Generalized q‐sampling imaging‐based fiber tracking measures of white matter tracts in cognitive impairment. (20th December 2022)

• Record Type: Journal Article
• Authors: Duran, Tugce; Bateman, James R.; Williams, Benjamin J.; Hughes, Tim M.; Barcus, Richard A.; Peavey, Jeremy J.; Okonmah‐Obazee, Stephanie E.; Whitlow, Christopher T.; Craft, Suzanne; Lockhart, Samuel N.
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 1
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.066574
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Diffusion magnetic resonance imaging (dMRI) is a non‐invasive technique for exploring white matter fiber organization of the brain. As traditional diffusion tensor imaging (DTI) methods may provide inaccurate descriptions of local crossing, kissing and diverging fibers, we examined generalized q ‐sampling imaging (GQI), a novel method providing better directional and quantitative information using dMRI data from community‐dwelling older adults. Method: We examined baseline dMRI data from 227 adults aged 71±8 on average from the Wake Forest Alzheimer's Disease Research Center's Clinical Core, who had baseline and year‐1 follow‐up global Clinical Dementia Rating (CDR) score of 0 (indicates normal cognitive function) and 0.5 (akin to mild cognitive impairment [MCI]). The primary outcome measure was cognitive migration status determined by CDR increase/decrease between baseline and follow‐up (mean difference=13.9 months), shown in Figure . We performed atlas‐based fiber tracking analysis using GQI in DSI Studio to generate normalized quantitative anisotropy (NQA), DTI fractional anisotropy (DTI_FA) and restricted diffusion (RDI) measures in several AD‐related white matter (WM) tracts: Corpus Callosum (CC), Fornix, Cingulum bundle/Parahippocampal tracts (CFP; CP; CPP), Uncinate Fasciculus (UF), Superior Longitudinal Fasciculus (SLF) and Inferior Fronto‐Occipital Fasciculus (IFOF). We performed multinomial logistic regression (MLR) models adjusted for age, sex, and education, using cognitive migration status as the outcome, and standardized GQI‐based diffusion indices sNQA, sRDI, and sDTI_FA as predictors. Result: Table 1 lists the baseline demographics and biomarker characteristics for cognitive migration groups. MLR results in Table 2 show significant associations between diffusion indices and tracts for outcome 4 (Reversion) in reference to outcome 1 (stability of normal cognition). Decrease in sNQA in bilateral CP and right Fornix was significantly associated with higher odds of Reversion relative to the reference. Increase in sDTI_FA in left IFOF was associated with higher odds of reversion relative to reference. Decrease in sRDI in bilateral CFP, CPP, Fornix and UF and right CP was associated with higher reversion relative to reference. Conclusion: In early trajectories of cognitive impairment, RDI may be a sensitive diffusion marker for cognitive reversion in several AD‐related WM tracts. Future work will validate these markers in other cognitive migration groups (negative migration and MCI stability).