Neuromelanin-sensitive MRI and its method of use

Abstract

We provide neuromelanin-sensitive MRI technology as a non-invasive measure of neurological condition. [Solution] Neuromelanin-sensitive magnetic resonance imaging ("MRI") techniques, methods, and computer-accessible media for measuring the degree of one or more neurological conditions, providing a diagnosis of one or more neurological conditions, monitoring the treatment of one or more neurological conditions, evaluating novel treatments for one or more neurological conditions, or determining the prognosis for one or more neurological conditions.

Description

[Technical Field]

[0001] Cross-reference of related applications This application claims priority and benefits of U.S. Provisional Application No. 63 / 114,304 filed on 16 November 2020, U.S. Provisional Application No. 63 / 120,105 filed on 1 December 2020, and U.S. Provisional Application No. 63 / 277,490 filed on 9 November 2021, the contents of which are incorporated herein by reference in their entirety.

[0002] This disclosure relates, in general, to magnetic resonance imaging ("MRI"), and more specifically to exemplary systems, methods, and computer-accessible media for neuromelanin-sensitive MRI techniques as a non-invasive measure of neurological condition. [Background technology]

[0003] Alzheimer's disease (AD) is one of the common forms of neurodegenerative diseases that cause dementia, also known as Alzheimer's type senile dementia, Alzheimer's type primary degenerative dementia, or Alzheimer's disease (AD). This dementia is a major public health concern, with a new case diagnosed somewhere in the world every seven seconds. Alzheimer's disease was first described in 1906 by the German psychiatrist and neuropathologist Alois Alzheimer and named after him. There is no cure for the disease, and it worsens as it progresses, ultimately leading to death within seven years. Less than 3% of individuals survive longer than 14 years after diagnosis. People diagnosed with AD are usually over 65 years old and are diagnosed by standard verbal and visual memory tests, decision-making, and problem-solving tasks. In 2006, there were 26.6 million patients worldwide, of which 5 million were in the United States. It is predicted that by 2050, one in 85 people worldwide will have Alzheimer's disease. Early symptoms are often mistakenly attributed to "age-related" issues or a manifestation of stress. If AD is suspected, the diagnosis is usually confirmed by tests that assess behavioral, memory, cognitive, and thinking abilities, followed by a brain scan.

[0004] Neurodegenerative diseases are divided into two broad categories encompassing all brain disorders. These diseases can be broadly divided into two groups: 1. conditions affecting memory, typically associated with dementia such as Alzheimer's disease, and 2. conditions causing problems with movement, such as Parkinson's disease. The most widely known neurodegenerative diseases include Alzheimer's disease (or Alzheimer's disease) and its precursor, mild cognitive impairment (MCI), Parkinson's disease (including Parkinson's dementia), as well as multiple sclerosis and many other diseases. Less well-known neurodegenerative diseases include dozens of conditions listed on the website of the National Institute of Neurological Disorders and Stroke (NINDS) of the National Institutes of Health (NIH) of the United States. Such diseases are often referred to by two or more names, and it should be understood that disease classifications may oversimplify conditions that occur in combination or are not typical or standard. Certain other impairments, such as postoperative cognitive impairment, have recently been reported, and these impairments may also be associated with neurodegeneration following anesthesia and surgery. Other impairments, such as epilepsy, may not primarily involve neurodegeneration, but at certain points in the progression of the impairment, they may be accompanied by neurodegeneration.

[0005] Despite the fact that at least some aspects of the pathology of each of the neurodegenerative diseases described above differ, they can often be treated with similar therapeutic agents and methods due to the common pathologies and symptoms they share. Therefore, the methods described herein can be used in conjunction with the selected therapeutic agents described to treat the majority of these neurodegenerative diseases. Many publications describe the common features of neurodegenerative diseases (Dale E. Bredesen, Rammohan V. Rao, Patrick Mehlen. Cell death in the nervous system. Nature 443(2006):796-802; Christian Haass. Initiation and propagation of neurodegeneration. Nature Medicine 16(2010):1201-1204; Michael T. Lin and M. Flint Beal. Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases. Nature 443(2006)787-795).

[0006] Symptoms of Alzheimer's disease (AD) include confusion, irritability, aggression, mood swings, speech difficulties, and long-term memory loss. Patients often withdraw from family and society. AD is a degenerative, incurable disease in which patients become dependent on the assistance and care of others. Caregivers are usually family members, spouses, or close relatives, placing a significant burden on them, making it one of the most costly diseases for society and families.

[0007] The causes and progression of Alzheimer's disease are not well understood. Research has shown that the disease is associated with plaques and changes in the brain. Current treatments are only symptomatic. There are no available treatments to stop or slow the progression of the disease. As of 2008, more than 500 clinical trials were underway to find a way to treat the disease, but it is unclear whether any of the tested treatments will be successful. Psychological stimulation, exercise, NSAID intake, and a balanced diet have been suggested as ways that may delay symptoms in healthy older adults. However, these methods have not been proven effective as treatments after symptoms have appeared.

[0008] The course of the disease is divided into four stages, with distinct patterns of progression in cognitive and functional impairment: 1. Pre-dementia, 2. Mild early onset of the disease, 3. Moderate gradual deterioration, and 4. Severe or severe – the final stage where the person becomes completely dependent and bedridden.

[0009] Alzheimer's disease is characterized by the accumulation of neurofibrillary tangles (tau protein) and neurite plaques (amyloid-beta) in the brain, particularly affecting the degeneration of neurons in the olfactory bulb and its connected brain structures. These include the endocortex (EC), hippocampus, amygdala nucleus, nucleus basalis of Meynert, locus coeruleus, and brainstem raphe nuclei, all of which project to the olfactory bulb (Figure 14). The degenerative changes result in the loss of memory and cognitive function. There is a significant loss of cortical and hippocampal choline acetyltransferase activity and degeneration of basoprestebral cholinergic neurons. Loss of smell in Alzheimer's disease is due to necrosis and / or apoptosis of olfactory neurons, the olfactory bulb, olfactory duct, pre-pirihom cortex, and endometrial cortex.

[0010] Etiology and Neuropathophysiology: The cause of most Alzheimer's disease remains unknown. The amyloid hypothesis assumed that amyloid-beta (Aβ) deposition is the essential cause of the disease. Furthermore, APOE4, a major genetic risk factor for AD, causes excessive amyloid accumulation in the brain before AD symptoms appear. Therefore, Aβ deposition precedes clinical AD. Interestingly, an experimental vaccine was found to remove amyloid plaques in early human trials, but had no significant effect on dementia. Studies have shown that a close relative of beta-amyloid protein, not necessarily beta-amyloid itself, may be a major cause of the disease. A 2004 study found that amyloid plaque deposition did not correlate with neuronal loss and memory loss. This observation supports the theory and proposal that tau protein abnormalities initiate the disease cascade. Ultimately, they form neurofibrillary tangles within nerve cell bodies, leading to microtubule breakdown, disruption of the neuronal transport system, malfunction of biochemical communication between neurons, and subsequent cell death. Herpes simplex virus type 1 has been shown to play a causative role in individuals carrying a susceptible version of the ApoE gene. Another hypothesis claims that demyelination in older adults causes axonal transport impairment, leading to neuronal loss. Iron and its vascular complexes released during myelin degradation have been hypothesized and suggested as causative factors. The inventors believe that the disruption of BV by the release of iron from hemoglobin around myelin and neuropiles leads to an iron-catalyzed hydrogen peroxide reaction called the Fenton reaction, which generates reactive oxygen species (ROS) during these demyelination episodes, adversely affecting neurons and causing neurogenesis, potentially leading to Alzheimer's disease.

[0011] Interestingly, individuals with AD show a 70% loss of locus coeruleus cells, which provide norepinephrine. Locus coeruleus cells are located in the pons, processes and spinal cord, brainstem, cerebellum, hypothalamus, thalamic relay nuclei, amygdala, basal brain, and cortex. Norepinephrine from the LC excitates most of the brain, mediates arousal, and primes neurons in the brain activated by stimulation. Norepinephrine from this nucleus stimulates microglia to suppress Aβ-induced cytokine production, and the phagocytosis of Aβ by cytokines suggests that degeneration of the locus coeruleus may be the initial cause of increased Aβ deposition in the AD brain. This nucleus in the pons (part of the brainstem) is involved in the physiological response to stress and panic and is a major site of norepinephrine (noradrenaline) synthesis in the brain, in addition to the adrenal glands.

[0012] To date, there is no definitive diagnosis for Alzheimer's disease, and there is a high clinical need to develop a highly sensitive, non-invasive diagnostic method. Diagnosis and monitoring of Alzheimer's patients are crucial for assessing the severity of progression and managing it with appropriate preventive care. Timely intervention at the onset of Alzheimer's disease can save lives. A comprehensive imaging modality for evaluating Alzheimer's disease remains an important clinical need that is not yet met.

[0013] In vivo measurement of dopamine activity is used to understand how this important neuromodulator contributes to cognition, neurodevelopment, aging, and neuropsychiatric disorders in humans. In medicine, such measurements can ideally provide objective biomarkers that predict clinical outcomes, including Alzheimer's disease, by using procedures that readily acquire and capture fundamental pathophysiological functions in a clinical setting. There is evidence that the locus coeruleus (LC), a major site of noradrenergic neurons in the human brain, is the first brain region to begin degenerating in the early stages of Alzheimer's disease (AD) and to accumulate hyperphosphorylated tau protein in Braak stage 0. While this structure has been extensively studied in the context of AD, much remains unclear about the correlation between the timing of LC changes and characteristic aspects of AD pathogenesis and clinical features.

[0014] The central noradrenergic system plays a crucial role in the arousal and reinforcement of emotional memories. The locus coeruleus (LC), the primary region of noradrenergic neurons in the brain, possesses a topographic pattern of projections, with the caudal region of the LC transmitting descending projections that regulate autonomic signaling. Dysregulation of the noradrenergic system has been suggested as a theoretical explanation for PTSD, particularly in its involvement in hyperarousal and major depressive disorder (MDD) symptoms. Despite a strong theoretical foundation, the understanding of noradrenergic dysfunction in PTSD and MDD remains incomplete, hindering research into novel therapies targeting this system in PTSD. Recent studies have developed the use of a specialized neuroimaging technique, neuromelanin-sensitive MRI (NM-MRI). NM-MRI is a non-invasive method for investigating the function of the human noradrenergic system in vivo by examining signal contrast in the LC. NM-MRI signals in this area are positively correlated with emotional memory performance and autonomic function (indicated by salivary α-amylase or heart rate variability), but this has not yet been investigated in PTSD patients. On the other hand, low LC NM-MRI signals have been observed in major depressive disorder. The inventors hypothesized that hyperarousal symptoms in PTSD and MDD are positively correlated with NM-MRI signals in the caudal LC.

[0015] Neuromelanin ("NM") is a dark pigment synthesized through iron-dependent oxidation of cytoplasmic dopamine and its subsequent interaction with proteins and lipids in midbrain dopaminergic neurons. NM pigments accumulate in certain autophagy organelles containing NM-iron complexes, along with lipids and various proteins. NM-containing organelles gradually accumulate over the lifespan of somatic cells of dopaminergic neurons in the substantia nigra ("SN"), the nucleus from which the name derives its dark appearance due to high concentrations of NM, and are removed only from post-death tissue by the action of microglia. Given that NM-iron complexes are paramagnetic, they can be imaged using MRI. A family of MRI sequences known as NM-MRI captures groups of neurons with high NM content, such as neurons in the SN, as high-concentration regions.

[0016] Therefore, it would be beneficial to provide a system, process, method, and computer-accessible medium for neuromelanin-sensitive MRI that can overcome the above-mentioned deficiencies. Various neurological and psychiatric diseases are associated with changes in neuromelanin within two major regions, namely the substantia nigra pars compacta (SNc) and the locus coeruleus (LC). Distinguishing between different disorders with similar clinical findings based only on the presentation of symptoms is difficult because the symptoms often overlap between related conditions.

[0017] Currently, there is no software approved by the FDA as a medical device for measuring NM within the SN or LC.

[0018] The unmet medical need addressed herein is the ability to distinguish between related diseases such as Parkinson's disease, multiple system atrophy, progressive supranuclear palsy, and different dementias such as Alzheimer's disease and dementia with Lewy bodies. An increased ability to distinguish between related diseases promotes the improvement of patient outcomes. SUMMARY OF THE INVENTION

[0019] Herein, in particular, a method is provided for determining the presence of Alzheimer's disease in a subject and for determining changes in the concentration of neuromelanin over time within the subject. The concentration of neuromelanin may change as a result of the normal progression of Alzheimer's disease or as a result of therapeutic intervention. In a first aspect, a method is provided for determining whether a change in the concentration of neuromelanin occurs over time within the brain of a subject. In a preferred embodiment, the subject is a patient with Alzheimer's disease. The method includes obtaining a first neuromelanin magnetic resonance image of the subject at a first point in time. Thereafter, a second neuromelanin magnetic resonance image at a second point in time is obtained. The first magnetic resonance image is compared with the second magnetic resonance image to determine whether a change in the concentration of neuromelanin has occurred between the first point in time and the second point in time.

[0020] This disclosure describes the combined use of two fully automated algorithms for measuring neuromelanin (NM) concentration and volume within two different brain regions (SNc and LC) to improve the ability to differentiate related disorders. A voxel-based analysis algorithm (already described in WO2020 / 077098 and WO2021 / 034770, the entire contents of each of these are incorporated herein by reference) is used to measure NM within the SNc. However, because the LC is much smaller and may not be well-suited to voxel-based analysis on 3T MRI (the most commonly available scanner in clinical practice), a new algorithm for measuring NM within the LC has been invented at the University of Ottawa. This LC algorithm is called a segmentation-based analysis algorithm. This disclosure describes a combination of the two algorithms in a software package that can be used to assist in the diagnosis and differentiation of neuropsychiatric disorders that are difficult to distinguish based solely on symptoms.

[0021] In this disclosure, the software uses two algorithms. A voxel-based analysis algorithm is used to measure NM within the SNc, and a segmentation-based analysis algorithm is used to measure NM changes within the LC. The software reports NM levels and volumes in both brain regions to the physician. Combining these two algorithms increases the ability to distinguish between related neurological conditions. By incorporating the algorithms into fully automated software, it has the potential for widespread use in clinics.

[0022] In one embodiment, this disclosure relates to a method for diagnosing Alzheimer's disease in a subject. (i) Perform a neuromelanin-magnetic resonance imaging (NM-MRI) scan to measure the level of neuromelanin, (ii) Compare neuromelanin levels to previous scans and / or reference values, (iii) providing a diagnosis of Alzheimer's disease, including.

[0023] In one embodiment, this disclosure relates to a method for monitoring the progression of Alzheimer's disease in a subject. (i) Perform a neuromelanin-magnetic resonance imaging (NM-MRI) scan to measure the level of neuromelanin, (ii) Compare neuromelanin levels to previous scans and / or reference values, (iii) including determining the progression of Alzheimer's disease.

[0024] In one embodiment, the disclosure relates to a method for providing a prognosis for Alzheimer's disease in a subject. (i) Perform a neuromelanin-magnetic resonance imaging (NM-MRI) scan to measure the level of neuromelanin, (ii) Compare neuromelanin levels to previous scans and / or reference values, (iii) providing an optional prognosis for Alzheimer's disease, including

[0025] In one embodiment, this disclosure relates to a method for monitoring the treatment of Alzheimer's disease in a subject. (i) Perform a neuromelanin-magnetic resonance imaging (NM-MRI) scan to measure the level of neuromelanin, (ii) Compare neuromelanin levels to previous scans and / or reference values, (iii) including evaluating the effectiveness of treatments for Alzheimer's disease.

[0026] In one embodiment, the present disclosure relates to determining a first signal intensity from a first neuromelanin magnetic resonance image and determining a second signal intensity from a second neuromelanin magnetic resonance image, wherein comparing the first magnetic resonance image with the second magnetic resonance image includes comparing the first signal intensity with the second signal intensity.

[0027] In one embodiment, the control is the level of neuromelanin present at approximately the same level within the target population, or the standard control is the approximately average level of neuromelanin present within the target population.

[0028] In one embodiment, a neuromelanin gradient phantom is used to measure the level, signal, and / or concentration of neuromelanin.

[0029] In one embodiment, the neuromelanin phantom concentration gradient is scanned approximately once per patient, approximately once per hour, approximately once per day, approximately once per week, or approximately once per month.

[0030] In one embodiment, the neuromelanin phantom gradient is scanned daily.

[0031] In one embodiment, the neuromelanin phantom gradient is scanned for each patient.

[0032] In one embodiment, this disclosure relates to a method for evaluating neuromelanin within a subject. Performing neuromelanin-magnetic resonance imaging (NM-MRI) scans on the subject, Obtaining neuromelanin datasets from NM-MRI scans, Selectively encrypting the neuromelanin dataset, Uploading the neuromelanin dataset to a remote server, Decoding the dataset at random, This involves performing an analysis of the neuromelanin dataset, and the analysis is performed by (i) Compare the neuromelanin dataset with one or more neuromelanin datasets previously obtained from the above subjects. (ii) Compare the neuromelanin dataset with the control dataset. (iii) Comparing a neuromelanin dataset with one or more neuromelanin datasets previously obtained from different subjects, and performing one or more of the above, To generate a report that includes neuromelanin analysis, The option to encrypt the report, Uploading the report to the remote server, This includes optionally decrypting the report.

[0033] In one embodiment, this disclosure relates to an in vivo method for determining the progression of Alzheimer's disease over time in a subject, wherein the method is (i) Obtain a first neuromelanin magnetic resonance image at a first time point, (ii) After step (i), compare the first neuromelanin magnetic resonance image with an age-matched control, (iii) Determining the level, signal, and / or concentration of neuromelanin that occurred between the first time point and the second time point.

[0034] In one embodiment, this disclosure relates to an in vivo method for diagnosing Alzheimer's disease, wherein the method is (i) Obtain a first neuromelanin magnetic resonance image at a first time point, (ii) After step (i), a second neuromelanin magnetic resonance image is obtained at a second time point, (iii) Comparing a first neuromelanin magnetic resonance image with a second neuromelanin magnetic resonance image to determine whether one or more changes in neuromelanin level, signal, or concentration occurred between the first time point and the second time point.

[0035] In one embodiment, the Disclosure relates to a method for providing a treatment plan to a patient, which includes performing an NM-MRI scan, obtaining an NM signal from the NM-MRI scan in a region of interest, comparing the NM signal from the NM-MRI scan in the region of interest with an age-matching database number if the NM signal is below a predetermined value, and implementing the corresponding treatment plan.

[0036] In one embodiment, the subject exhibits symptoms of Alzheimer's disease.

[0037] In one embodiment, the patient has a disorder that is commonly misdiagnosed as Alzheimer's disease.

[0038] In one embodiment, NM-MRI scans and analyses can distinguish between Alzheimer's disease and Parkinson's disease. In one embodiment, NM-MRI scans and analyses can distinguish between related disorders (e.g., dementia with Lewy bodies) and identify them separately. In one embodiment, NM-MRI scans and analyses can monitor the progression of disorders associated with Alzheimer's disease, monitor the treatment of disorders, and provide prognoses for disorders.

[0039] In one embodiment, the Disclosure relates to a method for determining whether a subject has Alzheimer's disease or is at risk of developing Alzheimer's disease, the method comprising analyzing one or more neuromelanin magnetic resonance imaging (NM-MRI) scans of the subject's brain region of interest, the analysis of which Receiving imaging information of brain regions of interest, This includes determining NM concentration in brain regions of interest using segmented analysis based on imaging information. Determining whether a subject has Alzheimer's disease or is at risk of developing Alzheimer's disease is (1) If one or more NM-MRI scans show reduced NM signaling compared to one or more control scans without Alzheimer's disease, the subject is considered to have Alzheimer's disease or to be at risk of developing it. (2) If one or more NM-MRI scans have NM signals comparable to those of one or more control scans that do not have Alzheimer's disease, the subjects include those who do not have Alzheimer's disease or are not at risk of developing it.

[0040] In one embodiment, the present disclosure relates to a method for treating a subject having Alzheimer's disease, wherein the method includes analyzing neuromelanin magnetic resonance imaging (NM-MRI) scans of a brain region of interest of the subject, and the analysis involves Receiving imaging information of the brain region of interest at the first time point, Receiving imaging information of the brain region of interest at the second time point, Based on imaging information, segmented analysis is used to determine the NM concentration in the brain region of interest at the first and second time points, This includes comparing the NM concentration at the first time point with that at the second time point. Treatment methods, (1) If the NM-MRI scan at the second time point shows a reduced NM signal compared to the NM signal at the first time point, administer one or more of levodopa and carbidopa. (2) If the NM-MRI scan at the second time point shows an increased NM signal compared to the NM signal at the first time point, the administration of one or more of levodopa and carbidopa is withheld.

[0041] In one embodiment, the subject exhibits one or more symptoms of Alzheimer's disease.

[0042] In one embodiment, the method provides a diagnosis of Alzheimer's disease before symptoms are clinically present.

[0043] In one embodiment, NM-MRI is used to distinguish between Alzheimer's disease and Parkinson's disease.

[0044] In one embodiment, the NM-MRI method diagnoses whether a patient has Alzheimer's disease or not, and presents the diagnosis to the user via a user interface.

[0045] In one embodiment, the analysis is a segmented analysis.

[0046] In one embodiment, the segmented analysis includes determining at least one topographical pattern within a brain region of interest.

[0047] In one embodiment, the method further includes a calculation using a value representing the volume of a neuromelanin voxel or segment.

[0048] In one embodiment, the segmented region of interest is the locus coeruleus.

[0049] In one embodiment, this disclosure relates to a diagnostic system for providing diagnostic information for Alzheimer's disease, and the diagnostic system is An MRI system configured to generate and acquire neuromelanin-sensitive MRI scans, along with a series of neuromelanin data for voxels or segments located within a region of interest in the target brain region, A signal processor configured to process a series of neuromelanin data and generate a processed neuromelanin MRI spectrum, A diagnostic processor is provided, and the diagnostic processor processes the processed neuromelanin MRI spectrum, By extracting measurements from the region of interest corresponding to neuromelanin at a given point in time, The measured value is compared with one or more control measured values ​​obtained prior to that point in time. It is configured to provide a diagnosis of Alzheimer's disease when the measured value is approximately 25% lower than the control value.

[0050] In one embodiment, the disclosure relates to a method for determining whether a brain tissue in question contains abnormal levels of neuromelanin. The method includes detecting levels of neuromelanin in the tissue. The level of neuromelanin is compared to a standard control. If a low level of neuromelanin is detected compared to the standard control, this indicates Alzheimer's disease.

[0051] In one embodiment, a method is provided for determining whether an Alzheimer's disease therapy administered to a subject is effective. The method includes the step of detecting the level of endogenous neuromelanin in the tissue at a first time point. In a subsequent step, the therapy is administered to the subject. Then, at a second time point, the level of neuromelanin in the tissue is determined. Subsequently, the level of neuromelanin at the first time point is compared to the level of neuromelanin at the second time point. A higher or constant level of neuromelanin at the second time point compared to the first time point indicates that the therapy was effective. Alternatively, a lower level of neuromelanin at the second time point compared to the first time point indicates that the therapy administered to the subject was ineffective.

