Godfrey D. Pearlson

This paper introduces a chronnectogram-ICA framework to track continuously evolving and overlapping brain states in fMRI data, revealing new insights into schizophrenia's connectivity dynamics.

This study identifies three psychosis neurosubtypes using imaging data, showing distinct connectivity patterns and supporting their biological validity through relative alignment.

This study shows environmental enrichment reduces restricted behaviors in C58 mice by altering brain structure and microstructure, with strain and sex-dependent effects.

This paper introduces a method to estimate directed causal brain networks from fMRI data, showing it detects more group-differentiating edges in schizophrenia compared to existing methods.

This paper develops a model using resting-state fMRI to identify functional connectivity patterns in schizophrenia, finding a robust cerebellothalamic disruption signature.

The paper proposes a dynamic biosocial model of hypomanic vulnerability, linking neural mechanisms to behavioral and social factors, and presents preliminary evidence of neural reconfiguration in hypomanic tendencies.

DESINE quantifies dynamic spatial network interactions in schizophrenia, finding reduced variability and rigidity linked to cognitive and clinical symptoms.

This study identifies microstructural abnormalities in the forceps minor white matter tract in schizophrenia using DTI, suggesting it as a potential biomarker and linking it to social behavior dysfunction.

This paper introduces a variational autoencoder framework to interpolate functional network connectivity data across neuropsychiatric disorders, revealing continuous gradients and heterogeneity.

This study identifies spatiotemporal complexity disruptions in the psychotic brain using resting-state fMRI, revealing altered information integration and network instability.

STRiDE improves dynamic brain state estimation by integrating structural and functional connectivity, revealing disruptions in schizophrenia.

This study introduces a method to identify dynamic brain states from resting-state fMRI data, revealing distinct convergence and divergence patterns in four psychiatric disorders.

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 study identifies distinct functional connectivity patterns in mental disorders using dFIPs derived from rs-fMRI data, revealing disorder-specific and shared neural signatures.

This paper introduces a matrix-based entropy functional to analyze multiscale brain networks beyond pairwise interactions, offering new insights into complex brain dynamics and psychiatric conditions.

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.

The paper analyzes psychosis biotypes using ancestry-adjusted polygenic risk scores and identifies genomic associations with specific biotypes in brain transcriptomes.

This study examines dynamic spatial brain networks in resting-state fMRI data, finding atypical variability and coupling in schizophrenia linked to cognitive impairment.

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 study compares AD and SZ using rs-FNC with confounder control and machine learning, finding shared and distinct functional abnormalities and classification accuracies of 85% and 68%.

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

This study examines structural brain heterogeneity in schizophrenia using multimodal MRI data from the ENIGMA dataset, finding higher frontotemporal heterogeneity and left parahippocampal homogeneity in patients.

The paper replicates and compares intrinsic neural timescales in autism spectrum disorder and schizophrenia, finding reduced timescales in specific brain regions for schizophrenia compared to both typically developed individuals and autism spectrum disorder patients.

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

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.

The paper introduces EVOdFNCs, a method to analyze dynamic brain connectivity patterns in rs-fMRI data, revealing associations with schizophrenia symptoms.