Network approaches to neurodegenerative diseases

Abstract

A method for determining a patient's response to a neuropharmacological intervention, a method for determining a patient's likelihood of developing one or more neurological disorders, and a system for the method are disclosed. The method for determining a patient's likelihood of developing one or more neurological disorders comprises the following steps: obtaining data indicating electrical activity within the patient's brain; 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, wherein the network indicates a flow of electrical activity within the patient's brain; calculating, for each node, a difference between the number and / or strength of connections entering the node and the number and / or strength of connections leaving the node; and determining the patient's likelihood of developing one or more neurological disorders using the calculated difference.

Description

Technical Field

[0001] The present invention relates to the application of network methods in studying neurocognitive disorders. Background Art

[0002] Models of the human brain as a complex network of interconnected subunits have improved the understanding of normal brain organization and have made it possible to address functional changes in neurological disorders. These subunits constitute so-called brain modules, groups of regions with high connection density within them and lower connection density between groups. It has been proposed that the modular organization of the brain supports effective integration between spatially isolated neural processes, thereby supporting various cognitive and behavioral functions. Changes in brain networks can help identify patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD).

[0003] For example, changes in regional volume have been identified in schizophrenia patients through studies of structural networks in health and disease, where pairwise correlations of cortical regional volume or thickness, as in vivo measurements derived from T1-weighted magnetic resonance images (MRI), have been examined. This approach has shown clinical relevance by revealing changes in regional volume in schizophrenia patients. However, volumetric measures that represent the product of cortical thickness (CT) and surface area (SA) may confound potential differences. For example, consideration of changes in cortical thickness can provide insight into how the disease alters the size, density, and arrangement of cells within the cortical layers. On the other hand, changes in surface area can provide information about perturbations in functional integration between column groups in the diseased brain.

[0004] Previously, whole-brain or lobar volumetry has been used to monitor the effects of neuropharmacological interventions. However, this is a relatively crude level of analysis.

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

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

[0007] Another poster titled "Divergent changes in structural correlation networks in Alzheimer's disease and behavioural variant frontotemporal dementia" was presented by Vuksanovic et al at the ARUK meeting held on March 20-21, 2018 in London, UK.

[0008] The presentation titled "Modular organisation of cortical thickness and surface area structural correlation networks in Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD)" was given by Vuksanovic, V at the 10th SINAPSE Annual Scientific Meeting in Edinburgh on 25 June 2018. Summary of the invention

[0009] In a first aspect, the present invention provides a method of determining a patient's response to a neuropharmacological intervention, the method comprising the steps of:

[0010] obtaining structural neurological data from a plurality of patients prior to a neuropharmacological intervention, the structural neurological data indicating the physical structure of a plurality of cortical regions;

[0011] A first correlation matrix is ​​generated from the structural neurological data by:

[0012] assigning a plurality of structural nodes corresponding to cerebral cortical regions; and

[0013] determining pairwise correlations between pairs of structural nodes based at least in part on corresponding data of the structural neurological data;

[0014] obtaining further structural neurological data from the plurality of patients after neuropharmacological intervention, the further structural neurological data indicating a physical structure of the plurality of cortical regions; and

[0015] A second correlation matrix is ​​generated from the further structural neurological data by:

[0016] determining pairwise correlations between pairs of structural nodes based at least in part on corresponding data of the further structural neurological data;

[0017] The method comprises:

[0018] The first correlation matrix is ​​compared to the second correlation matrix to determine a patient's response to the neuropharmacological intervention.

[0019] The optional features of the invention will now be described. These are applicable alone or in any combination with any aspect of the invention.

[0020] A correlation matrix can mean generating a structural correlation network which can then be represented as a matrix.

[0021] In one embodiment, the patient response may be in the context of a clinical trial, e.g., for evaluating the efficacy of a drug in the treatment of a neurocognitive disease. Thus, the patient group (s) may be a treatment group that has been diagnosed as having the disease, or may be a control ("normal") group. Finally, the efficacy of the drug may be evaluated in whole or in part based on the patient group response determined according to the invention, optionally compared to a comparison group that has not received the intervention.

[0022] The measured or obtained physical structure can be cortical thickness and / or surface area. The value of cortical thickness and / or surface area can be an average value obtained from the structural neurological data. The structured neurological data can be collected from magnetic resonance imaging (MRI) data or computed tomography data of each patient. The structural neurological data and the further structural neurological data are obtained at different time points. As discussed herein, the structural neurological data can be obtained by magnetic resonance imaging, computed tomography or positron emission tomography of each patient. These techniques themselves 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 Supplement D: 5-12; Pascual-Marqui RD et al.

[0023] The plurality of cortical areas may be at least 60 or at least 65. For example, 68. The cortical areas may for example be those provided by the Desikan-Killiany atlas (Desikan et al. 2006).

[0024] The p-value can be determined for each pairwise correlation in multiple subjects and can be compared to a significance level, wherein only p-values ​​less than the significance level are used to generate the corresponding correlation matrix. When determining the pairwise correlations between pairs of structural nodes, the corresponding value of each structural node can be compared to a reference value and its covariance is determined. The significance level can be referred to as Alpha ("α").

[0025] Comparing the first correlation matrix to the second correlation matrix may include comparing the number and / or density of inverse correlations in the first correlation matrix to the number and / or density of inverse correlations in the second correlation matrix. In making the comparison, groups of structural nodes corresponding to the same lobe may be identified, and the comparison between the first correlation matrix and the second correlation matrix may utilize the same lobe.

[0026] Assigning the plurality of structural nodes corresponding to the cerebral cortical regions may also include defining groups containing structural nodes corresponding to homologous or non-homologous lobes. Comparing the first correlation matrix with the second correlation matrix may include comparing the number and / or density of correlations between different groups of structural nodes. In other words, comparing the first correlation matrix with the second correlation matrix may include comparing the correlations between pairs of non-homologous structural nodes.

[0027] In some examples, comparing the first correlation matrix to the second correlation matrix can include 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. It has been found that in examples of effective neuropharmacological interventions, the number and / or density of inverse correlations between the anterior nodes and the posterior nodes decreases. Since inverse correlations are assumed to indicate compensatory linkage formation, whereby atrophy in one node is associated with hypertrophy in a functionally linked node, it should be understood that a decrease in the number and / or density of inverse correlations indicates a decrease in the number of compensatory linkages.

[0028] ***

[0029] Typically, the neurocognitive disease or cognitive disorder is a degenerative disorder of the nervous system causing dementia, such as a tauopathy.

[0030] The patient may have been diagnosed with a neurocognitive disease, such as Alzheimer's disease or behavioral variant frontotemporal dementia. The disease may be mild or moderate Alzheimer's disease. The disease may be mild cognitive impairment. However, the inventors' findings described herein are also applicable to other neurocognitive diseases.

[0031] Diagnostic criteria and treatments for tauopathy and other neurocognitive diseases are known in the art and are discussed, for example, in WO 2018 / 019823 and references cited therein.

[0032] The disease may be behavioral variant frontotemporal dementia (bvFTD). The diagnostic criteria and treatment of bvFTD are discussed in, for example, WO 2018 / 041739 and references cited therein.

[0033] As explained herein, the topology of perturbations in the structural networks differs in these two pathologies (AD and bvFTD), and both differ from normal aging. The changes relative to normal are global and not limited to the frontotemporal and temporoparietal lobes in bvFTD and AD, respectively, and indicate increases in both global correlation strength and specific non-homologous inter-lobar connectivity defined by inverse correlations.

[0034] These changes appear to be adaptive, reflecting coordinated increases in cortical thickness and surface area that compensate for corresponding impairments in functionally linked nodes. The effects were more pronounced in the cortical thickness network in bvFTD and in the surface area network in AD.

[0035] The inventors have observed that an important change that distinguishes the two forms of dementia from normal elderly controls is the emergence of a significant network of inverse correlations linking anterior and posterior brain regions, which may be related to functional adaptation or compensation to damage caused by the pathology. Specifically, given that inverse correlations indicate compensatory linkage formation, whereby atrophy in one node is associated with hypertrophy in a functionally linked node, it will be appreciated that a decrease in the number and / or density of inverse correlations indicates a decrease in the number of compensatory linkages.

[0036] Therefore, if neuropharmacological intervention is effective, it is expected that the network organization will be restored to the state observed when using a normal (non-diseased) comparison group. If the disease is treated at an early enough stage, the network organization can be restored to a state that is completely equivalent to the normal control. The method therefore provides an objective means of distinguishing disease-modifying treatment from symptomatic treatment: symptomatic treatment focuses on abnormal network architecture, and may actually focus on the risk of (for example) prion-like disease processes spreading to healthy brain areas. On the contrary, disease-modifying drugs work in the opposite direction, reducing the need for compensatory input from relatively small brain areas that are damaged by normalizing the function in the area affected by the pathology.

[0037] It will be appreciated from the disclosure herein that analysis of structural or network organization has particular utility in providing higher 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 time period (see WO 2009 / 060191).

[0038] Typically, therefore, the neuropharmacological intervention will be a drug intervention.

[0039] The neuropharmacological intervention can be a symptomatic treatment. Such compounds include acetylcholinesterase inhibitors (AChEl) - these include tacrine, donepezil, rivastigmine and galantamine. Another symptomatic treatment is memantine. These treatments are described in WO 2018 / 041739.

[0040] As explained above, the inventors have discovered an increase in compensatory networks (the number and / or density of non-homologous inverse correlations present in a group of patients that have received such treatment).

[0041] The neuropharmacological intervention can be a disease-modifying drug rather than a symptomatic drug. These treatments can be distinguished, for example, based on what happens when a patient withdraws from active treatment. After the initial treatment period, symptomatic agents postpone the symptoms of the disease without affecting the underlying disease process, and do not change (or at least do not improve) the rate of longer-term decline. If after withdrawal, the patient returns to the state in which he was originally in the absence of treatment, then the treatment is considered to be symptomatic treatment (Cummings, JL (2006) Challenges to demonstrating disease-modifying effects in Alzheimer's disease clinical trials. Alzheimer's and Dementia, 2: 263-271).

[0042] For example, a disease-modifying treatment may be an inhibitor of pathological protein aggregation, such as 3,7-diaminophenothiazine (DAPTZ) compounds. Such compounds are described in WO 2018 / 041739, WO 2007 / 110627, and WO 2012 / 107706. The latter describes colorless bis(hydrogen methanesulfonate) methylene blue, also known as colorless methanesulfonate methylene blue (LMTM; USAN name: methylthionine hydrogen methanesulfonate).

[0043]

[0044] The contents of all of these WO publications with respect to the DAPTZ compounds they define are expressly included by cross reference.

[0045] Treatment with LMTM has been shown to reduce compensatory network correlations (especially non-homologous positive and negative correlations).

[0046] The neuropharmacological intervention may be a disease-modifying drug, and efficacy may be established by a decrease in the number and / or density of correlations between forebrain and hindbrain regions of the first correlation matrix and the second correlation matrix.

[0047] 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 anterior and posterior nodes is reduced.

[0048] The present invention can also be used to identify functional adaptation or compensation to damage due to pathology in a patient population, for example to study "cognitive reserve". The present invention can be used in combination with conventional diagnostic or prognostic measurements. These measurements include the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog); National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA); Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSMIV); and Clinical Dementia Rating (CDR) scale.

[0049] As explained above, the method for determining the patient's response to a neuropharmacological intervention can then be used to evaluate different patient cohorts in a clinical trial of the neuropharmacological intervention. For example, the method can be used to determine the effectiveness of a neuropharmacological intervention in a patient group. The method can be used to define a patient group according to their patient response (e.g., according to a determined correlation / inverse correlation). The patient group can be identified with respect to its previous use of the neuropharmacological intervention, and optionally selected for further treatment suitable for the patient response.

[0050] ***

[0051] In a second aspect, the present invention provides a method of determining the likelihood that a patient will suffer from one or more neurological disorders, the method comprising the steps of:

[0052] obtaining data indicative of electrical activity within a brain of the patient;

[0053] 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, wherein the network indicates a flow of electrical activity within a brain of the patient;

[0054] calculating, 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

[0055] The calculated differences are used to determine a likelihood that the patient suffers from one or more neurological disorders.

[0056] The inventors have shown that even very short use (for example) brain EEG analysis can be used to potentially identify patients susceptible to one or more neurocognitive diseases (such as AD). Specifically, such individuals (patients or subjects, the terms are used interchangeably) can have a relatively large number of "sinks" or relatively strong sinks in the posterior lobe, and a relatively large number of "sources" or relatively strong sources in the temporal lobe and / or frontal lobe. In apparently normal or prodromal subjects, in preferred embodiments, the method can be more sensitive than the psychological measurements commonly used to determine this risk.

[0057] ***

[0058] The optional features of the invention will now be described. These are applicable alone or in any combination with any aspect of the invention.

[0059] The likelihood of a patient suffering from one or more neurological disorders may be referred to as the patient's susceptibility to the one or more neurological disorders. The method may include the step of defining a state of each node, whereby a node is defined as a sink or a source based on the calculated difference.

