Local Field Potentials: Myths and Misunderstandings

Clinical asymmetry in Parkinson's disease is characterized by prevalence of subthalamic pause-burst neurons and alpha-beta oscillations

OBJECTIVE

We aimed to establish specific biomarkers of Parkinson's disease (PD) by comparing activity of more affected (MA) and less affected (LA) subthalamic nucleus (STN) of patients with prominent clinical asymmetry.

METHODS

We recorded single unit activity and local field potentials (LFP) of the STN during deep brain stimulation surgeries. Neuronal firing patterns and discharge rate, as well as oscillatory features of both single cells and LFP, were analyzed.

RESULTS

We observed notable differences in proportions of irregular-burst and pause-burst, but not tonic neurons, between the hemispheres. Oscillations of pause-burst neurons correlated significantly with the bradykinesia and rigidity scores of the corresponding hemibody. LFP derived from MA STN featured greater power in 12-15 Hz.

CONCLUSIONS

Our results provide evidence that the increased proportion of units with prolonged pauses may be associated with PD. We also speculate that some of them may gain rhythmicity in the alpha-beta range in relation to hypokinetic symptoms, long-term disease, or both.

SIGNIFICANCE

Our findings highlight the relation between specific oscillatory features of the STN, predominance of subthalamic pause-burst units and PD pathophysiology.

Why so slow? Models of parkinsonian bradykinesia

Bradykinesia, or slowness of movement, is a defining feature of Parkinson disease (PD) and a major contributor to the negative effects on quality of life associated with this disorder and related conditions. A dominant pathophysiological model of bradykinesia in PD has existed for approximately 30 years and has been the basis for the development of several therapeutic interventions, but accumulating evidence has made this model increasingly untenable. Although more recent models have been proposed, they also appear to be flawed. In this Perspective, I consider the leading prior models of bradykinesia in PD and argue that a more functionally related model is required, one that considers changes that disrupt the fundamental process of accurate information transmission. In doing so, I review emerging evidence of network level functional connectivity changes, information transfer dysfunction and potential motor code transmission error and present a novel model of bradykinesia in PD that incorporates this evidence. I hope that this model may reconcile inconsistencies in its predecessors and encourage further development of therapeutic interventions.

Mental workload evaluation using weighted phase lag index and coherence features extracted from EEG data

Electroencephalogram (EEG) represents an effective, non-invasive technology to study mental workload. However, volume conduction, a common EEG artifact, influences functional connectivity analysis of EEG data. EEG coherence has been used traditionally to investigate functional connectivity between brain areas associated with mental workload, while weighted Phase Lag Index (wPLI) is a measure that improves on coherence by reducing susceptibility to volume conduction, a common EEG artifact. The goal of this study was to compare two methods of functional connectivity analysis, wPLI and coherence, in the context of mental workload evaluation. The study involved model development for mental workload domains and comparing their performance using coherence-based features, wPLI-based features, and a combination of both. Generalized linear mixed-effects model (GLMM) with the least absolute shrinkage and selection operator (LASSO) feature selection method was used for model development. Results indicated that the model developed using a combination of both feature types demonstrated improved predictive performance across all mental workload domains, compared to models that used each feature type individually. The R2 values were 0.82 for perceived task complexity, 0.71 for distraction, 0.91 for mental demand, 0.85 for physical demand, 0.74 for situational stress, and 0.74 for temporal demand. Furthermore, task complexity and functional connectivity patterns in different brain areas were identified as significant contributors to perceived mental workload (p-value<0.05). Findings showed the potential of using EEG data for mental workload evaluation which suggests that combination of coherence and wPLI can improve the accuracy of mental workload domains prediction. Future research should aim to validate these results on larger, diverse datasets to confirm their generalizability and refine the predictive models.

Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network

Recent studies have explored functional and effective neural networks in animal models; however, the dynamics of information propagation among functional modules under cognitive control remain largely unknown. Here, we addressed the issue using transfer entropy and graph theory methods on mesoscopic neural activities recorded in the dorsal premotor cortex of rhesus monkeys. We focused our study on the decision time of a Stop-signal task, looking for patterns in the network configuration that could influence motor plan maturation when the Stop signal is provided. When comparing trials with successful inhibition to those with generated movement, the nodes of the network resulted organized into four clusters, hierarchically arranged, and distinctly involved in information transfer. Interestingly, the hierarchies and the strength of information transmission between clusters varied throughout the task, distinguishing between generated movements and canceled ones and corresponding to measurable levels of network complexity. Our results suggest a putative mechanism for motor inhibition in premotor cortex: a topological reshuffle of the information exchanged among ensembles of neurons.

