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Browsing by Subject "Multimodal MRI"
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Item Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup(Wiley, 2021) Faria, Andreia V.; Zhao, Yi; Ye, Chenfei; Hsu, Johnny; Yang, Kun; Cifuentes, Elizabeth; Wang, Lei; Mori, Susumu; Miller, Michael; Caffo, Brian; Sawa, Akira; Biostatistics and Health Data Science, School of MedicineMulti-institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels. We studied 87 first episode psychosis (FEP) patients and 62 healthy subjects by combining supervised integrated factor analysis (SIFA) with a novel pipeline for automated structure-based analysis, an efficient and comprehensive method for dimensional data reduction that our group recently established. We integrated multiple MRI features (volume, DTI indices, resting state fMRI-rsfMRI) in the whole brain of each participant in an unbiased manner. The automated structure-based analysis showed widespread DTI abnormalities in FEP and rs-fMRI differences between FEP and healthy subjects mostly centered in thalamus. The combination of multiple modalities with SIFA was more efficient than the use of single modalities to stratify a subgroup of FEP (individuals with schizophrenia or schizoaffective disorder) that had more robust deficits from the overall FEP group. The information from multiple MRI modalities and analytical methods highlighted the thalamus as significantly abnormal in FEP. This study serves as a proof-of-concept for the potential of this methodology to reveal disease underpins and to stratify populations into more homogeneous sub-groups.Item Multimodal MRI examination of structural and functional brain changes in older women with breast cancer in the first year of antiestrogen hormonal therapy(Springer Nature, 2022) McDonald, Brenna C.; Van Dyk, Kathleen M.; Deardorff, Rachael L.; Bailey, Jessica N.; Zhai, Wanting; Carroll, Judith E.; Root, James C.; Ahles, Tim A.; Mandelblatt, Jeanne S.; Saykin, Andrew J.; Radiology and Imaging Sciences, School of MedicinePurpose: Cancer patients are concerned about treatment-related cognitive problems. We examined effects of antiestrogen hormonal therapy on brain imaging metrics in older women with breast cancer. Methods: Women aged 60 + treated with hormonal therapy only and matched non-cancer controls (n = 29/group) completed MRI and objective and self-reported cognitive assessment at pre-treatment/enrollment and 12 months later. Gray matter was examined using voxel-based morphometry (VBM), FreeSurfer, and brain age calculations. Functional MRI (fMRI) assessed working memory-related activation. Analyses examined cross-sectional and longitudinal differences and tested associations between brain metrics, cognition, and days on hormonal therapy. Results: The cancer group showed regional reductions over 12 months in frontal, temporal, and parietal gray matter on VBM, reduced FreeSurfer cortical thickness in prefrontal, parietal, and insular regions, and increased working memory-related fMRI activation in frontal, cingulate, and visual association cortex. Controls showed only reductions in fusiform gyrus on VBM and FreeSurfer temporal and parietal cortex thickness. Women with breast cancer showed higher estimated brain age and lower regional gray matter volume than controls at both time points. The cancer group showed a trend toward lower performance in attention, processing speed, and executive function at follow-up. There were no significant associations between brain imaging metrics and cognition or days on hormonal therapy. Conclusion: Older women with breast cancer showed brain changes in the first year of hormonal therapy. Increased brain activation during working memory processing may be a sign of functional compensation for treatment-related structural changes. This hypothesis should be tested in larger samples over longer time periods.