Local shape volume alterations in subcortical structures of suicide attempters with major depressive disorder.

Suicide is among the most important global health concerns; accordingly, an increasing number of studies have shown the risks for suicide attempt(s) in terms of brain morphometric features and their clinical correlates. However, brain studies addressing suicidal vulnerability have been more focused on demonstrating impairments in cortical structures than in the subcortical structures. Using local shape volumes (LSV) analysis, we investigated subcortical structures with their clinical correlates in depressed patients who attempted suicide. Then we compared them with depressed patients without a suicidal history and age- and sex-matched healthy controls (HCs; i.e., 47 suicide attempters with depression, 47 non-suicide attempters with depression, and 109 HCs). Significant volumetric differences were found between suicidal and nonsuicidal depressed patients in several vertices: 16 in the left amygdala; 201 in the left hippocampus; 1,057 in the left putamen; and 140 in the left pallidum; 1 in the right pallidum; and 6 in the bilateral thalamus. These findings indicated subcortical alterations in LSV in components of the limbic-cortical-striatal-pallidal-thalamic circuits. Moreover, our results demonstrated that the basal ganglia was correlated with perceived stress levels, and the thalamus was correlated with suicidal ideation. We suggest that suicidality in major depressive disorder may involve subcortical volume alterations.

Amyloid involvement in subcortical regions predicts cognitive decline.

PURPOSE: We estimated whether amyloid involvement in subcortical regions may predict cognitive impairment, and established an amyloid staging scheme based on degree of subcortical amyloid involvement. METHODS: Data from 240 cognitively normal older individuals, 393 participants with mild cognitive impairment, and 126 participants with Alzheimer disease were acquired at Alzheimer's Disease Neuroimaging Initiative sites. To assess subcortical involvement, we analyzed amyloid deposition in amygdala, putamen, and caudate nucleus. We staged participants into a 3-stage model based on cortical and subcortical amyloid involvement: 382 with no cortical or subcortical involvement as stage 0, 165 with cortical but no subcortical involvement as stage 1, and 203 with both cortical and subcortical involvement as stage 2. RESULTS: Amyloid accumulation was first observed in cortical regions and spread down to the putamen, caudate nucleus, and amygdala. In longitudinal analysis, changes in MMSE, ADAS-cog 13, FDG PET SUVR, and hippocampal volumes were steepest in stage 2 followed by stage 1 then stage 0 (p value <0.001). Stage 2 showed steeper changes in MMSE score (ß [SE] = -0.02 [0.004], p < 0.001), ADAS-cog 13 (0.05 [0.01], p < 0.001), FDG PET SUVR (-0.0008 [0.0003], p = 0.004), and hippocampal volumes (-4.46 [0.65], p < 0.001) compared to stage 1. CONCLUSIONS: We demonstrated a downward spreading pattern of amyloid, suggesting that amyloid accumulates first in neocortex followed by subcortical structures. Furthermore, our new finding suggested that an amyloid staging scheme based on subcortical involvement might reveal how differential regional accumulation of amyloid affects cognitive decline through functional and structural changes of the brain.

Normalization of cortical thickness measurements across different T1 magnetic resonance imaging protocols by novel W-Score standardization.

BACKGROUND: The use of different 3D T1-weighted magnetic resonance (T1 MR) imaging protocols induces image incompatibility across multicenter studies, negating the many advantages of multicenter studies. A few methods have been developed to address this problem, but significant image incompatibility still remains. Thus, we developed a novel and convenient method to improve image compatibility. METHODS: W-score standardization creates quality reference values by using a healthy group to obtain normalized disease values. We developed a protocol-specific w-score standardization to control the protocol effect, which is applied to each protocol separately. We used three data sets. In dataset 1, brain T1 MR images of normal controls (NC) and patients with Alzheimer's disease (AD) from two centers, acquired with different T1 MR protocols, were used (Protocol 1 and 2, n = 45/group). In dataset 2, data from six subjects, who underwent MRI with two different protocols (Protocol 1 and 2), were used with different repetition times, echo times, and slice thicknesses. In dataset 3, T1 MR images from a large number of healthy normal controls (Protocol 1: n = 148, Protocol 2: n = 343) were collected for w-score standardization. The protocol effect and disease effect on subjects' cortical thickness were analyzed before and after the application of protocol-specific w-score standardization. RESULTS: As expected, different protocols resulted in differing cortical thickness measurements in both NC and AD subjects. Different measurements were obtained for the same subject when imaged with different protocols. Multivariate pattern difference between measurements was observed between the protocols. Classification accuracy between two protocols was nearly 90%. After applying protocol-specific w-score standardization, the differences between the protocols substantially decreased. Most importantly, protocol-specific w-score standardization reduced both univariate and multivariate differences in the images while maintaining the AD disease effect. Compared to conventional regression methods, our method showed the best performance for in terms of controlling the protocol effect while preserving disease information. CONCLUSIONS: Protocol-specific w-score standardization effectively resolved the concerns of conventional regression methods. It showed the best performance for improving the compatibility of a T1 MR post-processed feature, cortical thickness.