[0052] In one embodiment, a method for treating a patient with Alzheimer's disease is provided. In one embodiment, the method includes administering an initial dose of Alzheimer's disease treatment to the patient. In one embodiment, the method includes monitoring neuromelanin concentration in a region of interest in the patient's brain and evaluating treatment-related adverse events over the initial treatment period. In one embodiment, during the initial treatment period, the patient, i) A decrease in neuromelanin concentration in the region of interest within the patient's brain, and ii) If one or more of the following conditions are met: no adverse effects or side effects related to the treatment, During the subsequent treatment period, the dose of Alzheimer's disease treatment was increased. Treatment leads to an improvement in the symptoms of Alzheimer's disease in patients.

[0053] In one embodiment, the treatment method is: Step c) includes repeating steps a) to c) until the patient no longer exhibits one or more of the symptoms i) to ii).

[0054] In one aspect, the disclosure relates to a method for diagnosing neurological disorders in a subject, determining the temporal progression of neurological disorders, or providing a prognosis for neurological disorders, wherein the method relates to a method for diagnosing neurological disorders in a subject, determining the temporal progression of neurological disorders, or providing a prognosis for neurological disorders. (i) Obtaining a first neuromelanin magnetic resonance imaging (NM-MRI) scan at a first time point, (ii) After step (i), a second NM-MRI scan is obtained at a second time point, (iii) Perform a segmentation-based algorithmic analysis to determine the level, concentration, and / or volume of neuromelanin (NM) in the locus coeruleus (LC), (iv) Perform voxel-based algorithmic analysis to determine the level, concentration, and / or volume of neuromelanin in substantia nigra pars compacta (SNc), (v) By comparing the first neuromelanin magnetic resonance image with the second neuromelanin magnetic resonance image, determine whether changes in neuromelanin level, signal, and / or concentration occurred between the first time point and the second time point, both in SNc using a voxel-based algorithm and LC using a segmentation-based algorithm. (vi) providing a diagnosis, progression over time, or prognosis of a neurological disorder based on the difference in the level of NM within the SNc between the first scan and the second scan, and the difference in the level of NM within the LC between the first scan and the second scan.

[0055] In one aspect, the disclosure relates to an in vivo method for selecting a treatment plan for the prevention or treatment of a target neurological disorder, wherein the method is (i) Obtaining a first neuromelanin magnetic resonance imaging (NM-MRI) scan at a first time point, (ii) After step (i), a second NM-MRI scan is obtained at a second time point, (iii) Perform segmentation-based algorithmic analysis to determine the level, concentration, and / or volume of neuromelanin (NM) within the locus coeruleus (LC), (iv) Perform voxel-based algorithmic analysis to determine the level, concentration, and / or volume of neuromelanin within the substantia nigra pars compacta (SNc), (v) By comparing the first neuromelanin magnetic resonance image with the second neuromelanin magnetic resonance image, determine whether changes in neuromelanin level, signal, and / or concentration occurred between the first time point and the second time point, both in SNc using a voxel-based algorithm and LC using a segmentation-based algorithm. (vi) To provide a diagnosis, time progression, or prognosis of neurological disorders based on the difference in the level of NM within the SNc between the first and second scans, and the difference in the level of NM within the LC between the first and second scans. (vi) including implementing a treatment plan corresponding to the diagnosed neurological disorder.

[0056] In one embodiment, the disclosure relates to a method for distinguishing between similarly symptomatic motor disorders. (i) Conduct tests to determine the Unified Parkinson's Disease Rating Scale score, (ii) Obtaining a first neuromelanin magnetic resonance imaging (NM-MRI) scan at a first time point, (iii) After steps (i) and (ii), a second NM-MRI scan is obtained at a second time point, (iv) Perform voxel-based analysis to determine the concentration and / or volume of NM in SNc, (v) Perform a segmentation-based analysis to determine the concentration and / or volume of NM in the LC, (vi) by comparing the first neuromelanin magnetic resonance image with the second neuromelanin magnetic resonance image, it is determined whether changes in neuromelanin level, signal, and / or concentration occurred in both SNc and LC between the first time point and the second time point, (vi) providing a diagnosis, progression over time, or prognosis of a neurological disorder based on the difference in the level of NM within the SNc between the first scan and the second scan, and the difference in the level of NM within the LC between the first scan and the second scan.

[0057] In one aspect, the present disclosure relates to a method for diagnosing a patient with a neurological disorder, and the said method is (i) Measuring the concentration and / or volume of neuromelanin in SNc using a voxel-based analytical method, and measuring the concentration and / or volume of neuromelanin in LC using a segmentation-based analytical method, (ii) Compare the level of neuromelanin in SNc to the standard control level of neuromelanin in SNc, and compare the level of neuromelanin in LC to the standard control level of neuromelanin in LC, (iii) Providing a diagnosis of neurological condition when the size or ratio of SNc and LC neuromelanin is lower or higher for each of the respective regions compared to a standard control.

[0058] In one aspect, the present disclosure relates to a method for diagnosing a patient with a neurological disorder, and the said method is (i) Measuring the concentration and / or volume of neuromelanin in SNc using a voxel-based analytical method, and measuring the concentration and / or volume of neuromelanin in LC using a segmentation-based analytical method, (ii) Compare the level of neuromelanin in SNc to the standard control level of neuromelanin in SNc, and compare the level of neuromelanin in LC to the standard control level of neuromelanin in LC, (iii) Providing a diagnosis of neurological condition when the size or ratio of SNc and LC neuromelanin is lower or higher than a standard control for each of the respective regions, according to a predetermined value.

[0059] In one embodiment, the method described herein is used in conjunction with a second imaging method, the second imaging method being selected from the group consisting of positron emission tomography (PET), tau-PET, structural MRI, functional MRI (fMRI), blood oxygen level-dependent (BOLD) fMRI, iron-sensitive MRI, quantitative susceptibility mapping (QSM), diffusion tensor imaging (DTI), and single-photon emission computed tomography (SPECT), DaTscan, and DaTquant.

[0060] In one embodiment, the method described herein is used in conjunction with a second imaging method, the second imaging method being positron emission tomography (PET).

[0061] In one embodiment, the method described herein is used in conjunction with a second imaging method, the second imaging method being structural MRI.

[0062] In one embodiment, the method described herein is used in conjunction with a second imaging method, the second imaging method being functional MRI (fMRI).

[0063] In one embodiment, the method described herein is used in conjunction with a second imaging method, the second imaging method being blood oxygen level-dependent (BOLD) fMRI.

[0064] In one embodiment, the method described herein focuses on neuromelanin levels, concentrations, volumes, or patterns within symptom-specific voxels and / or disease-specific voxels in SNc.

[0065] In one embodiment, the method described herein focuses on neuromelanin levels, concentrations, volumes, or patterns within symptom-specific and / or disease-specific segments in LC.

[0066] In one embodiment, the method described herein focuses on neuromelanin levels, concentrations, volumes, or patterns within symptom-specific voxels in SNc and / or neuromelanin levels, concentrations, volumes, or patterns within symptom-specific segments and / or disease-specific segments in LC.

[0067] In one embodiment, the method described herein focuses on neuromelanin levels, concentrations, volumes, or patterns within SNc, as well as neuromelanin levels, concentrations, volumes, or patterns within symptom-specific segments and / or disease-specific segments within LC.

[0068] In one embodiment, the method described herein focuses on neuromelanin levels, concentrations, volumes, or patterns in symptom-specific voxels and / or disease-specific voxels within SNc, as well as neuromelanin levels, concentrations, volumes, or patterns within LC.

[0069] In one embodiment, the method described herein targets one or more neurological conditions.

[0070] In one embodiment, the method described herein targets one or more neurological conditions selected from schizophrenia, cocaine use disorder, Parkinson's disease, Alzheimer's disease without neuropsychiatric symptoms, Alzheimer's disease with neuropsychiatric symptoms, major depressive disorder, and / or post-traumatic stress disorder.

[0071] The accompanying drawings included to provide a further understanding of this disclosure are incorporated herein and constitute part thereof, illustrating aspects of this disclosure and serving to illustrate the principles of this disclosure together with a detailed description. The patent or application file includes at least one drawing drawn in color. A copy of this patent or patent application document, including the color drawing, will be provided by the Patent Office upon request and payment of the necessary fees. [Brief explanation of the drawing]

[0072] [Figure 1] Neuroimaging measurements are shown. Top: PET imaging of tau loading (left) using the radiotracer [18F]MK6420 and β-amyloid loading (right) using [18F]AZD4694 in representative cognitively normal (CN) participants and participants with Alzheimer's disease (AD). Bottom: Imaging of the locus coeruleus (LC). Bottom left: NM-MRI image acquired in vivo from a CN elderly person. Bottom center: Magnified view of the pons from this participant and a representative AD patient. This non-invasive procedure clearly depicts the LC as a high-intensity voxel (yellow arrow). In AD, LC degeneration begins in the early stages of the disease, causing a visible decrease in the LC NM-MRI signal. Bottom right: 3D structure of human LC revealed by computer reconstruction showing the distribution of noradrenergic LC cells (orange) based on post-mortem cell count. [Figure 2] This shows predictions of neuropsychiatric symptom severity in n=73 elderly individuals with cognitive impairment based on LC NM-MRI (left), tau loading (center), or β-amyloid loading (right). NPS severity was adjusted for covariates age, sex, dementia severity, and PET scale (LC plot) or LC signal (tau and amyloid plot). All of these scales significantly predicted the MBI total score (Pearson r=0.37, 0.44, and 0.40, respectively) and the MBI impulse control disorder subdomain score (not shown, r=0.35, 0.30, and 0.29, respectively). [Figure 3]NM-MRI images acquired at 7 Tesla and 3 Tesla from representative subjects are shown. The yellow arrows point to LC. Compared to 3T, the ultra-high magnetic field strength (7T) allows for improved in-plane resolution (0.7 × 0.7 mm at 3T vs. 0.4 × 0.4 mm at 7T, coronal view) and thinner slices (1.8 mm vs. 1.0 mm, coronal view), and therefore the voxel volume is 5.5 times smaller at 7T. Low resolution introduces noise into the LC NM-MRI signal due to the partial volume effect where a single voxel combines LC and non-LC tissue. For this reason, ultra-high field-of-view NM-MRI is preferred for measuring signals from these small structures. [Figure 4] This shows the measurement of the LC NM-MRI signal. A. NM-MRI visualization template created by averaging many NM-MRI images in MNI space. B, C. Enlarged view of the template with a manually traced over-inclusive mask of the LC superimposed. This mask is divided into four rostral-caudal segments (colored in B). D. NM-MRI image of a representative target showing the bridge in native space. The over-inclusive LC mask is transformed from MNI space to native space to create a search space (yellow) for positioning the LC. E. The LC (yellow) is secondarily identified as the brightest cluster of four adjacent voxels in the search space. F. The contrast-to-noise ratio (CNR) is calculated for all voxels in the reference region that does not contain the NM (white circle). Averaging the CNR values ​​from all LC voxels in the rostral-caudal segment yields the LC NM-MRI signal. The mid-rostral segment (yellow) shows the most significant degeneration in AD, and the signal here is used for all analyses. [Figure 5]This shows the relationship between LC NM-MRI signaling and Braak disease stage and dementia severity. Left: Schematic diagram of LC in a coronal plot showing the small-region pattern of NM-MRI signal loss in tau-positive individuals. LC was divided into five segments (each 3 mm long) on ​​the left and right sides. Tau status was divided into three levels (tau-negative, Braak region 1 positive, Braak region 3 positive). LC segments are color-coded based on the relationship between NM-MRI signaling within the segment and tau status (t-statistic derived from robust linear regression adjusted for age and sex). The strongest relationship was observed in the middle LC segment (MNI spatial z-coordinate = -22 to -25, enclosed in yellow, corresponding to the yellow LC segment shown in Figure 1). The bilateral LC NM signaling from this segment was selected as the NM-MRI metric in all subsequent analyses. Center: Scatter plot showing LC NM-MRI signaling in all test groups. Black 3-positive cases (dark red) showed reduced signal compared to tau-negative and Black 1-positive cases. Error bars represent the standard error of the mean. Right: Scatter plot showing the correlation between LC NM-MRI signal and cognitive impairment (MMSE error, top) and dementia stage (CDR score, bottom). L: left, R: right, CN-: tau-negative individuals with normal cognitive function, CI+: Black 1-positive individuals with cognitive impairment, CI++: Black 3-positive individuals with cognitive impairment, MMSE: Mini-Mental State Examination, CDR: Clinical Dementia Rating Scale. [Figure 6] This shows the voxel-by-voxel correlation of LC NM-MRI signals to [18F]MK-6240 intake across the entire brain. [Figure 7]This shows the measurement of LC NM-MRI signals. Left: Visualization template of the MNI space created by averaging spatially normalized NM-MRI images from all participants. Center: Magnified view of the visualization template with an over-inclusive LC mask superimposed. This mask was manually traced on the visualization template over the high-sensitivity region surrounding the LC and divided into five rostral-caudal segments (shown in different colors). Each segment spans 3 mm along the z-axis. Top right: Unprocessed NM-MRI image showing the pons of a representative individual. The central pons reference region is outlined in white. The contrast-to-noise ratio for all voxels was calculated for the signal from this region. Bottom right: Segmentation of the LC in native space. The over-inclusive LC mask (yellow) was transformed from MNI space to native space to provide a search space in which the LC was identified on both the left and right sides as the four brightest adjacent voxels. To minimize partial volume effects, the LC NM-MRI signal was calculated by retaining only the brightest of four voxels for each side and slice, and averaging the CNR values ​​from these voxels for each of the five segments. [Figure 8] A positive correlation was observed between caudal LC NM-MRI signaling and CAPS-5 hyperarousal symptom severity. [Figure 9] A negative correlation is observed between LC NM-MRI signals and the severity of BDI depression. [Figure 10] The LC signal is shown in PTSD patients and healthy individuals. Caudal LC signal was significantly increased in the age-adjusted PTSD group (t34=2.08, p=0.046, Cohen's d=0.71, age-adjusted linear regression). [Figure 11]This shows the relationship between LC NM-MRI signaling and clinical and physiological measures of hyperarousal. Left: The magnitude of the LC NM-MRI signaling was significantly associated with more severe symptoms of hyperarousal in 24 Canadian military veterans with operational experience (r=0.52, p=0.019). Right: Preliminary data suggest a positive correlation between LC NM-MRI signaling and skin conduction response during an fMRI fear conditioning procedure (maximum phase, conditioned stimulus-unconditioned stimulus) in seven young healthy individuals (age and diagnostic-related increases in NM signaling likely explain the lower LC NM-MRI signaling values ​​compared to the left plot). [Figure 12] We demonstrate that LC localization via NM-MRI supports fMRI analysis. Due to its small size, it is not recommended to examine LC activity using standard methods for bold fMRI preprocessing and analysis. Recent studies have demonstrated an improved method that performs first-level fMRI analysis in native space without smoothing and leverages NM-MRI signals to provide a personal LC localizer [46, 47]. We applied this approach to a subject with PTSD and examined the functional connectivity of the LC. At rest (top), a pattern of functional connectivity was observed that was very similar to previous reports

[46] , centered on the LC (white) and including structures within the brainstem and cerebellum. In this same individual (bottom), after presentation of a personal trauma word, connectivity of the LC to many structures known to project to or from the LC increased, including the hypothalamus, hippocampus, and cerebral cortex. In our current proposal, the fMRI paradigm consists of fear conditioning rather than trauma evocation as shown here. Nevertheless, these results demonstrate the feasibility of an approach that uses bold fMRI to investigate LC functional activity. [Figure 13] The SNc and LC masks are shown. The software automatically applies a custom SN mask to the SNc to select regions for voxel-based algorithms and a custom LC mask to the LC to select regions for segmentation algorithms. [Figure 14]This document demonstrates the application of voxel-based and segmentation-based algorithms to measure NM in Parkinson's disease patients. The voxel-based algorithm shows a significant change in SNc compared to healthy controls (left figure). The segmentation-based algorithm shows no significant change in LC compared to healthy controls (right figure). [Figure 15] This document demonstrates the application of voxel-based and segmentation-based algorithms to measure NM in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). The voxel-based algorithm shows no significant change in SNc compared to healthy controls (left figure). The segmentation-based algorithm shows a significant change in LC compared to healthy controls (right figure). [Figure 16] This paper demonstrates the application of voxel-based and segmentation-based algorithms to measure neuropsychiatric symptoms (NM) in Alzheimer's disease (AD). The segmentation-based algorithm shows a significant increase in NM within the LC compared to healthy controls (left figure). The voxel-based algorithm shows a significant decrease in NM within the SNc compared to healthy controls (right figure). [Figure 17] This document demonstrates the application of voxel-based and segmentation-based algorithms to measure NM in patients with schizophrenia. The voxel-based algorithm shows a significant change in SNc compared to healthy controls (left figure). The segmentation-based algorithm shows no significant change in LC compared to healthy controls (right figure). [Figure 18] This study demonstrates the application of voxel-based and segmentation-based algorithms to post-traumatic stress disorder (PTSD). The voxel-based algorithm shows no significant association between NM levels within SNc and disease severity compared to healthy controls (left figure). The segmentation-based algorithm shows significant changes within LC compared to healthy controls, indicating a significant association between increased NM levels and disease severity (right figure). [Figure 19]This report demonstrates the application of voxel-based and segmentation-based algorithms to patients with depression. The voxel-based algorithm shows no significant association between disease severity and NM levels within SNc compared to healthy controls (left figure). The segmentation-based algorithm shows a tendency for NM levels to decrease with increasing disease severity in LC compared to healthy controls (right figure). [Figure 20] This paper demonstrates the application of voxel-based and segmentation-based algorithms to cocaine use disorder. The voxel-based algorithm shows that an increase in NM within SNc is significantly associated with cocaine use disorder compared to healthy controls (left figure). The segmentation-based algorithm shows a tendency for NM within LC to decrease compared to healthy controls (right figure). [Modes for carrying out the invention]

[0073] Before describing this disclosure in more detail, please understand that it is not limited to the specific embodiments described herein and, therefore, can certainly be modified. Also, please understand that the terminology used herein is merely for describing specific embodiments and is not intended to limit the scope of this disclosure, as it is limited only by the appended claims.

[0074] definition The details provided herein are, for example, intended solely for illustrative purposes of embodiments of the Disclosure and are presented to provide what is considered to be the most useful and readily understandable explanation of the principles and conceptual aspects of the Disclosure. In this regard, no attempt is made to provide structural details of the Disclosure in more detail than is necessary for a basic understanding of the Disclosure, and explanations are provided with drawings that will make it clear to those skilled in the art how the forms of the Disclosure may actually be embodied.

[0075] As used herein, the singular forms "a," "an," and "the" include multiple references unless the context explicitly indicates otherwise.

[0076] Unless otherwise indicated, all numbers representing quantities of components, reaction conditions, etc., used herein and in the claims should be understood in all cases to be modified by the term "approximately." Therefore, unless otherwise indicated, the numerical parameters described in the following specification and the appended claims are approximations that may vary depending on the desired properties to be obtained by this disclosure. Each numerical parameter should be interpreted in light of the number of significant figures and common rounding rules, at least so as not to be considered an attempt to limit the application of the doctrine of equivalents to the claims.

[0077] In addition, any disclosure of numerical ranges within this specification shall be considered as a disclosure of numbers and ranges within that range. For example, if the range is about 1 to about 50, it shall be considered to include, for example, 1, 7, 34, 46.1, 23.7, or any other value or range within the range. Furthermore, terms shall include at least the number stated; for example, “at least 50” shall include 50.

[0078] The term "MR" refers to magnetic resonance, a physical principle on which various experimental procedures known in the art and / or described herein are based, including MRI ("magnetic resonance imaging") and MRS ("magnetic resonance spectroscopy"). The terms neuromelanin-sensitive MRI or neuromelanin MRI refer to the use of MRI in the study of neuromelanin in the brain. In this specification, the general terms, magnetic resonance imaging, magnetic resonance imaging or MRI encompass neuromelanin-sensitive variations.

[0079] As used herein, the term "NM-MRI" and similar names refer to MRI scans and corresponding voxel-by-voxel analyses independently, separately, and collectively.

[0080] As used herein, the terms "T1" and "T2" refer to their conventional meanings as are well known in the art (i.e., "spin-lattice relaxation time" and "spin-spin relaxation time," respectively).

[0081] In the context of MRI imaging, the term "T1 weighting" refers to an image created using a pulsed spin echo or inversion recovery sequence with appropriately shortened TR and TE, which can show contrast between tissues with different T1 values, as is well known in the art. In this context, the term "TR" refers to the repetition time between excitation pulses. The term "excitation pulse" is understood to refer to a 90-degree radio frequency (RF) excitation pulse. The term "TE" refers to the echo time between the excitation pulse and the MR signal sampling.

[0082] The term "subject" can refer to a mammalian subject such as a mouse, rat, horse, cattle, sheep, dog, cat, or human. In some embodiments of the methods described herein, the subject is a mouse, and in other embodiments, the subject is a human. In this context, the term "patient" refers to a human subject.

[0083] As used herein, the term “reduce” means to describe a process by which the severity of a sign or symptom of a disorder is reduced. Importantly, the sign or symptom may be reduced without elimination. In preferred embodiments, the use of the therapeutic methods disclosed herein results in the elimination of the sign or symptom, but elimination is not required. The effective doses guided by this disclosure are expected to reduce the severity of the sign or symptom.

[0084] Dosage and administration are adjusted to provide a sufficient level of activator(s) or to maintain the desired effect. Factors to consider include the severity of the disease state, the subject's overall health status, age, weight, and sex, diet, timing and frequency of administration, drug interactions(s), response sensitivity, and tolerance / response to therapy. The effective dose of the drug provides an objectively identifiable improvement.

[0085] The term “neurological” is used interchangeably with “neurological disorder” and “neurological disease” and is intended to encompass conditions / disorders well known in the art, at least some of which are listed herein.

[0086] As used herein, “stable” refers to a reproducible measurement. In one embodiment, “stable neuromelanin levels” refers to a series of scans in which neuromelanin levels remain relatively constant. In some contexts, “stable neuromelanin levels” are maintained for more than one hour, more than one day, more than one week, or for more than one treatment cycle.

[0087] In the context of disease, terms such as “treat” and “cure” refer to improving, suppressing, eradicating, and / or delaying the onset of the disease being treated. In some embodiments, the methods described herein are performed on subjects requiring treatment. As used herein, terms such as “requiring treatment” refer to subjects at risk of developing the disease, subjects with a condition, who are understood by those skilled in the art of medicine or veterinary medicine to be highly likely to develop the disease and / or actually have the disease. Treatment for Alzheimer’s disease includes currently approved therapies and investigational therapies. Conventional MRI lacks the spatial and quantitative data necessary to predict clinical outcomes. However, the methods considered herein detect levels of neuromelanin in the brain that can predict clinical progression, severity, and response in Alzheimer’s disease by considering the dispersion of neuromelanin in the brain or the loss of neuromelanin-containing neurons.

[0088] The NM-MRI of this disclosure can monitor the effectiveness of Alzheimer's disease treatments. The NM-MRI of this disclosure can determine the effectiveness of investigational treatments. A non-exclusive list of Alzheimer's disease treatments that can be monitored according to one embodiment of this disclosure includes one or more of the following:

[0089] Treatment for Alzheimer's disease includes disease-modifying therapies. These therapies aim to prevent, delay, or halt the overall progression of Alzheimer's disease (PD). They target different proteins and pathways thought to play a role in the disease.

[0090] In some embodiments, NM-MRI provides a method for dose titration for the treatment of Alzheimer's disease while avoiding adverse effects or side effects from currently approved or investigational therapies. Specifically, it is possible to increase efficacy by administering treatment and guiding the dosing plan while monitoring NM signals using the voxel-by-voxel approach described herein.

