Patterns of single-neuron activity during associative recognition memory in the human medial temporal lobe.

Electrophysiological activity in medial temporal lobe (MTL) structures is pivotal for declarative long-term memory. Single-neuron and microcircuit findings capitalizing on human microwire recordings from the medial temporal lobe are still fragmentary. In particular, it is an open question whether identical or different groups of neurons participate in different memory functions. Here, we investigated category-specific responses in the human MTL based on single-neuron recordings in presurgical epilepsy patients performing an associative long-term memory task. Additionally, auditory beat stimuli were presented during encoding and retrieval to modulate memory performance. We describe the proportion of neurons in amygdala, entorhinal cortex, hippocampus and parahippocampal cortex belonging to different response classes. These entail neurons coding stimulus-familiarity, neurons coding successful item memory, and neurons coding associated source memory, as well as the overlap between these classes. As major results we demonstrate that neurons responding to stimulus familiarity (old/new effect) can be identified in the MTL even when using previously known rather than entirely novel stimulus material (words). We observed a significant overlap between familiarity-related neurons and neurons coding item retrieval (remembered/forgotten effect). The largest fraction of familiarity-related neurons was found in the parahippocampal cortex, and a considerable fraction of all parahippocampal neurons was related to successful item retrieval. Neurons related to successful source retrieval were different from the neurons coding the associated information. Most importantly, there was no overlap between neurons coding item memory and those coding associated source memory strongly suggesting that these functions are facilitated by different sets of neurons.

Neurons in the Human Left Amygdala Automatically Encode Subjective Value Irrespective of Task.

The amygdala plays an important role in the computation of internal reward signals. In animals it has been shown to enable a stimulus to indicate the current value of a reinforcer. However, the exact nature of the current value representations in humans remains unknown. Specifically, do neurons of the human amygdala represent current value signals only in tasks requiring valuation? We recorded from 406 neurons in the amygdala, orbitofrontal cortex, parahippocampal cortex, entorhinal cortex, and hippocampus of 6 neurosurgical patients while subjects repeatedly viewed 40 different pictures of sweet or salty "junk food" items in 2 different tasks. Neural activity during stimulus inspection in a valuation task reflected food preferences in the amygdala, orbitofrontal cortex, hippocampus, and entorhinal cortex. Notably, only left amygdala activity represented these food preferences even in a sweet-salty classification task. Valuation signals of the left amygdala thus appear to be stimulus-, not-task driven.

An online adaptive screening procedure for selective neuronal responses.

BACKGROUND: A common problem in neurophysiology is to identify stimuli that elicit neuronal responses in a given brain region. Particularly in situations where electrode positions are fixed, this can be a time-consuming task that requires presentation of a large number of stimuli. Such a screening for response-eliciting stimuli is employed, e.g., as a standard procedure to identify 'concept cells' in the human medial temporal lobe. NEW METHOD: Our new method evaluates neuronal responses to stimuli online during a screening session, which allows us to successively exclude stimuli that do not evoke a response. Using this method, we can screen a larger number of stimuli which in turn increases the chances of finding responsive neurons and renders time-consuming offline analysis unnecessary. RESULTS: Our method enabled us to present 30% more stimuli in the same period of time with additional presentations of the most promising candidate stimuli. Our online method ran smoothly on a standard computer and network. COMPARISON WITH AN EXISTING METHOD: To analyze how our online screening procedure performs in comparison to an established offline method, we used the Wave_Clus software package. We did not observe any major drawbacks in our method, but a much higher efficiency and analysis speed. CONCLUSIONS: By transitioning from a traditional offline screening procedure to our new online method, we substantially increased the number of visual stimuli presented in a given time period. This allows to identify more response-eliciting stimuli, which forms the basis to better address a great number of questions in cognitive neuroscience.

Using bivariate signal analysis to characterize the epileptic focus: the benefit of surrogates.

The disease epilepsy is related to hypersynchronous activity of networks of neurons. While acute epileptic seizures are the most extreme manifestation of this hypersynchronous activity, an elevated level of interdependence of neuronal dynamics is thought to persist also during the seizure-free interval. In multichannel recordings from brain areas involved in the epileptic process, this interdependence can be reflected in an increased linear cross correlation but also in signal properties of higher order. Bivariate time series analysis comprises a variety of approaches, each with different degrees of sensitivity and specificity for interdependencies reflected in lower- or higher-order properties of pairs of simultaneously recorded signals. Here we investigate which approach is best suited to detect putatively elevated interdependence levels in signals recorded from brain areas involved in the epileptic process. For this purpose, we use the linear cross correlation that is sensitive to lower-order signatures of interdependence, a nonlinear interdependence measure that integrates both lower- and higher-order properties, and a surrogate-corrected nonlinear interdependence measure that aims to specifically characterize higher-order properties. We analyze intracranial electroencephalographic recordings of the seizure-free interval from 29 patients with an epileptic focus located in the medial temporal lobe. Our results show that all three approaches detect higher levels of interdependence for signals recorded from the brain hemisphere containing the epileptic focus as compared to signals recorded from the opposite hemisphere. For the linear cross correlation, however, these differences are not significant. For the nonlinear interdependence measure, results are significant but only of moderate accuracy with regard to the discriminative power for the focal and nonfocal hemispheres. The highest significance and accuracy is obtained for the surrogate-corrected nonlinear interdependence measure.

Internetwork and intranetwork communications during bursting dynamics: applications to seizure prediction.

We use a simple dynamical model of two interacting networks of integrate-and-fire neurons to explain a seemingly paradoxical result observed in epileptic patients indicating that the level of phase synchrony declines below normal levels during the state preceding seizures (preictal state). We model the transition from the seizure free interval (interictal state) to the seizure (ictal state) as a slow increase in the mean depolarization of neurons in a network corresponding to the epileptic focus. We show that the transition from the interictal to preictal and then to the ictal state may be divided into separate dynamical regimes: the formation of slow oscillatory activity due to resonance between the two interacting networks observed during the interictal period, structureless activity during the preictal period when the two networks have different properties, and bursting dynamics driven by the network corresponding to the epileptic focus. Based on this result, we hypothesize that the beginning of the preictal period marks the beginning of the transition of the epileptic network from normal activity toward seizing.

Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state.

We compare dynamical properties of brain electrical activity from different recording regions and from different physiological and pathological brain states. Using the nonlinear prediction error and an estimate of an effective correlation dimension in combination with the method of iterative amplitude adjusted surrogate data, we analyze sets of electroencephalographic (EEG) time series: surface EEG recordings from healthy volunteers with eyes closed and eyes open, and intracranial EEG recordings from epilepsy patients during the seizure free interval from within and from outside the seizure generating area as well as intracranial EEG recordings of epileptic seizures. As a preanalysis step an inclusion criterion of weak stationarity was applied. Surface EEG recordings with eyes open were compatible with the surrogates' null hypothesis of a Gaussian linear stochastic process. Strongest indications of nonlinear deterministic dynamics were found for seizure activity. Results of the other sets were found to be inbetween these two extremes.

Nonlinear EEG analysis in epilepsy: its possible use for interictal focus localization, seizure anticipation, and prevention.

Several recent studies emphasize the high value of nonlinear EEG analysis particularly for improved characterization of epileptic brain states. In this review the authors report their work to increase insight into the spatial and temporal dynamics of the epileptogenic process. Specifically, they discuss possibilities for seizure anticipation, which is one of the most challenging aspects of epileptology. Although there are numerous studies exploring basic neuronal mechanisms that are likely to be associated with seizures, to date no definite information is available regarding how, when, or why a seizure occurs. Nonlinear EEG analysis now provides strong evidence that the interictal-ictal state transition is not an abrupt phenomenon. Rather, findings indicate that it is indeed possible to detect a preseizure phase. The unequivocal definition of such a state with a sufficient length would enable investigations of basic mechanisms leading to seizure initiation in humans, and development of adequate seizure prevention strategies.