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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 Stimulation (TMS).

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Optical Clearing and Labeling for Light-sheet Fluorescence Microscopy in Large-scale Human Brain Imaging

Optical Clearing and Labeling for Light-sheet Fluorescence Microscopy in Large-scale Human Brain Imaging

Published on: January 26, 2024

Risk-calibrated sharing of human brain data.

Saskia Hendriks^1,2, Andrea C Beckel-Mitchener³, James Eberwine⁴

¹Department of Bioethics, NIH Clinical Center, Bethesda, MD 20892, USA.

Brain : a Journal of Neurology

|June 19, 2026

Summary

This summary is machine-generated.

Sharing human brain data accelerates discovery but raises privacy concerns. Responsible sharing requires risk-based protections to balance scientific value with participant safety and trust.

Keywords:

biomedical research data management ethics neurosciences secondary data analysis

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Area of Science:

Neuroscience
Bioethics
Data Science

Background:

Growing pressure to share human brain data for neurological and psychiatric disorder research.
Concerns exist regarding brain data misuse and mental privacy threats.
Need for clearer ethical guidance on brain data sharing.

Purpose of the Study:

Review bioethics and neuroscience literature on human brain data sharing risks.
Analyze risks using NIH workshop insights and normative analysis.
Propose a risk-based framework for responsible brain data sharing.

Main Methods:

Literature review of bioethics and neuroscience.
Analysis of National Institutes of Health (NIH) workshop insights.
Normative ethical analysis of brain data risks and benefits.

Main Results:

Brain data sharing risks are not uniform; they depend on re-identification likelihood and inference sensitivity.
Certain brain data types pose higher risks due to re-identification and sensitive inferences.
Risk mitigation strategies may impact scientific data value.

Conclusions:

Responsible brain data sharing necessitates calibrating protections to dataset-specific risks.
A tiered approach (lower, medium, higher risk) based on inferential sensitivity and re-identification likelihood is proposed.
Safeguards include access restrictions, informed consent, and data-use governance to maximize value while protecting participants.