Epileptic seizures are traditionally characterized as the ultimate expression of monolithic, hypersynchronous neuronal activity arising from unbalanced runaway excitation. Here we report the first examination of spike train patterns in large ensembles of single neurons during seizures in persons with epilepsy. Contrary to the traditional view, neuronal spiking activity during seizure initiation and spread was highly heterogeneous, not hypersynchronous, suggesting complex interactions among different neuronal groups even at the spatial scale of small cortical patches. In contrast to earlier stages, seizure termination is a nearly homogenous phenomenon followed by an almost complete cessation of spiking across recorded neuronal ensembles. Notably, even neurons outside the region of seizure onset showed significant changes in activity minutes before the seizure. These findings suggest a revision of current thinking about seizure mechanisms and point to the possibility of seizure prevention based on spiking activity in neocortical neurons.

The neurophysiological mechanisms by which anesthetic drugs cause loss of consciousness are poorly understood. Anesthetic actions at the molecular, cellular, and systems levels have been studied in detail at steady states of deep general anesthesia. However, little is known about how anesthetics alter neural activity during the transition into unconsciousness. We recorded simultaneous multiscale neural activity from human cortex, including ensembles of single neurons, local field potentials, and intracranial electrocorticograms, during induction of general anesthesia. We analyzed local and global neuronal network changes that occurred simultaneously with loss of consciousness. We show that propofol-induced unconsciousness occurs within seconds of the abrupt onset of a slow (<1 Hz) oscillation in the local field potential. This oscillation marks a state in which cortical neurons maintain local patterns of network activity, but this activity is fragmented across both time and space. Local (<4 mm) neuronal populations maintain the millisecond-scale connectivity patterns observed in the awake state, and spike rates fluctuate and can reach baseline levels. However, neuronal spiking occurs only within a limited slow oscillation-phase window and is silent otherwise, fragmenting the time course of neural activity. Unexpectedly, we found that these slow oscillations occur asynchronously across cortex, disrupting functional connectivity between cortical areas. We conclude that the onset of slow oscillations is a neural correlate of propofol-induced loss of consciousness, marking a shift to cortical dynamics in which local neuronal networks remain intact but become functionally isolated in time and space. electrophysiology | single units | GABA | cortical networks G eneral anesthesia is a drug-induced reversible coma commonly initiated by administering a large dose of a fast-acting drug to induce unconsciousness within seconds (1). This state can be maintained as long as needed to execute surgical and many nonsurgical procedures. One of the most widely used anesthetics is propofol, an i.v. drug that enhances GABAergic inhibitory input to neurons (2-4), with effects in cortex, thalamus, brainstem, and spinal cord (5-7). Despite the understanding of propofol's molecular actions, it is not clear how these effects at molecular targets affect single neurons and larger-scale neural circuits to produce unconsciousness.The effects on macroscopic dynamics are noticeable in the EEG, which contains several stereotyped patterns during maintenance of propofol general anesthesia. These patterns include increased delta (0.5-4 Hz) power (8, 9); increased gamma (25-40 Hz) power (9); an alpha (∼10 Hz) rhythm (10-12) that is coherent across frontal cortex; and burst suppression, an alternation between bursts of high-voltage activity and periods of flat EEG lasting for several seconds (13,14). In addition, slow oscillations (<1 Hz) have been well characterized in deeply anesthetized animals and are associated with an alternation of the neuron...

Background: Deep brain stimulation (DBS) is used to modulate the activity of dysfunctional brain circuits. The safety and efficacy of DBS in dementia is unknown.Objective: To assess DBS of memory circuits as a treatment for patients with mild Alzheimer’s disease (AD).Methods: We evaluated active “on” versus sham “off” bilateral DBS directed at the fornix-a major fiber bundle in the brain’s memory circuit-in a randomized, double-blind trial (ClinicalTrials.gov NCT01608061) in 42 patients with mild AD. We measured cognitive function and cerebral glucose metabolism up to 12 months post-implantation.Results: Surgery and electrical stimulation were safe and well tolerated. There were no significant differences in the primary cognitive outcomes (ADAS-Cog 13, CDR-SB) in the “on” versus “off” stimulation group at 12 months for the whole cohort. Patients receiving stimulation showed increased metabolism at 6 months but this was not significant at 12 months. On post-hoc analysis, there was a significant interaction between age and treatment outcome: in contrast to patients <65 years old (n = 12) whose results trended toward being worse with DBS ON versus OFF, in patients≥65 (n = 30) DBS-f ON treatment was associated with a trend toward both benefit on clinical outcomes and a greater increase in cerebral glucose metabolism.Conclusion: DBS for AD was safe and associated with increased cerebral glucose metabolism. There were no differences in cognitive outcomes for participants as a whole, but participants aged≥65 years may have derived benefit while there was possible worsening in patients below age 65 years with stimulation.