[0091] In addition, administering therapeutic agents according to specific variable dosing plans derived by NM-MRI can reduce potential administration-related side effects. For example, administering treatment according to specific dosing plans derived by NM-MRI voxel analysis as disclosed herein can significantly reduce, or in some cases even completely eliminate, treatment-related side effects.

[0092] In one embodiment, the area of ​​interest is Alzheimer's disease symptom-related voxels. Dose variation increases patient compliance, improves treatment, and reduces unwanted and / or adverse effects. In certain embodiments, the therapeutic methods of the present disclosure provide an improved overall therapy compared to the administration of the therapeutic agent itself.

[0093] In certain embodiments, when using the induction intervention of the present disclosure, the dose or frequency of administration of existing therapeutic agents can be reduced, thereby increasing patient compliance, improving therapy, and reducing unwanted or adverse effects. In one embodiment, by monitoring NM-MRI therapy of the present disclosure, patients can experience benefits from a longer time frame of treatment.

[0094] Neuromelanin-sensitive MRI data can be used as a biomarker for Alzheimer's disease, or the risk of developing Alzheimer's disease, severity, disease progression, treatment response, and / or clinical outcomes. Neuromelanin-sensitive MRI satisfies the need for objective biomarkers to track Alzheimer's disease, severity, or the risk of developing it. Neuromelanin-sensitive MRI can be used as a safe alternative to invasive / radioactive imaging measurements (e.g., PET). Neuromelanin-sensitive MRI can also be used to monitor progression, although this is not currently possible due to the risk of repeated exposure to radiation. Neuromelanin-sensitive MRI is non-invasive, less expensive, safer, and easier to obtain in clinical settings. Neuromelanin-sensitive MRI significantly increases anatomical resolution (5-10 times), thereby enabling the resolution of anatomical details within relevant brain structures.

[0095] In a particular embodiment, neuromelanin-sensitive magnetic resonance imaging is performed periodically, for example, every 1, 2, 3, 4, 5, 6, or 7 days, or every 1, 2, 3, or 4 weeks. The images are taken every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months, or every 1, 2, 3, 4, or 5 years. In certain embodiments, the first magnetic resonance imaging (MMRI) is taken before the onset of symptoms. In certain embodiments, the first MMRI is taken before symptoms associated with Alzheimer's disease. The second MMRI may be taken either before or after the onset of symptoms. In other embodiments, the second MMRI may be taken one year after the first MMRI.

[0096] In some embodiments, neuromelanin-sensitive magnetic resonance imaging ("NM-MRI") techniques are effective in non-invasively diagnosing Alzheimer's disease, measuring the effects of Alzheimer's disease, and / or providing prognosis for Alzheimer's disease.

[0097] In some embodiments, NM-MRI technology is used as a tool for diagnosing Alzheimer's disease before symptoms appear. In some embodiments, NM-MRI technology is effective in distinguishing Alzheimer's disease from other neurological conditions, including, but not limited to, Parkinson's disease and / or dementia with Lewy bodies. In other embodiments, NM-MRI technology is effective in selecting and / or monitoring the course of treatment, and, optionally, such treatment is effective in treating Alzheimer's disease.

[0098] In some embodiments, NM-MRI technology is used as a tool for monitoring the progression of Alzheimer's disease. In some embodiments, NM-MRI technology is effective for longitudinal evaluation of the progression of Alzheimer's disease.

[0099] In some embodiments, the technique directly or indirectly measures neuromelanin. In other embodiments, the technique directly or indirectly measures dopamine function. In some embodiments, there is a correlation between neuromelanin-sensitive MRI (NM-MRI) signals and the severity of Alzheimer's disease.

[0100] In some embodiments, NM-MRI technology can determine the concentration of neuromelanin across all sections of brain tissue. In other embodiments, NM-MRI technology can determine the regional concentration of neuromelanin. In other embodiments, NM-MRI technology can determine the regional level of neuromelanin. In other embodiments, NM-MRI technology can determine the regional signal intensity of neuromelanin.

[0101] In other embodiments, NM-MRI technology is used to determine neuromelanin concentration within a small region of the locus coeruleus (LC). In further embodiments, NM-MRI technology is used to determine dopamine release in the dorsal striatum and resting blood flow in the locus coeruleus, either directly or indirectly.

[0102] In some embodiments, NM-MRI signals are directly correlated with the severity of Alzheimer's disease. In some embodiments, NM-MRI signals are inversely correlated with the severity of Alzheimer's disease. In other embodiments, NM-MRI shows a lower signal in the substantia nigra-striatal pathway in patients with Alzheimer's disease. In some embodiments, NM-MRI captures dopamine dysfunction. In yet another embodiment, NM-MRI can be used as a biomarker for Alzheimer's disease. In further embodiments, NM-MRI can be used to determine the severity of Alzheimer's disease. In yet another embodiment, NM-MRI can be used to diagnose and / or provide prognosis for Alzheimer's disease.

[0103] In some embodiments, the analysis is performed in comparison to previous NM-MRI. In other embodiments, the analysis is performed in comparison to reference values ​​and / or ranges. In some embodiments, reference values ​​and / or ranges are generated using edited neuromelanin data from healthy individuals. In some embodiments, reference values ​​and / or ranges are generated using edited neuromelanin data from individuals with Alzheimer's disease. In some embodiments, reference values ​​and / or ranges are generated using edited neuromelanin data from individuals with and without Alzheimer's disease.

[0104] In some embodiments, the NM-MRI signal is acquired from the substantia nigra or the locus coeruleus. In some embodiments, the NM-MRI signal is acquired from both the sniola nigra and the locus coeruleus.

[0105] Certain embodiments of the present disclosure can provide objective tests to improve diagnostic accuracy, advance the recognition of Alzheimer's disease to the pre-symptomatic stage, and serve as monitors for treatment. Generally, embodiments of the present disclosure can be used to diagnose neuromelanin using stored templates, to differentiate several different conditions or diseases, and to monitor subjects over a period of time.

[0106] In one embodiment, this disclosure is used in conjunction with a second imaging method, the second imaging method being positron emission tomography (PET). In one embodiment, this disclosure is used in conjunction with a second imaging method, the second imaging method being structural MRI. In one embodiment, this disclosure is used in conjunction with a second imaging method, the second imaging method being functional MRI (fMRI). In one embodiment, this disclosure is used in conjunction with a second imaging method, the second imaging method being blood oxygen level-dependent (BOLD) fMRI. In one embodiment, this disclosure is used in conjunction with a second imaging method, the second imaging method being iron-sensitive MRI. In one embodiment, this disclosure is used in conjunction with a second imaging method, the second imaging method being quantitative susceptibility mapping (QSM). In one embodiment, this disclosure is used in conjunction with a second imaging method, the second imaging method being diffusion tensor imaging (DTI). In one embodiment, this disclosure is used in conjunction with a second imaging method, the second imaging method being single-photon emission computed tomography (SPECT). In one embodiment, the present disclosure is used in conjunction with a second imaging method, the second imaging method being DaTscan. In one embodiment, the present disclosure is used in conjunction with a second imaging method, the second imaging method being DaTquant.

[0107] In some embodiments, neuromelanin concentration and / or levels are measured relative to a control, and a diagnosis of Alzheimer's disease is supported if the neuromelanin concentration and / or levels are about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 50%, 60%, 70%, 80%, or 90% lower than the control. In some embodiments, the neuromelanin change is evaluated as a net concentration or level change over a year. In some embodiments, the neuromelanin change is evaluated as a percentage change over a year. In some embodiments, neuromelanin concentration and / or levels are measured relative to a control, and the neuromelanin concentration and / or levels are about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, or 15% lower than the control. In some embodiments, neuromelanin concentration and / or levels are measured relative to a control, and the neuromelanin concentration and / or levels are about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, or 15% lower per year compared to the control.

[0108] In one embodiment, the control is a previous NM-MRI scan of the patient. In one embodiment, neuromelanin concentration and / or levels are measured relative to the control, and neuromelanin concentration and / or levels are measured annually, every two years, every three years, every four years, every five years, every six years, every seven years, every eight years, every nine years, every ten years, and every twenty years. In one embodiment, the second time point is about three months, six months, nine months, twelve months, two years, three years, four years, five years, six years, seven years, eight years, nine years, ten years, fifteen years, twenty years, twenty-five years, or thirty years after the first time point. In a particular embodiment, if the neuromelanin concentration and / or levels are measured to be below the control level, the patient is diagnosed with Alzheimer's disease. In certain embodiments, a patient is diagnosed with Alzheimer's disease if their neuromelanin concentration and / or level is measured to be a predetermined amount lower than the control, either per year or in net overall change. In further embodiments, the measured neuromelanin is more than about 20% lower than the control. In further embodiments, the measured neuromelanin is more than about 25% lower than the control. In further embodiments, the measured neuromelanin is more than about 30% lower than the control. In further embodiments, the measured neuromelanin is more than about 35% lower than the control. In further embodiments, the measured neuromelanin is more than about 45% lower than the control. In further embodiments, the measured neuromelanin is more than about 40% lower than the control. In further embodiments, the measured neuromelanin is more than about 50% lower than the control. In certain embodiments, the control is arbitrarily selected from previous neuromelanin MRI scans of the same patient. In other embodiments, the control includes a reference number arbitrarily determined from a database of neuromelanin MRI scans from at least one other person with the disease.

[0109] In one embodiment, a diagnosis of Alzheimer's disease is provided if the change in neuromelanin level, signal, and / or concentration at the second time point is more than about 5% less or more than about 10% less than the change in neuromelanin level, signal, and / or concentration at the first time point, and the first and second time points are separated by about 1 year, about 2 years, about 3 years, about 4 years, about 5 years, about 6 years, about 7 years, about 8 years, about 9 years, or about 10 years.

[0110] In one embodiment, a diagnosis of Alzheimer's disease is provided if the change in neuromelanin level, signal, and / or concentration at the second time point is more than 35%, more than 40%, more than 45%, or more than 50% less than the neuromelanin signal, and / or concentration at the first time point, and the first and second time points are separated by about 1 year, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, or 10 years.

[0111] In one embodiment, the degree of reduction in neuromelanin volume, signal, or concentration in a given patient compared to a control is proportional to the progression and / or severity of Alzheimer's disease.

[0112] In one embodiment, the degree of increase in neuromelanin volume, signal, or concentration in a given patient compared to a control is proportional to the progression of Alzheimer's disease and / or improvement and / or effectiveness of treatment.

[0113] In one embodiment, the standard control is either the level of neuromelanin present at approximately the same level within the population of study, or the standard control is the approximately average level of neuromelanin present within the population of study.

[0114] A series of validation studies are presented to illustrate the extended use of NM-MRI for such applications. The first procedure is provided to demonstrate that NM-MRI may have sufficient sensitivity to detect regional variations in tissue concentrations of NM, which are presumed to depend not only on the loss of NM-containing neurons but also on inter-individual and inter-regional differences in dopamine function (including synthesis and storage capacity). To test this, MRI measurements were compared to neurochemical measurements of NM concentrations in postmortem tissue without Alzheimer's disease. Because variability in dopamine function may not occur uniformly across all SN layers, the next procedure demonstrates that NM-MRI, with its higher anatomical resolution compared to standard molecular imaging procedures, has sufficient anatomical specificity. NM-MRI is used to test the ability of a novel voxel-by-voxel approach to capture known topographical patterns of cellular loss within the SN in Alzheimer's disease. The next procedure uses a segmented approach to provide direct evidence about the relationship between NM-MRI and Alzheimer's disease.

[0115] As discussed in WO2020 / 077098, which is incorporated in its entirety herein by reference, the NM-MRI signal correlates with a well-validated positron emission tomography ("PET") measure of dopamine release to the stritum, i.e., the primary projection site of SN neurons, as well as with a functional MRI measure of regional blood flow in the SN, i.e., an indirect measure of activity in SN neurons. The level of neuromelanin increase (SNc concentration, volume of NM within SNc) measured by the method of this disclosure results in improvement of UPDRS with L-DOPA therapy.

[0116] In one embodiment, any representative treatment for Alzheimer's disease is used. In one embodiment, the treatment is gene therapy. In one embodiment, the treatment dose remains constant if the neuromelanin concentration remains stable, unchanged, or constant. In one embodiment, the treatment dose increases if the neuromelanin concentration remains stable. In one embodiment, the Alzheimer's disease treatment dose increases if the neuromelanin concentration decreases by more than about 1%, more than about 2%, more than about 3%, more than about 5%, more than about 10%, more than about 15%, more than about 20%, or more than about 25%.

[0117] In one embodiment, neuromelanin is monitored. In one embodiment, the set of controls from other patients is age-matched. In one embodiment, the set of controls from other patients is sex-matched.

[0118] In some embodiments, neuromelanin is measured at least every other day, weekly, every two weeks, monthly, every other month, every three months, every six months, annually, every two years, every three years, every four years, every five years, every six years, every seven years, every eight years, every nine years, every ten years, every fifteen years, every twenty years, every twenty-five years, and every thirty years. In certain embodiments, a second therapeutic dose is administered weekly or every two weeks. In certain embodiments, the therapeutic agent is administered every hour, every two hours, every three hours, every four hours, every five hours, every six hours, every eight hours, every ten hours, every twelve hours, every fourteen hours, every sixteen hours, every eighteen hours, every twenty-four hours, every twenty-four hours, every day, every two days, every three days, every four days, every five days, every six days, every seven days, or at least every fourteen days.

[0119] In one embodiment, the treatment period (either initial or subsequent) or monitoring period as discussed herein is daily, every other day, every 28 days, weekly, every 2 weeks, every 3 weeks, every 4 weeks, every 5 weeks, every 6 weeks, every 7 weeks, every 8 weeks, every 9 weeks, every 10 weeks, every 11 weeks, every 12 weeks, every 13 weeks, every 14 weeks, every 15 weeks, every 16 weeks, every 17 weeks, every 18 weeks, every 19 weeks, or every 20 weeks, about once a month, about every other month, about every 3 months, about every 6 months, or about once a year.

[0120] In some embodiments, the degree of reduction in neuromelanin volume, signal, or concentration in a given patient compared to a control is proportional to the progression and / or severity of Alzheimer's disease (AD) and neuropsychiatric symptoms (NPS).

[0121] In some embodiments, the degree of increase in neuromelanin volume, signal, or concentration in a given patient compared to a control is proportional to the progression of AD and / or NPS and / or improvement and / or effectiveness of treatment.

[0122] In some embodiments, the standard control is either the level of neuromelanin present at approximately the same level within the population of study, or the standard control is the approximately average level of neuromelanin present within the population of study.

[0123] This disclosure demonstrates that by correlating AD and / or NPS symptoms measured by the AD and / or NPS scale (CAPS) administered by clinicians with specific LC segments and applying a segmentation-based analysis method, specific LC segments (referred to as AD segments and / or NPS segments) that correlate with specific symptoms on CAPS and are unique to each patient or consistent across patient populations with the same disease can be found, and the correlation between changes in neuromelanin scale after the initiation of therapy and improvements in CAPS scores can be determined, and the neuromelanin scale (e.g., total NM concentration in the locus coeruleus (LC) (micrograms of neuromelanin per microgram of moist tissue), NM concentration in small area LC, volume of neuromelanin in total LC, volume of small area LC) in patients with AD and / or NPS from the normal range in the control group can be determined. Determine the difference and the difference in neuromelanin levels from the control group that justifies a diagnosis of AD and / or NPS; correlate changes in neuromelanin scales after the initiation of therapy with improvements in CAPS; determine the level of neuromelanin increase that results in improvements in CAPS; verify that NM levels can be used to monitor the response to treatment; correlate AD and / or NPS segments with AD and / or NPS symptoms measured via CAPS scores; apply segment-based analytical methods to find specific segments (referred to as AD and / or NPS segments) that are unique to each patient or consistent across the entire patient population with the same disease that correlate with specific symptoms on CAPS; and correlate NM-MRI scans with both tau-PET or p-tau181 or ptau217 blood tests and CAPS scores.

[0124] In one embodiment, a region of interest is determined, the segment covering that region is measured, and the volume of neuromelanin within that region is determined.

[0125] In one embodiment, the region of interest is subdivided, and segments covering smaller areas are measured to determine the volume of neuromelanin within those areas.

[0126] In one embodiment, these segments are compared to a reference dataset and used to calculate the concentration of neuromelanin within a region of interest or within a subregion of interest.

[0127] In one embodiment, these segments are compared to a reference dataset and used to calculate the total amount of neuromelanin within a region of interest or within a subregion of interest.

[0128] In one embodiment, multiple comparisons are made between all segments identified in the region of interest and specific symptoms or symptom severity scales, or disease status, or demographic information, or other patient or disease-specific information, and correlations are found between subgroups of individual segments and levels of specific symptoms or symptom severity on the disease surveillance scale. These are called symptom-specific segments.

[0129] In one embodiment, multiple comparisons are made between all segments identified in the region of interest and specific disease diagnoses or demographic information, or other patient or disease-specific information, and associations are found between subgroups of individual segments and conditions diagnosed as having a specific disease. These are called disease-specific segments, and in one embodiment, they may include AD and / or NPS disease-specific segments.

[0130] In one embodiment, these symptom-specific or disease-specific segments may exhibit similarity across multiple patients with the same symptoms in the context of the same disease and can be used to compare multiple patients with the same disease (e.g., two patients with AD and / or NPS disease, both of whom have symptoms of hyperarousal, sleep disturbance, or nightmares). In this case, similarities between patients can be compared, symptom-specific segments may function as symptom-specific diagnostic biomarkers, and disease-specific segments may function as diagnostic biomarkers for specific diseases.

[0131] In one embodiment, these symptom-specific or disease-specific segments differ among patients who have the same symptoms occurring in the context of different diseases. In this case, the differences between symptom-specific segments can be used to distinguish between two different disorders that share the same symptoms.

[0132] In one embodiment, either a symptom-specific segment, a disease-specific segment, or the neuromelanin concentration or neuromelanin volume of a specific region or subregion can be used as a non-invasive biomarker to determine diagnostic information and diagnose the presence of a specific disease (in this case, AD and / or NPS disorder, or related stress disorders such as acute stress disorder (ASD)).

[0133] In one embodiment, this can be achieved by comparing baseline measurements of neuromelanin concentration or neuromelanin volume in a specific patient with future measurements of these values ​​in the same patient.

[0134] In one embodiment, this can be achieved by comparing a measurement of neuromelanin concentration or neuromelanin volume in a specific patient with a standard control, whether it be a symptom-specific segment, a disease-specific segment, or a specific region or subregion.

[0135] In one embodiment, a symptom-specific segment, a disease-specific segment, or the neuromelanin concentration or volume of a specific region or subregion can be used as a non-invasive biomarker to determine diagnostic information, rule out the presence of an associated disorder, or distinguish between AD and / or associated disorders such as NPS and ASD.

[0136] In one embodiment, this can be achieved by comparing baseline measurements of neuromelanin concentration or neuromelanin volume in a specific patient with future measurements of these values ​​in the same patient.

[0137] In one embodiment, this can be achieved by comparing a measurement of neuromelanin concentration or neuromelanin volume in a specific patient with a standard control, whether it be a symptom-specific segment, a disease-specific segment, or a specific region or subregion.

[0138] In one embodiment, symptom-specific segments, disease-specific segments, or neuromelanin concentrations or volumes in specific regions or subregions can be used as non-invasive biomarkers to classify or grade specific diseases or symptoms, and to distinguish or classify this information in patients. For example, this could be used to determine the stage of AD and / or NPS or stress disorder in a particular patient.

[0139] In one embodiment, the current severity of symptoms in a patient can be determined by using either a symptom-specific segment, a disease-specific segment, or the neuromelanin concentration or neuromelanin volume of a specific region or subregion as a non-invasive biomarker.

[0140] In one embodiment, a symptom-specific segment, a disease-specific segment, or the neuromelanin concentration or neuromelanin volume of a specific region or subregion can be used as a non-invasive biomarker to predict the onset of new symptoms that a patient has not yet experienced.

[0141] In one embodiment, a symptom-specific segment, a disease-specific segment, or the neuromelanin concentration or neuromelanin volume of a specific region or subregion can be used as a non-invasive biomarker to predict the severity of current symptoms, predict the future onset of the disease course, or predict the response of any of the specific symptoms or disease responses as an overall response to treatment, thus functioning as a non-invasive prognostic biomarker.

[0142] In one embodiment, a symptom-specific segment, a disease-specific segment, or the neuromelanin concentration or volume of a specific region or subregion can be used as a non-invasive biomarker to monitor the response to treatment for a specific symptom or overall disease state.

[0143] In one embodiment, symptom-specific segments, disease-specific segments, or neuromelanin concentrations or volumes in specific regions or subregions can be used as non-invasive biomarkers to guide the selection of the correct treatment for either specific symptoms or the overall disease state.

[0144] In one embodiment, a symptom-specific segment, a disease-specific segment, or the neuromelanin concentration or neuromelanin volume of a specific region or subregion can be used as a non-invasive biomarker to determine the status of treatment and whether an appropriate response to treatment was obtained for a specific symptom or the overall disease state.

[0145] In one embodiment, a symptom-specific segment, a disease-specific segment, or the neuromelanin concentration or volume of a specific region or subregion can be used as a non-invasive biomarker to predict future responses to treatment for a specific symptom or an overall disease state.

[0146] In any embodiment, a comparison may be made between the following:

[0147] Baseline measurements in a specific patient of either symptom-specific or disease-specific segments, or neuromelanin concentration or neuromelanin volume in a specific region or subregion, compared to future measurements of these values ​​in the same patient.

[0148] A measurement of neuromelanin concentration or neuromelanin volume in a specific patient, compared to a standard control, in a symptom-specific segment, a disease-specific segment, or a specific region or subregion.

[0149] Further PTSD and MDD Embodiments In some embodiments, the degree of reduction in neuromelanin volume, signal, or concentration in a given patient compared to a control is proportional to the progression and / or severity of PTSD.

[0150] In some embodiments, the degree of increase in neuromelanin volume, signal, or concentration in a given patient compared to a control is proportional to the progression of PTSD and / or MDD and / or improvement and / or effectiveness of treatment.

[0151] In some embodiments, the standard control is either the level of neuromelanin present at approximately the same level within the population of study, or the standard control is the approximately average level of neuromelanin present within the population of study.

[0152] This disclosure demonstrates that by correlating PTSD symptoms, as measured by the Clinical Diagnostic Scale for PTSD (CAPS) and / or MDD using DSM-5 or MINI criteria, with specific LC segments and applying a segmentation-based analysis method, specific LC segments (referred to as MDD segments and / or PTSD segments) that are unique to each patient or consistent across patient populations with the same disease and correlate with specific symptoms on CAPS, and determines the correlation between changes in neuromelanin scales after the initiation of therapy and improvements in CAPS scores, and analyzes neuromelanin scales (e.g., total NM concentration in the locus coeruleus (LC) (micrograms of neuromelanin per microgram of moist tissue), NM concentration in small area LC, total LC) in patients with PTSD and / or MDD from the normal range in the control group. The study involves determining the difference in neuromelanin volume (volume of small regions of LC), identifying the difference in neuromelanin levels from a control group that justifies a diagnosis of PTSD, correlating changes in neuromelanin scales after the initiation of therapy with improvements in CAPS scores, determining the level of neuromelanin increase that leads to CAPS improvement, verifying that NM levels can be used to monitor response to treatment, correlating PTSD and / or MDD segments with PTSD symptoms and / or BDI-II total score or HAMD or MADRS scales as measured via CAPS scores, and applying segment-based analytical methods to find specific segments (referred to as PTSD and / or MDD segments) that are unique to each patient or consistent across the entire patient population with the same disorder and correlate with specific symptoms on CAPS.

