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New memory traces are believed to be reactivated and reorganized during sleep, mediated by the fine-tuned temporal interplay of neocortical slow oscillations, thalamo-cortical spindles and hippocampal ripples. The authors used intracranial recordings in humans to provide, for the first time, direct evidence for a systematic interaction of these oscillations in the human hippocampus. During systems-level consolidation, mnemonic representations initially reliant on the hippocampus are thought to migrate to neocortical sites for more permanent storage, with an eminent role of sleep for facilitating this information transfer. Mechanistically, consolidation processes have been hypothesized to rely on systematic interactions between the three cardinal neuronal oscillations characterizing non–rapid eye movement (NREM) sleep. Under global control of de- and hyperpolarizing slow oscillations (SOs), sleep spindles may cluster hippocampal ripples for a precisely timed transfer of local information to the neocortex. We used direct intracranial electroencephalogram recordings from human epilepsy patients during natural sleep to test the assumption that SOs, spindles and ripples are functionally coupled in the hippocampus. Employing cross-frequency phase-amplitude coupling analyses, we found that spindles were modulated by the up-state of SOs. Notably, spindles were found to in turn cluster ripples in their troughs, providing fine-tuned temporal frames for the hypothesized transfer of hippocampal memory traces.

Recording extracellulary from neurons in the brains of animals in vivo is among the most established experimental techniques in neuroscience, and has recently become feasible in humans. Many interesting scientific questions can be addressed only when extracellular recordings last several hours, and when individual neurons are tracked throughout the entire recording. Such questions regard, for example, neuronal mechanisms of learning and memory consolidation, and the generation of epileptic seizures. Several difficulties have so far limited the use of extracellular multi-hour recordings in neuroscience: Datasets become huge, and data are necessarily noisy in clinical recording environments. No methods for spike sorting of such recordings have been available. Spike sorting refers to the process of identifying the contributions of several neurons to the signal recorded in one electrode. To overcome these difficulties, we developed Combinato: a complete data-analysis framework for spike sorting in noisy recordings lasting twelve hours or more. Our framework includes software for artifact rejection, automatic spike sorting, manual optimization, and efficient visualization of results. Our completely automatic framework excels at two tasks: It outperforms existing methods when tested on simulated and real data, and it enables researchers to analyze multi-hour recordings. We evaluated our methods on both short and multi-hour simulated datasets. To evaluate the performance of our methods in an actual neuroscientific experiment, we used data from from neurosurgical patients, recorded in order to identify visually responsive neurons in the medial temporal lobe. These neurons responded to the semantic content, rather than to visual features, of a given stimulus. To test our methods with multi-hour recordings, we made use of neurons in the human medial temporal lobe that respond selectively to the same stimulus in the evening and next morning.

Sensory experience elicits complex activity patterns throughout the neocortex. Projections from the neocortex converge onto the medial temporal lobe (MTL), in which distributed neocortical firing patterns are distilled into sparse representations. The precise nature of these neuronal representations is still unknown. Here, we show that population activity patterns in the MTL are governed by high levels of semantic abstraction. We recorded human single-unit activity in the MTL (4,917 units, 25 patients) while subjects viewed 100 images grouped into 10 semantic categories of 10 exemplars each. High levels of semantic abstraction were indicated by representational similarity analyses (RSAs) of patterns elicited by individual stimuli. Moreover, pattern classifiers trained to decode semantic categories generalised successfully to unseen exemplars, and classifiers trained to decode exemplar identity more often confused exemplars of the same versus different categories. Semantic abstraction and generalisation may thus be key to efficiently distill the essence of an experience into sparse representations in the human MTL. Although semantic abstraction is efficient and may facilitate generalisation of knowledge to novel situations, it comes at the cost of a loss of detail and may be central to the generation of false memories.

