Zachary V. Freudenburg

Stable and dynamic cortical electrophysiology of induction and emergence with propofol anesthesia

The mechanism(s) by which anesthetics reversibly suppress consciousness are incompletely understood. Previous functional imaging studies demonstrated dynamic changes in thalamic and cortical metabolic activity, as well as the maintained presence of metabolically defined functional networks despite the loss of consciousness. However, the invasive electrophysiology associated with these observations has yet to be studied. By recording electrical activity directly from the cortical surface, electrocorticography (ECoG) provides a powerful method to integrate spatial, temporal, and spectral features of cortical electrophysiology not possible with noninvasive approaches. In this study, we report a unique comprehensive recording of invasive human cortical physiology during both induction and emergence from propofol anesthesia. Propofol-induced transitions in and out of consciousness (defined here as responsiveness) were characterized by maintained large-scale functional networks defined by correlated fluctuations of the slow cortical potential (<0.5 Hz) over the somatomotor cortex, present even in the deeply anesthetized state of burst suppression. Similarly, phase-power coupling between θ- and γ-range frequencies persisted throughout the induction and emergence from anesthesia. Superimposed on this preserved functional architecture were alterations in frequency band power, variance, covariance, and phase–power interactions that were distinct to different frequency ranges and occurred in separable phases. These data support that dynamic alterations in cortical and thalamocortical circuit activity occur in the context of a larger stable architecture that is maintained despite anesthetic-induced alterations in consciousness.

Nonuniform High-Gamma (60–500 Hz) Power Changes Dissociate Cognitive Task and Anatomy in Human Cortex

High-gamma-band (>60 Hz) power changes in cortical electrophysiology are a reliable indicator of focal, event-related cortical activity. Despite discoveries of oscillatory subthreshold and synchronous suprathreshold activity at the cellular level, there is an increasingly popular view that high-gamma-band amplitude changes recorded from cellular ensembles are the result of asynchronous firing activity that yields wideband and uniform power increases. Others have demonstrated independence of power changes in the low- and high-gamma bands, but to date, no studies have shown evidence of any such independence above 60 Hz. Based on nonuniformities in time-frequency analyses of electrocorticographic (ECoG) signals, we hypothesized that induced high-gamma-band (60–500 Hz) power changes are more heterogeneous than currently understood. Using single-word repetition tasks in six human subjects, we showed that functional responsiveness of different ECoG high-gamma sub-bands can discriminate cognitive task (e.g., hearing, reading, speaking) and cortical locations. Power changes in these sub-bands of the high-gamma range are consistently present within single trials and have statistically different time courses within the trial structure. Moreover, when consolidated across all subjects within three task-relevant anatomic regions (sensorimotor, Brocas area, and superior temporal gyrus), these behavior- and location-dependent power changes evidenced nonuniform trends across the population. Together, the independence and nonuniformity of power changes across a broad range of frequencies suggest that a new approach to evaluating high-gamma-band cortical activity is necessary. These findings show that in addition to time and location, frequency is another fundamental dimension of high-gamma dynamics.

Fully Implanted Brain–Computer Interface in a Locked-In Patient with ALS

Options for people with severe paralysis who have lost the ability to communicate orally are limited. We describe a method for communication in a patient with late-stage amyotrophic lateral sclerosis (ALS), involving a fully implanted brain-computer interface that consists of subdural electrodes placed over the motor cortex and a transmitter placed subcutaneously in the left side of the thorax. By attempting to move the hand on the side opposite the implanted electrodes, the patient accurately and independently controlled a computer typing program 28 weeks after electrode placement, at the equivalent of two letters per minute. The brain-computer interface offered autonomous communication that supplemented and at times supplanted the patients eye-tracking device. (Funded by the Government of the Netherlands and the European Union; ClinicalTrials.gov number, NCT02224469 .).

