Properties and hemispheric differences of theta oscillations in the human hippocampus.

The left and right primate hippocampi (LH and RH) are thought to support distinct functions, but little is known about differences between the hemispheres at the neuronal level. We recorded single-neuron and local field potentials from the human hippocampus in epilepsy patients implanted with depth electrodes. We detected theta-frequency bouts of oscillatory activity while patients performed a visual recognition memory task. Theta appeared in bouts of 3.16 cycles, with sawtooth-shaped oscillations that had a prolonged downswing period. Outside the seizure onset zone, the average frequency of theta bouts was higher in the RH compared to the LH (6.0 vs. 5.3 Hz). LH theta bouts had lower amplitudes and a higher prevalence compared to the RH (26% vs. 21% of total time). Additionally, the RH contained a population of thin spiking visually tuned neurons that were not present in the LH. These data show that human theta appears in short oscillatory bouts whose properties vary between hemispheres, thereby revealing neurophysiological properties of the hippocampus that differ between the hemispheres.

Single-neuron correlate of epilepsy-related cognitive deficits in visual recognition memory in right mesial temporal lobe.

OBJECTIVE: Impaired memory is a common comorbidity of refractory temporal lobe epilepsy (TLE) and often perceived by patients as more problematic than the seizures themselves. The objective of this study is to understand what the relationship of these behavioral impairments is to the underlying pathophysiology, as there are currently no treatments for these deficits, and it remains unknown what circuits are affected. METHODS: We recorded single neurons in the medial temporal lobes (MTLs) of 62 patients (37 with refractory TLE) who performed a visual recognition memory task to characterize the relationship between behavior, tuning, and anatomical location of memory selective and visually selective neurons. RESULTS: Subjects with a seizure onset zone (SOZ) in the right but not left MTL demonstrated impaired ability to recollect as indicated by the degree of asymmetry of the receiver operating characteristic curve. Of the 1973 recorded neurons, 159 were memory selective (MS) and 366 were visually selective (VS) category cells. The responses of MS neurons located within right but not left MTL SOZs were impaired during high-confidence retrieval trials, mirroring the behavioral deficit seen both in our task and in standardized neuropsychological tests. In contrast, responses of VS neurons were unimpaired in both left and right MTL SOZs. Our findings show that neuronal dysfunction within SOZs in the MTL was specific to a functional cell type and behavior, whereas other cell types respond normally even within the SOZ. We show behavioral metrics that detect right MTL SOZ-related deficits and identify a neuronal correlate of this impairment. SIGNIFICANCE: Together, these findings show that single-cell responses can be used to assess the causal effects of local circuit disruption by an SOZ in the MTL, and establish a neural correlate of cognitive impairment due to epilepsy that can be used as a biomarker to assess the efficacy of novel treatments.

Extent of Single-Neuron Activity Modulation by Hippocampal Interictal Discharges Predicts Declarative Memory Disruption in Humans.

Memory deficits are common in epilepsy patients. In these patients, the interictal EEG commonly shows interictal epileptiform discharges (IEDs). While IEDs are associated with transient cognitive impairments, it remains poorly understood why this is. We investigated the effects of human (male and female) hippocampal IEDs on single-neuron activity during a memory task in patients with medically refractory epilepsy undergoing depth electrode monitoring. We quantified the effects of hippocampal IEDs on single-neuron activity and the impact of this modulation on subjectively declared memory strength. Across all recorded neurons, the activity of 50 of 728 neurons were significantly modulated by IEDs, with the strongest modulation in the medial temporal lobe (33 of 416) and in particular the right hippocampus (12 of 58). Putative inhibitory neurons, as identified by their extracellular signature, were more likely to be modulated by IEDs than putative excitatory neurons (19 of 157 vs 31 of 571). Behaviorally, the occurrence of hippocampal IEDs was accompanied by a disruption of recognition of familiar images only if they occurred up to 2 s before stimulus onset. In contrast, IEDs did not impair encoding or recognition of novel images, indicating high temporal and task specificity of the effects of IEDs. The degree of modulation of individual neurons by an IED correlated with the declared confidence of a retrieval trial, with higher firing rates indicative of reduced confidence. Together, these data link the transient modulation of individual neurons by IEDs to specific declarative memory deficits in specific cell types, thereby revealing a mechanism by which IEDs disrupt medial temporal lobe-dependent declarative memory retrieval processes.SIGNIFICANCE STATEMENT Interictal epileptiform discharges (IEDs) are thought to be a cause of memory deficits in chronic epilepsy patients, but the underlying mechanisms are not understood. Utilizing single-neuron recordings in epilepsy patients, we found that hippocampal IEDs transiently change firing of hippocampal neurons and disrupted selectively the retrieval, but not encoding, of declarative memories. The extent of the modulation of the individual firing of hippocampal neurons by an IED predicted the extent of reduction of subjective retrieval confidence. Together, these data reveal a specific kind of transient cognitive impairment caused by IEDs and link this impairment to the modulation of the activity of individual neurons. Understanding the mechanisms by which IEDs impact memory is critical for understanding memory impairments in epilepsy patients.