[0060] The network may be a renormalized partially directional coherent network. Any step of the method may be performed offline, ie without relying on a patient. For example, obtaining the data may be performed by receiving data previously recorded from a patient via a network.

[0061] The data indicative of electrical activity within the brain may be electroencephalography data. The electroencephalography data may be beta band electroencephalography data. The data indicative of electrical activity within the brain may also be magnetoencephalography data or functional magnetic resonance imaging data.

[0062] 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.

[0063] The method may include the step of generating a heat map based at least in part on the states of the nodes, the heat map indicating the locations and / or intensities of nodes defined as sinks and nodes defined as sources within the patient's brain. Such a representation of the defined nodes may help (e.g., ergonomically) determine the patient's susceptibility.

[0064] In determining the susceptibility of the patient, a comparison may be made between the number and / or intensity of source points in the parietal and / or occipital lobes and the number and / or intensity of sink points in the frontal and / or temporal lobes. It has been experimentally observed that patients susceptible to one or more neurodegenerative diseases (and particularly Alzheimer's disease) have a relatively high intensity of sink points in the posterior lobe and a relatively high intensity of source points in the temporal and / or frontal lobes.

[0065] The method may further comprise the step of using the states of the nodes to derive an indication of the degree of left-right asymmetry in the positions and / or strengths of nodes in the brain corresponding to sinks and sources.

[0066] The neurological disorder may be a neurocognitive disease, which may be Alzheimer's disease.

[0067] The 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 comparing the number and / or strength of nodes defined as source points in the temporal lobe 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 if the number and / or strength of nodes defined as source points in the temporal lobe and / or frontal lobe exceeds a predetermined value (e.g., based on a "control" subject or a subject established as having a low risk, or reference data obtained therefrom (e.g., historical reference data)), then the patient can be determined to have a high risk of susceptibility. In other words, and more generally, determinations about susceptibility can be based on whether the patient has more and / or stronger source points and / or sinks in one area of ​​the brain relative to another area of ​​the brain. For example, based on data from control subjects, if there are more and / or stronger source points than expected in the temporal and / or frontal lobes, and / or if the patient has more and / or stronger sink points than expected in the posterior lobe, then the patient may be identified as being at risk for a neurodegenerative disorder. Such data from control subjects may have been established through longitudinal monitoring after a baseline assessment.

[0068] The inventors have further observed that one or more symptomatic treatments increase outward activity from the frontal lobe compared to the untreated group.

[0069] The method of the invention according to this aspect can be used to assess, test or classify the susceptibility of a subject to one or more neurological disorders for any purpose. For example, the score or other output of the test can be used to classify the mental state or disease state of the subject according to predetermined criteria.

[0070] ***

[0071] The subject may be any human subject. In one embodiment, the subject may be a subject suspected of having a neurocognitive disease or disorder as described herein (eg, a neurodegenerative or vascular disease), or may be a subject not identified as being at risk.

[0072] In one embodiment, the method is used for the purpose of early diagnosis or prognosis of cognitive impairment (eg, neurocognitive disease) in the subject.

[0073] The disease may be mild to moderate Alzheimer's disease.

[0074] The disease may be mild cognitive impairment.

[0075] However, the inventors' findings described herein are also applicable to other neurocognitive diseases. For example, the disease may be a different dementia, such as vascular dementia.

[0076] The methods may optionally be used to inform further diagnostic steps or interventions for the subject - for example other methods based on imaging or invasive or non-invasive biomarker assessments, where such methods are themselves known in the art.

[0077] In some embodiments, the method can be used for the purpose of determining the risk of neurocognitive disorder in the subject. Optionally, the risk can be calculated using other factors in addition, such as age, lifestyle factors, and other measured physical or mental criteria. The risk can be a classification of "high" or "low", or can be presented as a scale or spectrum.

[0078] ***

[0079] It will be clear from the disclosure herein that in addition to assessing the likelihood of developing one or more neurological disorders, the methods may also be used to assess the efficacy of a disease-modifying treatment to reduce the risk and / or treat the disease, i.e., assess the efficacy of a drug for preventing or treating the disease or disorder. This may optionally be in the context of a clinical trial as described herein, for example compared to a placebo or other normal control.

[0080] Specifically, the disclosure herein shows that the methods of the present invention (e.g., based on EEG technology) can provide effective and sensitive measurements of the effects of disease on subjects. This provides an opportunity to demonstrate the efficacy of disease-modifying treatments in smaller groups of subjects (e.g., less than or equal to 200, 150, 100, or 50 in treatment and comparison groups) and at shorter intervals (e.g., less than or equal to 6, 5, 4, or 3 months) and in earlier disease or less severe disease (e.g., prodromal AD, MCI, or even pre-MCI) than is possible using currently available methods.

[0081] Thus, as discussed above, the methods can be used in different patient cohorts of clinical trials of neuropharmacological interventions, for example, the patient group (multiple patients) can be a treatment group of patients already diagnosed with the disease (e.g., early stage disease) who are treated with a putative disease-modifying treatment versus a group treated with a placebo.

[0082] Thus, in another aspect, the method steps of the second aspect are used to determine the disease state or severity of the patient, rather than to determine the likelihood that the patient suffers from one or more neurological disorders. This state can in turn be monitored as part of clinical management or clinical trials.

[0083] Therefore, another aspect of the present invention provides a method for determining a patient's response to a neuropharmacological intervention for a neurological disorder, the method comprising the following steps prior to the neuropharmacological intervention:

[0084] (a) obtaining data indicative of electrical activity within the brain of the patient;

[0085] (b) 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, wherein the network is indicative of a flow of electrical activity within the brain of the patient;

[0086] (c) calculating, 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

[0087] (d) determining the patient's status with respect to the neurological disorder using the calculated difference;

[0088] (e) repeating steps (a)-(d) after the neuropharmacological intervention to determine the further status of the patient with respect to the neurological disorder; and

[0089] (f) determining the patient's response to the neuropharmacological intervention based on (e.g., by comparing) the first state and the second state.

[0090] Steps (e) and (f) are optionally repeated, and subsequent states are used to determine patient response over time.

[0091] Therefore, these methods of the second aspect and other aspects (and the corresponding systems discussed below) can be used for both clinical trials and clinical management. In terms of clinical management, a high degree of certainty (e.g., 70%, 80%, 90% or 95% probability) that the electrical activity in the patient's brain (e.g., as assessed using EEG) is abnormal in "normal" people (i.e., currently undiagnosed) can be a strong indication for immediate initiation of dementia medication. EEG can also be used to monitor the response to treatment at intervals of, for example, 1, 2, 3, 4, 5 or 6 months. On the contrary, people with a lower probability (e.g., 30%, 40%, 50%, 55% or 60%) of abnormal EEG can be more closely tracked at intervals of once a month, once every two months or once every three months. Further testing by other means suitable for the disorder (e.g., biomarker assessments based on amyloid or tau protein PET or CSF) can be optionally used in combination with the method.

[0092] The optional features of the method in relation to the second aspect apply mutatis mutandis to this aspect.

[0093] ***

[0094] In a third aspect, the present invention provides a system for determining a patient's response to a neuropharmacological intervention, the system comprising:

[0095] a data acquisition component configured to obtain structural neurological data from a plurality of patients prior to a neuropharmacological intervention, the structural neurological data indicating a physical structure of a plurality of cortical regions;

[0096] A correlation matrix generating component is configured to generate a first correlation matrix from the structural neurological data by:

[0097] assigning a plurality of structural nodes corresponding to cerebral cortical regions; and

[0098] determining pairwise correlations between pairs of structural nodes based at least in part on corresponding data of the structural neurological data;

[0099] wherein the data acquisition component is further configured to obtain further structural neurological data from the plurality of patients after the neuropharmacological intervention, the further structural neurological data being indicative of the physical structure of the plurality of cortical regions; and

[0100] The correlation matrix generating means is further configured to generate a second correlation matrix from the further structural neurological data by:

[0101] determining pairwise correlations between pairs of structural nodes based at least in part on corresponding data of the further structural neurological data;

[0102] The system further comprises:

[0103] a display component for presenting the first correlation matrix and the second correlation matrix; or

[0104] Comparison means for comparing the first correlation matrix with the second correlation matrix to determine a patient's response to the neuropharmacological intervention.

[0105] The optional features of the invention will now be described. These are applicable alone or in any combination with any aspect of the invention.

[0106] A correlation matrix can mean generating a structural correlation network which can then be represented as a matrix.

[0107] The measured or obtained physical structure can be cortical thickness and / or surface area. The value of cortical thickness and / or surface area can be an average value obtained from the structural neurological data. The structured neurological data can be collected from magnetic resonance imaging (MRI) data or computed tomography data of each patient. The structural neurological data and the further structural neurological data are obtained at different time points. As discussed herein, the structural neurological data can be obtained by magnetic resonance imaging, computed tomography or positron emission tomography of each patient. These techniques themselves 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 Supplement D: 5-12; Pascual-Marqui RD et al.

[0108] The plurality of cortical areas may be at least 60 or at least 65. For example, 68. The cortical areas may for example be those provided by the Desikan-Killiany atlas (Desikan et al. 2006).

[0109] The display means may provide each of the first correlation matrix and the second correlation matrix on a display, wherein the correlation values ​​in each correlation matrix are presented in a colour corresponding to the relative magnitude or strength of the correlation.

[0110] The validation component can be configured to determine the p-value for each pairwise correlation, and compare the p-values ​​for each pairwise correlation, and the p-values ​​can be compared to a significance level, and the correlation matrix generation component can be configured to use only p-values ​​less than a corrected significance level when generating the correlation matrix. The significance level can be referred to as alpha ("α").

[0111] The comparison means may be configured to compare the number and / or density of inverse correlations in the first correlation matrix with the number and / or density of inverse correlations in the second correlation matrix. In performing the comparison, groups of structural nodes corresponding to the same lobe may be identified, and the comparison between the first correlation matrix and the second correlation matrix may utilize the same lobe.

[0112] Assigning the plurality of structural nodes corresponding to the cerebral cortical areas may also include defining groups containing structural nodes corresponding to homologous or non-homologous lobes. The comparison component may be configured to compare the first correlation matrix with the second correlation matrix by comparing the number and / or density of correlations between different structural node groups. In other words, comparing the first correlation matrix with the second correlation matrix may include comparing non-homologous structural node pairs.

[0113] The comparison means may be configured to compare the first correlation matrix with 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. It has been found that in instances of effective neuropharmacological interventions, the number and / or density of inverse correlations between the anterior nodes and the posterior nodes decreases. Since it is assumed that inverse correlations indicate compensatory linkage formation, whereby atrophy in one node is associated with hypertrophy in a functionally linked node, it will be appreciated that a decrease in the number and / or density of inverse correlations indicates a decrease in the number of compensatory linkages.

[0114] ***

[0115] In one embodiment, the patient response may be in the context of a clinical trial, e.g., for evaluating the efficacy of a drug for a neurocognitive disease. Thus, the patient group (patients) may be a treatment group that has been diagnosed with the disease, or may be a control ("normal") group. Finally, the efficacy of the drug may be evaluated in whole or in part based on the patient group response determined according to the invention.

[0116] As explained in relation to the first aspect, the neurocognitive disease will typically be a degenerative disorder of the nervous system causing dementia, such as a tauopathy.

[0117] The patient may have been diagnosed with the neurocognitive disease, such as Alzheimer's disease or behavioral variant frontotemporal dementia. The disease may be mild or moderate Alzheimer's disease. The disease may be mild cognitive impairment.

[0118] Diagnostic criteria and treatment of tauopathies and these disorders are discussed, for example, in WO 2018 / 019823 and references cited therein.

[0119] The disease may be behavioral variant frontotemporal dementia (bvFTD). The diagnostic criteria and treatment of bvFTD are discussed in, for example, WO 2018 / 041739 and references cited therein.

[0120] As explained herein, the topology of perturbations in structural networks differs in these two pathologies (AD and bvFTD), and both are distinct from normal aging. These changes appear to be adaptive, reflecting coordinated increases in cortical thickness and surface area that compensate for corresponding impairments in functionally linked nodes.

[0121] Thus, if the neuropharmacological intervention is effective, it is expected that the network organization will be restored to a normal state. If the condition is treated at an early enough stage, the network organization may be restored to a normal state indicating cessation or reversal of the disease state. Thus, the system provides an objective means of distinguishing disease-modifying treatments as described above from symptomatic treatments.

[0122] Typically, the neuropharmacological intervention will be a drug intervention.

[0123] The neuropharmacological intervention may be a symptomatic treatment as described above.

[0124] For example, the disease modifying treatment may be an inhibitor of pathological protein aggregation, such as a 3,7-diaminophenothiazine (DAPTZ) compound as described above.

[0125] ***

[0126] In a fourth aspect, the present invention provides a system for determining a patient's susceptibility to one or more neurological disorders, the system comprising:

[0127] a data acquisition component configured to obtain data indicative of electrical activity within a brain of the patient;

[0128] a network generating component configured to generate a network based at least in part on the obtained data, the network comprising a plurality of nodes and directed connections between the nodes, wherein the network indicates a flow of electrical activity within a brain of the patient;

[0129] a difference calculation component configured to calculate, for each node, a 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 one of the following components:

[0130] display means configured to display a representation of the calculated difference; or

[0131] A determining means is configured to determine the patient's susceptibility to one or more neurological disorders using the calculated difference.

