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Successful outcomes in epilepsy surgery rely on the accurate localization of the seizure onset zone. Localizing the seizure onset zone is often a costly and time-consuming process wherein a patient undergoes intracranial EEG monitoring, and a team of clinicians wait for seizures to occur. Clinicians then analyse the intracranial EEG before each seizure onset to identify the seizure onset zone and localization accuracy increases when more seizures are captured. In this study, we develop a new approach to guide clinicians to actively elicit seizures with electrical stimulation. We propose that a brain region belongs to the seizure onset zone if a periodic stimulation at a particular frequency produces large amplitude oscillations in the intracranial EEG network that propagate seizure activity. Such responses occur when there is ‘resonance’ in the intracranial EEG network, and the resonant frequency can be detected by observing a sharp peak in the magnitude versus frequency response curve, called a Bode plot. To test our hypothesis, we analysed single-pulse electrical stimulation response data in 32 epilepsy patients undergoing intracranial EEG monitoring. For each patient and each stimulated brain region, we constructed a Bode plot by estimating a transfer function model from the intracranial EEG ‘impulse’ or single-pulse electrical stimulation response. The Bode plots were then analysed for evidence of resonance. First, we showed that when Bode plot features were used as a marker of the seizure onset zone, it distinguished successful from failed surgical outcomes with an area under the curve of 0.83, an accuracy that surpassed current methods of analysis with cortico-cortical evoked potential amplitude and cortico-cortical spectral responses. Then, we retrospectively showed that three out of five native seizures accidentally triggered in four patients during routine periodic stimulation at a given frequency corresponded to a resonant peak in the Bode plot. Last, we prospectively stimulated peak resonant frequencies gleaned from the Bode plots to elicit seizures in six patients, and this resulted in an induction of three seizures and three auras in these patients. These findings suggest neural resonance as a new biomarker of the seizure onset zone that can guide clinicians in eliciting native seizures to more quickly and accurately localize the seizure onset zone.

Cognitive control involves flexibly combining multiple sensory inputs with task-dependent goals during decision making. Several tasks have been proposed to examine cognitive control, including Stroop, Eriksen-Flanker, and the Multi-source interference task. Because these tasks have been studied independently, it remains unclear whether the neural signatures of cognitive control reflect abstract control mechanisms or specific combinations of sensory and behavioral aspects of each task. To address this question, here we recorded invasive neurophysiological signals from 16 subjects and directly compared the three tasks against each other. Neural activity patterns in the theta and high-gamma frequency bands differed between incongruent and congruent conditions, revealing strong modulation by conflicting task demands. These neural signals were specific to each task, generalizing within a task but not across tasks. These results highlight the complex interplay between sensory inputs, motor outputs, and task demands and argue against a universal and abstract representation of conflict.

Cross-frequency coupling in general and phase-amplitude coupling (PAC) as a particular form of it, provides an opportunity to investigate the complex interactions between neural oscillations in the human brain and neurological disorders such as epilepsy. Using PAC detection methods on temporal sliding windows, we developed a map of dynamic PAC evolution to investigate the spatiotemporal changes occurring during ictal transitions in a patient with intractable mesial temporal lobe epilepsy. The map is built by computing the modulation index between the amplitude of high frequency oscillations and the phase of lower frequency rhythms from the intracranial stereoelectroencephalography recordings during seizure. Our preliminary results show early abnormal PAC changes occurring in the preictal state prior to the occurrence of clinical or visible electrographic seizure onset, and suggest that dynamic PAC measures may serve as a potential clinical technique for analyzing seizure dynamics.Clinical Relevance-Application of a dynamic temporal PAC map as a new tool may provide novel insights into the neurophysiology of epileptic seizure activity and its spatio-temporal dynamics.

Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes (‘sources’) and the inhibited nodes themselves (‘sinks’). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians’ predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.

Epilepsy is a global epidemic and 30% of the 60 million patients do not respond to medication treatment. The only treatment options for patients with medically refractory epilepsy are surgical removal or electrical stimulation of the epileptogenic zone (EZ) i.e. the source of their seizures. Despite extensive evaluations with neuroimaging, visual EEG analysis and clinical testing, surgical success rates vary between 30-70%. Currently, no computational methods have been translated into the clinic to assist in localizing the EZ. Here, we applied a dynamical network model that quantifies the fragility of nodes within a patient9s intracranial EEG (iEEG) brain network. Fragility is quantified as the minimal amount of perturbation that must to be applied to a node9s influence on a "balanced" network to cause imbalance. Here, a balanced network is one in which the connectivity between excitatory and inhibitory nodes render a stable system, and an imbalanced network is unstable and hence can generate seizures. Using iEEG data from 91 patients treated across 5 epilepsy centers (44 successes, 47 failures), we demonstrated that nodal fragility is greater in electrodes within the EZ. In addition, we compared fragility of iEEG nodes to 7 frequency-based and 14 graph theoretic features of the EZ in both seizure (n=91) and non-seizure data (n=54). We calculated a confidence statistic, defined as the ratio of the value of a given feature averaged across electrodes in the clinically annotated seizure onset zone to its average across all other electrodes. Fragility has a significantly greater effect size difference between surgical outcomes when compared to other features. This novel feature, outperformed the most popular iEEG features when comparing across surgical outcomes, possibly defining a superior network-based EEG fingerprint for the EZ.

Children with ADHD show developmentally abnormal levels of mirror overflow-unintentional movements occurring symmetrically opposite of intentional movements. Because mirror overflow correlates with ADHD behavioral symptoms, the study of disinhibition in motor control may shed light on physiologic mechanisms underlying impaired behavioral/cognitive control. This is a case-controlled study of EEG recording from 25 children with ADHD and 25 typically developing (TD) controls performing unilateral sequential finger tapping, with overflow movements measured using electronic goniometers. Consistent with previously published findings, children with ADHD showed increased mirror overflow as compared with TD peers. EEG findings revealed less lateralized alpha modulation (event-related desynchronization; ERD) and decreased magnitude of beta ERD in ADHD; both alpha and beta ERD reflect cortical activation. Moderation analysis revealed a significant association between beta ERD and overflow, independent of diagnosis; and an equivocal (p = .08) effect of diagnosis on the relationship between alpha ERD and overflow, with a significant effect in children with ADHD but not TD children. These results suggest two mechanisms involved with mirror overflow: one reflected in beta ipsilateral to the intentional movement and relevant to both children with ADHD and controls, and the other seemingly more specific to ADHD (alpha, contralateral to movement).

