Motor conversion disorder (CD) entails genuine disturbances in the subjective experience of patients who maintain they are unable to perform a motor function, despite lack of apparent neurological damage. Abilities by which individuals assess their own capacities during performance in a task are called metacognitive, and distinctive impairment of such abilities is observed in several disorders of self-awareness such as blindsight and anosognosia. In CD, previous research has focused on the recruitment of motor and emotional brain systems, generally linking symptoms to altered limbic-motor interactions; however, metacognitive function has not been studied to our knowledge. Here we tested ten CD patients and ten age-gender matched controls during a visually-guided motor paradigm, previously employed in healthy controls (HC), allowing us to probe for motor awareness and metacognition. Participants had to draw straight trajectories towards a visual target while, unbeknownst to them, deviations were occasionally introduced in the reaching trajectory seen on the screen. Participants then reported both awareness of deviations and confidence in their response. Activity in premotor and cingulate cortex distinguished between conscious and unconscious movement corrections in controls better than patients. Critically, whereas controls engaged the left superior precuneus and middle temporal region during confidence judgments, CD patients recruited bilateral parahippocampal and amygdalo-hippocampal regions instead. These results reveal that distinct brain regions subserve metacognitive monitoring for HC and CD, pointing to different mechanisms and sources of information used to monitor and form confidence judgments of motor performance. While brain systems involved in sensory-motor integration and vision are more engaged in controls, CD patients may preferentially rely on memory and contextual associative processing, possibly accounting for how affect and memories can imbue current motor experience in these patients.

Acute mesenteric ischemia (AMI) is a severe condition associated with poor prognosis, ultimately leading to death due to multiorgan failure. Several mechanisms may lead to AMI, and non-occlusive mesenteric ischemia (NOMI) represents a particular form of AMI. NOMI is prevalent in intensive care units in critically ill patients. In NOMI management, promptness and accuracy of diagnosis are paramount to achieve decisive treatment, but the last decades have been marked by failure to improve NOMI prognosis, due to lack of tools to detect this condition. While real-life diagnostic management relies on a combination of physical examination, several biomarkers, imaging, and endoscopy to detect the possibility of several grades of NOMI, research studies only focus on a few elements at a time. In the era of artificial intelligence (AI), which can aggregate thousands of variables in complex longitudinal models, the prospect of achieving accurate diagnosis through machine-learning-based algorithms may be sought. In the following work, we bring you a state-of-the-art literature review regarding NOMI, its presentation, its mechanics, and the pitfalls of routine work-up diagnostic exams including biomarkers, imaging, and endoscopy, we raise the perspectives of new biomarker exams, and finally we discuss what AI may add to the field, after summarizing what this technique encompasses.

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Physiological evidence suggests that neighboring brain regions have similar perfusion characteristics (vascular supply, collateral blood flow). It is largely unknown whether integrating perfusion CT (pCT) information from the area surrounding a given voxel (i.e. the receptive field (RF)) improves the prediction of infarction of this voxel. Based on general linear regression models (GLMs) and using acute pCT-derived maps, we compared the added value of cuboid RF to predict the final infarct. To this aim, we included 144 stroke patients with acute pCT and follow-up MRI, used to delineate the final infarct. Overall, the performance of GLMs to predict the final infarct improved when using RF for all pCT maps (cerebral blood flow, cerebral blood volume, mean transit time and time-to-maximum of the tissue residual function (Tmax)). The highest performance was obtained with Tmax (glm(Tmax); AUC = 0.89 ± 0.03 with RF vs. 0.78 ± 0.02 without RF; p < 0.001) and with a model combining all perfusion parameters (glm(multi); AUC 0.89 ± 0.02 with RF vs. 0.79 ± 0.02 without RF; p < 0.001). These results suggest that prediction of infarction improves by integrating perfusion information from adjacent tissue. This approach may be applied in future studies to better identify ischemic core and penumbra thresholds and improve patient selection for acute stroke treatment.