Decoding hand gestures from primary somatosensory cortex using high-density ECoG

ABSTRACT Electrocorticography (ECoG) based Brain‐Computer Interfaces (BCIs) have been proposed as a way to restore and replace motor function or communication in severely paralyzed people. To date, most motor‐based BCIs have either focused on the sensorimotor cortex as a whole or on the primary motor cortex (M1) as a source of signals for this purpose. Still, target areas for BCI are not confined to M1, and more brain regions may provide suitable BCI control signals. A logical candidate is the primary somatosensory cortex (S1), which not only shares similar somatotopic organization to M1, but also has been suggested to have a role beyond sensory feedback during movement execution. Here, we investigated whether four complex hand gestures, taken from the American sign language alphabet, can be decoded exclusively from S1 using both spatial and temporal information. For decoding, we used the signal recorded from a small patch of cortex with subdural high‐density (HD) grids in five patients with intractable epilepsy. Notably, we introduce a new method of trial alignment based on the increase of the electrophysiological response, which virtually eliminates the confounding effects of systematic and non‐systematic temporal differences within and between gestures execution. Results show that S1 classification scores are high (76%), similar to those obtained from M1 (74%) and sensorimotor cortex as a whole (85%), and significantly above chance level (25%). We conclude that S1 offers characteristic spatiotemporal neuronal activation patterns that are discriminative between gestures, and that it is possible to decode gestures with high accuracy from a very small patch of cortex using subdurally implanted HD grids. The feasibility of decoding hand gestures using HD‐ECoG grids encourages further investigation of implantable BCI systems for direct interaction between the brain and external devices with multiple degrees of freedom. HIGHLIGHTSPrimary somatosensory cortex offers discriminative patterns between four gestures.Spatiotemporal information is robust and reliable for fine movement decoding.A new trial alignment method to reduce decoding temporal jitter is introduced.Optimal coverage and consistent execution are essential for accurate decoding.

Decoding Motor Signals From the Pediatric Cortex: Implications for Brain-Computer Interfaces in Children

OBJECTIVE: To demonstrate the decodable nature of pediatric brain signals for the purpose of neuroprosthetic control. We hypothesized that children would achieve levels of brain-derived computer control comparable to performance previously reported for adults. PATIENTS AND METHODS: Six pediatric patients with intractable epilepsy who were invasively monitored underwent screening for electrocortical control signals associated with specific motor or phoneme articulation tasks. Subsequently, patients received visual feedback as they used these associated electrocortical signals to direct one dimensional cursor movement to a target on a screen. RESULTS: All patients achieved accuracies between 70% and 99% within 9 minutes of training using the same screened motor and articulation tasks. Two subjects went on to achieve maximum accuracies of 73% and 100% using imagined actions alone. Average mean and maximum performance for the 6 pediatric patients was comparable to that of 5 adults. The mean accuracy of the pediatric group was 81% (95% confidence interval [CI]: 71.5–90.5) over a mean training time of 11.6 minutes, whereas the adult group had a mean accuracy of 72% (95% CI: 61.2–84.3) over a mean training time of 12.5 minutes. Maximum performance was also similar between the pediatric and adult groups (89.6% [95% CI: 83–96.3] and 88.5% [95% CI: 77.1–99.8], respectively). CONCLUSIONS: Similarly to adult brain signals, pediatric brain signals can be decoded and used for BCI operation. Therefore, BCI systems developed for adults likely hold similar promise for children with motor disabilities.