Subcortical shape analysis of progressive mild cognitive impairment in Parkinson's disease.

BACKGROUND: Cortical neural correlates of ongoing cognitive decline in Parkinson's disease (PD) have been suggested; however, the role of subcortical structures in longitudinal change of cognitive dysfunction in PD has not been fully investigated. Here, we used automatic analysis to explore subcortical brain structures in patients with PD with mild cognitive impairment that converts into PD with dementia. METHODS: One hundred eighty-two patients with PD with mild cognitive impairment were classified as PD with mild cognitive impairment converters (n = 74) or nonconverters (n = 108), depending on whether they were subsequently diagnosed with dementia in PD. We used surface-based analysis to compare atrophic changes of subcortical brain structures between PD with mild cognitive impairment converters and nonconverters. RESULTS: PD with mild cognitive impairment converters had lower cognitive composite scores in the attention and frontal executive domains than did nonconverters. Subcortical shape analysis revealed that PD with mild cognitive impairment converters had smaller local shape volumes than did nonconverters in the bilateral thalamus, right caudate, and right hippocampus. Logistic regression analysis showed that local shape volumes in the bilateral thalamus and right caudate were significant independent predictors of PD with mild cognitive impairment converters. In the PD with mild cognitive impairment converter group, thalamic local shape volume was associated with semantic fluency and attentional composite score. CONCLUSIONS: The present data suggest that the local shape volumes of deep subcortical structures, especially in the caudate and thalamus, may serve as important predictors of the development of dementia in patients with PD. © 2017 International Parkinson and Movement Disorder Society.

Amyloid deposition and its association with depressive symptoms and cognitive functions in late-life depression: a longitudinal study using amyloid-ß PET images and neuropsychological measurements.

BACKGROUND: Although depression is linked to an increased risk of dementia, the association between late-onset depression (LOD) and amyloid burden remains unclear. This study aimed to determine amyloid deposition in patients with LOD compared to healthy controls (HC) using amyloid-beta (Aß) positron emission tomography (PET) images and neuropsychological assessments. METHODS: Forty patients first diagnosed with major depressive disorder after the age of 60 (LOD) and twenty-one healthy volunteers (HC) were enrolled. Depression and anxiety were evaluated using the 17-item Hamilton Depression Scale, Hamilton Anxiety Rating Scale, and Clinical Global Impression Scale. Cognitive function was assessed using the Korean versions of the Mini-Mental Status Examination, Montreal Cognitive Assessment, and Seoul Neuropsychological Screening Battery at baseline and 3-month follow-up. 18F-florbetapir PET images were co-registered with T1-weighted magnetic resonance images. RESULTS: There was no significant difference in Aß deposition between LOD and HC groups. No significant correlation between Aß burden and depressive symptom severity was found in LOD patients. Higher somatic anxiety was correlated with lower Aß burden in multiple brain regions, including the left inferior frontal lobe (p = 0.009), right anterior cingulate (p = 0.003), and right superior frontal lobe (p = 0.009). Despite cognitive recovery in areas such as attention (Digit Span Forward, p = 0.026), memory (Auditory Verbal Learning Test Recall Total, p = 0.010; Rey Complex Figure Test Delayed Recall, p = 0.039), and frontal executive function (Contrasting Program, p = 0.033) after three months of antidepressant treatment, cognitive improvement showed no association with amyloid deposition. CONCLUSIONS: These findings suggest distinct mechanisms may underlie amyloid deposition in neurodegenerative changes associated with depression. While amyloid burden in specific brain regions negatively correlated with somatic anxiety, it showed no significant correlation with the severity of depression or overall cognitive function.

