Dimitri Van De Ville

This paper introduces a method to cluster instantaneous phase-locking states using the Bures-Wasserstein distance, improving sensitivity to phase-lag size and spatial patterns in EEG data.

The paper shows that excitation-inhibition balance is reflected in long-range cortical dynamics and is state-dependent, extending beyond fMRI to arousal-linked dynamics.

The paper uses surface-based morphometry to analyze claustrum structure in 22q11DS, finding age-related shape changes and reduced deformation in the syndrome.

The paper evaluates how well population-level connectome harmonics represent individual brains across different dimensions and finds they can be useful despite not perfectly matching individual data.

This paper uses higher-order topology to analyze brain activity during naturalistic emotion tasks, finding that it captures fine-grained affective structure while pairwise connectivity captures broad arousal.

This study evaluates microstructure-weighted connectomes using diffusion MRI data and finds that DePerp provides the most consistent structure-function coupling across spatial scales.

The paper identifies five distinct depression phenotypes using MEG-oscillation functional connectivity and low-dimensional brain-symptom associations.

This study identifies early cerebrovascular reactivity impairments in 22q11.2DS as a biomarker for psychosis susceptibility, particularly in regions like the anterior cingulate cortex and globi pallidi.

This study examines how individual differences in anxiety and openness relate to brain activity during emotional film scenes, using emotion-informed functional gradients to improve prediction of these traits.

This study uses TMS and spinal fMRI to show how spinal cord activity changes with TMS intensity, revealing intensity-dependent BOLD responses in specific spinal regions.

This paper shows that individualized connectome harmonics reveal a hierarchical organization linking brain structure, function, and cognition across 875 participants.

The paper identifies dissociated functional gradients across cortical layers using fMRI, showing superficial layers align with sensory-to-association gradients and deep layers with visual-to-somatomotor gradients.

The paper uses connectome-based brain fingerprinting to identify early cognitive decline markers in Parkinson's patients with minor hallucinations using resting-state fMRI.

This study maps age-related structural and functional changes in the spinal cord and brain, identifying shared aging patterns across the central nervous system.

The paper examines how BOLD variability in preterm infants relates to cortical microstructure and gene expression, finding increased variability in specific regions with decreased diffusivity and upregulated gliogenesis genes.

This study tracks how brain connectivity patterns change after stroke, showing early stabilization of functional fingerprints and long-term circuit remodeling that predicts recovery outcomes.

This paper introduces directed connectomics using bicommunities to reveal asymmetric pathways in brain networks and validates them with electrophysiological data.

This study shows that motor cortex and peripheral muscles are reactivated during memory retrieval, supporting the embodied nature of episodic memory.

This paper demonstrates that functional connectivity patterns in the human cervical spinal cord exhibit individual-specific properties, suggesting the existence of a spinal cord 'fingerprint' similar to brain fingerprints.

This paper develops a whole-brain model to predict conduction velocity from MRI-derived microstructural features, explaining 29% of variance in neurophysiological conduction velocity.

The paper shows that individual functional brain connectivity improves the explanation of tau PET patterns in Alzheimer's disease compared to group-level data.

The paper introduces Emo-FilM, a multimodal dataset combining fMRI, physiological recordings, and detailed emotion annotations from films to study emotion processing.

This paper introduces a laterality index to quantify hemispheric asymmetry in cortical maps using geometric eigenmodes, validated on fMRI and tau-PET data from Alzheimer's disease patients.

This study evaluates the accuracy of reconstructing cortical functional maps using subject-specific geometric and connectome eigenmodes, finding that spatially smooth eigenmodes best explain functional data.

This study uses graph learning to analyze resting-state MEG data and identifies frequency-specific functional connectome differences in tinnitus patients compared to controls.

This study examines how different temporal resolutions in resting-state fMRI affect subject identifiability through functional connectivity fingerprinting.

The paper uses functional MRI data to study thalamic functional organization in newborns, identifying age-related changes and specific thalamic units.

The paper introduces EPISeg, a deep learning model for automated spinal cord segmentation on gradient-echo EPI data, showing improved performance over existing methods.

This study shows body size parameters significantly correlate with brain and spinal cord MRI metrics and should be included in neuroimaging studies.

This study examines how neural excitation-inhibition balance dynamics change with cognitive load and their impact on functional connectivity during working memory tasks.