Vince D. Calhoun

DECODE decomposes brain functional states into structure-coupled and decoupled components, revealing AD-related differences in temporal dynamics and connectivity organization.

The paper links functional brain network connectivity across age and psychiatric disorders to neurobiological microprofiles, showing stage-specific associations.

The paper shows that alpha-range EEG oscillations are linked to dynamic BOLD brain networks, with low-alpha periods associated with unique configurations and both low- and high-alpha periods linked to overlapping networks.

NeuroMark dFNC uses reference-state templates to estimate subject-specific dynamic connectivity states across datasets, enabling cross-cohort comparison while preserving individual patterns.

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 a dynamic directional coordination axis in the cortical hierarchy that is selectively reorganized in schizophrenia, linked to symptoms and cognition.

This paper introduces an amplitude-resolved framework using ICA to analyze fMRI data, showing that discriminative information varies non-monotonically across amplitude ranges.

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

This paper introduces spatio-dynamical decomposition for fMRI BSS, showing it better recovers sources using both spatial and temporal criteria than methods that use only one.

SILICA-SNC transforms whole brain tractography into a network representation, revealing organized white matter architecture and coupling patterns.

This paper introduces class-conditional conformal prediction to address uneven coverage in multi-class psychiatric neuroimaging, showing improved coverage consistency across diagnostic classes.

The paper analyzes how dynamic brain connectivity states change across different echo times (TEs) in fMRI data using a bi-exponential model and finds TE-dependent variance and state transitions.

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

This study compares functional and structural brain organization between pigs and humans using MRI data, finding homologous large-scale networks and white matter pathways.

CSU-RSC identifies sex-dominant subgroups in psychosis by integrating supervised and unsupervised clustering on neuroimaging data, revealing sex-specific biological heterogeneity.

The paper identifies sex-dominant neurosubtypes in psychosis using fMRI data and shows their unique dysconnectivity patterns and cognitive implications.

The paper describes the OpenNeuroPET initiative, which develops standards and open-source tools to standardize and share PET neuroimaging data, enhancing reproducibility and collaboration.

This paper explores privacy-preserving visualization techniques for brain functional connectivity using differential privacy to protect participant data while maintaining visual utility.

This paper introduces the intrinsic network flow framework to explain cognitive states through phase dynamics, showing that task classification accuracy is higher using phase information than activation patterns.

The paper identifies a dominant beta oscillatory brain state in the days after psilocybin or 5-MeO-DMT use using resting-state EEG and dynamic functional connectivity analysis.

NeuroFLAME provides a secure, scalable federated platform for multi-site neuroimaging analysis, achieving consistency with centralized methods while preserving data privacy and enabling institutional compliance.

This paper introduces DyCoM, a framework that unifies dynamic connectivity analysis in neuroimaging by decomposing estimators into fundamental operations, revealing distinct biological interpretations from different computational choices.

This paper introduces a dynamic fusion framework to integrate structural and functional connectivity data, revealing differences between schizophrenia patients and controls.

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.

SILICA decomposes white matter tractograms into population-level statistical components while preserving individual streamline geometry, revealing recognizable brain systems and enabling trajectory-resolved visualization.

This study analyzes nonlinear developmental trajectories of brain networks in infants using a cubic model, revealing network-specific growth patterns and interactions.

The paper develops a timescale-aligned framework to measure amplitude balance in brain networks and finds greater imbalance and slower recovery in schizophrenia, linked to symptoms and cognition.

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

This study shows that sleep continuity after encoding affects memory accuracy and neural reinstatement of context memory in younger and older adults.

This study evaluates the robustness of NeuroMark-derived functional networks to fMRI spatial normalization across different age groups, finding high stability regardless of normalization strategy.