[0153] In one embodiment, a region of interest is determined, the segment covering that region is measured, and the volume of neuromelanin within that region is determined.

[0154] In one embodiment, the region of interest is subdivided, and segments covering smaller areas are measured to determine the volume of neuromelanin within those areas.

[0155] In one embodiment, these segments are compared to a reference dataset and used to calculate the concentration of neuromelanin within a region of interest or within a subregion of interest.

[0156] In one embodiment, these segments are compared to a reference dataset and used to calculate the total amount of neuromelanin within a region of interest or within a subregion of interest.

[0157] In one embodiment, multiple comparisons are made between all segments identified in the region of interest and specific symptoms or symptom severity scales including CAPS, or disease status including PTSD and / or MDD, or demographic information, or other patient or disease-specific information, and associations are found between subgroups of individual segments and levels of specific symptoms or symptom severity on the disease surveillance scale. These are called symptom-specific segments.

[0158] In one embodiment, multiple comparisons are made between all segments identified in the region of interest and specific disease diagnoses or demographic information, or other patient or disease-specific information, and associations are found between subgroups of individual segments and conditions diagnosed as having a specific disease. These are called disease-specific segments, and in one embodiment, they may include PTSD-disease-specific segments or MDD-disease-specific segments.

[0159] In one embodiment, these symptom-specific or disease-specific segments may exhibit similarity across multiple patients with the same symptoms in the context of the same disease and can be used to make comparisons between multiple patients with the same disease (e.g., two PTSD patients, both with symptoms of hyperarousal, sleep disturbance, or nightmares, and / or two MDD patients, both with symptoms of anhedonia). In this case, similarities between patients can be compared, the symptom-specific segments may function as symptom-specific diagnostic biomarkers, and the disease-specific segments may function as diagnostic biomarkers for specific diseases.

[0160] In one embodiment, these symptom-specific or disease-specific segments differ among patients who have the same symptoms occurring in the context of different diseases. In this case, the differences between symptom-specific segments can be used to distinguish between two different disorders that share the same symptoms.

[0161] In one embodiment, a symptom-specific segment, a disease-specific segment, or the neuromelanin concentration or volume of a specific region or subregion can be used as a non-invasive biomarker to determine diagnostic information and diagnose the presence of a specific disease (in this case, PTSD or acute stress disorder (ASD), or related stress disorders such as panic disorder and / or MDD, or related depressive disorders including dystohymia, cyclothymia, bipolar disorder type I and II, adjustment disorder, or bereavement).

[0162] In one embodiment, this can be achieved by comparing baseline measurements of neuromelanin concentration or neuromelanin volume in a specific patient with future measurements of these values ​​in the same patient.

[0163] In one embodiment, this can be achieved by comparing a measurement of neuromelanin concentration or neuromelanin volume in a specific patient with a standard control, whether it be a symptom-specific segment, a disease-specific segment, or a specific region or subregion.

[0164] In one embodiment, a symptom-specific segment, a disease-specific segment, or neuromelanin concentration or neuromelanin volume in a specific region or subregion can be used as a non-invasive biomarker to determine diagnostic information, rule out the presence of related disorders, or distinguish related disorders (in this case, PTSD or acute stress disorder ASD, or related stress disorders such as panic disorder and / or MDD, or related depressive disorders including dystohymia, cyclothymia, bipolar disorder type I and II, adjustment disorder, or bereavement).

[0165] In one embodiment, this can be achieved by comparing baseline measurements of neuromelanin concentration or neuromelanin volume in a specific patient with future measurements of these values ​​in the same patient.

[0166] In one embodiment, this can be achieved by comparing a measurement of neuromelanin concentration or neuromelanin volume in a specific patient with a standard control, whether it be a symptom-specific segment, a disease-specific segment, or a specific region or subregion.

[0167] In one embodiment, symptom-specific segments, disease-specific segments, or neuromelanin concentration or volume in a specific region or subregion can be used as non-invasive biomarkers to classify or grade specific diseases or symptoms, and to differentiate or classify this information in patients. For example, this can be used to determine the stage of PTSD, ASD, panic disorder, or related stress disorders in a particular patient.

[0168] In one embodiment, the current severity of symptoms in a patient, including hyperarousal, sleep disorders, and nightmares, can be determined using either a symptom-specific segment, a disease-specific segment, or the neuromelanin concentration or volume of a specific region or subregion as a non-invasive biomarker.

[0169] In one embodiment, a symptom-specific segment, a disease-specific segment, or the neuromelanin concentration or neuromelanin volume of a specific region or subregion can be used as a non-invasive biomarker to predict the onset of new symptoms that a patient has not yet experienced.

[0170] In one embodiment, symptom-specific segments, disease-specific segments, or neuromelanin concentrations or volumes in specific regions or subregions can be used as non-invasive biomarkers to predict the severity of current symptoms, predict the future onset of disease progression, or predict the response of any particular symptom or disease response as an overall response to treatment, thus functioning as non-invasive prognostic biomarkers. These treatments may include stellate ganglion block, vagus nerve stimulation, venlafaxine, beta-blockers, prazosin, brexpiprazole and aripiprazole, iloperidone, and NMDA antagonists including 3,4-methylenedioxymethamphetamine (MDMA), selective serotonin reuptake inhibitors (SSRIs), SNRIs, and ketamine.

[0171] In one embodiment, a symptom-specific segment, a disease-specific segment, or neuromelanin concentration or volume in a specific region or subregion can be used as a non-invasive biomarker to monitor the response to treatment for a specific symptom or overall disease state. These treatments may include stellate ganglion block, vagus nerve stimulation, venlafaxine, beta-blockers, prazosin, brexpiprazole and aripiprazole, iloperidone, and NMDA antagonists including 3,4-methylenedioxymethamphetamine (MDMA), selective serotonin reuptake inhibitors (SSRIs), SNRIs, and ketamine.

[0172] In one embodiment, symptom-specific segments, disease-specific segments, or neuromelanin concentrations or volumes in specific regions or subregions can be used as non-invasive biomarkers to guide the selection of the correct treatment for either specific symptoms or an overall disease state. These treatments may include stellate ganglion block, vagus nerve stimulation, venlafaxine, beta-blockers, prazosin, brexpiprazole and aripiprazole, iloperidone, and NMDA antagonists including 3,4-methylenedioxymethamphetamine (MDMA), selective serotonin reuptake inhibitors (SSRIs), SNRIs, and ketamine.

[0173] In one embodiment, either a symptom-specific segment or a disease-specific segment, or either neuromelanin concentration or neuromelanin volume in a specific region or subregion, can be used as a non-invasive biomarker to determine the status of treatment and whether an appropriate response to treatment was obtained for any specific symptom or overall disease state. These treatments may include stellate ganglion block, vagus nerve stimulation, venlafaxine, beta-blockers, prazosin, brexpiprazole and aripiprazole, iloperidone, and NMDA antagonists including 3,4-methylenedioxymethamphetamine (MDMA), selective serotonin reuptake inhibitors (SSRIs), SNRIs, and ketamine.

[0174] In one embodiment, neuromelanin concentration or neuromelanin volume in either a symptom-specific segment or a disease-specific segment, or in a specific region or subregion, can be used as a non-invasive biomarker to predict future responses to treatment for either a specific symptom or an overall disease state. These treatments may include stellate ganglion block, vagus nerve stimulation, venlafaxine, beta-blockers, prazosin, brexpiprazole and aripiprazole, iloperidone, and NMDA antagonists including 3,4-methylenedioxymethamphetamine (MDMA), selective serotonin reuptake inhibitors (SSRIs), SNRIs, and ketamine.

[0175] In any embodiment, a comparison may be made between the following:

[0176] Baseline measurements in a specific patient of either symptom-specific or disease-specific segments, or neuromelanin concentration or neuromelanin volume in a specific region or subregion, compared to future measurements of these values ​​in the same patient.

[0177] A measurement of neuromelanin concentration or neuromelanin volume in a specific patient, compared to a standard control, in a symptom-specific segment, a disease-specific segment, or a specific region or subregion.

[0178] By combining measurements of neuromelanin concentration or neuromelanin volume in a specific patient, either in a symptom-specific segment or a disease-specific segment, or in a specific region or subregion, with information from a second imaging test including PET imaging, fMRI, and BOLD, a more accurate diagnosis can be obtained using the algorithm of the present invention.

[0179] In some embodiments, the level of NM in SNc is measured by a voxel-based algorithm, and the level of NM in LC is measured via a segmentation-based algorithm. Combining these two measurements allows for a more accurate diagnosis than using either measurement alone.

[0180] In some embodiments, the concentration, volume, signal, and / or level of neuromelanin within the SNc are measured by a voxel-based algorithm.

[0181] In some embodiments, the concentration, volume, signal, and / or level of neuromelanin within the LC are measured via a segmentation-based algorithm.

[0182] In some embodiments, the concentration, volume, signal, and / or level of neuromelanin in the SNc are measured by a voxel-based algorithm, and the level of NM in the LC is measured via a segmentation-based algorithm.

[0183] In some embodiments, combining two measurements allows for a more accurate diagnosis than using either measurement algorithm alone. In some embodiments, combining two measurements allows for differentiation of diagnoses compared to using either measurement algorithm alone. In some embodiments, combining two measurements allows for differentiation of similar diagnoses and selection of a more useful treatment plan compared to using either measurement algorithm alone.

[0184] In some embodiments, the absolute difference in the degree of decrease in neuromelanin volume, signal, or concentration in the SNc and LC in a given patient, compared to a control, is proportional to the progression and / or severity of Parkinson's disease.

[0185] In some embodiments, the degree of increase in neuromelanin volume, signal, or concentration in the SNc and LC of a given patient, compared to a control, is proportional to the progression of Parkinson's disease and / or improvement and / or effectiveness of treatment.

[0186] In some embodiments, the standard control is either the level of neuromelanin present at approximately the same level within the population of study, or the standard control is the approximately average level of neuromelanin present within the population of study.

[0187] In some embodiments, this disclosure demonstrates that by correlating Parkinson's voxels with Parkinson's symptoms measured by UPDRS and applying a voxel-based analysis method, specific voxels unique to each patient (referred to as PD voxels) that correlate with specific symptoms on UPDRS, and by determining the correlation between changes in neuromelanin scale after initiation of L-DOPA therapy and improvement in UPDRS scores, the neuromelanin scale in patients with PD (e.g., total NM concentration in substantia nigra pars compacta (SNc) (micrograms of neuromelanin per microgram of moist tissue), NM concentration in small SNc areas, volume of neuromelanin in total SNc, SN) from the normal range of the control group. The study aimed to determine the difference in volume of a small region of c, identify the difference in neuromelanin levels from the control group that justifies a diagnosis of PD, correlate changes in neuromelanin scale after initiation of L-DOPA therapy with improvements in UPDRS scores, determine the level of neuromelanin increase that leads to UPDRS improvement, verify that NM levels can be used to monitor response to treatment, correlate Parkinson's voxels with Parkinson's symptoms measured via UPDRS scores, and apply voxel-based analytical methods to find specific voxels unique to each patient (called PD voxels) that correlate with specific symptoms on UPDRS, correlating them with both NM-MRI scans and DaTscan and UPDRS scores.

[0188] In the voxels discussed herein, the diagnostic or prognostic values ​​of a particular voxel are enhanced when combined with data on NM segments within the LC obtained by a segmentation-based algorithm.

[0189] In one embodiment, a region of interest is determined, the voxels covering that region are measured, and the volume of neuromelanin within that region is determined.

[0190] In one embodiment, the region of interest is subdivided, voxels covering the subregions are measured, and the volume of neuromelanin within those regions is determined.

[0191] In one embodiment, these voxels are compared to a reference dataset and used to calculate the concentration of neuromelanin in a region of interest or a subregion within the region of interest.

[0192] In one embodiment, these voxels are compared to a reference dataset and used to calculate the total amount of neuromelanin in a region of interest or a subregion within the region of interest.

[0193] In one embodiment, multiple comparisons are made between all voxels identified in the region of interest and specific symptoms or symptom severity, disease status, demographic information, or other patient or disease-specific information, and associations are found between subgroups of individual voxels on the disease surveillance scale and specific symptoms or symptom severity. These are called symptom-specific voxels. In some embodiments, this capability is enhanced when combined with information on NM levels within segments of LC determined by a segmentation-based algorithm.

[0194] In one embodiment, multiple comparisons are made between all voxels identified within a target region and specific disease diagnoses or demographic information, or other patient or disease-specific information, and associations are found between subgroups of individual voxels and conditions diagnosed with a specific disease. These are called disease-specific voxels, and in one embodiment, may include Parkinson's disease-specific voxels. This capability is enhanced when combined with information on NM levels within LC segments determined by a segmentation-based algorithm.

[0195] In one embodiment, these symptom-specific or disease-specific voxels exhibit similarity across multiple patients with identical symptoms in the context of the same disease and can be used to compare multiple patients with the same disease (e.g., two Parkinson's disease patients, both exhibiting psychomotor blunting). In this case, similarities between patients can be compared, and the symptom-specific voxels can function as diagnostic biomarkers. This capability is enhanced when combined with information on NM levels within segments of LC determined by a segmentation-based algorithm.

[0196] In one embodiment, these symptom-specific or disease-specific voxels differ among patients who have the same symptoms occurring in the context of different diseases. In this case, the differences between symptom-specific voxels can be used to distinguish between two different disorders that share the same symptoms. This ability is enhanced when combined with information about NM levels within segments of LC determined by a segmentation-based algorithm.

[0197] In one embodiment, symptom-specific voxels, disease-specific voxels, or neuromelanin concentration or volume in a specific region or subregion can be used as non-invasive biomarkers to determine diagnostic information and diagnose the presence of a specific disease (in this case, Parkinson's disease, or related disorders such as MSA, PSP, Parkinsonian syndromes, dyskinesia, dystonia, or essential tremor). This capability is enhanced when combined with information on NM levels within LC segments determined by a segmentation-based algorithm.

[0198] In one embodiment, this can be achieved by comparing baseline measurements in a particular patient of either symptom-specific voxels or disease-specific voxels, or neuromelanin concentration or neuromelanin volume in a specific region or subregion, with future measurements of these values ​​in the same patient. This capability is enhanced when combined with information on NM levels within segments of LC determined by a segmentation-based algorithm.

[0199] In one embodiment, this can be achieved by comparing a measurement of either symptom-specific voxels or disease-specific voxels, or neuromelanin concentration or neuromelanin volume in a specific region or subregion, in a particular patient to a standard control. This capability is enhanced when combined with information on NM levels within segments of LC determined by a segmentation-based algorithm.

[0200] In one embodiment, symptom-specific voxels, disease-specific voxels, or neuromelanin concentration or volume in a specific region or subregion can be used as non-invasive biomarkers to determine diagnostic information, rule out the presence of associated disorders, or distinguish Parkinson's disease from associated disorders such as MSA, PSP, Parkinson's syndrome, dyskinesia, dystonia, or essential tremor. This ability is enhanced when combined with information on NM levels within LC segments determined by a segmentation-based algorithm.

[0201] In one embodiment, this can be achieved by comparing baseline measurements in a particular patient of either symptom-specific voxels or disease-specific voxels, or neuromelanin concentration or neuromelanin volume in a specific region or subregion, with future measurements of these values ​​in the same patient. This capability is enhanced when combined with information on NM levels within segments of LC determined by a segmentation-based algorithm.

[0202] In one embodiment, this can be achieved by comparing a measurement of either symptom-specific voxels or disease-specific voxels, or neuromelanin concentration or neuromelanin volume in a specific region or subregion, in a particular patient to a standard control. This capability is enhanced when combined with information on NM levels within segments of LC determined by a segmentation-based algorithm.

[0203] In one embodiment, symptom-specific voxels, disease-specific voxels, or neuromelanin concentration or volume in a specific region or subregion can be used as non-invasive biomarkers to stage or grade specific diseases or symptoms, and to differentiate or classify this information in patients. For example, this can be used to determine the stage of Parkinson's disease (PD) or associated motor impairment in a particular patient. This capability is enhanced when combined with NM-level information of LC segments determined by a segmentation-based algorithm.

[0204] In one embodiment, symptom-specific voxels, disease-specific voxels, or neuromelanin concentration or volume in a specific region or subregion can be used as non-invasive biomarkers to determine the current severity of symptoms in a patient. This capability is enhanced when combined with information on NM levels within LC segments determined by a segmentation-based algorithm.

[0205] In one embodiment, symptom-specific voxels, disease-specific voxels, or neuromelanin concentration or volume in a specific region or subregion can be used as non-invasive biomarkers to predict the onset of new symptoms that a patient has not yet experienced. This capability is enhanced when combined with information on NM levels within LC segments determined by a segmentation-based algorithm.

[0206] In one embodiment, symptom-specific voxels, disease-specific voxels, or neuromelanin concentration or volume in a specific region or subregion can be used as non-invasive biomarkers to predict the current severity of symptoms, predict the future progression of the disease course, or predict the response of specific symptoms or the overall disease to treatment, and can function as non-invasive prognostic biomarkers. This capability is enhanced when combined with information on NM levels within LC segments determined by a segmentation-based algorithm.

[0207] In one embodiment, symptom-specific voxels, disease-specific voxels, or neuromelanin concentration or volume in a specific region or subregion can be used as non-invasive biomarkers to monitor the response to treatment for either a specific symptom or an overall disease state. This capability is enhanced when combined with information on NM levels within LC segments determined by a segmentation-based algorithm.

[0208] In one embodiment, symptom-specific voxels, disease-specific voxels, or neuromelanin concentration or volume in a specific region or subregion can be used as non-invasive biomarkers to guide the selection of the correct treatment for either a specific symptom or an overall disease state. This capability is enhanced when combined with information on NM levels within LC segments determined by a segmentation-based algorithm.

[0209] In one embodiment, symptom-specific voxels, disease-specific voxels, or neuromelanin concentration or volume in a specific region or subregion can be used as non-invasive biomarkers to determine the status of treatment and whether an appropriate response to treatment was obtained for a specific symptom or the overall disease state. This capability is enhanced when combined with information on NM levels within LC segments determined by a segmentation-based algorithm.

[0210] In one embodiment, symptom-specific voxels, disease-specific voxels, or neuromelanin concentration or volume in a specific region or subregion can be used as non-invasive biomarkers to predict future responses to treatment for either specific symptoms or an overall disease state. This capability is enhanced when combined with information on NM levels within LC segments determined by a segmentation-based algorithm.

[0211] In any embodiment, a comparison may be made between the following:

[0212] Baseline measurements in a specific patient of either symptom-specific or disease-specific voxels, or neuromelanin concentration or neuromelanin volume in a specific region or subregion, compared to future measurements of these values ​​in the same patient. This capability is enhanced when combined with information on NM levels within LC segments determined by a segmentation-based algorithm.

[0213] Measurements in a specific patient of either symptom-specific or disease-specific voxels, or neuromelanin concentration or volume in a specific region or subregion, compared to a standard control. This capability is enhanced when combined with information on NM levels within LC segments determined by a segmentation-based algorithm.

[0214] In some embodiments, any method considered herein with respect to a single neurological condition can be applied to any other neurological condition.

[0215] Dual analysis of LC and SNc In one embodiment, the prognosis and / or diagnosis of one or more neurological conditions can be determined using any of the methods discussed herein according to the following table. [Table 1]

[0216] In some embodiments, diagnostic or prognostic information is provided by determining changes in NM levels, volume, or concentration of both LC and SNc, where LC neuromelanin is determined by a segmentation approach and SNc neuromelanin is determined by a voxel-by-voxel approach.

[0217] In some embodiments, neurological conditions are diagnosed according to the table above using changes detected during an NM-MRI scan or against a standard control, according to any of the methods described herein.

[0218] In some embodiments, the prognosis of the neurological condition is provided according to the table above, using changes detected between NM-MRI scans or against a standard control, according to any of the methods described herein.

[0219] Computer-based analysis Exemplary procedures that comply with the disclosures described herein can be performed by cloud-based processing and / or computing configurations (e.g., computer hardware configurations). Such processing / computing configurations may be, for example, computer / processors in whole that include one or more microprocessors and can use instructions stored in computer-accessible media (e.g., RAM, ROM, hard drives, or other storage devices), or may include, but are not limited to, a computer / processor as part of a processing / computing configuration.

[0220] For example, a computer-accessible medium (such as a storage device, such as an encrypted cloud file, hard disk, floppy disk, memory stick, CD-ROM, RAM, ROM, or a collection thereof, as described above herein) can be provided (for example, to communicate with the processing configuration). The computer-accessible medium may contain executable instructions on the medium. In addition, or alternatively, a storage configuration may be provided separately from the computer-accessible medium, and the computer-accessible medium can provide instructions to the processing unit, thereby configuring the processing unit to perform, for example, certain exemplary procedures, processes, and methods as described above.

[0221] Furthermore, the exemplary processing configuration may or may include input / output ports that may include, for example, wired networks, wireless networks, the Internet, intranets, data acquisition probes, sensors, etc. The exemplary processing configuration may communicate with an exemplary display configuration, which, according to certain exemplary embodiments of this disclosure, may be, for example, a touchscreen configured to input information to the processing configuration in addition to outputting information from the processing configuration. Furthermore, the exemplary display configuration and / or storage configuration may be used to display and / or store data in a user-accessible and / or user-readable format. [Examples]

[0222] This disclosure is further illustrated by the following embodiments, but the embodiments should not be construed as limiting the scope or spirit of this disclosure to the specific procedures described herein. It should be understood that the embodiments are provided to illustrate specific embodiments and are not intended to limit the scope of this disclosure. It should also be understood that various other embodiments, modifications, and equivalents thereof may be available to those skilled in the art without departing from the spirit of this disclosure and / or the appended claims.

[0223] Example 1: Correlation between neuromelanin-sensitive MRI signals in the locus coeruleus, cortical tau proliferation, and neuropsychiatric symptoms. Neuropsychiatric symptoms (NPS) are a common and troublesome aspect of Alzheimer's disease (AD). Managing these symptoms, as well as cognitive impairment, often requires residential care. Some of these symptoms may not manifest until later stages of the disease, while others may appear even in the prodromal or pre-morbid stages. Effective treatment of NPS in the early stages can slow its progression and minimize associated complications. The most common existing treatments, antidepressants or antipsychotics, are inconsistently effective because they may not target the neurobiological causes of the symptoms in specific patients.

[0224] The underlying physiological mechanisms of AD pathophysiology and NPS are not well understood. These symptoms may be associated with major pathophysiological changes that occur in AD, including the accumulation of β-amyloid and phosphorylated tau. Tau and amyloid load in AD patients has been shown to correlate with aggression, psychosis, and other NPS symptoms. The LC, the main site of noradrenergic neurons, is the first brain region to begin degenerating early in AD and accumulate hyperphosphorylated tau protein in Braak stage 0. Compensatory changes occur in the noradrenergic system to restore balance, potentially leading to hyperactivity of remaining LC neurons. The noradrenergic system is becoming a major target in the treatment of AD, particularly with respect to NPS. Noradrenergic dysfunction may have a causal role, as symptoms of agitation / aggression and depression correlate with NPS in AD and respond to treatment with noradrenergic drugs. Regarding the direction of the relationships, depressive symptoms in AD are associated with LC degeneration and low noradrenergic function, while the overall, aggressive, and psychotic symptoms of AD are associated with high or conserved noradrenergic function. The inventor's research investigating these characteristic pathophysiological AD functions is clearly positioned to extend existing models of how these episodes interact to promote NPS in the early stages of the disease.

