The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated wit... more The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multimodality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM-MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our ...
Past research shows that major depression is associated with lower white matter integrity in fron... more Past research shows that major depression is associated with lower white matter integrity in fronto-limbic and other areas. But it is not known whether the integrity of these white matter connections is associated with subsyndromal depression symptoms, a marker of risk for major depression, in family dementia caregivers (dCGs) who reported stress. If specific aspects of white matter integrity are related to depression symptoms in this high-risk group, this could provide a biomarker of vulnerability or target for treatment. Participants included 41 dCGs (average age=69, standard deviation=6.4), who underwent a 7 Tesla 64-direction (12-minute) diffusion-weighted imaging sequence. Analyses compared dCGs with (n=20) and without (n=21) subsyndromal depression symptoms (nine-item Patient Health Questionnaire scores ≥5). Using fractional anisotropy (FA), we assessed differences in the integrity of 11 white matter aspects implicated in prior studies of major depression. We found that caregi...
Objectives: Non-Latino Black adults have greater risk for Alzheimer's dementia compared to non-La... more Objectives: Non-Latino Black adults have greater risk for Alzheimer's dementia compared to non-Latino White adults, possibly due to factors disproportionally affecting Black adults including cardiovascular disease (CVD). Chronic peripheral inflammation is implicated in both Alzheimer's dementia and CVD and is known to impact cognition and cerebral white matter, yet little work has examined these associations by race. This study examined associations between inflammation, cognition, and cerebral white matter generally, and by race. Methods: Eighty-six non-demented older Black and White participants (age = 69.03; 50% female; 45% Black participants) underwent fasting venipuncture, cognitive testing, and MRI. Serum was assayed for interleukin-6 (IL-6), C-reactive protein (CRP), and interleukin 1-beta. Cognitive domains included memory, executive function, and attention/information processing. MRI measures included white matter hyperintensity volumes (WMH) and quantification of white matter integrity in areas outside WMHs via DTI-derived fractional anisotropy (FA) and mean diffusivity, as well as multi-component relaxometry derived myelin water fraction (MWF). Results: Black and White participants did not differ on age, sex, or CVD risk. Separate linear regression models adjusting for relevant confounders revealed that higher IL-6 associated with lower executive function and higher CRP levels associated with lower FA and MWF. Stratified analyses revealed that these association were significant for Black participants only. Discussion: These findings suggest that peripheral inflammation is inversely associated with select cognitive domains and white matter integrity (but not WMHs), particularly in older Black adults. It is important to consider race when investigating inflammatory associates of brain and behavior.
This paper introduces a novel method that unifies structural connectivity and functional time ser... more This paper introduces a novel method that unifies structural connectivity and functional time series to form a signed coupling interaction network or "signed resting state structural connectome" (signed rs-SC) to describe neural excitation and inhibition. We employ an energy representation of neural activity based on the Ising model from statistical mechanics, hereby bypassing traditional BOLD correlations. The spin model is a function of a coupling interaction (traditionally positive or negative) and spin-states of paired brain regions. Observed functional time series represent brain states over time. A maximum pseudolikelihood with a constraint is used to estimate the coupling interaction. The constraint is introduced as a penalty function such that the learned interactions are scaled relative to structural connectivity; the sign of the interactions may infer inhibition or excitation over an underlying structure. We evaluate our method by comparing a group of otherwise healthy APOE-e4 carriers with a control group of non APOE-e4 subjects. Our results identify a global shift in the excitation-inhibition balance of the APOE e4 signed rs-SC compared to the control group, providing the first connectomics-based support for hyperexcitation related to APOE e4.
Neural activity coordinated across different scales from neuronal circuits to large-scale brain n... more Neural activity coordinated across different scales from neuronal circuits to large-scale brain networks gives rise to complex cognitive functions. Bridging the gap between micro- and macroscale processes, we present a novel framework based on the maximum entropy model to infer a hybrid resting-state structural connectome, representing functional interactions constrained by structural connectivity. We demonstrate that the structurally informed network outperforms the unconstrained model in simulating brain dynamics, wherein by constraining the inference model with the network structure we may improve the estimation of pairwise BOLD signal interactions. Further, we simulate brain network dynamics using Monte Carlo simulations with the new hybrid connectome to probe connectome-level differences in excitation-inhibition balance between apolipoprotein E (APOE)-ε4 carriers and noncarriers. Our results reveal sex differences among APOE-ε4 carriers in functional dynamics at criticality; sp...
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020
Applying network science approaches to investigate the functions and anatomy of the human brain i... more Applying network science approaches to investigate the functions and anatomy of the human brain is prevalent in modern medical imaging analysis. Due to the complex network topology, for an individual brain, mining a discriminative network representation from the multimodal brain networks is non-trivial. The recent success of deep learning techniques on graph-structured data suggests a new way to model the non-linear cross-modality relationship. However, current deep brain network methods either ignore the intrinsic graph topology or require a network basis shared within a group. To address these challenges, we propose a novel end-to-end deep graph representation learning (Deep Multimodal Brain Networks-DMBN) to fuse multimodal brain networks. Specifically, we decipher the cross-modality relationship through a graph encoding and decoding process. The higher-order network mappings from brain structural networks to functional networks are learned in the node domain. The learned network representation is a set of node features that are informative to induce brain saliency maps in a supervised manner. We test our framework in both synthetic and real image data. The experimental results show the superiority of the proposed method over some other state-of-the-art deep brain network models.
Synaptic dysfunction is hypothesized to be one of the earliest brain changes in Alzheimer’s disea... more Synaptic dysfunction is hypothesized to be one of the earliest brain changes in Alzheimer’s disease, leading to “hyperexcitability” in neuronal circuits. In this study, we evaluated a novel hyperexcitation indicator (HI) for each brain region using a hybrid resting-state structural connectome to probe connectome-level excitation–inhibition balance in cognitively intact middle-aged apolipoprotein E (APOE) ε4 carriers with noncarriers (16 male/22 female in each group). Regression with three-way interactions (sex, age, and APOE-ε4 carrier status) to assess the effect of APOE-ε4 on excitation–inhibition balance within each sex and across an age range of 40–60 years yielded a significant shift toward higher HI in female carriers compared with noncarriers (beginning at 50 years). Hyperexcitation was insignificant in the male group. Further, in female carriers the degree of hyperexcitation exhibited significant positive correlation with working memory performance (evaluated via a virtual M...
Cardiovascular disease risk factors (CVD-RFs) are associated with decreased gray and white matter... more Cardiovascular disease risk factors (CVD-RFs) are associated with decreased gray and white matter integrity and cognitive impairment in older adults. Less is known regarding the interplay between CVD-RFs, brain structural connectome integrity, and cognition. We examined whether CVD-RFs were associated with measures of tract-based structural connectivity in 94 nondemented/non-depressed older adults and if alterations in connectivity mediated associations between CVD-RFs and cognition. Participants (age=68.2 years; 52.1% female; 46.8% Black) underwent CVD-RF assessment, MRI, and cognitive evaluation. Framingham 10-year stroke risk (FSRP-10) quantified CVD-RFs. Graph theory analysis integrated T1-derived gray matter regions of interest (ROIs; 23 a-priori ROIs associated with CVD-RFs and dementia), and diffusion MRIderived white matter tractography into connectivity matrices analyzed for local efficiency and nodal strength. A principal component analysis resulted in three rotated factor scores reflecting executive function (EF; FAS, Trail Making Test (TMT) B-A, Letter-Number Sequencing, Matrix Reasoning); attention/information processing (AIP; TMT-A, TMT-Motor, Digit Symbol); and memory (CVLT-II Trials 1-5 Total, Delayed Free Recall, Recognition Discriminability). Linear regressions between FSRP-10 and connectome ROIs adjusting for word reading, intracranial volume, and white matter hyperintensities revealed negative associations with nodal strength in eight ROIs (p-values<.05) and negative associations with efficiency in two ROIs, and a positive
ABSTRACTBackgroundAccumulating evidence suggests that higher Mediterranean diet (MedDiet) adheren... more ABSTRACTBackgroundAccumulating evidence suggests that higher Mediterranean diet (MedDiet) adherence is associated with higher global cognitive performance and brain structural integrity as well as decreased risk of Alzheimer disease (AD) and vascular dementia (VaD).ObjectivesWe directly examined cross-sectional associations between the MedDiet and cognitive and neuroimaging phenotypes associated with AD and VaD (separately) in a cohort of nondemented, nondepressed older adults.MethodsCommunity-dwelling older adults (n = 82; aged ∼68.8 y; 50% female, 50% minority) underwent dietary (Block Food Frequency Questionnaire 2005) and neuropsychological assessments and neuroimaging. MedDiet scores were quantified with the use of published criteria, and participants were divided into High and Low (median split) adherence groups. We focused our neuropsychological investigation on cognitive phenotypes primarily associated with AD [i.e., learning and memory (L&M)] and VaD (i.e., information proc...
