Search

Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post‐operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre‐ and intra‐operatively to delineate brain regions which are "eloquent" and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non‐invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre‐existing deficits. Connectome fingerprinting (CF) is a machine‐learning approach that learns associations between resting‐state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting‐state data. Here we utilized CF to train models on high‐quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting‐state fMRI (rs‐fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%–100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task‐related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out‐of‐sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments. Practitioner Points: Precision motor network prediction using connectome fingerprinting.Carefully trained models' performance limited by stability of task‐fMRI data.Successful cross‐scanner predictions and motor network mapping in patients with tumor. [ABSTRACT FROM AUTHOR]

Abstract: Over the past two decades, many magnetic resonance spectroscopy (MRS) studies reported lower N-acetylaspartate (NAA) in key brain regions of patients with schizophrenia (SZ) compared to healthy subjects. A smaller number of studies report no difference in NAA. Many sources of variance may contribute to these discordant results including heterogeneity of the SZ subject populations and methodological differences such as MRS acquisition parameters, and post-acquisition analytic methods. The current study reviewed proton MRS literature reporting measurements of NAA in SZ with a focus on methodology. Studies which reported lower NAA were significantly more likely to have used longer echo times (TEs), while studies with shorter TEs reported no concentration difference. This suggests that NAA quantitation using MRS was affected by the choice of TE, and that published MRS literature reporting NAA in SZ using a long TE is confounded by apparent differential T 2 relaxation effects between SZ and healthy control groups. Future MRS studies should measure T 2 relaxation times. This would allow for spectral concentration measurements to be appropriately corrected for these relaxation effects. In addition, as metabolite concentration and T 2 relaxation times are completely independent variables, this could offer distinct information about the metabolite of interest. [Copyright &y& Elsevier]

Situation awareness (SA) is important in many demanding tasks (e.g. driving). Assessing SA during training can indicate whether someone is ready to perform in the real world. SA is typically assessed by interrupting the task to ask questions about the situation or asking questions after task completion, assessing only momentary SA. An objective and continuous means of detecting SA is needed. We examined whether neurophysiological sensors are useful to objectively measure Level 3 SA (projection of events into the future) during a driving task. We measured SA by the speed at which participants responded to SA questions and the accuracy of responses. For EEG, beta and theta power were most sensitive to SA response time. For fNIRS, oxygenated haemoglobin (HbO) was most sensitive to accuracy. This is the first evidence to our knowledge that neurophysiological measures are useful for assessing Level 3 SA during an ecologically valid task.

In cocaine-dependent individuals, sleep is disturbed during cocaine use and abstinence, highlighting the importance of examining the behavioral and homeostatic response to acute sleep loss in these individuals. The current study was designed to identify a differential effect of sleep deprivation on brain bioenergetics, cognitive performance, and sleep between cocaine-dependent and healthy control participants. 14 healthy control and 8 cocaine-dependent participants experienced consecutive nights of baseline, total sleep deprivation, and recovery sleep in the research laboratory. Participants underwent ³¹P magnetic resonance spectroscopy (MRS) brain imaging, polysomnography, Continuous Performance Task, and Digit Symbol Substitution Task. Following recovery sleep, ³¹P MRS scans revealed that cocaine-dependent participants exhibited elevated global brain β-NTP (direct measure of adenosine triphosphate), α-NTP, and total NTP levels compared to those of healthy controls. Cocaine-dependent participants performed worse on the Continuous Performance Task and Digit Symbol Substitution Task at baseline compared to healthy control participants, but sleep deprivation did not worsen cognitive performance in either group. Enhancements of brain ATP levels in cocaine dependent participants following recovery sleep may reflect a greater impact of sleep deprivation on sleep homeostasis, which may highlight the importance of monitoring sleep during abstinence and the potential influence of sleep loss in drug relapse.