Hermes, Dora

OBJECTIVE Stereoelectroencephalography (SEEG) has become the predominant method for intracranial seizure localization. When imaging, semiology, and scalp EEG findings are not in full agreement or definitively localizing, implanted SEEG recordings are used to test candidate seizure onset zones (SOZs). Discovered SOZs may then be targeted for resection, laser ablation, or neurostimulation. If an SOZ is eloquent, resection and ablation are both contraindicated, so identifying functional representation is crucial for therapeutic decision-making. The authors present a novel functional brain mapping technique that utilizes task-based electrophysiological changes in SEEG during behavioral tasks and test this in pediatric and adult patients. METHODS SEEG was recorded in 20 patients with epilepsy who ranged in age from 6 to 39 years (12 female, 18 of 20 patients < 21 years of age) and underwent implanted monitoring to identify seizure onset. Each performed 1) visually cued simple repetitive movements of the hand, foot, or tongue while electromyography was recorded; and 2) simple picture-naming or verb-generation speech tasks while audio was recorded. Broadband changes in the power spectrum of the SEEG recording were compared between behavior and rest. RESULTS Electrophysiological functional mapping of movement and/or speech areas was completed in all 20 patients. Eloquent representation was identified in both cortex and white matter and generally corresponded to classically described functional anatomical organization as well as other clinical mapping results. Robust maps of brain activity were identified in healthy brain, regions of developmental or acquired structural abnormality, and SOZs. CONCLUSIONS Task-based electrophysiological mapping using broadband changes in the SEEG signal reliably identifies movement and speech representation in pediatric and adult epilepsy patients.

Abstract The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

Abstract Blood and cerebrospinal fluid (CSF) pulse and flow throughout the brain, driven by the cardiac cycle. These fluid dynamics, which are essential to healthy brain function, are characterized by several noninvasive magnetic resonance imaging (MRI) methods. Recent developments in fast MRI, specifically simultaneous multislice acquisition methods, provide a new opportunity to rapidly and broadly assess cardiac‐driven flow, including CSF spaces, surface vessels and parenchymal vessels. We use these techniques to assess blood and CSF flow dynamics in brief (3.5 min) scans on a conventional 3 T MRI scanner in five subjects. Cardiac pulses are measured with a photoplethysmography (PPG) on the index finger, along with functional MRI (fMRI) signals in the brain. We, retrospectively, align the fMRI signals to the heartbeat. Highly reliable cardiac‐gated fMRI temporal signals are observed in CSF and blood on the timescale of one heartbeat (test–retest reliability within subjects R 2 > 50%). In blood vessels, a local minimum is observed following systole. In CSF spaces, the ventricles and subarachnoid spaces have a local maximum following systole instead. Slower resting‐state scans with slice timing, retrospectively, aligned to the cardiac pulse, reveal similar cardiac‐gated responses. The cardiac‐gated measurements estimate the amplitude and phase of fMRI pulsations in the CSF relative to those in the arteries, an estimate of the local intracranial impedance. Cardiac aligned fMRI signals can provide new insights about fluid dynamics or diagnostics for diseases where these dynamics are important.

Brain–computer interfaces (BCIs) provide a way for the brain to interface directly with a computer. Many different brain signals can be used to control a device, varying in ease of recording, reliability, stability, temporal and spatial resolution, and noise. Electrocorticography (ECoG) electrodes provide a highly reliable signal from the human brain surface, and these signals have been used to decode movements, vision, and speech. ECoG-based BCIs are being developed to provide increased options for treatment and assistive devices for patients who have functional limitations. Decoding ECoG signals in real time provides direct feedback to the patient and can be used to control a cursor on a computer or an exoskeleton. In this review, the authors describe the current state of ECoG-based BCIs that are approaching clinical viability for restoring lost communication and motor function in patients with amyotrophic lateral sclerosis or tetraplegia. These studies provide a proof of principle and the possibility that ECoG-based BCI technology may also be useful in the future for assisting in the cortical rehabilitation of patients who have suffered a stroke.

Gamma oscillations in visual cortex have been hypothesized to be critical for perception, cognition, and information transfer. However, observations of these oscillations in visual cortex vary widely; some studies report little to no stimulus-induced narrowband gamma oscillations, others report oscillations for only some stimuli, and yet others report large oscillations for most stimuli. To better understand this signal, we developed a model that predicts gamma responses for arbitrary images and validated this model on electrocorticography (ECoG) data from human visual cortex. The model computes variance across the outputs of spatially pooled orientation channels, and accurately predicts gamma amplitude across 86 images. Gamma responses were large for a small subset of stimuli, differing dramatically from fMRI and ECoG broadband (non-oscillatory) responses. We propose that gamma oscillations in visual cortex serve as a biomarker of gain control rather than being a fundamental mechanism for communicating visual information.