State-dependent complexity of the local field potential in the primary visual cortex

The local field potential (LFP) is as a measure of the combined activity of neurons within a region of brain tissue. While biophysical modeling schemes for LFP in cortical circuits are well established, there is a paramount lack of understanding regarding the LFP properties along the states assumed in cortical circuits over long periods. Here we use a symbolic information approach to determine the statistical complexity based on Jensen disequilibrium measure and Shannon entropy of LFP data recorded from the primary visual cortex (V1) of urethane-anesthetized rats and freely moving mice. Using these information quantifiers, we find consistent relations between LFP recordings and measures of cortical states at the neuronal level. More specifically, we show that LFP's statistical complexity is sensitive to cortical state (characterized by spiking variability), as well as to cortical layer. In addition, we apply these quantifiers to characterize behavioral states of freely moving mice, where we find indirect relations between such states and spiking variability.

Deep brain stimulation pulse sequences to optimally modulate frequency-specific neural activity

Objective. Precise neuromodulation systems are needed to identify the role of neural oscillatory dynamics in brain function and to advance the development of brain stimulation therapies tailored to each patient's signature of brain dysfunction. Low-frequency, local field potentials (LFPs) are of increasing interest for the development of these systems because they can reflect the synaptic inputs to a recorded neuronal population and can be chronically recorded in humans. In this computational study, we aim to identify stimulation pulse patterns needed to optimally maximize the suppression or amplification of frequency-specific neural activity.Approach. We derived DBS pulse patterns to minimize or maximize the 2-norm of frequency-specific neural oscillations using a generalized mathematical model of spontaneous and stimulation-evoked LFP activity, and a subject-specific model of neural dynamics in the pallidum of a Parkinson's disease patient. We leveraged convex and mixed-integer optimization tools to identify these pulse patterns, and employed constraints on the pulse frequency and amplitude that are required to keep electrical stimulation within its safety envelope.Main results. Our analysis revealed that a combination of phase, amplitude, and frequency pulse modulation is needed to attain optimal suppression or amplification of the targeted oscillations. Phase modulation is sufficient to modulate oscillations with a constant amplitude envelope. To attain optimal modulation for oscillations with a time-varying envelope, a trade-off between frequency and amplitude pulse modulation is needed. The optimized pulse sequences were invariant to changes in the dynamics of stimulation-evoked neural activity, including changes in damping and natural frequency or complexity (i.e. generalized vs. patient-specific model).Significance. Our results provide insight into the structure of pulse patterns for future closed-loop brain stimulation strategies aimed at controlling neural activity precisely and in real-time.

Rhythmic Attention and ADHD: A Narrative and Systematic Review

In recent decades, a growing body of evidence has confirmed the existence of rhythmic fluctuations in attention, but the effect of inter-individual variations in these attentional rhythms has yet to be investigated. The aim of this review is to identify trends in the attention deficit/hyperactivity disorder (ADHD) literature that could be indicative of between-subject differences in rhythmic attention. A narrative review of the rhythmic attention and electrophysiological ADHD research literature was conducted, and the commonly-reported difference in slow-wave power between ADHD subjects and controls was found to have the most relevance to an understanding of rhythmic attention. A systematic review of the literature examining electrophysiological power differences in ADHD was then conducted to identify studies with conditions similar to those utilised in the rhythmic attention research literature. Fifteen relevant studies were identified and reviewed. The most consistent finding in the studies reviewed was for no spectral power differences between ADHD subjects and controls. However, the strongest trend in the studies reporting power differences was for higher power in the delta and theta frequency bands and lower power in the alpha band. In the context of rhythmic attention, this trend is suggestive of a slowing in the frequency and/or increase in the amplitude of the attentional oscillation in a subgroup of ADHD subjects. It is suggested that this characteristic electrophysiological modulation could be indicative of a global slowing of the attentional rhythm and/or an increase in the rhythmic recruitment of neurons in frontal attention networks in individuals with ADHD.