Heterogeneity of factors associated with cognitive decline and cortical atrophy in early- versus late-onset Alzheimer's disease.

The objectives of this study were to investigate the variable factors associated with cognitive function and cortical atrophy and estimated variable importance of those factors in affecting cognitive function and cortical atrophy in patients with EOAD and LOAD. Patients with EOAD (n = 40), LOAD (n = 34), and healthy volunteers with normal cognition were included (n = 65). All of them performed 3T MRI, [18F]THK5351 PET (THK), [18F]flutemetamol PET (FLUTE), and detailed neuropsychological tests. To investigate factors associated with neuropsychological test results and cortical thickness in each group, we conducted multivariable linear regression models, including amyloid, tau, cerebral small vessel disease markers on MRI, and vascular risk factors. Then, we estimated variable importance in associating cognitive functions and cortical thickness, using relative importance analysis. In patients with EOAD, global THK retention was the most important contributor to the model variances for most neuropsychological tests, except for memory. However, in patients with LOAD, multiple contributors beyond tau were important in explaining variance of neuropsychological tests. In analyses with mean cortical thickness, global THK retention was the main contributor in patients with EOAD, while in LOAD patients, multiple factors contributed equally to mean cortical thickness. Therefore, EOAD and LOAD may have different pathomechanistic courses.

Diverging Relationships among Amyloid, Tau, and Brain Atrophy in Early-Onset and Late-Onset Alzheimer's Disease.

PURPOSE: Alzheimer's disease (AD) dementia may not be a single disease entity. Early-onset AD (EOAD) and late-onset AD (LOAD) have been united under the same eponym of AD until now, but disentangling the heterogeneity according to the age of sonset has been a major tenet in the field of AD research. MATERIALS AND METHODS: Ninety-nine patients with AD (EOAD, n=54; LOAD, n=45) and 66 cognitively normal controls completed both [18F]THK5351 and [18F]flutemetamol (FLUTE) positron emission tomography scans along with structural magnetic resonance imaging and detailed neuropsychological tests. RESULTS: EOAD patients had higher THK retention in the precuneus, parietal, and frontal lobe, while LOAD patients had higher THK retention in the medial temporal lobe. Intravoxel correlation analyses revealed that EOAD presented narrower territory of local FLUTE-THK correlation, while LOAD presented broader territory of correlation extending to overall parieto-occipito-temporal regions. EOAD patients had broader brain areas which showed significant negative correlations between cortical thickness and THK retention, whereas in LOAD, only limited brain areas showed significant correlation with THK retention. In EOAD, most of the cognitive test results were correlated with THK retention. However, a few cognitive test results were correlated with THK retention in LOAD. CONCLUSION: LOAD seemed to show gradual increase in tau and amyloid, and those two pathologies have association to each other. On the other hand, in EOAD, tau and amyloid may develop more abruptly and independently. These findings suggest LOAD and EOAD may have different courses of pathomechanism.

Effects of the APOEɛ4 Allele on the Relationship Between Tau and Amyloid-ß in Early- and Late-Onset Alzheimer's Disease.