Background Language mapping with high-gamma modulation (HGM) has compared well with electrical cortical stimulation mapping (ESM). However, there is limited prospective data about its functional validity. We compared changes in neuropsychological evaluation (NPE) performed before and 1-year after epilepsy surgery, between patients with/without resection of cortical sites showing HGM during a visual naming task. Methods Pediatric drug-resistant epilepsy (DRE) patients underwent pre-surgical language localization with ESM and HGM using a visual naming task. Surgical decisions were based solely on ESM results. NPE difference scores were compared between patients with/without resection of HGM naming sites using principal component (PC) analysis. Follow-up NPE scores were modeled with resection group as main effect and respective pre-surgical score as a covariate, using analysis of covariance. Results Seventeen native English speakers (12 females), aged 6.5–20.2 years, were included. One year after epilepsy surgery, first PC score increased by (mean ± standard deviation) 14.4 ± 16.5 points in patients without resection, whereas it decreased by 7.6 ± 24.6 points in those with resection of HGM naming sites (p = 0.040). This PC score represented verbal comprehension, working memory, perceptual reasoning (Wechsler subscales); Woodcock-Johnson Tests of Achievement; and Peabody Picture Vocabulary Test. Subsequent analysis showed significant difference in working memory score between patients with/without resection of HGM naming sites (−15.2 points, 95% confidence limits −29.7 to −0.7, p = 0.041). Conclusion We highlight the functional consequences of resecting HGM language sites, and suggest that NPE of DRE patients should include comprehensive assessment of multiple linguistic and cognitive domains besides naming ability.

Objective The aim of the present study was to investigate the optimal stimulation parameters for eliciting cortico-cortical evoked potentials (CCEPs) for mapping functional and epileptogenic networks. Methods We studied 13 patients with refractory epilepsy undergoing intracranial EEG monitoring. We systematically titrated the intensity of single-pulse electrical stimulation at multiple sites to assess the effect of increasing current on salient features of CCEPs such as N1 potential magnitude, signal to noise ratio, waveform similarity, and spatial distribution of responses. Responses at each incremental stimulation setting were compared to each other and to a final set of responses at the maximum intensity used in each patient (3.5–10 mA, median 6 mA). Results We found that with a biphasic 0.15 ms/phase pulse at least 2–4 mA is needed to differentiate between non-responsive and responsive sites, and that stimulation currents of 6–7 mA are needed to maximize amplitude and spatial distribution of N1 responses and stabilize waveform morphology. Conclusions We determined a minimum stimulation threshold necessary for eliciting CCEPs, as well as a point at which the current-dependent relationship of several response metrics all saturate. Significance This titration study provides practical, immediate guidance on optimal stimulation parameters to study specific features of CCEPs, which have been increasingly used to map both functional and epileptic brain networks in humans.

The complex nature of neural connections throughout the cerebral cortex has led to broad interest in understanding cortical functional networks of tactile perception and sensorimotor integration. Cortico-cortical evoked potentials (CCEPs) can be used as physiological markers to study and map cerebral networks in the brain. In a human participant with bi-hemispheric microelectrode array implants in sensorimotor regions of the brain, we found that intracortical microstimulation (ICMS) of the primary somatosensory cortex can lead to evoked responses in the motor cortex in the same hemisphere, indicating connectivity between these sensorimotor regions. Single ICMS pulses were not consciously perceived, but elicited a rapid evoked potential approximately 20 ms after stimulus onset. Multi-pulse ICMS trains, perceived as tactile sensations in the thumb, sustained over an approximately 33 ms period, led to a delayed evoked response roughly 80 ms after stimulus onset. This work is important not only for better understanding the functional relationship between cortical areas, specifically somatosensory and motor cortices, but also to provide insight on pathways where neuromodulation techniques could be employed for rehabilitation or mitigation of sensorimotor neurodegenerative effects.

Functional human brain mapping is commonly performed during invasive monitoring with intracranial electroencephalographic (iEEG) electrodes prior to resective surgery for drug­ resistant epilepsy. The current gold standard, electrocortical stimulation mapping (ESM), is time ­consuming, sometimes elicits pain, and often induces after discharges or seizures. Moreover, there is a risk of overestimating eloquent areas due to propagation of the effects of stimulation to a broader network of language cortex. Passive iEEG spatial-temporal functional mapping (STFM) has recently emerged as a potential alternative to ESM. However, investigators have observed less correspondence between STFM and ESM maps of language than between their maps of motor function. We hypothesized that incongruities between ESM and STFM of language function may arise due to propagation of the effects of ESM to cortical areas having strong effective connectivity with the site of stimulation. We evaluated five patients who underwent invasive monitoring for seizure localization, whose language areas were identified using ESM. All patients performed a battery of language tasks during passive iEEG recordings. To estimate the effective connectivity of stimulation sites with a broader network of task-activated cortical sites, we measured cortico-cortical evoked potentials (CCEPs) elicited across all recording sites by single-pulse electrical stimulation at sites where ESM was performed at other times. With the combination of high gamma power as well as CCEPs results, we trained a logistic regression model to predict ESM results at individual electrode pairs. The average accuracy of the classifier using both STFM and CCEPs results combined was 87.7%, significantly higher than the one using STFM alone (71.8%), indicating that the correspondence between STFM and ESM results is greater when effective connectivity between ESM stimulation sites and task-activated sites is taken into consideration. These findings, though based on a small number of subjects to date, provide preliminary support for the hypothesis that incongruities between ESM and STFM may arise in part from propagation of stimulation effects to a broader network of cortical language sites activated by language tasks, and suggest that more studies, with larger numbers of patients, are needed to understand the utility of both mapping techniques in clinical practice.

Surgical resection of the seizure onset zone (SOZ) could potentially lead to seizure-freedom in medically refractory epilepsy patients. However, localizing the SOZ can be a time consuming and tedious process involving visual inspection of intracranial electroencephalographic (iEEG) recordings captured during passive patient monitoring. Cortical stimulation is currently performed on patients undergoing invasive EEG monitoring for the main purpose of mapping functional brain networks such as language and motor networks. We hypothesized that evoked responses from single pulse electrical stimulation (SPES) can also be used to localize the SOZ as they may express the natural frequencies and connectivity of the iEEG network. To test our hypothesis, we constructed patient specific transfer function models from the evoked responses recorded from 22 epilepsy patients that underwent SPES evaluation and iEEG monitoring. We then computed the frequency and connectivity dependent "peak gain" of the system as measured by the H ∞ norm from systems theory. We found that in cases for which clinicians had high confidence in localizing the SOZ, the highest peak gain transfer functions with the smallest "floor gain" (gain at which the dipped H ∞ 3dB below DC gain) corresponded to when the clinically annotated SOZ and early spread regions were stimulated. In more complex cases, there was a large spread of the peak-to-floor (PF) ratios when the clinically annotated SOZ was stimulated. Interestingly for patients who had successful surgeries, our ratio of gains, agreed with clinical localization, no matter the complexity of the case. For patients with failed surgeries, the PF ratio did not match clinical annotations. Our findings suggest that transfer function gains and their corresponding frequency responses computed from SPES evoked responses may improve SOZ localization and thus surgical outcomes.