The physiology of coordination: self-resolving diverse affinities via the sparse order in relevant noise

Living systems at any given moment enact a very constrained set of end-directed and contextually appropriate actions that are self-initiated from among innumerable possible alternatives. However, these constrained actions are not necessarily because the system has reduced its sensitivities to themselves and their surroundings. Quite the contrary, living systems are continually open to novel and unanticipated stimulations that require a physiology of coordination. To address these competing demands, this paper offers a novel heuristic model informed by neuroscience, systems theory, biology and sign study to explain how organisms situated in diverse, complex and ever-changing environments might draw upon the sparse order made available by 'relevant noise'. This emergent order facilitates coordination, habituation and, ultimately, understanding of the world and its relevant affordances. Inspired by the burgeoning field of coordination dynamics and physiologist Denis Noble's concept of 'biological relativity', this model proposes a view of coordination on the neuronal level that is neither sequential nor stochastic, but instead implements a causal logic of phasic alignment, such that an organism's learned and inherited sets of diverse biological affinities and sympathies can be resolved into a continuous and complex range of patterns that will implement the kind of novel orientations and radical generativity required of such organisms to adaptively explore their environments and to learn from their experiences.

Differential Modulation of Local Field Potentials in the Primary and Premotor Cortices during Ipsilateral and Contralateral Reach to Grasp in Macaque Monkeys

Hand movements are associated with modulations of neuronal activity across several interconnected cortical areas, including the primary motor cortex (M1) and the dorsal and ventral premotor cortices (PMd and PMv). Local field potentials (LFPs) provide a link between neuronal discharges and synaptic inputs. Our current understanding of how LFPs vary in M1, PMd, and PMv during contralateral and ipsilateral movements is incomplete. To help reveal unique features in the pattern of modulations, we simultaneously recorded LFPs in these areas in two macaque monkeys performing reach and grasp movements with either the right or left hand. The greatest effector-dependent differences were seen in M1, at low (≤13 Hz) and γ frequencies. In premotor areas, differences related to hand use were only present in low frequencies. PMv exhibited the greatest increase in low frequencies during instruction cues and the smallest effector-dependent modulation during movement execution. In PMd, δ oscillations were greater during contralateral reach and grasp, and β activity increased during contralateral grasp. In contrast, β oscillations decreased in M1 and PMv. These results suggest that while M1 primarily exhibits effector-specific LFP activity, premotor areas compute more effector-independent aspects of the task requirements, particularly during movement preparation for PMv and production for PMd. The generation of precise hand movements likely relies on the combination of complementary information contained in the unique pattern of neural modulations contained in each cortical area. Accordingly, integrating LFPs from premotor areas and M1 could enhance the performance and robustness of brain-machine interfaces.

Local contribution to the somatosensory evoked potentials in rat's thalamus

Local Field Potential (LFP), despite its name, often reflects remote activity. Depending on the orientation and synchrony of their sources, both oscillations and more complex waves may passively spread in brain tissue over long distances and be falsely interpreted as local activity at such distant recording sites. Here we show that the whisker-evoked potentials in the thalamic nuclei are of local origin up to around 6 ms post stimulus, but the later (7-15 ms) wave is overshadowed by a negative component reaching from cortex. This component can be analytically removed and local thalamic LFP can be recovered reliably using Current Source Density analysis. We used model-based kernel CSD (kCSD) method which allowed us to study the contribution of local and distant currents to LFP from rat thalamic nuclei and barrel cortex recorded with multiple, non-linear and non-regular multichannel probes. Importantly, we verified that concurrent recordings from the cortex are not essential for reliable thalamic CSD estimation. The proposed framework can be used to analyze LFP from other brain areas and has consequences for general LFP interpretation and analysis.

