Network methods for neurodegenerative diseases

Abstract

To provide applications of network methods in investigating neurocognitive disorders.SOLUTION: The invention provides a method of determining patient response to a neuropharmacological intervention, a method of determining a patient's likelihood of developing one or more neurological disorders, and systems for them. The method of determining a patient's likelihood of developing one or more neurological disorders comprises the steps of: obtaining data indicative of electrical activity within the brain of the patient; generating a network, based at least in part on the obtained data, the network comprising a plurality of nodes and directed connections between the nodes, where the network is indicative of a flow of the electrical activity within the brain of the patient; calculating, for each node, a difference in the number and / or strength of connections into the node and the number and / or strength of connections out of the node; and determining, using the calculated differences, the patient's likelihood of developing one or more neurological disorders.SELECTED DRAWING: Figure 41

Description

[Technical Field]

[0001] FIELD OF THE INVENTION The present invention relates to the application of network methods in the investigation of neurocognitive disorders. [Background technology]

[0002] background The model of the human brain as a complex network of interconnected subunits has improved our understanding of normal brain organization, making it possible to address functional changes in neurological disorders. These subunits constitute so-called brain modules, i.e., groups of regions with dense intraregional connections and sparse interregional connections. The brain's modular organization has been suggested to support efficient integration between spatially separated neural processes that support diverse cognitive and behavioral functions. Alterations in brain networks may aid in the identification of patients with Alzheimer's disease (AD) and behavioral frontotemporal dementia (bvFTD).

[0003] For example, alterations in regional volume have been identified in patients with schizophrenia through structural network studies in healthy and diseased individuals, where pairwise correlations of cortical area volumes or thicknesses derived from in vivo measurements of T1-weighted magnetic resonance imaging (MRI) were examined. This approach has demonstrated clinical relevance by revealing alterations in regional volume in patients with schizophrenia. However, volumetric measurements that represent the product of cortical thickness (CT) and surface area (SA) may confound underlying differences. For example, examination of changes in cortical thickness may provide insight into how disease alters the size, density, and arrangement of cells within cortical layers. On the other hand, changes in surface area may provide information about impaired functional integration between groups of brain columns in disease.

[0004] Whole brain or lobe volumes have already been used to monitor the effects of neuropharmacological interventions, however this is a relatively coarse level of analysis.

[0005] As a further example of the use of networks in understanding the brain, EEG data collected from a patient can be used to detect the strength and directionality of electrical flow in the brain, as discussed in WO 2017 / 118733, the entire contents of which are incorporated herein by reference.

[0006] A poster titled "Organization of cortical thickness networks in Alzheimer's disease and behavioral variant frontotemporal dementia across brain lobes" was presented by Vuksanovic et al. at the 6th Cambridge Neuroscience Symposium, Neural Networks in Health and Disease, September 7-8, 2017.

[0007] A further poster entitled "Divergent changes in structural correlation networks in Alzheimer's disease and behavioral variant frontotemporal dementia" was presented by Vuksanovic et al. at the ARUK Conference 2018, 20-21 March, London, UK.

[0008] A presentation entitled "Modular organization of cortical thickness and surface area structural correlation networks in Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD)" was given by Vuksanovic, V at the 10th SINAPSE Annual Scientific Meeting, 25 June 2018, Edinburgh. Summary of the Invention [Means for solving the problem]

[0009] overview In a first aspect, the present invention provides a method for determining a patient response to a neuropharmacological intervention, comprising: obtaining structural neurological data from a plurality of patients prior to neuropharmacological intervention, said structural neurological data being indicative of the physical structure of a plurality of cortical regions; Assigning a plurality of structural nodes corresponding to cortical regions of the brain; and determining pairwise correlations between pairs of structural nodes based at least in part on correspondence data of the structural neurological data; creating a first correlation matrix from the structural neurological data by: obtaining additional structural neurological data from a plurality of patients after the neuropharmacological intervention, wherein the additional structural neurological data is indicative of the physical structure of a plurality of cortical regions; and determining pairwise correlations between pairs of structural nodes based at least in part on correspondence data of the further structural neurological data; generating a second correlation matrix from the further structural neurological data by Including, comparing the first correlation matrix and the second correlation matrix, thereby determining the patient response to the neuropharmacological intervention. The present invention provides a method comprising:

[0010] Optional features of the present invention are now described, which are applicable alone or in any combination with any aspect of the present invention.

[0011] The correlation matrix may then mean creating a structural correlation network that can be represented by a matrix.

[0012] In one embodiment, patient response may be in the context of a clinical trial, for example, to evaluate the efficacy of a pharmaceutical agent in treating a neurocognitive disorder. Thus, the patient group(s) may be a treatment group diagnosed with the disease, or may be a control ("normal") group. Ultimately, the efficacy of a pharmaceutical agent may be assessed based in whole or in part on the patient group response determined by the present invention, and optionally compared to a comparison group that did not receive the intervention.

[0013] The physical structure measured or obtained may be cortical thickness and / or surface area. The values ​​for cortical thickness and / or surface area may be average values ​​obtained from structural neurological data. The structural neurological data may be collected from magnetic resonance imaging (MRI) data or computed tomography data for each patient. The structural neurological data and further structural neurological data may be obtained at different time points. As discussed herein, the structural neurological data may be obtained for each patient via magnetic resonance imaging, computed tomography, or positron emission tomography. These techniques are well known to those skilled in the art—see, for example, Mangrum, Wells, et al., Duke Review of MRI Principles: Case Review Series E-book. Elsevier Health Sciences, 2018, and "Standardized low-resolution electromagnetic tomography (sLORETA): technical details" Methods Find Exp. Clin. Pharmacol. 2002:24 Suppl. D:5-12; Pascual-Marqui RD et al.

[0014] The plurality of cortical regions can be at least 60, or at least 65, e.g., 68. The cortical regions can be, for example, those provided by the Desikan-Killiany atlas (Desikan et al. 2006).

[0015] A p-value can be determined for each pairwise correlation across multiple subjects and compared with a significance level, and only p-values ​​below the significance level are used to create a corresponding correlation matrix. In determining the pairwise correlation between pairs of structural nodes, the corresponding value of each structural node can be compared with a reference value to determine their covariates. The significance level can be referred to as alpha ("α").

[0016] Comparing the first and second correlation matrices may include comparing the number and / or density of anti-correlations in the first correlation matrix with the number and / or density of anti-correlations in the second correlation matrix. In the comparison, groups of structure nodes corresponding to the same leaf may be identified, and the comparison between the first and second correlation matrices may utilize the same leaf.

[0017] Assigning the plurality of structural nodes corresponding to cortical regions of the brain may further include defining groups containing structural nodes corresponding to homologous or non-homologous brain lobes. Comparing the first and second correlation matrices may include comparing the number and / or density of correlations between different groups of structural nodes. In other words, comparing the first and second correlation matrices may include comparing correlations between pairs of non-homologous structural nodes.

[0018] In some examples, comparing the first correlation matrix and the second correlation matrix may include comparing the number and / or density of correlations between groups of structural nodes located in the frontal lobe (anterior nodes) and the temporal and occipital lobes (posterior nodes).In examples of effective neuropharmacological interventions, it has been found that the number and / or density of anti-correlations between anterior and posterior nodes decreases.It is recognized that a decrease in the number and / or density of anti-correlations indicates a decrease in the number of compensatory links, since anti-correlations are hypothesized to indicate compensatory link construction, where atrophy of a node is associated with hypertrophy of a functional link node.

[0019] Typically, the neurocognitive disease or cognitive disorder is a neurodegenerative disorder that leads to dementia, such as a tauopathy.

[0020] The patient may have been diagnosed with a neurocognitive disease, such as Alzheimer's disease or behavioral frontotemporal dementia. The disease may be mild or moderate Alzheimer's disease. The disease may be mild cognitive impairment. However, the findings of the inventors described herein also have applicability to other neurocognitive diseases.

[0021] Diagnostic criteria and treatments for tauopathies, and other neurocognitive disorders, are known in the art and are discussed, for example, in WO 2018 / 019823 and the references cited therein.

[0022] The disease can be behavioral vFTD (bvFTD). Diagnostic criteria and treatment of bvFTD are discussed, for example, in WO 2018 / 041739 and the references cited therein.

[0023] As explained herein, the topology of the disturbances in the structural networks differs in the two pathologies (AD and bvFTD), and both differ from normal aging. The changes from normal are global features, not limited to the fronto-temporal lobes in bvFTD and the temporo-parietal lobes in AD, but are indicative of increased connectivity between non-homogenous lobes as defined by both global correlation strengths, particularly inverse correlations.

[0024] These changes appear to be adaptive features, reflecting coordinated increases in cortical thickness and surface area that compensate for corresponding impairments in functional link nodes. The effect was more pronounced in the cortical thickness network in bvFTD and in the surface area network in AD.

[0025] The inventors have observed that a key change that distinguishes both forms of dementia from normal elderly controls is the emergence of significant anti-correlated networks connecting anterior and posterior brain regions, which may be related to functional adaptation or compensation for the impairment caused by the lesion. Specifically, it is hypothesized that anti-correlations are indicative of compensatory link construction, where atrophy of a node is associated with hypertrophy of a functional link node, and it is recognized that a decrease in the number and / or density of anti-correlations is indicative of a decrease in the number of compensatory links.

[0026] Thus, if neuropharmacological intervention is effective, it is predicted that network organization will be restored to that observed in a normal (non-diseased) comparison population. If the pathology is treated at a sufficiently early stage, network organization can be restored to full equivalence with normal controls. Thus, this method provides an objective means of distinguishing disease-modifying treatment from symptomatic treatment: symptomatic treatment may highlight abnormal network architecture and actually highlight the risk of transmission of (for example) prion-like disease processes to healthy brain regions. Conversely, disease-modifying drugs work in the opposite direction, normalizing function in regions affected by the lesion and thereby reducing the need for compensatory input from relatively mildly affected brain regions.

[0027] In light of the disclosure herein, it is recognized that analysis of structure or network organization is particularly useful in providing greater power in clinical trials, thereby allowing the use of fewer subjects and shorter treatment times. In particular, in diseases such as mild AD, mild cognitive impairment, and pre-mild cognitive impairment, clinical trial endpoints (cognition and function) may be relatively insensitive, thus requiring a large number of subjects and / or a longer duration (see WO 2009 / 060191).

[0028] Thus, typically, neuropharmacological intervention is a pharmaceutical intervention.

[0029] Neuropharmacological intervention can be symptomatic treatment.Such compounds include acetylcholinesterase inhibitors (AChEl), including tacrine, donepezil, rivastigmine and galantamine.Another symptomatic treatment is memantine.These treatments are described in International Publication No. 2018 / 041739.

[0030] As explained above, the inventors found an increase in compensatory networks (the number and / or density of non-homogeneous inverse correlations present in the patient population receiving such treatment).

[0031] Neuropharmacological interventions can be disease-modifying rather than symptomatic. These treatments can be distinguished, for example, based on what happens when a patient discontinues active treatment. Symptomatic drugs delay disease symptoms without affecting the underlying disease process and do not change (or at least improve) the rate of long-term functional decline after the initial treatment period. If, after discontinuation, the patient returns to a treatment-free state, the treatment is considered symptomatic (Cummings, JL (2006) Challenges to demonstrating disease-modifying effects in Alzheimer's disease clinical trials. Alzheimer's and Dementia, 2:263-271).

[0032] For example, the disease-modifying treatment can be an inhibitor of pathogenic protein aggregation, such as a 3,7-diaminophenothiazine (DAPTZ) compound. Such compounds are described in WO 2018 / 041739, WO 2007 / 110627, and WO 2012 / 107706. The latter describes leucomethylthioninium bis(hydromethanesulfonate), which is also known as leucomethylthioninium mesylate (LMTM; USAN name: hydromethylthionine mesylate). [ka]

[0033] The entire contents of these international publications relating to the DAPTZ compounds they define are specifically incorporated by cross-reference.

[0034] Treatment with LMTM has been shown to reduce compensatory network correlations (especially non-cognate, positive and negative correlations).

[0035] Neuropharmacological interventions can be disease-modifying drugs, and efficacy can be established by a reduction in the number and / or density of correlations between anterior and posterior brain regions in the first correlation matrix and the second correlation matrix.

[0036] Therefore, it can be concluded that in instances of effective neuropharmacological intervention (e.g., disease-modifying treatment), the number and / or density of inverse correlations between the anterior and posterior lobes is reduced.

[0037] The present invention can also be used to identify functional adaptations or compensations of pathologically-induced impairments in patient populations (e.g., to investigate "cognitive reserve"). The present invention can be used in combination with conventional diagnostic or prognostic assessment criteria, such as the Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog), the National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA), and the Diagnostic and Statistical Manual of Mental Disorders, 4 th These include the Edn (DSMIV), and the Clinical Dementia Rating Scale (CDR).

[0038] As described above, the method for determining patient response to neuropharmacological intervention can be sequentially used to evaluate different patient cohorts in clinical trials of neuropharmacological intervention.For example, this method can be a method for determining the effectiveness of neuropharmacological intervention in a patient group.This method can be used to define patient groups according to their patient response (e.g., with respect to the determined correlation / inverse correlation).Patient groups can be identified based on their prior use of neuropharmacological intervention, and can optionally be selected for further treatment appropriate to the patient response.

