Amy F. Kuceyeski

The paper examines sex-independent and sex-specific relationships between functional connectivity and cognitive abilities in healthy adults using the Human Connectome Project data.

This study uses graph matching to analyze functional connectivity reorganization in stroke patients, showing it correlates with motor recovery and structural disconnection.

This study finds that smaller spared subcortical nuclei are linked to worse sensorimotor outcomes after stroke, varying by time since stroke and type of impairment.

The paper quantifies regional structural-functional connectome coupling in adults and finds it varies with age, sex, cognitive scores, and is highly heritable.

This study shows deep neural networks can predict cortical responses to naturalistic stimuli across large brain areas beyond visual and auditory regions.

The paper integrates functional and structural brain connectivity to predict individual cognitive abilities, finding that multimodal integration outperforms single-modal approaches.

This paper compares models using observed and estimated connectivity networks to classify multiple sclerosis patients into disability groups, finding estimated networks perform better.

This study uses fMRI to compare emotional response-inhibition patterns between young adults with major depression and subthreshold bipolar symptoms, finding distinct neural activation differences.

This study identifies sex differences in brain regions using temporal dependence and volume measures from resting-state fMRI data, achieving high classification accuracy.

The paper examines how changes in structural and functional connectomes relate to cognitive recovery after mild TBI, finding that increased network diffusion model propagation time, structural segregation, and functional integration correlate with better recovery.

This study uses machine learning to predict motor improvement in chronic stroke patients after therapy, finding that pre-intervention assessments and motor thresholds are key predictors.