## 4 Discussion
Moreover, evidence of remote tract-specific reductions in axonal connectivity indicated by diffusion imaging measures suggest a model of losing connecting fibres in the cerebellum and interhemispheric sensorimotor areas in the somatosensory network after a stroke [31]. The second 'golden feature' grouping with the second highest mean importance value of 0.21 identified the ratio involving the relationship between the right temporal and left cerebellum and left frontal operculum and supplementary motor area (SMA). This grouping is a valuable predictor of the achieved TDT score across the majority of the group. The involvement of these connected regions is consistent with cerebro-cerebellar interactions involved in perceptual and motor aspects of temporal processing and the simulation of timing information through feed-forward computation in the cerebellum [66]. The second grouping also involved an association between the left inferior frontal operculum and SMA. The SMA is connected to the frontal lobe and opercular region via the superior longitudinal fasciculus [67]. The touch function of the ipsilesional hand has been associated with the superior longitudinal fasciculus after stroke [31], and the pre-SMA has been shown to have somatosensory organisation [68]. Other regions that were identified as being associated with TDT outcomes were the right frontal inferior orbital region and right insula (0.13 importance) and left hippocampus pus and right cerebellum (0.10 importance). The right orbitofrontal cortex has extensive connections with sensory areas, as well as limbic system structure, and may have a specific role in attending to tactile stimuli [69]. Differential links with pain and pain-related areas are also reported [70]. Finally, the hippocampus and cerebellum are functionally connected in a bidirectional manner such that the cerebellum can influence hippocampal activity and vice versa [71]. Functional connectivity between the cerebellum and somatosensory area has also been associated with the attenuation of self-generated touch [72], and the hippocampus has been associated with the ownership of one's limb [73]. Together, our findings are consistent with a distributed model of somatosensory processing, wherein multiple networks are involved in separate subfunctions [73]. Finally, using automated ML, we have demonstrated new insights to functional connectivity networks associated with clinical outcomes, specifically touch discrimination, after stroke. This approach has identified brain regions and patterns of connectivity, i.e., combined 'golden features', that have importance in predicting clinical outcomes, beyond the predictions that are possible via manual human investigation. The next step, as highlighted in this Special Issue on the applications of AI in neuroscience, is to investigate the following question: how might we apply these new insights in clinical practice to personalise stroke recovery and rehabilitation? For example, the right insula and its correlated connections were identified as having an important role in two of the 'golden feature' groupings for stroke survivors with either right or left hemisphere lesions. This connected region plays an important role in multisensory integration. If this region is infarcted by the stroke in an individual, it might directly impact touch discrimination function. Alternatively, touch discrimination might also be indirectly impacted via interruption to correlated parts of the network. Conversely, if not impacted, this residual strength in the network could be manipulated in therapy, via its known behavioural function, to enhance cross-modal calibration and multisensory integration. Thus, the knowledge of the importance of these connected regions and networks has the potential to not only predict clinical outcome for an individual but also personalise treatment. The predictive analysis between brain networks and clinical outcomes reported was conducted using functional resting state connectivity data.