[0132] As described above in relation to the second aspect, the inventors have shown that even very brief analysis using, for example, brain EEG can be used to potentially identify patients susceptible to one or more neurocognitive diseases, such as AD.

[0133] The system can be used for both clinical trials and clinical management.

[0134] Thus, in another aspect, there is provided a system as described above for determining a patient's response to a neuropharmacological intervention for a neurological disorder. In this aspect, the determining means system may be configured to determine the patient's state with respect to the neurological disorder using the calculated difference.

[0135] The system may be used to determine a further state of the patient following the neuropharmacological intervention and optionally configured to determine a response of the patient to the neuropharmacological intervention based on the first state and one or more subsequent states, as described above with respect to the corresponding method.

[0136] Further optional features of the invention will now be described. These are applicable alone or in any combination with any aspect of the invention.

[0137] The system may include a state definition component configured to define a node as a sink or a source based on the calculated difference.

[0138] The network may be a renormalized partially directional coherent network. The system may operate "offline", ie without a patient. For example, obtaining the data may be performed by receiving data previously recorded from a patient via a network.

[0139] The data indicative of electrical activity within the brain may be electroencephalography data. The electroencephalography data may be beta band electroencephalography data. The data indicative of electrical activity within the brain may also be magnetoencephalography data or functional magnetic resonance imaging data.

[0140] The determining means may be configured to determine the patient's susceptibility to one or more neurological disorders using a machine learning classifier, such as a Markov model, a support vector machine, a random forest, or a neural network.

[0141] The display member may be configured to present a heat map indicating the location and / or strength of nodes defined as sinks and nodes defined as sources within the brain. This representation of the defined nodes may help (e.g. ergonomically) determine the susceptibility of the patient.

[0142] The system may also include a heat map generating component configured to generate a heat map based at least in part on the states of the nodes, the heat map indicating the locations and / or intensities of nodes defined as sinks and nodes defined as sources within the patient's brain. Such a representation of the defined nodes may help (e.g., ergonomically) determine the patient's susceptibility.

[0143] The determining means may compare the number and / or intensity of source points in the parietal and / or occipital lobes with the number and / or intensity of sink points in the frontal and / or temporal lobes compared to the number and / or intensity of sink points in the frontal and / or temporal lobes. It has been experimentally observed that patients susceptible to one or more neurodegenerative diseases (and in particular Alzheimer's disease) have a relatively high number and / or intensity of sink points in the posterior lobe and a relatively high number and / or intensity of source points in the temporal and / or frontal lobes.

[0144] The system may further comprise an asymmetry map generating component configured to derive an indication of the degree of left-right asymmetry in the positions and / or densities of nodes corresponding to sinks and sources in the brain using the states of the nodes.

[0145] The neurological disorder may be a neurocognitive disease, which is optionally Alzheimer's disease.

[0146] The determination component may compare the number and / or strength of nodes defined as sinks in the posterior lobe with a predetermined value, and / or compare the number and / or strength of nodes defined as source points in the temporal lobe and / or frontal lobe with a predetermined value. 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 source points in the temporal lobe and / or frontal lobe exceeds a predetermined value, then the determination component may determine the patient as having a high risk of susceptibility. In other words, and more generally, the determination of susceptibility may be based on whether the patient has more and / or stronger source points and / or sinks in one area of ​​the brain relative to another area of ​​the brain. For example, if there are more and / or stronger source points than expected in the temporal lobe and / or frontal lobe, and / or if the patient has more and / or stronger sinks in the posterior lobe than expected, then the patient may be determined to be at risk of a neurodegenerative disorder.

[0147] The inventors have further observed that one or more symptomatic treatments increase outward activity from the frontal lobe compared to the untreated group.

[0148] The system of the present invention according to this aspect can be used for any purpose to assess, test or classify the susceptibility of a subject to one or more neurological disorders. For example, the score or other output of the test can be used to classify the mental state or disease state of the subject according to predetermined criteria.

[0149] The subject may be any human subject. In one embodiment, the subject may be a subject suspected of having a neurocognitive disease or disorder as described herein (eg, a neurodegenerative or vascular disease), or may be a subject not identified as being at risk.

[0150] In one embodiment, the system is used for early diagnosis or prognosis of cognitive impairment, such as a neurocognitive disease, in the subject, as described above.

[0151] The system may optionally be used to inform further diagnostic steps or interventions for the subject - for example based on other systems for imaging or invasive or non-invasive biomarker assessment, wherein such systems themselves are known in the art.

[0152] In some embodiments, the system can be used to determine the risk of neurocognitive disorder in the subject. Optionally, the risk can be calculated using other factors, such as age, lifestyle factors, and other measured physical or mental criteria. The risk can be a classification of "high" or "low", or can be presented as a scale or spectrum.

[0153] As with the methods described herein, the system can be used in the context of clinical trials to evaluate the efficacy of neuropharmacological interventions. The system can be used to demonstrate the efficacy of disease-modifying treatments (e.g., LMTM) in a relatively small number of subjects (e.g., 50) over a relatively short time scale (e.g., 6 months) and in early disease stages (e.g., mild cognitive impairment or possible pre-mild cognitive impairment).

[0154] Other aspects of the present invention provide: a computer program comprising an executable code that, when executed on a computer, causes the computer to execute the method of the first or second aspect; a computer-readable medium that stores a computer program comprising a code that, when executed on a computer, causes the computer to execute the method of the first or second aspect; and a computer system that is programmed to execute the method of the first or second aspect. For example, a computer system may be provided, the system comprising: one or more processors configured to: execute the method of the first or second aspect. The system thus corresponds to the method of the first or second aspect. The system may also include: one or more computer-readable media operatively connected to the processor, the one or more media storing computer-executable instructions corresponding to the method of the first or second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0155] Embodiments of the present invention will now be described by way of example with reference to the accompanying drawings, in which:

[0156] Figure 1 An example of the Desikan-Killiany brain atlas is shown;

[0157] Figure 2 An exemplary cortical surface area correlation matrix for a group of subjects diagnosed with behavioral variant frontotemporal dementia is shown, with pairwise correlations grouped by lobe;

[0158] FIG. 3A to FIG. 3C Group-based cortical thickness correlation networks depicted as pairwise correlation matrices are shown for: (i) a group of HE subjects, (ii) a group of bvFTD subjects, and (iii) a group of AD subjects, respectively;

[0159] FIG. 4A to FIG. 4C Group-based surface area correlation networks depicted as pairwise correlation matrices are shown for: (i) a group of HE subjects, (ii) a group of bvFTD subjects, and (iii) a group of AD subjects, respectively;

[0160] Figure 5 Plots showing mean edge strength of cortical thickness-related networks averaged across lobes and compared between HE, bvFTD, and AD groups, with the upper plot being the positively correlated network and the lower plot being the inversely correlated network;

[0161] Figure 6 Plots showing mean edge strength of surface area correlation networks averaged across lobes and compared between HE, bvFTD, and AD groups, with inversely correlated networks on the left and positively correlated networks on the right;

[0162] Figure 7 Plots showing the node degrees of the cortical thickness correlation network averaged across lobes between the HE, bvFTD, and AD groups, with the upper plot being the positively correlated network and the lower plot being the inversely correlated network;

[0163] Figure 8 Plots of nodal interlobar participation indices of the cortical thickness-related network averaged across lobes and compared between HE, bvFTD, and AD groups for positive correlations are shown;

[0164] Fig. 9 Plots showing the node degrees of the surface area correlation networks averaged across cerebral lobes and compared between the HE, bvFTD, and AD groups, with the upper plot being the positively correlated network and the lower plot being the inversely correlated network;

[0165] Fig.10 Plots showing the nodal interlobar involvement index of the surface area-correlated networks averaged across lobes and compared between the HE, bvFTD, and AD groups, with the upper plot being the positively correlated networks and the lower plot being the inversely correlated networks;

[0166] Fig.11 Visualization of hubs in the cortical thickness network in brain space is shown for positively correlated nodes (in the upper panel) and inversely correlated nodes (in the lower panel) for the HE, bvFTD, and AD groups.

[0167] Fig.12 Visualization of hubs in the surface area network in brain space is shown for the positively correlated nodes (in the upper panel) and the inversely correlated nodes (in the lower panel) for the HE, bvFTD, and AD groups;

[0168] Fig.13 Visualization of the interaction between the positive network of cortical thickness and cortical surface area in brain space for the HE, bvFTD, and HE groups is shown;

[0169] Fig.14 Histograms of preserved edges in the networks related to cortical thickness (upper three plots) and surface area (lower three plots) are shown;

[0170] Fig.15 is a graph showing the distribution of modularity index (Q) in regional cortical thickness correlation networks generated on 100 alternative datasets;

[0171] Fig.16 Binarized correlation matrices of the cortical thickness networks (upper three graphs) and surface areas (lower three graphs) of the HE, bvFTD, and AD groups are shown, with white representing significant positive correlations and black representing significant inverse correlations;

[0172] FIG. 17A to FIG. 17D A correlation matrix according to treatment status with symptomatic AD medication (cholinesterase inhibitors and / or memantine) at baseline (i.e., week 01) is shown, with ach0 indicating no treatment and ach1 indicating the presence of such treatment;

[0173] 18A to 18D Plot showing nonhomologous interlobar correlated node degrees at baseline node degree according to treatment status with symptomatic AD medications (acetylcholinesterase inhibitors and / or memantine);

[0174] Fig.19A and Fig.19B Shown are temporally separated cortical thickness correlation matrices at baseline (Week 01) and after 65 weeks (Week 65) in patients receiving 8 mg / day LMTM in combination with symptomatic treatment;

[0175] FIG. 20A to FIG. 20D Figures showing the correlation of positive and negative nonhomologous interlobar nodal degrees in cortical thickness (CT) and surface area (SA) at baseline and after 65 weeks in patients receiving 8 mg / day LMTM in combination with symptomatic treatment;

[0176] Fig.21A and Fig. 21B Shown are temporally separated cortical thickness correlation matrices at baseline (Week 01) and after 65 weeks (Week 65) in patients receiving 8 mg / day LMTM as monotherapy (i.e., not in combination with symptomatic AD treatment);

[0177] Fig.22A and Fig. 22B Plots showing non-homologous interlobar node degrees of the cortical thickness-related network at baseline and after 65 weeks in patients receiving 8 mg / day of LMTM as monotherapy (i.e., not in combination with symptomatic AD treatment);

[0178] Fig.23A and Fig. 23B Shown are the temporally separated surface area correlation matrices at baseline (Week 01) and after 65 weeks (Week 65) in patients receiving 8 mg / day LMTM as monotherapy (i.e., not in combination with symptomatic AD treatment);

[0179] Fig.24A and Fig. 24B Plots showing non-homologous interlobar node degrees of the surface area-related network at baseline and after 65 weeks in patients receiving 8 mg / day of LMTM as monotherapy (i.e., not in combination with symptomatic AD treatment);

[0180] FIG. 25A to FIG. 25D Cortical thickness correlation matrices separated in time for both AD (Clinical Dementia Rating 0.5, 1, and 2) and elderly controls (HE) at baseline and after 65 weeks of treatment with 8 mg / day LMTM as monotherapy are shown to show normalization of the matrices after treatment;

[0181] FIG. 26A to FIG. 26D Shown are temporally separated surface area correlation matrices for both AD (Clinical Dementia Rating 0.5, 1, and 2) and elderly controls (HE) at baseline and after 65 weeks of treatment with 8 mg / day LMTM as monotherapy to show normalization of the matrices after treatment;

[0182] FIG. 27A to FIG. 27DCortical thickness correlation matrices separated in time for both AD (Clinical Dementia Rating 0.5) and elderly controls (HE) at baseline and after 65 weeks of treatment with 8 mg / day LMTM as monotherapy are shown to show normalization of the matrices after treatment;

[0183] FIG. 28A to FIG. 28D Shown are surface area correlation matrices separated in time for both AD (Clinical Dementia Rating 0.5) and elderly controls (HE) at baseline and after 65 weeks of treatment with 8 mg / day LMTM as monotherapy to show normalization of the matrices after treatment;

[0184] Fig.29 Examples of resting electroencephalography data are shown;

[0185] Fig.30 Examples of directed networks derived from electroencephalography data are shown;

[0186] Fig.31 Schematic showing determination of node state based on difference between incoming and outgoing electrical activity flows;

[0187] Fig.32 Heatmap showing the locations of net sinks (yellow / red) and net sources (blue) in the brains of a group of subjects;

[0188] Fig.33 Shows the indication Fig.32 Heatmap showing asymmetric distribution of source and sink points between the left and right sides of the middle heatmap;

[0189] Fig.34 A heat map showing the locations of sources and sinks in the brains of a group of subjects diagnosed with Alzheimer's disease;

[0190] Fig.35 Heatmap showing the locations of sources and sinks in the brains of a group of subjects not diagnosed with Alzheimer's disease (i.e., matched volunteers);

[0191] Fig.36 Heatmap showing the locations of sources and sinks in the brains of subjects not diagnosed with Alzheimer's disease (i.e., matched volunteers);

[0192] Fig.37 Heatmap showing the locations of sources and sinks in the brains of subjects diagnosed with Alzheimer's disease;

[0193] Fig.38 a heat map showing the locations of sources and sinks in the brain of a group of subjects identified as being at risk for dementia or cognitive decline, e.g., due to having Alzheimer's disease;

[0194] Fig.39 Heatmap showing the locations of sources and sinks in the brain of a group of subjects determined not to be at risk for Alzheimer's disease;

[0195] Fig.40 are box plots comparing sources and sinks in EEG networks from frontal and posterior brain regions at the group level in subjects with and without AD risk;

[0196] Fig.41 Comparison between the cortical thickness correlation matrix in the AD group (which shows an increase in the strength and number of significant inverse correlations between non-homologous lobes, left panel) and a heat map of the locations of sources and sinks in the brain of a group of subjects who have been diagnosed with AD is shown, which shows the correspondence between the increase in compensatory structural non-homologous inverse correlations in cortical thickness directed to posterior brain regions and the increase in the strength and number of incoming connections to posterior brain regions as sinks revealed by rPDC coherence analysis of resting state EEG;

[0197] Fig.42 Comparison between the surface area correlation matrix of the HE group and heat maps of the locations of sources and sinks in the brain of a group of healthy elderly subjects is shown, showing the correspondence between the relative lack of compensatory structural nonhomologous anticorrelations in cortical thickness directed toward posterior brain regions and the reduction in the number and strength of incoming connections to posterior brain regions as sinks revealed by rPDC coherence analysis of resting-state EEG;

[0198] Fig.43 is a box plot showing the quantitative distinction between mild AD and elderly controls;

[0199] Fig.44 Three heat maps comparing medicated and unmedicated AD patients to matched volunteers at the group level are shown; and

[0200] Fig.45 is a group-level network boxplot comparing medicated and unmedicated AD patients with matched volunteers. DETAILED DESCRIPTION

[0201] Various aspects and embodiments of the present invention will now be discussed with reference to the accompanying drawings. Other aspects and embodiments will be clear to those skilled in the art. All documents mentioned in this text are incorporated herein by reference.

[0202] Figure 1An example of the Desikan-Killiany brain atlas is given. The Desikan-Killiany brain atlas divides the human cerebral cortex into target regions based on gyri on MRI scans. Although 18 regions are shown in the figure, the full Desikan-Killiany brain atlas divides the human cortex into 68 target regions.

[0203] The subjects discussed in this document participated in three global Phase 3 clinical trials, which have now been completed. Two of the clinical trials were conducted in mild to moderate AD (Gauthier et al., 2016; Wilcock et al., 2018), and the third was a large study from bvFTD (Feldman et al., 2016). Comparable data can be obtained from well-characterized healthy elderly (HE) subjects who participated in an ongoing longitudinal study of the Aberdeen Birth Cohort of 1936 (ABC36) (Murray et al., 2011). In the example discussed in this article, there were a total of 628 subjects, 213 in each dementia group, and 202 healthy elderly subjects. bvFTD patients were diagnosed with mild severity according to the international consensus criteria for bvFTD, with a Mini-Mental State Examination (MMSE) score of 20-30, including end values. AD patients were diagnosed with mild to moderate severity according to the criteria of the National Institute of Aging and the Alzheimer's Association, defined as MMSE scores of 14-26 (inclusive) and a Clinical Dementia Rating (CDR) total score of 1 or 2. They were drawn from a corresponding larger group (N=1132) to match the number of participants in the bvFTD group. Healthy elderly (HE) subjects were selected from the well-characterized 1936 Aberdeen birth cohort.

[0204] The multi-side source imaging data set used to generate the correlation matrix discussed below is a standard T1-weighted MRI image acquired sequentially using the proprietary 3DT1 of the same manufacturer. The data from the trial patients were merged to allow for overall intergroup comparisons. The training scanner was limited to 1.5T and 3T (30%) field strengths from three manufacturers (Philips, GE and Siemens). The MRI images in the ABC36 cohort were all acquired using the same (Philips) 3T scanner. The images were processed using an automated processing pipeline implemented in a manner known per se. In addition to the volume-based image processing method, the pipeline also produces surface-based regional measurements of cortical morphology (such as thickness, local curvature or surface area). An example of an automated processing pipeline suitable for the above method is FreeSurfer v5.3.0 available from the Athinoula A.Martinos Biomedical Imaging Center at Massachusetts General Hospital.

[0205] Surface area was calculated from the imaging data set using triangulation 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 of the distances from the white matter surface to the nearest point on the pial surface and from that point back to the nearest point on the white matter surface. The cortical thickness and surface area of ​​68 cortical regions of the two hemispheres were extracted based on the Desikan-Killiany atlas using a segmentation scheme known per se. A list of regions and their lobe assignments is given in Table A.1 of Appendix A.

[0206] Figure 2 The cortical surface area correlation matrix of a group of subjects diagnosed with bvFTD is shown. Each matrix element represents the correlation strength ("edge strength") between 68 pairs of cortical surface areas from the Desikan-Killiany atlas. The intensity bar on the right indicates the correlation / edge strength. Sixty-eight cortical surface areas (network nodes) are sorted according to their affiliation with the frontal lobe, temporal lobe, parietal lobe, and occipital lobe. Individual lobe areas are enclosed in a square and are sorted from top to bottom / left to right as: frontal lobe, temporal lobe, parietal lobe, and occipital lobe. In essence, the correlation matrix represents a network constructed from the partial correlations between 68 pairs of cortical thicknesses. FIG. 3A to FIG. 3CCortical surface area correlation matrices are shown for healthy elderly, behavioral variant frontotemporal dementia subjects, and Alzheimer's disease subjects, respectively. Significant differences can be observed between healthy elderly and both bvFTD and AD subjects. It is noteworthy that within the lobe, the correlation strength is significantly increased in bvFTD and AD subjects. In addition, the number of inverse correlations between non-homologous nodes is increased. As can be observed, HE subjects have sparse correlations, and these are mostly positive correlations between homologous lobes. However, compared to the HE group, both bvFTD and AD have a significantly increased number of nodes linked by positive and inverse correlations. The increase in the number of correlations in both forms of dementia can be between the same lobe (homologous, mainly positive) or between different lobes (non-homologous, mainly negative). In general, non-homologous inverse correlations between lobes are highly abnormal. Similarly, it can be observed that bvFTD is particularly associated with a higher density of non-homologous inverse correlations in cortical thickness.

[0207] FIG. 4A to FIG. 4C The surface area correlation matrices for healthy elderly, behavioral variant frontotemporal dementia subjects, and Alzheimer's disease subjects are shown separately. Significant differences can be observed between healthy elderly and both bvFTD and AD subjects. In addition, it should be noted that AD is specifically associated with a higher density of non-homologous inverse correlations in surface area.

[0208] In these plots, zero entries correspond to nonsignificant correlations. Significant network correlations were found to have both positive and negative values ​​(see for an explanation). Fig.14 and Fig.16 ). Due to the apparent increase in the number of significant inverse correlations and network correlation strength in the bvFTD and AD groups relative to the HE group, subnetworks of significant positive and inverse correlations were considered separately. Given the apparent differences in lobar network structure according to diagnostic group, we sought to determine whether these differences could be quantified.

[0209] The networks represented as correlation matrices in these figures can be constructed by correlating surface area or cortical thickness between all subjects within a particular diagnostic category (i.e., HE, bvFTD, and AD). Cortical regions (as defined by the Desikan-Killiany brain atlas) represent nodes, and pairwise correlations between nodes represent graph edges, or linkages / connections are constructed by correlating SA or CT between all participants within each diagnostic category. Each correlation matrix is ​​calculated based on an S x N array containing N regional CT / SA values ​​from S subjects within each group. In this way, six N x N (e.g., 68 x 68) correlation matrices are obtained (one CT or SA structural correlation matrix for each study group). The matrix elements e ij is the region between i and j (i, j = 1, 2, ... N) (i.e., vector x i With xj After first removing the effects of all other regions m≠(i;j), x is adjusted for the control variables (stored in a separate array S x C, where C represents the number of control variables). i and x j After that, the partial correlation is calculated as x i With x j The linear Pearson correlation coefficient between pairs. This means that before the correlation analysis, for each x i Linear regression was performed to remove the effects of age, sex, and mean CT (mean cortical thickness of all regions) or total surface area (sum of total body surface area). Autocorrelations (expressed as the main matrix diagonnetwork measures) were calculated on the lower triangular part of the matrix. Partial correlations e ij (i.e. edge weight) can be calculated according to the following general equation:

[0210] e ij =ρ i ≡corr(x i , x j |x c )

[0211] where x i；j represents a variable array, and x c represents any subset of the conditioning variables. To achieve this general form of partial correlation, the process starts with i, j, c = 1, 2, 3:

[0212]

[0213] Thus, for any subset c of the condition variables:

[0214]

[0215] In some examples, to verify that the network retains only statistically significant correlations, the calculated correlation coefficients are adjusted for a variety of tests using a false discovery rate (FDR) procedure as described in Storey, 2002. The FDR procedure tests each calculated p-value (from the pairwise correlation calculation) against a corrected significance level, in this example α = 0.05, and only accepts p-values ​​less than the adjusted significance level as truly significant. Those pairwise correlations that fail the FDR test can be set to zero; otherwise, all non-zero correlations (whether positive or negative) are retained (see Fig.14 , discussed in more detail below).

[0216] In this way, a 68×68 correlation matrix can be constructed for CT or SA in each clinical group, which represents the structural correlation network of surface area or cortical thickness. The matrix elements quantify the strength of correlations between cortical areas with respect to cortical thickness or surface area, and do not essentially represent actual physical connections. In the case of structural correlation network analysis of neurodegenerative disorders, such correlations are considered to imply co-atrophic relationships (if positive) or inverse atrophy / hypertrophy relationships (if negative) between brain regions.

[0217] With respect to the structural correlation networks and / or matrices generated using the above methods, it is useful to compare the structural network properties of the three clinical groups using the following measures: edge strength, node degree, node module intra-degree z score, and participation index. Edge strength and node degree represent two basic network attributes; they quantify the strength of correlation between nodes and the number of pairwise correlations of each node, respectively. In order to assess whether the cortical lobes represent modules, two network measures of modularity in network interactions, i.e., module intra-degree z score and participation index, are used. All measures (except node degree) are calculated on weighted graphs and estimated as the average between the four lobes (described below). The measures are calculated on binary or weighted graphs (as discussed below). It is known from purely theoretical studies that the calculated network topological properties depend on the choice of thresholds (van Wijk et al. 2010). In this document, a fixed threshold is selected for each group-based correlation matrix.

[0218] Node Degree

[0219] Node degree k i Represents the number of significant correlations for each node in the network. Typically, node degrees are calculated from a binary correlation matrix, where each significant correlation in the matrix is ​​replaced by 1 (if it is significant) or 0 (if it is not significant). An example of a binary matrix is ​​shown in Fig.16 The binary matrix can also be called the adjacency matrix. Fig.16 In the figure, the upper three plots correspond to cortical thickness, and the lower three plots correspond to surface area. Significant positive correlations are shown in white, while significant inverse correlations are shown in black.

[0220] The node degree of node i (i.e. the number of significant links connecting to the node) can be calculated as follows:

[0221]

[0222] Where N is the number of nodes, and a ij Represents the connection between nodes i and j. Its value is 1 when there is a direct connection between the nodes, otherwise it is 0.

[0223] Modularity Index

[0224] Node participation index and intra-module degree z-score assess the role of nodes in terms of modules. Network modules (also called community structure) represent densely connected subgraphs of the network, i.e., subsets of nodes whose intra-network connections are denser and whose inter-network connections are sparser. It is useful to examine the modular organization of frontal, temporal, parietal, and occipital parcellations of cortical thickness or surface area networks defined as modules. Since these lobar parcellations of cortical surface area are not necessarily modular in themselves, it may be desirable to first test whether the lobar parcellations are intrinsically modular. In one example, this can be done by computing a modularity index (Q) for the network per lobe. The modularity index quantifies the fraction of observed intra-module degree values ​​relative to those expected if connections were randomly distributed across the network. Since the constructed cortical thickness and surface area networks contain both positive and negative edge strengths, an asymmetric generalization of the modularity quality function can be used. For example, as introduced in Rubinov and Sporns (2011):

[0225]

[0226] If the strength of the pairwise correlations between cortical areas ω ij >0, then is equal to the i, jth element of the correlation matrix, i.e. ω ij , otherwise it is equal to zero. Similarly, if ω ij >0, then Equal to -ω ij , otherwise it is equal to zero. represents the expected density of positive or negative connection weights, given a random null model that preserves strength, where and When the i-th and j-th nodes are in the same module, the Kronecker delta function is equal to one, otherwise equal to zero. The performance of a given separation of the network into modules is tested by applying a community detection function known per se in the art while employing vectors of node affiliations, with a specific node as the initial community affiliation vector.

