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- W2896921208 abstract "There is an urgent, unmet need for biomarkers of risk for Alzheimer's disease related dementia (ADRD) suitable for routine screening in clinical settings. Multimodal MRI may offer multi-dimensional information about the underlying pathology in addition to affordability and abundance. In AD biomarker research, white matter structural connectomes are relatively understudied. Here we tested utility of structural connectomes in predictive modeling of AD using multi-site, multi-parametric, multi-modal MRI data. Participants. 211 elders visited a dementia clinic at Korean National Health Insurance Service Ilsan Hospital, Ilsan, South Korea from 2009 to 2013 were included:110 with AD (median age=82), 64 with mild cognitive impairment (MCI; median age=73), and 37 subjective memory complaints (SMC; median age=74). ADNI-2 data were used as a generalizability set: 179 individuals with the baseline structural and diffusion MRI were included (49 AD, 22 MCI-to-AD converter, 38 stable MCI, 71 SMC). MRI analysis. Morphometry, white matter tractography, white matter hyper-intensity were conducted using T1-, T2-FLAIR MRI; individualized structural connectomes were estimated. Classification. Machine learning models were trained on the large-scale brain phenotypes to predict diagnosis or disease progression. Random forest-based feature selection was used. Iterative, nested, stratified 10-fold cross validation was used. In ADNI data, gold standard CSF biomarker measures were used as a benchmark (i.e., Aβ, tau, p-tau, Aβ/p-tau, tau/Aβ). In Korean data, machine learning models showed optimal classification of AD/SMC (accuracy=97±4%, s.d.), MCI/SMC (83±11%), and AD/MCI (98±3%). In the generalizability data (ADNI-2), models showed more accurate classification of AD/SMC (accuracy=82±1%) in relative to the CSF benchmark model (accuracy=78±1%), similar classification of MCI/SMC (accuracy=61±1%) to the CSF benchmark model (accuracy=61±1%). Finally, models predicted MCI conversion to AD (mean yrs of conversion = 2.1±1.8) with accuracy of 68±4%, similar to the CSF benchmark (70±2%). Comparison analyses revealed structural connectome estimates contributed to these models. Figure. Receiver Operator Characteristic plots. DMRI=diffusion MRI-structural connectome; SMRI= structural MRI (T1 and T2-FLAIR)-morphometry; WHM=T2-FLAIR based White Matter Hyperintensity." @default.
- W2896921208 created "2018-10-26" @default.
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- W2896921208 date "2018-07-01" @default.
- W2896921208 modified "2023-10-16" @default.
- W2896921208 title "P3‐417: INDIVIDUALIZED STRUCTURAL CONNECTOME FOR DIAGNOSTIC AND PROGNOSTIC PREDICTION OF ALZHEIMER'S DISEASE" @default.
- W2896921208 doi "https://doi.org/10.1016/j.jalz.2018.06.1780" @default.
- W2896921208 hasPublicationYear "2018" @default.
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