[0039] In a second aspect, the present invention provides a method for determining the likelihood of a patient developing one or more neurological disorders, comprising: obtaining data indicative of electrical activity in the patient's brain; creating a network based at least in part on the obtained data, the network including a plurality of nodes and directed connections between the nodes, the network being indicative of the flow of electrical activity in the patient's brain; calculating, for each node, the difference between the number and / or strength of the connections going into the node and the number and / or strength of the connections going out of the node; and Using the calculated difference to determine the patient's likelihood of developing one or more neurological disorders. The present invention provides a method comprising:

[0040] The present inventors have shown that even very short-term analysis using (for example) brain EEG can potentially identify patients who are susceptible to one or more neurocognitive disorders (e.g., AD). Specifically, such individuals (patients or subjects, the terms are used interchangeably) may have a relatively large number of "sinks" or relatively strong sinks in the posterior lobes, and a relatively large number of "sources" or relatively strong sources in the temporal and / or frontal lobes. In apparently normal or prodromal subjects, in preferred embodiments, the method may be more sensitive than commonly used psychometric assessment criteria for determining such risk.

[0041] Optional features of the present invention are now described, which are applicable alone or in any combination with any aspect of the present invention.

[0042] The likelihood of a patient developing one or more neurological disorders may be referred to as the patient's susceptibility to one or more neurological disorders. The method may include defining a state for each node, defining the node as either a sink or a source based on the calculated difference.

[0043] The network can be a renormalized partial directed coherence network. Any of the steps of the method can be performed offline, i.e., without operation on the patient. For example, obtaining data can be performed by receiving previously recorded data from the patient over a network.

[0044] The data indicative of electrical activity in the brain may be electroencephalogram data. The electroencephalogram data may be beta band electroencephalogram data. The data indicative of electrical activity in the brain may also be magnetic electroencephalogram data or functional magnetic resonance imaging data.

[0045] Determining the patient's susceptibility can be performed using a machine learning classifier, such as a Markov model, a support vector machine, a random forest, or a neural network.

[0046] The method may include generating a heat map based at least in part on the state of the nodes, the heat map being indicative of the location and / or strength of nodes defined as sinks and nodes defined as sources within the patient's brain. This representation of the defined nodes may aid in determining the patient's susceptibility (e.g., ergonomically).

[0047] In determining a patient's susceptibility, a comparison can be made between the number and / or strength of sources in the parietal and / or occipital lobes and the number and / or strength of sinks in the frontal and / or temporal lobes. It has been experimentally determined that patients who are susceptible to one or more neurodegenerative diseases (particularly Alzheimer's disease) have relatively high-intensity sinks in the posterior lobes and relatively high-intensity sources in the temporal and / or frontal lobes.

[0048] The method may further comprise using the state of the nodes to derive an indication of the degree of asymmetry in the localization and / or strength of nodes in the brain corresponding to sinks and sources.

[0049] The neurological disorder may be a neurocognitive disorder, which may be Alzheimer's disease.

[0050] A patient's susceptibility to one or more neurological disorders can be determined by comparing the number and / or strength of nodes defined as sinks in the posterior lobe to a predetermined value, and / or the number and / or strength of nodes defined as sources in the temporal and / or frontal lobe to a predetermined value. If the number and / or strength of nodes defined as sinks in the posterior lobe exceeds a predetermined value, and / or the number and / or strength of nodes defined as sources in the temporal and / or frontal lobe exceeds a predetermined value (e.g., based on "control" subjects or subjects established as having a low risk, or reference data obtained from such subjects (e.g., medical history reference data)), the patient can be determined to be at high risk of susceptibility. In other words, and more generally, a determination regarding susceptibility can be based on whether the patient has many and / or strong sources and / or sinks in one region of the brain relative to another region of the brain. For example, a patient can be determined to be at risk for a neurodegenerative disorder if there are more and / or stronger sources than expected in the temporal and / or frontal lobes, and / or if the patient has more and / or stronger sinks in the posterior lobes than expected based on data from control subjects. Such data from control subjects can be established by longitudinal monitoring after a baseline assessment.

[0051] We further observed that symptomatic treatment increased outward activity from the frontal lobe compared to the unmedicated group.

[0052] The method of the invention according to this aspect can be used to assess, test, or classify a subject's susceptibility to one or more neurological disorders for any purpose, for example, the score value or other output of the test can be used to classify the subject's mental or medical condition according to predetermined criteria.

[0053] The subject can be any human subject. In one embodiment, the subject can be a subject suspected of having a neurocognitive disease or disorder, such as a neurodegenerative or vascular disease described herein, or a subject not identified as at risk.

[0054] In one embodiment, the method is for the purpose of early diagnosis or prognosis of a cognitive disorder, eg, a neurocognitive disease, in a subject.

[0055] The disease can be mild to moderate Alzheimer's disease.

[0056] The condition can be mild cognitive impairment.

[0057] However, the inventors' findings described herein also have applicability to other neurocognitive diseases, for example the disease may be a different dementia, such as vascular dementia.

[0058] The method can optionally be used to inform the subject of further diagnostic steps or interventions, for example based on imaging or other methods of invasive or non-invasive biomarker assessment, such methods being known per se in the art.

[0059] In some embodiments, the method may be for the purpose of determining the risk of neurocognitive disorder in a subject. Optionally, the risk can be further calculated using additional factors, such as age, lifestyle factors, and other measured physical or mental criteria. The risk can be categorized as "high" or "low," or presented as a scale or spectrum.

[0060] It is clear from the disclosure herein that in addition to assessing the likelihood of developing one or more neurological disorders, the same methods can be used to assess the efficacy of disease-modifying treatments to reduce said risk and / or treat said disease, i.e., to assess the efficacy of a pharmaceutical agent for preventing or treating a disease or disorder, optionally in the context of a clinical trial as described herein, for example in comparison to a placebo or other normal control.

[0061] Specifically, the disclosure herein demonstrates that the methods of the present invention (e.g., based on EEG technology) can provide a powerful and sensitive measure of disease impact on subjects, opening up opportunities to demonstrate the efficacy of disease-modifying treatments in smaller groups of subjects (e.g., 200, 150, 100, or 50 or fewer in treatment and comparison arms) and over shorter intervals (e.g., 6, 5, 4, or 3 months or fewer) than are possible using currently available methods, and in earlier stages of disease or less severe disease (e.g., prodromal AD, MCI, or even pre-MCI).

[0062] Thus, as discussed above, the methods can be used with different patient cohorts in clinical trials of neuropharmacological interventions, for example, a patient group (or groups) diagnosed with a disease (e.g., early stage disease) that is to be treated with a putative disease-modifying treatment and a group treated with a placebo.

[0063] Thus, in a further embodiment, the method steps of the second embodiment are used to determine the disease state or severity in a patient rather than to determine the patient's likelihood of developing one or more neurological disorders, which state can then be monitored as part of clinical management or a clinical trial.

[0064] Thus, in a further aspect of the invention there is provided a method of determining a patient response to a neuropharmacological intervention for a neurological disorder, comprising: Prior to neuropharmacological intervention: (a) obtaining data indicative of electrical activity in the patient's brain; (b) creating a network based at least in part on the obtained data, the network including a plurality of nodes and directed connections between the nodes, the network being indicative of the flow of electrical activity in the patient's brain; (c) for each node, calculating the difference between the number and / or strength of the connections going into the node and the number and / or strength of the connections going out of the node; and (d) using the calculated difference to determine the patient's status with respect to neuropathy; (e) repeating steps (a)-(d) after neuropharmacological intervention to determine the patient's further status with respect to the neurological disorder; and (f) determining the patient response to the neuropharmacological intervention based on the first state and the second state (e.g., by comparing the two); A method is provided which includes:

[0065] Optionally, steps (e) and (f) are repeated and subsequent states are used to determine patient response over time.

[0066] Thus, the methods of the second and further aspects (and the corresponding systems discussed below) can be used for both clinical trials and clinical management. With regard to clinical management, a high degree of certainty (e.g., 70%, 80%, 90%, or 95% probability) that a patient's brain electrical activity (e.g., assessed using EEG) is abnormal in a "normal" (i.e., currently undiagnosed) individual can be a strong indicator for the immediate initiation of dementia medication. EEG can also be used, for example, at 1-, 2-, 3-, 4-, 5-, or 6-month intervals to monitor response to treatment. Conversely, individuals with a lower probability of abnormal EEG (e.g., 30%, 40%, 50%, 55%, or 60%) can be followed more closely, at monthly, bimonthly, or trimonthly intervals. Further testing by other means appropriate for the disorder, such as those known in the art (e.g., amyloid or tau PET or CSF-based biomarker assessment), can optionally be used in conjunction with the present methods.

[0067] The optional features relating to the method of the second aspect apply mutatis mutandis to this aspect.

[0068] In a third aspect, the present invention provides a system for determining a patient response to a neuropharmacological intervention, comprising: a data collection means configured to obtain structural neurological data from a plurality of patients prior to neuropharmacological intervention, said structural neurological data being indicative of the physical structure of a plurality of cortical regions; Assigning a plurality of structural nodes corresponding to cortical regions of the brain; and determining pairwise correlations between pairs of structural nodes based at least in part on correspondence data of the structural neurological data; a correlation matrix generation means configured to generate a first correlation matrix from the structural neurological data by Including, the data collection means is also configured to obtain additional structural neurological data from the plurality of patients following the neuropharmacological intervention, the additional structural neurological data indicative of the physical structure of the plurality of cortical regions; The correlation matrix creation method is determining pairwise correlations between pairs of structural nodes based at least in part on correspondence data of the further structural neurological data; and also configured to generate a second correlation matrix from the further structural neurological data by The system is one of the following: a display means for presenting the first correlation matrix and the second correlation matrix; or a comparison means for comparing the first correlation matrix and the second correlation matrix, thereby determining a patient response to the neuropharmacological intervention; The system further comprises:

[0069] Optional features of the present invention are now described, which are applicable alone or in any combination with any aspect of the present invention.

[0070] The correlation matrix may then mean creating a structural correlation network that can be represented by a matrix.

[0071] The physical structure measured or obtained may be cortical thickness and / or surface area. The values ​​for cortical thickness and / or surface area may be average values ​​obtained from structural neurological data. The structural neurological data may be collected from magnetic resonance imaging (MRI) data or computed tomography data for each patient. The structural neurological data and further structural neurological data may be obtained at different time points. As discussed herein, the structural neurological data may be obtained for each patient via magnetic resonance imaging, computed tomography, or positron emission tomography. These techniques are well known to those skilled in the art—see, for example, Mangrum, Wells, et al., Duke Review of MRI Principles: Case Review Series E-book. Elsevier Health Sciences, 2018, and "Standardized low-resolution electromagnetic tomography (sLORETA): technical details" Methods Find Exp. Clin. Pharmacol. 2002:24 Suppl. D:5-12; Pascual-Marqui RD et al.

[0072] The plurality of cortical regions can be at least 60, or at least 65, e.g., 68. The cortical regions can be, for example, those provided by the Desikan-Killiany atlas (Desikan et al. 2006).

[0073] The display means may provide each of the first and second correlation matrices on the display, marking correlation values ​​in each correlation matrix with a color corresponding to the relative amplitude or strength of the correlation.

[0074] The validation means may be configured to determine a p-value for each pairwise correlation and compare the p-values ​​for each pairwise correlation and may compare the p-values ​​to a significance level, and the correlation matrix creation means may be configured to use only p-values ​​that are less than the corrected significance level when creating the correlation matrix. The significance level may be referred to as alpha ("α").

[0075] The comparing means may be configured to compare the number and / or density of anti-correlations in the first correlation matrix with the number and / or density of anti-correlations in the second correlation matrix. In the comparison, groups of structure nodes corresponding to the same leaf may be identified, and the comparison between the first and second correlation matrices may utilize the same leaf.

[0076] Assigning the plurality of structural nodes corresponding to cortical regions of the brain may further include defining groups containing structural nodes corresponding to homologous or non-homologous brain lobes. The comparing means may be configured to compare the first and second correlation matrices by comparing the number and / or density of correlations between different groups of structural nodes. In another expression, comparing the first and second correlation matrices may include comparing pairs of structural nodes that are non-homologous.

[0077] The comparison means can be configured to compare the first correlation matrix and the second correlation matrix by comparing the number and / or density of correlations between groups of structural nodes located in the frontal lobe (anterior nodes) and the parietal and occipital lobes (posterior nodes), respectively. In examples of effective neuropharmacological interventions, the number and / or density of anti-correlations between anterior and posterior nodes has been found to decrease. It is recognized that a decrease in the number and / or density of anti-correlations is indicative of a decrease in the number of compensatory links, since anti-correlations are hypothesized to be indicative of compensatory link building, where atrophy of a node is associated with hypertrophy of a functional link node.

[0078] In one embodiment, the patient response may be in the context of a clinical trial, for example, to evaluate the efficacy of a pharmaceutical agent in treating a neurocognitive disorder. Thus, the patient group(s) may be a treatment group diagnosed with the disease, or may be a control ("normal") group. Ultimately, the efficacy of a pharmaceutical agent may be assessed based in whole or in part on the patient group response determined by the present invention.

[0079] As explained in relation to the first aspect, neurocognitive diseases are generally neurodegenerative disorders that lead to dementia, such as tauopathies.

[0080] The patient may have been diagnosed with a neurocognitive disorder, such as Alzheimer's disease or behavioral frontotemporal dementia. The disorder may be mild or moderate Alzheimer's disease. The disorder may be mild cognitive impairment.

[0081] Tauopathies, and the diagnostic criteria and treatment of these disorders, are discussed, for example, in WO 2018 / 019823 and the references cited therein.

