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This paper investigates scalp electroencephalogram (EEG) data from 14 subjects with unilateral prefrontal cortex (pFC) lesions and 20 healthy controls during lateral visuospatial working memory (WM) tasks. The goal is to differentiate the brain networks involved in WM processing between these groups. The EEG recordings are transformed into graph signals, with proximity-weighted brain connectivity measures as edges and centrality measures as nodal features. Graph convolutional network (GCN) layers are used for feature representation, followed by a fully connected layer for classification. The GCN-based model effectively handles nine classification tasks, proving that graph-based network representation is versatile for describing brain interactions. The sparse MI-GCI-based graph model’s accuracy effectively captures the functional segregation of distinct WM tasks. The classifier using mutual information-guided Granger causality index (MI-GCI) with 20% of top edges matched prior classification performance with 67% fewer parameters and 80% less graph density, identifying the correct class of all 34 subjects in group identification using leave-one-out cross-validation and two-thirds majority voting. 

This paper describes a group-level classification of 14 patients with prefrontal cortex (pFC) lesions from 20 healthy controls using multi-layer graph convolutional networks (GCN) with features inferred from the scalp EEG recorded from the encoding phase of working memory (WM) trials. We first construct undirected and directed graphs to represent the WM encoding for each trial for each subject using distance correlation- based functional connectivity measures and differential directed information-based effective connectivity measures, respectively. Centrality measures of betweenness centrality, eigenvector centrality, and closeness centrality are inferred for each of the 64 channels from the brain connectivity. Along with the three centrality measures, each graph uses the relative band powers in the five frequency bands - delta, theta, alpha, beta, and gamma- as node features. The summarized graph representation is learned using two layers of GCN followed by mean pooling, and fully connected layers are used for classification. The final class label for a subject is decided using majority voting based on the results from all the subject's trials. The GCN-based model can correctly classify 28 of the 34 subjects (82.35% accuracy) with undirected edges represented by functional connectivity measure of distance correlation and classify all 34 subjects (100% accuracy) with directed edges characterized by effective connectivity measure of differential directed information. 

This paper describes a group-level analysis of 14 subjects with prefrontal cortex (pFC) lesions and 20 healthy controls performing multiple lateralized visuospatial working memory (WM) trials. Using effective brain connectivity measures inferred from directed information (DI) during memory encoding, we first show that DI features can correctly classify 18 control subjects and 11 subjects with pFC lesions, providing an overall accuracy of 85.3%. Second, we show that differential DI, the change in DI during the encoding phase from pretrial, can successfully overcome inter-subject variability and correctly identify the class of all 34 subjects (100% accuracy). These accuracy results are based on two-thirds majority thresholding among all trials. Finally, we use Welch’s t-test to identify the crucial differences in the two classes’ sub-networks responsible for memory encoding. While the inflow of information to the prefrontal region is significant among subjects with pFC lesions, the outflow from the prefrontal to the frontal and central regions is diminished compared to the control subjects. We further identify specific neural pathways that are exclusively activated for each group during the encoding phase. 

This paper analyzes the scalp electroencephalogram (EEG) recorded from 14 human subjects with pFC lesions and 20 healthy controls while performing lateral visuospatial working memory tasks to identify the directional brain networks responsible for memory encoding. First, we show that effective connectivity features using directed information (DI) are more accurate and robust than the functional connectivity measure of correlation coefficients in classifying the memory encoding stage from the pretrial phase, with a mean accuracy of 99.36%. Second, we identify the functional segregation of memory encoding to a much smaller sub-network by showing that the top 2.5% of the observed DI features can distinguish memory encoding from the pretrial phase with a mean accuracy of 93.1%. Finally, using graph features, we reveal the increased significance of frontocentral, centroparietal, and temporal regions in memory encoding for subjects with pFC lesions and reduced information flow in the prefrontal, frontal and parietooccipital areas when compared to healthy control. 

This paper investigates the effect of filtering (or modulating) the functional magnetic resonance imaging (fMRI) time-series on intelligence metrics predicted using dynamic functional connectivity (dFC). Thirteen brain regions that have highest correlation with intelligence are selected and their corresponding time-series are filtered. Using filtered time-series, the modified intelligence metrics are predicted. This experiment investigates whether modulating the time-series of one or two regions of the brain will increase or decrease the fluid ability and fluid intelligence among healthy humans. Two sets of experiments are performed. In the first case, each of the thirteen regions is separately filtered using four different digital filters with passbands: i) 0 - 0.25π, ii) 0.25π - 0.5π, iii) 0.5π - 0.75π, and iv) 0.75π – π, respectively. In the second case, two of the thirteen regions are filtered simultaneously using a low-pass filter of passband 0 - 0.25π. In both cases, the predicted intelligence declined for 45-65% of the subjects after filtering in comparison with the ground truths. In the first case, the low-pass filtering process had the highest predicted intelligence among the four filters. In the second case, it was noticed that the filtering of two regions simultaneously resulted in a higher prediction of intelligence for over 80% of the subjects compared to low-pass filtering of a single region.