Intracranial recording is an important diagnostic method routinely used in a number of neurological monitoring scenarios. In recent years, advancements in such recordings have been extended to include unit activity of an ensemble of neurons. However, a detailed functional characterization of excitatory and inhibitory cells has not been attempted in human neocortex, particularly during the sleep state. Here, we report that such feature discrimination is possible from high-density recordings in the neocortex by using 2D multielectrode arrays. Successful separation of regular-spiking neurons (or bursting cells) from fast-spiking cells resulted in well-defined clusters that each showed unique intrinsic firing properties. The high density of the array, which allowed recording from a large number of cells (up to 90), helped us to identify apparent monosynaptic connections, confirming the excitatory and inhibitory nature of regular-spiking and fast-spiking cells, thus categorized as putative pyramidal cells and interneurons, respectively. Finally, we investigated the dynamics of correlations within each class. A marked exponential decay with distance was observed in the case of excitatory but not for inhibitory cells. Although the amplitude of that decline depended on the timescale at which the correlations were computed, the spatial constant did not. Furthermore, this spatial constant is compatible with the typical size of human columnar organization. These findings provide a detailed characterization of neuronal activity, functional connectivity at the microcircuit level, and the interplay of excitation and inhibition in the human neocortex.spontaneous activity | ensemble recordings | single unit | functional dynamics F rom columnar microcircuits (1-3) to higher-order neuronal functional units, neocortical dynamics are characterized by a large range of spatial and temporal scales (4, 5). Recent technical improvements have allowed the nature of these dynamics in the human brain to be directly explored: Single-neuron activity in conjunction with local field potentials (LFPs) can be detected from the cerebral cortex and hippocampus in the course of intense monitoring of brain activity before surgical treatment of epileptic foci (6). Modern electrode systems provide the possibility of extracellular recordings of neuronal ensembles by using either microwires (7) or high-density microelectrode arrays (8,9). Prior efforts have demonstrated excellent recordings of single-neuron activity in human cerebral cortex (10-12).Separation of units between "regular-spiking" (RS) and "fastspiking" (FS) neurons, presumably excitatory (pyramidal) and inhibitory (interneuron) cells, respectively, is commonly practiced in animal experiments. In the neocortex of various mammalian species, RS and FS cells can be reliably separated based on spike waveform, duration, and firing rates (13,14). Similar criteria were also used to successfully separate units into putative pyramidal (Pyr) cells and inhibitory interneurons (Int) in human hippocampus...

The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.focal epilepsy | seizure localization | network analysis | eigenvector centrality | ECoG signals E pilepsy affects over 60 million people worldwide, and approximately 40% of patients have drug-resistant epilepsy (DRE) with recurrent seizures that are not controlled by available medications (1-3). It is now routine to consider drugresistant partial epilepsy patients, who represent the largest cohort of patients with uncontrolled seizures, for possible resective seizure surgery (4). Successful seizure surgery is predicated upon the ability to localize the seizure onset zone. Although some patients (e.g., those with mesial temporal sclerosis or lesional epilepsy) can proceed to surgery following scalp recordings of seizures delineating a seizure onset zone (5), a significant number of patients have seizures that are challenging to localize with scalp ictal (i.e., seizure) recordings. In this case, ictal recordings using intracranial electrodes (e.g., subdural strips, grids, or depth electrode arrays) are necessary. The purpose of these intracranial recording arrays is to provide information about seizure onset and propagation, representing spatiotemporal changes in cerebral function.Using intracranial electrocorticographic (ECoG) recordings taken over several days to capture ictal events, clinicians visually inspect the ECoG recordings at the onset of the seizures an...

Objective We used native sensorimotor representations of fingers in a brain-machine interface to achieve immediate online control of individual prosthetic fingers. Approach Using high gamma responses recorded with a high-density ECoG array, we rapidly mapped the functional anatomy of cued finger movements. We used these cortical maps to select ECoG electrodes for a hierarchical linear discriminant analysis classification scheme to predict: 1) if any finger was moving, and, if so, 2) which digit was moving. To account for sensory feedback, we also mapped the spatiotemporal activation elicited by vibrotactile stimulation. Finally, we used this prediction framework to provide immediate online control over individual fingers of the Johns Hopkins University Applied Physics Laboratory (JHU/APL) Modular Prosthetic Limb (MPL). Main Results The balanced classification accuracy for detection of movements during the online control session was 92% (chance: 50%). At the onset of movement, finger classification was 76% (chance: 20%), and 88% (chance: 25%) if the pinky and ring finger movements were coupled. Balanced accuracy of fully flexing the cued finger was 64%, and 77% had we combined pinky and ring commands. Offline decoding yielded a peak finger decoding accuracy of 96.5% (chance: 20%) when using an optimized selection of electrodes. Offline analysis demonstrated significant finger-specific activations throughout sensorimotor cortex. Activations either prior to movement onset or during sensory feedback led to discriminable finger control. Significance Our results demonstrate the ability of ECoG-based BMIs to leverage the native functional anatomy of sensorimotor cortical populations to immediately control individual finger movements in real time.

Neural oscillations are important features in a working central nervous system, facilitating efficient communication across large networks of neurons. They are implicated in a diverse range of processes such as synchronization and synaptic plasticity, and can be seen in a variety of cognitive processes. For example, hippocampal theta oscillations are thought to be a crucial component of memory encoding and retrieval. To better study the role of these oscillations in various cognitive processes, and to be able to build clinical applications around them, accurate and precise estimations of the instantaneous frequency and phase are required. Here, we present methodology based on autoregressive modeling to accomplish this in real time. This allows the targeting of stimulation to a specific phase of a detected oscillation. We first assess performance of the algorithm on two signals where the exact phase and frequency are known. Then, using intracranial EEG recorded from two patients performing a Sternberg memory task, we characterize our algorithm’s phase-locking performance on physiologic theta oscillations: optimizing algorithm parameters on the first patient using a genetic algorithm, we carried out cross-validation procedures on subsequent trials and electrodes within the same patient, as well as on data recorded from the second patient.

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 seconds for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.