The apolipoprotein E (APOE) ε4 allele is the best characterized genetic risk factor for Alzheimer... more The apolipoprotein E (APOE) ε4 allele is the best characterized genetic risk factor for Alzheimer's disease to date. Older APOE ε4 carriers (aged 60+ years) are known to have disrupted structural and functional connectivity, but less is known about APOE-associated network integrity in middle age. The goal of this study was to characterize APOE-related differences in network topology in middle age, as disentangling the early effects of healthy versus pathological aging may aid early detection of Alzheimer's disease and inform treatments. We performed resting state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) in healthy, cognitively normal, middle-aged adults (age 40-60; N = 76, 38 APOE ε4 carriers). Graph theoretical analysis was used to calculate local and global efficiency of 1) a whole brain rs-fMRI network; 2) a whole brain DTI network; and 3) the resting state structural connectome (rsSC), an integrated functionalstructural network derived using functional-by-structural hierarchical (FSH) mapping. Our results indicated no APOE ε4-associated differences in network topology of the rs-fMRI or DTI networks alone. However, ε4 carriers had significantly lower global and local efficiency of the integrated rsSC compared to non-carriers. Furthermore, ε4 carriers were less resilient to targeted node failure of the rsSC, which mimics the neuropathological process of Alzheimer's disease. Collectively, these findings suggest that integrating multiple neuroimaging modalities and employing graph theoretical analysis may reveal network-level vulnerabilities that may serve as biomarkers of agerelated cognitive decline in middle age, decades before the onset of overt cognitive impairment.
Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2015
Mild cognitive impairment (MCI) is an intermediate stage between normal aging and Alzheimer's... more Mild cognitive impairment (MCI) is an intermediate stage between normal aging and Alzheimer's disease (AD), and around 10-15% of people with MCI develop AD each year. More recently, MCI has been further subdivided into early and late stages, and there is interest in identifying sensitive brain imaging biomarkers that help to differentiate stages of MCI. Here, we focused on anatomical brain networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying early versus late MCI.
Occurring in at least 1 in 3,000 live births, chromosome 22q11.2 deletion syndrome (22q11DS) prod... more Occurring in at least 1 in 3,000 live births, chromosome 22q11.2 deletion syndrome (22q11DS) produces a complex phenotype that includes a constellation of medical complications such as congenital cardiac defects, immune deficiency, velopharyngeal dysfunction, and characteristic facial dysmorphic features. There is also an increased incidence of psychiatric diagnosis, especially intellectual disability and ADHD in childhood, lifelong anxiety, and a strikingly high rate of schizophrenia spectrum disorders, which occur in around 30% of adults with 22q11DS. Using innovative computational connectomics, we studied how 22q11DS affects high-level network signatures of hierarchical modularity and its intrinsic geometry in 55 children with confirmed 22q11DS and 27 Typically Developing (TD) children. Results identified 3 subgroups within our 22q11DS sample using a K-means clustering approach based on several midline structural measures-of-interests. Each subgroup exhibited distinct patterns of...
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 2018
Modern resting-state functional magnetic resonance imaging (rs-fMRI) provides a wealth of informa... more Modern resting-state functional magnetic resonance imaging (rs-fMRI) provides a wealth of information about the inherent functional connectivity of the human brain. However, understanding the role of negative correlations and the nonlinear topology of rs-fMRI remains a challenge. To address these challenges, we propose a novel graph embedding technique, phase angle spatial embedding (PhASE), to study the "intrinsic geometry" of the functional connectome. PhASE both incorporates negative correlations as well as reformulates the connectome modularity problem as a kernel two-sample test, using a kernel method that induces a maximum mean discrepancy (MMD) in a reproducing kernel Hilbert space (RKHS). By solving a graph partition that maximizes this MMD, PhASE identifies the most functionally distinct brain modules. As a test case, we analyzed a public rs-fMRI dataset to compare male and female connectomes using PhASE and minimum spanning tree inferential statistics. These results show statistically significant differences between male and female resting-state brain networks, demonstrating PhASE to be a robust tool for connectome analysis.
