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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Updated: Sep 25, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing.

Elina Thibeau-Sutre¹, Mauricio Díaz¹, Ravi Hassanaly¹

¹Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, F-75013, France.

Computer Methods and Programs in Biomedicine

|April 28, 2022

Summary

This summary is machine-generated.

ClinicaDL offers a solution for deep learning in neuroimaging, addressing data format, preprocessing, and reproducibility issues. This software ensures reliable results by preventing data leakage and standardizing analyses for better scientific studies.

Keywords:

Data leakage Deep learning Neuroimaging Open-source Reproducibility

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Area of Science:

Neuroimaging
Machine Learning
Computational Neuroscience

Background:

Deep learning in neuroimaging faces reproducibility challenges and methodological flaws.
Existing tools aim to assist deep learning users with neuroimaging datasets.
ClinicaDL is presented as a software tool to address these issues.

Purpose of the Study:

To provide a safe environment for deep learning users in neuroimaging.
To help users avoid common pitfalls that bias and discredit results.
To address issues related to data format, preprocessing, data leakage, and reproducibility.

Main Methods:

ClinicaDL interacts with the Brain Imaging Data Structure (BIDS) standard.
It checks for data leakage during inference with trained networks.
It saves information to guarantee the reproducibility of results.
ClinicaDL, combined with Clinica, enables end-to-end neuroimaging analysis.

Main Results:

ClinicaDL facilitates the use of diverse neuroimaging datasets through BIDS compatibility.
The software prevents data leakage, ensuring the integrity of evaluation procedures.
It saves all necessary information for reproducible results.

Conclusions:

ClinicaDL addresses key challenges in deep learning for neuroimaging: data handling, preprocessing, data leakage, and reproducibility.
The software aims to improve the reliability and value of scientific studies in the field.
Its use is expected to lead to more robust and trustworthy neuroimaging research.