Defining eloquent cortex intraoperatively, traditionally performed by neurosurgeons to preserve patient function, can now help target electrode implantation for restoring function. Brain-machine interfaces (BMIs) have the potential to restore upper-limb motor control to paralyzed patients but require accurate placement of recording and stimulating electrodes to enable functional control of a prosthetic limb. Beyond motor decoding from recording arrays, precise placement of stimulating electrodes in cortical areas associated with finger and fingertip sensations allows for the delivery of sensory feedback that could improve dexterous control of prosthetic hands. In this study, the authors demonstrated the use of a novel intraoperative online functional mapping (OFM) technique with high-density electrocorticography to localize finger representations in human primary somatosensory cortex. In conjunction with traditional pre- and intraoperative targeting approaches, this technique enabled accurate implantation of stimulating microelectrodes, which was confirmed by postimplantation intracortical stimulation of finger and fingertip sensations. This work demonstrates the utility of intraoperative OFM and will inform future studies of closed-loop BMIs in humans.

Defining eloquent cortex intraoperatively, traditionally performed by neurosurgeons to preserve patient function, can now help target electrode implantation for restoring function. Brain-machine interfaces (BMIs) have the potential to restore upper-limb motor control to paralyzed patients but require accurate placement of recording and stimulating electrodes to enable functional control of a prosthetic limb. Beyond motor decoding from recording arrays, precise placement of stimulating electrodes in cortical areas associated with finger and fingertip sensations allows for the delivery of sensory feedback that could improve dexterous control of prosthetic hands. In our study, we demonstrated the use of a novel intraoperative online functional mapping (OFM) technique with high-density electrocorticography (ECoG) to localize finger representations in human primary somatosensory cortex. In conjunction with traditional pre- and intraoperative targeting approaches, this technique enabled accurate implantation of stimulating microelectrodes, which was confirmed by post-implantation intra-cortical stimulation of finger and fingertip sensations. This work demonstrates the utility of intraoperative OFM and will inform future studies of closed-loop BMIs in humans.

Surgical removal of the seizure onset zone (SOZ) in epilepsy patients is a potentially curative treatment, but the process heavily relies on accurate localization of the SOZ via visual inspection. SPES (Single-pulse electrical stimulation) is a method recently used to explore inter-areal connectivity in vivo to probe functional brain networks such as language and motor networks, and to a much lesser degree, seizure networks. We hypothesized that a dynamical quantification of the connectivity networks derived from the evoked responses induced by SPES could also be used to localize the SOZ. To test our hypothesis, we used an intracranial EEG (iEEG) data set in which five epilepsy patients underwent extensive SPES evaluation. For each patient, and for each dataset that stimulated a different pair of electrodes, we constructed a state-space model from the patient's data. Specifically, we simultaneously estimated model parameters under an exogenous pulse input to a dynamical system whose state vector consisted of the response iEEG signals. Then, the size of the reachable state space, as quantified by the maximum singular value of the reachability matrix, σ max (R), was computed and denoted as the "largest" network response possible when stimulating the given pair. Our results suggest high agreement between σ max (R) and clinically annotated SOZ for patients with localizable SOZs.Clinical Relevance— Our study applies dynamical systems theory to identify epileptogenic brain regions, creating a novel tool that clinicians may use in surgical planning for medically-refractory epilepsy patients.

Surgical resection of the seizure onset zone (SOZ) could potentially lead to seizure-freedom in medically refractory epilepsy patients. However, localizing the SOZ can be a time consuming and tedious process involving visual inspection of intracranial electroencephalographic (iEEG) recordings captured during passive patient monitoring. Single pulse electrical stimulation (SPES) is currently performed on patients undergoing invasive EEG monitoring for the main purposes of mapping functional brain networks such as language and motor networks. We hypothesize that evoked responses from SPES can also be used to localize the SOZ as they may express the natural frequencies and connectivity of the iEEG network. To test our hypothesis, we construct patient specific single-input multi-output transfer function models from the evoked responses recorded from five epilepsy patients that underwent SPES evaluation and iEEG monitoring. Our preliminary results suggest that the stimulation electrodes that produced the highest gain transfer functions, as measured by the ${\mathcal{H}_\infty }$ norm, correspond to those electrodes clinically defined in the SOZ in successfully treated patients.Clinical Relevance- This study creates an innovative tool that allows clinicians to identify the seizure onset zone in medically refractory epilepsy patients using quantitative metrics thereby increasing surgical success outcomes, mitigating patient risks, and decreasing costs.

The topography and temporal stability of movement representations in sensorimotor cortex underlie the quality and durability of neural decoders for brain machine interface (BMI) technology. While single- and multi-unit activity (SUA and MUA) in sensorimotor cortex has been used to characterize the layout of the sensorimotor map, quantifying its stability has not been done outside of injury or targeted interventions. Here we aimed to characterize 1) the bilateral sensorimotor body map associated to isolated muscle group contractions and 2) the stability of multiunit firing responses for a single muscle (the extensor carpi radialis, ECR) over short (minutes) and long (days) time intervals. We concurrently recorded surface electromyograms (EMG) and MUA in a participant with incomplete high-spinal-cord injury as he executed (or attempted to execute) different metronome-paced, isolated muscle group contractions. Furthermore, for 8 recording sessions over 2 months, we characterized the sensorimotor map associated to ECR motions both within and across sessions. For each measurement period, we compared the stability of somatotopy (defined by the number of the channels on which a response was consistently detected) and firing pattern stability for each responsive channel. Stability was calculated for each channel in peri-EMG or peri-cue windows using both mean MUA firing rates and the full time-varying responses (i.e., MUA “shape”). First, we found that cortical representations of isolated group muscle contractions overlapped, even for muscles from disparate body regions such as facial and distal leg muscles; this was the case for both intact and de-efferented muscles, in both motor and sensory channels. Second, the spatial stability of somatotopy significantly changed over the course of both minutes and days, with the consistency between sessions decreasing across longer bouts of time. Firing pattern stabilities showed distinct profiles; mean MUA firing rates became less stable over time whereas MUA shape remained consistent. Interestingly, sensory channels were overall more consistent than motor channels in terms of spatial stability, mean MUA firing rates, and MUA shape. Our findings suggest that the encoding of muscle-driven specific activity in sensorimotor cortex at the level of MUA is redundant and widespread with complex spatial and temporal characteristics. These findings extend our understanding of how sensorimotor cortex represents movements, which could be leveraged for the design of non-traditional BMI approaches.