[0227] The organization of the cortical surface lobes into frontal, parietal, temporal and occipital partitions was found to be modular in nature (see Appendix A). Therefore, the contribution of individual nodes to the lobar modules can then be calculated as a node participation index and an intra-module z-score, which are referred to as the node inter-lobar participation index and the node intra-lobar z-score.

[0228] Nodal interlobar involvement index

[0229] Typically, the participation index p assesses inter-module connectivity. It can be thought of as the ratio of a node's edges within a lobe to all other lobe modules in the network, where a node's p is 0 if it has linkages only within its own module.i tends to 0, and tends to 1 if the node has linkages only outside its own module. The weighted network participation is calculated by the following equation:

[0230]

[0231] where M is the module group and is the weighted number of linkages of the ith node with all other nodes in module m - the inter-module degree, and is the total degree of the ith node. In this document, the term interlobar involvement is used for this network metric.

[0232] Node intralobar degree z score

[0233] The complement of the interlobar participation index is the normalized intralobar degree z i , which assesses intralobar connectivity with the aid of a z-score, i.e., the normalized deviation of the interlobar degrees of nodes with the corresponding mean degree distribution. Thus, the node intralobar z-score z i Nodes with more intra-module connections relative to the average inter-module connectivity are larger. For networks that preserve correlation strength, the node intra-module degree z-score is calculated as follows:

[0234] in As mentioned above, It is module m i the mean of the inner degree distribution, and It is module m i The standard deviation of the inlier degree distribution.

[0235] Node roles in network module organization

[0236] The role of a node with modular lobar organization depends on its i Position in parameter space. Nodes can have four possible roles in the network, which are assigned based on above-average node property measures. It is useful to consider two of these roles, the so-called connectors or global network hubs (which have high interlobar involvement and high intralobar degree z-scores) and the so-called local hubs (which have high intralobar degree z-scores and low interlobar involvement). i and p i The thresholds for high and low values ​​are set above 1.5 and 0.05, respectively.

[0237] Statistical analysis

[0238] Statistical differences in demographics and cognitive scores of subjects were assessed using one-way ANOVA or two-tailed t-tests. The one-sample Kolmogorov-Smirnov test was used to check the normality of the distribution of data. The chi-square test was used to test for differences in the distribution of males and females between groups. For unbalanced sample sizes, one-way ANOVA was used to test for statistical differences in the strength of global network correlations according to diagnostic group (to account for the uneven number of significant correlations between networks). Node degree, intralobar z scores, and scores were compared between groups using the Kruskal-Wallis test (a nonparametric one-way ANOVA test). i and interlobar involvement index p i Results were reported as significant at the p < 0.05 level.

[0239] result

[0240] Table 1 below shows the demographics, cognition, and mean CT and SA of each group according to clinical diagnosis. The age of the three groups was significantly different, with AD patients being older than HE and bvFTD (p<10 in all tests). -4 Significant differences were also observed in the cognitive scores of the MMSE scale, with AD patients being the most impaired and bvFTD patients being more impaired compared with HE subjects (p<10 in all tests). -4 ). Mean CT and total SA were different between groups.

[0241] Table 1

[0242]

[0243] Abbreviations: HE-healthy elderly, bvFTD-behavioral variant frontotemporal dementia, AD-Alzheimer's disease, M-male, F-female, MMSE-mini-mental state examination, CT-cortical thickness, SA-surface area. Significant differences between groups: a-HE / bvFTD, b-HE / AD, c-bvFTD / AD (p<0.05).

[0244] The difference between HE and the two patient groups was in mean CT (p < 10 -4 ) and total SA (p<0.003 in both tests), but the bvFTD and AD groups did not differ from each other. The mean CT and total SA values ​​averaged by lobe are given in Table A.3 of Appendix A. Thus, although AD and bvFTD differ in the lobar distribution of pathology, age, and severity of cognitive impairment, neither the overall degree of cortical thinning nor changes in mean surface area provide a means of distinguishing the two disorders.

[0245] Lobe properties of structural correlation networks

[0246] Since the definition of correlation-based network organization depends on the choice of threshold, it is useful to ensure that the networks defined in this paper are non-random in their global topology by calculating a density / sparseness value (κ). If κ > 0.1, then the brain network is considered to display a non-random (small-world) topology, which is the case for all networks considered here. It is also useful to ensure that inverse correlations are not ignored after thresholding (see Fig.14 ). Therefore, all the positive and negative CT and SA correlation networks considered in this paper are non-random. See also Table A.3 for the total values ​​of κ for CT and SA in these three groups.

[0247] A study was conducted using the modularity index to determine whether the cortical lobes as conventionally defined corresponded to network modules in the CT network. It was found that only two homologue pairs in the CT network (posterior cingulate gyrus and precentral cortex) and two homologue pairs in the SA network (posterior cingulate cortex and paracentral gyrus and right posterior bank of the superior temporal sulcus) were misassigned in the modularity index algorithm. Table A.2 (in Appendix A) gives details of the algorithm input and output. It is recognized in practice that Q values ​​above 0.3 are good indicators of the presence of important modules in the network. In order to estimate the confidence interval of the Q value of the dataset, the calculation was repeated for 100 CT matrices generated on alternative datasets. Each of the 100 alternative CT and SA matrices was generated in the following way: 213 subjects were randomly selected from the three study cohorts, and the Q value was calculated on the correlation matrix obtained for CT and SA. The values ​​of Q are shown in Fig.15 , which is a graph of the distribution of the modularity index Q in regional CT networks generated on 100 alternative data sets. The central line indicates the mean, and the upper and lower lines indicate 1.5 standard deviations from the mean (Q = 0.36 ± 0.02), i.e. random, where the Q value is similar to the Q value of a random graph. For the study group, the following values ​​were obtained: Q HE =0.49, Q bvFTD =0.49 and Q AD = 0.45 (for the “positive” subnetwork) and Q HE =0.39, Q bvFTD =0.28 and Q AD =0.29 (for the “negative” subnetwork), indicating a non-random modular topological organization in the frontal, temporal, parietal, and occipital subregions of the CT / SA-related network.

[0248] Therefore, it can be concluded that the cortical lobes as conventionally described correspond to non-random modules in the CT network.

[0249] Average correlation strength of CT and SA networks

[0250] Figure 5The edge strength of each cortical thickness-related network, averaged across lobes and compared between the HE, bvFTD, and AD groups, is shown. Data are shown for positively correlated (upper panels) and inversely correlated (lower panels) networks. Asterisks indicate significant differences between the three groups (*p<0.05; **p<0.01). As in Figure 5 As can be observed in the literature, the mean correlation intensity of 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 strengths in bvFTD and AD subjects were higher than those in HE subjects in the frontal, temporal, parietal, and occipital lobes (p ≤ 0.003 for all pairwise comparisons). -4 The mean correlation strength was higher in bvFTD than in AD in the temporal lobe (p=0.005) and temporal lobe (p=0.005).

[0251] The average strength of the anticorrelated networks in the CT network also differs between the frontal and temporal lobes, see Figure 5 Likewise, in the frontal and temporal lobes, both the bvFTD and AD groups showed higher mean correlation strengths than the HE group (p≤0.003 in all tests), and in the frontal lobe, the mean inverse correlation strength of the bvFTD group was higher than that of the AD group (p=0.003).

[0252] Figure 6 The edge strength of each surface shown is a correlation network averaged over the frontal lobe of the brain and compared between the HE, bvFTD, and AD groups. Data are shown for networks of positive correlation (right panel) and inverse correlation (left panel). Asterisks indicate significant differences between the three groups (*p<0.05; **p<0.01). The figure shows significant differences in the average correlation strength between SA networks. The diagnostic groups differed only in the frontal lobe, with the average correlation strength in the AD group being lower than that in the HE group (p=0.03). Similarly, the inverse SA network correlations in the bvFTD and AD groups were significantly different in the frontal lobe when compared to the HE group, with lower average correlation strengths (p≤0.02 in both tests). This is due to the greater number of correlations found in the disease and the wider frequency distribution of the strength compared to the sparser networks with narrower frequency distributions in healthy elderly subjects (see Fig.14 ).

[0253] Node Metrics in CT Networks

[0254] Node Degree

[0255] The node degree quantifies the average number of significant positive correlations per node. For the CT network, the node degrees averaged over the frontal, temporal, parietal, and occipital lobes are shown in Figure 7Middle. Node degrees were compared between HE, bvFTD, and AD groups. Data for positively correlated (upper panel) and inversely correlated (lower panel) networks are shown. Asterisks indicate significant differences among the three groups (*p<0.05; **p<0.01).

[0256] There were significant differences between the groups in the frontal, temporal, parietal, and occipital lobes (p ≤ 10 -4 ). Both bvFTD and AD subjects had higher node degrees in the frontal and temporal lobes compared to HE subjects (p<0.006 for all tests). The bvFTD group had significantly higher node degrees than the AD group in the parietal and occipital lobes (p≤0.02 for all tests). Similar patterns were found for the number of inverse correlations in the CT network in the frontal, temporal, parietal, and occipital lobes (p≤0.02 in all tests). These differences were driven by the greater number of significant inverse correlations in the bvFTD and AD groups than in the HE group across all four lobes (p<0.01 in all tests). None of the differences between the bvFTD and AD groups were significant.

[0257] Nodal interlobar involvement index

[0258] Group differences were found in the nodal interlobar involvement index of CT. The index measures the extent of significant positive correlations with nodes in different lobes. This was significant for lobes located in the temporal, parietal, and occipital lobes (p≤0.003 for all tests). The differences were reflected in higher index values ​​relative to 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). This was shown in Figure 8 Figure 3, in which the nodal interlobar participation index of the cortical thickness correlation network averaged across lobes was compared between the HE, bvFTD, and AD groups. Only data for positive correlations are shown in the figure. Asterisks indicate significant differences between groups (*p<0.05; **p<0.01). For the inverse correlations in the CT network, the interlobar participation index comparisons were not significantly different in any lobe.

[0259] Node Metrics in SA Networks

[0260] Node Degree

[0261] For the frontal, temporal, parietal, and occipital lobes, the node degree values ​​in the SA network are shown in Fig. 9 In the figure, the node degrees of the surface area correlation network were averaged across lobes and compared between the HE, bvFTD, and AD groups. Data for positively correlated (upper panel) and inversely correlated (lower panel) networks are shown. Asterisks indicate significant differences between the three groups (*p<0.05; **p<0.01).

[0262] In the frontal, temporal, parietal, and occipital lobes, positive correlations differed between diagnostic groups (p ≤ 0.03). As in the CT network, in the frontal, temporal, and parietal lobes, both the bvFTD and AD groups had higher SA node degrees than the HE group (p < 10 -4 ). For the occipital lobe, the only significant difference was between the AD and HE groups (p=0.04). Compared with the CT network, the node degree in the parietal lobe was also significantly higher in AD than in bvFTD (p=0.004).

[0263] The anticorrelated SA network also showed significant group differences in the frontal, temporal, parietal, and occipital lobes (p≤0.001 in all tests). Similarly, in all four lobes, both the bvFTD and AD groups had higher node degrees than the HE group (p<0.001 for all tests). Compared with 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.

[0264] Nodal interlobar involvement index

[0265] Fig.10 Group differences in nodal interlobar participation index for SA network organization are shown. The figure shows nodal interlobar participation index averaged across lobes and compared between HE, bvFTD, and AD groups. Data for positively correlated (upper panel) and inversely correlated (lower panel) networks are shown. Asterisks indicate significant differences between the three groups (*p<0.05; **p<0.01).

[0266] In all four lobes, for the positive SA-related networks, the index values ​​of both the bvFTD and AD groups were higher than those of the HE group (p<10 -4 ). Compared with the CT-related network, the inverse SA-related network also showed significant differences in the frontal and parietal lobes (p≤0.04 for both patient groups) and in the temporal lobe (p≤0.001 for the AD group) relative to the HE group.

[0267] Hubs of structural correlation networks

[0268] CT Network Hub

[0269] There are four possible combinations of the mean values ​​of the inter-lobar involvement index (phigh / low) and the intra-lobar z-score (high / low). Here, only cases with high inter-lobar indices and high intra-lobar z-scores were considered to focus on nodes with high hub-like characteristics. Tables A.4 to A.6 (see Appendix A) provide data on global and local network hubs. The remaining two combinations were examined, but no information was provided. In the positive CT-correlation networks, the number and distribution of network hubs within high p-values ​​and high z-scores 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 proceeded differently in the two disease groups. This is shown in Fig.11 In the figure above, the figure is a visualization of the hubs of the cortical thickness network in brain space. In bvFTD, the number of hubs increased from 4 to 9 in the frontal lobe, decreased from 2 to 1 in the occipital lobe, and disappeared completely in the parietal and temporal lobes. In contrast, in AD, the hubs were almost equally distributed in all four lobes. The number of hubs decreased in the frontal lobe (2 vs. 4), while the number increased in the temporal and occipital lobes relative to the HE group (1 vs. 3 and 2 vs. 3, respectively). A complete list of nodes with hub-like characteristics in the CT network and the lobe locations is provided in Table A.4 (see Appendix A).

