BackgroundFunctional magnetic resonance imaging (fMRI) resting-state studies show generalized social anxiety disorder (gSAD) is associated with disturbances in networks involved in emotion regulation, emotion processing, and perceptual functions, suggesting a network framework is integral to elucidating the pathophysiology of gSAD. However, fMRI does not measure the fast dynamic interconnections of functional networks. Therefore, we examined whole-brain functional connectomics with electroencephalogram (EEG) during resting-state.MethodsResting-state EEG data was recorded for 32 patients with gSAD and 32 demographically-matched healthy controls (HC). Sensor-level connectivity analysis was applied on EEG data by using Weighted Phase Lag Index (WPLI) and graph analysis based on WPLI was used to determine clustering coefficient and characteristic path length to estimate local integration and global segregation of networks.ResultsWPLI results showed increased oscillatory midline coherence in the theta frequency band indicating higher connectivity in the gSAD relative to HC group during rest. Additionally, WPLI values positively correlated with state anxiety levels within the gSAD group but not the HC group. Our graph theory based connectomics analysis demonstrated increased clustering coefficient and decreased characteristic path length in theta-based whole brain functional organization in subjects with gSAD compared to HC.ConclusionsTheta-dependent interconnectivity was associated with state anxiety in gSAD and an increase in information processing efficiency in gSAD (compared to controls). Results may represent enhanced baseline self-focused attention, which is consistent with cognitive models of gSAD and fMRI studies implicating emotion dysregulation and disturbances in task negative networks (e.g., default mode network) in gSAD.
Brain network embedding is the process of converting brain network data to discriminative representations of subjects, so that patients with brain disorders and normal controls can be easily separated. Computer-aided diagnosis based on such representations is potentially transformative for investigating disease mechanisms and for informing therapeutic interventions. However, existing methods either limit themselves to extracting graph-theoretical measures and subgraph patterns, or fail to incorporate brain network properties and domain knowledge in medical science. In this paper, we propose t-BNE, a novel Brain Network Embedding model based on constrained tensor factorization. t-BNE incorporates 1) symmetric property of brain networks, 2) side information guidance to obtain representations consistent with auxiliary measures, 3) orthogonal constraint to make the latent factors distinct with each other, and 4) classifier learning procedure to introduce supervision from labeled data. The Alternating Direction Method of Multipliers (ADMM) framework is utilized to solve the optimization objective. We evaluate t-BNE on three EEG brain network datasets. Experimental results illustrate the superior performance of the proposed model on graph classification tasks with significant improvement 20.51%, 6.38% and 12.85%, respectively. Furthermore, the derived factors are visualized which could be informative for investigating disease mechanisms under different emotion regulation tasks.
Connectomics is a framework that models brain structure and function interconnectivity as a network, rather than narrowly focusing on select regions-of-interest. MRI-derived connectomes can be structural, usually based on diffusion-weighted MR imaging, or functional, usually formed by examining fMRI blood-oxygen-level-dependent (BOLD) signal correlations. Recently, we developed a novel method for assessing the hierarchical modularity of functional brain networks—the probability associated community estimation (PACE). PACE uniquely permits a dual formulation, thus yielding equivalent connectome modular structure regardless of whether positive or negative edges are considered. This method was rigorously validated using the 1,000 functional connectomes project data set (F1000, RRID:SCR_005361) (1) and the Human Connectome Project (HCP, RRID:SCR_006942) (2, 3) and we reported novel sex differences in resting-state connectivity not previously reported. (4) This study further examines sex differences in regard to hierarchical modularity as a function of age and clinical correlates, with findings supporting a basal configuration framework as a more nuanced and dynamic way of conceptualizing the resting-state connectome that is modulated by both age and sex. Our results showed that differences in connectivity between men and women in the 22–25 age range were not significantly different. However, these same non-significant differences attained significance in both the 26–30 age group (p = 0.003) and the 31–35 age group (p < 0.001). At the most global level, areas of diverging sex difference include parts of the prefrontal cortex and the temporal lobe, amygdala, hippocampus, inferior parietal lobule, posterior cingulate, and precuneus. Further, we identified statistically different self-reported summary scores of inattention, hyperactivity, and anxiety problems between men and women. These self-reports additionally divergently interact with age and the basal configuration between sexes.
Long-term survivors of childhood Hodgkin lymphoma (HL) experience high burden of chronic health morbidities. Correlates of neurocognitive and psychosocial morbidity have not been well established. 1,760 survivors of HL (mean[SD] age 37.5[6.0] years, time since diagnosis 23.6[4.7] years, 52.1% female) and 3,180 siblings (age 33.2[8.5] years, 54.5% female) completed cross-sectional surveys assessing neurocognitive function, emotional distress, quality of life, social attainment, smoking, and physical activity. Treatment exposures were abstracted from medical records. Chronic health conditions were graded according to NCI CTCAE v4.3 (1=mild, 2=moderate, 3=severe/disabling, 4=life-threatening). Multivariable analyses, adjusted for age, sex, and race, estimated relative risk (RR) of impairment in survivors vs. siblings and, among survivors, risk of impairment associated with demographic, clinical, treatment factors and grade 2+ chronic health conditions. Compared with siblings, survivors had significant higher risk (p's<0.05) of neurocognitive impairment (e.g. memory 8.1% vs. 5.7%), anxiety (7.0%%vs. 5.4%),depression (9.1% vs. 7%), unemployment (9.6% vs. 4.4%), and impaired physical/mental quality of life (e.g. physical function 11.2% vs. 3.0%). Smoking was associated with higher risk of impairment in task efficiency (RR=1.56[1.02-2.39]), emotional regulation (RR=1.84[1.35-2.49]), anxiety (RR=2.43[1.51-3.93]), and depression (RR=2.73[1.85-4.04]). Meeting CDC exercise guidelines was associated with lower risk of impairment in task efficiency (RR=0.70[0.52-0.95]), organization (RR=0.60[0.45-0.80]), depression (RR=0.66[0.48-0.92]), and multiple quality of life domains. Cardiovascular and neurologic conditions were associated with impairment in nearly all domains. Survivors of HL are at elevated risk for neurocognitive and psychosocial impairment, and risk is associated with modifiable factors that provide targets for interventions to improve long-term functional outcomes.
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