Most daily tasks require simultaneous control of both hands. Here we demonstrate simultaneous classification of gestures in both hands using multi-unit activity recorded from bilateral motor and somatosensory cortices of a tetraplegic participant. Attempted gestures were classified using hierarchical linear discriminant models trained separately for each hand. In an online experiment, gestures were continuously classified and used to control two robotic arms in a center-out movement task. Bimanual trials that required keeping one hand still resulted in the best performance (70.6%), followed by symmetric movement trials (50%) and asymmetric movement trials (22.7%). Our results indicate that gestures can be simultaneously decoded in both hands using two independently trained hand models concurrently, but online control using this approach becomes more difficult with increased complexity of bimanual gesture combinations. This study demonstrates the potential for restoring simultaneous control of both hands using a bilateral intracortical brain-machine interface.

The restoration of cutaneous sensation to fingers and fingertips is critical to achieving dexterous prosthesis control for individuals with sensorimotor dysfunction. However, localized and reproducible fingertip sensations in humans have not been reported via intracortical microstimulation (ICMS) in humans. Here, we show that ICMS in a human participant was capable of eliciting percepts in 7 fingers spanning both hands, including 6 fingertip regions (i.e., 3 on each hand). Median percept size was estimated to include 1.40 finger or palmar segments (e.g., one segment being a fingertip or the section of upper palm below a finger). This was corroborated with a more sensitive manual marking technique where median percept size corresponded to roughly 120% of a fingertip segment. Percepts showed high intra-day consistency, including high performance (99%) on a blinded finger discrimination task. Across days, there was more variability in percepts, with 75.8% of trials containing the modal finger or palm region for the stimulated electrode. These results suggest that ICMS can enable the delivery of localized fingertip sensations during object manipulation by neuroprostheses.

Reading is a rapid, distributed process that engages multiple components of the ventral visual stream. To understand the neural constituents and their interactions that allow us to identify written words, we performed direct intra-cranial recordings in a large cohort of humans. This allowed us to isolate the spatiotemporal dynamics of visual word recognition across the entire left ventral occipitotemporal cortex. We found that mid-fusiform cortex is the first brain region sensitive to lexicality, preceding the traditional visual word form area. The magnitude and duration of its activation are driven by the statistics of natural language. Information regarding lexicality and word frequency propagates posteriorly from this region to visual word form regions and to earlier visual cortex, which, while active earlier, show sensitivity to words later. Further, direct electrical stimulation of this region results in reading arrest, further illustrating its crucial role in reading. This unique sensitivity of mid-fusiform cortex to sub-lexical and lexical characteristics points to its central role as the orthographic lexicon-the long-term memory representations of visual word forms.

Reading is a rapid, distributed process that engages multiple components of the ventral visual stream. To understand the neural constituents and their interactions that allow us to identify written words, we performed direct intra-cranial recordings in a large cohort of humans. This allowed us to isolate the spatiotemporal dynamics of visual word recognition across the entire left ventral occipitotemporal cortex. We found that mid-fusiform cortex is the first brain region sensitive to lexicality, preceding the traditional visual word form area. The magnitude and duration of its activation are driven by the statistics of natural language. Information regarding lexicality and word frequency propagates posteriorly from this region to visual word form regions and to earlier visual cortex, which, while active earlier, show sensitivity to words later. Further, direct electrical stimulation of this region results in reading arrest, further illustrating its crucial role in reading. This unique sensitivity of mid-fusiform cortex to sub-lexical and lexical characteristics points to its central role as the orthographic lexicon-the long-term memory representations of visual word forms.

The subthalamic nucleus (STN) is proposed to participate in pausing, or alternately, in dynamic scaling of behavioral responses, roles that have conflicting implications for understanding STN function in the context of deep brain stimulation (DBS) therapy. To examine the nature of event-related STN activity and subthalamic-cortical dynamics, we performed primary motor and somatosensory electrocorticography while subjects (n = 10) performed a grip force task during DBS implantation surgery. Phase-locking analyses demonstrated periods of STN-cortical coherence that bracketed force transduction, in both beta and gamma ranges. Event-related causality measures demonstrated that both STN beta and gamma activity predicted motor cortical beta and gamma activity not only during force generation but also prior to movement onset. These findings are consistent with the idea that the STN participates in motor planning, in addition to the modulation of ongoing movement. We also demonstrated bidirectional information flow between the STN and somatosensory cortex in both beta and gamma range frequencies, suggesting robust STN participation in somatosensory integration. In fact, interactions in beta activity between the STN and somatosensory cortex, and not between STN and motor cortex, predicted PD symptom severity. Thus, the STN contributes to multiple aspects of sensorimotor behavior dynamically across time.

Persons with epilepsy (PWE) often report that seizure triggers can influence the occurrence and timing of seizures. Some previous studies of seizure triggers have relied on retrospective daily seizure diaries or surveys pertaining to all past seizures, recent and/or remote, in respondents. To assess the characteristics of seizure triggers at the granularity of individual seizures, we used a seizure-tracking app, called EpiWatch, on a smart watch system (Apple Watch and iPhone) in a national study of PWE. Participants tracked seizures during a 16-month study period using the EpiWatch app. Seizure tracking was initiated during a pre-ictal state or as the seizure was occurring and included collection of biosensor data, responsiveness testing, and completion of an immediate post-seizure survey. The survey evaluated seizure types, auras or warning symptoms, loss of awareness, use of rescue medication, and seizure triggers for each tracked seizure. Two hundred and thirty four participants tracked 2493 seizures. Ninety six participants reported triggers in 650 seizures: stress (65.8%), lack of sleep (30.5%), menstrual cycle (19.7%), and overexertion (18%) were the most common. Participants often reported having multiple combined triggers, frequent stress with lack of sleep, overexertion, or menses. Participants who reported triggers were more likely to be taking 3 or more anti-seizure medications compared to participants who did not report triggers. Participants were able to interact with the app and use mobile technology in this national study to record seizures and report common seizure triggers. These findings demonstrate the promise of longitudinal, self-reported data to improve our understanding of epilepsy and its related comorbidities.

OBJECTIVE This prospective study compared the topography of high-gamma modulation (HGM) during a story-listening task requiring negligible patient cooperation, with the conventional electrical stimulation mapping (ESM) using a picture-naming task, for presurgical language localization in pediatric drug-resistant epilepsy. METHODS Patients undergoing extraoperative monitoring with subdural electrodes were included. Electrocorticographic signals were recorded during quiet baseline and a story-listening task. The likelihood of 70- to 150-Hz power modulation during the listening task relative to the baseline was estimated for each electrode and plotted on a cortical surface model. Sensitivity, specificity, accuracy, and diagnostic odds ratio (DOR) were estimated compared to ESM, using a meta-analytic framework. RESULTS Nineteen patients (10 with left hemisphere electrodes) aged 4-19 years were analyzed. HGM during story listening was observed in bilateral posterior superior temporal, angular, supramarginal, and inferior frontal gyri, along with anatomically defined language association areas. Compared to either cognitive or both cognitive and orofacial sensorimotor interference with naming during ESM, left hemisphere HGM showed high specificity (0.82-0.84), good accuracy (0.66-0.70), and DOR of 2.23 and 3.24, respectively. HGM was a better classifier of ESM language sites in the left temporoparietal cortex compared to the frontal lobe. Incorporating visual naming with the story-listening task substantially improved the accuracy (0.80) and DOR (13.61) of HGM mapping, while the high specificity (0.85) was retained. In the right hemisphere, no ESM sites for aphasia were seen, and the results of HGM and ESM comparisons were not significant. SIGNIFICANCE HGM associated with story listening is a specific determinant of left hemisphere ESM language sites. It can be used for presurgical language mapping in children who cannot cooperate with conventional language tasks requiring active engagement. Incorporation of additional language tasks, if feasible, can further improve the diagnostic accuracy of language localization with HGM.

