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How brain functions in the distorted ischemic state before and after reperfusion is unclear. It is also uncertain whether there are any indicators within ischemic brain that could predict surgical outcomes. To alleviate these issues, we applied individual brain connectome in chronic steno‐occlusive vasculopathy (CSOV) to map both ischemic symptoms and their postbypass changes. A total of 499 bypasses in 455 CSOV patients were collected and followed up for 47.8 ± 20.5 months. Using multimodal parcellation with connectivity‐based and pathological distortion‐independent approach, areal MR features of brain connectome were generated with three measurements of functional connectivity (FC), structural connectivity, and PageRank centrality at the single‐subject level. Thirty‐three machine‐learning models were then trained with clinical and areal MR features to obtain acceptable classifiers for both ischemic symptoms and their postbypass changes, among which, 11 were deemed acceptable (AUC > 0.7). Notably, the FC feature‐based model for long‐term neurological outcomes performed very well (AUC > 0.8). Finally, a Shapley additive explanations plot was adopted to extract important individual features in acceptable models to generate "fingerprints" of brain connectome. This study not only establishes brain connectomic fingerprint databases for brain ischemia with distortion, but also provides informative insights for how brain functions before and after reperfusion. [ABSTRACT FROM AUTHOR]

Background and Purpose: Hemispatial neglect is characterized by a reduced awareness to stimuli on the contralateral side. Current literature suggesting that damage to the right parietal lobe and attention networks may cause hemispatial neglect is conflicting and can be improved by investigating a connectomic model of the "neglect system" and the anatomical specificity of regions involved in it. Methods: A meta‐analysis of voxel‐based morphometry magnetic resonance imaging (MRI) studies of hemispatial neglect was used to identify regions associated with neglect. We applied parcellation schemes to these regions and performed diffusion spectrum imaging (DSI) tractography to determine their connectivity. By overlaying neglect areas and maps of the attention networks, we studied the relationship between them. Results: The meta‐analysis generated a list of 13 right hemisphere parcellations. These 13 neglect‐related parcellations were predominantly linked by the superior longitudinal fasciculus (SLF) throughout a fronto‐parietal‐temporal network. We found that the dorsal and ventral attention networks showed partial overlap with the neglect system and included various other higher‐order networks. Conclusions: We provide an anatomically specific connectomic model of the neurobehavioral substrates underlying hemispatial neglect. Our model suggests a fronto‐parietal‐temporal network linked via the SLF supports the functions impaired in neglect and implicates various higher‐order networks which are not limited to the attention networks. [ABSTRACT FROM AUTHOR]

Objective: Despite its prevalence, insomnia disorder (ID) remains poorly understood. In this study, we used machine learning to analyze the functional connectivity (FC) disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation (rTMS) and pharmacotherapy. Materials and methods: 51 adult patients with chronic insomnia and 42 healthy age and education matched controls underwent baseline anatomical T1 magnetic resonance imaging (MRI), resting-stage functional MRI (rsfMRI), and diffusion weighted imaging (DWI). Imaging was repeated for 24 ID patients following four weeks of treatment with pharmacotherapy, with or without rTMS. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into ID and control groups based on their FC, and derive network and parcel-based FC features contributing to each model. The number of FC anomalies within each network was also compared between responders and non-responders using median absolute deviation at baseline and follow-up. Results: Subjects were classified into ID and control with an area under the receiver operating characteristic curve (AUC-ROC) of 0.828. Baseline FC anomaly counts were higher in responders than non-responders. Response as measured by the Insomnia Severity Index (ISI) was associated with a decrease in anomaly counts across all networks, while all networks showed an increase in anomaly counts when response was measured using the Pittsburgh Sleep Quality Index. Overall, responders also showed greater change in all networks, with the Default Mode Network demonstrating the greatest change. Conclusion: Machine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets. [ABSTRACT FROM AUTHOR]