Javid Dadashkarimi

This study uses connectome-based predictive modeling to predict chronological age from structural and functional brain connectivity across different lifespan stages and finds that both modalities contribute complementary age-related information.

PIGMENT is a deep learning framework that automates the segmentation and quantification of APP-positive pathology in porcine white matter histology, achieving high detection rates with limited expert annotations.

The paper identifies inflated effect sizes in functional neuroimaging studies and proposes a correction method to improve study planning and detection of brain effects.

This study analyzes self-citation rates across 100,347 articles from 63 neuroscience journals between 2000-2020, revealing trends in self-citation by author position, country, gender, and field.

The paper shows that overlooked brain connectivity features can yield different neurobiological interpretations with similar prediction accuracy in neuroimaging studies.

The paper tests predictive models' ability to generalize across diverse, unharmonized neuroimaging datasets and finds that cross-dataset training improves prediction robustness.

This paper extends the CAROT method to stack multiple optimal transport mappings between atlases, improving efficiency while maintaining performance in connectome reconstruction.

CAROT remaps connectomes between atlases without raw data, enabling reuse of processed data across different atlases.

The paper uses task-based functional MRI connectomes to predict cognitive phenotypes across psychiatric diseases and shows differences in connectomes by disease category and symptom severity.