Body dysmorphic disorder (BDD) is characterized by preoccupation with misperceived defects of app... more Body dysmorphic disorder (BDD) is characterized by preoccupation with misperceived defects of appearance, causing significant distress and disability. Previous studies suggest abnormalities in information processing characterized by greater local relative to global processing. The purpose of this study was to probe whole-brain and regional white matter network organization in BDD, and to relate this to specific metrics of symptomatology. We acquired diffusion-weighted 34-direction MR images from 14 unmedicated participants with DSM-IV BDD and 16 healthy controls, from which we conducted whole-brain deterministic diffusion tensor imaging tractography. We then constructed white matter structural connectivity matrices to derive whole-brain and regional graph theory metrics, which we compared between groups. Within the BDD group, we additionally correlated these metrics with scores on psychometric measures of BDD symptom severity as well as poor insight/delusionality. The BDD group showed higher whole-brain mean clustering coefficient than controls. Global efficiency negatively correlated with BDD symptom severity. The BDD group demonstrated greater edge betweenness centrality for connections between the anterior temporal lobe and the occipital cortex, and between bilateral occipital poles. This represents the first brain network analysis in BDD. Results suggest disturbances in whole brain structural topological organization in BDD, in addition to correlations between clinical symptoms and network organization. There is also evidence of abnormal connectivity between regions involved in lower-order visual processing and higher-order visual and emotional processing, as well as interhemispheric visual information transfer. These findings may relate to disturbances in information processing found in previous studies.
A number of studies have shown an association between diabetes and depression. However, the under... more A number of studies have shown an association between diabetes and depression. However, the underlying mechanisms are still unclear. Previous findings indicate a role for the prefrontal cortex and subcortical gray matter regions in type 2 diabetes and major depressive disorder (MDD). The purpose of this study was to examine the white matter integrity in the fibers that are part of the anterior limb of internal capsule (ALIC) in MDD and diabetic subjects using diffusion tensor imaging tractography. We studied 4 groups of subjects including 1) 42 healthy controls (HC), 2) 28 MDD subjects (MD), 3) 24 patients diagnosed with type 2 diabetes without depression (DC), and 4) 22 patients diagnosed with diabetes and depression (DD). Results revealed significantly decreased fractional anisotropy (FA; P ¼ .021) and a trend towards significant increase in radial diffusivity (RD; P ¼ .078) of the right ALIC in depressed subjects (MD þ DD) compared to non-depressed subjects (HC þ DC). While there were no significant diabetes effects or interactions between depression and diabetes, subjects with high depression ratings and high hemoglobin A1c levels had the lowest mean FA values in the right ALIC. In addition, we found a significant negative correlation between FA of the left ALIC with hemoglobin A1c in diabetic subjects (DC þ DD; P ¼ .016). Our study demonstrated novel findings of white matter abnormalities of the ALIC in depression and diabetes. These findings have implications for clinical manifestations of depression and diabetes as well as their pathophysiology.
Over the past decade a wide spectrum of machine learning models have been developed to model the ... more Over the past decade a wide spectrum of machine learning models have been developed to model the neurodegenerative diseases, associating biomarkers, especially non-intrusive neuroimaging markers, with key clinical scores measuring the cognitive status of patients. Multi-task learning (MTL) has been commonly utilized by these studies to address high dimensionality and small cohort size challenges. However, most existing MTL approaches are based on linear models and suffer from two major limitations: 1) they cannot explicitly consider upper/lower bounds in these clinical scores; 2) they lack the capability to capture complicated non-linear interactions among the variables. In this paper, we propose Subspace Network, an efficient deep modeling approach for non-linear multi-task censored regression. Each layer of the subspace network performs a multi-task censored regression to improve upon the predictions from the last layer via sketching a low-dimensional subspace to perform knowledge...
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015
Genetic and environmental factors affect white matter connectivity in the normal brain, and they ... more Genetic and environmental factors affect white matter connectivity in the normal brain, and they also influence diseases in which brain connectivity is altered. Little is known about genetic influences on brain connectivity, despite wide variations in the brain's neural pathways. Here we applied the "DICCCOL" framework to analyze structural connectivity, in 261 twin pairs (522 participants, mean age: 21.8 y ± 2.7SD). We encoded connectivity patterns by projecting the white matter (WM) bundles of all "DICCCOLs" as a tracemap (TM). Next we fitted an A/C/E structural equation model to estimate additive genetic (A), common environmental (C), and unique environmental/error (E) components of the observed variations in brain connectivity. We found 44 "heritable DICCCOLs" whose connectivity was genetically influenced (2 >1%); half of them showed significant heritability (2 >20%). Our analysis of genetic influences on WM structural connectivity suggests high heritability for some WM projection patterns, yielding new targets for genome-wide association studies.