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients.

Intracranial recordings from patients with intractable epilepsy provide a unique opportunity to study the activity of individual human neurons during active behavior. An important tool for quantifying behavior is eye tracking, which is an indispensable tool for studying visual attention. However, eye tracking is challenging to use concurrently with invasive electrophysiology and this approach has consequently been little used. Here, we present a proven experimental protocol to conduct single-neuron recordings with simultaneous eye tracking in humans. We describe how the systems are connected and the optimal settings to record neurons and eye movements. To illustrate the utility of this method, we summarize results that were made possible by this setup. This data shows how using eye tracking in a memory-guided visual search task allowed us to describe a new class of neurons called target neurons, whose response was reflective of top-down attention to the current search target. Lastly, we discuss the significance and solutions to potential problems of this setup. Together, our protocol and results suggest that single-neuron recordings with simultaneous eye tracking in humans are an effective method to study human brain function. It provides a key missing link between animal neurophysiology and human cognitive neuroscience.

Improving P300 Spelling Rate using Language Models and Predictive Spelling.

The P300 Speller Brain-Computer Interface (BCI) provides a means of communication for those suffering from advanced neuromuscular diseases such as amyotrophic lateral sclerosis (ALS). Recent literature has incorporated language-based modelling, which uses previously chosen characters and the structure of natural language to modify the interface and classifier. Two complementary methods of incorporating language models have previously been independently studied: predictive spelling uses language models to generate suggestions of complete words to allow for the selection of multiple characters simultaneously, and language model-based classifiers have used prior characters to create a prior probability distribution over the characters based on how likely they are to follow. In this study, we propose a combined method which extends a language-based classifier to generate prior probabilities for both individual characters and complete words. In order to gauge the efficiency of this new model, results across 12 healthy subjects were measured. Incorporating predictive spelling increased typing speed using the P300 speller, with an average increase of 15.5% in typing rate across subjects, demonstrating that language models can be effectively utilized to create full word suggestions for predictive spelling. When combining predictive spelling with language model classification, typing speed is significantly improved, resulting in better typing performance.

Online BCI Typing using Language Model Classifiers by ALS Patients in their Homes.

The P300 speller is a common brain-computer interface system that can provide a means of communication for patients with amyotrophic lateral sclerosis (ALS). Recent studies have shown that incorporating language information in signal classification can improve system performance, but they have largely been tested on healthy volunteers in a laboratory setting. The goal of this study was to demonstrate the functionality of the P300 speller system with language models when used by ALS patients in their homes. Six ALS patients with functional ratings ranging from two to 28 participated in this study. All subjects had improved offline performance when using a language model and five subjects were able to type at least six characters per minute with over 84% accuracy in online sessions. The results of this study indicate that the improvements in performance using language models in the P300 speller translate into the ALS population, which could help to make it a viable assistive device.

A comparison of stimulus types in online classification of the P300 speller using language models.

The P300 Speller is a common brain-computer interface communication system. There are many parallel lines of research underway to overcome the system's low signal to noise ratio and thereby improve performance, including using famous face stimuli and integrating language information into the classifier. While both have been shown separately to provide significant improvements, the two methods have not yet been implemented together to demonstrate that the improvements are complimentary. The goal of this study is therefore twofold. First, we aim to compare the famous faces stimulus paradigm with an existing alternative stimulus paradigm currently used in commercial systems (i.e., character inversion). Second, we test these methods with language model integration to assess whether different optimization approaches can be combined to further improve BCI communication. In offline analysis using a previously published particle filter method, famous faces stimuli yielded superior results to both standard and inverting stimuli. In online trials using the particle filter method, all 10 subjects achieved a higher selection rate when using the famous faces flashing paradigm than when using inverting flashes. The improvements achieved by these methods are therefore complementary and a combination yields superior results to either method implemented individually when tested in healthy subjects.