[0082] The disease can be behavioral vFTD (bvFTD). Diagnostic criteria and treatment of bvFTD are discussed, for example, in WO 2018 / 041739 and the references cited therein.

[0083] As explained herein, the topology of the lesions in the structural network differs in the two pathologies (AD and bvFTD), and both differ from normal aging. These changes appear to be adaptive in nature, reflecting coordinated increases in cortical thickness and surface area that compensate for corresponding damage in functional link nodes.

[0084] Therefore, if neuropharmacological intervention is effective, it is predicted that network organization will be returned to normal. If the disease state is treated at a sufficiently early stage, network organization will be returned to normal, and the disease state may be arrested or reversed. Thus, the system provides an objective means of distinguishing disease-modifying treatments from the symptomatic treatments described above.

[0085] Typically, the neuropharmacological intervention is a pharmaceutical intervention.

[0086] Neuropharmacological interventions can be symptomatic treatments as described above.

[0087] For example, the disease-modifying treatment can be an inhibitor of pathogenic protein aggregation, such as the 3,7-diaminophenothiazine (DAPTZ) compounds described above.

[0088] In a fourth aspect, the present invention provides a system for determining a patient's susceptibility to one or more neurological disorders, comprising: data collection means configured to obtain data indicative of electrical activity in the patient's brain; network generation means configured to generate a network based at least in part on the obtained data, the network including a plurality of nodes and directed connections between the nodes, the network being indicative of the flow of electrical activity in the patient's brain; difference calculation means configured to calculate, for each node, the difference between the number and / or strength of connections entering the node and the number and / or strength of connections leaving the node; and any of the following: a display means configured to display a representation of the calculated difference; or determining means configured to use the calculated difference to determine the patient's susceptibility to one or more neurological disorders; The present invention provides a system including:

[0089] As described above in relation to the second aspect, the inventors have shown that even very short-term analysis using (for example) EEG of the brain can potentially be used to identify patients who are susceptible to one or more neurocognitive diseases (e.g. AD).

[0090] The system can be used for both clinical trials and clinical management.

[0091] Thus, in a further aspect, there is provided the above system for determining a patient's response to a neuropharmacological intervention for a neurological disorder, in which aspect the decision means system may be configured to use the calculated difference to determine the patient's status with respect to the neurological disorder.

[0092] The system can be used to determine a further state of the patient after the neuropharmacological intervention, and can optionally be configured to determine the patient's response to said neuropharmacological intervention in terms of a corresponding method based on the first and one or more subsequent states.

[0093] Other optional features of the present invention will now be described, which may be applied alone or in any combination with any aspect of the present invention.

[0094] The system may include state defining means configured to define the node as either a sink or a source based on the calculated difference.

[0095] The network may be a renormalized partially directed coherence network. The system may operate "offline," i.e., without operation on the patient. For example, data acquisition may be performed by receiving previously recorded data from the patient over the network.

[0096] The data indicative of electrical activity in the brain may be electroencephalogram data. The electroencephalogram data may be beta band electroencephalogram data. The data indicative of electrical activity in the brain may also be magnetic electroencephalogram data or functional magnetic resonance imaging data.

[0097] The determining means may be configured to determine the patient's susceptibility to one or more neurological disorders using a machine learning classifier, for example a Markov model, a support vector machine, a random forest, or a neural network.

[0098] The display means may be configured to present a heat map indicative of the localization and / or intensity of nodes defined as sinks and nodes defined as sources within the brain, this representation of the defined nodes may aid (e.g., ergonomically) in determining the susceptibility of the patient.

[0099] The system may further include a heat map generator configured to generate a heat map based at least in part on the state of the nodes, said heat map being indicative of the localization and / or intensity of nodes defined as sinks and nodes defined as sources within the patient's brain. This representation of the defined nodes may aid in determining the patient's susceptibility (e.g., ergonomically).

[0100] The determining means may compare the number and / or strength of sources in the parietal and / or occipital lobes with the number and / or strength of sinks in the frontal and / or temporal lobes. It has been experimentally determined that patients who are susceptible to one or more neurodegenerative diseases (particularly Alzheimer's disease) have a relatively high number and / or strength of sinks in the posterior lobes and a relatively high number and / or strength of sources in the temporal and / or frontal lobes.

[0101] The system may further include an asymmetry mapping means configured to use the state of the nodes to derive an indication of the degree of left-right asymmetry in the localization and / or density of nodes in the brain corresponding to sinks and sources.

[0102] The neurological disorder may optionally be a neurocognitive disorder, which may be Alzheimer's disease.

[0103] The determining means may compare the number and / or strength of nodes defined as sinks in the posterior lobe with a predetermined value and / or the number and / or strength of nodes defined as sources in the temporal and / or frontal lobe with a predetermined value. The determining means may determine that a patient is at high risk of susceptibility if the number and / or strength of nodes defined as sinks in the posterior lobe exceeds a predetermined value and / or if the number and / or strength of nodes defined as sources in the temporal and / or frontal lobe exceeds a predetermined value. In other words, and more generally, a determination regarding susceptibility may be based on whether a patient has more and / or stronger sources and / or sinks in one region of the brain relative to another region of the brain. For example, a patient may be determined to be at risk of a neurodegenerative disorder if there are more and / or stronger sources in the temporal and / or frontal lobe than expected and / or if the patient has more and / or stronger sinks in the posterior lobe than expected based on data from control subjects.

[0104] We further observed that symptomatic treatment increased outward activity from the frontal lobe compared to the unmedicated group.

[0105] The system of the invention according to this aspect can be used to assess, test, or classify a subject's susceptibility to one or more neurological disorders for any purpose, for example, the score value or other output of the test can be used to classify the subject's mental or medical condition according to predetermined criteria.

[0106] The subject can be any human subject. In one embodiment, the subject can be a subject suspected of having a neurocognitive disease or disorder, such as a neurodegenerative or vascular disease described herein, or a subject not identified as at risk.

[0107] In one embodiment, the system is for the purpose of early diagnosis or prognosis of a cognitive disorder, such as a neurocognitive disease, in the subject.

[0108] The system can optionally be used to inform the subject of further diagnostic steps or interventions, for example based on imaging or other systems of invasive or non-invasive biomarker assessment, such systems being known per se in the art.

[0109] In some embodiments, the system may be for the purpose of determining the risk of neurocognitive impairment in a subject. Optionally, the risk can be further calculated using additional factors, such as age, lifestyle factors, and other measured physical or mental criteria. The risk can be categorized as "high" or "low," or presented as a scale or spectrum.

[0110] Similar to the methods described herein, the system can be used in the context of a clinical trial to evaluate the efficacy of neuropharmacological interventions. The system can be used to demonstrate the efficacy of disease-modifying treatments, such as LMTM, in relatively small numbers of subjects (e.g., 50), over relatively short timescales (e.g., 6 months), and in early disease stages (e.g., mild cognitive impairment or possible pre-mild cognitive damage).

[0111] Further aspects of the present invention provide a computer program comprising executable code that, when executed on a computer, causes the computer to perform the method of the first or second aspect; a computer-readable medium storing a computer program comprising code that, when executed on a computer, causes the computer to perform the method of the first or second aspect; and a computer system programmed to perform the method of the first or second aspect. For example, a computer system can be provided that includes one or more processors configured to perform the method of the first or second aspect. Thus, the system corresponds to the method of the first or second aspect. The system may further include one or more computer-readable media operably connected to the processor, the one or more computer-readable media storing computer-executable instructions corresponding to the method of the first or second aspect.

[0112] Embodiments of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which: [Brief explanation of the drawings]

[0113] [Figure 1] An example of the Desikan-Killiany brain atlas is shown. [Figure 2] 1 shows an example of a cortical surface area correlation matrix with pairwise correlations grouped by lobe for a group of subjects diagnosed with behavioral disorder frontotemporal dementia. [Figure 3A] (i) Group-based cortical thickness correlation network shown as pairwise correlation matrix for a group of HE subjects. [Figure 3B] (ii) Group-based cortical thickness correlation network shown as pairwise correlation matrix for a group of bvFTD subjects. [Figure 3C] (iii) Group-based cortical thickness correlation network shown as pairwise correlation matrix for a group of AD subjects. [Figure 4A] (i) Group-based surface area correlation network shown as pairwise correlation matrix for groups of HE subjects. [Figure 4B] (ii) Group-based surface area correlation network shown as pairwise correlation matrix for groups of bvFTD subjects. [Figure 4C] (iii) Group-based surface area correlation network shown as pairwise correlation matrix for groups of AD subjects. [Figure 5] Figure 1 shows plots of mean edge strength of cortical thickness correlation networks averaged across brain lobes and compared between HE, bvFTD, and AD groups; the top plot is for positively correlated networks, and the bottom plot is for negatively correlated networks. [Figure 6] Plots of mean edge strength of surface area correlation networks averaged across brain lobes and compared between HE, bvFTD, and AD groups are shown; the left plot is for anti-correlated networks and the right plot is for positively correlated networks. [Figure 7] Figure 1 shows plots of node degree of the cortical thickness correlation network averaged across brain lobes across HE, bvFTD, and AD groups, where the top plot is for the positively correlated network and the bottom plot is for the negatively correlated network. [Figure 8] 10 shows plots of node inter-lobar participation indices of the cortical thickness correlation network for positive correlations averaged across lobes and compared across HE, bvFTD, and AD groups. [Figure 9] Plots of node degree for surface area correlation networks averaged across brain lobes and compared across HE, bvFTD, and AD groups are shown; the top plot is for the positively correlated network and the bottom plot is for the negatively correlated network. [Figure 10] Plots of nodal interlobar participation indices for surface area correlation networks averaged across brain lobes and compared across HE, bvFTD, and AD groups are shown; the top plot is for positively correlated networks and the bottom plot is for negatively correlated networks. [Figure 11]Brain spatial visualization of hubs in the cortical thickness network for HE, bvFTD, and AD groups for positively correlated nodes for the top plot and for negatively correlated nodes for the bottom plot. [Figure 12] Brain space visualization of hubs in the surface area network for HE, bvFTD, and AD groups for positively correlated nodes for the top plot and for negatively correlated nodes for the bottom plot. [Figure 13] 1 shows brain spatial visualization of interactions between positive networks of cortical thickness and cortical surface area for HE, bvFTD, and HE groups. [Figure 14] Histograms of retained edges in cortical thickness (top three plots) and surface area (bottom three plots) correlation networks are shown. [Figure 15] 1 is a plot showing the distribution of modularity index (Q) in a regional cortical thickness correlation network created based on 100 surrogate datasets. [Figure 16] Dichotomized correlation matrices of the cortical thickness network (top three panels) and surface area (bottom three panels) for the HE, bvFTD, and AD groups are shown, with white representing significant positive correlations and black representing significant inverse correlations. [Figure 17] Figures 17A-17D show correlation matrices at baseline (i.e., week 1) by treatment status with symptomatic medications for AD (cholinesterase inhibitors and / or memantine), where ach0 indicates no treatment and ach1 indicates the presence of such treatment. [Figure 18] 18A-18D show plots of node degrees of non-homogeneous inter-lobar correlations at baseline node degrees by treatment status with symptomatic AD medications (acetylcholinesterase inhibitors and / or memantine). [Figure 19] Figures 19A and 19B show the cortical thickness correlation matrix at baseline (week 1) and after 65 weeks (week 65) in patients receiving 8 mg / day of LMTM in combination with symptomatic treatment. [Figure 20A] 1 shows a plot of the correlation of positive non-homogenous interlobar node order with cortical thickness (CT) at baseline and after 65 weeks in patients receiving 8 mg / day of LMTM in combination with symptomatic treatment. [Figure 20B] 1 shows a plot of the correlation of inverse non-homogenous interlobar nodal order with cortical thickness (CT) at baseline and after 65 weeks in patients receiving 8 mg / day of LMTM in combination with symptomatic treatment. [Figure 20C] 1 shows a plot of the correlation of positive non-homogenous interlobar node order with surface area (SA) at baseline and after 65 weeks in patients receiving 8 mg / day of LMTM in combination with symptomatic treatment. [Figure 20D] 1 shows a plot of the correlation of non-homogenous interlobar nodal degree with inverse surface area (SA) at baseline and after 65 weeks in patients receiving 8 mg / day of LMTM in combination with symptomatic treatment. [Figure 21A] 1 shows the cortical thickness correlation matrix at baseline (week 1) in patients receiving 8 mg / day of LMTM as monotherapy (i.e., not in combination with symptomatic AD treatment). [Figure 21B] FIG. 1 shows time-separated cortical thickness correlation matrices after 65 weeks (Week 65) in patients receiving 8 mg / day of LMTM as monotherapy (i.e., not in combination with symptomatic AD treatment). [Figure 22] Figures 22A and 22B show plots of non-homogenous interlobar node degree for the cortical thickness correlation network at baseline and after 65 weeks in patients receiving 8 mg / day of LMTM in combination as monotherapy (i.e., not in combination with symptomatic AD treatments). [Figure 23A] FIG. 1 shows the surface area correlation matrix at baseline (week 1) in patients receiving 8 mg / day of LMTM as monotherapy (i.e., not in combination with symptomatic AD treatment). [Figure 23B] FIG. 1 shows time-separated surface area correlation matrices after 65 weeks (Week 65) in patients receiving 8 mg / day of LMTM as monotherapy (i.e., not in combination with symptomatic AD treatment). [Figure 24] Figures 24A and 24B show plots of non-homogenous inter-lobar node degree for the surface area correlation network at baseline and after 65 weeks in patients receiving 8 mg / day of LMTM as monotherapy and in combination (i.e., not in combination with symptomatic AD treatments). [Figure 25A] 1 shows the cortical thickness correlation matrix for AD (Clinical Dementia Scale 0.5, 1, and 2) at baseline for treatment with LMTM 8 mg / day as monotherapy. [Figure 25B] Cortical thickness correlation matrices are shown for the elderly control group (HE) to demonstrate normalization of the matrices after treatment. [Figure 25C] FIG. 1 shows time-separated cortical thickness correlation matrices for AD (Clinical Dementia Scale 0.5, 1, and 2) after 65 weeks of treatment with LMTM 8 mg / day as monotherapy. [Figure 25D] Cortical thickness correlation matrices are shown for the elderly control group (HE) to demonstrate normalization of the matrices after treatment. [Figure 26A] 1 shows the surface area correlation matrix for AD (Clinical Dementia Scale 0.5, 1, and 2) at baseline for treatment with LMTM 8 mg / day as monotherapy. [Figure 26B] The surface area correlation matrix for the elderly control group (HE) is shown to demonstrate normalization of the matrix after treatment. [Figure 26C] FIG. 1 shows time-separated surface area correlation matrices for AD (Clinical Dementia Scale 0.5, 1, and 2) after 65 weeks of treatment with LMTM 8 mg / day as monotherapy. [Figure 26D] The surface area correlation matrix for the elderly control group (HE) is shown to demonstrate normalization of the matrix after treatment. [Figure 27A] 1 shows the cortical thickness correlation matrix for AD (Clinical Dementia Scale 0.5) at baseline for treatment with LMTM 8 mg / day as monotherapy. [Figure 27B] Cortical thickness correlation matrices are shown for the elderly control group (HE) to demonstrate normalization of the matrices after treatment. [Figure 27C]FIG. 1 shows time-separated cortical thickness correlation matrices for AD (Clinical Dementia Scale 0.5) after 65 weeks of treatment with LMTM 8 mg / day as monotherapy. [Figure 27D] Cortical thickness correlation matrices are shown for the elderly control group (HE) to demonstrate normalization of the matrices after treatment. [Figure 28A] 1 shows the surface area correlation matrix for AD (Clinical Dementia Scale 0.5) at baseline for treatment with LMTM 8 mg / day as monotherapy. [Figure 28B] The surface area correlation matrix for the elderly control group (HE) is shown to demonstrate normalization of the matrix after treatment. [Figure 28C] FIG. 1 shows the time-distant surface area correlation matrix for AD (clinical dementia rating 0.5) after 65 weeks of treatment with LMTM 8 mg / day as monotherapy. [Figure 28D] The surface area correlation matrix for the elderly control group (HE) is shown to demonstrate normalization of the matrix after treatment. [Figure 29] An example of resting electroencephalogram data is shown. [Figure 30] 1 shows an example of a directed network derived from EEG data. [Figure 31] 10A and 10B illustrate diagrammatically the determination of node state as the difference between the inflow of electrical activity and the outflow of electrical activity. [Figure 32] Heatmaps of the localization of net sinks (yellow / red) and net sources (blue) within the brain of a group of subjects are shown. [Figure 33] 33 shows a heatmap illustrating the asymmetry in the distribution of sources and sinks between the left and right sides of the heatmap of FIG. 32. [Figure 34] 1 shows heat maps of source and sink localization in the brain of a group of subjects diagnosed with Alzheimer's disease. [Figure 35] 1 shows heat maps of source and sink localization in the brain of a group of subjects not diagnosed with Alzheimer's disease (i.e., paired subjects). [Figure 36]1 shows heatmaps of source and sink localization in the brain of subjects not diagnosed with Alzheimer's disease (i.e., paired subjects). [Figure 37] 1 shows a heatmap of source and sink localization in the brain of a subject diagnosed with Alzheimer's disease. [Figure 38] For example, a heat map of source and sink localization in the brain of a group of subjects determined to be at risk of dementia or cognitive decline due to having Alzheimer's disease is shown. [Figure 39] 1 shows a heat map of source and sink localization in the brain of a group of subjects determined to be not at risk for Alzheimer's disease. [Figure 40] Boxplots comparing sources and sinks in EEG networks from anterior and posterior brain regions at the group level in subjects at risk for AD and not at risk for AD. [Figure 41] Figure 1 shows a comparison between the cortical thickness correlation matrix (left) for the AD group showing an increase in the strength and number of significant reverse non-homogenous inter-lobar correlations, and a heat map of source and sink localization in the brain of a group of subjects diagnosed with AD showing a correspondence between the increase in compensatory structural reverse non-homogenous correlations in cortical thickness directed towards posterior brain regions and the increase in the strength and number of incoming connections to posterior brain regions as sinks as shown by rPDC coherence analysis of resting-state EEG. [Figure 42] Figure 1 shows a comparison between the surface area correlation matrix for the HE group and a heat map of source and sink localization in the brain of a group of healthy elderly subjects showing a correspondence between the relative lack of compensatory structural inverse non-homogeneous correlations in cortical thickness directed towards posterior brain regions and the reduced number and strength of incoming connections to posterior brain regions as sinks as shown by rPDC coherence analysis of resting-state EEG. [Figure 43] Box plot showing quantitative difference of mild AD from elderly controls. [Figure 44] Three heatmaps comparing medicated and unmedicated AD patients and paired subjects at the group level are shown. [Figure 45]Boxplots of group-level networks comparing medicated and unmedicated AD patients and paired subjects. DETAILED DESCRIPTION OF THE INVENTION

