Dysfunctional prefrontal cortical network activity and interactions following cannabinoid receptor activation

Coordinated activity spanning anatomically distributed neuronal networks underpins cognition and mediates limbic-cortical interactions during learning, memory, and decision-making. We used CP55940, a potent agonist of brain cannabinoid receptors known to disrupt coordinated activity in hippocampus, to investigate the roles of network oscillations during hippocampal and medial prefrontal cortical (mPFC) interactions in rats. During quiet wakefulness and rest, CP55940 dose-dependently reduced 0.1-30 Hz local field potential power in CA1 of the hippocampus while concurrently decreasing 30-100 Hz power in mPFC; these contrasting population-level effects were paralleled by differential effects on underlying single-unit activity in the two structures. During decision-making phases of a spatial working memory task, CP5540-induced deficits in hippocampal theta and prefrontal gamma oscillations were observed alongside disrupted theta-frequency coherence between the two structures. These changes in coordinated limbic-cortical network activities correlated with (1) reduced accuracy of task performance, (2) impaired phase-locking of prefrontal single-unit spiking to the local gamma and hippocampal theta rhythms, and (3) impaired task-dependent activity in a subset of mPFC units. In addition to highlighting the importance of CA1-mPFC network oscillations for cognition, these results implicate disrupted theta-frequency coordination of CA1-mPFC activity in the cognitive deficits caused by exogenous activation of brain cannabinoid receptors

Electrical Stimulation Modulates High γ Activity and Human Memory Performance.

Direct electrical stimulation of the brain has emerged as a powerful treatment for multiple neurological diseases, and as a potential technique to enhance human cognition. Despite its application in a range of brain disorders, it remains unclear how stimulation of discrete brain areas affects memory performance and the underlying electrophysiological activities. Here, we investigated the effect of direct electrical stimulation in four brain regions known to support declarative memory: hippocampus (HP), parahippocampal region (PH) neocortex, prefrontal cortex (PF), and lateral temporal cortex (TC). Intracranial EEG recordings with stimulation were collected from 22 patients during performance of verbal memory tasks. We found that high γ (62-118 Hz) activity induced by word presentation was modulated by electrical stimulation. This modulatory effect was greatest for trials with poor memory encoding. The high γ modulation correlated with the behavioral effect of stimulation in a given brain region: it was negative, i.e., the induced high γ activity was decreased, in the regions where stimulation decreased memory performance, and positive in the lateral TC where memory enhancement was observed. Our results suggest that the effect of electrical stimulation on high γ activity induced by word presentation may be a useful biomarker for mapping memory networks and guiding therapeutic brain stimulation

Human Verbal Memory Encoding Is Hierarchically Distributed in a Continuous Processing Stream.

Processing of memory is supported by coordinated activity in a network of sensory, association, and motor brain regions. It remains a major challenge to determine where memory is encoded for later retrieval. Here, we used direct intracranial brain recordings from epilepsy patients performing free recall tasks to determine the temporal pattern and anatomical distribution of verbal memory encoding across the entire human cortex. High γ frequency activity (65-115 Hz) showed consistent power responses during encoding of subsequently recalled and forgotten words on a subset of electrodes localized in 16 distinct cortical areas activated in the tasks. More of the high γ power during word encoding, and less power before and after the word presentation, was characteristic of successful recall and observed across multiple brain regions. Latencies of the induced power changes and this subsequent memory effect (SME) between the recalled and forgotten words followed an anatomical sequence from visual to prefrontal cortical areas. Finally, the magnitude of the memory effect was unexpectedly found to be the largest in selected brain regions both at the top and at the bottom of the processing stream. These included the language processing areas of the prefrontal cortex and the early visual areas at the junction of the occipital and temporal lobes. Our results provide evidence for distributed encoding of verbal memory organized along a hierarchical posterior-to-anterior processing stream

Participant and spectator scaling of spectator fragments in Au + Au and Cu + Cu collisions at √sNN = 19.6 and 22.4 GeV