Recent findings indicate that the hippocampus supports not only long-term memory encoding but also plays a role in working memory (WM) maintenance of multiple items; however, the neural mechanism underlying multi-item maintenance is still unclear. Theoretical work suggests that multiple items are being maintained by neural assemblies synchronized in the gamma frequency range (25-100 Hz) that are locked to consecutive phase ranges of oscillatory activity in the theta frequency range (4-8 Hz). Indeed, cross-frequency coupling of the amplitude of high-frequency activity to the phase of slower oscillations has been described both in animals and in humans, but has never been linked to a theoretical model of a cognitive process. Here we used intracranial EEG recordings in human epilepsy patients to test pivotal predictions from theoretical work. First, we show that simultaneous maintenance of multiple items in WM is accompanied by cross-frequency coupling of oscillatory activity in the hippocampus, which is recruited during multi-item WM. Second, maintenance of an increasing number of items is associated with modulation of beta/gamma amplitude with theta band activity of lower frequency, consistent with the idea that longer cycles are required for an increased number of representations by gamma cycles. This effect cannot be explained by a difference in theta or beta/gamma power. Third, we describe how the precision of cross-frequency coupling predicts individual WM performance. These data support the idea that working memory in humans depends on a neural code using phase information.

Our brains create new memories by capturing the ‘who/what’, ‘where’ and ‘when’ of everyday experiences. On a neuronal level, mechanisms facilitating a successful transfer into episodic memory are still unclear. We investigated this by measuring single neuron activity in the human medial temporal lobe during encoding of item-location associations. While previous research has found predictive effects in population activity in human MTL structures, we could attribute such effects to two specialized sub-groups of neurons: concept cells in the hippocampus, amygdala and entorhinal cortex (EC), and a second group of parahippocampal location-selective neurons. In both item- and location-selective populations, firing rates were significantly higher during successfully encoded trials. These findings are in line with theories of hippocampal indexing, since selective index neurons may act as pointers to neocortical representations. Overall, activation of distinct populations of neurons could directly support the connection of the ‘what’ and ‘where’ of episodic memory. Whether specialized neuronal firing in the human MTL predicts successful memory encoding remains unknown. Here, the authors find this to be the case for two distinct populations of single neurons responding to items and locations, respectively.

Epileptic seizures vary greatly in clinical phenomenology and can markedly affect the patient's quality of life. As therapeutic interventions focus on reduction or elimination of seizures, the accurate documentation of seizure occurrence is essential. However, patient self-evaluation compared with objective evaluation by video-electroencephalography (EEG) monitoring or long-term ambulatory EEG revealed that patients document fewer than 50% of their seizures, on average, and that documentation accuracy varies significantly over time. For good clinical practice in epilepsy, novel and feasible seizure detection techniques for ambulatory long-term use are needed. Generalised tonic-clonic seizures can already be detected reliably by methods that rely on motion recording (eg, surface electromyography). However, the automatic detection of other seizure types, such as complex partial seizures, will require multimodal approaches that combine the measurement of ictal autonomic alterations (eg, heart rate) and of characteristic movement patterns (eg, accelerometry). Innovative and feasible tools for automatic seizure detection are likely to advance both monitoring of the outcome of a treatment in a patient and clinical research in epilepsy.

The ability to evaluate outcomes of previous decisions is critical to adaptive decision-making. The feedback-related negativity (FRN) is an event-related potential (ERP) modulation that distinguishes losses from wins, but little is known about the effects of outcome probability on these ERP responses. Further, little is known about the frequency characteristics of feedback processing, for example, event-related oscillations and phase synchronizations. Here, we report an EEG experiment designed to address these issues. Subjects engaged in a probabilistic reinforcement learning task in which we manipulated, across blocks, the probability of winning and losing to each of two possible decision options. Behaviorally, all subjects quickly adapted their decision-making to maximize rewards. ERP analyses revealed that the probability of reward modulated neural responses to wins, but not to losses. This was seen both across blocks as well as within blocks, as learning progressed. Frequency decomposition via complex wavelets revealed that EEG responses to losses, compared to wins, were associated with enhanced power and phase coherence in the theta frequency band. As in the ERP analyses, power and phase coherence values following wins but not losses were modulated by reward probability. Some findings between ERP and frequency analyses diverged, suggesting that these analytic approaches provide complementary insights into neural processing. These findings suggest that the neural mechanisms of feedback processing may differ between wins and losses.