[0225] Advanced neuroimaging techniques for AD, including MRI and PET imaging, have enabled the measurement of LC degeneration and β-amyloid and tau overload in the ultrastructure of the human brain. The inventors' group has extensive experience with all of these tools. Using the tracers [18F]AZD4694 (for amyloid) and [18F]MK6240 (for tau, see Figure 1), over 300 PET scans have been successfully acquired. These validated tracers enable in vivo AD diagnosis and Blaak staging. The inventors' group validated NM-MRI as a measure of the structure and function of catecholamine neurons. As seen in Figure 1, NM-MRI captures noradrenergic degeneration in AD as loss of signal in LC (secondary to loss of neuromelanin pigment). A caveat with LC NM-MRI is the small size of this structure (cross-sectional diameter 1-2 mm, Figure 1), which limits what can be detected using high-field (3 Tesla) MRI. Recent advances have led to the development of ultra-high field (7T) NM-MRI sequences that increase image resolution (5.5× in this invention) and thereby reduce measurement noise (Figure 3). While no studies have yet validated the advantages of 7T NM-MRI in AD, at 3T, this method has not only revealed LC degeneration in AD but has also correlated in other populations with symptoms similar to NPS, including depression, sleep disorders, and autonomic dysfunction.

[0226] By combining these advanced neuroimaging techniques with NPS assessments using highly sensitive instruments designed for use before the onset of dementia (MBI), we can model the mechanisms by which AD-related brain changes lead to the emergence of NPS throughout the disease stage.

[0227] Participants and clinical scales Study participants from the McGill University Research Centre for Studies in Aging, either from the community or outpatients, were enrolled in the Translational Biomarkers of Aging and Dementia (TRIAD) cohort at McGill University, Canada. Cohort participants underwent detailed clinical assessments, including the Clinical Dementia Scale (CDR) and the Mini-Mental State Examination (MMSE). Participants without cognitive impairment had no objective cognitive impairment and a CDR score of 0. Individuals with mild cognitive impairment (MCI) had subjective and objective cognitive impairment, maintained activities of daily living, and had a CDR score of 0.5. Patients with mild to moderate sporadic Alzheimer's disease dementia had CDR scores of 0.5–2 and met the National Institute of Aging and Alzheimer's Association criteria for likely Alzheimer's disease as determined by a physician (McKhann et al., 2011). Sporadic early-onset Alzheimer's disease dementia was defined as individuals who developed dementia before the age of 65 (Snowden et al., 2011). Participants who were receiving inappropriate treatment, abusing active substances, had recently suffered a head injury or major surgery, or had MRI / PET safety contraindications were excluded. Alzheimer's disease patients did not discontinue their medication for this study.

[0228] The severity of NPS was assessed using the Mild Behavioral Disorder Checklist (MBI-C, http: / / www.MBItest.org). The MBI-C was completed by the participant's primary informant, usually their spouse. The MBI-C consists of 34 questions and is subdivided into five domains: (1) decreased vitality and motivation (apathy), (2) mood dysregulation (symptoms of depression and anxiety), (3) impulsivity control (agitation, impulsivity, abnormal reward prominence), (4) social inappropriateness (impairment of social cognition), and (5) abnormal perceptions and thoughts (psychotic symptoms). Each question is answered with "yes" or "no," and for each question answered "yes," the severity is assessed as 1=mild, 2=moderate, or 3=severe. To give a "yes" rating, the symptoms must have persisted for at least 6 months. This study was approved by the Research Ethics Board of the Douglas Mental Health University Institute and the working committee of the Montreal Neurological Institute's PET program, and written informed consent was obtained from all participants. [Table 2]

[0229] MRI acquisition All neuroimaging data was acquired at the Montreal Neurological Institute. Magnetic resonance (MR) images were acquired using a 3T Prisma scanner. NM-MRI images were acquired via turbo spin echo (TSE) sequencing using the following parameters: repetition time (TR) = 600 ms, echo time (TE) = 10 ms, flip angle = 120°, and in-plane resolution = 0.7 × 0.7 mm. 2Partial brain coverage by field of view (FoV) = 165 × 220, number of slices = 20, slice thickness = 1.8 mm, average number = 7, acquisition time = 8.45 minutes. The slice specification protocol consisted of oriented the image stack along the anterior-commissural-posterior-commissural lines, with the upper slice positioned 3 mm above the floor of the third ventricle when viewed in the sagittal plane of the center of the brain. High-resolution T1-weighted whole-brain MRI images were also acquired to preprocess NM-MRI and PET data using the MPRAGE sequence (inversion time = 1050 ms, TR = 2500, TE = 1.69 ms, inversion angle = 7°, FoV = 192 × 192, matrix = 192 × 256, number of slices = 256, isotropic voxel size = 1 mm, acquisition time = 5.47 minutes). MRI image quality was visually inspected for artifacts immediately after acquisition, and scans were repeated as time permitted if necessary.

[0230] Image preprocessing of NM-MRI images The initial preprocessing steps were performed in the same manner as in conventional work examining NM-MRI signals from the substantia nigra (5) using SPM12. Although the final analysis of LC signals was performed on native space NM-MRI images, it was nevertheless necessary to spatially normalize the NM-MRI images to align the universal LC search space from MNI space to native space for each participant. First, the NM-MRI scans were aligned to the participants' T1-weighted scans. Next, tissue segmentation was performed using the T1-weighted images. The NM-MRI scans were normalized to MNI space using the DARTEL routine with gray and white matter templates generated from all study participants. The resampled voxel size of these normalized NM-MRI scans was isotropic at 1 mm. After each of these steps, all images were visually inspected. A visualization template was created by averaging the spatially normalized NM-MRI images from all participants.

[0231] The subsequent steps involved using a custom Matlab script to specifically examine the LC signals. An over-inclusive LC mask was drawn on a visualization template to cover high-intensity voxels at the anterolateral rim of the fourth ventricle over a 15 mm span along the rostral-caudal axis (MNI spatial coordinates z=-16 to -31, see Figure 1). Rostral-caudal boundaries were defined by cross-referencing distances to anatomical landmarks in brain atlases (superior colliculus above the rostral end and posterior recess of the fourth ventricle above the caudal end). A segmented version of the mask was created by dividing it into five rostral-caudal segments of equal length. The entire over-inclusive LC mask and the segmented masks were then warped into native space and resampled into NM-MRI image space using the inverse of the flow field generated in the spatial normalization step. The warped entire over-inclusive LC mask could then be used to define a search space for finding the LC in each participant. Using a clustering algorithm, the LCs in this space are segmented, and the four adjacent voxels (1.96 mm) with the highest average signal are selected. 2 ) was defined as follows. This operation was repeated for the left and right LC. The contrast-to-noise ratio (CNR) of each voxel v in a given axial slice is given below: CNR V =(I V -Mode (I RR )) / mode(I RR) was calculated as the relative difference in NM-MRI signal intensity I from a reference region RR within the same slice. A region known to have low NM density, i.e., the pontine center centered 32.6 mm from the axis connecting the left and right LCs, was used as the reference. All axial slices in native space were identified as belonging to one of five rostral-caudal LC segments based on which of the five divided LC masks was present on the slice (if two of these masks were present on the same slice, the LC segment was defined by the mask covering the most LC voxels). For each of the five segments, the NM-MRI CNR value from the brightest voxel on each side was calculated and averaged across all slices determined to be within that segment. To minimize partial volume effects, the brightest voxel per slice was selected rather than the average of all LC voxels.

[0232] PET acquisition and analysis All subjects underwent 18 F-AZD4694 and 18 F-MK-6240 PET scans using a Siemens High Resolution Research Tomograph specifically designed for the brain. For detailed PET procedures, please refer to previous studies. Tau changes 18 F-MK-6240 images were acquired 90–110 minutes after intravenous bolus injection of the tracer and reconstructed on a 4D volume with 4 frames (4 300s) using the OSEM algorithm (Pascoal et al., 2018b). Amyloid-b was acquired 40–70 minutes after intravenous bolus injection of the tracer. 18F-AZD4694 images were acquired and scans were reconstructed using the same OSEM algorithm on a 4D volume over 3 frames (3600 s) (Cselenyi et al., 2012). At the end of each PET acquisition, a 6-minute transmission scan was performed using a rotating 137Cs point source for attenuation correction. Images were further corrected for motion, dead time, attenuation, and random and scattered coincidences. Briefly, T1-weighted MRI was corrected for inhomogeneity and field distortion. Subsequently, the PET images were automatically registered to the T1-weighted image space, and the T1-weighted images were registered linearly and nonlinearly to the MNI standard space (Mazziotta et al., 1995). For the PET images, the meninges and skull were stripped and nonlinearly registered to the MNI space using the transformation from the T1-weighted images to the MNI space and the transformation from the PET images to the T1-weighted image space. 18 F-MK-6240 standardized uptake value ratio (SUVR) and 18 F-AZD4694 SUVR was used with the inferior cerebellum and whole cerebellar gray matter as reference regions, respectively (Cselenyi et al., 2012; Pascoal et al., 2018b). The PET images were spatially smoothed to a full width at half maximum resolution Global 18 F-AZD4694 SUVR values were estimated for the entire cortex (Pascoal et al., 2018a). 18F-MK-6240 SUVR values ​​are Braak (Braak and Braak,1991,1997, Braak et al.,2006, Braak et al.,2011, Braak I (transentorhinal), Braak II (entorhinal and hippocampus), Braak III (amygdala, parahippocampal gyrus, fusiform gyrus, lingual gyrus), Braak IV(insula,inferior temporal,lateral temporal,posterior cingulate,and inferior parietal),Braak V(orbitofrontal,superior temporal,inferior frontal,cuneus,anterior cingulate,supramarginal gyrus,lateral occipital,precuneus,superior parietal,superior frontal,rostro medial frontal),Braak VI,(paracentral,postcentral,precentral,and The Braak disease stage region proposed by pericalcarine was estimated. The subjects were divided into three groups based on the Braak disease stage. Tau-negative (in Braak region 1 < 1.2) 18 F-MK-6240 SUVR), positive for stage 1 of Braak disease (in the Braak stage 1 region > 1.2). 18 F-MK-6240SUVR (these cases are Braak stage 1 or 2), and Braak stage 3 positive (in Braak stage 3 region > 1.5). 18 F-MK-6240 SUVR (These cases are Braak stage 3 or higher). In the stage 3 region, tau levels were above the threshold, but there were no inconsistent cases where they were not above the threshold in the stage 1 region.

[0233] statistical analysis Statistical studies relating the final imaging measurements to each other and to clinical measurements were performed using Matlab software. These included Tukey's post-hoc test, linear regression analysis, and ANCOVA with Spearman partial correlation. For details of the specific models used, please refer to the results. Using the VoxelStats package (Mathotaarachchi et al., 2016) and Matlab software version 9.2 (http: / / www.mathworks.com), 18 Voxel-by-voxel statistics were performed for the F-MK-6240 SUVR map.

[0234] result LC NM-MRI signal and Braak's stage First, we confirmed that LC NM-MRI signals were reduced in AD. LC NM-MRI signals were averaged across the entire LC in 191 elderly individuals segmented based on cognitive status (two levels: normal cognitive and cognitive impairment (AD and MCI individuals)) and tau status (three levels: tau negative, tau positive in Braak region 1 (threshold SUV of region 1 ROI = 1.2), and tau positive in Braak stage 3 region (threshold SUV of region 3 ROI = 1.5; see Figure 3 for definition of ROI)). A two-way ANOVA for all LC NM-MRI signals, adjusted for age and sex, showed a significant impact of tau status (F 2、184 (=4.53, p=0.012) and cognitive status did not show a significant effect (F 1、184 =0.34, p=0.56, adjusted for age and sex; more complex models did not show a significant tau status x cognitive status interaction, p=0.48). Post-hoc testing showed a significant difference between tau-negative individuals and blaak-3 positive individuals (p=0.012), but no difference was found between blaak-1 (single) positive individuals and either tau-negative (p=0.17) or blaak-3 positive individuals (p=0.27, Tukey's HSD).

[0235] Next, within the lower subdivision of the LC, the structure of tau-status related signal loss was examined. All segments, except for the rostral and caudal ends of the LC, were significantly different between tau groups, with the middle segment showing the strongest effect (Figure 1). When examining the middle LC segment (average of NM-MRI signals from this segment on the left and right), a very strong relationship was observed with tau status (F2,184 = 15.3 p<0.00001), and a decrease in signal was observed in black 3 positive individuals compared to both tau negative individuals (p<0.00001) and black 1 positive individuals (p = 0.0045), but no decrease in signal was observed in black 1 positive individuals compared to tau negative individuals (p = 0.075, Tukey's HSD, Figure 1 shows the LC middle segment signals for all study groups). Considering these strong effects present in the middle LC segment, this was retained as the LC NM-MRI measure for all subsequent analyses.

[0236] These results suggested that LC signal loss was minimal in black disease stage 1 and became prominent from black disease stage 3 onwards. To determine whether there was evidence of progressive loss of LC signal from black disease stage 3, the cognitive impairment and dementia stages were correlated with the LC signal in black 3 positive individuals. Both measures were significantly correlated with the loss of LC signal (MMSE error: t 23 = -2.95, p = 0.0072, robust linear regression, Clinical Dementia Rating Scale: Spearman ρ = -0.52, p = 0.012, n = 25, partial correlation, both analyses adjusted for age and gender, see Figure 1).

[0237] LC NM-MRI signal and AD pathology To more fully examine the relationship between the LC NM-MRI signal and tau propagation, voxel-wise analyses relating the LC signal to 18 F]MK-6240 uptake were performed across the whole brain. Significant clusters were seen in regions (see Figure 6). To examine the extent to which the link between the LC signal and tau load was direct, the inventors performed subsequent voxel analyses after also adjusting for cortical beta-amyloid load and cortical gray and white matter volumes.

[0238] To examine which aspects of AD pathophysiology independently predicted loss of LC NM-MRI signal, linear regression analyses were performed in individuals with cognitive impairment (n = 75). Because of very high collinearity (r = 0.91), aggregate measures of tau and amyloid were not included together in the same model. LC NM-MRI signal was significantly predicted by tau load in black region 3 (t 70 = -2.36, p = 0.021, linear regression adjusted for CDR score, age, and gender) and cortical amyloid load (t 70 = -2.50, p = 0.015, linear regression with the same covariates). However, when cortical gray-white matter volume was included in either of these models, it became a significant predictor of LC NM-MRI signal (in the tau model: t 68 = 2.13, p = 0.037, linear regression adjusted for tau load in black region 3, cortical gray-white matter volume, total intracranial volume, CDR score, age, and gender), and neither tau nor amyloid remained as significant predictors.

[0239] LC NM-MRI signal and clinical findings Finally, the clinical correlates of LC NM-MRI signal loss were investigated. Specifically, both cognitive impairment and neuropsychiatric symptoms related to LC signal were examined while adjusting for major pathophysiological measures. In individuals with cognitive impairment (n = 72), LC signal was not significantly correlated with the degree of cognitive impairment (MMSE score: t 64 = 0.83, linear regression adjusted for tau load in black region 3, cortical gray-white matter volume, total intracranial volume, CDR score, age, and gender).

[0240] Next, neuropsychiatric symptoms measured by the total score of the Mild Behavioral Impairment Checklist (MBI) were examined. This measure was significantly associated with LC NM-MRI signal in individuals with cognitive impairment (n = 73) regardless of which pathophysiological measures were included as covariates (Table xx). In the preferred model, both LC NM-MRI signal and tau load in black region 3 predicted the MBI total score (t 65=3.48, p=0.0009 and t 65 =2.48, p=0.016. Linear regression was adjusted for cortical gray matter volume, total intracranial volume, CDR score, age, and sex (Figure 4). The correlation between LC NM-MRI signaling and total MBI score was confirmed in a non-parametric test using the same covariates as in linear regression (Table xx, preferred model Spearman ρ=0.40). This positive relationship suggests that maintaining LC is associated with worsening NPS, and it is noteworthy that this effect is stronger when any measure of cortical pathology is included in the model. In a post-hoc analysis, MBI subdomains were examined, and the domain with the strongest correlation to LC NM-MRI signaling was found to be impulse control disorder (Spearman ρ=0.36, p=0.003, partial correlation adjusted for tau load, cortical gray matter volume, total intracranial volume, CDR score, age, and sex).

[0241] When predicting the total MBI score in cognitively normal older adults, LC NM-MRI signaling was not a significant predictor (t 92 = -0.19, p = 0.85). Also, tau loading in Braak region 1 was not a significant predictor (t 92 =1.91, p=0.059, linear regression controlling for cortical gray matter volume, total intracranial volume, CDR score, age, and sex. [Table 3]

[0242] The analysis included age, sex, and CDR score as covariates (total intracranial volume was also included as a covariate in analyses including cortical gray matter volume). *p<0.05, **p<0.01, ***p<0.001

[0243] Example 2: A longitudinal multimodal neuroimaging study to determine the LC, amyloid, and tau signatures of future progression of neuropsychiatric symptoms in patients with MCI and AD, as well as elderly patients with CN. LC NM-MRI signals, along with amyloid and tau load in major brain regions, predict the progression of NPS at 18 months. This was examined separately in cognitively impaired (AD and MCI) and non-impaired elderly individuals to determine the prediction even in the earliest stages of the disease.

[0244] The obtained data demonstrate that NPS is associated with tau accumulation, β-amyloid accumulation, and LC integrity when measured in vivo using PET[18F]MK6240, [18F]AZD4694, and neuromelanin-sensitive MRI (NM-MRI), respectively.

[0245] Without being bound by any theory, NPS reflects an imbalance of major pathophysiological changes occurring in AD, on the one hand indicating LC integrity and on the other hand indicating amyloid and tau accumulation in the cortex. The combined effect of these processes may lead to an imbalance in the cortical and subcortical regulation of behavior, potentially causing the emergence of NPS. Our preliminary data reveal a pattern associated with NPS in the early stages of the disease, where cortical tau accumulation, coupled with LC preservation, reflects a dysregulation of cortical regulation of LC, possibly intact or possibly hyperactive, leading to the manifestation of NPS, including impulse control and emotional dysregulation. This is also consistent with reports of increased noradrenergic activity and tau pathogenesis (measured in CSF), both of which correlate with the exacerbation of NPS and the use of noradrenergic inhibitors to treat NPS. Linear regression models have been used to predict NPS including all neuroimaging measures. Specifically, LC NM-MRI signaling, tau load, and amyloid load are all positively associated with the progression / onset of NPS, and composite predictions in models including all measures are superior to predictions using any single measure alone. Identifying which neuropathological processes are most relevant to a particular patient is a crucial step before targeted NPS treatment can become a reality.

[0246] NPS severity from LC NM-MRI signals (total MBI score, n=73), tau load (Brak stage 3 ROI)18 F] MK6240 SUVR), and β-amyloid loading (in cortical ROI [ 18 F] SUVR of AZD4694). Multivariate prediction is superior to univariate prediction for all measures. [Table 4]

[0247] The heterogeneity of NPS is an important consideration. In the inventors' sample, the MBI total score was most highly correlated with the impulse control disorder subdomain (r=0.85), a symptom type with a strong theoretical link to the noradrenergic system. On the other hand, depressive symptoms were associated with low noradrenergic function (decreased LC NM-MRI signaling), and therefore, depressive symptoms were considered separately in the analysis.

[0248] Research design and timeline This study consists of baseline clinical and neuropsychological assessments, amyloid and tau PET scans, and MRI scans in n=70 elderly individuals with cognitive impairment (CN), n=35 elderly individuals with mild cognitive impairment (MCI), and n=35 elderly individuals with dementia (AD). Participants will return after 18 months for further clinical and neuropsychological assessments. Participants are already involved in a longitudinal protocol called Translational Biomarkers in Aging and Dementia (TRIAD), which facilitates follow-up contact. Timeline: Months 1-6: Ethical review approval, staff training, and optimization of 7T NM-MRI sequencing. Months 7-30: Participant recruitment. To achieve a sample of n=140 (after natural attenuation), the inventors recruited 77 individuals per year. Similar to the TRIAD cohort, 100-130 participants will complete MRI and PET imaging procedures annually. Neuroimaging quality control and preprocessing will proceed as data is collected, with preliminary analysis performed when half of the sample has been collected. Months 24-48: Follow-up clinical and cognitive assessments.

[0249] Recruitment and consent: All participants will be recruited from the McGill Centre for Studies in Aging (MCSA)'s Translational Biomarkers in Aging and Dementia (TRIAD) longitudinal PET biomarker cohort. MCSA has a clinical database of 4,000 patients, and the TRIAD cohort is primarily drawn from this database. More than 1,000 people have already been recruited to participate in the study.

[0250] Recruitment criteria: Participants aged 55 to 90 years with a history of academic achievement to exclude intellectual disability will be enrolled. Participants who are unable to provide informed consent or who become unable to provide consent during the study will be excluded. The ability to provide consent will be determined using a screening battery including the MMSE and MoCA. Cognitively normal older adults (CN) are defined by a Clinical Dementia Rating (CDR) of 0. MCI is defined by a CDR of 0.5, subjective and objective memory loss, and normal activities of daily living. The CN and MCI groups do not have dementia based on the Petersen and National Institute of Ageing-Alzheimer's Association Criteria. AD cases are defined as having a mild severity and a CDR of 0.5 to 1.0, and are diagnosed using the National Institute of Ageing-Alzheimer's Association criteria.

[0251] Exclusion criteria: (i) Illiteracy (not due to cognitive impairment), (ii) use of recreational drugs, (iii) major structural abnormalities or major vascular pathologies on MRI examination, (iv) participation in an interventional trial within the previous 4 weeks or exposure to ionizing radiation within the past 12 months (through research studies or radiation examinations), (v) contraindications to MRI / PET scans, (vi) chronic and recurrent mental health conditions, i.e., past history of mental disorders (e.g., schizophrenia, major depression, PTSD). Include patients with newly developed mental symptoms, provided the severity does not prevent participation (e.g., violence or aggression).

[0252] Neuropsychiatric symptoms and other clinical and cognitive measures: The NPS scales enable comprehensive evaluation of a wide range of types of NPS and detect their expression with high sensitivity at various disease stages. These include the standard questionnaires used in AD, namely the Neuropsychiatric Inventory (NPI), the Apathy Inventory, and the Epworth Sleepiness Questionnaire. Additionally, a special questionnaire, the Mild Behavioral Impairment Checklist (developed by Dr. Ismail), which is designed with high sensitivity for the evaluation of NPS in preclinical AD, is employed. The standard protocol used in the TRIAD cohort follows clinical profile and cognitive evaluations. Cognitive measures include the Rey Auditory Verbal Learning Test (RAVLT), the Digit Span and Digit Symbol from WAIS-III, IQ (WASI-II, Matrix Reasoning, Vocabulary, part of a 3-hour battery administered by a neuropsychologist). These measures are recorded every 24 months in the TRIAD protocol and are not repeated for participants tested within 60 days of baseline or follow-up evaluations. The inventors' main measure of interest is the change in MBI over 18 months. Preliminary follow-up data indicate that this measure can capture changes over 1 year or 2 years.

