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001     908614
005     20220721190604.0
037 _ _ |a FZJ-2022-02723
100 1 _ |a More, Shammi
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|e Corresponding author
111 2 _ |a Organisation for Human Brain Mapping
|c Glasgow, Scotland
|d 2022-06-19 - 2022-06-23
|w UK
245 _ _ |a Brain-age prediction: a systematic comparison of machine learning workflows
260 _ _ |c 2022
336 7 _ |a Conference Paper
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336 7 _ |a Other
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520 _ _ |a Prediction of age using anatomical brain MRI, i.e., brain age, is proving valuable in exploring accelerated aging (brain age delta) as a proxy for aging-related diseases and crucial future health outcomes [1]. While various data representations and machine learning (ML) algorithms have been used for brain-age prediction [2,3], the impact of these choices on prediction accuracy remains uncharacterized. Moreover, several methodological challenges remain before a predictive model can be deployed in the real world; (1) robust within-site performance, (2) accurate cross-site prediction and, (3) consistent prediction for the same individual. To fill this gap, we systematically evaluated 70 workflows consisting of ten feature spaces derived from grey matter (GM) images and seven ML algorithms with diverse inductive biases to establish guidelines for designing brain-age prediction workflows.
536 _ _ |a 5251 - Multilevel Brain Organization and Variability (POF4-525)
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700 1 _ |a Antonoupolous, Georgios
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700 1 _ |a Hoffstaedter, Felix
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700 1 _ |a Caspers, Julian
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700 1 _ |a Eickhoff, Simon
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700 1 _ |a Patil, Kaustubh
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|v Decoding Brain Organization and Dysfunction
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