## 5 Conclusions  
Automated machine learning can identify patterns of brain network activity associated with quantitative touch discrimination in survivors of stroke. A comparison across six candidate AutoML approaches revealed the best-performing algorithm for this stroke cohort (LightGBM), with the subsequent identification of two 'golden feature' patterns involving brain regions and networks putatively associated with touch discrimination. The potential to use this automated ML approach to predict quantitative clinical outcomes and identify associated brain networks and interacting brain networks, even with a small sample of stroke survivors, is demonstrated. The value of this approach lies in the following: the automation of model selection; the identification of correlated activity between brain imaging and clinical data filtered for robust associations; feature selection with the naming of brain regions; and the identification of 'golden features' that represent derived combined brain patterns based on decision tree analysis. Thus, the process is rigorous, provides an outcome based on fused data, and is able to be applied in small cohorts, as was the case in the current analysis. This approach has the potential to provide new insights into stroke recovery, as demonstrated in the identification of brain networks with correlated brain activity associated with clinical outcomes at 3 months and 12 months post-stroke. Conceptualization and methodology, A.W., P.G. and L.M.C. ; formal analysis, A.W. and P.G. ; data curation, A.W., P.G. and L.M.C. ; data interpretation, A.W., P.G. and L.M.C. ; writing--original draft preparation, A.W., P.G. and L.M.C. ; writing--review and editing, A.W., P.G. and L.M.C. ; data visualization, A.W. ; supervision, P.G. and L.M.C. ; funding acquisition, L.M.C. ; software, A.W. All authors have read and agreed to the published version of the manuscript. We acknowledge support from the Commonwealth Scientific Industrial Research Organisation (CSIRO) of the Australia Preventative Health Flagship grant (START cohort); the National Health and Medical Research Council (NHMRC) of Australia Partnership grant (GNT 1134495); the NHMRC Project grant (GNT 1022694); and the NHMRC Ideas grant (GNT 2004443) awarded to L.M.C. This study was conducted in accordance with the Declaration of Helsinki and approved by the Human Ethics Committee of Austin Health (HREC/17/Austin/281, approved 4 September 2017 ) and the La Trobe University Human Ethics Committee (HREC/17/Austin/281, externally approved project, 14 March 2018 ), Melbourne, Victoria, Australia for studies involving humans. Central ethical approval for original data collection for the START_PrePARE cohort study was obtained from the Melbourne Health Human Research Ethics Committee (2009,079, approved 13 January 2010) and the Austin Health Human Research Ethics Committee (H2010/03588, approved 6 January 2010). Informed Consent Statement: Informed consent was obtained from all participants involved in the study. Due to the personal nature of the data and original ethics approval, the data will not be made available broadly. De-identified data may be made available for related research and analysis by the research group and collaborators with additional ethics approval. We would like to thank the stroke survivors who participated in the original studies and the members of the START research group and Neurorehabilitation and Recovery research team who contributed to data collection. All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The following abbreviations are used in this manuscript: