###### Abstract  
Stroke recovery is multifaceted and complex. Machine learning approaches have potential to identify patterns of brain activity associated with clinical outcomes, providing new insights into recovery. We aim to use machine learning to characterise the contribution of and potential interaction between resting state functional connectivity networks in predicting touch discrimination outcomes in a well-phenotyped, but small, stroke cohort. We interrogated and compared a suite of automated machine learning approaches to identify patterns of brain activity associated with clinical outcomes. Using feature reduction, the identification of combined 'golden features', and five-fold cross-validation, two golden features patterns emerged. These golden features identified patterns of resting state connectivity involving interactive relationships: 1. The difference between right insula and right superior temporal lobe correlation and left cerebellum and vermis correlation; 2. The ratio between right inferior temporal lobe and left cerebellum correlation and left frontal inferior operculum and left supplementary motor area correlation. Our findings demonstrate evidence of the potential for automated machine learning to provide new insights into brain network patterns and their interactions associated with the prediction of quantitative touch discrimination outcomes, through the automated identification of robust associations and golden feature brain patterns, even in a small cohort of stroke survivors. Stroke recovery; brain connectivity; somatosensation; prediction +
Footnote †: journal: Medical  
Artificial intelligence (AI) is a broad concept that refers to the development of computer systems that are capable of simulating human intelligence [8]. Machine learning (ML) is a subset of AI, focused on training algorithms to learn patterns from data and make predictions or decisions without explicit programming [9]. Models are investigated for their predictive power, with this index providing a means of benchmarking the value of the model generated. In essence, ML is a key technology within the field of AI and contains the concept of an error metric that the model can reference to gauge improvement in successive training runs [10]. Automated machine learning (AutoML) refers to the process of automating the end-to-end process of applying ML to real-world problems [11]. It involves automating tasks such as data pre-processing, feature engineering, model selection, hyperparameter tuning, and model evaluation. The goal is to create ML models with minimal manual intervention, making it more accessible for individuals with limited ML expertise and reducing the time taken to arrive at an accurate and reliable model. The final state is a balance between the competing goals of specificity and bias. Machine learning (ML) and automated machine learning (AutoML) represent transformative approaches in the analysis of complex clinical data and the prediction of patient outcomes. ML and AutoML can be used to identify interactions between several variables and detect useful information in clinical and imaging data [12]. Machine learning models, such as random forest, logistic regression, and deep neural networks, have been increasingly applied in medical research, including stroke rehabilitation [13]. For example, these models are utilized for predicting functional recovery [14], predicting favourable outcomes following an intervention [15], and individualising stroke rehabilitation [16], typically employing a large amount of clinical and imaging data as input [13; 17]. Additionally, convolutional neural networks (CNNs), a subset of deep learning models, have been employed for image analysis in stroke research, showcasing the ability to improve accuracy in predicting motor functions and aiding in rehabilitation planning [13; 18].