Matthew Rosenblatt

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.

The paper models the genetic architecture of cortical expansion using GWAS data and links it to neuropsychiatric disorders.

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 identifies 1,294 genomic regions and 961 effector genes associated with externalizing disorders, revealing neural mechanisms and polygenic predictors of variance in these conditions.

PRISME is a MATLAB toolbox for neuroimaging power benchmarking that enables large-scale empirical power analysis across diverse data types and test types.

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 evaluates the statistical power of external validation in brain-phenotype predictions and finds that sample size impacts power and effect size inflation.

This study evaluates how data leakage affects machine learning models using neuroimaging data, finding that some leakage types significantly inflate performance while others reduce it or affect model interpretations.

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

This paper improves brain-behavior prediction by imputing missing data in connectome-based models, increasing sample size and explained variance.

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.