Daniel H. Mathalon

This paper introduces a canonical microcircuit model to estimate E/I balance from non-invasive M/EEG recordings and shows its relevance to schizophrenia biomarkers.

This study finds no objective cognitive deficits in individuals with post-COVID-19 subjective cognitive complaints, but links them to higher depression/anxiety and biological markers like APOE-ε4 and sCD14.

The paper examines how sleep stages and PTSD status affect emotional memory accuracy through neural responses to visual stimuli in men and women.

This study identifies a shared genetic factor underlying brain age gaps across 30 cohorts, finding 19 SNPs and associations with health traits.

The paper identifies altered default mode network connectivity dynamics in schizophrenia using a novel iterative feature-removal clustering approach on resting-state fMRI data.

This study identifies small but consistent anatomical correlates of negative symptom dimensions in schizophrenia, linking them to prefrontal cortical thickness and limbic subcortical volumes.

The paper introduces a framework using SHAP to identify FNC biomarkers distinguishing between schizophrenia patients and controls, and young vs. old adults with high accuracy.

This paper introduces a novel method to analyze static and dynamic functional connectivity in schizophrenia patients, finding elevated entropy in connectivity patterns compared to healthy controls.

The paper presents a WM intrinsic connectivity network template derived from 100,000 fMRI scans, showing distinct WM and GM networks with task-related activity and differences in schizophrenia.

This paper develops a method to detect linked features across neuroimaging modalities and identifies markers related to age, sex, cognition, and schizophrenia in large datasets.

The paper compares functional brain measures between 22qDel and CHR cohorts, finding distinct patterns of connectivity and variability disruptions.

This paper introduces dynamic fusion, a method to link brain structure and function over time, showing it captures dynamic functional processes and detects nuanced structural components.

This paper introduces a biophysical, connectivity-based generative model for rs-fMRI that integrates spectral and structural information to quantify structure-function relationships.

The paper develops a method to estimate time-resolved spatial networks and applies it to study sex-specific schizophrenia disruptions and their genetic links.

The paper investigates how NREM sleep spindles affect emotion regulation and memory consolidation in PTSD after stress exposure, finding increased spindles correlate with reduced anxiety but poorer memory recall.

This study examines brain morphometric deviations in individuals at clinical high-risk for psychosis and finds they largely overlap with healthy variation and are not strongly associated with symptom severity or conversion to psychosis.

This paper introduces a multi-scale spatially constrained ICA approach to analyze fMRI data, revealing consistent group differences and predictive patterns in schizophrenia.

This study finds that individuals at clinical high risk for psychosis have larger choroid plexus volumes, associated with neuroanatomical changes and plasma analyte markers.

The paper develops a reliable network template and methods to estimate individualized functional networks from resting-state fMRI data across 100k+ individuals, showing how data length and spatial resolution affect network estimation.

This study introduces a method to analyze explicitly nonlinear dynamic functional connectivity in resting-state fMRI data and finds significant group differences between schizophrenia patients and healthy controls.

This study shows that intrinsic connectivity networks in fMRI are continuously present and overlapping, not just during discrete events, and that analyzing all time points provides more comprehensive insights into functional patterns and schizophrenia symptoms.

This study combines EEG and fMRI to show that aperiodic EEG measures correlate with specific hemodynamic networks during rest, indicating a balance of top-down and bottom-up processing.

This paper introduces multi-model order ICA to evaluate brain functional network connectivity across multiple spatial scales and finds that FNC between model orders 25 and 50 best predicts schizophrenia vs. healthy controls.

This paper introduces a method to quantify nonlinear functional connectivity in resting-state fMRI data, showing distinct nonlinear patterns in schizophrenia patients compared to healthy controls.

The paper introduces a novel tri-clustering method for dynamic functional network connectivity to identify schizophrenia effects across subgroups in resting fMRI data.

This paper introduces msICA to analyze dynamic functional interactions across spatial scales in schizophrenia, finding sex-specific differences in connectivity patterns and their association with symptom scores.

The paper investigates dynamic functional connectivity (dFC) of the default mode network (DMN) in schizophrenia and links it to symptom severity using resting-state fMRI data from two datasets.

The paper examines alpha event-related desynchronization during reward processing in schizophrenia, finding altered alpha ERD magnitude between reward outcomes and associations with trait rumination.

This study uses EEG to examine reward anticipation and outcome processing during slot machine play, finding distinct neural responses to winning, near misses, and total misses.