[0114] Detailed Description and Further Optional Features Aspects and embodiments of the present invention will now be discussed with reference to the accompanying drawings. Further aspects and embodiments will be apparent to those skilled in the art. All documents cited in this text are incorporated herein by reference.

[0115] Figure 1 shows an example of a Desikan-Killiany brain atlas. The Desikan-Killiany brain atlas divides the human cerebral cortex into gyral-based regions of interest on MRI scans. While 18 regions are shown in the figure, the Desikan-Killiany whole-brain atlas divides the human cortex into 68 regions of interest.

[0116] The subjects discussed in this paper participated in three currently completed global Phase 3 clinical trials. Two of these trials were in mild to moderate AD (Gauthier et al., 2016; Wilcock et al., 2018), and the third was from a larger study of bvFTD (Feldman et al., 2016). Comparative data were available from well-characterized healthy elderly (HE) subjects participating in the ongoing longitudinal study of the Aberdeen 1936 Birth Cohort (ABC36) (Murray et al., 2011). In all, there were 628 subjects in the examples discussed herein, with 213 and 202 healthy elderly subjects in the dementia groups, respectively. bvFTD patients were diagnosed according to the International Consensus Criteria for bvFTD and had a mild severity of bvFTD with a Mini-Mental State Examination (MMSE) score of 20–30 (inclusive). AD patients were diagnosed according to criteria from the National Institute of Aging and the Alzheimer's Association and had mild to moderate severity as defined by an MMSE score of 14-26 (inclusive) and a Clinical Dementia Rating Scale (CDR) total score of 1 or 2. They were drawn from a corresponding larger cohort (N = 1132) to match the number of participants in the bvFTD cohort. Healthy elderly (HE) subjects were selected from the well-characterized Aberdeen 1936 Birth Cohort.

[0117] The multi-side source imaging datasets used to generate the correlation matrices discussed below were standard T1-weighted MRI images acquired using equivalent manufacturer-specification 3DT1 sequences. Data from study patients were pooled to allow for overall group comparisons. Train scanners were limited to 1.5T and 3T (30%) field strengths from three manufacturers (Philips, GE, and Siemens). All MRI images in the ABC36 cohort were acquired using the same (Philips) 3T scanner. Images were processed using an automated processing pipeline implemented in a manner known per se. In addition to volume-based methods of image processing, the pipeline also generates surface-based area measurements of cortical morphology, such as thickness, regional curvature, or surface area. One example of an automated processing pipeline suitable for the above method is FreeSurfer v5.3.0, available from the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital.

[0118] Surface area was calculated from the imaging dataset using a triangular tessellation of the gray matter / white matter interface and the white matter / cerebrospinal fluid boundary (referred to as the pial surface). Cortical thickness was calculated as the average distance from the white matter surface to the closest point on the pial surface and back to the closest point to the white matter surface. Cortical thickness and surface area were extracted for 68 cortical regions from both hemispheres based on the Desikan-Killiany atlas using a known parcellation scheme. A list of regions and their lobe assignments is provided in Table A.1 in Appendix A.

[0119] Figure 2 shows the cortical surface area correlation matrix for a group of subjects diagnosed with bvFTD. Each matrix element represents the correlation strength ("edge strength") between 68 pairs of cortical surface areas from the Desikan-Killiany atlas. The intensity bar to the right indicates the correlation / edge strength. The 68 cortical surface regions (network nodes) are arranged according to their frontal, temporal, parietal, and occipital lobe location. Single lobe regions are enclosed within boxes and arranged from top to bottom / left to right by frontal, temporal, parietal, and occipital. Essentially, the correlation matrix represents a network constructed from the partial correlations between 68 pairs of cortical thicknesses. Figures 3A-3C show the cortical surface area correlation matrices for healthy elderly subjects, subjects with behavioral disorder-type frontotemporal dementia, and subjects with Alzheimer's disease, respectively. Significant differences can be observed between healthy elderly subjects and both bvFTD and AD subjects. In particular, intralobar correlations were significantly increased in strength for bvFTD and AD subjects. Furthermore, the number of reverse correlations increased between non-homogenous nodes. As can be seen, HE subjects have sparse correlations, which are mostly positive correlations between the same brain lobes. In contrast, both bvFTD and AD have a significantly increased number of nodes linked by positive and reverse correlations compared to the HE group. The increased number of correlations in both forms of dementia can be between the same lobes (homogenous, mostly positive) or between different lobes (non-homogenous, mostly negative). In general, reverse inter-lobe non-homogenous correlations are highly abnormal. It can also be seen that bvFTD is associated with denser reverse non-homogenous correlations, especially in cortical thickness.

[0120] Figures 4A-4C show the surface area correlation matrices for healthy elderly subjects, subjects with behavioral impairment-type frontotemporal dementia, and subjects with Alzheimer's disease, respectively. Significant differences can be seen between healthy elderly subjects and both bvFTD and AD subjects. Furthermore, it should be noted that AD is particularly associated with a higher density of inverse non-homogenous correlations in surface area.

[0121] In these figures, zero entries correspond to non-significant correlations. Significant network correlations were found to have both positive and negative values ​​(see Figures 14 and 16 for illustration). Due to the number of significant inverse correlations and the apparent increase in correlation strength of networks in bvFTD and AD relative to the HE group, subnetworks with significant positive and negative correlations were examined separately. Given the apparent differences in leaf network structure according to diagnostic group, we attempted to determine whether these differences could be quantified.

[0122] Networks, represented in these figures as correlation matrices, can be constructed by correlating surface area or cortical thickness across all subjects within a particular diagnostic category (i.e., HE, bvFTD, and AD). Cortical regions (defined by the Desikan-Killiany brain atlas) represent nodes, and pairwise correlations between nodes represent graph edges, or links / connections were constructed that correlate either SA or CT across all participants within each diagnostic category. Each correlation matrix was calculated based on an S × N array containing N regional CT / SA values ​​from S subjects within each group. In this way, six N × N (e.g., 68 × 68) correlation matrices were obtained (one CT or SA structural correlation matrix for each study group). The matrix element e ij is the vector x containing the region measurements from subjects within each group between regions i and j (i, j = 1, 2, ... N) i and x j The partial correlation is calculated by first removing the influence of all other regions m ≠ (i; j), and then calculating x for the control variables (stored in a separate array S × C, where C represents the number of control variables). i and x j After adjusting both x i and x j The correlation coefficient was calculated as the linear Pearson correlation coefficient between pairs of x. This means that a linear regression was performed on every x before the correlation analysis. iThis means that the effects of age, sex, and mean CT (average cortical thickness of all areas) or total surface area (sum of all surface areas) are removed. Autocorrelation (represented by the main matrix diagonal network criterion) was calculated based on the lower triangular part of the matrix. Partial correlation e ij (i.e., edge load) can be calculated according to the following general formula:

number

[0123] In the formula, x i;j indicates an array of variables, and x c denotes any subset of conditioning variables. To derive this general form of partial correlation, the process starts with i, j, c=1, 2, 3:

number

[0124] Thus, for any subset of c conditioning variables:

number

[0125] In some cases, to verify that the network retained only statistically significant correlations, the calculated correlation coefficients were adjusted for multiple testing using the false discovery rate (FDR) procedure described in Storey, 2002. The FDR procedure tests each calculated p-value (from the pairwise correlation calculations) against an adjusted significance level, α=0.05 in this example, and only accepts p-values ​​smaller than the adjusted significance level as truly significant. Those pairwise correlations that did not pass the FDR test could be set to zero; otherwise, all non-zero correlations, whether positive or negative, were retained (see Figure 14, discussed in more detail below).

[0126] In this way, a 68x68 correlation matrix can be constructed for either CT or SA in each clinical group, which represents the structural correlation network for either cortical thickness or surface area. The matrix elements quantify the strength of correlation between cortical regions for either cortical thickness or surface area, which does not represent actual physical connections in themselves. In the context of structural correlation network analysis in neurodegenerative disorders, such correlations are considered to imply either a simultaneous atrophy relationship (if positive) or an inverse atrophy / hypertrophy relationship (if negative) between brain regions.