Any given area in human cortex may receive input from multiple, functionally heterogeneous areas, potentially representing different processing threads. Alpha (8–13 Hz) and beta oscillations (13–20 Hz) have been hypothesized by other investigators to gate local cortical processing, but their influence on cortical responses to input from other cortical areas is unknown. To study this, we measured the effect of local oscillatory power and phase on cortical responses elicited by single-pulse electrical stimulation (SPES) at distant cortical sites, in awake human subjects implanted with intracranial electrodes for epilepsy surgery. In 4 out of 5 subjects, the amplitudes of corticocortical evoked potentials (CCEPs) elicited by distant SPES were reproducibly modulated by the power, but not the phase, of local oscillations in alpha and beta frequencies. Specifically, CCEP amplitudes were higher when average oscillatory power just before distant SPES (−110 to −10 ms) was high. This effect was observed in only a subset (0–33%) of sites with CCEPs and, like the CCEPs themselves, varied with stimulation at different distant sites. Our results suggest that although alpha and beta oscillations may gate local processing, they may also enhance the responsiveness of cortex to input from distant cortical sites.

How do humans flexibly respond to changing environmental demands on a sub-second temporal scale? Extensive research has highlighted the key role of the prefrontal cortex in flexible decision-making and adaptive behavior, yet the core mechanisms that translate sensory information into behavior remain undefined. Utilizing direct human cortical recordings, we investigated the temporal and spatial evolution of neuronal activity, indexed by the broadband gamma signal, while sixteen participants performed a broad range of self-paced cognitive tasks. Here we describe a robust domain- and modality-independent pattern of persistent stimulus-to-response neural activation that encodes stimulus features and predicts motor output on a trial-by-trial basis with near-perfect accuracy. Observed across a distributed network of brain areas, this persistent neural activation is centered in the prefrontal cortex and is required for successful response implementation, providing a functional substrate for domain-general transformation of perception into action, critical for flexible behavior.

Objective This study examined the incidence, thresholds, and determinants of electrical cortical stimulation (ECS)-induced after-discharges (ADs) and seizures. Methods Electrocorticograph recordings were reviewed to determine incidence of ECS-induced ADs and seizures. Multivariable analyses for predictors of AD/seizure occurrence and their thresholds were performed. Results In 122 patients, the incidence of ADs and seizures was 77% (94/122) and 35% (43/122) respectively. Males (odds ratio [OR] 2.92, 95% CI 1.21–7.38, p = 0.02) and MRI-negative patients (OR 3.69, 95% CI 1.24–13.7, p = 0.03) were found to have higher odds of ECS-induced ADs. A significant trend for decreasing AD thresholds with age was seen (regression co-efficient −0.151, 95% CI −0.267 to −0.035, p = 0.011). ECS-induced seizures were more likely in patients with lateralized functional imaging (OR 6.62, 95% CI 1.36–55.56, p = 0.036, for positron emission tomography) and presence of ADs (OR 3.50, 95% CI 1.12–13.36, p = 0.043). Conclusions ECS is associated with a high incidence of ADs and seizures. With age, current thresholds decrease and the probability for AD/seizure occurrence increases. Significance ADs and seizures during ECS brain mapping are potentially hazardous and affect its functional validity. Thus, safer method(s) for brain mapping with improved neurophysiologic validity are desirable.

Functional connectivity differences between children with autism spectrum disorder (ASD) and typically developing children have been described in multiple datasets. However, few studies examine the task-related changes in connectivity in disorder-relevant behavioral paradigms. In this paper, we examined the task-related changes in functional connectivity using EEG and a movement-based paradigm that has behavioral relevance to ASD. Resting-state studies motivated our hypothesis that children with ASD would show a decreased magnitude of functional connectivity during the performance of a motor-control task. Contrary to our initial hypothesis, however, we observed that task-related modulation of functional connectivity in children with ASD was in the direction opposite to that of TDs. The task-related connectivity changes were correlated with clinical symptom scores. Our results suggest that children with ASD may have differences in cortical segregation/integration during the performance of a task, and that part of the differences in connectivity modulation may serve as a compensatory mechanism. Autism Res 2017. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary Decreased connectivity between brain regions is thought to cause the symptoms of autism. Because most of our knowledge comes from data in which children are at rest, we do not know how connectivity changes directly lead to autistic behaviors, such as impaired gestures. When typically developing children produced complex movements, connectivity decreased between brain regions. In children with autism, connectivity increased. It may be that behavior-related changes in brain connectivity are more important than absolute differences in connectivity in autism.

Objective The articulatory loop is a fundamental component of language function, involved in the short-term buffer of auditory information followed by its vocal reproduction. We characterized the network dynamics of the human articulatory loop, using invasive recording and stimulation. Methods We measured high-gamma activity 70–110 Hz recorded intracranially when patients with epilepsy either only listened to, or listened to and then reproduced two successive tones by humming. We also conducted network analyses, and analyzed behavioral responses to cortical stimulation. Results Presentation of the initial tone elicited high-gamma augmentation bilaterally in the superior-temporal gyrus (STG) within 40ms, and in the precentral and inferior-frontal gyri (PCG and IFG) within 160ms after sound onset. During presentation of the second tone, high-gamma augmentation was reduced in STG but enhanced in IFG. The task requiring tone reproduction further enhanced high-gamma augmentation in PCG during and after sound presentation. Event-related causality (ERC) analysis revealed dominant flows within STG immediately after sound onset, followed by reciprocal interactions involving PCG and IFG. Measurement of cortico-cortical evoked-potentials (CCEPs) confirmed connectivity between distant high-gamma sites in the articulatory loop. High-frequency stimulation of precentral high-gamma sites in either hemisphere induced speech arrest, inability to control vocalization, or forced vocalization. Vocalization of tones was accompanied by high-gamma augmentation over larger extents of PCG. Conclusions Bilateral PCG rapidly and directly receives feed-forward signals from STG, and may promptly initiate motor planning including sub-vocal rehearsal for short-term buffering of auditory stimuli. Enhanced high-gamma augmentation in IFG during presentation of the second tone may reflect high-order processing of the tone sequence. Significance The articulatory loop employs sustained reciprocal propagation of neural activity across a network of cortical sites with strong neurophysiological connectivity.

