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MRI-based quantification of cardiac-driven brain biomechanics for early detection of neurological disorders.
Diorio, Tyler C; Abderezzai, Javid; Nauman, Eric; Kurt, Mehmet; Tong, Yunjie; Rayz, Vitaliy L.
Affiliation
  • Diorio TC; Purdue University, (Weldon School of Biomedical Engineering), West Lafayette, (IN), USA.
  • Abderezzai J; University of Washington, (Department of Mechanical Engineering), Seattle, (WA), USA.
  • Nauman E; University of Cincinnati, (Department of Biomedical Engineering), Cincinnati, (OH), USA.
  • Kurt M; University of Washington, (Department of Mechanical Engineering), Seattle, (WA), USA.
  • Tong Y; Purdue University, (Weldon School of Biomedical Engineering), West Lafayette, (IN), USA.
  • Rayz VL; Purdue University, (Weldon School of Biomedical Engineering), West Lafayette, (IN), USA.
bioRxiv ; 2024 Aug 06.
Article in En | MEDLINE | ID: mdl-39149257
ABSTRACT
We present a pipeline to quantify biomechanical environment of the brain using solely MRI-derived data in order to elucidate the role of biomechanical factors in neurodegenerative disorders. Neurological disorders, like Alzheimer's and Parkinson's diseases, are associated with physical changes, including the accumulation of amyloid-ß and tau proteins, damage to the cerebral vasculature, hypertension, atrophy of the cortical gray matter, and lesions of the periventricular white matter. Alterations in the external mechanical environment of cells can trigger pathological processes, and it is known that AD causes reduced stiffness in the brain tissue during degeneration. However, there appears to be a significant lag time between microscale changes and macroscale obstruction of neurological function in the brain. Here, we present a pipeline to quantify the whole brain biomechanical environment to bridge the gap in understanding how underlying brain changes affect macroscale brain biomechanics. This pipeline enables image-based quantification of subject-specific displacement field of the whole brain to subject-specific strain, strain rate, and stress across 133 labeled functional brain regions. We have focused our development efforts on utilizing solely MRI-derived data to facilitate clinical applicability of our approach and have emphasized automation in all aspects of our methods to reduce operator dependance. Our pipeline has the potential to improve early detection of neurological disorders and facilitate the identification of disease before widespread, irreversible damage has occurred.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: BioRxiv Year: 2024 Document type: Article Affiliation country: United States Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: BioRxiv Year: 2024 Document type: Article Affiliation country: United States Country of publication: United States