BACKGROUND: Little is known regarding the differential effects of the apolipoprotein E (APOE) ɛ4 on the regional topography of amyloid and tau in patients with both early-onset (EOAD) and late-onset Alzheimer's disease (LOAD). OBJECTIVE: To compare the distribution and association of tau, amyloid, and cortical thickness among groups classified by the presence of APOEɛ4 allele and onset age. METHODS: A total of 165 participants including 54 EOAD patients (29 ɛ4-; 25 ɛ4+), 45 LOAD patients (21 ɛ4-; 24 ɛ4+), and 66 age-matched controls underwent 3T MRI, 18F-THK5351 (THK) and 18F-flutemetamol (FLUTE) PET scans, APOE genotyping, and neuropsychological tests. Data for voxel-wise and standardized uptake values from PET scans were analyzed in the context of APOE and age at onset. RESULTS: EOAD ɛ4- patients showed greater THK retention in the association cortices, whereas their EOAD ɛ4+ counterparts had more retention in medial temporal areas. THK topography of LOAD ɛ4+ was similar to EOAD ɛ4 + . THK correlated positively with FLUTE and conversely with mean cortical thickness, being lowest in EOAD ɛ4-, highest in LOAD ɛ4-, and modest in ɛ4+ groups. Even in the APOEɛ4+ groups, THK tended to correlate with FLUTE and mean cortical thickness in the inferior parietal region in EOAD and in the medial temporal region in LOAD. LOAD ɛ4- manifested with prevalent small vessel disease markers and the lowest correlation between THK retention and cognition. CONCLUSION: Our observations suggest the differential effects of the APOEɛ4 on the relationship between tau and amyloid in EOAD and LOAD.

Distinctive Mediating Effects of Subcortical Structure Changes on the Relationships Between Amyloid or Vascular Changes and Cognitive Decline.

Objective: We investigated the mediation effects of subcortical volume change in the relationship of amyloid beta (Aß) and lacune with cognitive function in patients with mild cognitive impairment (MCI). Methods: We prospectively recruited 101 patients with MCI who were followed up with neuropsychological tests, MRI, or Pittsburgh compound B (PiB) PET for 3 years. The mediation effect of subcortical structure on the association of PiB or lacunes with cognitive function was analyzed using mixed effects models. Results: Volume changes in the amygdala and hippocampus partially mediated the effect of PiB changes on memory function (direct effect = -0.168/-0.175, indirect effect = -0.081/-0.077 for amygdala/hippocampus) and completely mediated the effect of PiB changes on clinical dementia rating scale sum of the box (CDR-SOB) (indirect effect = 0.082/0.116 for amygdala/hippocampus). Volume changes in the thalamus completely mediated the effect of lacune on memory, frontal executive functions, and CDR-SOB (indirect effect = -0.037, -0.056, and 0.047, respectively). Conclusions: Our findings provide a better understanding of the distinct role of subcortical structures in the mediation of the relationships of amyloid or vascular changes with a decline in specific cognitive domains.

Deformities of the Globus Pallidus are Associated with Severity of Suicidal Ideation and Impulsivity in Patients with Major Depressive Disorder.

Neuroimaging research increasingly suggests there are biological features related to suicidal risk, including brain morphometric features, leading to an elaborate suicide risk assessment. However, few studies have focused on the severity of suicidal ideation and its association with subcortical anatomy in patients with major depressive disorder (MDD). Here, we mainly investigated whether specific structural differences were present in MDD patients with and without suicidal ideation; and supplemented comparison with and without suicidal attempt. We hypothesized that structures associated with suicidal ideation would be derived from a combination of depression and impulsivity. Local atrophy of subcortical structures in 48 patients with MDD (24 with suicidal ideation and 24 without) and 25 age- and sex-matched healthy controls were compared using a surface-based shape analysis method. There was no difference in brain volume between MDD patients with or without suicidal ideations; or MDD patients with or without suicidal attempt. However, the atrophy level in the left pallidum showed a positive correlation with severity of suicidal risk in MDD patients with suicidal ideation. Local atrophy of the left hippocampus, right caudate, and right pallidum had a positive correlation with total impulsivity. These findings possibly suggest that vulnerability to suicidal attempt can be derived from suicidal ideation combined with depression and impulsivity, related to reduced motivational control.

Multiple cortical thickness sub-networks and cognitive impairments in first episode, drug naïve patients with late life depression: A graph theory analysis.