Spectator fragments resulting from relativistic heavy ion collisions, consisting of single protons and neutrons along with groups of stable nuclear fragments up to nitrogen (Z=7), are measured in PHOBOS. These fragments are observed in Au+Au (√sNN =19.6GeV) and Cu+Cu (22.4 GeV) collisions at high pseudorapidity (η). The dominant multiply-charged fragment is the tightly bound helium (α), with lithium, beryllium, and boron all clearly seen as a function of collision centrality and pseudorapidity. We observe that in Cu+Cu collisions, it becomes much more favorable for the α fragments to be released than lithium. The yields of fragments approximately scale with the number of spectator nucleons, independent of the colliding ion. The shapes of the pseudorapidity distributions of fragments indicate that the average deflection of the fragments away from the beam direction increases for more central collisions. A detailed comparison of the shapes for α and lithium fragments indicates that the centrality dependence of the deflections favors a scaling with the number of participants in the collision.United States. Department of Energy (Grant DE-AC02-98CH10886)United States. Department of Energy (Grant DE-FG02-93ER40802)United States. Department of Energy (Grant DE-FG02-94ER40818)United States. Department of Energy (Grant DE-FG02-94ER40865)United States. Department of Energy (Grant DE-FG02- 99ER41099)United States. Department of Energy (Grant DE-AC02-06CH11357)National Science Foundation (U.S.) (Grant 9603486)National Science Foundation (U.S.) (Grant 0072204)National Science Foundation (U.S.) (Grant 0245011

Distinct hippocampal-prefrontal neural assemblies coordinate memory encoding, maintenance, and recall

: Short-term memory enables incorporation of recent experience into subsequent decision-making. This processing recruits both the prefrontal cortex and hippocampus, where neurons encode task cues, rules, and outcomes. However, precisely which information is carried when, and by which neurons, remains unclear. Using population decoding of activity in rat medial prefrontal cortex (mPFC) and dorsal hippocampal CA1, we confirm that mPFC populations lead in maintaining sample information across delays of an operant non-match to sample task, despite individual neurons firing only transiently. During sample encoding, distinct mPFC subpopulations joined distributed CA1-mPFC cell assemblies hallmarked by 4-5 Hz rhythmic modulation; CA1-mPFC assemblies re-emerged during choice episodes but were not 4-5 Hz modulated. Delay-dependent errors arose when attenuated rhythmic assembly activity heralded collapse of sustained mPFC encoding. Our results map component processes of memory-guided decisions onto heterogeneous CA1-mPFC subpopulations and the dynamics of physiologically distinct, distributed cell assemblies

A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection

Computational cost is an important consideration for memory encoding prediction models that use data from dozens of implanted electrodes. We propose a method to reduce computational expense by selecting a subset of all the electrodes to build the prediction model. The electrodes were selected based on their likelihood of measuring brain activity useful for predicting memory encoding better than chance (in terms of AUC). A logistic regression prediction model was built using spectral features of intracranial electroencephalography (iEEG) from the selected electrodes. We demonstrate our method on iEEG data from 37 human subjects performing free recall verbal short-term memory tasks. The method achieves a 36.3% reduction in the number of electrodes used for prediction, resulting in a 64.9 % reduction in inference computation time with just a 0.3 % loss in prediction performance compared to the case when all electrodes were used. The electrodes selected using our method provided improved prediction performance compared to those electrodes that were not selected in 31 out of 37 patients. Building upon this observation, we also developed a method to identify the subjects for whom the proposed electrode selection method would be beneficial

Dissecting gamma frequency activity during human memory processing

Gamma frequency activity (30-150 Hz) is induced in cognitive tasks and is thought to reflect underlying neural processes. Gamma frequency activity can be recorded directly from the human brain using intracranial electrodes implanted in patients undergoing treatment for drug-resistant epilepsy. Previous studies have independently explored narrowband oscillations in the local field potential and broadband power increases. It is not clear, however, which processes contribute to human brain gamma frequency activity, or their dynamics and roles during memory processing. Here a large dataset of intracranial recordings obtained during encoding of words from 101 patients was used to detect, characterize and compare induced gamma frequency activity events. Individual bursts of gamma frequency activity were isolated in the time-frequency domain to determine their spectral features, including peak frequency, amplitude, frequency span, and duration. We found two distinct types of gamma frequency activity events that showed either narrowband or broadband frequency spans revealing characteristic spectral properties. Narrowband events, the predominant type, were induced by word presentations following an initial induction of broadband events, which were temporally separated and selectively correlated with evoked response potentials, suggesting that they reflect different neural activities and play different roles during memory encoding. The two gamma frequency activity types were differentially modulated during encoding of subsequently recalled and forgotten words. In conclusion, we found evidence for two distinct activity types induced in the gamma frequency range during cognitive processing. Separating these two gamma frequency activity components contributes to the current understanding of electrophysiological biomarkers, and may prove useful for emerging neurotechnologies targeting, mapping and modulating distinct neurophysiological processes in normal and epileptogenic brain