[0127] For the structural correlation networks and / or matrices created using the above method, it is useful to use the following metrics to compare the structural network properties of the three clinical groups: edge strength, node degree, node intra-module degree z-score, and participation index. Edge strength and node degree represent two fundamental network attributes; they quantify the correlation strength between nodes and the number of pairwise correlations for each node, respectively. To evaluate whether cortical lobes represent modules, we utilized two network metrics that assess modularity in network interactions: intra-module degree z-score and participation index. All metrics (except node degree) were computed based on weighted graphs and estimated as averages across the four lobes (see below). The metrics were computed based on either binary or weighted graphs (as discussed below). From purely theoretical studies, it is known that the computed topological properties of networks depend on the choice of threshold (van Wijk et al. 2010). In this paper, a fixed threshold for each group-based correlation matrix is ​​chosen.

[0128] Node degree Node degree k irepresents the number of significant correlations for each node in the network. Generally, node degree is calculated from a binarized correlation matrix, where each significant correlation in the matrix is ​​replaced with 1 if it is significant or 0 if it is not significant. An example of a binarized matrix is ​​shown in Figure 16. A binarized matrix can also be referred to as an adjacency matrix. In Figure 16, the top three plots correspond to cortical thickness, and the bottom three plots correspond to surface area. Significant positive correlations are shown in white, while significant negative correlations are shown in black.

[0129] The degree of node i, i.e., the number of significant links connected to the node, is given by

number

[0130] Modularity Index The node participation index and intramodular degree z-score assess the role of nodes according to modules. A network module (also known as a community structure) represents a subgraph of a densely connected network, i.e., a subset of nodes with denser and sparser network connections. It is useful to examine the modular organization of the frontal, temporal, parietal, and occipital lobes of a cortical thickness or surface area network, defined as modules. Because these lobes of the cortical surface area are not necessarily modular themselves, it may be necessary to first test whether the lobes are modular in nature. In one example, this can be done by calculating the modularity index (Q) of the network according to each lobe. The modularity index quantifies the fraction of observed intramodular degree values ​​relative to those expected if connections were randomly distributed across the network. Because the constructed cortical thickness and surface area networks contain both positive and negative edge strengths, it is possible to use an asymmetric generalization of the modularity quality function. For example, as introduced in Rubinov and Sporns (2011):

number

[0131] During the ceremony,

number

number

number

number

number

number

[0132] The lobe organization of the cortical surface into frontal, parietal, temporal, and occipital regions was found to be modular in nature (see Appendix A). Therefore, it is then possible to calculate the contribution of individual nodes to lobe modules as node participation indices and intramodule z-scores, which we refer to as node inter-lobe participation indices and node intra-lobe z-scores.

[0133] Node-leaf participation index In general, the participation index p measures the inter-module connectivity. It can be viewed as the ratio of the intra-leaf node edges to all other leaf modules in the network, and the node p i is a propensity of 0 if a node has links exclusively within its own module, and a propensity of 1 if a node has links exclusively outside its own module. Weighted network participation is

number

number

number

[0134] Node leaf degree z score The complement of the inter-leaf participation index is the normalized intra-leaf degree z i It evaluates the intra-leaf connectivity by z-score, i.e., by the normalized deviation of the inter-leaf degrees of the nodes with their respective mean degree distributions. Therefore, the intra-leaf z-score of a node, z i is large for nodes with many intra-modular connections relative to the average inter-modular connectivity. For networks where correlation strength is preserved, the node intra-modular degree z-score is

number

number

number

number

[0135] The role of nodes in network modular organization The node role for modular leaf organization is zp iIt depends on its position in the parameter space. There are four possible roles that a node can have in the network, which are assigned based on a higher than average measure of node characteristics. It is useful to consider two of these roles: so-called connectors or global network hubs (with high inter-leaf participation and high within-leaf degree z scores) and so-called local hubs (with high within-leaf degree z scores and low inter-leaf participation). i and p i The thresholds for high and low values ​​of were set at >1.5 and >0.05, respectively.

[0136] statistical analysis Statistical differences in demographic and cognitive scores among subjects were assessed using either one-way analysis of variance or two-tailed t-tests. Data were checked for normal distribution using one-sample Kolmogorov-Smirnov tests. Chi-square tests were used to test for differences in the distribution of men and women between groups. Statistical differences in global network correlation strength according to diagnostic group were tested using one-way analysis of variance for unequal sample sizes (to account for odd significant correlations across the network). Node degree, intra-leaf z-score, z i , and the leaf-to-leaf participation index p i Results were compared across groups using the Kruskal-Wallis test, a nonparametric one-way analysis of variance test. Results were reported as significant at the p<0.05 level.

[0137] result Table 1 below shows the demographics, cognition, and mean CT and SA for each group according to clinical diagnosis. The three groups differed significantly in age, with AD patients being older than HE and bvFTD patients (p<10 for all tests). -4 Significant differences were also observed in cognitive scores on the MMSE scale, with AD patients being most impaired and bvFTD being more impaired than HE subjects (p<10 for all tests). -4 ). Mean CT and total SA differed across groups.

[0138] [Table 1]

[0139] Differences between HE and both patient groups were observed for mean CT (p<10 for both tests). -4 While the differences were significant for both CT and total SA (p<0.003 for both tests), the bvFTD and AD groups did not differ from each other. Mean CT and total SA values ​​averaged by lobe are shown in Table A.3 in Appendix A. Thus, although AD and bvFTD differ with respect to lobar distribution of lesions, age, and severity of cognitive impairment, neither the overall degree of cortical thinning nor the change in mean surface area provides a means of distinguishing between the two conditions.

[0140] Leaf properties of structural correlation networks Because the definition of correlation-based network organization depends on the choice of threshold, it is useful to ensure that the networks defined herein are non-random in their global topology by calculating a density / sparseness value (κ). Brain networks are considered to exhibit non-random (small-world) topology if κ > 0.1, which was the case for all networks considered herein. It is also useful to ensure that anti-correlations were not omitted after thresholding (see Figure 14). Thus, all direct and anti-CT and SA correlation networks considered herein are non-random. See also Table A.3 for global values ​​for κ for CT and SA in the three groups.

[0141] Using the modularity index, an investigation was performed to determine whether conventionally defined cortical lobes corresponded to network modules in the CT network. Only two homologous pairs in the CT network (posterior cingulate cortex and precentral cortex) and two homologous pairs in the SA network (posterior cingulate cortex and the right bank of the paracentral sulcus and superior temporal sulcus) were found to be incorrectly assigned in the modularity index algorithm. Table A.2 (Appendix A) gives details of the algorithm inputs and outputs. In practice, it is accepted that a Q value above 0.3 is a good indicator of the presence of significant modules in the network. To estimate the confidence interval of the Q value for the dataset, iterative calculations were performed on 100 CT matrices created on the surrogate dataset. Each of the gated CT and SA matrices was created by randomly drawing 213 subjects from the three test cohorts and calculating the Q value for the correlation matrices obtained for CT and SA. The Q values ​​are shown in Figure 15, which is a plot of the distribution of the modularity index Q in the regional CT network created based on 100 surrogate datasets. The middle line indicates the mean value, and the upper and lower lines indicate 1.5 standard deviations away from the mean (Q = 0.36 ± 0.02), i.e., random, in which case the Q value is similar to that of a random graph. For the test group, the following values ​​were obtained: Q for the "positive" sub-network HE =0.49, Q bvFTD = 0.49, and Q AD = 0.45 and for "negative" subnetworks, Q HE =0.39, Q bvFTD = 0.28, and Q AD = 0.29, which indicates a non-random modular topological organization in the frontal, temporal, parietal, and occipital CT / SA correlation networks.

[0142] Therefore, we can conclude that the conventionally described cortical lobes correspond to non-random modules in the CT network.

[0143] Average correlation strength of CT and SA networks Figure 5 shows the edge strength of each cortical thickness correlation network averaged across brain lobes and compared between the HE, bvFTD, and AD groups. Data are shown for networks of positive (top plot) and negative (bottom plot) correlations. Asterisks indicate significant differences between the three groups ( * p<0.05; ** p<0.01). As can be seen in Figure 5, the mean correlation strength for CT showed significant differences between HE, bvFTD, and AD subjects in the frontal, temporal, parietal, and occipital lobes (p<10 for all tests). -4 ). The mean correlation strength was higher in bvFTD and AD than in HE subjects in the frontal, temporal, parietal, and occipital lobes (p≦0.003 for all pairwise correlations). The mean correlation strength was higher in the frontal (p<10 -4 ) and temporal (p=0.005) lobes were higher in bvFTD than in AD.

[0144] The mean strength of the anticorrelated network in the CT network also differed in the frontal and temporal lobes (see bottom plot in Figure 5). Again, both the bvFTD and AD groups showed higher mean correlation strengths in the frontal and temporal lobes than the HE group (p ≤ 0.03 for all tests), and the bvFTD group had a higher mean anticorrelated strength in the frontal lobe than the AD group (p = 0.003).

[0145] Figure 6 shows that the edge intensities of each surface were averaged across the frontal lobe and correlation networks were compared between the HE, bvFTD, and AD groups. Data are shown for networks of positive (right plot) and negative (left plot) correlations. Asterisks indicate significant differences between the three groups ( * p<0.05; **p<0.01). The plot shows significant differences in mean correlation strength across the SA network. Diagnostic groups differed only in the frontal lobe, with the AD group having lower mean correlation strength than the HE group (p=0.03). Similarly, reverse SA network correlations were significantly different in the frontal lobe, with lower mean correlation strength in the bvFTD and AD groups compared to the HE group (p≦0.02 for both tests). This is due to the large number of correlations with a wide frequency distribution in strength found in disease compared to the sparse networks with narrow frequency distributions in healthy elderly subjects (see Figure 14).

[0146] Node evaluation criteria in CT networks Node degree Node degree, which quantifies the average number of significant positive correlations per node, averaged across the frontal, temporal, parietal, and occipital lobes for the CT networks, is shown in Figure 7. Node degree is compared across the HE, bvFTD, and AD groups. Data are shown for networks of positive (top plot) and inverse (bottom plot) correlations. Asterisks indicate significant differences between the three groups ( * p<0.05; ** p<0.01).

[0147] There were significant differences between groups in the frontal, temporal, parietal, and occipital lobes (p ≤ 10 for all tests). -4 ). Both bvFTD and AD subjects had higher node degree in the frontal and temporal lobes compared with HE subjects (p<0.006 for all tests). The bvFTD group had notably higher node degree than the AD group in the parietal and occipital lobes (p≦0.02 for all tests). A similar pattern was found for the number of anticorrelations in the CT network in the frontal, temporal, parietal, and occipital lobes (p≦0.02 for all tests). These differences were driven by a larger number of significant anticorrelations in the bvFTD and AD than in the HE group across all four lobes (p<0.01 for all tests). Differences between the bvFTD and AD groups were not significant.

[0148] Node-leaf participation index Group differences were found in the node-lobar participation index for CT. This index measures the degree of significant positive correlation with nodes in different lobes. It was significant for lobes located in the temporal, parietal, and occipital lobes (p≦0.03 for all tests). Differences were reflected in higher index values ​​for the HE group in the parietal (p<0.003 in both groups), temporal (p=0.01 in AD), and occipital (p=0.002 in bvFTD) lobes. This is shown in Figure 8, which compares the node-lobar participation index of the cortical thickness correlation network averaged across brain lobes across the HE, bvFTD, and AD groups. Data are shown in the plots only for positive correlations. Asterisks indicate significant differences between groups ( * p<0.05; ** p<0.01). Interlobar participation index comparisons showed that no lobes were significantly different in terms of inverse correlation in the CT network.

[0149] Node evaluation criteria in SA networks Node degree Node degree values ​​in the SA network are shown in Figure 9 for the frontal, temporal, parietal, and occipital lobes. In the figure, node degrees of the surface area correlation network are averaged across lobes and compared across the HE, bvFTD, and AD groups. Data are shown for networks of positive (upper plot) and negative (lower plot) correlation. Asterisks indicate significant differences between the three groups ( * p<0.05; ** p<0.01).

[0150] Positive correlations differed between diagnostic groups in the frontal, temporal, parietal, and occipital lobes (p ≤ 0.03). Similar to the CT network, both the bvFTD and AD groups had higher SA node degrees than the HE group in the frontal, temporal, and parietal lobes (p < 10 for all tests). -4 For the occipital lobe, the only significant difference was between the AD and HE groups (p = 0.04). In contrast to the CT network, node degree in the parietal lobe was also significantly higher in AD than in bvFTD (p = 0.004).

[0151] The anticorrelated SA network also showed significant group differences in the frontal, temporal, parietal, and occipital lobes (p≦0.001 for all tests). Again, both the bvFTD and AD groups had higher node degrees than the HE group in all four lobes (p<0.001 for all tests). In contrast to the CT anticorrelated network, the AD group had higher node degrees than the bvFTD group in the frontal (p=0.02) and parietal (p=0.01) lobes.

[0152] Node-leaf participation index Figure 10 shows group differences in node interlobar participation indices for SA network organization. The figure shows node interlobar participation indices averaged across brain lobes and compared across HE, bvFTD, and AD groups. Data are shown for networks with positive (top plot) and negative (bottom plot) correlations. Asterisks indicate significant differences between the three groups ( * p<0.05; ** p<0.01).