Identification of Early Hippocampal Dynamics during Recognition Memory with Independent Component Analysis

The hippocampus is generally considered to have relatively late involvement in recognition memory, its main electrophysiological signature being between 400 and 800 ms after stimulus onset. However, most electrophysiological studies have analyzed the hippocampus as a single responsive area, selecting only a single-site signal exhibiting the strongest effect in terms of amplitude. These classical approaches may not capture all the dynamics of this structure, hindering the contribution of other hippocampal sources that are not located in the vicinity of the selected site. We combined intracerebral electroencephalogram recordings from epileptic patients with independent component analysis during a recognition memory task involving the recognition of old and new images. We identified two sources with different responses emerging from the hippocampus: a fast one (maximal amplitude at ∼250 ms) that could not be directly identified from raw recordings and a latter one, peaking at ∼400 ms. The former component presented different amplitudes between old and new items in 6 out of 10 patients. The latter component had different delays for each condition, with a faster activation (∼290 ms after stimulus onset) for recognized items. We hypothesize that both sources represent two steps of hippocampal recognition memory, the faster reflecting the input from other structures and the latter the hippocampal internal processing. Recognized images evoking early activations would facilitate neural computation in the hippocampus, accelerating memory retrieval of complementary information. Overall, our results suggest that the hippocampal activity is composed of several sources with an early activation related to recognition memory.

Convergent effects of different anesthetics are due to changes in phase alignment of cortical oscillations

Many different anesthetics cause loss of responsiveness despite having diverse underlying molecular and circuit actions. To explore the convergent effects of these drugs, we examined how ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist, and dexmedetomidine, an α2 adrenergic receptor agonist, affected neural oscillations in the prefrontal cortex of nonhuman primates. Previous work has shown that anesthesia increases phase locking of low-frequency local field potential activity across cortex. We observed similar increases with anesthetic doses of ketamine and dexmedetomidine in the ventrolateral and dorsolateral prefrontal cortex, within and across hemispheres. However, the nature of the phase locking varied between regions. We found that oscillatory activity in different prefrontal subregions within each hemisphere became more anti-phase with both drugs. Local analyses within a region suggested that this finding could be explained by broad cortical distance-based effects, such as a large traveling wave. By contrast, homologous areas across hemispheres increased their phase alignment. Our results suggest that the drugs induce strong patterns of cortical phase alignment that are markedly different from those in the awake state, and that these patterns may be a common feature driving loss of responsiveness from different anesthetic drugs.

Sequentially activated discrete modules appear as traveling waves in neuronal measurements with limited spatiotemporal sampling

Numerous studies have identified traveling waves in the cortex and suggested they play important roles in brain processing. These waves are most often measured using macroscopic methods that are unable to assess the local spiking activity underlying wave dynamics. Here, we investigated the possibility that waves may not be traveling at the single neuron scale. We first show that sequentially activating two discrete brain areas can appear as traveling waves in EEG simulations. We next reproduce these results using an analytical model of two sequentially activated regions. Using this model, we were able to generate wave-like activity with variable directions, velocities, and spatial patterns, and to map the discriminability limits between traveling waves and modular sequential activations. Finally, we investigated the link between field potentials and single neuron excitability using large-scale measurements from turtle cortex ex vivo. We found that while field potentials exhibit wave-like dynamics, the underlying spiking activity was better described by consecutively activated spatially adjacent groups of neurons. Taken together, this study suggests caution when interpreting phase delay measurements as continuously propagating wavefronts in two different spatial scales. A careful distinction between modular and wave excitability profiles across scales will be critical for understanding the nature of cortical computations.

Laminar evoked responses in mouse somatosensory cortex suggest a special role for deep layers in cortical complexity

It has been suggested that consciousness is closely related to the complexity of the brain. The perturbational complexity index (PCI) has been used in humans and rodents to distinguish conscious from unconscious states based on the global cortical responses (recorded by electroencephalography, EEG) to local cortical stimulation (CS). However, it is unclear how different cortical layers respond to CS and contribute to the resulting intra- and inter-areal cortical connectivity and PCI. A detailed investigation of the local dynamics is needed to understand the basis for PCI. We hypothesized that the complexity level of global cortical responses (PCI) correlates with layer-specific activity and connectivity. We tested this idea by measuring global cortical dynamics and layer-specific activity in the somatosensory cortex (S1) of mice, combining cortical electrical stimulation in deep motor cortex, global electrocorticography (ECoG) and local laminar recordings from layers 1-6 in S1, during wakefulness and general anaesthesia (sevoflurane). We found that the transition from wake to sevoflurane anaesthesia correlated with a drop in both the global and local PCI (PCIst ) values (complexity). This was accompanied by a local decrease in neural firing rate, spike-field coherence and long-range functional connectivity specific to deep layers (L5, L6). Our results suggest that deep cortical layers are mechanistically important for changes in PCI and thereby for changes in the state of consciousness.

