Brain Camp: A Summer Pipeline Program to Increase Diversity in Neurosciences.

BACKGROUND: Despite calls to increase diversity in the health care workforce, most medical fields including neurology have seen minimal advances, owing in part to the lack of developing a robust pipeline for trainees from underrepresented backgrounds. We sought to create an immersive, replicable neurology-themed summer camp and longitudinal mentorship program for underrepresented-in-medicine (URM) high-school students to encourage them to enter the training pipeline in neuroscience-related fields. METHODS: We established an annual, no-cost 1-week camp for local URM students with the goals of exposing them to different health care professions within neuroscience while providing them with college application resources and long-term mentorship. A postprogram survey was distributed to assess the students' attitudes towards the camp and their desires to pursue health care careers. RESULTS: Over the 4 years since the founding of the camp (2016-2020), a total of 96 students participated, of whom 53% were URM, 74% came from very low-income households, and 61% had parents who did not attend college. In total, 87 students (91%) completed the postcamp survey. Nearly all (97%) of the respondents were likely to recommend the camp to their peers, and the vast majority (85%) felt that Brain Camp made them more likely to pursue careers in health care. CONCLUSIONS: Brain Camp seeks to address the unmet need for low barrier-to-entry programs designed for URM high-school students interested in health care careers. We envision that our camp may serve as a blueprint for other similar programs across the nation with the goal of addressing the URM pipeline in neuroscience.

Spatial integration during active tactile sensation drives orientation perception.

Active haptic sensation is critical for object identification, but its neural circuit basis is poorly understood. We combined optogenetics, two-photon imaging, and high-speed behavioral tracking in mice solving a whisker-based object orientation discrimination task. We found that orientation discrimination required animals to summate input from multiple whiskers specifically along the whisker arc. Animals discriminated the orientation of the stimulus per se as their performance was invariant to the location of the presented stimulus. Populations of barrel cortex neurons summated across whiskers to encode each orientation. Finally, acute optogenetic inactivation of the barrel cortex and cell-type-specific optogenetic suppression of layer 4 excitatory neurons degraded performance, implying that infragranular layers alone are not sufficient to solve the task. These data suggest that spatial summation over an active haptic array generates representations of an object's orientation, which may facilitate encoding of complex three-dimensional objects during active exploration.

Medial Orbitofrontal Cortex, Dorsolateral Prefrontal Cortex, and Hippocampus Differentially Represent the Event Saliency.

Two primary functions attributed to the hippocampus and prefrontal cortex (PFC) network are retaining the temporal and spatial associations of events and detecting deviant events. It is unclear, however, how these two functions converge into one mechanism. Here, we tested whether increased activity with perceiving salient events is a deviant detection signal or contains information about the event associations by reflecting the magnitude of deviance (i.e., event saliency). We also tested how the deviant detection signal is affected by the degree of anticipation. We studied regional neural activity when people watched a movie that had varying saliency of a novel or an anticipated flow of salient events. Using intracranial electroencephalography from 10 patients, we observed that high-frequency activity (50-150 Hz) in the hippocampus, dorsolateral PFC, and medial OFC tracked event saliency. We also observed that medial OFC activity was stronger when the salient events were anticipated than when they were novel. These results suggest that dorsolateral PFC and medial OFC, as well as the hippocampus, signify the saliency magnitude of events, reflecting the hierarchical structure of event associations.

Systematic comparison between a wireless EEG system with dry electrodes and a wired EEG system with wet electrodes.

Recent advances in dry electrodes technology have facilitated the recording of EEG in situations not previously possible, thanks to the relatively swift electrode preparation and avoidance of applying gel to subject's hair. However, to become a true alternative, these systems should be compared to state-of-the-art wet EEG systems commonly used in clinical or research applications. In our study, we conducted a systematic comparison of electrodes application speed, subject comfort, and most critically electrophysiological signal quality between the conventional and wired Biosemi EEG system using wet active electrodes and the compact and wireless F1 EEG system consisting of dry passive electrodes. All subjects (n = 27) participated in two recording sessions on separate days, one with the wet EEG system and one with the dry EEG system, in which the session order was counterbalanced across subjects. In each session, we recorded their EEG during separate rest periods with eyes open and closed followed by two versions of a serial visual presentation target detection task. Each task component allows for a neural measure reflecting different characteristics of the data, including spectral power in canonical low frequency bands, event-related potential components (specifically, the P3b), and single trial classification based on machine learning. The performance across the two systems was similar in most measures, including the P3b amplitude and topography, as well as low frequency (theta, alpha, and beta) spectral power at rest. Both EEG systems performed well above chance in the classification analysis, with a marginal advantage of the wet system over the dry. Critically, all aforementioned electrophysiological metrics showed significant positive correlations (r = 0.54-0.89) between the two EEG systems. This multitude of measures provides a comprehensive comparison that captures different aspects of EEG data, including temporal precision, frequency domain as well as multivariate patterns of activity. Taken together, our results indicate that the dry EEG system used in this experiment can effectively record electrophysiological measures commonly used across the research and clinical contexts with comparable quality to the conventional wet EEG system.

Integrated analysis of anatomical and electrophysiological human intracranial data.

Human intracranial electroencephalography (iEEG) recordings provide data with much greater spatiotemporal precision than is possible from data obtained using scalp EEG, magnetoencephalography (MEG), or functional MRI. Until recently, the fusion of anatomical data (MRI and computed tomography (CT) images) with electrophysiological data and their subsequent analysis have required the use of technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that enables complex raw human iEEG data to be converted into more readily comprehensible illustrative representations. The protocol uses an open-source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and used by a large research community. In this protocol, we describe how to analyze complex iEEG datasets by providing an intuitive and rapid approach that can handle both neuroanatomical information and large electrophysiological datasets. We provide a worked example using an example dataset. We also explain how to automate the protocol and adjust the settings to enable analysis of iEEG datasets with other characteristics. The protocol can be implemented by a graduate student or postdoctoral fellow with minimal MATLAB experience and takes approximately an hour to execute, excluding the automated cortical surface extraction.