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Investigating the effect of changing parameters when building prediction models for post-stroke aphasia

Nature Human Behaviour volume 4, pages 725–735 (2020)Cite this article

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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 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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Fig. 1: A schematic representation of the prediction model creations.
Fig. 2: Cross-validation approach to determine the optimal number of components for PCA.
Fig. 3: Lesion overlap map for 69 patients with left-hemisphere post-stroke aphasia.
Fig. 4: Model performance across different input features.
Fig. 5: Model performance across machines and brain partitions.

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Data availability

The conditions of our ethical approval do not permit the public archiving of anonymized study data. The anonymized data necessary for reproducing the results in this article can be requested from the corresponding authors.

Code availability

The computer code that supports the findings of this study is available from the corresponding author on reasonable request.

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Acknowledgements

We thank all the patients, families, carers and community support groups for their continued, enthusiastic support of our research programme. This research was supported by grants from The Rosetrees Trust (no. A1699) and ERC (GAP: 670428 - BRAIN2MIND_NEUROCOMP). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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  1. MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK

    Ajay D. Halai & Matthew A. Lambon Ralph

  2. Division of Neuroscience and Experimental Psychology, School of Biological Sciences, University of Manchester, Manchester, UK

    Anna M. Woollams

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A.D.H., A.M.W. and M.A.L.R. conceived and designed the experiment. A.D.H. collected and analysed the data. A.D.H., A.M.W. and M.A.LR. wrote the paper.

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Correspondence to Ajay D. Halai or Matthew A. Lambon Ralph.

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Halai, A.D., Woollams, A.M. & Lambon Ralph, M.A. Investigating the effect of changing parameters when building prediction models for post-stroke aphasia. Nat Hum Behav 4, 725–735 (2020). https://doi.org/10.1038/s41562-020-0854-5

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