Bioelectronic Medicine: a multidisciplinary roadmap from biophysics to precision therapies

Bioelectronic Medicine stands as an emerging field that rapidly evolves and offers distinctive clinical benefits, alongside unique challenges. It consists of the modulation of the nervous system by precise delivery of electrical current for the treatment of clinical conditions, such as post-stroke movement recovery or drug-resistant disorders. The unquestionable clinical impact of Bioelectronic Medicine is underscored by the successful translation to humans in the last decades, and the long list of preclinical studies. Given the emergency of accelerating the progress in new neuromodulation treatments (i.e., drug-resistant hypertension, autoimmune and degenerative diseases), collaboration between multiple fields is imperative. This work intends to foster multidisciplinary work and bring together different fields to provide the fundamental basis underlying Bioelectronic Medicine. In this review we will go from the biophysics of the cell membrane, which we consider the inner core of neuromodulation, to patient care. We will discuss the recently discovered mechanism of neurotransmission switching and how it will impact neuromodulation design, and we will provide an update on neuronal and glial basis in health and disease. The advances in biomedical technology have facilitated the collection of large amounts of data, thereby introducing new challenges in data analysis. We will discuss the current approaches and challenges in high throughput data analysis, encompassing big data, networks, artificial intelligence, and internet of things. Emphasis will be placed on understanding the electrochemical properties of neural interfaces, along with the integration of biocompatible and reliable materials and compliance with biomedical regulations for translational applications. Preclinical validation is foundational to the translational process, and we will discuss the critical aspects of such animal studies. Finally, we will focus on the patient point-of-care and challenges in neuromodulation as the ultimate goal of bioelectronic medicine. This review is a call to scientists from different fields to work together with a common endeavor: accelerate the decoding and modulation of the nervous system in a new era of therapeutic possibilities.

Multielectrode array use in insect auditory neuroscience to unravel the spatio-temporal response pattern in the prothoracic ganglion of Mecopoda elongata

Mechanoreceptors in hearing organs transduce sound-induced mechanical responses into neuronal signals, which are further processed and forwarded to the brain along a chain of neurons in the auditory pathway. Bushcrickets (katydids) have their ears in the front leg tibia, and the first synaptic integration of sound-induced neuronal signals takes place in the primary auditory neuropil of the prothoracic ganglion. By combining intracellular recordings of the receptor activity in the ear, extracellular multichannel array recordings on top of the prothoracic ganglion and hook electrode recordings at the neck connective, we mapped the timing of neuronal responses to tonal sound stimuli along the auditory pathway from the ears towards the brain. The use of the multielectrode array allows the observation of spatio-temporal patterns of neuronal responses within the prothoracic ganglion. By eliminating the sensory input from one ear, we investigated the impact of contralateral projecting interneurons in the prothoracic ganglion and added to previous research on the functional importance of contralateral inhibition for binaural processing. Furthermore, our data analysis demonstrates changes in the signal integration processes at the synaptic level indicated by a long-lasting increase in the local field potential amplitude. We hypothesize that this persistent increase of the local field potential amplitude is important for the processing of complex signals, such as the conspecific song.

Spontaneous slow wave oscillations in extracellular field potential recordings reflect the alternating dominance of excitation and inhibition