The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated wit... more The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multimodality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM-MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our ...
Past research shows that major depression is associated with lower white matter integrity in fron... more Past research shows that major depression is associated with lower white matter integrity in fronto-limbic and other areas. But it is not known whether the integrity of these white matter connections is associated with subsyndromal depression symptoms, a marker of risk for major depression, in family dementia caregivers (dCGs) who reported stress. If specific aspects of white matter integrity are related to depression symptoms in this high-risk group, this could provide a biomarker of vulnerability or target for treatment. Participants included 41 dCGs (average age=69, standard deviation=6.4), who underwent a 7 Tesla 64-direction (12-minute) diffusion-weighted imaging sequence. Analyses compared dCGs with (n=20) and without (n=21) subsyndromal depression symptoms (nine-item Patient Health Questionnaire scores ≥5). Using fractional anisotropy (FA), we assessed differences in the integrity of 11 white matter aspects implicated in prior studies of major depression. We found that caregi...
Objectives: Non-Latino Black adults have greater risk for Alzheimer's dementia compared to non-La... more Objectives: Non-Latino Black adults have greater risk for Alzheimer's dementia compared to non-Latino White adults, possibly due to factors disproportionally affecting Black adults including cardiovascular disease (CVD). Chronic peripheral inflammation is implicated in both Alzheimer's dementia and CVD and is known to impact cognition and cerebral white matter, yet little work has examined these associations by race. This study examined associations between inflammation, cognition, and cerebral white matter generally, and by race. Methods: Eighty-six non-demented older Black and White participants (age = 69.03; 50% female; 45% Black participants) underwent fasting venipuncture, cognitive testing, and MRI. Serum was assayed for interleukin-6 (IL-6), C-reactive protein (CRP), and interleukin 1-beta. Cognitive domains included memory, executive function, and attention/information processing. MRI measures included white matter hyperintensity volumes (WMH) and quantification of white matter integrity in areas outside WMHs via DTI-derived fractional anisotropy (FA) and mean diffusivity, as well as multi-component relaxometry derived myelin water fraction (MWF). Results: Black and White participants did not differ on age, sex, or CVD risk. Separate linear regression models adjusting for relevant confounders revealed that higher IL-6 associated with lower executive function and higher CRP levels associated with lower FA and MWF. Stratified analyses revealed that these association were significant for Black participants only. Discussion: These findings suggest that peripheral inflammation is inversely associated with select cognitive domains and white matter integrity (but not WMHs), particularly in older Black adults. It is important to consider race when investigating inflammatory associates of brain and behavior.
This paper introduces a novel method that unifies structural connectivity and functional time ser... more This paper introduces a novel method that unifies structural connectivity and functional time series to form a signed coupling interaction network or "signed resting state structural connectome" (signed rs-SC) to describe neural excitation and inhibition. We employ an energy representation of neural activity based on the Ising model from statistical mechanics, hereby bypassing traditional BOLD correlations. The spin model is a function of a coupling interaction (traditionally positive or negative) and spin-states of paired brain regions. Observed functional time series represent brain states over time. A maximum pseudolikelihood with a constraint is used to estimate the coupling interaction. The constraint is introduced as a penalty function such that the learned interactions are scaled relative to structural connectivity; the sign of the interactions may infer inhibition or excitation over an underlying structure. We evaluate our method by comparing a group of otherwise healthy APOE-e4 carriers with a control group of non APOE-e4 subjects. Our results identify a global shift in the excitation-inhibition balance of the APOE e4 signed rs-SC compared to the control group, providing the first connectomics-based support for hyperexcitation related to APOE e4.
Neural activity coordinated across different scales from neuronal circuits to large-scale brain n... more Neural activity coordinated across different scales from neuronal circuits to large-scale brain networks gives rise to complex cognitive functions. Bridging the gap between micro- and macroscale processes, we present a novel framework based on the maximum entropy model to infer a hybrid resting-state structural connectome, representing functional interactions constrained by structural connectivity. We demonstrate that the structurally informed network outperforms the unconstrained model in simulating brain dynamics, wherein by constraining the inference model with the network structure we may improve the estimation of pairwise BOLD signal interactions. Further, we simulate brain network dynamics using Monte Carlo simulations with the new hybrid connectome to probe connectome-level differences in excitation-inhibition balance between apolipoprotein E (APOE)-ε4 carriers and noncarriers. Our results reveal sex differences among APOE-ε4 carriers in functional dynamics at criticality; sp...
