Research Overview of MRI for Neurodegenerative Diseases & Mental Illnesses
DOI: https://doi.org/10.62381/ACS.ATSS2025.12
Author(s)
He Zirui
Affiliation(s)
Communications Engineering, Guangdong University of Technology, Guangzhou, Guangdong, China
Abstract
Since its inception in 1990, fMRI has become a critical tool in clinical settings for surgical planning, treatment monitoring, and disease biomarker identification. Technological advancements have mitigated early challenges, enhancing image quality and data reliability. The paper discusses the evolution towards pattern classification and statistical methods to infer cognitive brain states, with a focus on advanced techniques such as task-based fMRI (tb-fMRI), resting-state fMRI (rs-fMRI), and various analytical methods including Seed-based Correlation Analysis (SCA), Independent Component Analysis (ICA), and Convolutional Neural Networks (CNNs). The research addresses the limitations in fMRI data interpretation, including noise, artifacts, and the heterogeneity of psychiatric disorders, and emphasizes the need for standardized protocols and broader validation across populations. The aim is to refine fMRI techniques for understanding psychiatric disorders, improve sensitivity and accuracy, address technical issues, define biomarkers, enhance clinical utility, and understand neurobiological mechanisms. The literature review synthesizes findings from studies on Major Depressive Disorder (MDD), the principles of fMRI, Quantitative Susceptibility Mapping (QSM), echo-planar imaging sequences, and the impact of head motion on neuroimaging data. The research methods section outlines the use of fMRI data collection, statistical and computational analysis, literature reviews, validation and experimental design, and techniques for mitigating artifacts. The conclusion highlights the transformative role of fMRI in understanding brain function and the need for future improvements in methodology and integration of advanced techniques for personalized medicine approaches. The research underscores the complexity of fMRI use in psychiatric the complexity of fMRI use in psychiatric research and the persistent challenges that need to be addressed to enhance diagnosis and treatment strategies in brain disorders.
Keywords
Fmri; Imaging; Analysis; Neurodegenerative Diseases; Mental Illnesses
References
[1] Pilmeyer, J., Huijbers, W., Lamerichs, R., Jansen, J. F. A., Breeuwer, M., & Zinger, S. (2022). Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects. Journal of neuroimaging : official journal of the American Society of Neuroimaging, 32(4), 582–595. https://doi.org/10.1111/jon.13011
[2] Glover G. H. (2011). Overview of functional magnetic resonance imaging. Neurosurgery clinics of North America, 22(2),133–vii. https://doi.org/10.1016/j.nec.2010.11.001
[3] Schweser, F., & Zivadinov, R. (2018). Quantitative susceptibility mapping (QSM) with an extended physical model for MRI frequency contrast in the brain: a proof-of-concept of quantitative susceptibility and residual (QUASAR) mapping. NMR in biomedicine, 31(12), e3999. https://doi.org/10.1002/nbm.3999
[4] Kirilina E, Lutti A, Poser BA, et al. The quest for the best: the impact of different EPI sequences on the sensitivity of random effect fMRI group analyses. Neuroimage 2016;126:49‐59.
[5] Yang QX, Mao W, Wang J, et al. Manipulation of image intensity distribution at 7.0 T: passive RF shimming and focusing with dielectric materials. J Magn Reson Imaging 2006;24:197‐202.
[6] Makowski C, Lepage M, Evans AC. Head motion: the dirty little secret of neuroimaging in psychiatry. J Psychiatry Neurosci 2019;44:62‐8.
[7] Havsteen I, Ohlhues A, Madsen KH, et al. Are movement artifacts in magnetic resonance imaging a real problem?—A narrative review. Front Neurol 2017;8:232.
[8] Kazemifar, S., Manning, K. Y., Rajakumar, N., Gómez, F. A., Soddu, A., Borrie, M. J., Menon, R. S., Bartha, R., & Alzheimer’s Disease Neuroimaging Initiative (2017). Spontaneous low frequency BOLD signal variations from resting-state fMRI are decreased in Alzheimer disease. PloS one, 12(6), e0178529. https://doi.org/10.1371/journal.pone.0178529
[9] Etkin, A., Maron-Katz, A., Wu, W., Fonzo, G. A., Huemer, J., Vértes, P. E., Patenaude, B., Richiardi, J., Goodkind, M. S., Keller, C. J., Ramos-Cejudo, J., Zaiko, Y. V., Peng, K. K., Shpigel, E., Longwell, P., Toll, R. T., Thompson, A., Zack, S., Gonzalez, B., Edelstein, R., … O'Hara, R. (2019). Using fMRI connectivity to define a treatment-resistant form of post-traumatic stress disorder. Science translational medicine, 11(486), eaal3236. https://doi.org/10.1126/scitranslmed.aal3236