In this Review, Hoppe and Elger discuss the etiological factors underlying depression as a comorbidity of epilepsy, comprising psychological factors such as learned helplessness and the 'burden of epilepsy', and neurobiological factors directly related to the seizures. The authors highlight the need for more clinical studies of antidepressants and psychotherapy, and emphasize the importance of an integrated approach to treatment that addresses both the psychological and the neurobiological factors. Depression is a serious and frequent comorbidity of epilepsy and other neurological conditions. Here, we review recent studies on the relationship between epilepsy and depression with regard to diagnostic criteria, epidemiology, etiology and treatment. Depression in epilepsy can be described in the general framework of the diathesis–stress model: chronic stress exposure owing to the 'burden of epilepsy' and learned helplessness due to the threat of recurrent seizures as unpredictable aversive events represent psychological risk factors for the development of depression. Epilepsy-related factors (for example, focus site or side) have shown little effect on mood. Nonepileptic individuals who are adversely affected by seizures (for example, parents of pediatric patients with epilepsy, and patients with psychogenic nonepileptic seizures) show increased levels of depression, similar to patients with epilepsy. However, seizures, subclinical hypersynchronous neural discharges and some antiepileptic drugs may cause acute states of depressive mood or depression on a purely neurobiological basis. Antidepressant drugs and psychotherapy have shown moderate efficacy in the treatment of depression comorbidity, but randomized controlled trials in patients with epilepsy are lacking, especially for drugs. Key Points Depression is a frequent comorbidity of epilepsy Depression comorbidity in epilepsy can be explained in the framework of the diathesis–stress model Seizures, subclinical hypersynchronous neural discharges and some antiepileptic drugs may cause acute states of depressive mood on a purely neurobiological basis Nonepileptic individuals who are adversely affected by seizures (for example, parents of pediatric patients, and patients with psychogenic seizures) show elevated levels of depression, similar to patients with epilepsy Antidepressants and psychotherapy seem to have a comparable, moderate antidepressant effect, but randomized controlled trials are lacking, particularly for drugs More-comprehensive approaches to the understanding and treatment of depression and suicidality in epilepsy, which combine drug treatment with psychotherapy and rehabilitative care, need to be developed

Imagine how flicking through your photo album and seeing a picture of a beach sunset brings back fond memories of a tasty cocktail you had that night. Computational models suggest that upon receiving a partial memory cue (‘beach’), neurons in the hippocampus coordinate reinstatement of associated memories (‘cocktail’) in cortical target sites. Here, using human single neuron recordings, we show that hippocampal firing rates are elevated from ~ 500–1500 ms after cue onset during successful associative retrieval. Concurrently, the retrieved target object can be decoded from population spike patterns in adjacent entorhinal cortex (EC), with hippocampal firing preceding EC spikes and predicting the fidelity of EC object reinstatement. Prior to orchestrating reinstatement, a separate population of hippocampal neurons distinguishes different scene cues (buildings vs. landscapes). These results elucidate the hippocampal-entorhinal circuit dynamics for memory recall and reconcile disparate views on the role of the hippocampus in scene processing vs. associative memory. The hippocampus is involved both in episodic memory recall and scene processing. Here, the authors show that hippocampal neurons first process scene cues before coordinating memory-guided pattern completion in adjacent entorhinal cortex.

Concept neurons in the medial temporal lobe respond to semantic features of presented stimuli. Analyzing 61 concept neurons recorded from twelve patients who underwent surgery to treat epilepsy, we show that firing patterns of concept neurons encode relations between concepts during a picture comparison task. Thirty-three of these responded to non-preferred stimuli with a delayed but well-defined onset whenever the task required a comparison to a response-eliciting concept, but not otherwise. Supporting recent theories of working memory, concept neurons increased firing whenever attention was directed towards this concept and could be reactivated after complete activity silence. Population cross-correlations of pairs of concept neurons exhibited order-dependent asymmetric peaks specifically when their response-eliciting concepts were to be compared. Our data are consistent with synaptic mechanisms that support reinstatement of concepts and their relations after activity silence, flexibly induced through task-specific sequential activation. This way arbitrary contents of experience could become interconnected in both working and long-term memory. It is unclear how distinct concepts are processed in the brain. Here, the authors recorded from concept cells in human subjects with epilepsy and found that a subset of concept cells responded to non-preferred concepts if those non-preferred concepts required comparison to a preferred concept.