[0153] Both the bvFTD and AD groups had higher index values ​​for the positive SA correlation network in all four lobes than the HE group (p<10 -4 In contrast to the CT correlation network, the reverse SA correlation network also showed significant differences in the frontal and parietal lobes (p≦0.04 for both patient groups) and in the temporal lobe for the AD group versus the HE group (p<0.001).

[0154] Hub of the structural correlation network CT Network Hub There are four possible combinations of interlobar participation index (p high / low) and mean within-lobe z scores (high / low). Here, we consider only cases with high interlobar indices and high within-lobe z scores, focusing on nodes with high hub-like features. Tables A.4–A.6 (see Appendix A) provide data on global and local network hubs. The remaining two combinations were tested but were uninformative. The number and distribution of network hubs within high p and high z values ​​in the positive CT correlation network differed between the study groups. In HE subjects, hubs were distributed throughout the cortex; each lobe had at least one hub, with four hubs in the frontal lobe. Reorganization of hub topology occurred differently in the two disease groups. This is shown in the top panel of Figure 11, which is a visualization of the hubs in the cortical thickness network in brain space. In bvFTD, the number of hubs in the frontal lobe increased from four to nine, decreased from two to one in the occipital lobe, and completely disappeared in the parietal and temporal lobes. In contrast, in AD, hubs were distributed approximately equally across all four lobes. The number of hubs in the frontal lobe decreased relative to the HE group (2 vs. 4), while the number increased in the temporal and occipital lobes (1 vs. 3 and 2 vs. 3, respectively). A complete list of nodes with hub-like properties in the CT network, along with their lobe location, is provided in Table A.4 (see Appendix A).

[0155] Nodes with hub-like properties in the decorrelation CT matrix were present exclusively in the frontal and temporal lobes in all three groups, and their topological distribution differed between groups (see bottom panel of Figure 11 and Table A.4 in Appendix A).

[0156] SA Network Hub The hubs in the positively correlated SA network are shown in the top panel of Figure 12. Table A.5 (see Appendix A) provides a list of nodes and lobar localizations, sorted according to interlobar participation index and intralobar z-score. Visual comparison of hub topology between groups indicates that the left hemisphere had more nodes with hub-like characteristics in all diagnostic groups. However, HE subjects had only one SA hub (left insula), while both disease groups had more hubs in each lobe. The AD group had twice as many SA hubs as the bvFTD group (14 vs. 7). Strikingly, the bvFTD group had more SA hubs in the temporal lobe than in the frontal lobe (4 vs. 1), while AD subjects had more frontal hubs than temporal hubs (6 vs. 4). AD subjects had three hubs in the parietal lobe compared to one in the bvFTD group.

[0157] Hubs in the anticorrelated SA network were present exclusively in either the frontal or temporal lobe in all three groups. However, the HE group had one hub in the parietal (precuneus) and bvFTD had two hubs (inferior parietal gyrus and paracentral gyrus) (see Table A.5 in Appendix A). Interestingly, most of the anticorrelated SA hubs in AD were found in the frontal lobe.

[0158] Cortical thickness-cortical surface area coupling topology The connectivity strength between CT and SA nodes was calculated by element-wise multiplication of the corresponding CT and SA correlation matrices. Figure 13 shows the CT / SA connectivity strength visualized in brain space. It can be seen that in HE subjects, interhemispheric homolog pairs exhibited coupled CT / SA correlations. In contrast, the CT / SA connectivity in the AD and bvFTD groups was very similar to each other and different from the HE group. Both the bvFTD and AD groups showed more connectivity between non-homologous nodes in the ipsilateral and contralateral hemispheres. Interlobar correlations also differed significantly between the bvFTD and AD groups. In the bvFTD group, most of the interlobar CT / SA correlations were due to fronto-temporal interactions. In AD, most of the interlobar CT / SA connectivity was due to fronto-parietal interactions. A list of hubs in the CT / SA connectivity topology is shown in Table A.6 in Appendix A.

[0159] Consideration We examined baseline structural correlation networks in subjects clinically diagnosed with either bvFTD or AD in three large-scale global clinical trials and compared them with healthy elderly subjects in a well-characterized birth cohort. For each group, networks were constructed from partial correlations between 68 × 68 pairs of cortical surface regions (nodes) for thickness and surface area. The approach employed allowed for systematic analysis of both direct and inverse network correlations across the three clinical settings. The methods and data discussed herein represent the first systematic comparative analysis of cortical thickness and surface area in a large patient population. The overall study size was determined by the number of bvFTD subjects available, as numbers needed to be comparable across the three groups. Because this is a rare disease, the bvFTD component of the study needed to be global, with patients participating from 70 study sites in 13 countries. 213 patients were included in the study, representing the largest set of MRI scan data in bvFTD subjects available to date. To this end, 213 patients were randomly drawn from a much larger group of 1,131 AD patients from 116 centers in 12 countries for Study TRx-237-005 and 128 centers in 16 countries for Study TRx-237-015 (e.g., accessible from the US National Library of Medicine). 202 normal elderly subjects were enrolled from a well-characterized birth cohort that had been studied over a long period of time. Therefore, the reported findings are robust and can be considered representative of the diagnostic criteria approved by the International Population Conference.

[0160] Leaf-wise network modularity Structural correlations across the frontal, temporal, parietal, and occipital regions of the cortical surface were shown to be inherently modular for both cortical thickness and surface area networks. That is, the results confirm that standard lobe divisions of the cortex share common network modularity attributes, which consequently differ from those expected in comparable random networks. Highly clustered network modules confer so-called "small-world" network properties, which are thought to provide an optimal balance between local specialization and global integration. The results from healthy elderly subjects are comparable to previous studies in smaller, younger, healthy groups, revealing an underlying modular architecture in the area-thickness correlation network. The results also show that intrinsic lobe-by-lobe modularity persists in both bvFTD and AD, indicating that the overall lobe architecture of the network is preserved in the presence of neurodegenerative changes. As discussed further below, this contrasts with the hub-like organization of networks, which is altered in disease-specific ways.

[0161] Similarities and differences between AD and bvFTD in healthy elderly subjects The morphological correlation networks for both patient groups (bvFTD and AD) were found to be highly significantly different from the corresponding networks for healthy elderly subjects. Both groups showed a significant increase in overall correlation strength in the thickness and surface area networks compared to healthy elderly subjects. The effect was more pronounced in the cortical thickness network in all lobes for both positive and negative correlations. This contrasts with the significantly lower correlation strength for surface area in the frontal lobe in AD and directionally similar differences in bvFTD compared to normal. This may be due to the large number of correlations with broad frequency distributions in disease compared to the sparse networks with narrow frequency distributions in healthy elderly subjects. In addition to the increased overall correlation strength, the number of intralobar positive and negative correlations, as measured by node degree, was higher in all lobes in both dementia groups than in healthy elderly controls. The number of interlobar positive correlations in thickness, as measured by interlobar participation index, was also higher in all lobes. The number of intralobar and interlobar positive surface area correlations was also greater in both bvFTD and AD than in healthy elderly subjects in the frontal, temporal, and parietal lobes. Both disease groups also differed from healthy elderly subjects with respect to the correlation in connectivity between cortical thickness and surface area. Thus, both diseases are characterized by an overall increase in the strength and extent of structural correlations that occur locally within lobes and globally between lobes.

[0162] The similarities between the two pathologies with respect to the significant increase in the overall strength and extent of structural correlations may seem to call into question the clinical distinction between bvFTD and AD, which served as the basis for the classification of subjects in the study. Indeed, there were no differences between the two pathologies with respect to overall cortical thickness and surface area. However, there were numerous significant network differences between the two pathologies. In the cortical thickness network, the overall positive correlation strength was greater in bvFTD than in AD in the frontal and temporal lobes, and the inverse correlation strength was also greater in bvFTD than in AD in the frontal lobe. The number of significant positive intralobar correlations was greater in bvFTD than in AD in the parietal and occipital lobes. Conversely, the number of positive and inverse intralobar correlations was greater in AD than in bvFTD in the frontal and parietal lobes. Most of the inverse correlations in cortical thickness and surface area were associated with interhemispheric fronto-temporal non-coordination in bvFTD and fronto-parietal lobes in AD.

[0163] The hub-like organization of correlation networks also differed significantly between the two pathologies. Network connector hubs are thought to provide network integration, while local hubs provide network isolation. Hubs have been proposed to provide resilience to damage in neurodegenerative disorders. Alternatively, hubs have been suggested to represent specific localized vulnerabilities. Therefore, it is of interest to examine how hubs change in the context of neurodegenerative diseases. bvFTD was characterized by an increased number of cortical thickness hubs in the frontal lobe and a reduction or elimination of hubs in the temporal, parietal, and occipital lobes. In contrast, AD was characterized by hubs distributed in all lobes, a reduced number of hubs in the frontal cortex compared with bvFTD, and an increase in hubs in the temporal and occipital lobes. In the surface area positive correlation network, AD subjects had twice as many hubs overall than bvFTD, and the topology of these hubs differed. Thus, in summary, AD is characterized by a significantly more distributed pattern of hubs than bvFTD in both the thickness and surface area degeneration networks. In contrast, hub-like organization is significantly more localized in bvFTD. It has been argued that bvFTD is a clinical syndrome characterized by focal but heterogeneous atrophy concentrated around hubs. The identification of the insular region as one of the inverse network hubs (in both bvFTD and AD groups for CT networks) is consistent with recent unexpected findings from diffusion MRI, which showed increased hub-like fiber connectivity in the insula in bvFTD. On the other hand, hubs in healthy elderly subjects were highly connected within and between lobes in a homogenous manner and were not otherwise linked to each other. The difference in hub-like organization between AD and bvFTD indicates differences in the hierarchy of nodal vulnerability and the organization of network adaptation, which are differentially compensated for in the two pathologies. Therefore, unlike the preserved lobe modularity in neurodegenerative diseases, a consistent hub-like organization is not preserved, implying that existing hubs are not an inherent structural property of cortical network organization.

[0164] AD is also characterized by changes in cortical thickness, but these are less pronounced overall than in bvFTD, whereas surface area changes are more pronounced in AD, suggesting coordinated changes in the number of adjacent affected columns. These differences are consistent with bvFTD pathology affecting interneurons and astrocytes, which have more localized links. The predominance of surface area correlation in AD is consistent with pathology primarily affecting the long-tract cortico-cortical projection system mediated by principal cells. bvFTD differs from AD in a number of important respects: there is no cholinergic deficit in bvFTD, there is no therapeutic benefit from treatment with either acetylcholinesterase inhibitors or memantine, bvFTD is characterized by prominent astrocytic pathology, affected neurons in the neocortex are primarily spiny interneurons in layers II and VI (pyramidal cells in layers III and V are primarily affected in AD) and the dentate gyrus of the hippocampus (affected neurons in AD are in CA1-4, not the dentate gyrus), and bvFTD, but not AD, is characterized by increased glutamate levels in the neocortex. However, these pathologies do not provide a simple explanation for the distinct distribution patterns of correlated structural changes described herein.

[0165] Global characteristics and significance of network changes in dementia The overall picture emerging from the two disease groups examined is that network architecture changes coordinately throughout the entire brain, with both positive and negative correlations. This is surprising, given that the neurodegenerative processes in these two conditions are generally considered anatomically restricted to the frontal and temporal lobes in bvFTD cases and the temporal and parietal lobes in AD. Rather, network analysis suggests that there are changes in cortical thickness and surface area networks in both conditions that globally affect all lobes, but that there are differences in the anatomical topology of the changes. Both tau and TDP-43 aggregate pathology are known to spread in a prion-like manner, whereby pathology in affected neuronal populations can initiate pathology in connected but previously unaffected neuronal populations. Thus, positive correlations may partially reflect the spread of pathology in existing normal networks, thereby affecting or sparing existing functional networks together. Alternatively, such correlations may represent functional independence, such that loss of function in a cooperating member results in a parallel loss of function in partners that are normally functionally synchronized with the affected node. This interpretation is consistent with previous studies of cortical thickness correlations in healthy adults, where positive correlations were found to converge on diffusion-based axonal connectivity.

[0166] The studies discussed herein first highlight the significance of anticorrelated networks. It should be noted that the anticorrelations observed in both neurodegenerative disorders primarily reflect interlobar non-cognate connections and therefore would not have been detected using only a lobe-based approach to analysis. It is particularly the emergence of these non-cognate inverse interlobar correlations and their increased strength that represents the most distinct overall difference between neurodegenerative disease and normal aging. In contrast, the normal aging brain is characterized by significantly weaker homogeneous positive correlations. The prevailing hypothesis is that as certain nodes become functionally impaired, other, still unaffected, nodes compensate, accentuating the non-cognate connections in disease. This implies that the major reorganization in the observed structural network may, in part, be adaptive in nature. Structural plasticity has been demonstrated in other contexts, and functional compensation is known to occur in focal disease.

[0167] The studies discussed herein represent the first comparative examination of correlated structural network abnormalities in bvFTD and AD relative to healthy aging. These correlations arise from both forward and reverse link changes in cortical thickness and surface area in the two pathologies, which are completely distinct from those seen in normal aging subjects. The changes seen in disease are global in nature and not restricted to the fronto-temporal and temporo-parietal lobes in bvFTD and AD, respectively. Rather, they are thought to represent structural adaptations to the different neurodegeneration in the two pathologies. Furthermore, all of the correlated networks showed a distinct hub-like organization that differed from normal and between the two forms of dementia. Unlike the network lobe organization, which remains constant in disease, the hub-like organization varied depending on the underlying pathology. This means that hub-like organization is not a fixed property of the brain, and attempts to explain disease in terms of hubs may be inappropriate. The reported differences between AD and bvFTD confirm that clinical differences between the two dementia populations correspond to systematic differences in the underlying network structure of the cortex. The topological differences in thickness and surface area of ​​the hub-like organization, as well as the underlying networks of positive and negative correlations, may provide a basis for the development of analytical tools to aid in differential diagnosis in two pathologies that can be difficult to distinguish by mere clinical criteria.

