Laminar analysis of the slow wave activity in the somatosensory cortex of anesthetized rats.

Rhythmic slow waves characterize brain electrical activity during natural deep sleep and under anesthesia, reflecting the synchronous membrane potential fluctuations of neurons in the thalamocortical network. Strong evidence indicates that the neocortex plays an important role in the generation of slow wave activity (SWA), however, contributions of individual cortical layers to the SWA generation are still unclear. The anatomically correct laminar profiles of SWA were revealed under ketamine/xylazine anesthesia, with combined local field potential recordings, multiple-unit activity (MUA), current source density (CSD) and time-frequency analyses precisely co-registered with histology. The up-state related negative field potential wave showed the largest amplitude in layer IV, the CSD was largest in layers I and III, while MUA was maximal in layer V, suggesting spatially dissociated firing and synaptic/transmembrane processes in the rat somatosensory cortex. Up-state related firing could start in virtually any layers (III-VI) of the cortex, but were most frequently initiated in layer V. However, in a subset of experiments, layer IV was considerably active in initiating up-state related MUA even in the absence of somatosensory stimulation. Somatosensory stimulation further strengthened up-state initiation in layer IV. Our results confirm that cortical layer V firing may have a major contribution to the up-state generation of ketamine/xylazine-induced SWA, however, thalamic influence through the thalamorecipient layer IV can also play an initiating role, even in the absence of sensory stimulation. This article is protected by copyright. All rights reserved

Beta oscillations underlie top-down, feedback control while gamma oscillations reflect bottom-up, feedforward influences

Prefrontal cortex (PFC) is critical to behavioral flexibility and, hence, the top-down control over bottom-up sensory information. The mechanisms underlying this capacity have been hypothesized to involve the propagation of alpha/beta (8-30 Hz) oscillations via feedback connections to sensory regions. In contrast, gamma (30-160 Hz) oscillations are thought to arise as a function of bottom-up, feedforward stimulation. To test the hypothesis that such oscillatory phenomena embody such functional roles, we assessed the performance of nine monkeys on tasks of learning, categorization, and working memory concurrent with recording of local field potentials (LFPs) from PFC. The first set of tasks consisted of two classes of learning: one, explicit and, another, implicit. Explicit learning is a conscious process that demands top-down control, and in these tasks alpha/beta oscillations tracked learning. In contrast, implicit learning is an unconscious process that is automatic (i.e. bottom up), and in this task alpha/beta oscillations did not track learning. We next looked at dot-pattern categorization. In this task, category exemplars were generated by jittering the dot locations of a prototype. By chance, some of these exemplars were similar to the prototype (low distortion), and others were not (high distortion). Behaviorally, the monkeys performed well on both distortion levels. However, alpha/beta band oscillations carried more category information at high distortions, while gamma-band category information was greatest on low distortions. Overall, the greater the need for top-down control (i.e. high distortion), the greater the beta, and the lesser the need (i.e. low distortion), the greater the gamma. Finally, laminar electrodes were used to record from animals trained on working memory tasks. Each laminar probe was lowered so that its set of contacts sampled all cortical layers. During these tasks, gamma oscillations peaked in superficial layers, while alpha/beta peaked in deep layers. Moreover, these deep-layer alpha/beta oscillations entrained superficial alpha/beta, and modulated the amplitude of superficial-layer gamma oscillations. These laminar distinctions are consistent with anatomy: feedback neurons originate in deep layers and feedforward neurons in superficial layers. In summary, alpha/beta oscillations reflect top-down control and feedback connectivity, while gamma oscillations reflect bottom-up processes and feedforward connectivity

Mapping brain activity with flexible graphene micro-transistors

Establishing a reliable communication interface between the brain and electronic devices is of paramount importance for exploiting the full potential of neural prostheses. Current microelectrode technologies for recording electrical activity, however, evidence important shortcomings, e.g. challenging high density integration. Solution-gated field-effect transistors (SGFETs), on the other hand, could overcome these shortcomings if a suitable transistor material were available. Graphene is particularly attractive due to its biocompatibility, chemical stability, flexibility, low intrinsic electronic noise and high charge carrier mobilities. Here, we report on the use of an array of flexible graphene SGFETs for recording spontaneous slow waves, as well as visually evoked and also pre-epileptic activity in vivo in rats. The flexible array of graphene SGFETs allows mapping brain electrical activity with excellent signal-to-noise ratio (SNR), suggesting that this technology could lay the foundation for a future generation of in vivo recording implants