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020
Applying network science approaches to investigate the functions and anatomy of the human brain i... more Applying network science approaches to investigate the functions and anatomy of the human brain is prevalent in modern medical imaging analysis. Due to the complex network topology, for an individual brain, mining a discriminative network representation from the multimodal brain networks is non-trivial. The recent success of deep learning techniques on graph-structured data suggests a new way to model the non-linear cross-modality relationship. However, current deep brain network methods either ignore the intrinsic graph topology or require a network basis shared within a group. To address these challenges, we propose a novel end-to-end deep graph representation learning (Deep Multimodal Brain Networks-DMBN) to fuse multimodal brain networks. Specifically, we decipher the cross-modality relationship through a graph encoding and decoding process. The higher-order network mappings from brain structural networks to functional networks are learned in the node domain. The learned network representation is a set of node features that are informative to induce brain saliency maps in a supervised manner. We test our framework in both synthetic and real image data. The experimental results show the superiority of the proposed method over some other state-of-the-art deep brain network models.
Synaptic dysfunction is hypothesized to be one of the earliest brain changes in Alzheimer’s disea... more Synaptic dysfunction is hypothesized to be one of the earliest brain changes in Alzheimer’s disease, leading to “hyperexcitability” in neuronal circuits. In this study, we evaluated a novel hyperexcitation indicator (HI) for each brain region using a hybrid resting-state structural connectome to probe connectome-level excitation–inhibition balance in cognitively intact middle-aged apolipoprotein E (APOE) ε4 carriers with noncarriers (16 male/22 female in each group). Regression with three-way interactions (sex, age, and APOE-ε4 carrier status) to assess the effect of APOE-ε4 on excitation–inhibition balance within each sex and across an age range of 40–60 years yielded a significant shift toward higher HI in female carriers compared with noncarriers (beginning at 50 years). Hyperexcitation was insignificant in the male group. Further, in female carriers the degree of hyperexcitation exhibited significant positive correlation with working memory performance (evaluated via a virtual M...
Cardiovascular disease risk factors (CVD-RFs) are associated with decreased gray and white matter... more Cardiovascular disease risk factors (CVD-RFs) are associated with decreased gray and white matter integrity and cognitive impairment in older adults. Less is known regarding the interplay between CVD-RFs, brain structural connectome integrity, and cognition. We examined whether CVD-RFs were associated with measures of tract-based structural connectivity in 94 nondemented/non-depressed older adults and if alterations in connectivity mediated associations between CVD-RFs and cognition. Participants (age=68.2 years; 52.1% female; 46.8% Black) underwent CVD-RF assessment, MRI, and cognitive evaluation. Framingham 10-year stroke risk (FSRP-10) quantified CVD-RFs. Graph theory analysis integrated T1-derived gray matter regions of interest (ROIs; 23 a-priori ROIs associated with CVD-RFs and dementia), and diffusion MRIderived white matter tractography into connectivity matrices analyzed for local efficiency and nodal strength. A principal component analysis resulted in three rotated factor scores reflecting executive function (EF; FAS, Trail Making Test (TMT) B-A, Letter-Number Sequencing, Matrix Reasoning); attention/information processing (AIP; TMT-A, TMT-Motor, Digit Symbol); and memory (CVLT-II Trials 1-5 Total, Delayed Free Recall, Recognition Discriminability). Linear regressions between FSRP-10 and connectome ROIs adjusting for word reading, intracranial volume, and white matter hyperintensities revealed negative associations with nodal strength in eight ROIs (p-values<.05) and negative associations with efficiency in two ROIs, and a positive
ABSTRACTBackgroundAccumulating evidence suggests that higher Mediterranean diet (MedDiet) adheren... more ABSTRACTBackgroundAccumulating evidence suggests that higher Mediterranean diet (MedDiet) adherence is associated with higher global cognitive performance and brain structural integrity as well as decreased risk of Alzheimer disease (AD) and vascular dementia (VaD).ObjectivesWe directly examined cross-sectional associations between the MedDiet and cognitive and neuroimaging phenotypes associated with AD and VaD (separately) in a cohort of nondemented, nondepressed older adults.MethodsCommunity-dwelling older adults (n = 82; aged ∼68.8 y; 50% female, 50% minority) underwent dietary (Block Food Frequency Questionnaire 2005) and neuropsychological assessments and neuroimaging. MedDiet scores were quantified with the use of published criteria, and participants were divided into High and Low (median split) adherence groups. We focused our neuropsychological investigation on cognitive phenotypes primarily associated with AD [i.e., learning and memory (L&M)] and VaD (i.e., information proc...
