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Wang J.,Beijing Normal University | Wang J.,Hangzhou Normal University | Wang J.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments | Wang X.,Peking University | And 4 more authors.
Human Brain Mapping | Year: 2015

The apolipoprotein E (APOE) e{open}4 allele is a well-established genetic risk factor for Alzheimer's disease (AD). Recent research has demonstrated an APOE e{open}4-mediated modulation of intrinsic functional brain networks in cognitively normal individuals. However, it remains largely unknown whether and how APOE e{open}4 affects the brain's functional network architecture in patients with AD. Using resting-state functional MRI and graph-theory approaches, we systematically investigated the topological organization of whole-brain functional networks in 16 APOE e{open}4 carriers and 26 matched noncarriers with AD at three levels: global whole-brain, intermediate module, and regional node/connection. Neuropsychological analysis showed that the APOE e{open}4 carriers performed worse on delayed memory but better on a late item generation of a verbal fluency task (associated with executive function) than noncarriers. Whole-brain graph analyses revealed that APOE e{open}4 significantly disrupted whole-brain topological organization as characterized by (i) reduced parallel information transformation efficiency; (ii) decreased intramodular connectivity within the posterior default mode network (pDMN) and intermodular connectivity of the pDMN and executive control network (ECN) with other neuroanatomical systems; and (iii) impaired functional hubs and their rich-club connectivities that primarily involve the pDMN, ECN, and sensorimotor systems. Further simulation analysis indicated that these altered connectivity profiles of the pDMN and ECN largely accounted for the abnormal global network topology. Finally, the changes in network topology exhibited significant correlations with the patients' cognitive performances. Together, our findings suggest that the APOE genotype modulates large-scale brain networks in AD and shed new light on the gene-connectome interaction in this disease. © 2015 Wiley Periodicals, Inc.

Wang J.,Beijing Normal University | Wang J.,Hangzhou Normal University | Wang J.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments | Wang X.,Beijing Normal University | And 5 more authors.
Frontiers in Human Neuroscience | Year: 2015

Recent studies have suggested that the brain's structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface (GUI); (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website. © 2015 Wang, Wang, Xia, Liao, Evans and He.

Liu Y.,Capital Medical University | Liu Y.,VU University Amsterdam | Liu Y.,Tianjin Medical University | Wang J.,Hangzhou Normal University | And 14 more authors.
Neurology | Year: 2015

Objective: To investigate spinal cord and brain atrophy in neuromyelitis optica (NMO), and its relationship with other MRI measurements and clinical disability, compared with patients with multiple sclerosis (MS) and healthy controls (HC). Methods: We recruited 35 patients with NMO, 35 patients with MS, and 35 HC, who underwent both spinal cord and brain MRI. Mean upper cervical cord area (MUCCA), brain parenchymal fraction (BPF), gray matter fraction (GMF), white matter fraction (WMF), and spinal cord and brain lesion loads were measured and compared among groups. Multivariate associations between MUCCA and brain volume measurement and clinical variables were assessed by partial correlations and multiple linear regression. Results: Patients with NMO showed smaller MUCCA than HC (p 0.004), and patients with MS had a trend of smaller MUCCA compared to HC (p 0.07), with no significant difference between the patient groups. Patients with NMO showed lower BPF than HC, and patients with MS had lower BPF and GMF than patients with NMO. In NMO, MUCCA was correlated with Expanded Disability Status Scale score (EDSS), number of relapses, and total spinal cord lesion length, while in MS, MUCCA was correlated with WMF and EDSS. MUCCA was the only independent variable for predicting clinical disability measured by EDSS in NMO (R 2 0.55, p < 0.001) and MS (R 2 0.17, p 0.013). Conclusion: NMO showed predominately spinal cord atrophy with mild brain atrophy, while MS demonstrated more brain atrophy, especially in the gray matter. MUCCA is the main MRI-derived parameter for explaining clinical disability in NMO and MS, and may serve as a potential biomarker for further clinical trials, especially in NMO. © 2015 American Academy of Neurology.

An L.,Peking University | Cao Q.-J.,Peking University | Sui M.-Q.,Peking University | Sun L.,Peking University | And 5 more authors.
Neuroscience Bulletin | Year: 2013

Regional homogeneity (ReHo) and the amplitude of low-frequency fluctuation (ALFF) are two approaches to depicting different regional characteristics of resting-state functional magnetic resonance imaging (RS-fMRI) data. Whether they can complementarily reveal brain regional functional abnormalities in attention-deficit/hyperactivity disorder (ADHD) remains unknown. In this study, we applied ReHo and ALFF to 23 medication-naïve boys diagnosed with ADHD and 25 age-matched healthy male controls using whole-brain voxel-wise analysis. Correlation analyses were conducted in the ADHD group to investigate the relationship between the regional spontaneous brain activity measured by the two approaches and the clinical symptoms of ADHD. We found that the ReHo method showed widely-distributed differences between the two groups in the fronto-cingulo-occipito-cerebellar circuitry, while the ALFF method showed a difference only in the right occipital area. When a larger smoothing kernel and a more lenient threshold were used for ALFF, more overlapped regions were found between ALFF and ReHo, and ALFF even found some new regions with group differences. The ADHD symptom scores were correlated with the ReHo values in the right cerebellum, dorsal anterior cingulate cortex and left lingual gyrus in the ADHD group, while no correlation was detected between ALFF and ADHD symptoms. In conclusion, ReHo may be more sensitive to regional abnormalities, at least in boys with ADHD, than ALFF. And ALFF may be complementary to ReHo in measuring local spontaneous activity. Combination of the two may yield a more comprehensive pathophy-siological framework for ADHD. © 2013 Shanghai Institutes for Biological Sciences, CAS and Springer-Verlag Berlin Heidelberg.

Lv Y.,Max Planck Institute for Human Cognitive and Brain Sciences | Lv Y.,Hangzhou Normal University | Margulies D.S.,Max Planck Institute for Human Cognitive and Brain Sciences | Margulies D.S.,Humboldt University of Berlin | And 5 more authors.
PLoS ONE | Year: 2013

Resting-state functional magnetic resonance imaging (RS-fMRI) has been widely used to investigate temporally correlated fluctuations between distributed brain areas, as well as to characterize local synchronization of low frequency (<0.1 Hz) spontaneous fMRI signal. Regional homogeneity (ReHo) was proposed as a voxel-wise measure of the synchronization of the timecourses of neighboring voxels and has been used in many studies of brain disorders. However, the interpretation of ReHo remains challenging because the effect of high frequency task on ReHo is still not clear. In order to investigate the effect of a high-frequency task on the modulation of local synchronization of resting-state activity, we employed three right-finger movement scanning sessions: slow-event related ('Slow'), fast-event related ('Fast'), and continuous finger pressure ('Tonic'), from 21 healthy participants and compared the ReHo of the three task states with that of resting-state ('Rest'). In the contralateral sensorimotor cortex, 'Slow' task state showed greater ReHo than 'Rest' in low frequency band (0-0.08Hz) fMRI signal, but lower ReHo in high frequency band (0.08-1.67 Hz); 'Fast' task state showed lower ReHo than 'Rest' in both the low and high frequency band; 'Tonic' state did not show any significant difference compared to 'Rest'. The results in the contralateral sensorimotor cortex suggest that local synchronization of BOLD signal varies with different finger tapping speed. In the ipsilateral sensorimotor cortex, all the three task states had lower ReHo than the 'Rest' state both in the low and high frequency, suggesting a similar effect of fast and slow finger tapping frequencies on local synchronization of BOLD signal in the ipsilateral motor cortex. © 2013 Lv et al.

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