Anders J. Perrone

BIBSNet is a deep learning model for infant brain segmentation in MRI scans that outperforms JLF in accuracy and speed, with compatible outputs for further analysis.

The paper estimates heritability of subcortical brain volumes in adolescents using a multi-site cohort, finding high heritability in cerebellum cortex and hippocampus.

The paper introduces SHAMAN, a method to assess motion impact on brain-behavior associations in fMRI studies, finding significant motion overestimation and underestimation in various traits.

The paper introduces Baby Open Brains (BOBs), an open-source repository of manually curated infant brain segmentations to improve reproducibility in neuroimaging research.

This paper introduces Synth, a field map-less distortion correction method for fMRI data, showing comparable or better performance than field map-based approaches in pediatric and adult datasets.

The paper defines personalized functional networks in children to predict cognitive differences, finding that fronto-parietal network representation correlates with general cognition.

CuBIDS streamlines reproducible curation of large BIDS datasets by validating metadata, managing variability, and preparing data for preprocessing pipelines.

The paper develops a precision functional brain atlas to capture individual-specific network topography and demonstrates its utility in generating reproducible statistical maps of brain function.

A curated collection of processed ABCD neuroimaging derivatives plus utilities for access, sharing and analysis, letting researchers exchange derivatives they cannot share through the central archive.

QSIPrep is a software platform for preprocessing diffusion MRI data that supports multiple sampling schemes and ensures reproducibility through standardized workflows and quality measures.

Brain-behaviour correlations in ABCD are tiny (median |r|=0.01), so samples of typical size (N=25) give inflated, irreproducible results; estimates only stabilise around N⪆2,000.

This paper develops a band-stop filter to correct respiratory artifacts in MRI motion estimates, improving data quality and demonstrating real-time implementation.

The paper describes neuroimaging processing methods for the ABCD Study to extract brain phenotypes from multimodal imaging data.

This study establishes data and scanning conditions for reproducible, stable, and internally valid areal parcellations in non-human primates using fMRI.