Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan.

Pomponio, R., G. Erus, M. Habes, J. Doshi, D. Srinivasan, E. Mamourian, V. Bashyam, I. Nasrallah, T. Satterthwaite, Y. Fan, L. Launer, C. Masters, P. Maruff, C. Zhuo, H. Völzke, S. Johnson, J. Fripp, N. Koutsouleris, D. Wolf, R. Gur, R. Gur, J. Morris, M. Albert, H. Grabe, S. Resnick, R. Bryan, D. Wolk, R. Shinohara, H. Shou, and C. Davatzikos. “Harmonization of Large MRI Datasets for the Analysis of Brain Imaging Patterns Throughout the Lifespan.”. NeuroImage, Vol. 208, 2020, p. 116450.

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

DOI: 10.1016/j.neuroimage.2019.116450

PubMed: 31821869