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Related Experiment Video

Updated: Jun 18, 2026

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Published on: July 1, 2014

A multi-site, multi-disorder resting-state magnetic resonance image database.

Saori C Tanaka¹, Ayumu Yamashita^2,3, Noriaki Yahata^2,4,5

¹Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan. xsaori@atr.jp.

Scientific Data

|August 31, 2021

Summary

This study created a large neuroimaging database for psychiatric disorders using resting-state fMRI. The goal is to improve machine learning classifiers for better diagnosis and understanding of brain circuit abnormalities.

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Last Updated: Jun 18, 2026

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

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Published on: August 28, 2021

Area of Science:

Neuroscience
Psychiatry
Machine Learning

Background:

Machine learning classifiers using resting-state functional magnetic resonance imaging (rs-fMRI) are emerging tools for understanding psychiatric disorders.
Accurate and generalizable classifiers require large, diverse datasets.

Purpose of the Study:

To develop accurate and generalizable machine learning classifiers for psychiatric disorders.
To create a large-scale, multi-site, multi-disorder neuroimaging database.

Main Methods:

Compiled a database of rs-fMRI and structural brain images from 993 patients and 1,421 healthy individuals.
Harmonized multi-site data using "traveling subjects" and 12 scanners.
Collected demographic and clinical rating scale data.

Main Results:

Published four datasets: SRPBS Multi-disorder Connectivity, SRPBS Multi-disorder MRI (restricted and unrestricted), and SRPBS Traveling Subject MRI.
Ensured data sharing and analysis across multiple institutions.

Conclusions:

The established database and harmonized data are crucial for advancing machine learning in psychiatric neuroimaging.
Facilitates research into neural circuit relationships with psychiatric disorders.