Differences in the functional brain architecture of sustained attention and working memory in youth and adults

Sustained attention (SA) and working memory (WM) are critical processes, but the brain networks supporting these abilities in development are unknown. We characterized the functional brain architecture of SA and WM in 9- to 11-year-old children and adults. First, we found that adult network predictors of SA generalized to predict individual differences and fluctuations in SA in youth. A WM model predicted WM performance both across and within children—and captured individual differences in later recognition memory—but underperformed in youth relative to adults. We next characterized functional connections differentially related to SA and WM in youth compared to adults. Results revealed 2 network configurations: a dominant architecture predicting performance in both age groups and a secondary architecture, more prominent for WM than SA, predicting performance in each age group differently. Thus, functional connectivity (FC) predicts SA and WM in youth, with networks predicting WM performance differing more between youths and adults than those predicting SA

Trial-level fluctuations in Attention Networks in fMRI

Fitting GLM to edge co-fluctuation timeserie

Alteration in the Local and Global Functional Connectivity of Resting State Networks in Parkinson's Disease

Objective Parkinson's disease (PD) is a neurodegenerative disorder that mainly leads to the impairment of patients' motor function, as well as of cognition, as it progresses. This study tried to investigate the impact of PD on the resting state functional connectivity of the default mode network (DMN), as well as of the entire brain. Methods Sixty patients with PD were included and compared to 60 matched normal control (NC) subjects. For the local connectivity analysis, the resting state fMRI data were analyzed by seed-based correlation analyses, and then a novel persistent homology analysis was implemented to examine the connectivity from a global perspective. Results The functional connectivity of the DMN was decreased in the PD group compared to the NC, with a stronger difference in the medial prefrontal cortex. Moreover, the results of the persistent homology analysis indicated that the PD group had a more locally connected and less globally connected network compared to the NC. Conclusion Our findings suggest that the DMN is altered in PD, and persistent homology analysis, as a useful measure of the topological characteristics of the networks from a broader perspective, was able to identify changes in the large-scale functional organization of the patients' brain

Skeleton of nested cross-validation.

<p>The skeleton of the nested cross-validation for measuring the performance of the proposed method.</p

An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts

<div><p>We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.</p></div

Skeleton of nested cross-validation.

<p>The skeleton of the nested cross-validation for measuring the performance of the proposed method.</p

Sensitivity histogram for tract group and direct tract labeling.

<p>The x-axis and y-axis represents sensitivity ranges and percentage of bundles that are included in the corresponding sensitivity ranges, respectively.</p

Skeleton of our automatic classification algorithm.

<p>The skeleton of the voting algorithm to determine the label of an input group.</p