In the resting state, cortical neurons can fire action potentials spontaneously but synchronously (Up state), followed by a quiescent period (Down state) before the cycle repeats. Extracellular recordings in the infragranular layer of cortex with a micro-electrode display a negative deflection (depth-negative) during Up states and a positive deflection (depth-positive) during Down states. The resulting slow wave oscillation (SWO) has been studied extensively during sleep and under anaesthesia. However, recent research on the balanced nature of synaptic excitation and inhibition has highlighted our limited understanding of its genesis. Specifically, are excitation and inhibition balanced during SWOs? We analyse spontaneous local field potentials (LFPs) during SWOs recorded from anaesthetised rats via a multi-channel laminar micro-electrode and show that the Down state consists of two distinct synaptic states: a Dynamic Down state associated with depth-positive LFPs and a prominent dipole in the extracellular field, and a Static Down state with negligible (≈ 0 mV $ \approx 0{\mathrm{\;mV}}$ ) LFPs and a lack of dipoles extracellularly. We demonstrate that depth-negative and -positive LFPs are generated by a shift in the balance of synaptic excitation and inhibition from excitation dominance (depth-negative) to inhibition dominance (depth-positive) in the infragranular layer neurons. Thus, although excitation and inhibition co-tune overall, differences in their timing lead to an alternation of dominance, manifesting as SWOs. We further show that Up state initiation is significantly faster if the preceding Down state is dynamic rather than static. Our findings provide a coherent picture of the dependence of SWOs on synaptic activity. KEY POINTS: Cortical neurons can exhibit repeated cycles of spontaneous activity interleaved with periods of relative silence, a phenomenon known as 'slow wave oscillation' (SWO). During SWOs, recordings of local field potentials (LFPs) in the neocortex show depth-negative deflection during the active period (Up state) and depth-positive deflection during the silent period (Down state). Here we further classified the Down state into a dynamic phase and a static phase based on a novel method of classification and revealed non-random, stereotypical sequences of the three states occurring with significantly different transitional kinetics. Our results suggest that the positive and negative deflections in the LFP reflect the shift of the instantaneous balance between excitatory and inhibitory synaptic activity of the local cortical neurons. The differences in transitional kinetics may imply distinct synaptic mechanisms for Up state initiation. The study may provide a new approach for investigating spontaneous brain rhythms.

News without the buzz: reading out weak theta rhythms in the hippocampus

Local field potentials (LFPs) reflect the collective dynamics of neural populations, yet their exact relationship to neural codes remains unknown1. One notable exception is the theta rhythm of the rodent hippocampus, which seems to provide a reference clock to decode the animal's position from spatiotemporal patterns of neuronal spiking2 or LFPs3. But when the animal stops, theta becomes irregular4, potentially indicating the breakdown of temporal coding by neural populations. Here we show that no such breakdown occurs, introducing an artificial neural network that can recover position-tuned rhythmic patterns (pThetas) without relying on the more prominent theta rhythm as a reference clock. pTheta and theta preferentially correlate with place cell and interneuron spiking, respectively. When rats forage in an open field, pTheta is jointly tuned to position and head orientation, a property not seen in individual place cells but expected to emerge from place cell sequences5. Our work demonstrates that weak and intermittent oscillations, as seen in many brain regions and species, can carry behavioral information commensurate with population spike codes.

Subthalamic nucleus shows opposite functional connectivity pattern in Huntington's and Parkinson's disease

Huntington's and Parkinson's disease are two movement disorders representing mainly opposite states of the basal ganglia inhibitory function. Despite being an integral part of the cortico-subcortico-cortical circuitry, the subthalamic nucleus function has been studied at the level of detail required to isolate its signal only through invasive studies in Huntington's and Parkinson's disease. Here, we tested whether the subthalamic nucleus exhibited opposite functional signatures in early Huntington's and Parkinson's disease. We included both movement disorders in the same whole-brain imaging study, and leveraged ultra-high-field 7T MRI to achieve the very fine resolution needed to investigate the smallest of the basal ganglia nuclei. Eleven of the 12 Huntington's disease carriers were recruited at a premanifest stage, while 16 of the 18 Parkinson's disease patients only exhibited unilateral motor symptoms (15 were at Stage I of Hoehn and Yahr off medication). Our group comparison interaction analyses, including 24 healthy controls, revealed a differential effect of Huntington's and Parkinson's disease on the functional connectivity at rest of the subthalamic nucleus within the sensorimotor network, i.e. an opposite effect compared with their respective age-matched healthy control groups. This differential impact in the subthalamic nucleus included an area precisely corresponding to the deep brain stimulation 'sweet spot'-the area with maximum overall efficacy-in Parkinson's disease. Importantly, the severity of deviation away from controls' resting-state values in the subthalamic nucleus was associated with the severity of motor and cognitive symptoms in both diseases, despite functional connectivity going in opposite directions in each disorder. We also observed an altered, opposite impact of Huntington's and Parkinson's disease on functional connectivity within the sensorimotor cortex, once again with relevant associations with clinical symptoms. The high resolution offered by the 7T scanner has thus made it possible to explore the complex interplay between the disease effects and their contribution on the subthalamic nucleus, and sensorimotor cortex. Taken altogether, these findings reveal for the first time non-invasively in humans a differential, clinically meaningful impact of the pathophysiological process of these two movement disorders on the overall sensorimotor functional connection of the subthalamic nucleus and sensorimotor cortex.