[0270] In all three groups, nodes with hub-like characteristics in the inverse correlation CT matrix were present only in the frontal and temporal lobes, and their topological distribution varied between the groups. Fig.11 See the figure below and Table A.4 in Appendix A.

[0271] SA network hub

[0272] The hubs in the positively correlated SA network are shown in Fig.12 . Table A.5 (see Appendix A) provides a list of nodes and lobe locations classified according to the inter-lobar involvement index and intra-lobar z-score. Visual comparison of the hub topology between groups showed that there were more nodes with hub-like properties in the left hemisphere 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). Surprisingly, 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 lobe hubs than temporal lobe hubs (6 vs. 4). AD subjects had 3 hubs in the parietal lobe compared to 1 hub in bvFTD subjects.

[0273] In all three groups, hubs in the anticorrelated SA network were found only in the frontal or temporal lobes. However, the HE group had one hub in the parietal lobe (precuneus) and bvFTD had two hubs (inferior parietal lobule and paracentral gyrus) (see Table A.5 in Appendix A). Interestingly, most anticorrelated SA hubs in AD were found in the frontal lobe.

[0274] Topology of cortical thickness-cortical surface area coupling

[0275] The coupling strength between CT and SA nodes is calculated by element-wise multiplication of the corresponding CT and SA correlation matrices. Fig.13 The CT / SA coupling strength visualized in brain space is shown. It can be observed that in HE subjects, inter-hemispheric homolog pairs showed coupled CT / SA correlations. In contrast, the CT / SA coupling 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 coupling between non-homologous nodes in the ipsilateral and contralateral hemispheres. The inter-lobar correlations were also significantly different between the bvFTD and AD groups. In the bvFTD group, most of the inter-lobar CT / SA correlations were due to frontotemporal interactions. In AD, most of the inter-lobar CT / SA couplings were due to fronto-parietal interactions. A list of the hub points of the CT / SA coupling topology is given in Table A.6 of Appendix A.

[0276] discuss

[0277] Baseline structural correlation networks of subjects clinically diagnosed with bvFTD or AD have been examined in three large global clinical trials and compared with healthy elderly subjects in a well-characterized birth cohort. For each group, a network was constructed from the partial correlations between 68x68 pairs of cortical surface areas (nodes) in terms of their thickness and surface area. The approach taken allows for a systematic analysis of both positive and inverse network correlations in three clinical settings. The methods and data discussed in this article represent the first systematic comparative analysis of cortical thickness and surface area in a large subject population. Due to the need for comparable numbers in the three groups, the overall study size was determined based on the number of available bvFTD subjects. Because this is a rare disease, the bvFTD component of the study must be global, with patients entering the study from 70 trial sites in 13 countries. 213 patients were included in the study, representing the largest MRI scan dataset in bvFTD subjects available to date. To match this, 213 patients were randomly selected from a much larger group of 1131 AD patients from 116 sites in 12 countries in the study TRx-237-005 and 128 sites in 16 countries in the study TRx-237-015 (e.g., accessible from the U.S. National Library of Medicine). The 202 normal elderly subjects were from a well-characterized birth cohort that has been studied longitudinally. Therefore, the reported findings are robust and can be considered representative of an international population that meets recognized diagnostic criteria.

[0278] Network modularity by lobe

[0279] It has been shown that the structural correlations in the frontal, temporal, parietal and occipital partitions of the cortical surface are inherently modular for both cortical thickness and surface area networks. In other words, the results confirm that the standard lobe partitions of the cortex share a common network modularity attribute, making them different from what would be expected in comparable random networks. The modules of highly clustered networks confer so-called "small world" network characteristics, and are considered to provide the best balance between local specialization and global integration. The results from healthy elderly subjects are comparable to previous work carried out in smaller and younger healthy groups, thereby revealing the potential module architecture in regional thickness-related networks. The results also show that the inherent modularity by lobe 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 central point-like organization of the network that changes in a disease-specific manner.

[0280] Similarities and differences between AD and bvFTD subjects relative to healthy elderly subjects

[0281] It was found that the morphological correlation networks of both patient groups (bvFTD and AD) were different from the corresponding networks of healthy elderly subjects in a highly significant manner. Compared with healthy elderly subjects, both groups showed a significant increase in the overall correlation strength of thickness and surface area networks. For both positive and reverse correlations, this effect was more significant in the cortical thickness network of all lobes. This is in contrast to the significantly lower correlation strength of the frontal lobe surface area in AD relative to normal and the directional similar differences in bvFTD. This may be due to the larger number of correlations in the disease and the wide frequency distribution compared to the sparser network with narrower frequency distribution in healthy elderly subjects. In addition to the increased overall correlation strength, the number of positive and reverse correlations within the lobe as measured by node degree in all lobes in both dementia groups was higher than that in healthy elderly controls. The number of positive correlations between lobes in thickness as measured by the interlobar participation index is also higher in all lobes. In the frontal, temporal and parietal lobes, the number of positive correlations in lobar and interlobar surface area is also greater in bvFTD and AD than in healthy elderly subjects. Both disease groups also differed from healthy elderly subjects in terms of correlations in the coupling between cortical thickness and surface area. Thus, both diseases are characterized by an overall increase in the strength and magnitude of structural correlations both locally within lobes and globally between lobes.

[0282] The similarities between the two conditions in terms of the overall strength and significant increase in the extent of structural correlations might seem to raise questions about the clinical distinction between bvFTD and AD, based on which subjects were categorized in the study. In fact, there were no differences between the two conditions in terms of overall cortical thickness and surface area. However, there were a number of important network differences between the two conditions. In the cortical thickness network, the overall strength of positive correlations was greater in bvFTD than in AD in both the frontal and temporal lobes, and in the frontal lobe, the strength of inverse correlations was also greater in bvFTD than in AD. In the parietal and occipital lobes, the number of significant intralobar positive correlations was greater in bvFTD than in AD. In contrast, in both the frontal and parietal lobes, the number of positive and inverse intralobar correlations was greater in AD than in bvFTD. Most of the inverse correlations in cortical thickness and surface area were associated with interhemispheric non-homologous frontotemporal lobes in bvFTD and frontoparietal lobes in AD.

[0283] The hub-like organization of the associated network is also significantly different in these two diseases. Although network connection hubs are considered to provide network integration, local hubs provide network differentiation at the same time. It has been proposed that hubs provide resilience to damage in neurodegenerative disorders. Alternatively, it has been proposed that hubs represent sites with specific vulnerabilities. Therefore, people are interested in studying how hubs change in the context of neurodegenerative diseases. bvFTD is characterized by an increase in the number of hubs in cortical thickness in the frontal lobe and a reduction or elimination of hubs in the temporal, parietal and occipital lobes. In contrast, AD is characterized by the distribution of hubs in all lobes, a decrease in the number of hubs in the frontal cortex, and an increase in hubs in the temporal and occipital lobes compared to bvFTD. In the positive correlation network of surface area, the overall number of hubs in AD subjects is twice that of bvFTD, and the topology of these hubs is different. Therefore, overall, AD is characterized by a much more extensive distribution pattern of hubs in both thickness and surface area degeneration networks compared to bvFTD. In contrast, in bvFTD, the hub-like organization is much more localized. It has been argued that bvFTD is a clinical syndrome with focal but heterogeneous atrophy centered on hubs. The identification of the insula as one of the reverse network hubs (for the CT network, in both the bvFTD and AD groups) is consistent with recent unexpected findings from diffusion MRI, namely that the hub-like fiber connectivity of the insula in bvFTD is increased. On the other hand, the hubs in the healthy elderly group are highly connected in a homologous manner within and between lobes, and are not otherwise interconnected. The difference in hub-like organization between AD and bvFTD indicates differences in the hierarchy of node vulnerability and differences in the organization of network adaptations for different compensations in these two diseases. Therefore, unlike the lobar modularity preserved in neurodegenerative diseases, there is no preserved unchanged hub-like organization, meaning that pre-existing hubs are not an inherent structural property of cortical network organization.

[0284] Although AD is also characterized by changes in cortical thickness, these changes are not as significant as in bvFTD overall, while changes in surface area are more significant in AD, indicating coordinated changes in the number of adjacent affected columns. These differences are consistent with the pathology of interneurons and astrocytes that affect more localized linkages in bvFTD. The surface area-related advantage in AD is consistent with the pathology of the long tract cortico-cortical projection system that mainly affects principal cells. bvFTD is different from AD in many important aspects: there is no cholinergic deficiency in bvFTD, there is no therapeutic benefit from the treatment of acetylcholinesterase inhibitors or memantine, bvFTD is characterized by significant astrocytic pathology, the neurons affected in the neocortex are mainly the multi-spiny interneurons in layers II and VI (in AD, the pyramidal cells in layers III and V are mainly affected) and the dentate gyrus of the hippocampus (the neurons affected in AD are in CA1-4 rather than the dentate gyrus), and bvFTD is characterized by increased glutamate levels in the neocortex, but AD is not. However, none of these conditions provide a simple explanation for the different distribution patterns of the related structural changes described herein.

[0285] Global characteristics and significance of network changes in dementia

[0286] The overall situation that occurs in the two disease groups studied is that the network architecture changes in a coordinated manner in the whole brain with respect to both positive and negative correlations. This is surprising because it is generally believed that the neurodegenerative process in these two diseases is anatomically limited to the frontal lobe and temporal lobe (in the case of bvFTD) and the temporal lobe and parietal lobe (in AD). On the contrary, network analysis shows that in both diseases, the cortical thickness and surface area network exist to affect the changes of all lobes in a global manner, but there are differences in the anatomical topology of the changes. Known tau protein and TDP-43 both aggregate pathology in a prion-like manner, whereby the pathology in the affected neuronal population can start pathology in a connected but previously unaffected neuronal population. Therefore, positive correlation can partially reflect the propagation of pathology in the existing normal network, whereby the existing functional network is affected or spared together. Alternatively, such correlation may represent functional dependence, so that the functional loss of one member in the partner relationship leads to a parallel loss of function in the partner, usually functionally synchronized with the affected node. This interpretation is consistent with previous work conducted on cortical thickness correlations in healthy adults, where positive correlations were found to be consistent with diffusion-based axonal connectivity.

[0287] The work discussed here highlights for the first time the importance of inverse correlation networks. It should be noted that because the inverse correlations observed in both neurodegenerative disorders primarily reflect interlobar nonhomologous associations, they cannot be detected using only lobar-based analysis methods. In particular, the emergence of these nonhomologous interlobar inverse correlations and their increased strength represent the clearest overall difference between neurodegenerative diseases and normal aging. In contrast, the normally aged brain is characterized by significantly weaker homologous positive correlations. An attractive hypothesis is that when certain nodes are functionally impaired, other nodes that remain unaffected compensate, thereby emphasizing nonhomologous associations in the disease. This would mean that the major reorganization observed in the structural network may be partially adaptive. Structural plasticity has been demonstrated in other contexts, and functional compensation is known to occur in focal diseases.

[0288] The work discussed in this paper represents the first comparative study of abnormalities in related structural networks in bvFTD and AD relative to healthy aging. These correlations arise from both positive and inverse linkage changes in cortical thickness and surface area in these two pathologies, which are quite different from those observed in normal elderly subjects. The changes observed in the disease are global and are not limited to the frontotemporal and temporoparietal lobes in bvFTD and AD, respectively. Instead, they appear to represent structural adaptations to different neurological degenerations in these two conditions. In addition, all related networks showed a rather unique hub-point-like organization that differed from normal conditions and between these two forms of dementia. Unlike the lobar organization of networks that remain unchanged in disease, the hub-point-like organization varies with the underlying pathology. This means that the hub-point-like organization is not a fixed feature of the brain, and attempts to explain the disease in terms of hubs may be insufficient. The documented differences between AD and bvFTD confirm that the clinical differences between these two dementia groups correspond to systematic differences in the underlying network structure of the cortex. The topological differences in thickness and surface area hub-like organization and potential positive and inverse correlation networks may provide a basis for the development of analytical tools to aid in differential diagnosis in these two conditions, which may be difficult to distinguish by clinical criteria alone.

[0289] Use of correlation matrices in determining responses of patient groups to neuropharmacological interventions

[0290] The methods discussed above have been used to determine the responses of patient groups to neuropharmacological interventions.

[0291] FIG. 17A to FIG. 17D Depicted is a correlation matrix for two patient groups: those being treated with symptomatic AD medication (cholinesterase inhibitors and / or memantine, ach1 in the figure title) and those not on such treatment (ach0 in the figure title). Subjects had a Clinical Dementia Rating (CDR) score range of 0.5, 1, or 2. Fig.17Ais the correlation matrix of cortical thickness at baseline (i.e., week 0) for 96 subjects diagnosed with AD who were not taking one or more symptomatic treatments. Fig. 17B Correlation matrix of cortical thickness at baseline for 445 subjects diagnosed with AD and taking one or more symptomatic treatments. Fig. 17C is the surface area correlation matrix at baseline for 96 subjects diagnosed with AD and not taking one or more symptomatic treatments, and Fig.17D is the surface area correlation matrix at baseline for 445 subjects diagnosed with AD and taking one or more symptomatic treatments.

[0292] As from FIG. 17A to FIG. 17D It can be observed that compared with untreated patients ( Fig.17A and Fig. 17C ), one or more symptomatic treatments for AD induced nonhomologous anticorrelated networks between lobes ( Fig. 17B and Fig.17D This is particularly noticeable for the surface area network.

[0293] These connections represent inverse correlations whereby a decrease in volume or surface area of ​​an affected region in a specific node (usually located in the posterior part of the brain) is associated in a statistically significant manner with a linked node in which there is a corresponding increase in volume or surface area. As discussed above, the presence of these nonhomologous inverse correlations is indicative of neurodegenerative disease and most likely represents frontal compensation for posterior dysfunction resulting from pathology. Symptomatic AD treatment induces an increase in these nonhomologous compensatory linkages.

[0294] 18A to 18D are plots of non-homologous inter-lobar node degrees (as discussed above) for cortical thickness-positive correlation, cortical thickness-inverse correlation, surface area-positive correlation, and surface area-inverse correlation, respectively. As can be observed from these plots, the number of significant non-homologous inter-lobar compensatory inverse correlations was significantly increased by symptomatic AD treatment.

[0295] Fig.19A and Fig.19B Cortical thickness correlation matrices based on temporally separated structural neurological data are shown. Fig.19A is a correlation matrix of cortical thickness at week 0 (i.e. at baseline) in a group of 445 AD-diagnosed patients being treated with symptomatic AD therapy. Fig.19Bis the cortical thickness correlation matrix of the same group of 445 AD-diagnosed patients at week 65. During the intervention period, the group was also treated with the tau protein aggregation inhibitor colorless methylene blue methanesulfonate (LMTM; USAN name: methylthionine hydrogen methanesulfonate) at a dose of 8 mg / day (4 mg given twice a day at this time and afterwards). As can be observed, in patients receiving symptomatic treatment for AD, LMTM has minimal effect on the structural correlation network.

[0296] FIG. 20A to FIG. 20D It is a graph of non-homologous interlobar node degrees compared between week 0 and week 65 in the ach1 group (simultaneous symptomatic AD treatment), which is respectively for cortical thickness-positive correlation, cortical thickness-inverse correlation, surface area-positive correlation and surface area-inverse correlation. As can be observed, in 65 weeks, LMTM as an add-on has minimal overall effect on brain network related structures. It is important to note that this is an intra-cohort analysis, whereby patients at baseline are used as their own controls for changes that occur after 65 weeks of treatment with LMTM.

[0297] Fig.21A and Fig. 21B Cortical thickness correlation matrices based on temporally separated structural neurological data are shown. Fig.21A is a cortical thickness correlation matrix at week 0 (ie, at baseline) for a group of 96 AD-diagnosed patients taking LMTM at a dose of 8 mg / day as monotherapy. Fig. 21B is the cortical thickness correlation matrix for the same group of 96 AD diagnosed patients at week 65. The 96 patients in this cohort did not receive one or more symptomatic AD treatments in combination with LMTM. As can be observed, LMTM as a monotherapy significantly reduced thickness correlations, both within-lobar (positive) correlations and between-lobar compensatory (inverse) correlations. This is a within-cohort analysis whereby the patients at baseline served as their own controls for changes occurring after 65 weeks of treatment with LMTM.

[0298] Fig.22A and Fig. 22B It is a graph of interlobar nodal degrees compared between week 0 and week 65 in the ach0 group, for cortical thickness-positive correlation and cortical thickness-inverse correlation. The graph indicates a high significant effect of 8 mg / day of LMTM as a monotherapy on the number of interlobar correlations in the AD group. A significant decrease in the number of positive and inverse non-homologous cortical thickness correlations was observed after 65 weeks. This may be due to the normalization of neuronal function in the posterior brain, whereby LMTM reduces pathology and alleviates neuronal dysfunction derived from pathology, thereby reducing the need for compensatory input from unaffected or less affected frontal lobe areas in the brain.

[0299] Fig.23A and Fig. 23B The surface area-thickness correlation matrix based on temporally separated structural neurological data is shown. Fig.23A is the surface area correlation matrix at week 0 (ie, baseline) for a group of 96 AD-diagnosed patients who continued to take LMTM as monotherapy at a dose of 8 mg / day. Fig. 23B is the surface area correlation matrix for the same cohort of 96 AD diagnosed patients at week 65. The 96 patients in this cohort were not on one or more concomitant symptomatic AD treatments. As can be observed, LMTM as monotherapy significantly reduced surface area correlations, both within-lobar (positive) correlations and between-lobar compensatory (inverse) correlations. This was a within-cohort analysis whereby the patients at baseline served as their own controls for changes occurring after 65 weeks of treatment with LMTM.

[0300] Fig.24A and Fig. 24B ] is a graph of non-homologous interlobar nodal degrees compared between week 0 and week 65 in the ach0 group for surface area-positive and surface area-inverse correlations. The graph indicates a significant effect of 8 mg / day of LMTM as a monotherapy on the number of interlobar correlations in the AD group. Notably, after 65 weeks, there was a significant reduction in the number of positive and inverse / compensatory surface area correlations.

[0301] FIG. 25A to FIG. 25D Shown is a cortical thickness correlation matrix comparing the AD group of 96 patients (CDR 0.5, 1, or 2) to a healthy elderly control group of 202 subjects at baseline and week 65. As can be observed, 8 mg / day of LMTM as monotherapy brought the cortical thickness network closer to normal.

[0302] FIG. 26A to FIG. 26D Shown is the surface area correlation matrix comparing the AD group of 96 patients (CDR 0.5, 1 or 2) versus a healthy elderly control group of 202 subjects at baseline and week 65. As can be observed, 8 mg / day of LMTM as monotherapy normalized the surface area network.

[0303] FIG. 27A to FIG. 27D Shown is a cortical thickness correlation matrix comparing the AD group of 54 patients (CDR only 0.5) to a healthy elderly control group of 202 subjects at baseline and week 65. As can be observed, 8 mg / day of LMTM as monotherapy reduced the number of inverse / compensatory non-homologous correlations to become equivalent to normal elderly controls.

[0304] FIG. 28A to FIG. 28DShown is a surface area correlation matrix comparing the AD group of 54 patients (CDR only 0.5) to a healthy elderly control group of 202 subjects at baseline and week 65. As can be observed, 8 mg / day of LMTM as monotherapy reduced the number of inverse / compensatory non-homologous correlations to be equal to or below that of normal elderly controls.

[0305] In summary, the structural correlation network analysis discussed above revealed the emergence of highly abnormal non-homologous inter-lobar anticorrelations in AD and bvFTD. It is assumed that these represent compensatory inputs from frontal brain regions that are not affected by the disease or are less affected. Symptomatic treatment and LMTM play a role in structural correlation networks in AD in fundamentally different ways. Symptomatic treatment induces a significant increase in compensatory networks. LMTM as a monotherapy reduces the need for these compensatory networks by reducing primary pathology, thereby allowing affected neurons to function more normally. These results confirm that the abnormal non-homologous anticorrelations observed in neurodegenerative diseases such as AD are adaptive because they can be reversed or weakened by disease-modifying treatments rather than symptomatic AD treatments. The effects were observed within the cohort before / after the analysis, in which subjects at baseline were used as their own controls for changes that occurred after receiving LMTM treatment as a monotherapy at 8 mg / day for 65 weeks. These analyses are far more sensitive to treatment effects than coarse analyses of whole-brain or lobar volumes. Furthermore, as will be discussed below, the results observed with respect to the structurally related networks are consistent with the functional effects observed using the renormalized partial directional coherence electroencephalography analysis technique.

[0306] Structural / functional correlation using electroencephalography (EEG)

[0307] The renormalized partial directional coherence (rPDC) network method for EEG data allows the direction and strength of the electrical activity in the brain to be studied using the network method to be indicated. This is discussed in, for example, WO 2017 / 118733 (the entire contents of which are incorporated herein by reference). Fig.29 shows examples of raw EEG data, and Fig.30 An exemplary rPDC network derived from the collected EEG data is shown.

[0308] like Fig.30 The resulting network shown in contains a number of nodes indicating approximate locations within the brain (the figure is drawn in a schematic manner looking down on the head, with the triangle at the top indicating the nose). The locations of the nodes are determined by placing electrodes on the surface of the scalp, which are used to obtain EEG data, such as Fig.29 EEG data shown in . The directed connections between nodes indicate the flow of electrical activity from one node to another in the brain.

[0309] By counting the number of directed connections entering and leaving a given node and / or measuring their relative strength, it is possible to define whether a node is a sink (and the incoming connections are more and / or stronger than the outgoing connections) or a source (and the outgoing connections are more and / or stronger than the incoming connections). This is schematically shown in Fig.31 , where the number / strength of incoming directed connections is subtracted from the number / strength of outgoing directed connections. Thus, in the extreme case, if the difference is negative, then the node acts as a net source, and if the difference is positive, then the node acts as a net sink. More generally, as can be observed in the figure, as in Fig.40 As shown in , lower values ​​indicate more / stronger outgoing connections, and higher values ​​indicate more / stronger incoming connections.

[0310] After deriving the difference between the incoming and outgoing connections for all nodes, a heat map can then be provided to indicate the location and strength of sinks and sources within the patient's brain. This can include the step of defining each node as either a sink or a source. An example of such a heat map is shown in Fig.32 In this example, the blue areas (arrow A) indicate more outgoing connections and therefore contain more source nodes, while the red / yellow areas (arrow B) indicate more incoming connections and therefore contain more sink nodes. This type of heat map can be called a "brainprint".

[0311] Fig.33 The diagram illustrates Fig.32 Asymmetric visualization of a heatmap comparing the number of sources and sinks on either side. A higher difference in sources and sinks between the left and right sides of the heatmap is shown in yellow (arrow A), while a lower difference is shown in black (arrow B).

[0312] The methods discussed above were used to analyze data provided by 329 subjects who were divided into 167 diagnosed subjects (DS) and 162 matched volunteers (PV) at their initial evaluation (Visit 1):

[0313]

[0314] MMSE-Mini Mental State Examination; ADAS-Cog-Alzheimer's Disease Assessment Scale-Cognitive Subscale

[0315] As can be observed, diagnosed subjects were significantly more cognitively impaired on the MMSE and ADAS-Cog psychometric scales, and also scored higher on the overall Clinical Dementia Rating (CDR) scale. Otherwise, there were no differences in age or gender distribution.

[0316] Fig.34A heat map visualizing the location of sinks and sources within the brain of the group of diagnosed subjects at baseline is shown. Arrows A indicate blue areas containing more / stronger sources, and arrows B indicate red areas containing more / stronger sinks. Fig.35 Shown is a heat map visualizing the location of sinks and sources within the brain of a paired volunteer group. When comparing the two images, it becomes clear that AD patients have significantly stronger sources (i.e., more / stronger outgoing connections, shown in blue) in their frontal lobes, and significantly stronger sinks (i.e., more / stronger incoming connections, shown in red / orange) in their posterior parietal, temporal, and occipital lobes compared to paired volunteers.

[0317] A machine learning classifier was trained based on the dataset provided by the 329 subjects discussed above. The rPDC network was prepared using beta band EEG data from 100 seconds of brain activity during the eyes closed resting state in each case. The machine learning classifier was then used to classify all 329 subjects as AD or paired volunteers (PV) with 95% accuracy. In addition, the machine learning classifier can be used to estimate the probability that a subject has AD, thereby allowing more than just a binary decision. For example, the heat map is shown in Fig.36 The subject in has AD. The patient is known to have AD by clinical diagnosis. The machine learning classifier estimates with 99% probability that the patient has AD, and therefore correctly classifies the subject. Fig.37 is another example of a heat map from a subject known to have AD via a clinical diagnosis. In this case, the machine learning classifier estimated that there is a 63% probability that the patient has AD and a 37% probability that the patient (therefore) does not have AD. This information can be used to determine a patient's susceptibility to AD where no clinical diagnosis has yet been made. Furthermore, specific distribution patterns of abnormal sink regions indicative of potential dysfunction can be correlated with specific patterns on clinical testing for further more detailed neuropsychological testing and clinical evaluation in the future. For example, Fig.36 The case illustrated in may have a form of dementia other than AD but is classified as having AD in this article.

[0318] Psychometric testing of an apparently healthy cohort showed a downward cognitive trajectory on the Hopkins Verbal Learning Test at 18 months in a subset of subjects. The characteristics of the cohort are as follows. As can be observed, there was no difference in cognitive scores at baseline on the MMSE scale between those subjects found to be at risk of decline and those found not to be at risk of decline.

[0319]

[0320] The heat map of the group of subjects at risk is shown in Fig.38, while the heat map of the risk-free subject group is shown in Fig.39 Because both groups were drawn from apparently healthy cohorts, the differences here are not as clear as those between the AD and PV groups above. Fig.38 It shows that there are more / stronger sinks in the posterior brain regions, indicated by stronger red / orange colors in the heatmap. Fig.40 is a box plot comparing sources and sinks in the EEG network from frontal and posterior brain regions at the group level. As can be observed from this figure, the at-risk group is characterized by increased activity out of the frontal cortex and increased activity into the posterior brain regions. At baseline, EEG recordings were performed before any measurable decline based on the Hopkins word learning task. Therefore, apparently normal subjects who were at risk of decline in the next 18 months may have been identified at baseline based on heat maps of their brain activity obtained non-invasively by EEG analysis.