BACKGROUND: Coordinated and pattern-wise changes in large scale gray matter structural networks reflect neural circuitry dysfunction in late life depression (LLD), which in turn is associated with emotional dysregulation and cognitive impairments. However, due to methodological limitations, there have been few attempts made to identify individual-level structural network properties or sub-networks that are involved in important brain functions in LLD. METHODS: In this study, we sought to construct individual-level gray matter structural networks using average cortical thicknesses of several brain areas to investigate the characteristics of the gray matter structural networks in normal controls and LLD patients. Additionally, we investigated the structural sub-networks correlated with several clinical measurements including cognitive impairment and depression severity. RESULTS: We observed that small worldness, clustering coefficients, global and local efficiency, and hub structures in the brains of LLD patients were significantly different from healthy controls. We further found that a sub-network including the anterior cingulate, dorsolateral prefrontal cortex and superior prefrontal cortex is significantly associated with attention control and executive function. The severity of depression was associated with the sub-networks comprising the salience network, including the anterior cingulate and insula. LIMITATIONS: We investigated cortico-cortical connectivity, but omitted the subcortical structures such as the striatum and thalamus. CONCLUSION: We report differences in patterns between several clinical measurements and sub-networks from large-scale and individual-level cortical thickness networks in LLD.

Atrophy patterns in cerebral amyloid angiopathy with and without cortical superficial siderosis.

OBJECTIVE: To investigate differential atrophy patterns based on the presence of cortical superficial siderosis (cSS) and the role of cSS in predicting amyloid positivity in memory clinic patients fulfilling the diagnostic criteria for probable cerebral amyloid angiopathy (CAA). METHODS: We retrospectively collected data from 44 cognitively impaired patients with probable CAA who underwent 3-dimensional, T1-weighted MRIs (cSS+, n = 27; cSS-, n = 17). Amyloid-positive patients with Alzheimer disease (AD) (n = 56) and amyloid-negative cognitively normal participants (n = 34) were recruited as controls. Among the patients with CAA who underwent amyloid-PET scans (75.0%), we investigated whether amyloid-negative cases were unevenly distributed based on cSS presentation. APOE genotypes, Mini-Mental State Examination scores, and cortical atrophy pattern along with hippocampal volume were compared across groups. RESULTS: Ten patients with probable CAA presented amyloid negativity and all of them belonged to the cSS- group (58.8%). Compared to the cSS- group, the cSS+ group presented higher APOE ε4 frequency, worse memory dysfunction, and lower hippocampal volume. Compared with cognitively normal participants, the cSS+ group exhibited atrophy in the precuneus, posterior cingulate, parietotemporal, superior frontal, and medial temporal areas, a pattern similar to AD-specific atrophy. The cSS- group exhibited atrophy in the parietotemporal, superior frontal, and precentral regions. CONCLUSION: Our findings imply that the current version of the Boston criteria may not be sufficient enough to remove non-CAA cases from a cognitively impaired population, especially in the absence of cSS. Patients with probable CAA presenting cSS appear to reflect a CAA phenotype that shares pathologic hallmarks with AD, providing insight into the CAA-to-AD continuum.

Biological Brain Age Prediction Using Cortical Thickness Data: A Large Scale Cohort Study.

Brain age estimation from anatomical features has been attracting more attention in recent years. This interest in brain age estimation is motivated by the importance of biological age prediction in health informatics, with an application to early prediction of neurocognitive disorders. It is well-known that normal brain aging follows a specific pattern, which enables researchers and practitioners to predict the age of a human's brain from its degeneration. In this paper, we model brain age predicted by cortical thickness data gathered from large cohort brain images. We collected 2,911 cognitively normal subjects (age 45-91 years) at a single medical center and acquired their brain magnetic resonance (MR) images. All images were acquired using the same scanner with the same protocol. We propose to first apply Sparse Group Lasso (SGL) for feature selection by utilizing the brain's anatomical grouping. Once the features are selected, a non-parametric non-linear regression using the Gaussian Process Regression (GPR) algorithm is applied to fit the final age prediction model. Experimental results demonstrate that the proposed method achieves the mean absolute error of 4.05 years, which is comparable with or superior to several recent methods. Our method can also be a critical tool for clinicians to differentiate patients with neurodegenerative brain disease by extracting a cortical thinning pattern associated with normal aging.

White Matter Network Disruption and Cognitive Dysfunction in Neuromyelitis Optica Spectrum Disorder.

