Pochon, J.-B. | bioRxiv Science

bioRxiv2026

Spectral and non-spectral EEG measures in the prediction of working memory task performance and psychopathology

Working memory (WM) supports the temporary maintenance of goal-relevant information and is disrupted across many neuropsychiatric disorders. We examined whether scalp electroencephalography (EEG) data features beyond spectral power, including waveform shape, broadband spectral structure, and signal complexity, provide complementary information for predicting cognitive and clinical outcomes. EEG was recorded from 200 adults spanning a broad range of neuropsychiatric symptom severity while they completed three WM task paradigms: Sternberg spatial WM (SWM), delayed face recognition (DFR), and dot pattern expectancy (DPX). Separate machine learning models were trained on EEG features from the encoding, delay, and probe phase of each task to predict participants task accuracy, reaction time (RT) variability, WM capacity, and psychopathology scores (Brief Psychiatric Rating Scale). A split-half analytic framework was used, with cross-validated model development in an exploratory dataset (N=100) and evaluation of statistically significant models in a held-out validation dataset (N=100). In the exploratory dataset, SWM task data best predicted WM capacity, DPX task data predicted RT variability, and DFR task data predicted psychopathology, suggesting that these three WM paradigms engage distinct neural processes relevant to different outcomes. No models reliably predicted task accuracy. Models incorporating features beyond spectral power generally outperformed power-only models, and task-derived features outperformed resting-state-derived features. However, only those models predicting WM capacity and RT variability generalized to the validation dataset; models predicting psychopathology did not. These findings demonstrate functional heterogeneity across WM paradigms, show that complementary EEG features enhance predictive modeling, and highlight the importance of rigorous validation for identifying robust brain-behavior relationships.

neuroscience↗

bioRxiv2021

Nicotine dependence and functional connectivity of insular cortex subregions

The insular cortex has been identified as a promising target in brain-based therapies for Tobacco Use Disorder, and has three major sub-regions (ventral anterior, dorsal anterior, and posterior) that serve distinct functional networks. How these subregions and associated networks contribute to nicotine dependence has not been well understood, and therefore was the subject of this study. Forty-seven individuals (24 women; 18-45 years old) who smoked cigarettes daily rated their dependence using the Fagerstrom Test for Nicotine Dependence (FTND), abstained from smoking overnight (~12 h), and underwent resting-state functional MRI. Correlations between dependence and resting-state functional connectivity (RSFC) of the major insular sub-regions were evaluated using whole-brain-corrected voxel-wise analyses and post-hoc region-of-interest (ROI) analyses. Dependence was analyzed both as a unitary (FTND total score) and bivariate construct - two FTND factors ("morning smoking" and "daytime smoking"). Dependence was negatively correlated with connectivity of both the right dorsal and left ventral anterior insula with the left precuneus, and with connectivity of the left posterior insula to the left putamen. In post-hoc analyses, dependence correlated negatively with connectivity between all anterior insula subregions and the left precuneus, and with bilateral posterior insula connectivity with the left posterior putamen. The latter finding was driven by "daytime smoking". These results suggest an anterior-posterior distinction in functional insular networks associated with different dimensions of nicotine dependence, with greater dependence linked to weaker connectivity. They may inform therapeutic approaches involving brain stimulation that may elicit differential clinical outcomes depending on the insular subnetwork targeted.

neuroscience↗