[0321] As shown above, there are clear differences in the networks between the diagnosed subjects and the matched volunteers. These differences are highly significant at the group level. As will be appreciated, the accuracy level of the first version of the machine learning classifier is higher than conventional superficial clinical assessments and gives the probability of having AD at the individual subject level, which can be used to make decisions in further clinical management.

[0322] Fig.41 A comparison between the cortical thickness correlation matrix (discussed above) of the ach0 AD group at week 0 is shown, compared to the heatmaps of the group-level diagnosed subjects. As can be observed, there are a large number of non-homologous inverse correlations between the frontal lobe and the posterior parietal and occipital brain regions. The heatmaps of the diagnosed subject group show the same phenomenon in brain connectivity as measured by EEG. Both the structural and EEG methods show the same pattern of increased frontal to posterior activity. Fig.42 Comparison between the cortical thickness correlation matrix (as discussed above) of the healthy elderly group at week 0 is shown, compared to the heatmaps of the paired volunteers at the group level. As can be observed, the absence of any non-homologous inverse correlations between the frontal and posterior parietal and occipital brain regions matches the absence of increased anterior to posterior electrical activity on the EEG.

[0323] Fig.41 The increase in nonhomologous interlobular compensatory anticorrelation networks observed in the cerebral cortex provides a structural basis for the characteristic heat map changes observed with functional EEG changes.

[0324] Fig.43 Box plots showing the quantitative distinction of mild AD from elderly controls are shown. As can be observed, in the beta band, AD subjects have more activity outgoing from the frontal cortex and more activity entering the posterior cortex.

[0325] Fig.44Three heat maps are shown, from left to right they are: a group of diagnosed AD subjects who have been treated with symptomatic medication (med), a group of diagnosed AD subjects who have not been treated with symptomatic medication (nonMed), and a group of matched volunteers. Fig.45 It is a box plot of the group level network comparing drug treatment, no drug treatment and paired volunteers. The data are from a preliminary study, which includes 53 diagnosed subjects (DS), 15 of whom are treated with standard medications, and 38 are not treated with standard medications. The characteristics of these two groups are shown in the table below. Although the untreated group is significantly younger, there is no difference between the two groups in terms of cognitive scores or gender distribution as measured by MMSE.

[0326] Medication No medication age 75.2(7.6) 67.5(9.5)*** gender 8F:7M 23F:15M MMSE 23.84(1.95)(N＝13) 23.37(2.41)

[0327] ***p<0.005

[0328] There were no statistically significant differences in the ADAS-Cog or CDR scales.

[0329] As in Fig.44 and Fig.45 As can be observed in the β band, both groups of AD subjects had more outgoing activity from the frontal cortex than the matched volunteers. In addition, it can be observed that one or more symptomatic treatments increased outgoing activity from the frontal cortex compared to the untreated group. This is shown in Fig.45 The medicated group had significantly more outgoing electrical activity from the frontal cortex. In the posterior brain regions, one or more symptomatic treatments reduced the need for supportive incoming electrical activity.

[0330] The frontal lobe is shown to be related to FIG. 17A to FIG. 17D and 18A to 18D The same phenomenon is shown by structural analysis of the correlation network. Fig.44 showed that differences detectable by structural analysis of MRI at the group level were also detectable by EEG. It should be noted that while structural analysis of network differences between patients receiving and not receiving one or more symptomatic treatments showed increased non-homologous interlobar connectivity directed to posterior brain regions, EEG analysis showed less incoming activity directed to posterior regions. It is currently hypothesized that, in other frequency bands, one or more symptomatic treatments increase activity into posterior brain regions.

[0331] In addition to the structural components and user interactions, the systems and methods of the above embodiments may be implemented in a computer system (specifically in computer hardware or in computer software).

[0332] The term "computer system" includes hardware, software and data storage devices for implementing the system or executing the method according to the above-mentioned embodiments. For example, the computer system may include a central processing unit (CPU), an input member, an output member and a data storage device. Preferably, the computer system has a monitor to provide a visual output display. The data storage device may include a RAM, a disk drive or other computer-readable media. The computer system may include multiple computing devices, which are connected through a network and can communicate with each other via the network.

[0333] 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, wherein the computer program is configured to perform one or more of the above methods when executed on a computer.

[0334] The term "computer-readable medium" includes, but is not limited to, any one or more non-transitory media that can be directly read and accessed by a computer or computer system. The media may include, but are not limited to, magnetic storage media, such as floppy disks, hard disk storage media, and magnetic tapes; optical storage media, such as compact disks or CD-OMs; electrical storage media, such as memories, including RAM, ROM, and flash memories; and mixtures and combinations of the above storage media, such as magnetic / optical storage media.

[0335] Although the present invention has been described in conjunction with the above exemplary embodiments, it will be clear to those skilled in the art that many equivalent modifications and changes will be made based on this disclosure. Therefore, it is considered that the above exemplary embodiments of the present invention are illustrative rather than restrictive. Various changes can be made to the embodiments without departing from the spirit and scope of the present invention.

[0336] In particular, although the methods of the above embodiments have been described as being implemented on the systems of the embodiments, the methods and systems of the present invention need not be implemented in conjunction with each other, but may be implemented separately on alternative systems or using alternative methods.

[0337] Annex A

[0338] Table A.1 - Cortical surfaces of frontal, temporal, parietal or occipital areas according to the Desikan-Killiany atlas (DKA). The cortical areas (nodes) of each structural correlation matrix are sorted throughout the document according to the following list:

[0339]

[0340] Table A.2 - Assignment of nodes to frontal, temporal, parietal, or occipital lobes by the algorithm and by DKA cortical parcellation. * For the cortical thickness network, nodes were incorrectly assigned to lobes; and + For surface area networks, nodes are those that are misassigned

[0341]

[0342]

[0343] Abbreviations: F-frontal lobe, T-temporal lobe, P-parietal lobe, O-occipital lobe, L-left, R-right.

[0344] Table A.3 - Mean cortical thickness (CT) and total surface area (SA) averaged across the four lobes in each study group

[0345]

[0346] Abbreviations: HE - healthy old age, bvFTD - behavioral variant frontotemporal dementia, AD - Alzheimer's disease

[0347] Table A.4 Hubs of frontal, temporal, parietal and occipital module organization of CT networks in HE, bvFTD and AD. Hubs are ranked according to their inter-lobar participation index (p) and intra-lobar z-score (z). High p / high z-scores indicate so-called integrative regions (i.e., nodes that interact between all lobes), and regions with low p / high z are so-called local hubs (i.e., nodes that interact within their own modules / lobes).

[0348] a) The central point of the positron network

[0349] High-p / High-z Node List

[0350]

[0351] Low p / high z node list

[0352]

[0353] b) The central point of the negative subnetwork

[0354] High-p / High-z Node List

[0355]

[0356] Abbreviations: HE - healthy old age, bvFTD - behavioral variant frontotemporal dementia, AD - Alzheimer's disease, F - frontal lobe, T - temporal lobe, P - parietal lobe, O - occipital lobe

[0357] Table A.5 - Hubs of frontal, temporal, parietal and occipital modular organization of SA networks in HE, bvFTD and AD. Hubs are ranked according to their inter-lobar participation index (p) and intra-lobar z-score (z). High p / high z-scores indicate so-called integrative nodes (i.e., nodes that interact between all lobes), and areas with low p / high z are so-called local hubs (i.e., nodes that interact within their own modules / lobes).

[0358] a) The central point of the positron network

[0359] High-p / High-z Node List

[0360]

[0361] Low p / high z node list

[0362]

[0363]

[0364] b) The central point of the negative subnetwork

[0365] High-p / High-z Node List

[0366]

[0367] Abbreviations: HE-healthy old age, bvFTD-behavioral variant frontotemporal dementia, AD-Alzheimer's disease, F-frontal lobe, T-temporal lobe, P-parietal lobe, O-occipital lobe.

[0368] Table A.6 Hubs of the frontal, temporal, parietal, and occipital organization of the CT-SA coupling network in HE, bvFTD, and AD. Regions are ranked according to their inter-lobar involvement index (p) and intra-lobar z-score (z). High p / z scores indicate so-called integrative regions (which interact between all lobes)

[0369] a) The central point of the positron network

[0370] High-p / High-z Node List

[0371]

[0372]

[0373] Abbreviations: HE-healthy old age, bvFTD-behavioral variant frontotemporal dementia, AD-Alzheimer's disease, F-frontal lobe, T-temporal lobe, P-parietal lobe, O-occipital lobe.

[0374] References

[0375] Gauthier,S.et al.“Efficacy and safety of tau-aggregation inhibitortherapy in patients with mild or moderate Alzheimer’s disease:a randomised,controlled,double-blind,parallel-arm,phase 3trial”,The Lancet 388,2873-2884(2016)

[0376] Wilcock,G.K.et al“Potential of low dose leuco-methylthioninium bis(hydromethanesulphonate)(lmtm)monotherapy for treatment of mild Alzheimer’sdisease:Cohort analysis as modified primary outcome in a phase iii clinicaltrial.Journal of Alzheimer’s disease 61,635-657(2018)

[0377] Feldman,H.et al“Aphase 3trial of the tau and tdp-43aggregationinhibitor,leuco-methylthioninium bis(hydromethanesulfonate)(lmtm),forbehavioural variant frontotemporal dementia(bvFTD)”Journal of Neurochemistry138,255(2016)

[0378] Murray,A.D.et al“The balance between cognitive reserve and brainimaging biomarkers of cerebrovascular and Alzheimer’s diseases”Brain 134,3687-3696(2011)

[0379] Storey, JD "A direct approach to false discovery rates" Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64, 479-498 (2002)

[0380] Van Wijk, BC, Stam, CJ & Daffertshofer, A. “Comparing brain networks of different size and connectivity density using graph theory.” PLoS One 5, e13701 (2010)

[0381] Rubinov M, Sporns O. "Weight-conserving characterization of complexfunctional brain networks", Neuroimage, 56(4):2068-79(2011)

[0382] All references mentioned above are hereby incorporated by reference.

Claims

1. A system for determining a patient's response to a neuropharmacological intervention, the system comprising: a data acquisition component configured to obtain structural neurological data from a plurality of patients prior to a neuropharmacological intervention, the structural neurological data indicating a physical structure of a plurality of cortical regions, wherein the physical structure is cortical thickness and / or surface area; A correlation matrix generating component is configured to generate a first correlation matrix from the structural neurological data by: assigning a plurality of structural nodes corresponding to cerebral cortical regions; and determining pairwise correlations between pairs of structural nodes based at least in part on corresponding data of the structural neurological data; wherein the data acquisition component is further configured to obtain further structural neurological data from the plurality of patients after the neuropharmacological intervention, the further structural neurological data being indicative of the physical structure of the plurality of cortical regions; and The correlation matrix generating means is further configured to generate a second correlation matrix from the further structural neurological data by: determining pairwise correlations between pairs of structural nodes based at least in part on corresponding data of the further structural neurological data; The system further comprises: a display component for presenting the first correlation matrix and the second correlation matrix; or a comparing component for comparing the first correlation matrix with the second correlation matrix to determine a patient's response to the neuropharmacological intervention; and wherein the comparison means is configured to compare the number and / or density of inverse correlations in the first correlation matrix with the number and / or density of inverse correlations in the second correlation matrix, wherein the neuropharmacological intervention is a disease modifying drug and efficacy is established by a decrease in the number and / or density of correlations between anterior and posterior brain regions of the first correlation matrix and the second correlation matrix.

2. A system according to claim 1, wherein the system further comprises a validation component configured to determine a p-value for each pairwise correlation and compare the p-value to a significance level, wherein the correlation matrix generation component is configured to only use p-values ​​less than the significance level when generating the correlation matrix.

3. The system of claim 1, wherein assigning the plurality of structural nodes corresponding to cerebral cortical regions further comprises defining a group containing structural nodes corresponding to homologous or non-homologous cerebral lobes. 4 . The system of claim 3 , wherein the comparison component is configured to compare the first correlation matrix with the second correlation matrix by comparing the number and density of correlations between different structural node groups.

5. The system of claim 4, wherein the comparison component is configured to compare the first correlation matrix with the second correlation matrix by comparing the number and / or density of correlations between groups of structural nodes located in the frontal lobe and the parietal and occipital lobes, respectively.

6. The system of claim 1, wherein the patient has been diagnosed with a neurocognitive disease.

7. The system of claim 1, wherein the neuropharmacological intervention is a disease-modifying drug.

8. The system of claim 7, wherein the disease-modifying drug is a tau protein aggregation inhibitor.

9. The system of claim 1, wherein the neuropharmacological intervention is symptomatic treatment.

10. The system of claim 1, wherein the structural neurological data is obtained by magnetic resonance imaging.

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