Background: In neuromyelitis optica spectrum disorder (NMOSD), brain involvement is common and cognitive dysfunction is frequently found. The study investigated alterations of white matter (WM) connectivity using graph theory and correlations with cognitive dysfunction in patients with NMOSD. Methods: We prospectively enrolled patients with NMOSD (N = 14) and age- and sex-matched healthy controls (N = 21). Structural connections between any pair of the 90 cortical and subcortical regions were established using diffusion tensor imaging and graph theory. Network-based statistics was employed to assess differences in WM connectivity between the NMOSD and healthy control groups. We further investigated the relationship between the topological network characteristics and cognitive test performances. Results: WM network analysis showed decreased total strength of brain networks and two disrupted sub-networks in patients with NMOSD. The first featured six hub nodes in the rectus, hippocampus, calcarine, cuneus, and precuneus with the left-sided predominance. The second had six hub nodes in the orbitomiddle frontal, post-central, superior parietal, superior, and middle temporal, and caudate with the right-sided predominance. Compared to healthy controls, NMOSD patients showed poor performance on tests for attention/working memory and processing speed, visuospatial processing, and executive function, which were associated with significant decreases in nodal clustering coefficient, local efficiency, and regional efficiency in the disrupted sub-networks (all p < 0.05). Conclusions: The data show the overall WM disruption and the relationship between poor cognitive function and sub-network alterations identified by the network analysis in NMOSD patients. We suggest that cognitive dysfunction is related to dysconnectivity of WM network including default mode network in NMOSD.

Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation.

To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject's cortical atrophy pattern with that of a representative AD patient cohort. In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia. Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively. In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p < 0.001) and first year visits (p < 0.001) relative to non-converters. Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p = 0.042), first year (p = 0.028), and third year visits (p = 0.027). The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level.

Changes in subcortical shape and cognitive function in patients with chronic insomnia.

OBJECTIVE: The aim of this study was to examine morphological changes in subcortical structures via surface-based analysis and to correlate local shape changes with cognitive function. METHODS: We analyzed subcortical brain morphology and compared the shape changes with clinical and neuropsychological features in patients with chronic insomnia. RESULTS: Hippocampal atrophy was associated with higher Pittsburgh Sleep Quality Index scores (r = -0.4, p = 0.0408) and higher arousal indices (r = -0.4, p = 0.0332). Local volume loss of the putamen was associated with higher arousal indices (r = -0.5, p = 0.0416). Atrophic change of subcortical structures including the hippocampus, amygdala, basal ganglia, and thalamus, correlated negatively with verbal fluency, frontal function, verbal memory, and visual memory, respectively, in these patients (|r| ≥ 0.3, p < 0.05). CONCLUSIONS: This study shows that sleep quality and fragmentation are closely related to atrophic changes in hippocampus and putamen. In addition, atrophic changes in global subcortical structures are associated with impaired cognitive function in patients with chronic insomnia.

Using Individualized Brain Network for Analyzing Structural Covariance of the Cerebral Cortex in Alzheimer's Patients.

Cortical thinning patterns in Alzheimer's disease (AD) have been widely reported through conventional regional analysis. In addition, the coordinated variance of cortical thickness in different brain regions has been investigated both at the individual and group network levels. In this study, we aim to investigate network architectural characteristics of a structural covariance network (SCN) in AD, and further to show that the structural covariance connectivity becomes disorganized across the brain regions in AD, while the normal control (NC) subjects maintain more clustered and consistent coordination in cortical atrophy variations. We generated SCNs directly from T1-weighted MR images of individual patients using surface-based cortical thickness data, with structural connectivity defined as similarity in cortical thickness within different brain regions. Individual SCNs were constructed using morphometric data from the Samsung Medical Center (SMC) dataset. The structural covariance connectivity showed higher clustering than randomly generated networks, as well as similar minimum path lengths, indicating that the SCNs are "small world." There were significant difference between NC and AD group in characteristic path lengths (z = -2.97, p < 0.01) and small-worldness values (z = 4.05, p < 0.01). Clustering coefficients in AD was smaller than that of NC but there was no significant difference (z = 1.81, not significant). We further observed that the AD patients had significantly disrupted structural connectivity. We also show that the coordinated variance of cortical thickness is distributed more randomly from one region to other regions in AD patients when compared to NC subjects. Our proposed SCN may provide surface-based measures for understanding interaction between two brain regions with co-atrophy of the cerebral cortex due to normal aging or AD. We applied our method to the AD Neuroimaging Initiative (ADNI) data to show consistency in results with the SMC dataset.