[0168] Use of correlation matrices in determining patient group response to neuropharmacological interventions. Patient group response to neuropharmacological intervention was determined using the methods discussed above.

[0169] Figures 17A-17D show correlation matrices for two patient groups: those treated with symptomatic AD medications (cholinesterase inhibitors and / or memantine; ach1 in the figure legend) and those not treated (ach0 in the figure legend). Subjects had a Clinical Dementia Scale (CDR) score range of 0.5, 1, or 2. Figure 17A shows the cortical thickness correlation matrix at baseline (i.e., week 0) for 96 subjects diagnosed with AD who were not receiving symptomatic treatment. In contrast, Figure 17B shows the cortical thickness correlation matrix at the base link for 445 subjects diagnosed with AD who were receiving symptomatic treatment. Figure 17C shows the surface area correlation matrix at baseline for 96 subjects diagnosed with AD who were not receiving symptomatic treatment, and Figure 17D shows the surface area correlation matrix at baseline for 445 subjects diagnosed with AD who were receiving symptomatic treatment.

[0170] As can be seen from Figures 17A-17D, symptomatic treatment for AD induces a significant increase in interlobar non-homogenous anti-correlated networks (blue in Figures 17B and 17D) compared to untreated patients (Figures 17A and 17C). This is particularly evident for the surface area network.

[0171] These connections represent inverse correlations, where a decrease in the volume or surface area of ​​the affected area at a particular node (typically located in the posterior part of the brain) is statistically significantly correlated with the link node, where there is a corresponding increase in volume or surface area. As discussed above, the existence of these non-homogenous inverse correlations indicates neurodegenerative disease and most likely represents anterior compensation for posterior dysfunction resulting from the lesion. Symptomatic AD treatment induces an increase in these non-homogenous compensatory connections.

[0172] 18A-18D are plots of the node degrees (discussed above) between non-homogenous lobes for cortical thickness-positive correlation, cortical thickness-inverse correlation, surface area-positive correlation, and surface area-inverse correlation, respectively. As can be seen from these plots, the number of significant non-homogenous lobe-to-lobe compensatory inverse correlations is significantly increased by symptomatic AD treatment.

[0173] Figures 19A and 19B show cortical thickness correlation matrices based on temporally separated structural neurological data. Figure 19A is the cortical thickness correlation matrix at week 0 (i.e., at baseline) for a group of 445 AD-diagnosed patients being treated with symptomatic AD treatment. Figure 19B is the cortical thickness correlation matrix at week 65 for the same group of 445 AD-diagnosed patients. During the intervention period, the group was also treated with leucomethylthioninium mesylate (LMTM; USAN name: hydromethylthionine mesylate), a tau aggregation inhibitor, at a dose of 8 mg / day (here and below, 4 mg given twice daily). As can be seen, LMTM has minimal effect on the structural correlation network in patients receiving symptomatic treatment for AD.

[0174] Figures 20A-20D are plots of non-homogenous interlobar node degrees compared at week 0 and week 65 in the ach1 group (concurrently receiving symptomatic AD treatment) for cortical thickness-positive correlation, cortical thickness-inverse correlation, surface area-positive correlation, and surface area-inverse correlation, respectively. As can be seen, there is a minimal overall effect of LMTM as an add-on on brain network correlation structure over 65 weeks. It is important to note that this is a within-cohort analysis in which patients at baseline serve as their own controls for changes occurring after 65 weeks of treatment with LMTM.

[0175] Figures 21A and 21B show cortical thickness correlation matrices based on temporally separated structural neurological data. Figure 21A shows the cortical thickness correlation matrix at week 0 (i.e., at baseline) for a group of 96 AD-diagnosed patients receiving LMTM as monotherapy at a dose of 8 mg / day. Figure 21B shows the cortical thickness correlation matrix at week 65 for the same group of 96 AD-diagnosed patients. None of the 96 patients in this cohort received symptomatic AD treatment in combination with LMTM. As can be seen, LMTM as monotherapy produces significant reductions in thickness correlations, both intralobar (positive) and interlobar compensatory (inverse) correlations. This is an intracohort analysis in which patients at baseline serve as their own controls for changes occurring after 65 weeks of treatment with LMTM.

[0176] Figures 22A and 22B are plots of interlobar node order compared at week 0 and week 65 in the ach0 group for cortical thickness-positive correlation and cortical thickness-negative correlation. The plots show a highly significant effect of 8 mg / day LMTM as monotherapy on the number of interlobar correlations in the AD group. A significant reduction in the number of positive and negative non-homogeneous cortical thickness correlations is seen after 65 weeks. This is likely due to normalization of neuronal function in the posterior brain, where LMTM reduces the lesion and neuronal dysfunction resulting from the lesion, thereby reducing the need for compensatory input from unaffected or mildly affected anterior regions of the brain.

[0177] Figures 23A and 23B show surface area-thickness correlation matrices based on temporally separated structural neurological data. Figure 23A shows the surface area correlation matrix at week 0 (i.e., baseline) for a group of 96 AD-diagnosed patients who continued to receive LMTM as monotherapy at a dose of 8 mg / day. Figure 23B shows the surface area correlation matrix at week 65 for the same group of 96 AD-diagnosed patients. None of the 96 patients in this cohort was receiving concurrent symptomatic AD treatment. As can be seen, LMTM as monotherapy produces significant reductions in surface area correlations, both intralobar (positive) and interlobar compensatory (inverse) correlations. This is an intracohort analysis in which patients at baseline serve as their own controls for changes occurring after 65 weeks of treatment with LMTM.

[0178] 24A and 24B are plots of non-homogenous inter-lobar node order compared at week 0 and week 65 in the ach0 group for surface area-positive correlation and surface area-negative correlation. The plots show a significant effect of 8 mg / day LMTM as monotherapy on the number of inter-lobar correlations in the AD group. In particular, there is a significant reduction in the number of positive and negative / compensatory surface area correlations after week 65.

[0179] 25A-25D show cortical thickness correlation matrices compared between the 96 patient AD group (with CDR 0.5, 1, or 2) at baseline and week 65 compared to the healthy elderly control group of 202 subjects. As can be seen, LMTM at 8 mg / day as monotherapy leads to cortical thickness networks approaching normal.

[0180] 26A-26D show surface area correlation matrices compared between the 96 patient AD group (with CDR 0.5, 1, or 2) at baseline and week 65 compared to a healthy elderly control group of 202 subjects. As can be seen, LMTM at 8 mg / day as monotherapy normalizes the surface area network.

[0181] 27A-27D show cortical thickness correlation matrices compared between the 54 patient AD group with only a CDR of 0.5 at baseline and week 65 compared to a healthy elderly control group of 202 subjects. As can be seen, LMTM at 8 mg / day as monotherapy reduces the number of reverse / compensatory non-homogeneous correlations to be comparable to normal elderly controls.

[0182] 28A-28D show surface area correlation matrices compared between the 54 patient AD group with only a CDR of 0.5 compared to the healthy elderly control group of 202 subjects at baseline and week 65. As can be seen, LMTM at 8 mg / day as monotherapy reduces the number of reverse / compensatory non-homologous correlations to be similar to or lower than normal elderly controls.

[0183] In summary, the structural correlation network analysis discussed above reveals the emergence of highly abnormal reverse non-homogenous interlobar correlations in AD and bvFTD. These are hypothesized to represent compensatory inputs from unaffected or mildly affected anterior brain regions. Symptomatic treatment and LMTM act in fundamentally different ways in AD with respect to the structural correlation network. Symptomatic treatment induces a significant increase in compensatory networks. LMTM as monotherapy reduces the need for these compensatory networks by reducing the primary pathology, thereby allowing affected neurons to function more normally. These results confirm that the abnormal reverse non-homogenous correlations observed in neurodegenerative diseases, such as AD, are adaptive in nature, as neurodegenerative diseases can be reversed or attenuated by disease-modifying treatments, but not by symptomatic AD treatments. Effects are observed in a within-cohort before / after analysis in which subjects at baseline served as their own controls for changes occurring after 65 weeks of LMTM treatment at 8 mg / day as monotherapy. These analyses are significantly more sensitive to treatment effects than crude whole-brain or lobe volume analyses. Furthermore, as discussed below, the results seen with respect to structural correlation networks are consistent with the functional effects seen with renormalized partial directed coherence EEG analysis techniques.

[0184] Structure / function correlation using electroencephalography (EEG) The renormalized partially directed coherence (rPDC) network approach to EEG data allows for indicators of the direction and intensity of electrical activity in the brain to be investigated using a network approach. These are discussed, for example, in International Publication No. 2017 / 118733, the entire contents of which are incorporated herein by reference. Figure 29 shows an example of raw EEG data, and Figure 30 shows an example of an rPDC network obtained from collected EEG data.

[0185] As shown in Figure 30, the resulting network contains a number of nodes that indicate their approximate location within the brain (the diagram is drawn in a schematic style looking down on the head with a triangle at the top indicating the nose). The location of the nodes is determined by the placement of electrodes on the scalp surface used to obtain EEG data, such as that shown in Figure 29. The directed connections between the nodes indicate the flow of electrical activity from one node to another within the brain.

[0186] By counting the number of directed connections entering and leaving a given node and / or measuring their strength, it is possible to define whether a node is a sink (and has more and / or stronger incoming connections than outgoing) or a source (and has more and / or stronger outgoing connections than incoming). This is shown schematically in FIG. 31, where the number / strength of incoming directed connections is subtracted from the number / strength of outgoing directed connections. Thus, in extreme cases, if the difference is negative, the node is acting as a net source, and if it is positive, the node is acting as a net sink. More generally, as can be seen in plots such as that shown in FIG. 40, lower values ​​indicate more / stronger outgoing connections and higher values ​​indicate more / stronger ingoing connections.

[0187] After deriving the difference between inward and outward connections for all nodes, it is then possible to provide a heatmap showing the location and strength of sinks and sources within the patient's brain. This may include defining each node as either a sink or a source. An example of such a heatmap is shown in Figure 32. In this example, blue regions (arrow A) indicate more outward connections and therefore contain more source nodes, while red / yellow regions (arrow B) indicate more inward connections and therefore contain more sink nodes. This type of heatmap can be referred to as a "brainprint."

[0188] Figure 33 illustrates the visualization of asymmetry in the heatmap of Figure 32, comparing the number of sources and sinks on one side. Higher differences in sources and sinks between the left and right sides of the heatmap appear as yellow (arrow A), while lower differences appear as black (arrow B).

[0189] Using the methods discussed above, data provided by 329 subjects divided into 167 diagnostic subjects (DS) and 162 paired subjects (PV) at initial evaluation (Visit 1) were analyzed.

[0190] [Table 2]

[0191] As can be seen, the diagnosed subjects are significantly more cognitively impaired on the MMSE and ADAS-Cog psychometric scales, and have higher scores on the global Clinical Dementia Scale (CDR) scale. Otherwise, there are no differences in age or sex distribution.

[0192] Figure 34 shows a heat map visualizing the localization of sinks and sources in the brains of a group of diagnostic subjects at baseline. Arrow A indicates blue areas containing more / stronger sources, and arrow B indicates red areas containing more / stronger sinks. Figure 35 shows a heat map visualizing the localization of sinks and sources in the brains of a group of paired subjects. Comparing the two images reveals that AD sufferers have significantly stronger sources (i.e., more / stronger outward connections, shown in blue) in their frontal lobes and significantly stronger sinks (i.e., more / stronger inward connections, shown in red / orange) in their posterior parietal, temporal, and occipital lobes than their paired subjects.

[0193] A machine learning classifier was trained on the data set provided by the 329 subjects discussed above. Beta-band EEG data from 100 seconds of brain activity during eyes-closed resting state was used in each case to prepare an rPDC network. The machine learning classifier was then used to classify all 329 subjects as either AD or paired subjects (PV), achieving 95% accuracy. Furthermore, the machine learning classifier can be used to estimate the probability that a subject has AD, allowing for more than just a binary decision. For example, the subject whose heatmap is shown in Figure 36 has AD. The subject is known to have AD via clinical diagnosis. The machine learning classifier estimated that the patient had AD with a 99% probability, thus correctly classifying this subject. Figure 37 is a further example of a heatmap from a subject known to have AD via clinical diagnosis. In this example, the machine learning classifier estimated that there was a 63% probability that the patient had AD and, therefore, a 37% probability that the patient did not have AD. This information can be used to determine a patient's susceptibility to AD in the absence of a clinical diagnosis. Furthermore, specific distribution patterns of abnormal sink regions, indicative of underlying dysfunction, could be correlated with specific patterns of clinical testing on further detailed neuropsychological testing and future clinical evaluation. For example, the example illustrated in Figure 36 may have a form of dementia other than AD, but was classified herein as suffering from AD.