[0253] NM-MRI acquisition at 7T Participants are scanned with a Siemens Terra 7T scanner equipped with an 8-channel transmit and 32-channel receive head coil (Nova Medical). A magnetization transfer preparation turbo flash sequence (MTw-TFL), developed by Christine Tardif's laboratory, is used to image the LC. The MT preparation consists of a pulse train of 15 pulses with a duration of 1 ms (2 ms gap), an offset frequency of 10 kHz, and a root mean square value of B1 of 9 μT. The polarity of the frequency offset alternates between pulses. Following each MT preparation block, a center-out TFL readout (TE / TR = 4.3 / 505 ms, turbo coefficient = 29, flip angle = 8°, GRAPPA = 2) is performed with a scan time of 4:31 minutes and a resolution of 0.4 x 0.4 x 1.0 mm³. This sequence is repeated twice and averaged to increase the SNR. Further optimization of this sequence is being undertaken to maximize the SNR and reliability of the LC signal, thereby improving this state-of-the-art sequence. A high-resolution (0.65mm isotropic) T1-weighted anatomical scan using the MP2RAGE sequence is performed: TI1 / TI2=1000 / 32000ms, TR=43000ms, TE=2.46, α=4°, echo interval=7.5ms, slice partial Fourier 6 / 8, GRAPPA=3, scan time=11 minutes. For subjects who wish to tolerate longer sessions, resting blood oxygen level-dependent (BOLD) functional MRI data are also collected for exploratory analysis of functional connectivity changes correlated with NPS. For this purpose, a 2D gradient echo EPI sequence at 1.8mm isotropy is used: TE=25ms, TR=2010ms, α=70°, GRAPPA=2, phase partial Fourier=6 / 8, scan time=10.5 minutes. Upon completion of all scans, the total scan time will be approximately 40 minutes.

[0254] NM-MRI pretreatment and analysis LC NM-MRI signals are measured on raw NM-MRI images in native space using custom scripts and the SPM12 tool, similar to previously used approaches, and LC is segmented. Analyzing signals from this small structure in native space is advantageous compared to conventional approaches in standardized space for MRI analysis. The NM-MRI preprocessing pipeline was developed by Dr. Cassidy.

[0255] PET acquisition PET scans are performed on a Siemens HRRT. Radiation tracers are generated by the center's Radiation Chemistry Laboratory and cyclotron. PET and MRI scans are performed on the same day. Another PET scan is performed on a subsequent day. [18F]AZD4694 or [18F]MK6240 PET scans are acquired after administration of 185 MBq of the tracer. Scans using [18F]MK6240 are 20 minutes long and begin 90-110 minutes after injection. Scans using [18F]AZD4694 are 30 minutes long and begin 40-70 minutes after injection. Subjects wear special glasses to correct head movement. Dynamic images are acquired using list-mode files. Transmitted images are acquired using a Ge-68 source. Tissue radioactivity images are re-binned using 4 frames and reconstructed using the OSM3 method with scattering and decay corrections. Motion correction is then applied.

[0256] PET analysis Quantification of [18F]AZD4694 or [18F]MK6240 PET scans is performed for both anatomical regions of interest (ROIs) and individual voxel maps. In both cases, the first step is to segment the MRI volume (T1-weighted images) to obtain gray matter maps, which are then aligned to the PET images via stiffness transformation using the MINC tool. Each [18F]AZD4694 SUVR50-70 or [18F]MK6240 SUVR90-110 is analyzed with the cerebellar cortex as the reference region. The MRI scans are transformed into a standard MNI space using nonlinear alignment. For ROI analysis, the inverse transformation of the alignment parameters is used to map the stochastic anatomical atlas to the PET images. The temporal activity curve (TAC) of the region is calculated via a mask obtained by convolution of the segmented gray matter images and the atlas of the subject. Next, both the region of interest (ROI) and voxel TACs are input into appropriate quantification procedures to obtain SUVRs of a set of anatomical ROIs or BP parametric maps for voxel-by-voxel analysis. Partial volume correction (PVC) is performed on all images. Voxel-based analysis is performed by first warping the parametric maps into MNI space using the nonlinear alignment procedure described above. The parametric maps are then smoothed (6mm) to reduce noise. Voxel-level univariate tests using a generalized linear model (GLM) are applied along with post-hoc multiple comparison corrections derived from random field theory, which are implemented in Voxel Statistics, a suite for running voxel-based generalized linear models developed at McGill University.

[0257] Statistical Analysis: The primary hypothesis testing analysis is linear regression predicting changes in NPS severity at 18 months post-baseline based on baseline LC NM-MRI signaling, amyloid load, and tau load in ROIs, where these substances appear in the early stages of the disease (Brak stage 3 region for tau, and the entire cerebral cortex for amyloid). Effects of interest are the independent effect of LC NM-MRI signaling, as well as the increased explained variance by additionally including amyloid and / or tau loads in the model. In the analysis of CN cases, the total MBI score is used as the outcome measure (a highly sensitive instrument favored for cases with minimal symptom burden). In CI individuals (MCI and AD), the analysis predicts total scores for both MBI and NPI. Post-hoc studies include voxel-by-voxel analysis using the same predictors and outcomes (within a mask of regions involved in early tau / amyloid accumulation to minimize penalty for multiple comparisons), as well as ROI analysis using more specific types of NPS as outcome measures (e.g., impulse control disorder, sleep disturbance, aggressive behavior). For all analyses, possible interactions between LC NM-MRI signals and amyloid / tau in predicting NPS severity will be examined. Covariates included age, sex, dementia severity (Clinical Dementia Rating Scale), and depression severity (NPI Depression Item). Norepinephrine function may have an inverse relationship with depression compared to symptoms such as aggression and impulsivity, and therefore adjustment for depression severity is necessary (aggression and impulsivity, etc., correlate very highly with the MBI total score, which is the inventors' primary measure of interest). Conversely, analyses predicting depressive symptoms (NPI Depression) will be adjusted for other NPS severity levels.

[0258] Sex and Gender-Based Analysis: Sex influences have been observed in late-stage depression, for example, with regard to cognitive impairment, functional impacts, and associations with brain structure. Furthermore, animal studies have reported sex dimorphism regarding the relationship between LC damage and taurinization, which is consistent with female predisposition to norepinephrine-related disorders. Therefore, sex may be a contributing factor to these relationships. An equal number of males and females will be recruited (63% female in previous recruitments from the TRIAD cohort). Primary analysis models will be run separately for males and females to determine whether the strength of the effect differs significantly by sex.

[0259] These results have lasting effects on patients with dementia and those at risk. Approximately 75% of AD patients and 50% of individuals with mild cognitive impairment (MCI) suffer from NPS, compared to 25% of individuals exhibiting normal cognitive aging. The presence of these symptoms is associated with rapid decline in cognitive function, reduced quality of life, early admission to nursing homes, and increased caregiver burden. Existing treatments for neuropsychiatric symptoms in AD have limited efficacy for many patients at high risk of adverse effects; therefore, understanding neurobiology is essential to finding and monitoring improved treatments. The biomarkers described herein will help guide the development of new NPS therapies and the optimization of existing treatments, ultimately supporting a precision medicine approach to identify patients most likely to respond to specific NPS therapies. One NPS therapeutic target is the noradrenaline system, whose integrity is measured via the LC NM-MRI signal described herein. In fact, since NM-MRI is a practical and non-invasive assay of the neurochemical changes underlying AD pathology, it can prove to be a useful tool, for example, as a potential moderator of NPS treatment response or as a marker of response to LC neuroprotective agents. There is promising evidence for these therapeutic applications. Aggressive behavior in AD patients responds to noradrenergic drug therapy in patients exhibiting noradrenergic dysfunction and is likely to lead to LC neuroprotective agents that reduce NPS-like behavior in animal models of AD.

[0260] Example 3: NM MRI for evaluating post-traumatic stress disorder (PTSD) and major depressive disorder Introduction Post-traumatic stress disorder (PTSD) is a heterogeneous condition that reduces the quality of life for veterans and poses a significant risk of suicide. Given the complex manifestation of the disorder, treatments targeting specific neurobiological disruption in a patient-specific manner may be necessary. However, the search for biomarkers to support targeted therapies in PTSD has been challenging. Recent research suggests that dysregulation of the neuromodulator norepinephrine (NE) may contribute to PTSD symptoms. The locus coeruleus (LC) is the central nucleus for NE release in the human brain, and the LC-NE system plays a crucial role in regulating stress responses, autonomic functioning, emotional memory, sleep, and wakefulness. These LC regulatory behaviors are particularly associated with the hyperarousal symptom domain of PTSD, as defined by the DSM-5 as exaggerated startle responses, hypervigilance, and sleep disturbances. For example, individuals with PTSD have been observed to show higher fMRI activation of LC BOLD in response to stimuli compared to controls. Furthermore, a relationship has been shown between autonomic nervous system dysregulation and the severity of hyperarousal symptoms. For example, a 2007 study by Blechert et al. found that PTSD patients "showed attenuation of parasympathetic control and enhancement of sympathetic control, as evidenced by hypopnea sinus arrhythmia (a measure of cardiac vagal control) and elevated cutaneous electrical activity." Given the evidence for the role of the NE system in PTSD, there is a growing effort to develop pharmacological therapies that target this system. Several drugs acting on this system have shown benefits, including venlafaxine, a mixed NE / serotonin reuptake inhibitor that is a common PTSD treatment and has been shown to be superior to certain serotonin reuptake inhibitors and beta-blockers when used in conjunction with trauma re-examination psychotherapy. Furthermore, the NEα-1 receptor antagonist prazosin has shown inconsistent evidence of efficacy in PTSD, highlighting the potential benefits of biomarkers tracking noradrenergic imbalance that could pre-select potential responders to noradrenergic agents like prazosin, thereby supporting trials of experimental noradrenergic therapies.

[0261] Neuromelanin-sensitive magnetic resonance imaging (NM-MRI) is a novel, non-invasive neuroimaging method that can be used to image NM-containing structures, LC, and dopaminergic substantia nigra in the human brain due to the paramagnetic properties of NM. The inventors previously showed that NM signals in the substantia nigra can provide a surrogate measure for PET imaging metrics of dopamine function, which has the practical advantages of being less expensive, non-invasive, and available at high resolution. Here, we propose that LC-NM signals can provide similar insights into the function of the NE system. Although LC-NM-MRI signals have not yet been investigated in PTSD, there is evidence that LC-NM-MRI signals track measures of NE or autonomic function, correlating with heart rate variability, alpha-amylase secretion, and anxiety-arousing symptoms in anxiety disorders. The inventors are particularly interested in the caudal LC because this region sends descending projections to the autonomic nervous system, enhances activity in PTSD, and correlates with autonomic nervous system measures.

[0262] In this study, the relationship between LC NM-MRI signals and hyperarousal symptoms, a dimensional scale of PTSD psychopathy, was investigated in a sample of 24 Canadian Armed Forces (CAF) veterans seeking assistance who had operational experience. The inventors hypothesized that NM-MRI signals in the caudal LC would be positively correlated with the severity of hyperarousal symptoms as measured by the DSM-5 Clinical Diagnostic Scale for PTSD (CAPS-5).

[0263] method participants Twenty-three CAF veterans with operational deployment experience were recruited from the Operational Stress Injury Clinic at the Royal Mental Health Center in Ottawa, Ontario. Eighteen of these individuals met the DSM-5 criteria for PTSD using the Clinical Diagnostic Scale for PTSD (CAPS-5). CAPS interviews were conducted by trained evaluators. See Table 1 for all clinical and demographic scales. Severity of depressive symptoms was assessed using the Beck Depression Inventory-II (21-item version). Other clinical assessments included the DSM-5 Life Events Checklist, the Pittsburgh Sleep Quality Index, and the Columbia Suicide Severity Scale. Recruitment criteria included being between 18 and 65 years of age and being a CAF veteran with operational deployment experience since 2000. Exclusion criteria included a history of manic / hypomanic or psychotic disorder, a diagnosis of substance use disorder (SUD) in the past six months, a significant medical condition, neurological condition, traumatic brain injury (or head injury with at least five minutes of unconsciousness), inability to abstain from alcohol, nicotine, cannabis, or caffeine for 24 hours, and current use of stimulants (due to potential impact on NM-MRI signals). This study was approved by the Institutional Review Board of the Royal Mental Health Centre in Ottawa, Ontario, and participants provided written informed consent.

[0264] MRI acquisition Magnetic resonance (MR) images were acquired for all study participants using a Siemens 3T PET BIOGRAPH mMR scanner with a 12-channel head coil. NM-MRI images were collected via a 2D gradient response echo sequence with magnetization transfer contrast (2D GRE-MT) using the following parameters: repetition time (TR) = 337 ms, echo time (TE) = 3.97 ms, flip angle = 50°, and in-plane resolution = 0.43 × 0.43 mm. 2The partial brain coverage by field of view (FoV) was 165 × 220 pixels, the matrix was 384 × 512 pixels, the number of slices was 10, the slice thickness was 3 mm, the slice gap was 0 mm, the magnetization migration frequency offset was 1200 Hz, the number of excitations (NEX) was 6, and the acquisition time was 7.24 minutes. The slice specification protocol consisted of oriented the image stack along the anterior-commissural-posterior-commissural lines, with the upper slice positioned 3 mm above the floor of the third ventricle when viewed in the sagittal plane of the center of the brain.

[0265] For preprocessing of NM-MRI data using the MEMPRAGE sequence, high-resolution T1-weighted whole-brain MRI images were also acquired (inversion time = 1050 ms, TR = 2500, TE = 1.69 ms, flip angle = 7°, FoV = 192 × 192, matrix = 192 × 256, number of slices = 256, isotropic voxel size = 1 mm, acquisition time = 5.47 mins). Regarding the quality of the NM-MRI images, artifacts were visually inspected immediately after acquisition, and scans were repeated as needed, time permitting.

[0266] Image preprocessing of NM-MRI images The initial preprocessing steps were performed in the same manner as in conventional work to examine NM-MRI signals from the substantia nigra (5) using SPM12. Although the final analysis of LC signals was performed on native space NM-MRI images, it was nevertheless necessary to spatially normalize the NM-MRI images to align the universal LC search space from MNI space to native space for each participant. First, the NM-MRI scans were aligned to the participants' T1-weighted scans. Then, tissue segmentation was performed using the T1-weighted images. The NM-MRI scans were normalized to MNI space using the DARTEL routine with gray and white matter templates generated from all study participants. The resampled voxel size of these normalized NM-MRI scans was isotropic at 1 mm. After each of these steps, all images were visually inspected. A visualization template was created by averaging the spatially normalized NM-MRI images from all participants.

[0267] The subsequent steps involved using a custom Matlab script to specifically examine the LC signals. An over-inclusive LC mask was drawn on a visualization template to cover high-intensity voxels at the anterolateral margin of the fourth ventricle, extending from z=xx-yy along the rostral-caudal axis (see Figure 1). Rostral-caudal boundaries were defined by cross-referencing distances to anatomical landmarks in brain atlases (superior colliculus above the rostral end and posterior recess of the fourth ventricle above the caudal end). A segmented version of the mask was created by dividing it into three rostral-caudal segments of equal length. The entire over-inclusive LC mask and the segmented masks were then warped into native space and resampled into NM-MRI image space using the inverse of the flow field generated in the spatial normalization step. The warped entire over-inclusive LC mask could then be used to define a search space for finding each participant's LC. A clustering algorithm was used to segment the LCs within this space, selecting the six adjacent voxels with the highest mean signal (2.58 mm). 2 ) was defined as follows. This operation was repeated for the left and right LC. The contrast-to-noise ratio (CNR) of each voxel v in a given axial slice is given below: CNR V =(I V -Mode (I RR )) / mode(I RR) was calculated as the relative difference in NM-MRI signal intensity I from a reference region RR within the same slice. The inventors used a reference region known to have low NM density, i.e., the central pons, centered x mm from the axis connecting the left and right LCs, defined by a circle of radius xx mm. All axial slices in native space were identified as belonging to LC segment 1 (rostral), 2 (central), or 3 (caudal) based on which of the three divided LC masks was present on the slice (if two of these masks were present on the same slice, the LC segment was defined as the one that coincided with the mask covering the brighter LC voxels). For each of the three segments, the LC signal was calculated by averaging the NM-MRI CNR values ​​from all voxels determined to be within that segment (for example, if a segment covers two axial slices in both the right and left native spaces, this would be the average CNR from 24 voxels - 6 voxels per LC*2 lateral*2 slices).

[0268] statistical analysis Final statistical analysis was performed using Matlab. Partial correlations examined the relationship between LC NM-MRI signals and clinical measures, including covariates age, sex, and PTSD diagnosis. The main measures (LC NM-MRI signals, severity of hyperarousal, and severity of depression) were found to be normally distributed based on the Relief Force test, supporting the inventors' use of parametric statistics.

[0269] result This sample of veterans with operational experience showed relatively high levels of hyperarousal and depressive symptoms (present in both those with and without a PTSD diagnosis; see Table 3). Hypothetically, NM-MRI signaling at the caudal LC was significantly positively correlated with the severity of CAPS-5 hyperarousal symptoms (r=0.52, p=0.019, partial correlation adjusted for depression severity, PTSD diagnosis, age, and sex; see Figure 2). The inventors observed a significant negative correlation between caudal LC-NM signaling and depression severity, consistent with studies in other populations (BDI-II total severity score, r=-0.48, p=0.033, partial correlation adjusted for hyperarousal severity, age, sex, and PTSD diagnosis). Finally, although the inventors' sample of participants not meeting PTSD criteria was very small (n=5), they tested whether there was evidence of an influence of PTSD diagnosis on NM-MRI signaling (Figure 4). The inventors found a propensity-level effect with a significant trend in this analysis (t 20 = -1.0, p=0.32, linear regression adjusted for hyperarousal severity, depression severity, age, and sex). This final analysis is very insufficient, and the inventors assume that the propensity level effect will become significant in larger future studies.

[0270] In summary, the above results indicate changes in LC activity in mental states such as PTSD and depression. Specifically, a significant positive correlation was found between LC NM-MRI signals and hyperarousal symptoms in individuals with PTSD. The inventors' current study also links increased LC-NE activity and its correlation with hyperarousal symptoms.

[0271] Furthermore, studies examining pharmacological interventions for PTSD have shown that numerous adrenergic agonists are somewhat effective in treating symptoms associated with the hyperarousal symptom cluster. Specifically, there is moderate evidence supporting the use of prazosin, an α-1 adrenergic receptor antagonist, for treating nightmares in PTSD patients. In particular, prazosin has been shown to help veterans suffering from hyperarousal symptoms associated with PTSD. However, the results have also shown that prazosin is not effective for PTSD in other individuals, further demonstrating the complexity associated with this condition. For example, a meta-analysis conducted among military personnel analyzed clinical trials involving many drugs, with a particular focus on the efficacy of each drug. Here, of 106 trials, only 6% of individuals showed a complete and correct response to prazosin. 51% did not respond to the drug at all. It is well known that PTSD is a heterogeneous state, and more research is needed to further identify biomarkers associated with this disorder.

[0272] Regarding the negative correlation between LC NM-MRI signals and the severity of depression, changes in LC-NE system activity are hypothesized in major depressive disorder, and the inventors' results support the use of NM-MRI as a biomarker for MDD. Pharmacological studies focusing on innervation targets for major depressive disorder have investigated NE receptors in the locus coeruleus. Here, when the selective NE reuptake inhibitor reboxetine was administered to depressed patients, the drug s= had efficacy very similar to that of tricyclic antidepressants. Other drugs targeting both the serotonin and norepinephrine systems (serotonin and norepinephrine reuptake inhibitors) have also been shown to be effective in treating symptoms associated with depression. Furthermore, postmortem studies conducted among individuals with depression have observed significant changes in NE neuron density and decreased NE transporter binding in the locus coeruleus in individuals with depression. Overall, the results of the present invention are consistent with current literature suggesting a decrease in NE, and therefore changes in LC-NE activity, in individuals with depression.

[0273] Regarding the method used in this study, the inventors previously demonstrated its usefulness and validity in capturing changes in the dopamine system. This study also allowed for further validation of the inventors' semi-automatic method for extracting LC NM-MRI signals within LCs. Here, the inventors' method provides a unique approach for NM image analysis of LCs. Based on this value, as well as the inventors' confirmed success in capturing and analyzing NM data, the inventors are confident in the inventors' method and its ability to capture changes in LC-NE systems, particularly in clinical settings.

[0274] Furthermore, by utilizing novel neuroimaging techniques within the field of psychiatry, researchers studying both the dopamine and norepinephrine neurotransmitter systems can overcome past challenges. Specifically, this method can increase the in-plane resolution of the LC, allowing for better capture of changes associated with LC activity. NM-MRI is also a novel imaging technique that has not been previously used in PTSD research, adding a unique aspect to current studies.

[0275] Limitations of the inventors' current study include a small sample size and the lack of a defined healthy control group, resulting in a significant trend observed in both LC NM-MRI signals and overall PTSD diagnosis. To address these limitations in the future, increased recruitment should be conducted for both the PTSD group and the defined control group. While a large healthy control group is lacking, obtaining such a control group may be difficult in PTSD-related studies because many individuals who experience a traumatic event but do not develop PTSD have other potential mental health problems. Therefore, the inventors compared their PTSD group with individuals who did not develop PTSD but suffered from depression. Furthermore, regarding the correlation between LC NM-MRI signals and PTSD diagnosis, an increase in sample size should make this correlation more apparent, and the inventors hypothesize that the significant trend observed here would reach significance if true healthy controls were incorporated.

[0276] In conclusion, the results of this study demonstrate that NM is a biomarker for PTSD and depression, and support the use of segmentation-based algorithms to measure NM in patients with these conditions. The correlation between LC NM-MRI signals and PTSD and depression provides clinical evidence supporting changes in NE activity, and thus provides further evidence supporting the role of the NE system in both conditions. This study may also provide insights into future noradrenergic targets for the treatment of both conditions. [Table 5]

[0277] Example 4. NM as a biomarker for PTSD using a segmentation-based approach overview Current research proposes neuromelanin-sensitive MRI (NM-MRI) as a novel biomarker that could enable directed therapies targeting hyperarousal symptom clusters in PTSD. These symptoms can lead to significant functional impairment and suicidal tendencies, and currently, no specific neuroscience-based treatment exists. NM-MRI, a short, non-invasive MRI scan, could provide a practical and reliable marker of norepinephrine (NE) excess in PTSD, thereby enabling neurobiologically informed treatment decisions. This proposal suggests validating this approach by correlating hyperarousal symptoms with NM-MRI signals and using NM-MRI to predict treatment response in subsequent clinical trials.

[0278] Neuromelanin is a pigment that gives a bluish color to NE neurons in the locus coeruleus (LC). Neuromelanin is formed by the metabolism of NE and accumulates slowly over its lifespan. Verification work by the inventors' group has demonstrated that, unlike most neurochemicals, NM content can be measured with high resolution using a special MRI sequence, NM-MRI. This method is practical for widespread clinical use, non-invasive, and can be performed on any MRI scanner in under 10 minutes. Research by the inventors' group and others suggests that NM-MRI may provide a neurochemical basis for important PTSD endophenotypes, excessive sympathetic nervous system responses, and hyperarousal responses. Therefore, this technique could provide a stable measure of NE imbalance in PTSD, although the method has not yet been validated in PTSD and is highly innovative and novel.

[0279] While excessive NE function may be a significant component of PTSD, this may only apply to certain individuals, such as those exhibiting hyperarousal. The primary function of the central NE system is to promote arousal, and clinical and preclinical studies link hyperarousal to excessive NE activity. Therefore, NM-MRI can guide treatment decisions in line with future clinical practices where treatments are selected based on objective neurobiological measures rather than subjective clinical measures excluded from the underlying neurobiology of pathology.

