Vince D. Calhoun

This study examines how habitual sleep quality affects episodic memory using EEG-based representational similarity analysis, finding mixed correlations between sleep metrics and memory performance.

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 study uses high-order ICA on PET scans to identify amyloid-beta networks in Alzheimer's Disease, revealing distinct gray and white matter patterns associated with cognitive impairment.

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 study identifies a structural connectivity pattern shared between physical activity and depressive mood using UK Biobank data, linking it to neurotransmitter receptors and pathways.

The paper creates a reference atlas of functional brain networks for typically developing adolescents using resting-state fMRI data and identifies age-related changes and sex effects.

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.

This paper introduces an image-assisted predictive modeling approach that integrates neuroimaging and behavioral data to predict future brain health constructs.

The paper introduces a fully automated pipeline to estimate subject-specific rs-fMRI states using constrained ICA of dFNC across 100,517 subjects.

This study examines time-varying spatial propagation of brain networks in fMRI data and links propagative patterns to schizophrenia.

This paper introduces a conditional DDPM with attention to synthesize subject-specific 3D brain networks from rs-fMRI data, outperforming existing methods in accuracy and detail.

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 develops and validates 12 new VADR models for human respiratory viruses, achieving high accuracy in genome annotation and identifying novel viral biology.

The paper describes the neuroimaging component of the A2CPS project, detailing data collection, processing, and initial findings linking brain measures to clinical variables.

This study identifies adult-like multi-scale brain networks in infants using rsfMRI, showing early emergence of functional connectivity patterns.

This paper introduces a multimodal vision transformer (MultiViT) to classify schizophrenia using structural and functional MRI data, achieving higher accuracy than unimodal and existing multimodal methods.

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.

This paper introduces TICA, a method for analyzing fMRI data across multiple spatial scales to detect functional brain networks and differences between healthy and schizophrenia cohorts.

The paper conducts GWAS on brain volumes, identifies genetic loci, and develops polygenic scores with cross-ancestry predictive ability and causal links to neuropsychiatric disorders.

This paper introduces a template-driven method to generate dynamic functional network connectivity features from neuroimaging data, showing improved prediction of medication response in mood disorders.

This paper uses phase portraits and fuzzy recurrence plots to analyze nonlinear dynamics in brain networks, showing they can capture functional connectivity and predict neural behavior.

This paper introduces a deep convolutional neural network method to extract nonlinear functional network connectivity from fMRI data and applies it to classify mental disorders with high accuracy.

The paper proposes a decentralized sparse federated learning approach for neuroimaging data to reduce communication overhead while maintaining data privacy.

This paper creates a whole-brain functional atlas from resting-state fMRI data to standardize terminology and improve consistency across neuroimaging studies.

The paper identifies four RSFC subtypes in children using ABCD data and shows they predict cognitive and mental health outcomes better than individual connections.

This paper develops an active learning framework to personalize brain stimulation therapies, showing it outperforms random sampling in efficiency and accuracy across in silico and in vivo experiments.

This paper introduces a method to identify linear and explicitly nonlinear dependencies in resting fMRI data and applies it to detect differences in schizophrenia.

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