Vince D. Calhoun

This study examines spatial changes in infant brain networks using resting-state fMRI data from 74 infants, revealing age-related patterns in network organization and intensity distribution.

The paper links brain entropy to molecular and cellular architecture in psychosis by analyzing differences in neurotransmitter systems and mitochondrial markers between healthy controls and individuals with schizophrenia or bipolar disorder.

This study integrates Florbetapir PET and functional network connectivity to identify disease progression markers and genetic risk associations in Alzheimer's disease.

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 introduces functional inertia as a measure of brain dynamics, showing it organizes activity into regimes and correlates with symptom severity in schizophrenia.

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.

The paper uses resting-state fMRI data to analyze dynamic metabolic demand patterns in children, finding sex-specific differences in neural flexibility linked to cognitive performance.

PhaseICA extracts reoccurring spatiotemporal brain patterns by incorporating phase information, improving the analysis of nonstationary brain activity.

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

This paper introduces FINE, a frequency-aware deep learning model for Alzheimer's disease classification using resting-state fMRI, achieving ROC-AUC 0.769 on OASIS-3.

This paper compares sliding window Pearson correlation and phase synchronization for time-resolved functional connectivity in fMRI data, finding they capture different aspects of brain dynamics and are complementary in their strengths and limitations.

The paper introduces NeuroMark PET, an automated framework using spatially constrained ICA to identify Aβ networks in PET data, showing higher sensitivity for age-related changes compared to traditional ROIs.

This study identifies two ASD subgroups with distinct brain morphology using cross-cultural datasets, highlighting key brain regions associated with subgroup characteristics and autistic traits.

This study uses dynamic inter-modality source coupling to identify sex-specific brain structural-functional connectivity patterns in children, showing different cognitive predictions for males and females.

This study compares functional network energy across multiscale brain networks in preterm and full-term adolescents from the ABCD study, identifying differences in network stability and flexibility.

The paper maps functional brain network maturation in early infancy using resting-state fMRI, showing age-related increases in network consistency and engagement.

This study introduces normalized dynamic time warping to improve sensitivity in detecting functional network connectivity differences in schizophrenia compared to standard DTW and correlation methods.

The paper links functional change patterns to brain structure using multimodal fusion in adolescents, revealing sex-specific differences in neural dynamics.

This paper introduces dFNG, a method to estimate dynamic functional network connectivity gradients from ICA, and applies it to schizophrenia data to reveal distinct connectivity patterns between patient and healthy groups.

This paper introduces a groupwise registration framework for longitudinal infant DTI images, improving alignment accuracy through clustering and sample-specific common space alignment.

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

This paper introduces a deep learning model to analyze resting-state fMRI dynamics, finding that context embeddings better predict schizophrenia diagnosis and correlate with age and symptom severity.

This study uses sgr-ICA with 500 components on a large rsfMRI dataset to identify fine-grained intrinsic connectivity networks (ICNs) and reveals dysconnectivity patterns in schizophrenia.

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 introduces a voxel-centric framework using information theory to analyze multi-way brain network interactions, revealing reduced Hamming weights in auditory, visual, and motor networks 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.

NeuroMark-HiFi detects high-spatial-frequency functional brain networks, revealing additional group differences in schizophrenia and healthy controls.

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 links short-term brain dynamics to long-term changes in the functional connectome during postnatal development, finding a strong association between specific brain states and non-linear developmental acceleration.

This study links multi-scale brain connectivity networks to cognitive and behavioral functions in adolescents, finding significant associations with attention, working memory, and behavioral regulation.