The apolipoprotein E (APOE) ε4 allele is the best characterized genetic risk factor for Alzheimer... more The apolipoprotein E (APOE) ε4 allele is the best characterized genetic risk factor for Alzheimer's disease to date. Older APOE ε4 carriers (aged 60+ years) are known to have disrupted structural and functional connectivity, but less is known about APOE-associated network integrity in middle age. The goal of this study was to characterize APOE-related differences in network topology in middle age, as disentangling the early effects of healthy versus pathological aging may aid early detection of Alzheimer's disease and inform treatments. We performed resting state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) in healthy, cognitively normal, middle-aged adults (age 40-60; N = 76, 38 APOE ε4 carriers). Graph theoretical analysis was used to calculate local and global efficiency of 1) a whole brain rs-fMRI network; 2) a whole brain DTI network; and 3) the resting state structural connectome (rsSC), an integrated functionalstructural network derived using functional-by-structural hierarchical (FSH) mapping. Our results indicated no APOE ε4-associated differences in network topology of the rs-fMRI or DTI networks alone. However, ε4 carriers had significantly lower global and local efficiency of the integrated rsSC compared to non-carriers. Furthermore, ε4 carriers were less resilient to targeted node failure of the rsSC, which mimics the neuropathological process of Alzheimer's disease. Collectively, these findings suggest that integrating multiple neuroimaging modalities and employing graph theoretical analysis may reveal network-level vulnerabilities that may serve as biomarkers of agerelated cognitive decline in middle age, decades before the onset of overt cognitive impairment.
Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2015
Mild cognitive impairment (MCI) is an intermediate stage between normal aging and Alzheimer's... more Mild cognitive impairment (MCI) is an intermediate stage between normal aging and Alzheimer's disease (AD), and around 10-15% of people with MCI develop AD each year. More recently, MCI has been further subdivided into early and late stages, and there is interest in identifying sensitive brain imaging biomarkers that help to differentiate stages of MCI. Here, we focused on anatomical brain networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying early versus late MCI.
Occurring in at least 1 in 3,000 live births, chromosome 22q11.2 deletion syndrome (22q11DS) prod... more Occurring in at least 1 in 3,000 live births, chromosome 22q11.2 deletion syndrome (22q11DS) produces a complex phenotype that includes a constellation of medical complications such as congenital cardiac defects, immune deficiency, velopharyngeal dysfunction, and characteristic facial dysmorphic features. There is also an increased incidence of psychiatric diagnosis, especially intellectual disability and ADHD in childhood, lifelong anxiety, and a strikingly high rate of schizophrenia spectrum disorders, which occur in around 30% of adults with 22q11DS. Using innovative computational connectomics, we studied how 22q11DS affects high-level network signatures of hierarchical modularity and its intrinsic geometry in 55 children with confirmed 22q11DS and 27 Typically Developing (TD) children. Results identified 3 subgroups within our 22q11DS sample using a K-means clustering approach based on several midline structural measures-of-interests. Each subgroup exhibited distinct patterns of...
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 2018
Modern resting-state functional magnetic resonance imaging (rs-fMRI) provides a wealth of informa... more Modern resting-state functional magnetic resonance imaging (rs-fMRI) provides a wealth of information about the inherent functional connectivity of the human brain. However, understanding the role of negative correlations and the nonlinear topology of rs-fMRI remains a challenge. To address these challenges, we propose a novel graph embedding technique, phase angle spatial embedding (PhASE), to study the "intrinsic geometry" of the functional connectome. PhASE both incorporates negative correlations as well as reformulates the connectome modularity problem as a kernel two-sample test, using a kernel method that induces a maximum mean discrepancy (MMD) in a reproducing kernel Hilbert space (RKHS). By solving a graph partition that maximizes this MMD, PhASE identifies the most functionally distinct brain modules. As a test case, we analyzed a public rs-fMRI dataset to compare male and female connectomes using PhASE and minimum spanning tree inferential statistics. These results show statistically significant differences between male and female resting-state brain networks, demonstrating PhASE to be a robust tool for connectome analysis.
