A practical framework for large-scale neuroimaging data collection in clinically diverse populations: experience from the Stanford Brain Stimulation Lab
- 1Brain Stimulation Lab, Department of Psychiatry and Behavioral Sciences, Stanford University
- 2Department of Radiological Sciences, Stanford University School of Medicine
Abstract
Novel neuroimaging technologies and data science techniques have allowed for large-scale applications of neuroimaging acquisition in clinical research, particularly within the fields of interventional psychiatry, neuropsychiatry, and precision medicine. These developments have enhanced our understanding of psychiatric and neurological disorders and highlight the need for optimized protocols that support scalable, high-quality neuroimaging data acquisition. This paper aims to describe a protocol for systematic MRI data collection in clinical trials, including details for subject recruitment, meticulous equipment calibration, rigorous real-time quality control of imaging data, standardized documentation (SOP, hardware and software logs), consent workflows, and personnel training. While existing MRI review papers largely focus on analytical methods, safety protocols, and results of large consortium efforts, few publications detail specific operational protocols of MRI data collection that allow for consistent, high-impact neuroimaging research including participant recruitment, staff training, and practical scanning considerations. The operational protocols presented here are designed to facilitate reproducible, high-quality neuroimaging acquisition across clinically diverse populations, with a particular focus on functional MRI. Importantly, many of the recommendations described are grounded in practical experience rather than prescriptive guidelines alone. They represent lessons learned through years of iterative refinement, troubleshooting, and trial-and-error while conducting thousands of MRI scans in clinical research settings. By sharing these operational insights, we hope to provide researchers with practical strategies that can reduce common challenges and accelerate the development of robust neuroimaging workflows.
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BibTeX
@article{azeez2026practical,
title = {{A practical framework for large-scale neuroimaging data collection in clinically diverse populations: experience from the Stanford Brain Stimulation Lab}},
author = {Azeezat Azeez and Ethan Makarewycz and Natalie Feng and Wiebke Struckmann and Jen Lissemore and Noriah D. Johnson and Malvika Sridhar and Andrew Geoly and Ruben Krueger and Brendan Wong and Seigo Ninomiya and Lauren Crowe and Derrick Matthew Buchanan and Sumaiya Ahmed and Cammie Rolle and Nolan Williams},
journal = {Frontiers in Neuroscience},
year = {2026},
month = oct,
doi = {10.3389/fnins.2026.1849837},
url = {https://www.frontiersin.org/articles/10.3389/fnins.2026.1849837/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A practical framework for large-scale neuroimaging data collection in clinically diverse populations: experience from the Stanford Brain Stimulation Lab
AU - Azeezat Azeez
AU - Ethan Makarewycz
AU - Natalie Feng
AU - Wiebke Struckmann
AU - Jen Lissemore
AU - Noriah D. Johnson
AU - Malvika Sridhar
AU - Andrew Geoly
AU - Ruben Krueger
AU - Brendan Wong
AU - Seigo Ninomiya
AU - Lauren Crowe
AU - Derrick Matthew Buchanan
AU - Sumaiya Ahmed
AU - Cammie Rolle
AU - Nolan Williams
PY - 2026
DA - 2026/10/05
JO - Frontiers in Neuroscience
DO - 10.3389/fnins.2026.1849837
UR - https://www.frontiersin.org/articles/10.3389/fnins.2026.1849837/full
AB - Novel neuroimaging technologies and data science techniques have allowed for large-scale applications of neuroimaging acquisition in clinical research, particularly within the fields of interventional psychiatry, neuropsychiatry, and precision medicine. These developments have enhanced our understanding of psychiatric and neurological disorders and highlight the need for optimized protocols that support scalable, high-quality neuroimaging data acquisition. This paper aims to describe a protocol for systematic MRI data collection in clinical trials, including details for subject recruitment, meticulous equipment calibration, rigorous real-time quality control of imaging data, standardized documentation (SOP, hardware and software logs), consent workflows, and personnel training. While existing MRI review papers largely focus on analytical methods, safety protocols, and results of large consortium efforts, few publications detail specific operational protocols of MRI data collection that allow for consistent, high-impact neuroimaging research including participant recruitment, staff training, and practical scanning considerations. The operational protocols presented here are designed to facilitate reproducible, high-quality neuroimaging acquisition across clinically diverse populations, with a particular focus on functional MRI. Importantly, many of the recommendations described are grounded in practical experience rather than prescriptive guidelines alone. They represent lessons learned through years of iterative refinement, troubleshooting, and trial-and-error while conducting thousands of MRI scans in clinical research settings. By sharing these operational insights, we hope to provide researchers with practical strategies that can reduce common challenges and accelerate the development of robust neuroimaging workflows.
SN - 1662-453X
SN - 1662-4548
ER -