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Riemannian frameworks for the harmonization of resting-state functional MRI scans.
Honnorat, Nicolas; Seshadri, Sudha; Killiany, Ron; Blangero, John; Glahn, David C; Fox, Peter; Habes, Mohamad.
Affiliation
  • Honnorat N; Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA. Electronic address: honnorat@uthscsa.edu.
  • Seshadri S; Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
  • Killiany R; Center for Biomedical Imaging, Boston University Medical School, Boston, MA, USA.
  • Blangero J; South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, USA.
  • Glahn DC; Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
  • Fox P; Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
  • Habes M; Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
Med Image Anal ; 91: 103043, 2024 Jan.
Article in En | MEDLINE | ID: mdl-38029722
ABSTRACT
Magnetic Resonance Imaging provides unprecedented images of the brain. Unfortunately, scanners and acquisition protocols can significantly impact MRI scans. The development of statistical methods able to reduce this variability without altering the relevant information in the scans, often coined harmonization methods, has been the topic of an increasing research effort supported by the recent growth of publicly available neuroimaging data sets and new possibilities for combining them to achieve greater statistical power. In this work, we focus on the challenges specifically raised by the harmonization of resting-state functional MRI scans. We propose to harmonize resting-state fMRI scans by reducing the impact of covariates such as scanner differences and scanning protocols on their associated functional connectomes and then propagating the changes back to the rs-fMRI time series. We use Riemannian geometric frameworks to preserve the mathematical properties of functional connectomes during their harmonization, and we demonstrate how state-of-the-art harmonization methods can be embedded within these frameworks to reduce covariates effects while preserving the relevant clinical information associated with aging or brain disorders. During our experiments, a large set of synthetic data was generated and processed to compare eighty variants of the proposed approach. The framework achieving the best harmonization was then applied to three low-dimensional data sets made of 712 sets of fMRI time series provided by the ABIDE consortium and two high-dimensional data sets obtained by processing 1527 rs-fMRI scans provided by the Human Connectome Project, the Framingham Heart Study and the Genetics of Brain Structure and Function study. These experiments established that our new framework could successfully harmonize low-dimensional connectomes and voxelwise functional time series and confirmed the need for preserving connectomes properties during their harmonization.
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Full text: 1 Database: MEDLINE Main subject: Magnetic Resonance Imaging / Connectome Limits: Humans Language: En Year: 2024 Type: Article

Full text: 1 Database: MEDLINE Main subject: Magnetic Resonance Imaging / Connectome Limits: Humans Language: En Year: 2024 Type: Article