Dorian Pustina

Mapping brain volume changes in the zQ175DN mouse model of Huntington's disease: a longitudinal MRI study

This study uses longitudinal MRI to analyze brain volume changes in zQ175DN mice, finding early striatal atrophy and later cortical and corpus callosum reductions similar to human HD.

Disruption of Striatal Intrinsic Ignition in Huntington's Disease: an In-Silico Perturbational Approach to Emulate Disease Progression and Recovery

This study uses computational modeling to simulate functional connectivity changes in Huntington's disease and assess recovery potential by perturbing striatal activity.

Large Scale Functional and Effective Connectivity Alterations cross the Huntington's Disease Integrated Staging System

This study uses fMRI to assess functional and effective connectivity changes across HD-ISS stages, finding attenuated connectivity in early stages and sensitivity to motion.

Evolution of aberrant brain-wide spatiotemporal dynamics of resting-state networks in a Huntington's disease mouse model

This study investigates altered spatiotemporal dynamics of resting-state networks in a Huntington's disease mouse model using dynamic functional connectivity and identifies age- and genotype-specific quasi-periodic patterns of brain activity.

Longitudinal investigation of changes in resting-state co-activation patterns and their predictive ability in the zQ175 DN mouse model of Huntington's disease

The paper investigates changes in resting-state co-activation patterns in a Huntington's disease mouse model and finds they predict disease states with high accuracy.

Resting-State fMRI reveals Longitudinal Alterations in Brain Network Connectivity in a Mouse Model of Huntington's Disease

This study uses resting-state fMRI to track longitudinal changes in brain network connectivity in a mouse model of Huntington's disease, revealing age-related alterations in cortical and subcortical networks.

Simulated attack reveals how lesions affect network properties in post-stroke aphasia

This study uses simulated attacks on brain networks to assess how stroke lesions affect network properties and aphasia severity, finding that global network measures explain some variance in aphasia symptoms beyond lesion volume.

Glass Half Full: Preserved Anatomical Bypasses Predict Variance in Language Functions After Stroke

This study examines how post-stroke bypasses in brain networks predict variance in language functions, finding that bypass length and overlap predict lexical production and auditory comprehension.

Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations

This study compares multivariate sparse canonical correlation analysis (SCCAN) with mass-univariate voxel-wise lesion-symptom mapping (VLSM) and finds SCCAN provides more accurate results across various scenarios.