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Published on: September 8, 2021
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The MR neuroimaging protocol for the Accelerating Medicines Partnership® Schizophrenia Program
Michael P Harms1, Kang-Ik K Cho2, Alan Anticevic3
1Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA. mharms@wustl.edu.
Schizophrenia (Heidelberg, Germany)
|April 2, 2025
Summary
This study details a neuroimaging protocol for individuals at clinical high risk (CHR) for psychosis, aiming to identify brain biomarkers. Results show participant variance is key for structural and fMRI, but scanner differences impact diffusion imaging quality.
Area of Science:
- Neuroimaging
- Psychosis Research
- Biomarker Discovery
Background:
- Magnetic Resonance Imaging (MRI) is crucial for studying individuals at clinical high risk (CHR) for psychosis.
- Existing studies often lack the statistical power for robust neuroimaging findings.
- The Accelerating Medicines Partnership® Schizophrenia Program (AMP® SCZ) aims to build a large-scale CHR cohort.
Purpose of the Study:
- To describe a prospective, advanced neuroimaging protocol for the AMP® SCZ initiative.
- To establish a standardized multi-site, multi-vendor data acquisition strategy.
- To analyze sources of variance in neuroimaging data to inform future analyses.
Main Methods:
- Implemented a protocol including T1/T2-weighted structural MRI, resting-state fMRI, and diffusion-weighted imaging.
- Collected data from participants at two time points, approximately two months apart.
- Conducted variance component analyses on signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and spatial smoothness.
Main Results:
- Site-related variance was generally small (<10%).
- Participant variance constituted the largest component for structural and fMRI SNR/CNR (40-76%).
- Substantial platform-related variance (>55%) was observed for diffusion imaging SNR/CNR and spatial smoothness due to scanner and vendor differences.
Conclusions:
- The described protocol provides a foundation for analyzing the largest CHR neuroimaging dataset to date.
- Understanding variance sources is critical for robust analysis of multi-site, multi-vendor neuroimaging data.
- Scanner and acquisition differences significantly impact diffusion imaging quality and spatial smoothness, requiring careful consideration in data analysis.