[0280] The inventors propose recruiting 60 individuals with a history of trauma (primarily from Operational Stress Injury Clinics), 30 of whom meet the CAPS-5 criteria for PTSD. This trauma-stricken group will serve as a representative sample across the entire PTSD phenotype, thereby facilitating RDoC-inspired investigations into the neurobiological correlations of specific symptoms. All participants will undergo MRI scans and clinical evaluations. The MRI sessions will consist of NM-MRI scans and functional MRI scans during the fear conditioning procedure. LC NM-MRI signals will be measured via an automated LC segmentation pipeline.

[0281] In functional MRI scans, segmented LCs are used as localizers to measure fear-related activation of LCs in native space. The analysis tests whether clinical and physiological measures of arousal correlate with NM signals and LC activation during fear conditioning in LCs, as seen in the inventors' pilot data.

[0282] Exploratory analyses will investigate the relationship between LC NM signals and the activation of structures within the fear circuit, such as the amygdala and prefrontal cortex. This study will then provide a basis for testing pharmacological and non-pharmacological interventions to address hyperarousal symptoms and their neurobiological correlations. Thus, this study provides novel and practical disease and therapeutic biomarkers that the inventors propose introducing into the clinical care of PTSD.

[0283] background PTSD is a troublesome and widespread mental health problem among military veterans. Between 1998 and 2015, 16.4% of regular military veterans who had seen operational deployments reported suffering from PTSD. Hyperarousal is one of the underlying syndromes of post-traumatic stress disorder (PTSD). This symptom group is characterized by excessive vigilance, excessive startle, hyper-irritability or reckless behavior, and sleep disturbances. Hyperarousal is common in PTSD and is highly detrimental, potentially leading to physical disability, physical health problems, and even suicide. More effective treatments for military PTSD will help mitigate these harmful downstream effects. While the subclassification of PTSD currently relies on clinical assessment, a rapid understanding of the neurobiological mechanisms underlying the pathogenesis of PTSD will allow biological measurements to become a preferred method for distinguishing different pathologies within PTSD. While peripheral physiological assessments of hyperarousal currently exist, this symptom cluster may depend on dysregulation within the central nervous system, and biomarkers for hyperarousal that track the symptom cluster at the source are best but more difficult to find.

[0284] Furthermore, for biomarkers to be useful in treatment, they must be practical for widespread implementation in clinical settings and help indicate optimal treatment strategies.

[0285] The current proposal tests the usefulness of a novel putative biomarker, neuromelanin-sensitive MRI (NM-MRI). Neuromelanin-sensitive MRI is practical, reliable, and can be linked to pharmacological therapeutic strategies. NM-MRI is an imaging method that provides a practical and specific assay of the central norepinephrine (NE) system that can identify a subset of PTSD patients with NE imbalance, thereby enabling targeted therapies to attack this imbalance using existing or experimental drugs (targeting the NE system) or specific psychotherapeutic strategies.

[0286] The brain's neuronal reuptake system (NE system) is recognized as a critical site of PTSD dysregulation, related to its role in stress response, arousal, and the reinforcement of fear memories. Very recent, impactful studies have provided compelling evidence that hyperarousal in PTSD patients is linked to activity in the locus coeruleus (LC), the location of NE neurons in the brain. This confirms a decades-old theory linking the NE system to hyperarousal, based on preclinical studies and human PET imaging and genetic research. The NE system is also a key target for common PTSD treatments, including NE reuptake inhibitors (SNRIs and SNDRIs), as well as the α1-adrenergic receptor antagonist prazosin, which can effectively treat hyperarousal in some individuals. Furthermore, drugs being developed for PTSD act on NE receptors. Current clinical trials of brexpiprazole are examining targeted engagement of LC NE neurons (indicated by pupil diameter), iloperidone is considered a drug of interest due to its high affinity for NE receptors, and recent findings suggest the potential of propranolol treatment prior to traumatic memory reactivation. Therefore, specific biomarkers of NE imbalance in PTSD would be extremely useful tools for characterizing the disease, guiding treatments, and evaluating the effectiveness of these new experimental therapies in targeted patients. Such novel tools may be provided by neuromelanin-sensitive MRI (NM-MRI, see Figure 3).

[0287] Neuromelanin (NM) is a dark pigment formed from the breakdown of catecholamine neurotransmitters NE and dopamine, and is present only in catecholamine neurons in the brain (NE neurons in the LC and dopamine neurons in the substantia nigra). Due to its unique properties, this pigment is one of the only neurochemicals that can be quantified with high spatial resolution using MR imaging, thereby enabling the investigation of NE system function without the invasiveness and cost of PET imaging. While this pigment offers similar advantages in assaying brain chemistry to obtain information for drug therapy, it is more practical for large-scale applications because it is inexpensive, quick to use (<10 minutes), non-invasive, and obtainable on any 3T MRI scanner. NM-MRI has the additional advantage of being a very stable measure with high test-retest reliability, as it accumulates gradually over life and does not degrade. In the LC, this signal can provide an alternative measure of persistent NE imbalance. This beneficial property means that the signal (unlike some unestablished biomarkers) does not change based on transient fluctuations in mental state or symptom severity.

[0288] The inventors possess extensive expertise in acquiring and analyzing this imaging method. Their validation and development work confirmed that NM-MRI actually senses NMs, which are markers of catecholamine neuron function, and that this signal correlates with hyperarousal symptoms of PTSD, consistent with its correlation to anxiety and autonomic function measures in other populations. Despite this evidence supporting a link to PTSD, no studies on PTSD NM-MRI have yet been published.

[0289] While the temporal stability of NM-MRI signals makes them usable as an assay for long-term NE system function, the usefulness of NM-MRI signals may be enhanced when combined with information from state-dependent measures of NE system function that can assay recent NE function. One such measure is LC activity measured by BOLD fMRI during a fear conditioning paradigm. This paradigm provides complementary information to LC NM-MRI signals because LC activity facilitates fear conditioning and generalization, and reinforced fear conditioning is one model of the pathophysiology of PTSD. A further complementary measure in this specification is pupillary measurement, where assessment of pupillary dilation is a highly sensitive measure of reflexive autonomic responses mediated by neural activity generated in the locus coeruleus.

[0290] NM-MRI signals in the LC are a biomarker of NE imbalance in the central nervous system. The inventors evaluated this in Canadian Armed Forces (CAF) veterans with PTSD, considering the association between hyperarousal symptoms and NE function in PTSD. This will help in efforts to move from clinical to neurobiological subclassification of PTSD to facilitate targeted therapies

[38] and accelerate the discovery of new treatments by pre-selecting individuals with a high likelihood of responding to experimental therapies. NM-MRI signals in the LC correlate with clinical and physiological measures of NE function in individuals who have experienced trauma.

[0291] The usefulness of NM-MRI signals in LC (and the possible mitigating role of sex in this relationship) will be evaluated as a biomarker for long-term NE function in traumatized individuals.

[0292] LC NM-MRI signals show a positive correlation with the severity of CAPS-5 hyperarousal symptoms, skin conduction responses during fear conditioning, and the rate of pupillary dilation in both sexes.

[0293] The usefulness of BOLD fMRI activation of LC during fear conditioning will be evaluated as a surrogate biomarker for instantaneous NE function that can complement NM-MRI signals in relation to hyperarousal. Exploratory objective 1b: Evaluate the correlation between fear-related BOLD activation of brain structures in conventional fear circuits and LC measures (NM-MRI and BOLD)

[40] . Exploratory objective 1c: Compare LC NM-MRI signals from individuals with trauma meeting the CAPS-5 criteria for PTSD with those from individuals with trauma not meeting the PTSD criteria.

[0294] Preliminary data Verification of NM-MRI as a measure of catecholamine system function NM-MRI actually senses neuromelanin. The NM-MRI signal corresponds to the local tissue concentration of NM in postmortem human midbrain (β=0.87, t114=5.05, p=10~6, mixed-effects model, 116 measurements, 7 samples). The inventors' in vivo PET imaging study confirmed the association between NM-MRI and the function of catecholamine neurotransmitters (partial rho=0.69, p=0.004, n=18; in this study, the probed catecholamine was dopamine, not NE, and dopamine generates dopamine-related NM-MRI signals in the substantia nigra, in contrast to NE-related signals in the LC). NE and its precursor dopamine are supplied to the same metabolic pathway leading to NM formation; therefore, the inventors' validation results from the substantia nigra NM-MRI signals of the dopaminergic system should be successfully replaced by LC NM-MRI signals of the NE system.

[0295] Correlation between NM-MRI in the locus coeruleus and hyperarousal symptoms in PTSD The inventors examined LC NM-MRI signals in their dataset of 24 patients treated at the Operational Stress Injury (OSI) Clinic at the Royal Ottawa Mental Health Centre (19 of whom met the CAPS criteria for PTSD). LC NM-MRI signals were measured using established methods. The percentage of signal change in LC was calculated compared to a reference region that did not contain neuromelanin.

[0296] In this sample, psychopathology was measured using CAPS-5. Hyperarousal symptom clusters were examined from this scale. Consistent with the inventors' hypothesis, the LC NM-MRI signal showed a positive correlation with the severity of CAPS-5 hyperarousal symptom clusters (r=0.52, p=0.019, partial correlation adjusted for age, sex, PTSD diagnosis, and depression severity [BDI total score], see Figure 1). As previously reported, the LC NM-MRI signal showed a negative correlation with depression severity (r=-0.48, p=0.033, partial correlation adjusted for age, sex, PTSD diagnosis, and hyperarousal severity).

[0297] Power calculation Based on the inventors' data, the influence of the relationship between LC NM-MRI signaling and hyperarousal syndrome is expected to be close to r=0.52. Even assuming a somewhat weaker effect (r=0.4), conservatively speaking, a sample size of n=60 has 90% power to detect a significant correlation. Therefore, we propose recruiting 60 participants with a history of trauma. Based on preliminary data from trauma-exposed veterans from the OSI Clinic (Figure 1) and internal data from the Clinic, we expect to observe a wide range of hyperarousal symptom severity in this population, which supports an analysis focused on this symptom dimension and is an approach compliant with the RDoC approach [9].

[0298] research participants The study participants were Canadian military veterans, male and female, with operational experience (surrogate trauma), aged 18 to 55 years. To minimize confounding due to early LC degeneration in some older individuals, those over 55 years of age were not included. Consistent with the objectives of the RDoC initiative[9], the inventors' study examines a single group of traumatized individuals representing the full range of trauma-related symptoms. Given the heterogeneity of PTSD, this approach maximizes the ability to identify neurobiological correlations by enrolling individuals who have similarly experienced trauma but differ in their symptom of interest domain (hyperarousal). The inventors aim to maximize variability on this symptom of interest scale while minimizing variability on other clinical scales (e.g., trauma exposure, comorbidities, and lifestyle factors important to match in the PTSD control group). To achieve the goal of 60 participants with available data, n=66 participants need to be recruited. Individuals with comorbid mental illnesses are eligible to participate. Exclusion criteria include active suicidal intent, major unstable medical conditions, methamphetamine use (>1 month in a lifetime), pregnancy, neurological disorders, and contraindications to MRI scans. There are no exclusions due to substance use or medication history (except for methamphetamine, which may affect NM-MRI signals). These inclusion criteria are consistent with many PTSD studies that seek to capture a representative sample in light of the prevalence of substance use disorders and the heterogeneity of prescribed medications in this population (see responses to previous reviews for further consideration of these issues).

[0299] Recruitment of veterans deployed in community operations is conducted in parallel through categorized advertisements and word-of-mouth (e.g., from participants recruited at the OSI Clinic). To facilitate secondary analysis comparing traumatized individuals with and without PTSD, ensure that n=20 individuals (from a total sample of 60 traumatized veterans) who do not meet the CAPS-5 criteria for PTSD are enrolled. This participant breakdown perfectly matches the proportion of individuals diagnosed with PTSD at the OSI Clinic (66% in 2017). Ensure that at least 40% of the sample are female to appropriately assay both sexes and support analysis of sex impact (the inventors' current neuroimaging data collected from this clinic includes 7 / 24 women, which represents 29%).

[0300] clinical scale Following screening and consent, all study participants will undergo a 3-4 hour test session at the Royal Ottawa Mental Health Centre, consisting of an MRI scan, physiological measurements, and a clinical interview. The following clinical scales will be collected from all participants through interviews or self-reporting: Interview scales: PTSD Clinical Diagnostic Scale (CAPS-5, inventors' primary clinical interest scale), Structured Clinical Interview for DSM-5 (SCID-5), self-report scales, PTSD Checklist-5 (PCL-5), Pittsburgh Sleep Quality Index, Life Events Checklist, Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), Dissociative Experiences Scale, Affective Disorder Scale, Substance Abuse and Dependence Scale (CUAD), and Columbia Suicide Severity Rating Scale.

[0301] Magnetic resonance imaging (MRI) and physiological scales All subjects will undergo MRI scans using a 3T MR-PET Siemens Biograph scanner at the Royal Ottawa Mental Health Centre. This will include structural scans (T1 and T2 weighted scans), NM-MRI scans, and BOLD functional MRI scans during fear conditioning. The total scan time for each participant will be approximately 50 minutes. A 32-channel head coil will be used for all scans. The NM-MRI scans will be 2D-GRE scans with magnetization transition contrast and the following parameters: TR=260ms, TE=2.68ms, flip angle=40°, in-plane resolution=0.39×0.39mm, FoV=162×200, matrix=416×512, number of slices=10, slice thickness=3.0mm, magnetization transition frequency offset=1200Hz, number of excitations=8, acquisition time=8.04 mins. BOLD-enabled MRI images are acquired with high temporal and anatomical resolution using the following sequence parameters: 66 slices, TR=864 ms, TE=34.8 ms, flip angle=52°, matrix=88x90, FoV=208x97.8 mm², voxel size=2.3 mm isotropic, multiband acceleration factor=6. Spin echo sequences and B0 field maps are also collected to help correct distortion and magnetic field inhomogeneity in the BOLD images.

[0302] BOLD imaging is performed during a fear state paradigm consisting of three different aversive conditioning tasks, each lasting 7 minutes. Between tasks, participants are presented with two computer-generated expressionless faces (created using FaceGen, www.facegen.com). Each task has a different face. Within each task, one face (conditioned stimulus, CS+) is followed by a mild electric shock (unconditioned stimulus) to the tibia in 33% of the trials. No shock is given after the other face (control stimulus, CS-). Skin conduction responses (SCRs) are calculated using Matlab's Ledalab by a method called continuous decomposition analysis (CDA). CDA decomposes SCR data into continuous signals of phasic activity (i.e., peak after a CS+ event) and sustained activity ("baseline"). In practice, the phasic maximum (maximum peak after a single event) is averaged for each event type (CS+, CS-). The final value compared is the CS+ / - contrast. A positive contrast value indicates that the conditioning to CS+ was successful.

[0303] The pupillary response scale is acquired using the Neuroptics PLR-3000 handheld pupillator, a validated instrument that produces a highly reproducible scale.

[48] The pupillator's soft cup is positioned facing the eye to minimize ambient light. The subject is fixed 10 feet away from a wall with the untested eye open. The protocol used by this device was adapted from another study in PTSD.

[0304] The measurement was completed in 5-6 seconds, during which time the pupil diameter was measured in a stationary state in response to the light pulse stimulation. This procedure was repeated under three ambient light conditions (bright, dim, and dark: 350, 5, and 0 lux, respectively), with a 4-minute interval to adjust the light level. The characteristics of the light pulses were as follows: positive pulse stimulation, pulse intensity = 50 uW, background intensity = 0 uW, measurement duration = 6.02 s, pulse duration = 0.30 s, pulse onset = 0.70 s (for the "bright" condition) or positive pulse stimulation, pulse intensity = 10 uW, background intensity = 0 uW, measurement duration = 12.03 s, pulse duration = 0.17 s, pulse onset = 2.04 s (for the "dim" and "dark" conditions). Resting blood pressure was also measured immediately before the clinical evaluation.

[0305] statistical analysis LC NM-MRI signals are measured directly from NM-MRI images using a custom automated method

[49] (Figure 3). The primary analysis is a linear regression to predict either CAPS hyperarousal score, compatibility skin conduction response to fear-conditioned stimuli, or pupillary dilation velocity, based on LC NM-MRI signals, with age, sex, and BDI severity as covariates. A secondary linear regression analysis tests a model predicting hyperarousal symptom severity based on LC NM-MRI signals, also including physiological measures (skin conduction, blood pressure, and pupil diameter) as covariates, to determine whether LC NM-MRI signals contribute more to symptom severity prediction than the contributions of more convenient peripheral measures. The secondary analysis compares veterans meeting CAPS-5 criteria for PTSD (n=40) with trauma veterans without PTSD (n=20) using linear regression analyses adjusted for age, sex, and depression severity. An additional secondary analysis considers the effect of sex.

[0306] Analysis of functional MRI data leverages NM-MRI images to provide segmentation of the LC as a subject-specific LC localizer, thus enabling examination of LC activity during fear conditioning. This method allows for improved estimation of BOLD fMRI activity within the LC compared to standard fMRI approaches

[47] (Figure 2). Final linear regression analysis includes LC NM-MRI signals and LC BOLD activation (contrast obtained by subtracting the unconditioned stimulus from the conditioned stimulus) to determine whether they are complementary measures of long-term and short-term NE system tension and to independently predict hyperarousal symptoms and physiological measures. We also leverage this rich dataset to investigate the correlation of our LC scales with fear-related activation of brain structures in conventional fear circuits, such as the amygdala, hypothalamus, and prefrontal cortex, and to develop an integrated model of brain mechanisms linking NE dysfunction to clinical symptoms (Figure 2).

[0307] Analysis based on sex and gender Significant sex differences exist in sex-specific risk factors for the fear system and PTSD

[50] , and research on autonomic dysfunction in women with PTSD is limited

[51] . Therefore, we investigate whether sex is a mitigating factor in the relationship between LC NM-MRI signals and hyperarousal. The inventors do this by including a sex*LC signal interaction term in a linear regression model that predicts the hyperarousal scale. To support this analysis, the inventors recruit at least 40% women in their sample. The inventors also conduct primary analyses exclusively in men and women to ensure that the effect sizes are similar in both groups and to support use in both sexes.

[0308] Example 5. Verification of the algorithm across indications Various neurological and psychiatric disorders are associated with neuromelanin changes in two main areas: the substantia nigra pars compacta (SNc) and the locus coeruleus (LC). Differentiating between different disorders with similar clinical findings based solely on symptom presentation is difficult because symptoms often overlap between related conditions.

[0309] This disclosure describes the combined use of two fully automated algorithms for measuring neuromelanin (NM) concentration and volume in two different brain regions (SNc and LC) to improve the ability to differentiate related disorders. This disclosure uses a voxel-based analysis algorithm (already invented and patented at Columbia University) to measure NM in SNc. However, because LC is much smaller and may not be well-suited to voxel-based analysis on 3T MRI (the most commonly available scanner in clinics), a new algorithm for measuring NM in LC was invented at the University of Ottawa. This LC algorithm is called a segmentation-based analysis algorithm. This disclosure describes a combination of the two algorithms in a software package that can be used to assist in the diagnosis and differentiation of neuropsychiatric disorders that are difficult to distinguish based solely on symptoms.

[0310] In this disclosure, a voxel-based analysis algorithm is used to measure NM within the SNc, and a segmentation-based analysis algorithm is used to measure NM changes within the LC. The software reports NM levels and volumes in both brain regions to the physician. Combining these two algorithms may increase the ability to differentiate related neurological disorders. Integrating the algorithms into fully automated software could lead to widespread use in clinics.

[0311] The unmet medical needs addressed here are the ability to distinguish between Parkinson's disease, multiple system atrophy, progressive supranuclear palsy and related disorders, as well as different dementias such as Alzheimer's disease and dementia with Lewy bodies. Increased ability to distinguish between related disorders should increase the clinical usefulness of the software, ultimately driving the use of NM software as a medical device, more broadly than possible with any single algorithm alone.

[0312] Results and supporting data by indication Figure 13. The software automatically applies a mask for selecting brain regions for an SNc voxel-based algorithm, and a second mask for selecting brain regions for an LC segmentation-based algorithm.

[0313] Parkinson's disease The two algorithms were validated by analyzing NM within SNc and LC in Parkinson's disease patients compared to healthy controls. The voxel-based analysis algorithm found a significant difference in NM contrast-to-noise ratio (CNR) in SNc in Parkinson's disease patients, while the segmentation-based algorithm found no difference in LC in this same patient population compared to healthy controls (Figure 14). This is consistent with previous literature showing that the main changes in NM in Parkinson's disease occur in SNc, but this is the first time that this has been achieved in a single brain scan using dual fully automated algorithms.

[0314] Diagnosis of Alzheimer's disease The algorithms were validated in patients with Alzheimer's disease compared to healthy controls. A segmentation-based analysis algorithm was applied to analyze the LC, and a voxel-based algorithm was applied to analyze the SNc. In contrast to patients with Parkinson's disease, the segmentation-based analysis algorithm found significant differences in NM within the LC in Alzheimer's disease patients, while the voxel-based algorithm did not find significant differences in SNc in this same patient population compared to healthy controls (Figure 15). This is also consistent with previous literature indicating that major NM changes in AD occur in the LC. The combination of these two datasets demonstrates that, when used together, voxel-based and segmentation-based algorithms can simultaneously detect significant changes in NM within different brain regions.

[0315] Prediction of neuropsychiatric symptoms of Alzheimer's disease We used a combination of these two algorithms to help determine the presence of neuropsychiatric symptoms in Alzheimer's disease patients. A segmentation-based analysis algorithm was applied to analyze the LC (Low Classification) and a voxel-based algorithm was applied to analyze the SNc (Small Sentence Continence) (Figure 16). Segmentation analysis of the LC revealed a significant increase in NM (Nutrition-Related Neuropsychiatric) within the LC compared to healthy controls. The voxel-based algorithm showed a significant decrease in NM within the SNc compared to healthy controls. This is the first time that NM levels within the SNc have been shown to significantly predict the presence of neuropsychiatric symptoms.

[0316] Schizophrenia The algorithm was validated in patients with schizophrenia. A segmentation-based analysis algorithm was applied to analyze the LC, and a voxel-based algorithm was applied to analyze the SNc (Figure 17). While it had previously been reported that NM levels change within the SNc in patients with schizophrenia, it was unclear whether NM levels change within the LC. Compared to healthy controls, significant changes were observed in NM levels within the SNc, with higher levels associated with increased psychosis severity as measured by the PANSS scale. Importantly, no significant changes were observed in NM levels within the LC. This is significant because patients with Alzheimer's disease experience psychiatric symptoms that overlap with those of schizophrenia (hallucinations and delusions). Importantly, this is the first data suggesting that NM levels measured in two brain regions may be useful in diagnosing these disorders. Importantly, psychotic symptoms in schizophrenia were associated only with increased NM within the SNc, while neuropsychiatric symptoms in Alzheimer's disease were associated with decreased NM within the SNc and increased NM within the LC.

[0317] PTSD The combination of algorithms was validated in patients with post-traumatic stress disorder (PTSD).

[0318] The voxel-based algorithm shows no significant association between disease severity and NM levels within SNc compared to healthy controls (Figure 18). The segmentation-based algorithm shows significant changes within LC compared to healthy controls, indicating that increased NM levels are significantly associated with disease severity (right figure).

[0319] Major Depressive Disorder The algorithm was validated in patients with major depressive disorder compared to healthy controls. A segmentation-based analysis algorithm was applied to analyze LC, and a voxel-based algorithm was applied to analyze SNc (Figure 19). The voxel-based algorithm shows no significant difference in NM levels within SNc compared to healthy controls (left figure). The segmentation-based algorithm shows a tendency for NM levels to decrease with increasing disease severity in LC compared to healthy controls (right figure).