[0194] Psychometric testing of the apparently healthy cohort showed a downward trajectory of cognitive function on the Hopkins Verbal Learning Test over 18 months in a subset of subjects. The characteristics of the cohort were as follows: As can be seen, there were no differences in baseline cognitive scores on the MMSE scale between those found to be at risk for decline and those not found to be at risk.

[0195] [Table 3]

[0196] A heatmap of the at-risk group of subjects is shown in Figure 38, while a heatmap of the not-at-risk group is shown in Figure 39. Because both groups were recruited from apparently healthy cohorts, the differences are not as clear as for the AD vs. PV group described above. Figure 38 shows more / stronger sinks in posterior brain regions, visible as more intense red / orange in the heatmap. Figure 40 shows boxplots comparing sources and sinks in EEG networks from anterior and posterior brain regions at the group level. As can be seen from this figure, the at-risk group is characterized by increased outward activity from the frontal cortex and increased inward activity to posterior brain regions. EEG recordings were performed at baseline, before any measurable functional decline based on the Hopkins Verbal Learning Task. Therefore, apparently normal subjects at risk for functional decline over the following 18 months can already be identified at baseline based on their brain activity heatmaps obtained noninvasively by EEG analysis.

[0197] As shown above, there are clear differences in the network between the diagnosed subjects and paired subjects.These differences are highly significant at the group level.As can be seen, the first version of the machine learning classifier has a higher level of accuracy than the routine superficial clinical assessment, and gives the probability of having AD at the individual subject level, which can be used to determine further clinical management.

[0198] Figure 41 shows a comparison between the cortical thickness correlation matrix (discussed above) at week 0 for the ach0AD group compared to the group-level diagnostic heatmap. As can be seen, there are a significant number of inverse non-homogenous correlations between the frontal lobe and posterior parietal and occipital brain regions. The diagnostic group heatmap shows the same phenomenon regarding brain connectivity as measured by EEG. Both structural and EEG approaches show the same pattern of increased anterior-to-posterior activity. Figure 42 shows a comparison between the cortical thickness correlation matrix (discussed above) at week 0 for the healthy elderly group compared to the group-level paired subject heatmap. As can be seen, the absence of any inverse non-homogenous correlations between anterior brain regions and posterior parietal and occipital brain regions is matched on the EEG by the absence of increased anterior-to-posterior electrical activity.

[0199] The increase in non-homologous interlobar compensatory anti-correlated networks seen in Figure 41 provides a structural basis for the characteristic heat map changes seen as functional EEG changes.

[0200] Figure 43 shows boxplots showing quantitative differences in mild AD from elderly controls. As can be seen, AD subjects have more outward activity from the anterior cortex and more inward activity to the posterior cortex in the beta band.

[0201] Figure 44 shows three heatmaps, from left to right: the group of diagnosed AD subjects receiving symptomatic medication (med), the group of diagnosed AD subjects not receiving symptomatic medication (nonMed), and the group of paired subjects. Figure 45 shows boxplots comparing group-level networks for medicated, unmedicated, and paired subjects. The data are from a pilot study involving 53 diagnosed subjects (DS), 15 subjects receiving standard medication, and 38 subjects not receiving standard medication. The characteristics of the two groups are shown in the table below. While the unmedicated group is significantly younger, there are no differences between the two groups in terms of cognitive scores as measured by the MMSE or gender distribution.

[0202] [Table 4]

[0203] There were no statistically significant differences in either the ADAS-Cog or CDR scales.

[0204] As can be seen in Figures 44 and 45, both groups of AD subjects have more outward activity from the anterior cortex in the beta band than their paired counterparts. Furthermore, it can be seen that symptomatic treatment increases outward activity from the frontal lobe compared to the non-medicated group. This is shown in the boxplot in Figure 45. The medicated group has significantly more outward electrical activity from the anterior cortex. In posterior brain regions, symptomatic treatment reduces the need for supportive inward electrical activity.

[0205] The frontal lobes exhibit the same phenomenon with EEG as is shown by the structural analysis of the correlation networks in Figures 17A-D and 18A-D. Figure 44 shows that the differences detectable by MRI structural analysis at the group level can also be detected by EEG. It should be noted that the structural analysis of network differences between patients receiving and not receiving symptomatic treatment suggests increased non-homogenous interlobar connectivity directed toward posterior brain regions, while the EEG analysis shows less inward activity directed toward posterior regions. It is currently hypothesized that symptomatic treatment increases inward activity toward posterior brain regions in other frequency bands.

[0206] The systems and methods of the above embodiments, in addition to the structural components and user interactions described, can be implemented in a computer system (particularly in computer hardware or computer software).

[0207] The term "computer system" includes hardware, software, and data storage devices for implementing the system or method according to the above embodiments. For example, a computer system may include a central processing unit (CPU), input means, output means, and data storage. Preferably, the computer system has a monitor for providing a visible output display. Data storage may include RAM, a disk drive, or other computer-readable medium. A computer system may include multiple computer devices linked by a network and communicating with each other over the network.

[0208] The methods of the above embodiments may be provided as a computer program or as a computer program product or a computer readable medium carrying a computer program arranged to perform the above methods when run on a computer.

[0209] The term "computer-readable medium" includes, but is not limited to, any non-transitory medium or media that can be read and accessed directly by a computer or computer system. Media can include, but are not limited to, magnetic storage media, such as floppy disks, hard disk storage media, and magnetic tape; optical storage media, such as optical disks or CD-OMs; electrical storage media, such as memory, e.g., RAM, ROM, and flash memory; and hybrids and combinations of the above, such as magnetic / optical storage media.

[0210] While the present invention has been described in conjunction with the above exemplary embodiments, many equivalent modifications and variations will be apparent to those skilled in the art in light of this disclosure. Accordingly, the above exemplary embodiments of the invention are considered to be illustrative and not restrictive. Various modifications of the described embodiments can be made without departing from the spirit and scope of the invention.

[0211] In particular, although the methods of the above embodiments have been described as being implemented on the systems of the described embodiments, the methods and systems of the present disclosure need not be implemented contemporaneously with each other, but may each be implemented on alternative systems or using alternative methods.

[0212] Appendix A

[0213] [Table 5]

[0214] [Table 6]

[0215] [Table 7]

[0216] [Table 8]

[0217] [Table 9]

[0218] [Table 10]

[0219] [Table 11]

[0220] [Table 12]

[0221] References Gauthier, S. et al. “Efficacy and safety of tau-aggregation inhibitor therapy in patients with mild or moderate Alzheimer's disease: a randomized, controlled, double-blind, parallel-arm, phase 3 trial”, The Lancet 388,2873-2884(2016) Wilcock, G. K. et al “Potential of low dose leuco-methylthioninium bis (hydromethanesulphonate) (lmtm) monotherapy for treatment of mild Alzheimer’s disease: Cohort analysis as modified primary outcome in a phase iii clinical trial. Journal of Alzheimer’s disease 61, 635-657 (2018) Feldman, H. et al “A phase 3 trial of the tau and tdp-43 aggregation inhibitor, leuco-methylthioninium bis(hydromethanesulfonate) (lmtm), for behavioural variant frontotemporal dementia(bvFTD)” Journal of Neurochemistry 138,255 (2016) Murray, A. D. et al “The balance between cognitive reserve and brain imaging biomarkers of cerebrovascular and Alzheimer’s diseases” Brain 134,3687-3696 (2011) Storey, J. D. “A direct approach to false discovery rates” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64, 479-498 (2002) Van Wijk, BC, Stam, CJ & Daffertshofer, A. “Comparing brain networks of different size and connectivity density using graph theory.” PLoS One 5,e13701 (2010) Rubinov M, Sporns O. “Weight-conserving characterization of complex functional brain networks”, Neuroimage, 56(4):2068-79 (2011)

[0222] All references mentioned above are incorporated herein by reference.

Claims

1. 1. A computer-implemented method for determining a patient's susceptibility to one or more neurological disorders, comprising: obtaining data indicative of electrical activity in the patient's brain; creating a network based at least in part on the obtained data, the network including a plurality of nodes and directed connections between the nodes, the network being indicative of the flow of the electrical activity in the brain of the patient; calculating, for each node, the difference between the number and / or strength of the connections going into said node and the number and / or strength of the connections going out of said node; and using the calculated difference to determine the patient's susceptibility to one or more neurological disorders. A method comprising:

2. The method of claim 1 , wherein the network is a renormalized partially directed coherence network.

3. The method according to claim 1 or 2, wherein the data indicative of electrical activity in the brain is electroencephalogram data.

4. The method of claim 3 , wherein the electroencephalogram data is beta band electroencephalogram data.

5. The method of any one of claims 1 to 4, wherein determining the patient's susceptibility is performed using a machine learning classifier.

6. 6. The method of claim 1, further comprising generating a heatmap based at least in part on the state of the nodes, the heatmap being indicative of the location and / or intensity of nodes defined as sinks and nodes defined as sources within the brain of the patient.

7. 7. The method of claim 6, further comprising using the states of the nodes to derive an indication of the degree of asymmetry in the localization and / or strength of nodes in the brain corresponding to sinks and sources.

8. The method of any one of claims 1 to 7, wherein the neurological disorder is a neurocognitive disorder.

9. 9. The method according to any one of claims 1 to 8, wherein the patient's susceptibility to one or more neurological disorders is determined by comparing the number and / or strength of nodes defined as sinks in the posterior lobe with predetermined values ​​and / or comparing the number and / or strength of nodes defined as sources in the temporal and / or frontal lobe with predetermined values.

10. 10. The method of claim 9, wherein a patient is determined to be at high risk of susceptibility if the number and / or strength of nodes defined as sinks in the posterior lobe exceeds a predetermined value and / or if the number and / or strength of nodes defined as sources in the temporal and / or frontal lobe exceeds a predetermined value.

11. 1. A system for determining a patient's susceptibility to one or more neurological disorders, comprising: data acquisition means configured to obtain data indicative of electrical activity in the patient's brain; network creation means configured to create a network based at least in part on the obtained data, the network including a plurality of nodes and directed connections between the nodes, the network being indicative of the flow of the electrical activity in the brain of the patient; difference calculation means configured to calculate, for each node, the difference between the number and / or strength of connections entering said node and the number and / or strength of connections leaving said node; and any of the following: a display means configured to display a representation of said calculated difference; or determining means configured to use said calculated difference to determine said patient's susceptibility to one or more neurological disorders. A system including:

12. The system of claim 11 , wherein the network is a renormalized partially directed coherence network.

13. The system according to claim 11 or 12, wherein the data indicative of electrical activity in the brain is electroencephalogram data.

14. The system of claim 13 , wherein the electroencephalogram data is beta band electroencephalogram data.

15. The system of any one of claims 11 to 14, wherein the determining means is configured to determine the patient's susceptibility to one or more neurological disorders using a machine learning classifier.

16. 16. The system of any one of claims 11 to 15, comprising heat map creation means configured to generate a heat map based at least in part on the state of the nodes, the heat map being indicative of the localization and / or intensity of nodes defined as sinks and nodes defined as sources within the brain of the patient.

17. 17. The system of claim 16, further comprising an asymmetry map generator configured to use the node states to derive an indication of the degree of left-right asymmetry in the localization and / or strength of nodes in the brain corresponding to sinks and sources.

18. The system according to any one of claims 11 to 17, wherein the neurological disorder is a neurocognitive disease.

19. 19. The system of any one of claims 11 to 18, wherein the determining means compares the number and / or strength of nodes defined as sinks in the posterior lobe with a predetermined value, and / or the number and / or strength of nodes defined as sources in the temporal and / or frontal lobe with a predetermined value.

20. 20. The system of claim 19, wherein the determining means determines a patient to be at high risk of susceptibility if the number and / or strength of nodes defined as sinks in the posterior lobe exceeds a predetermined value and / or if the number and / or strength of nodes defined as sources in the temporal and / or frontal lobe exceeds a predetermined value.

21. A computer program comprising executable code which, when executed on a computer, causes said computer to perform the method of any one of claims 1 to 10, the executable code being stored on a non-transitory storage medium.

22. 1. A computer-implemented method for determining a patient response to a neuropharmacological intervention for a neurological disorder, comprising determining a patient's response prior to said neuropharmacological intervention: (a) obtaining data indicative of electrical activity in the brain of said patient; (b) creating a network based at least in part on the obtained data, the network including a plurality of nodes and directed connections between the nodes, the network being indicative of the flow of electrical activity in the brain of the patient; (c) calculating, for each node, the difference between the number and / or strength of the connections going into said node and the number and / or strength of the connections going out of said node; and (d) using the calculated difference to determine a first susceptibility of the patient to the neuropathy; (e) repeating steps (a)-(d) after the neuropharmacological intervention to further determine the patient's second susceptibility to the neurological disorder; and (f) determining the patient response to the neuropharmacological intervention based on the first susceptibility and the second susceptibility. A method comprising:

23. (i) the network is a renormalized partially directed coherence network, and / or; (ii) the data indicative of electrical activity in the brain is electroencephalogram data, and / or; (iii) performing susceptibility testing on said patient using a machine learning classifier; and / or; (iv) the method further comprises generating a heat map based at least in part on the state of the nodes, the heat map being indicative of the localization and / or intensity of nodes defined as sinks and nodes defined as sources within the brain of the patient; and / or; (v) the method further comprises using the node states to derive an indication of the degree of asymmetry in the localization and / or strength of nodes in the brain corresponding to sinks and sources; 23. The method of claim 22.

24. 24. The method of claim 22 or 23, wherein the neurological disorder is a neurocognitive disorder.

25. A system adapted to carry out the method according to any one of claims 22 to 24.

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