Godfrey D. Pearlson

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.

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 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.

This study examines the effects of season and weather on brain volumes using MRI data from 3,279 individuals, finding significant relationships between weather variables and specific brain regions.

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.

This paper introduces regression dynamic causal modeling (rDCM) for resting-state fMRI, providing directional connectivity estimates and scalability to whole-brain networks.

The paper analyzes dynamic functional connectivity in the visual sensory network of schizophrenia patients and healthy controls, finding disrupted connectivity patterns and a correlation between connectivity states and visual learning.

The paper detects and corrects label noise in mental health diagnoses using MRI data from mood and psychosis disorders through a multi-label data cleansing approach.

This study identifies 45 structural-functional component maps across 9 functional domains using independent component analysis in two cohorts, showing varying S-F coherence in different brain regions.

The paper identifies whole-brain polarization states in fMRI data that are disrupted in schizophrenia and correlate with network connectivity patterns.

This paper introduces the spatial chronnectome, a method to analyze dynamic spatiotemporal brain network interactions, revealing transient network changes and cognitive relevance in schizophrenia.

This paper introduces a hierarchical approach to analyze spatial and temporal dynamics in brain function, revealing new insights into the chronnectome and identifying changes in schizophrenia patients.

The paper identifies brain-wide bi-directional connectivity alterations in cortico-striato-cerebellar circuits in schizophrenia using resting-state fMRI and validates findings with clustering and classification methods.

The paper discusses challenges in genetic analysis of deep phenotyping projects in common disorders and highlights advances in imputation and GWAS methods to address these challenges.

The paper reports GWAS findings linking common variants in NRXN1, LRP1B, and RORA to increased ventricular volumes in psychosis, using deep phenotyping data from 1,115 cases and controls.

The paper investigates the association between schizophrenia genetic risk and cognitive performance in healthy individuals, finding a link with current cognitive abilities but not with premorbid intelligence or education.