Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Big Five personality dimensions and their association with depressive symptom severity over the treatment course.

Journal of affective disorders·2026

Same author

Decomposing neuroanatomical heterogeneity in depression: insights from an ENIGMA major depressive disorder working group study in 5146 individuals.

Translational psychiatry·2026

Same author

In-patient treatment and structural dysconnectivity in major depression - A longitudinal connectome study.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology·2026

Same author

Distinguishing Chronic and Non-Chronic Depression: A Clinical Profile and Symptom Networks Approach.

Psychotherapy and psychosomatics·2026

Same author

Associations between CRP-related DNA methylation, stress exposure, and depression severity in a longitudinal clinical cohort.

Brain, behavior, and immunity·2026

Same author

Structural Brain Network Alterations in Relation to Treatment and Illness Severity in Bipolar Disorder.

Biological psychiatry·2026

Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026

Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026

Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026

Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026

Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026

Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 23, 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

Recommendations for machine learning benchmarks in neuroimaging.

Ramona Leenings¹, Nils R Winter², Udo Dannlowski²

¹University of Münster, Institute for Translational Psychiatry, Albert-Schweitzer-Campus 1, Münster 48149, Germany; University of Münster, Faculty of Mathematics and Computer Science, Münster, Germany.

|May 13, 2022

Summary

This summary is machine-generated.

Neuroimaging research needs better benchmarks for machine learning. This study proposes an extended evaluation framework and a collaborative platform to advance the field.

Keywords:

Agile model development Benchmark Machine learning Neuroimaging Recommendations Translation

More Related Videos

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Related Experiment Videos

Last Updated: Sep 23, 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

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

Neuroimaging
Machine Learning
Biomedical Imaging

Background:

Machine learning methods are increasingly used in neuroimaging.
Open-access neuroimaging datasets are available, but dedicated benchmarks are scarce.
Benchmarks in computer science and biomedical imaging have driven machine learning progress.

Purpose of the Study:

To highlight the importance of benchmarks in neuroimaging.
To outline requirements for establishing robust neuroimaging benchmarks.
To propose an extended evaluation framework and a collaborative platform.

Main Methods:

Reviewing benchmark methodologies in computer science and biomedical imaging.
Identifying unique characteristics of neuroimaging data.
Defining criteria for dataset composition and evaluation.
Proposing an extended evaluation procedure focusing on explainability, robustness, uncertainty, efficiency, and code quality.

Main Results:

Benchmarks are crucial for advancing machine learning in neuroimaging.
Specific considerations are needed for neuroimaging data when creating benchmarks.
An extended evaluation should include scientific aspects beyond performance metrics.
A collaborative platform can foster interdisciplinary research.

Conclusions:

Establishing well-designed neuroimaging benchmarks is essential for methodological progress.
An extended evaluation framework incorporating scientific rigor is proposed.
A collaborative platform is envisioned to facilitate joint efforts in neuroimaging and psychiatry research.