Decoding spoken phonemes from sensorimotor cortex with high-density ECoG grids

ABSTRACT For people who cannot communicate due to severe paralysis or involuntary movements, technology that decodes intended speech from the brain may offer an alternative means of communication. If decoding proves to be feasible, intracranial Brain‐Computer Interface systems can be developed which are designed to translate decoded speech into computer generated speech or to instructions for controlling assistive devices. Recent advances suggest that such decoding may be feasible from sensorimotor cortex, but it is not clear how this challenge can be approached best. One approach is to identify and discriminate elements of spoken language, such as phonemes. We investigated feasibility of decoding four spoken phonemes from the sensorimotor face area, using electrocorticographic signals obtained with high‐density electrode grids. Several decoding algorithms including spatiotemporal matched filters, spatial matched filters and support vector machines were compared. Phonemes could be classified correctly at a level of over 75% with spatiotemporal matched filters. Support Vector machine analysis reached a similar level, but spatial matched filters yielded significantly lower scores. The most informative electrodes were clustered along the central sulcus. Highest scores were achieved from time windows centered around voice onset time, but a 500 ms window before onset time could also be classified significantly. The results suggest that phoneme production involves a sequence of robust and reproducible activity patterns on the cortical surface. Importantly, decoding requires inclusion of temporal information to capture the rapid shifts of robust patterns associated with articulator muscle group contraction during production of a phoneme. The high classification scores are likely to be enabled by the use of high density grids, and by the use of discrete phonemes. Implications for use in Brain‐Computer Interfaces are discussed. HIGHLIGHTSDiscrete, spoken phonemes can be classified with high performance from sensorimotor cortex, even before voice onset.Rapid sequences of activity patterns in sensorimotor cortex reflect sequences of muscle contractions during spoken phonemes.Decoding spoken phonemes benefits from inclusion of the temporal evolution of high frequency band power.Decoding spoken phonemes benefits from sampling from the whole inferior sensorimotor region, with electrodes spaced 4 mm apart or less.

Neural tuning to low-level features of speech throughout the perisylvian cortex

Despite a large body of research, we continue to lack a detailed account of how auditory processing of continuous speech unfolds in the human brain. Previous research showed the propagation of low-level acoustic features of speech from posterior superior temporal gyrus toward anterior superior temporal gyrus in the human brain (Hullett et al., 2016). In this study, we investigate what happens to these neural representations past the superior temporal gyrus and how they engage higher-level language processing areas such as inferior frontal gyrus. We used low-level sound features to model neural responses to speech outside of the primary auditory cortex. Two complementary imaging techniques were used with human participants (both males and females): electrocorticography (ECoG) and fMRI. Both imaging techniques showed tuning of the perisylvian cortex to low-level speech features. With ECoG, we found evidence of propagation of the temporal features of speech sounds along the ventral pathway of language processing in the brain toward inferior frontal gyrus. Increasingly coarse temporal features of speech spreading from posterior superior temporal cortex toward inferior frontal gyrus were associated with linguistic features such as voice onset time, duration of the formant transitions, and phoneme, syllable, and word boundaries. The present findings provide the groundwork for a comprehensive bottom-up account of speech comprehension in the human brain. SIGNIFICANCE STATEMENT We know that, during natural speech comprehension, a broad network of perisylvian cortical regions is involved in sound and language processing. Here, we investigated the tuning to low-level sound features within these regions using neural responses to a short feature film. We also looked at whether the tuning organization along these brain regions showed any parallel to the hierarchy of language structures in continuous speech. Our results show that low-level speech features propagate throughout the perisylvian cortex and potentially contribute to the emergence of “coarse” speech representations in inferior frontal gyrus typically associated with high-level language processing. These findings add to the previous work on auditory processing and underline a distinctive role of inferior frontal gyrus in natural speech comprehension.

Fast-scale network dynamics in human cortex have specific spectral covariance patterns

