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Investigating the effect of changing parameters when building prediction models in post-stroke aphasia.
Authors:
HALAI, A., Woollams, A., LAMBON RALPH, M.A.
Reference:
Nature Human Behaviour, 4(7):725-735
Year of publication:
2020
CBU number:
8500
Abstract:
Neuroimaging has radically improved our understanding of how speech and language abilities map to the brain in normal and impaired participants, including the diverse, graded variations observed in post-stroke aphasia. A handful of studies have begun to explore the reverse inference: creating brain-to-behaviour prediction models. In this study, we explored the effect of three critical parameters on model performance: (1) brain partitions as predictive features; (2) combination of multimodal neuroimaging; and (3) type of machine learning algorithms. We explored the influence of these factors while predicting four principal dimensions of language and cognition variation in post-stroke aphasia. Across all four behavioural dimensions, we consistently found that prediction models derived from diffusion weighted data did not improve performance over and above models using structural measures extracted from T1 scans. Our results provide a set of principles to guide future work aiming to predict outcomes in neurological patients from brain imaging data.
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