Jean‐Baptiste Poline

The paper quantifies numerical variability in MRI analyses of Parkinson's disease and shows it can significantly affect statistical conclusions and p-values.

Nipoppy standardizes neuroimaging data workflows to enable reproducible and comparable multi-center data sharing.

This study improves brain age prediction by integrating features from multiple segmentation pipelines, showing that variability across pipelines reflects structured demographic differences.

The paper analyzes data availability statements in biomedical publications to assess open data sharing rates across funders, journals, and institutions.

This study benchmarks seven dynamic functional connectivity methods across 16 fMRI datasets to assess their reliability in tracking cognitive engagement during various tasks.

The paper investigates how sample size and cohort composition affect the replicability of brain-behaviour associations using Canonical Correlation Analysis on UK Biobank data.

The paper discusses challenges in federated analysis for multisite neuroinformatics studies and presents solutions from recent projects.

This study evaluates how deep learning models and dynamic functional connectivity estimation methods affect task presence prediction from fMRI data.

The paper provides tools for collecting, processing, and annotating biomedical literature to streamline text-mining and meta-science projects.

NiCLIP predicts cognitive tasks and domains from brain activation maps using a contrastive language-image model trained on neuroscientific articles.

This paper develops a framework for image-based meta-analysis using NeuroVault data, showing that manual selection provides precise results while heuristic methods are effective for heterogeneous domains.

This paper reviews neuroimaging meta-analyses, finding that most follow PRISMA guidelines but have issues with pre-registration, search strategies, and study numbers.

This study examines how different versions of FreeSurfer affect structural brain measure estimation in Parkinson's disease and shows significant software-induced variability impacting clinical outcomes.

This study evaluates how physiological factors affect functional connectivity in brain signals, finding that multivariate methods are more robust than bivariate ones and that physiological artifacts significantly distort connectivity estimates.

The paper identifies three ASD subtypes using morphometric similarity networks and finds distinct transcriptomic and functional connectome patterns associated with each subtype.

This study replicates findings from Hanganu et al. (2014) on Parkinson's disease neuroimaging biomarkers using PPMI data, finding partial replication of cortical thinning and cognitive decline associations.

This study evaluates the variability of dynamic functional connectivity methods using fMRI data, showing significant differences between methods and emphasizing the need for multi-analysis approaches.

This paper develops and evaluates methods for decoding functional connectivity gradients in fMRI data using meta-analytic techniques and k-means segmentation.

This study attempts to reproduce and replicate a machine learning model for predicting Parkinson's disease progression using MRI data, finding lower performance than the original study.

Micapipe is an open pipeline for processing multimodal MRI data, generating structural and functional connectomes, geodesic distances, and microstructural profiles across multiple brain parcellations and surface spaces.

NeuroCI evaluates result variability across neuroimaging pipelines and datasets using continuous integration to improve reproducibility.

The paper develops the abagen toolbox to standardize imaging transcriptomics workflows and shows that methodological variability significantly affects research outcomes using the Allen Human Brain Atlas.

This paper benchmarks five functional alignment methods for inter-subject decoding across four datasets, finding that alignment improves accuracy but the best method depends on context.

This study quantifies how population diversity affects brain-imaging prediction accuracy and pattern stability using two clinical cohorts.

This study evaluates how different preprocessing pipelines affect neuroimaging analyses, showing that software, parcellation, and quality control significantly impact results.

This study examines the genomic architecture of neuroanatomical diversity using SNP data and MRI, finding polygenic influences and environmental factors affecting brain asymmetry.

This paper introduces a diffeomorphic centroid method for shape analysis of large datasets, showing it achieves similar results to variational templates but with 70% faster computation.

The paper attempts to reproduce a bioinformatics method, identifies challenges in reusability, and proposes solutions to improve reproducibility and research efficiency.

The paper discusses the importance of sharing computational code in neuroscience and highlights challenges in current practices.