Peter V Kochunov

This study identifies thalamic dysconnectivity as a key feature of negative symptoms in schizophrenia spectrum disorder using structural and functional MRI data.

This paper identifies systematic prediction bias in aging clocks and introduces a constrained optimization framework to improve calibration and downstream inference.

The paper develops and evaluates the Regional Vulnerability Index (RVI-ASD) as a neuroimaging biomarker for autism, showing its stability, heritability, and associations with cognition and social outcomes.

This paper introduces MSIVA, a method to capture latent relationships between brain structure and function, showing it identifies phenotype-associated brain regions in neuroimaging data.

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

This paper introduces st-DenseViT, a weakly supervised spatiotemporal vision transformer that generates 4D dynamic brain networks from fMRI data, showing significant differences between schizophrenia patients and healthy controls.

This paper uses aNy-way ICA to identify a cortico-thalamo-cerebellar circuit in schizophrenia, showing altered connectivity and cognitive impairments across multiple datasets.

The paper creates lifespan reference curves for white matter microstructure using diffusion MRI data from multiple datasets and validates them with ApoE4 risk factor analysis.

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 introduces a nonlinear method to estimate brain networks from fMRI data, showing it better distinguishes schizophrenia patients from controls than linear methods.

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 evaluates test-retest reliability of brain imaging metrics across multiple sites and scanners, finding significant differences in reliability between scanner types.

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

The paper introduces a method to analyze voxel-wise genome-wide associations and identifies genetic loci influencing white matter integrity in the corpus callosum.

This paper introduces a multi-modal AI model for schizophrenia classification using imaging and genetic data, achieving 94.10% accuracy and identifying key brain regions and genes associated with the disorder.

This study investigates how structural brain alterations in schizophrenia relate to network architecture, finding hub susceptibility and shared epicenters across disease stages and disorders.

The paper investigates age and sex differences in BBB function using MRI in 186 participants, finding more pronounced decline in males starting in the early 60s, particularly in parietal and temporal regions.

The paper analyzes brain functional connectomes in 40,000 individuals to identify aging-related network changes, finding two motor-related subnetworks with age-related declines in functional connectivity.

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 examines how APOE4 modifies the effect of plasma LDL on white matter integrity in older adults, finding a significant moderation effect.

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.

This paper introduces a deep residual CNN model to analyze 5D spatiotemporal brain dynamics in schizophrenia, revealing widespread group-level differences in spatiotemporal features.

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.

The paper examines how genetic and phylogenetic factors influence structure-function coupling in the human transmodal cortex and finds reduced genetic control in regions linked to social cognition and memory.

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 new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures and finds it outperforms existing methods in sensitivity, bias, and false discovery rate.

This study examines the genetic and phenotypic relationships between affect and cognition in the superior frontal cortex using twin data from the Human Connectome Project.