Body dysmorphic disorder (BDD) is characterized by preoccupation with misperceived defects of app... more Body dysmorphic disorder (BDD) is characterized by preoccupation with misperceived defects of appearance, causing significant distress and disability. Previous studies suggest abnormalities in information processing characterized by greater local relative to global processing. The purpose of this study was to probe whole-brain and regional white matter network organization in BDD, and to relate this to specific metrics of symptomatology. We acquired diffusion-weighted 34-direction MR images from 14 unmedicated participants with DSM-IV BDD and 16 healthy controls, from which we conducted whole-brain deterministic diffusion tensor imaging tractography. We then constructed white matter structural connectivity matrices to derive whole-brain and regional graph theory metrics, which we compared between groups. Within the BDD group, we additionally correlated these metrics with scores on psychometric measures of BDD symptom severity as well as poor insight/delusionality. The BDD group showed higher whole-brain mean clustering coefficient than controls. Global efficiency negatively correlated with BDD symptom severity. The BDD group demonstrated greater edge betweenness centrality for connections between the anterior temporal lobe and the occipital cortex, and between bilateral occipital poles. This represents the first brain network analysis in BDD. Results suggest disturbances in whole brain structural topological organization in BDD, in addition to correlations between clinical symptoms and network organization. There is also evidence of abnormal connectivity between regions involved in lower-order visual processing and higher-order visual and emotional processing, as well as interhemispheric visual information transfer. These findings may relate to disturbances in information processing found in previous studies.
A number of studies have shown an association between diabetes and depression. However, the under... more A number of studies have shown an association between diabetes and depression. However, the underlying mechanisms are still unclear. Previous findings indicate a role for the prefrontal cortex and subcortical gray matter regions in type 2 diabetes and major depressive disorder (MDD). The purpose of this study was to examine the white matter integrity in the fibers that are part of the anterior limb of internal capsule (ALIC) in MDD and diabetic subjects using diffusion tensor imaging tractography. We studied 4 groups of subjects including 1) 42 healthy controls (HC), 2) 28 MDD subjects (MD), 3) 24 patients diagnosed with type 2 diabetes without depression (DC), and 4) 22 patients diagnosed with diabetes and depression (DD). Results revealed significantly decreased fractional anisotropy (FA; P ¼ .021) and a trend towards significant increase in radial diffusivity (RD; P ¼ .078) of the right ALIC in depressed subjects (MD þ DD) compared to non-depressed subjects (HC þ DC). While there were no significant diabetes effects or interactions between depression and diabetes, subjects with high depression ratings and high hemoglobin A1c levels had the lowest mean FA values in the right ALIC. In addition, we found a significant negative correlation between FA of the left ALIC with hemoglobin A1c in diabetic subjects (DC þ DD; P ¼ .016). Our study demonstrated novel findings of white matter abnormalities of the ALIC in depression and diabetes. These findings have implications for clinical manifestations of depression and diabetes as well as their pathophysiology.
Over the past decade a wide spectrum of machine learning models have been developed to model the ... more Over the past decade a wide spectrum of machine learning models have been developed to model the neurodegenerative diseases, associating biomarkers, especially non-intrusive neuroimaging markers, with key clinical scores measuring the cognitive status of patients. Multi-task learning (MTL) has been commonly utilized by these studies to address high dimensionality and small cohort size challenges. However, most existing MTL approaches are based on linear models and suffer from two major limitations: 1) they cannot explicitly consider upper/lower bounds in these clinical scores; 2) they lack the capability to capture complicated non-linear interactions among the variables. In this paper, we propose Subspace Network, an efficient deep modeling approach for non-linear multi-task censored regression. Each layer of the subspace network performs a multi-task censored regression to improve upon the predictions from the last layer via sketching a low-dimensional subspace to perform knowledge...
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015
Genetic and environmental factors affect white matter connectivity in the normal brain, and they ... more Genetic and environmental factors affect white matter connectivity in the normal brain, and they also influence diseases in which brain connectivity is altered. Little is known about genetic influences on brain connectivity, despite wide variations in the brain's neural pathways. Here we applied the "DICCCOL" framework to analyze structural connectivity, in 261 twin pairs (522 participants, mean age: 21.8 y ± 2.7SD). We encoded connectivity patterns by projecting the white matter (WM) bundles of all "DICCCOLs" as a tracemap (TM). Next we fitted an A/C/E structural equation model to estimate additive genetic (A), common environmental (C), and unique environmental/error (E) components of the observed variations in brain connectivity. We found 44 "heritable DICCCOLs" whose connectivity was genetically influenced (2 >1%); half of them showed significant heritability (2 >20%). Our analysis of genetic influences on WM structural connectivity suggests high heritability for some WM projection patterns, yielding new targets for genome-wide association studies.
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Papers by Liang Zhan