Non-invasive neuroimaging studies have shown that semantic category and attribute information are encoded in neural population activity. Electrocorticography (ECoG) offers several advantages over non-invasive approaches, but the degree to which semantic attribute information is encoded in ECoG responses is not known. We recorded ECoG while patients named objects from 12 semantic categories and then trained high-dimensional encoding models to map semantic attributes to spectral-temporal features of the task-related neural responses. Using these semantic attribute encoding models, untrained objects were decoded with accuracies comparable to whole-brain functional Magnetic Resonance Imaging (fMRI), and we observed that high-gamma activity (70-110Hz) at basal occipitotemporal electrodes was associated with specific semantic dimensions (manmade-animate, canonically large-small, and places-tools). Individual patient results were in close agreement with reports from other imaging modalities on the time course and functional organization of semantic processing along the ventral visual pathway during object recognition. The semantic attribute encoding model approach is critical for decoding objects absent from a training set, as well as for studying complex semantic encodings without artificially restricting stimuli to a small number of semantic categories.

Speaking in sentences requires selection from contextually determined lexical representations. Although posterior temporal cortex (PTC) and Broca’s areas play important roles in storage and selection, respectively, of lexical representations, there has been no direct evidence for physiological interactions between these areas on time scales typical of lexical selection. Using intracranial recordings of cortical population activity indexed by high-gamma power (70–150 Hz) modulations, we studied the causal dynamics of cortical language networks while epilepsy surgery patients performed a sentence completion task in which the number of potential lexical responses was systematically varied. Prior to completion of sentences with more response possibilities, Broca’s area was not only more active, but also exhibited more local network interactions with and greater top-down influences on PTC, consistent with activation of, and competition between, more lexical representations. These findings provide the most direct experimental support yet for network dynamics playing a role in lexical selection among competing alternatives during speech production.

Cortical stimulation mapping (CSM) is a common clinical procedure for mapping eloquent cortex in epilepsy patients. Electrical responses to the stimulation, or after-discharges (ADs), that occur in response to stimulation can point to unstable regions of cortex that are more prone to spontaneous seizures. Clinicians are interested in identifying regions that start seizures, i.e., the epileptogenic zone (EZ), so that they can target treatment. However, during CSM, not all regions are stimulated, as it would be time-consuming and potentially harmful to the patient. This limits the clinician's ability to fully explore ADs to reliably localize the EZ. In this paper, we develop a virtual CSM procedure that processes pre-seizure intracranial EEG recordings obtained from epilepsy patients being treated at three different epilepsy centers. First, we identify a linear time varying network (LTVN) model from electrocorticography (ECoG) and stereo-EEG (SEEG) data using sparse least squares estimation for each patient. We then construct an virtual CSM by applying impulse perturbations to each electrode contact in the LTVN model and then measuring the ADs of the network. We summarize the l2-norm of the responses in the form of a heatmap that shows the spatio-temporal evolution of the ADs before, during, and after seizures. Finally we compute an impulse response ratio (IRR) metric from each heatmap, that measures the ratio between the mean norm of ADs of clinically annotated EZ contacts and the mean norm of ADs of the remaining contacts. We find that the IRR is higher in maps derived from patients with successful surgical outcomes and lower in failed surgical outcomes. This suggests that virtual CSM may provide valuable information to clinicians regarding EZ location.

Wakefulness and sleep arise from global changes in brain physiology that may also govern the flow of neural activity between cortical regions responsible for perceptual processing versus planning and action. To test whether and how the sleep/wake cycle affects the overall propagation of neural activity in large-scale brain networks, we applied single-pulse electrical stimulation (SPES) in patients implanted with intracranial EEG electrodes for epilepsy surgery. SPES elicited cortico-cortical spectral responses at high-gamma frequencies (CCSR(HG), 80–150 Hz), which indexes changes in neuronal population firing rates. Using event-related causality (ERC) analysis, we found that the overall patterns of neural propagation among sites with CCSR(HG) were different during wakefulness and different sleep stages. For example, stimulation of frontal lobe elicited greater propagation toward parietal lobe during slow-wave sleep than during wakefulness. During REM sleep, we observed a decrease in propagation within frontal lobe, and an increase in propagation within parietal lobe, elicited by frontal and parietal stimulation, respectively. These biases in the directionality of large-scale cortical network dynamics during REM sleep could potentially account for some of the unique experiential aspects of this sleep stage. Together these findings suggest that the regulation of conscious awareness and sleep is associated with differences in the balance of neural propagation across large-scale frontal-parietal networks.

Neural keyword spotting could form the basis of a speech brain-computer-interface for menu-navigation if it can be done with low latency and high specificity comparable to the "wake-word" functionality of modern voice-activated AI assistant technologies. This study investigated neural keyword spotting using motor representations of speech via invasively-recorded electrocorticographic signals as a proof-of-concept. Neural matched filters were created from monosyllabic consonant-vowel utterances: one keyword utterance, and 11 similar non-keyword utterances. These filters were used in an analog to the acoustic keyword spotting problem, applied for the first time to neural data. The filter templates were cross-correlated with the neural signal, capturing temporal dynamics of neural activation across cortical sites. Neural vocal activity detection (VAD) was used to identify utterance times and a discriminative classifier was used to determine if these utterances were the keyword or non-keyword speech. Model performance appeared to be highly related to electrode placement and spatial density. Vowel height (/a/ vs /i/) was poorly discriminated in recordings from sensorimotor cortex, but was highly discriminable using neural features from superior temporal gyrus during self-monitoring. The best performing neural keyword detection (5 keyword detections with two false-positives across 60 utterances) and neural VAD (100% sensitivity, ~1 false detection per 10 utterances) came from high-density (2 mm electrode diameter and 5 mm pitch) recordings from ventral sensorimotor cortex, suggesting the spatial fidelity and extent of high-density ECoG arrays may be sufficient for the purpose of speech brain-computer-interfaces.

BCI2000 has been a popular platform for development of real-time brain computer interfaces (BCIs). Since BCI2000's initial release, web browsers have evolved considerably, enabling rapid development of internet-enabled applications and interactive visualizations. Linking the amplifier abstraction and signal processing native to BCI2000 with the host of technologies and ease of development afforded by modern web browsers could enable a new generation of browser-based BCIs and visualizations. We developed a server and filter module called BCI2000Web providing an HTTP connection capable of escalation into an RFC6455 WebSocket, which enables direct communication between a browser and a BCI2000 distribution in real-time, facilitating a number of novel applications. We also present a JavaScript module, bci2k.js, that allows web developers to create paradigms and visualizations using this interface in an easy-to-use and intuitive manner. To illustrate the utility of BCI2000Web, we demonstrate a browser-based implementation of a real-time electrocorticographic (ECoG) functional mapping suite called WebFM. We also explore how the unique characteristics of our browser-based framework make BCI2000Web an attractive tool for future BCI applications. BCI2000Web leverages the advances of BCI2000 to provide real-time browser-based interactions with human neurophysiological recordings, allowing for web-based BCIs and other applications, including real-time functional brain mapping. Both BCI2000 and WebFM are provided under open source licenses. Enabling a powerful BCI suite to communicate with today's most technologically progressive software empowers a new cohort of developers to engage with BCI technology, and could serve as a platform for internet-enabled BCIs.