[0320] Cocaine use disorder The algorithm was validated in patients with cocaine use disorder compared to healthy controls. A segmentation-based analysis algorithm was applied to analyze LC, and a voxel-based algorithm was applied to analyze SNc (Figure 20). The application of voxel-based and segmentation-based algorithms to cocaine use disorder is shown. The voxel-based algorithm shows that an increase in NM within SNc is significantly associated with cocaine use disorder compared to healthy controls (left figure). The segmentation-based algorithm shows a tendency for decreased NM in LC compared to healthy controls (right figure).

[0321] In one embodiment, the following summary table shows various NM levels within SN and LC, illustrating how this can guide the diagnosis of a particular disease in terms of patient or symptom severity. [Table 6]

[0322] Example 6. Verification of clinical trials across disease indications One of the challenges faced in developing new treatments is the need to enroll patients who are most likely to benefit from the treatment and exclude those who are unlikely to respond. Mis-enrollment of the wrong patients is a factor in the failure of clinical trials of new treatments and can otherwise lead to delays in the development of effective treatments. This is particularly important in Parkinson's disease (PD) because it is well known that it can be clinically difficult to distinguish Parkinson's disease from similar disorders that may present with symptoms, including multiple system atrophy with parkinsonian characteristics (MSA-P) and progressive supranuclear palsy (PSP), or certain cases of essential tremor (ET) and idiopathic normal pressure hydrocephalus (iNPH). Retrospective analyses have shown that the diagnostic accuracy for PD among general neurologists is 75%, while the diagnostic accuracy for atypical parkinsonism, including PSP and MSA, is only 61% among general neurologists and 71% among movement disorder specialists. The inventors concluded that a high rate of misdiagnosis increases noise in clinical trials of PD. One study on ET showed that 25% of patients initially diagnosed with PD were later found to have ET. Similarly, a review of iNPH reported that it can be difficult to distinguish iNPH from PD when gait impairment is present.

[0323] Imaging modalities that have shown promise in the differential diagnosis of PD have several limitations that reduce their usefulness as biomarkers in therapeutic clinical trials. These include tau protein (tau-PET) positron emission tomography, which may be useful in diagnosing PD versus PSP, and DaTscan, which may be useful in the differential diagnosis of PD versus ET and PD versus iNPH

[16] . Both methods are costly, require intravenous injection, expose patients to radioactive tracers, require long preparation and scan times, and require access to a PET scanner and a SPECT scanner, respectively. Taken together, these limitations make widespread deployment to large-scale clinical trials impossible.

[0324] This study evaluates NM-MRI as a useful biomarker for the differential diagnosis of Parkinson's Disease (PD) in patients with PSP, MSA, ET, and iNPH. A total of 50 subjects (10 per group) with established diagnoses of PD (pre-treatment), PSP, MSA-P, ET, and iNPH (pre-shunt intervention), and 10 healthy controls will undergo NM-MRI scans in addition to a clinical assessment including medical history, and neurological examinations including the Movement Disorders Association Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The absolute concentration and volume of neuromelanin in the SNc and LC of each hemisphere will be determined by Terrans NM-SAMD. Furthermore, Terrans' proprietary voxel-based analysis will be applied to determine the voxel-based pattern of each disorder. The primary outcome is the difference in absolute NM concentration and volume in the SNc and LC. The secondary outcome is the localized voxel-based pattern of NM in the SNc and LC that is specific to each disorder. The recruitment period will be 12 months. Specifically, for PD, MSA, PSP, and ET patients, recruitment of patients with iNPH will be conducted before shunt intervention.

[0325] This study validated the biomarker NM for differentiating Parkinson's disease spectrum disorders. NM-MRI can improve both the design and execution of future clinical trials by helping to differentiate PD, PSP, MSA, ET, and iNPH. It is an inexpensive, rapid, readily available, non-invasive biomarker and has broad applicability to clinical trials by increasing the likelihood of success in clinical trials of future therapies targeting PD or related diseases by reducing the enrollment of patients who are misdiagnosed and, in some cases, whose effectiveness is diminished by potentially valuable therapeutic agents. Finally, a large patient population can reduce the cost of future clinical trials by reducing the number of patients required to reach statistical significance.

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[0328] Equivalent The foregoing is merely illustrative of the principles of this disclosure. Various modifications and changes to the embodiments described will be apparent to those skilled in the art, given the teachings herein. Thus, it will be understood that a number of systems, configurations, and procedures not expressly shown or described herein may be devised to embody the principles of this disclosure and thus be within the spirit and scope of this disclosure. Various different exemplary embodiments may be used together and interchangeably, as should be understood by those skilled in the art. In addition, certain terms used in this disclosure, including the specification, drawings, and claims, may be used synonymously in certain examples, including, but not limited to, data and information. These words, and / or other words that may be synonymous with each other, may be used synonymously herein, but it should be understood that there may be cases where such words are not intended to be used synonymously. Furthermore, prior art knowledge is expressly incorporated herein in its entirety, to the extent that it is not expressly incorporated herein by the above references. All referenced documents are incorporated herein in their entirety by reference.

[0329] Where a range of values ​​is provided, it is understood that (unless otherwise explicitly indicated by the context) each intermediate value up to one-tenth of the lower limit between the upper and lower limits of that range, and any other stated values ​​or intermediate values ​​within that stated range, are included in this disclosure. The upper and lower limits of these smaller ranges may independently be included within smaller ranges and are included in this disclosure, subject to any specifically excluded limits within the stated range. If a stated range includes one or both limits, the range excluding one or both of the included limits is also included in this disclosure.

Claims

1. An in vivo method for determining the progression of Alzheimer's disease over time in a subject, (i) Obtaining a first neuromelanin magnetic resonance imaging (NM-MRI) scan at a first time point, (ii) After step (i), a second NM-MRI scan is obtained at a second time point, (iii) The method comprising determining whether a change in the level, signal, and / or concentration of the neuromelanin occurred between the first time point and the second time point by comparing the first neuromelanin magnetic resonance image with the second neuromelanin magnetic resonance image.

2. The method according to claim 1, wherein Alzheimer's disease is progressing if the change in the level, signal, and / or concentration of neuromelanin at the second time point is about 1%, about 2%, about 3%, about 4%, about 5%, about 6%, about 7%, about 8%, about 9%, about 10%, about 11%, about 12%, about 13%, about 14%, about 15%, about 20%, or about 25% lower than the level, signal, and / or concentration of neuromelanin at the first time point.

3. An in vivo method for diagnosing Alzheimer's disease, (i) Obtaining a first neuromelanin magnetic resonance image at a first time point, (ii) After step (i), a second neuromelanin magnetic resonance image is obtained at a second time point, (iii) The method comprising determining whether a change in the level, signal, and / or concentration of the neuromelanin occurred between the first time point and the second time point by comparing the first neuromelanin magnetic resonance image with the second neuromelanin magnetic resonance image.

4. The method according to any one of the prior claims, wherein a diagnosis of Alzheimer's disease is provided if the change in the level, signal, and / or concentration of the neuromelanin at the second time point is lower by more than about 1%, more than about 2%, more than about 3%, more than about 4%, more than about 5%, more than about 6%, more than about 7%, more than about 8%, more than about 9%, more than about 10%, more than about 11%, more than about 12%, more than about 13%, more than about 14%, more than about 15%, more than about 20%, or more than about 25% than the signal and / or concentration of the neuromelanin at the first time point for Alzheimer's disease.

5. A method for diagnosing patients with Alzheimer's disease, (i) Measuring neuromelanin levels, (ii) Comparing the neuromelanin level with a standard control, (iii) The method comprising, optionally, providing a diagnosis of Alzheimer's disease if the measured level of neuromelanin is lower than that of the standard control.

6. The method according to any one of the prior claims, further comprising determining a first signal intensity from a first neuromelanin magnetic resonance image and determining a second signal intensity from a second neuromelanin magnetic resonance image, wherein comparing the first magnetic resonance image with the second magnetic resonance image includes comparing the first signal intensity with the second signal intensity.

7. The method according to any one of the prior claims, wherein the standard control is the level of neuromelanin present at approximately the same level in the target population, or the standard control is the approximately average level of neuromelanin present in the target population.

8. The method according to any one of the prior claims, wherein a neuromelanin gradient phantom is used to measure the level, signal, and / or concentration of the neuromelanin.

9. The method according to any one of the prior claims, wherein the neuromelanin phantom concentration gradient is scanned approximately once per patient, approximately once per hour, approximately once per day, approximately once per week, or approximately once per month.

10. The method according to any one of the prior claims, wherein the neuromelanin phantom gradient is scanned daily.

11. The method according to any one of the prior claims, wherein a neuromelanin phantom gradient is scanned for each patient.

12. The method according to claims 5 to 11, wherein a diagnosis of Alzheimer's disease is provided if the change in the level, signal, and / or concentration of the neuromelanin at the second time point is more than about 5% lower or more than about 10% lower than the level, signal, and / or concentration of the neuromelanin at the first time point, and the first time point and the second time point are separated by about 1 year, about 2 years, about 3 years, about 4 years, about 5 years, about 6 years, about 7 years, about 8 years, about 9 years, or about 10 years.

13. The method according to any one of the prior claims, wherein a diagnosis of Alzheimer's disease is provided if the change in the level, signal, and / or concentration of the neuromelanin at the second time point is more than 35%, more than 40%, more than 45%, or more than 50% lower than the signal and / or concentration of the neuromelanin at the first time point, and the first time point and the second time point are separated by about 1 year, about 2 years, about 3 years, about 4 years, about 5 years, about 6 years, about 7 years, about 8 years, about 9 years, or about 10 years.

14. The method according to any one of the prior claims, wherein the second time point is approximately 3 months, 6 months, 9 months, 12 months, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, 10 years, 15 years, 20 years, 25 years, or 30 years after the first time point.

15. A method for evaluating neuromelanin concentration in a region of interest of the target brain, The subject is subjected to neuromelanin-magnetic resonance imaging (NM-MRI) scanning, Obtaining a neuromelanin dataset from the aforementioned NM-MRI scan, The optional encryption of the neuromelanin dataset, Uploading the aforementioned neuromelanin dataset to a remote server, Decoding the dataset at will, This includes performing an analysis of the neuromelanin dataset, wherein the analysis is performed (i) Comparing the neuromelanin dataset with one or more neuromelanin datasets previously obtained from the subject, (ii) Comparing the neuromelanin dataset with the control dataset, (iii) Comparing the neuromelanin dataset with one or more neuromelanin datasets previously obtained from different subjects, (iv) To generate a report including the neuromelanin analysis, (v) Optionally encrypting the report, (vi) Uploading the above report to a remote server, (vii) Decrypting the report at an optional rate, and the method comprising one or more of these.

16. A method for determining whether a subject has Alzheimer's disease or is at risk of developing Alzheimer's disease, comprising analyzing one or more neuromelanin-magnetic resonance imaging (NM-MRI) scans of the subject's brain region of interest, wherein the analysis is performed Receiving imaging information of the aforementioned brain region of interest, This includes determining the NM concentration in the brain region of interest using segmented analysis based on the imaging information, Determining whether the subject has developed Alzheimer's disease or is at risk of developing Alzheimer's disease is (1) If one or more NM-MRI scans show reduced NM signals compared to one or more control scans without Alzheimer's disease, then the subject has Alzheimer's disease or is at risk of developing it. (2) The method, wherein if one or more NM-MRI scans have NM signals comparable to the signals of one or more control scans that do not involve Alzheimer's disease, the subject does not have Alzheimer's disease or is not at risk of developing it.

17. A method for treating a subject with Alzheimer's disease, comprising analyzing neuromelanin-magnetic resonance imaging (NM-MRI) scans of brain regions of interest of the subject, wherein the analysis is performed (i) Receiving imaging information of the brain region of interest at a first time point, (ii) Receiving imaging information of the brain region of interest at a second time point, (iii) Using segmented analysis based on the imaging information, determine the NM concentration at the first and second time points in the brain region of interest, (iv) The treatment method comprises comparing the NM concentration at the first time point with the second time point, (1) If the NM-MRI scan at the second time point has a reduced NM signal compared to the NM signal at the first time point, the method includes administering one or more Alzheimer's disease treatments, or (2) If the NM-MRI scan at the second time point has an increased NM signal compared to the NM signal at the first time point, the method (a) Withholding the administration of one or more Alzheimer's disease medications, (b) The method further comprising repeating steps (i) to (iv).

18. The method according to any one of the prior claims, wherein the MRI scan is neuromelanin-sensitive.

19. A method for providing a treatment plan to a patient, comprising: performing an NM-MRI scan; obtaining an NM signal from the NM-MRI scan in a region of interest; comparing the NM signal from the NM-MRI scan in the region of interest with an age-matched database number; and, if the NM signal is less than a predetermined value, implementing a corresponding treatment plan.

20. The method according to any one of the prior claims, wherein the NM-MRI is compared with a standard control.

21. The method according to any one of the prior claims, wherein the patient exhibits symptoms of Parkinson's disease or dementia with Lewy bodies.

22. The method according to any one of the prior claims, wherein the NM-MRI scan distinguishes between Alzheimer's disease and Parkinson's disease, and distinguishes between Alzheimer's disease and dementia with Lewy bodies.

23. The method according to any one of the prior claims, wherein the subject or patient exhibits one or more symptoms of Alzheimer's disease.

24. The method according to any one of the prior claims, wherein a patient is diagnosed with Alzheimer's disease without showing any symptoms.

25. The method according to any one of the prior claims, further comprising diagnosing the patient as having or not having Alzheimer's disease, and showing the diagnosis to the user via a user interface.

26. The method according to any one of the prior claims, wherein the analysis is a segmented analysis.

27. The method according to any one of the prior claims, wherein the segmented analysis includes determining at least one topographical pattern within the brain region of interest.

28. The method according to any one of the prior claims, further comprising a calculation using a value representing the volume of a neuromelanin segment.

29. The method according to any one of the prior claims, wherein the region of interest of the segmented analysis is substantia nigra.

30. The method according to any one of the prior claims, wherein the region of interest of the segmented analysis is the locus coeruleus.

31. A diagnostic system for providing diagnostic information for Alzheimer's disease, An MRI system configured to generate and acquire neuromelanin-sensitive MRI scans, along with a series of neuromelanin data for voxels or segments located within a region of interest in the brain of a target, A signal processor configured to process the series of neuromelanin data and generate a processed neuromelanin MRI spectrum, A diagnostic processor is provided, wherein the diagnostic processor processes the processed neuromelanin MRI spectrum, Extracting measurement values ​​from the region of interest corresponding to neuromelanin at a certain point in time, The measured value is compared with one or more control measured values ​​obtained before the aforementioned time point, The system is configured to provide a diagnosis of Alzheimer's disease when the measured value is more than approximately 25% less than the control measured value.

32. A method for treating patients with Alzheimer's disease, a) Administering an initial dose of Alzheimer's disease medication to the patient, b) Performing serial NM-MRI scans of the patient, monitoring neuromelanin concentrations in regions of interest within the patient's brain, and evaluating treatment-related adverse events throughout the initial treatment period, c) During the initial treatment period, the patient i) A decrease in neuromelanin concentration in the region of interest within the brain of the patient, ii) If there are no adverse effects or side effects related to the treatment, This includes increasing the dose of the Alzheimer's disease medication during the subsequent treatment period. The method wherein the treatment with the Alzheimer's disease drug results in an improvement in the symptoms of Alzheimer's disease in the patient.

33. d) The method according to claim 32, further comprising the step of repeating steps a) to c) until the patient no longer exhibits one or more of the conditions i) to ii) in step c).

34. The method according to any one of the prior claims, wherein the method is used together with a second imaging method, the second imaging method being selected from the group consisting of positron emission tomography (PET), structural MRI, functional MRI (fMRI), blood oxygen level-dependent (BOLD) fMRI, iron-sensitive MRI, quantitative susceptibility mapping (QSM), diffusion tensor imaging DTI, and single-photon emission computed tomography (SPECT), DaTscan, and DaTquant.

35. The method according to any one of the prior claims, wherein the second imaging method includes positron emission tomography (PET).

36. The method according to any one of the prior claims, wherein the second imaging method includes structural MRI.

37. The method according to any one of the prior claims, wherein the second imaging method includes functional MRI (fMRI).

38. The method according to any one of the prior claims, wherein the second imaging method includes blood oxygen level-dependent (BOLD) fMRI.

39. The method according to any one of the prior claims, wherein the segmented analysis comprises determining at least one topographical pattern within the brain region of interest, the brain region of interest being one or more Alzheimer's disease symptom-related segments.

40. The method according to any one of the prior claims, wherein the segmented analysis comprises determining at least one topographical pattern within the brain region of interest, the brain region of interest being one or more patient-specific Alzheimer's disease symptom-associated segments.

41. The method according to any one of the prior claims, wherein the brain region of interest is the substantia nigra or the locus coeruleus.

42. The method according to claims 1 to 41, wherein the brain region of interest is the ventral substantia nigra.

43. The method according to claims 1 to 41, wherein the brain region of interest is the lateral substantia nigra.

44. The method according to claims 1 to 41, wherein the brain region of interest is the ventrolateral substantia nigra.

45. The method according to claims 1 to 41, wherein the brain region of interest is the substantia nigra pars compacta (SNPC).

46. The method according to claims 1 to 41, wherein the brain region of interest is the substantia nigra pars reticularis (SNpr).

47. The method according to claims 1 to 41, wherein the brain region of interest is the ventral intervertebral disc region (VTA).

48. The method according to claims 1 to 41, wherein the brain region of interest is the locus coeruleus.

49. A method for diagnosing neurological disorders in a subject, determining the temporal progression of neurological disorders, or providing a prognosis for neurological disorders, (i) Obtaining a first neuromelanin magnetic resonance imaging (NM-MRI) scan at a first time point, (ii) After step (i), a second NM-MRI scan is obtained at a second time point, (iii) Perform segmentation-based algorithmic analysis to determine the level, concentration and / or volume of neuromelanin (NM) in the locus coeruleus (LC), (iv) Perform voxel-based algorithmic analysis to determine the level, concentration, and / or volume of neuromelanin in the substantia nigra pars compacta (SNc), (v) By comparing the first neuromelanin magnetic resonance image with the second neuromelanin magnetic resonance image, it is determined whether a change in the level, signal, and / or concentration of the neuromelanin occurred between the first time point and the second time point, in both the voxel-based algorithm-based SNc and the segmentation-based algorithm-based LC. (vi) The method comprising providing a diagnosis, progression over time, or prognosis of the neurological disorder based on the difference in the level of the NM in the SNc between the first scan and the second scan, and the difference in the level of the NM in the LC between the first scan and the second scan.

50. An in vivo method for selecting a treatment plan for the prevention or treatment of neurological disorders in a subject, (i) Obtaining a first neuromelanin magnetic resonance imaging (NM-MRI) scan at a first time point, (ii) After step (i), a second NM-MRI scan is obtained at a second time point, (iii) Perform segmentation-based algorithmic analysis to determine the level, concentration, and / or volume of neuromelanin (NM) in the locus coeruleus (LC), (iv) Perform voxel-based algorithmic analysis to determine the level, concentration, and / or volume of neuromelanin in the substantia nigra pars compacta (SNc), (v) By comparing the first neuromelanin magnetic resonance image with the second neuromelanin magnetic resonance image, determine whether a change in the level, signal, and / or concentration of the neuromelanin occurred between the first time point and the second time point, in both the voxel-based algorithm-based SNc and the segmentation-based algorithm-based LC. (vi) To provide a diagnosis, temporal progression, or prognosis of the neurological disorder based on the difference in the level of NM in the SNc between the first scan and the second scan, and the difference in the level of NM in the LC between the first scan and the second scan. (vi) The method comprising carrying out the treatment plan corresponding to the determined neurological disorder.

51. A method for distinguishing between motor disorders that exhibit similar symptoms, (i) Conduct tests to determine the Unified Parkinson's Disease Rating Scale score, (ii) To obtain a first neuromelanin magnetic resonance imaging (NM-MRI) scan at a first time point, (iii) After steps (i) and (ii), a second NM-MRI scan is obtained at a second time point, (iv) Perform a voxel-based analysis to determine the concentration of NM in SNC, and / or Determining the volume, (v) Perform a segmentation-based analysis to determine the concentration and / or volume of NM in the LC, (vi) by comparing the first neuromelanin magnetic resonance image with the second neuromelanin magnetic resonance image, it is determined whether a change in neuromelanin level, signal, and / or concentration occurred in both the SNc and the LC between the first time point and the second time point, (vi) The method comprising providing a diagnosis, progression over time, or prognosis of a neurological disorder based on the difference in the level of the NM in the SNc between the first scan and the second scan, and the difference in the level of the NM in the LC between the first scan and the second scan.

52. A method for diagnosing patients with neurological disorders, (i) Measuring the concentration and / or volume of neuromelanin in SNc using a voxel-based analytical method, and measuring the concentration and / or volume of neuromelanin in LC using a segmentation-based analytical method, (ii) Comparing the level of neuromelanin in the SNc with the standard control level of neuromelanin in the SNc, and comparing the level of neuromelanin in the LC with the standard control level of neuromelanin in the LC, (iii) The method comprising providing a diagnosis of a neurological condition when the size or ratio of SNc and LC neuromelanin is lower or higher for each of their respective regions compared to the standard control.

53. The method according to any one of claims 48 to 52, wherein the method is used together with a second imaging method, the second imaging method being selected from the group consisting of positron emission tomography (PET), tau-PET, structural MRI, functional MRI (fMRI), blood oxygen level-dependent (BOLD) fMRI, iron-sensitive MRI, quantitative susceptibility mapping (QSM), diffusion tensor imaging DTI, and single-photon emission computed tomography (SPECT), DaTscan, and DaTquant.

54. The method according to claim 53, wherein the second imaging method includes positron emission tomography (PET).

55. The method according to claim 53, wherein the second imaging method includes structural MRI.

56. The method according to claim 53, wherein the second imaging method includes functional MRI (fMRI).

57. The method according to claim 53, wherein the second imaging method includes blood oxygen level-dependent (BOLD) fMRI.

58. The method according to any one of claims 1 to 57, wherein the analysis focuses on the neuromelanin levels, concentrations, volumes, or patterns within symptom-specific voxels and / or disease-specific voxels within the SNc.

59. The method according to any one of claims 1 to 57, wherein the analysis focuses on the neuromelanin level, concentration, volume, or pattern within the symptom-specific segment and / or disease-specific segment within the LC.

60. The method according to any one of claims 1 to 57, wherein the analysis focuses on the neuromelanin levels, concentrations, volumes, or patterns within symptom-specific voxels and / or disease-specific voxels in the SNc, and the neuromelanin levels, concentrations, volumes, or patterns within disease-specific segments and / or symptom-specific segments in the LC.

61. The method according to any one of claims 1 to 57, wherein the analysis focuses on the neuromelanin level, concentration, or volume within the SNc, and the neuromelanin level, concentration, volume, or pattern within disease-specific segments and / or symptom-specific segments within the LC.

62. The method according to any one of claims 1 to 57, wherein the analysis focuses on the neuromelanin levels, concentrations, volumes, or patterns in symptom-specific voxels and / or disease-specific voxels within the SNc, and the neuromelanin levels, concentrations, or volumes within the LC.

63. The method according to any one of the prior claims, wherein the neurological condition is selected from schizophrenia, cocaine use disorder, Parkinson's disease, Alzheimer's disease without neuropsychotic symptoms, Alzheimer's disease with neuropsychotic symptoms, major depressive disorder, and / or post-traumatic stress disorder.

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