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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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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Making big data open: data sharing in neuroimaging.

Russell A Poldrack¹, Krzysztof J Gorgolewski²

¹Department of Psychology, Stanford Univeristy, Stanford, California, USA.

Nature Neuroscience

|October 29, 2014

Summary

This summary is machine-generated.

Neuroimaging data sharing has advanced significantly, with over 8,000 magnetic resonance imaging (MRI) datasets online. This review focuses on task-based functional MRI (fMRI) data sharing, its utility, and future challenges.

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Area of Science:

Neuroscience
Medical Imaging
Data Science

Background:

Significant progress in shared neuroimaging data availability over the past decade.
Over 8,000 magnetic resonance imaging (MRI) datasets are now accessible online.

Purpose of the Study:

To review the current state of data sharing for task-based functional MRI (fMRI) data.
To discuss the utility of various fMRI data forms for downstream analyses.
To explore challenges and ethical considerations in neuroimaging data sharing.

Main Methods:

Literature review and synthesis of current practices in neuroimaging data sharing.
Analysis of the types and utility of shared task-based fMRI data.
Discussion of ethical frameworks and future directions for data sharing.

Main Results:

A large and growing repository of shared MRI datasets exists.
Task-based fMRI data sharing is crucial for advancing research.
Several challenges impede the full potential of data sharing.

Conclusions:

Maximizing the contribution of human subjects in research necessitates robust data sharing practices.
Addressing challenges in data sharing is vital for the future of neuroimaging research.
Ethical considerations underscore the importance of open data principles.