Brain–machine interface (BMI) researchers have traditionally focused on modeling endpoint reaching tasks to provide the control of neurally driven prosthetic arms. Most previous research has focused on achieving an endpoint control through a Cartesian-coordinate-centered approach. However, a joint-centered approach could potentially be used to intuitively control a wide range of limb movements. We systematically investigated the feasibility of discriminating between flexion and extension of different upper limb joints using electrocorticography(ECoG) recordings from sensorimotor cortex. Four subjects implanted with macro-ECoG (10-mm spacing), high-density ECoG (5-mm spacing), and/or micro-ECoG arrays (0.9-mm spacing and 4 mm $\times $ 4 mm coverage), performed randomly cued flexions or extensions of the fingers, wrist, or elbow contralateral to the implanted hemisphere. We trained a linear model to classify six movements using averaged high-gamma power (70–110 Hz) modulations at different latencies with respect to movement onset, and within a time interval restricted to flexion or extension at each joint. Offline decoding models for each subject classified these movements with accuracies of 62%–83%. Our results suggest that the widespread ECoG coverage of sensorimotor cortex could allow a whole limb BMI to sample native cortical representations in order to control flexion and extension at multiple joints.

A brain–computer interface (BCI) is a technology that uses neural features to restore or augment the capabilities of its user. A BCI for speech would enable communication in real time via neural correlates of attempted or imagined speech. Such a technology would potentially restore communication and improve quality of life for locked-in patients and other patients with severe communication disorders. There have been many recent developments in neural decoders, neural feature extraction, and brain recording modalities facilitating BCI for the control of prosthetics and in automatic speech recognition (ASR). Indeed, ASR and related fields have developed significantly over the past years, and many lend many insights into the requirements, goals, and strategies for speech BCI. Neural speech decoding is a comparatively new field but has shown much promise with recent studies demonstrating semantic, auditory, and articulatory decoding using electrocorticography (ECoG) and other neural recording modalities. Because the neural representations for speech and language are widely distributed over cortical regions spanning the frontal, parietal, and temporal lobes, the mesoscopic scale of population activity captured by ECoG surface electrode arrays may have distinct advantages for speech BCI, in contrast to the advantages of microelectrode arrays for upper-limb BCI. Nevertheless, there remain many challenges for the translation of speech BCIs to clinical populations. This review discusses and outlines the current state-of-the-art for speech BCI and explores what a speech BCI using chronic ECoG might entail. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s13311-018-00692-2) contains supplementary material, which is available to authorized users.

Electrophysiology and neuroimaging provide conflicting evidence for the neural contributions to target detection. Scalp electroencephalography (EEG) studies localize the P3b event-related potential component mainly to parietal cortex, whereas neuroimaging studies report activations in both frontal and parietal cortices. We addressed this discrepancy by examining the sources that generate the target-detection process using electrocorticography (ECoG). We recorded ECoG activity from cortex in 14 patients undergoing epilepsy monitoring, as they performed an auditory or visual target-detection task. We examined target-related responses in 2 domains: high frequency band (HFB) activity and the P3b. Across tasks, we observed a greater proportion of electrodes that showed target-specific HFB power relative to P3b over frontal cortex, but their proportions over parietal cortex were comparable. Notably, there was minimal overlap in the electrodes that showed target-specific HFB and P3b activity. These results revealed that the target-detection process is characterized by at least 2 different neural markers with distinct cortical distributions. Our findings suggest that separate neural mechanisms are driving the differential patterns of activity observed in scalp EEG and neuroimaging studies, with the P3b reflecting EEG findings and HFB activity reflecting neuroimaging findings, highlighting the notion that target detection is not a unitary phenomenon.

Language tasks require the coordinated activation of multiple subnetworks—groups of related cortical interactions involved in specific components of task processing. Although electrocorticography (ECoG) has sufficient temporal and spatial resolution to capture the dynamics of event-related interactions between cortical sites, it is difficult to decompose these complex spatiotemporal patterns into functionally discrete subnetworks without explicit knowledge of each subnetwork’s timing. We hypothesized that subnetworks corresponding to distinct components of task-related processing could be identified as groups of interactions with co-varying strengths. In this study, five subjects implanted with ECoG grids over language areas performed word repetition and picture naming. We estimated the interaction strength between each pair of electrodes during each task using a time-varying dynamic Bayesian network (tvDBN) model constructed from the power of high gamma (70–110 Hz) activity, a surrogate for population firing rates. We then reduced the dimensionality of this model using principal component analysis (PCA) to identify groups of interactions with co-varying strengths, which we term functional network components (FNCs). This data-driven technique estimates both the weight of each interaction’s contribution to a particular subnetwork, and the temporal profile of each subnetwork’s activation during the task. We found FNCs with temporal and anatomical features consistent with articulatory preparation in both tasks, and with auditory and visual processing in the word repetition and picture naming tasks, respectively. These FNCs were highly consistent between subjects with similar electrode placement, and were robust enough to be characterized in single trials. Furthermore, the interaction patterns uncovered by FNC analysis correlated well with recent literature suggesting important functional-anatomical distinctions between processing external and self-produced speech. Our results demonstrate that subnetwork decomposition of event-related cortical interactions is a powerful paradigm for interpreting the rich dynamics of large-scale, distributed cortical networks during human cognitive tasks.

The goal of this study was to identify a strategy for antiepileptic drug (AED) reduction to allow efficient recording of focal seizures (FS) in patients undergoing video-electroencephalography (EEG) in an epilepsy monitoring unit (EMU) while avoiding the risk of complications associated with more severe seizure types.We retrospectively reviewed consecutive patients admitted to our institution's EMU from July 1, 2016 to December 31, 2017. We included 114 presurgical patients who had AEDs reduced and at least one seizure during the admission. We compared AED dosages at which FS versus focal to bilateral tonic-clonic seizures (f-BTCS), seizure clusters, and lorazepam administration occurred. We also examined rate of AED reduction and seizure types. We used a receiver-operating characteristic (ROC) curve to identify a dose maximizing FS and minimizing other seizure types.Antiepileptic drug withdrawal rates ranged from 0 to 100% in the first 24 h (mean: 20%, standard deviation: 20%). Focal to bilateral tonic-clonic seizures and lorazepam administration occurred at a lower median AED dose than did FS (0%, 7.2%, and 43.8%, respectively, expressed as a percentage of the patient's outpatient daily AED dose; p 0.001). A daily EMU-administered dose of one-third of the patient's outpatient AED dose allowed 55.0% of FS to occur while avoiding 82.0% of more severe seizure types. The seizure types had no difference in rate of AED withdrawal in the first 24 h of EMU stay.Focal seizures occurred at a higher AED dose than did f-BTCS. This may imply that a low minimally effective dose of AED could allow FS to be recorded while providing protection against f-BTCS. This strategy could improve efficacy and safety in the EMU.

Behavioral responses to a perceptual stimulus are typically faster with repeated exposure to the stimulus (behavioral priming). This implicit learning mechanism is critical for survival but impaired in a variety of neurological disorders, including Alzheimer's disease. Many studies of the neural bases for behavioral priming have encountered an interesting paradox: in spite of faster behavioral responses, repeated stimuli usually elicit weaker neural responses (repetition suppression). Several neurophysiological models have been proposed to resolve this paradox, but noninvasive techniques for human studies have had insufficient spatial-temporal precision for testing their predictions. Here, we used the unparalleled precision of electrocorticography (ECoG) to analyze the timing and magnitude of task-related changes in neural activation and propagation while patients named novel vs repeated visual objects. Stimulus repetition was associated with faster verbal responses and decreased neural activation (repetition suppression) in ventral occipito-temporal cortex (VOTC) and left prefrontal cortex (LPFC). Interestingly, we also observed increased neural activation (repetition enhancement) in LPFC and other recording sites. Moreover, with analysis of high gamma propagation we observed increased top-down propagation from LPFC into VOTC, preceding repetition suppression. The latter results indicate that repetition suppression and behavioral priming are associated with strengthening of top-down network influences on perceptual processing, consistent with predictive coding models of repetition suppression, and they support a central role for changes in large-scale cortical dynamics in achieving more efficient and rapid behavioral responses.

The subthalamic nucleus (STN) is proposed to participate in pausing, or alternately, in dynamic scaling of behavioral responses, roles that have conflicting implications for understanding STN function in the context of deep brain stimulation (DBS) therapy. To examine the nature of event-related STN activity and subthalamic-cortical dynamics, we performed primary motor and somatosensory electrocorticography while subjects (n=10) performed a grip force task during DBS implantation surgery. The results provide the first evidence from humans that STN gamma activity can predict activity in the cortex both prior to and during movement, consistent with the idea that the STN participates in both motor planning and execution. We observed that STN activity appeared to facilitate movement: while both movement onset and termination both coincided with STN-cortical phase-locking, narrow-band gamma power was positively correlated with grip force, and event-related causality measures demonstrated that STN gamma activity predicted cortical gamma activity during movement. STN participation in somatosensory integration also was demonstrated by casual analysis. Information flow from the STN to somatosensory cortex was observed for both beta and gamma range frequencies, specific to particular movement periods and kinematics. Interactions in beta activity between the STN and somatosensory cortex, rather than motor cortex, predicted PD symptom severity. Thus, the STN contributes to multiple aspects of sensorimotor behavior dynamically across time.

Humans have a capacity for hierarchical cognitive control—the ability to simultaneously control immediate actions while holding more abstract goals in mind. Neuropsychological and neuroimaging evidence suggests that hierarchical cognitive control emerges from a frontal architecture whereby prefrontal cortex coordinates neural activity in the motor cortices when abstract rules are needed to govern motor outcomes. Here we utilize the improved temporal resolution of human intracranial electrocorticography to investigate the mechanisms by which frontal cortical oscillatory networks communicate in support of hierarchical cognitive control. Responding according to progressively more abstract rules results in greater frontal network theta phase encoding (4-8 Hz) and increased prefrontal local neuronal population activity (high gamma amplitude, 80-150 Hz), which predicts trial-by-trial response times. Theta phase encoding couples with high gamma amplitude during interregional information encoding, suggesting that interregional phase encoding is a mechanism for the dynamic instantiation of complex cognitive functions by frontal cortical subnetworks.

Praxis, the performance of complex motor gestures, is crucial to the development of motor and social/communicative capacities. Praxis relies on a network consisting of inferior parietal and premotor regions, particularly on the left, and is thought to require transformation of spatio-temporal representations (parietal) into movement sequences (premotor).We examined praxis network dynamics by measuring EEG effective connectivity while healthy subjects performed a praxis task.Propagation from parietal to frontal regions was not statistically greater on the left than the right. However, propagation from left parietal regions to all other regions was significantly greater during gesture preparation than execution. Moreover, during gesture preparation only, propagation from the left parietal region to bilateral frontal regions was greater than reciprocal propagations to the left parietal region. This directional specificity was not observed for the right parietal region.These findings represent direct electrophysiological evidence for directionally predominant propagation in left frontal-parietal networks during praxis behavior, which may reflect neural mechanisms by which representations in the human brain select appropriate motor sequences for subsequent execution.In addition to bolstering the classic view of praxis network function, these results also demonstrate the relevance of additional information provided by directed connectivity measures.

Summary Introduction This study describes development of a novel language mapping approach using high-γ modulation in electrocorticograph (ECoG) during spontaneous conversation, and its comparison with electrical cortical stimulation (ECS) in childhood-onset drug-resistant epilepsy. Methods Patients undergoing invasive pre-surgical monitoring and able to converse with the investigator were eligible. ECoG signals and synchronized audio were acquired during quiet baseline and during natural conversation between investigator and the patient. Using Signal Modeling for Real-time Identification and Event Detection (SIGFRIED) procedure, a statistical model for baseline high-γ (70–116 Hz) power, and a single score for each channel representing the probability that the power features in the experimental signal window belonged to the baseline model, were calculated. Electrodes with significant high-γ responses (HGS) were plotted on the 3D cortical model. Sensitivity, specificity, positive and negative predictive values (PPV, NPV), and classification accuracy were calculated compared to ECS. Results Seven patients were included (4 males, mean age 10.28 ± 4.07 years). Significant high-γ responses were observed in classic language areas in the left hemisphere plus in some homologous right hemispheric areas. Compared with clinical standard ECS mapping, the sensitivity and specificity of HGS mapping was 88.89% and 63.64%, respectively, and PPV and NPV were 35.29% and 96.25%, with an overall accuracy of 68.24%. HGS mapping was able to correctly determine all ECS+ sites in 6 of 7 patients and all false—sites (ECS+, HGS− for visual naming, n = 3) were attributable to only 1 patient. Conclusions This study supports the feasibility of language mapping with ECoG HGS during spontaneous conversation, and its accuracy compared to traditional ECS. Given long-standing concerns about ecological validity of ECS mapping of cued language tasks, and difficulties encountered with its use in children, ECoG mapping of spontaneous language may provide a valid alternative for clinical use.