Mapping brain activity with flexible graphene micro-transistors

Establishing a reliable communication interface between the brain and electronic devices is of paramount importance for exploiting the full potential of neural prostheses. Current microelectrode technologies for recording electrical activity, however, evidence important shortcomings, e.g. challenging high density integration. Solution-gated field-effect transistors (SGFETs), on the other hand, could overcome these shortcomings if a suitable transistor material were available. Graphene is particularly attractive due to its biocompatibility, chemical stability, flexibility, low intrinsic electronic noise and high charge carrier mobilities. Here, we report on the use of an array of flexible graphene SGFETs for recording spontaneous slow waves, as well as visually evoked and also pre-epileptic activity in vivo in rats. The flexible array of graphene SGFETs allows mapping brain electrical activity with excellent signal-to-noise ratio (SNR), suggesting that this technology could lay the foundation for a future generation of in vivo recording implants

Investigating multisensory integration in human early visual and auditory areas with intracranial electrophysiological recordings: insights and perspectives

Cross-modal processing and multisensory integration (MSI) can be observed at early stages of sensory processing in the cortex. However, the neurophysiological mechanisms underlying these processes and how they vary across sensory systems remain elusive. The aim of this study was to investigate how cross-modal processing and MSI are reflected in power and phase of oscillatory neuronal activity at different temporal scales in different sensory cortices. To this goal, we recorded stereo-electroencephalographic (SEEG) responses from early visual (calcarine and pericalcarine) and auditory (Heschl’s gyrus and planum temporale) regions in patients with drug-resistant epilepsy while performing an audio-visual oddball task. To Investigate crossmodal processing and MSI in the power domain of oscillatory activity, we explored a wide range of frequency bands (theta/alpha band: 5-13Hz; beta band: 13-30 Hz; gamma band: 30-80 Hz; high-gamma band: 80-200 Hz) during the first 150 ms post-stimulus onset. Differently, to investigate crossmodal processing and MSI in the phase domain of oscillatory activity, we explored a narrow range of frequency bands (theta/alpha band: 5-13Hz; beta band: 13-30 Hz; gamma band: 30-80 Hz) during the first 300 ms post-stimulus onset. In the power domain, we showed that cross-modal processing occurs mainly in the high-gamma band (80-200Hz) in both cortices. However, we evidenced that the way MSI is expressed across modalities differs considerably: in the visual cortex, MSI relies mainly on the beta band, however it is also evident, to a lesser extent, in the gamma and high-gamma band, while the auditory cortex reveals widespread MSI in the high-gamma band and, to a lesser extent, across the gamma band and the other investigated frequency bands. In the phase domain, we showed that cross-modal processing is differently expressed across modalities: in the auditory cortex it induces an increased phase concentration index (PCI) in ongoing oscillatory activity across all the investigated frequency bands, while, in the visual cortex, it induces an increased PCI particularly evident in the theta/alpha band with few or no effect respectively in the gamma and beta band. Importantly in both cortices, the most part of the COIs showing increased PCI, were not accompanied by a concomitant increase in power. These results indicate that in both auditory and visual cortex, cross-modal processing induces a pure phase resetting of the oscillatory activity. During MSI processing we observed, in both cortices, a stronger increase in PCI, in comparison to the intramodal processing, in the theta/alpha band and in the gamma band. Our results confirm the presence of cross-modal information representations at neuronal populations level and conform to a model where the cross-modal input induces phase-locked modulation of the ongoing oscillations. Importantly, our data showed that the way MSI is expressed in power modulations differs between the investigated sensory cortices suggesting the presence of different types of neurophysiological interactions during this process. These results are discussed in the framework of the current literature

The Multi-Dimensional Contributions of Prefrontal Circuits to Emotion Regulation during Adulthood and Critical Stages of Development

The prefrontal cortex (PFC) plays a pivotal role in regulating our emotions. The importance of ventromedial regions in emotion regulation, including the ventral sector of the medial PFC, the medial sector of the orbital cortex and subgenual cingulate cortex, have been recognized for a long time. However, it is increasingly apparent that lateral and dorsal regions of the PFC, as well as neighbouring dorsal anterior cingulate cortex, also play a role. Defining the underlying psychological mechanisms by which these functionally distinct regions modulate emotions and the nature and extent of their interactions is a critical step towards better stratification of the symptoms of mood and anxiety disorders. It is also important to extend our understanding of these prefrontal circuits in development. Specifically, it is important to determine whether they exhibit differential sensitivity to perturbations by known risk factors such as stress and inflammation at distinct developmental epochs. This Special Issue brings together the most recent research in humans and other animals that addresses these important issues, and in doing so, highlights the value of the translational approach

Investigating large-scale brain dynamics using field potential recordings: Analysis and interpretation

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (EEG, MEG, ECoG and LFP) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide best-practice recommendations for the analyses and interpretations using a forward model and an inverse model. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems