###### Abstract
Together, these studies provide evidence of robust brain-behaviour associations and demonstrate the value and potential of using machine learning approaches, particularly when large volumes of data are available. However, the selection and application of ML models are also often reliant on known relationships and prior datasets to train models [13]. In the domain of stroke research, where data are often scarce and highly dimensional, AutoML frameworks present a promising solution. These frameworks automate the process of model selection, feature engineering, and hyperparameter tuning, aiming to develop high-performing predictive models with minimal human intervention [19]. This has particular value when such parameters are not previously known or established for a particular population, such as stroke. By benchmarking various AutoML tools against traditional ML approaches, studies have demonstrated the potential of AutoML in enhancing disease prediction, including stroke outcome prediction, based on clinical and imaging data [19]. In the current study, we investigated different ML approaches to examining brain networks (functional brain imaging data) and their ability to predict clinical outcomes. The stroke cohort investigated is known as the START cohort (STroke imAging pRevention and Treatment) [5]. The cohort includes survivors of stroke over the first year post-stroke, from within hours after their stroke to 3 days, 3 months, and 12 months post-stroke. A subset of the START cohort received advanced imaging at 3 months and 12 months post-stroke. This included structural and functional MRI brain imaging. Here, we focus on functional brain imaging data, specifically resting state functional connectivity, given its potential to predict clinical outcomes [5]. Disruptions to functional resting state network connectivity have predicted impairment across multiple behavioural domains, with both specific and general changes in network patterns [6]. For example, [PERSON] et al. found an association between specific functional resting state brain networks and attention, visual memory, verbal memory, and language domains in a sample of 132 stroke survivors. They did not, however, investigate a relationship with somatosensation. We focus on the prediction of quantitative touch discrimination in the hands (contralesional and/or ipsilesional) of stroke survivors as the clinical outcome of interest at 3 months and 12 months post-stroke. The impairment of body sensations (somatosensation), including changes in the ability to discriminate touch sensations, is experienced by one in two survivors of stroke [20]. Evidence-based therapy is available to treat this impairment [21]. However, the potential for new personalised and optimised interventions based on the knowledge of neural networks and interactions between them has not been fully investigated. Evidence of altered functional connectivity has been reported in a stroke cohort within four pre-defined regions of the somatosensory network, i.e., the primary (S1) and secondary (S2) somatosensory cortices in both hemispheres [22]. This cohort of chronic stroke survivors had ongoing, and often marked, impairment of somatosensation, specifically touch discrimination. In comparison, the current stroke cohort has a wide range of impairments, many patients are defined as having mild stroke according to the National Institute of Health Stroke Scale (NIHSS) [23], and touch discrimination was tested at specific times post-stroke (3 and 12 months). We use the tactile discrimination test (TDT) to quantify touch discrimination impairment [24; 25]. To explore the complex effects of stroke on brain networks, this study introduces an innovative analytical approach centered on the identification and analysis of 'golden features'. These features, derived from the mathematical manipulation of brain area correlations, represent a novel method of examining the multi-level interactions within post-stroke brain networks.