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Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.

Eye tracking and other behavioral measurements collected from patient-participants in their hospital rooms afford a unique opportunity to study natural behavior for basic and clinical translational research. We describe an immersive social and behavioral paradigm implemented in patients undergoing evaluation for surgical treatment of epilepsy, with electrodes implanted in the brain to determine the source of their seizures. Our studies entail collecting eye tracking with other behavioral and psychophysiological measurements from patient-participants during unscripted behavior, including social interactions with clinical staff, friends, and family in the hospital room. This approach affords a unique opportunity to study the neurobiology of natural social behavior, though it requires carefully addressing distinct logistical, technical, and ethical challenges. Collecting neurophysiological data synchronized to behavioral and psychophysiological measures helps us to study the relationship between behavior and physiology. Combining across these rich data sources while participants eat, read, converse with friends and family, etc., enables clinical-translational research aimed at understanding the participants' disorders and clinician–patient interactions, as well as basic research into natural, real-world behavior. We discuss data acquisition, quality control, annotation, and analysis pipelines that are required for our studies. We also discuss the clinical, logistical, and ethical and privacy considerations critical to working in the hospital setting. [ABSTRACT FROM AUTHOR]

The human capacity for processing speech is remarkable, especially given that information in speech unfolds over multiple time scales concurrently. Similarly notable is our ability to filter out of extraneous sounds and focus our attention on one conversation, epitomized by the "Cocktail Party" effect. Yet, the neural mechanisms underlying on-line speech decoding and attentional stream selection are not well understood. We review findings from behavioral and neurophysiological investigations that underscore the importance of the temporal structure of speech for achieving these perceptual feats. We discuss the hypothesis that entrainment of ambient neuronal oscillations to speech's temporal structure, across multiple time-scales, serves to facilitate its decoding and underlies the selection of an attended speech stream over other competing input. In this regard, speech decoding and attentional stream selection are examples of "Active Sensing", emphasizing an interaction between proactive and predictive top-down modulation of neuronal dynamics and bottom-up sensory input. (Contains 2 figures.)

Intracranial recordings in human subjects provide a unique, fine‐grained temporal and spatial resolution inaccessible to conventional non‐invasive methods. A prominent signal in these recordings is broadband high‐frequency activity (approx. 70–150 Hz), generally considered to reflect neuronal excitation. Here we explored the use of this broadband signal to track, on a single‐trial basis, the temporal and spatial distribution of task‐engaged areas involved in decision‐making. We additionally focused on the alpha rhythm (8–14 Hz), thought to regulate the (dis)engagement of neuronal populations based on task demands. Using these signals, we characterized activity across cortex using intracranial recordings in patients with intractable epilepsy performing the Multi‐Source Interference Task, a Stroop‐like decision‐making paradigm. We analyzed recordings both from grid electrodes placed over cortical areas including frontotemporal and parietal cortex, and depth electrodes in prefrontal regions, including cingulate cortex. We found a widespread negative relationship between alpha power and broadband activity, substantiating the gating role of alpha in regions beyond sensory/motor cortex. Combined, these signals reflect the spatio‐temporal pattern of task‐engagement, with alpha decrease signifying task‐involved regions and broadband increase temporally locking to specific task aspects, distributed over cortical sites. We report sites that only respond to stimulus presentation or to the decision report and, interestingly, sites that reflect the time‐on‐task. The latter predict the subject's reaction times on a trial‐by‐trial basis. A smaller subset of sites showed modulation with task condition. Taken together, alpha and broadband signals allow tracking of neuronal population dynamics across cortex on a fine temporal and spatial scale. We examine the alpha‐band rhythm and broadband activity using intracranial recordings in a large cohort of patients with intractable epilepsy who performed a decision‐making task. We show that broadband activity, thought to be a proxy of local neuronal excitation, provides a readout of task‐specific information, such as task condition and reaction time. Critically, we find a negative correlation between broadband activity and the alpha rhythm across a widespread range of cortical areas, providing further evidence for the idea that alpha reflects a general gating mechanism, suppressing processing in task‐disengaged areas in favor of task‐engaged areas. [ABSTRACT FROM AUTHOR]