Significance How different cortical regions are coordinated during a cognitive task is fundamentally important to understanding brain function. At rest, the brain is subdivided into different functional networks that are bound together at very slow oscillating time scales. Less is understood about how this networked behavior operates during the brief moments of a cognitive operation. By recording brain signals directly from the surface of the human brain, we find that, when performing a simple speech task, broad cortical regions are transiently bound together by shared patterns of brain oscillations that are frequency specific. In addition to demonstrating that cortical areas are broadly networked, these findings provide a new analytic tool for understanding fast-scale dynamics in the brain. Whether measured by MRI or direct cortical physiology, infraslow rhythms have defined state invariant cortical networks. The time scales of this functional architecture, however, are unlikely to be able to accommodate the more rapid cortical dynamics necessary for an active cognitive task. Using invasively monitored epileptic patients as a research model, we tested the hypothesis that faster frequencies would spectrally bind regions of cortex as a transient mechanism to enable fast network interactions during the performance of a simple hear-and-repeat speech task. We term these short-lived spectrally covariant networks functional spectral networks (FSNs). We evaluated whether spectrally covariant regions of cortex, which were unique in their spectral signatures, provided a higher degree of task-related information than any single site showing more classic physiologic responses (i.e., single-site amplitude modulation). Taken together, our results showing that FSNs are a more sensitive measure of task-related brain activation and are better able to discern phonemic content strongly support the concept of spectrally encoded interactions in cortex. Moreover, these findings that specific linguistic information is represented in FSNs that have broad anatomic topographies support a more distributed model of cortical processing.

GridLoc : An automatic and unsupervised localization method for high-density ECoG grids

&NA; Precise localization of electrodes is essential in the field of high‐density (HD) electrocorticography (ECoG) brain signal analysis in order to accurately interpret the recorded activity in relation to functional anatomy. Current localization methods for subchronically implanted HD electrode grids involve post‐operative imaging. However, for situations where post‐operative imaging is not available, such as during acute measurements in awake surgery, electrode localization is complicated. Intra‐operative photographs may be informative, but not for electrode grids positioned partially or fully under the skull. Here we present an automatic and unsupervised method to localize HD electrode grids that does not require post‐operative imaging. The localization method, named GridLoc, is based on the hypothesis that the anatomical and vascular brain structures under the ECoG electrodes have an effect on the amplitude of the recorded ECoG signal. More specifically, we hypothesize that the spatial match between resting‐state high‐frequency band power (45–120 Hz) patterns over the grid and the anatomical features of the brain under the electrodes, such as the presence of sulci and larger blood vessels, can be used for adequate HD grid localization. We validate this hypothesis and compare the GridLoc results with electrode locations determined with post‐operative imaging and/or photographs in 8 patients implanted with HD‐ECoG grids. Locations agreed with an average difference of 1.94 ± 0.11 mm, which is comparable to differences reported earlier between post‐operative imaging and photograph methods. The results suggest that resting‐state high‐frequency band activity can be used for accurate localization of HD grid electrodes on a pre‐operative MRI scan and that GridLoc provides a convenient alternative to methods that rely on post‐operative imaging or intra‐operative photographs. HighlightsData‐driven method to localize intraoperative high‐density ECoG grids.GridLoc does not require post‐operative imaging or the use of a neuronavigator.Resting‐state ECoG HFB signals reveal vascular and anatomical structure.

Removal of epileptically compromised tissue in the frontal cortex restores oculomotor selection in the antisaccade task

The frontal cortex is heavily involved in oculomotor selection. Here, we investigated the neural correlates of eye movement selection during an antisaccade task in a young epileptic patient in whom the seizure focus included the frontal cortex and affected its function. Before resection surgery, the patient had difficulty in performing correct antisaccades towards the visual field contralateral to the seizure focus. Because the FEF is the only area in the human frontal cortex that is known to have a lateralized oculomotor function in the antisaccade task, this behavioural imbalance between the two visual fields suggests a disruption of FEF functioning by the nearby seizure focus. Electrocorticographic recordings at the seizure focus indeed showed that the seizure focus interfered with correct antisaccade performance. These results were in line with fMRI recordings revealing less task-related frontal activity for the hemisphere of the seizure focus, possibly reflecting diminished top-down engagement of the oculomotor system. Two months after removal of the compromised tissue, the seizures had disappeared, and antisaccade performance was the same for both visual hemifields. We conclude that a seizure focus in the frontal cortex can induce a dysfunction in the selection of eye movements, which is resolved after removal of interfering tissue.