The syndrome of Anosognosia for Hemiplegia (AHP) can provide unique insights into the neurocognitive processes of motor awareness. Yet, prior studies have only explored predominately discreet lesions. Using advanced structural neuroimaging methods in 174 patients with a right-hemisphere stroke, we were able to identify three neural systems that contribute to AHP, when disconnected or directly damaged: the (i) premotor loop (ii) limbic system, and (iii) ventral attentional network. Our results suggest that human motor awareness is contingent on the joint contribution of these three systems.

1The rare syndrome of Anosognosia for Hemiplegia (AHP) can provide unique insights into 2 the neurocognitive processes of motor awareness. Yet, prior studies have only explored 3 predominately discreet lesions. Using advanced structural neuroimaging methods in 174 patients with 4 a right-hemisphere stroke, we were able to identify three neural networks that contribute to AHP, 5 when disconnected: the (1) premotor loop (2) limbic system, and (3) ventral attention network. Our 6 results suggest that human motor awareness is contingent on the joint contribution of these three 7 systems. 83 Motor awareness allows individuals to have insight into their motor performance, a 9 fundamental aspect of self-awareness. However, following damage to the right hemisphere, patients 10 with left paralysis may show delusions of intact motor ability, or anosognosia for hemiplegia (AHP, 11 1). Hence, studying AHP offers unique opportunities to explore the neurocognitive mechanisms of 12 motor awareness. 13While early studies regarded AHP as secondary to concomitant spatial deficits such as hemineglect 14 2 caused by parietal lesions, more recent experimental and voxel-based, lesion-symptom mapping 15 (VLSM) results suggest that AHP is an independent syndrome. These earlier studies address AHP as 16 an impairment of action and body monitoring, with lesions to the lateral premotor cortex and the 17 anterior insula (3,4), affecting patients' ability to detect discrepancies between feed-forward motor 18 predictions and sensorimotor feedback. However, these hypotheses are insufficient to explain all the 19 AHP symptoms, such as patients' inability to update their beliefs based on social feedback or more 20 general difficulties experienced in their daily living (5,6). Indeed, others have suggested that AHP 21 can be caused by a functional disconnection between regions processing top-down beliefs about the 22

Stroke significantly impacts quality of life. However, the long-term cognitive evolution in stroke is poorly predictable at the individual level. There is an urgent need for a better prediction of long-term symptoms based on acute clinical neuroimaging data. Previous works have demonstrated a strong relationship between the location of white matter disconnections and clinical symptoms. However, rendering the entire space of possible disconnections-deficit associations optimally surveyable will allow for a systematic association between brain disconnections and cognitive-behavioural measures at the individual level. Here we present the most comprehensive framework, a composite morphospace to predict neuropsychological scores one year after stroke. Linking the latent disconnectome morphospace to neuropsychological outcomes yields biological insights available as the first comprehensive atlas of disconnectome-deficit relations across 86 neuropsychological scores. Out-of-sample prediction derived from this atlas achieved average accuracy over 80%, which is higher than any other framework. Our novel predictive framework is available as an interactive web application, the disconnectome symptoms discoverer (http://disconnectomestudio.bcblab.com), to provide the foundations for a new and practical approach to modelling cognition in stroke. Our atlas and web application will reduce the burden of cognitive deficits on patients, their families, and wider society while also helping to tailor personalized treatment programs and discover new targets for treatments. We expect the range of assessments and the predictive power of our framework to increase even further through future crowdsourcing.

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