Neuronal functional connectivity is impaired in a layer dependent manner near the chronically implanted microelectrodes

Objective

This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near the chronically implanted microelectrode. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how does the FBR impact affect the functional stability of neural circuits near implanted Brain-Computer Interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders.

Approach

This study utilized multisite Michigan-style microelectrodes that span all cortical layers and the hippocampal CA1 region to collect spontaneous and visually-evoked electrophysiological activity. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of LFP and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation.

Main Results

The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations.

Significance

This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study's understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.

Spatial transcriptomics reveals the distinct organization of mouse prefrontal cortex and neuronal subtypes regulating chronic pain

The prefrontal cortex (PFC) is a complex brain region that regulates diverse functions ranging from cognition, emotion and executive action to even pain processing. To decode the cellular and circuit organization of such diverse functions, we employed spatially resolved single-cell transcriptome profiling of the adult mouse PFC. Results revealed that PFC has distinct cell-type composition and gene-expression patterns relative to neighboring cortical areas-with neuronal excitability-regulating genes differently expressed. These cellular and molecular features are further segregated within PFC subregions, alluding to the subregion-specificity of several PFC functions. PFC projects to major subcortical targets through combinations of neuronal subtypes, which emerge in a target-intrinsic fashion. Finally, based on these features, we identified distinct cell types and circuits in PFC underlying chronic pain, an escalating healthcare challenge with limited molecular understanding. Collectively, this comprehensive map will facilitate decoding of discrete molecular, cellular and circuit mechanisms underlying specific PFC functions in health and disease.

Tau trajectory in Alzheimer's disease: Evidence from the connectome-based computational models

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an impairment of cognition and memory. Current research on connectomics have now related changes in the network organization in AD to the patterns of accumulation and spread of amyloid and tau, providing insights into the neurobiological mechanisms of the disease. In addition, network analysis and modeling focus on particular use of graphs to provide intuition into key organizational principles of brain structure, that stipulate how neural activity propagates along structural connections. The utility of connectome-based computational models aids in early predicting, tracking the progression of biomarker-directed AD neuropathology. In this article, we present a short review of tau trajectory, the connectome changes in tau pathology, and the dependent recent connectome-based computational modelling approaches for tau spreading, reproducing pragmatic findings, and developing significant novel tau targeted therapies.

Developing cognitive workload and performance evaluation models using functional brain network analysis

Cognition, defined as the ability to learn, remember, sustain attention, make decisions, and solve problems, is essential in daily activities and in learning new skills. The purpose of this study was to develop cognitive workload and performance evaluation models using features that were extracted from Electroencephalogram (EEG) data through functional brain network and spectral analyses. The EEG data were recorded from 124 brain areas of 26 healthy participants conducting two cognitive tasks on a robot simulator. The functional brain network and Power Spectral Density features were extracted from EEG data using coherence and spectral analyses, respectively. Participants reported their perceived cognitive workload using the SURG-TLX questionnaire after each exercise, and the simulator generated actual performance scores. The extracted features, actual performance scores, and subjectively assessed cognitive workload values were used to develop linear models for evaluating performance and cognitive workload. Furthermore, the Pearson correlation was used to find the correlation between participants' age, performance, and cognitive workload. The findings demonstrated that combined EEG features retrieved from spectral analysis and functional brain networks can be used to evaluate cognitive workload and performance. The cognitive workload in conducting only Matchboard level 3, which is more challenging than Matchboard level 2, was correlated with age (0.54, p-value = 0.01). This finding may suggest playing more challenging computer games are more helpful in identifying changes in cognitive workload caused by aging. The findings could open the door for a new era of objective evaluation and monitoring of cognitive workload and performance.

Low-frequency deep brain stimulation reveals resonant beta-band evoked oscillations in the pallidum of Parkinson's Disease patients

Introduction

Evidence suggests that spontaneous beta band (11-35 Hz) oscillations in the basal ganglia thalamocortical (BGTC) circuit are linked to Parkinson's disease (PD) pathophysiology. Previous studies on neural responses in the motor cortex evoked by electrical stimulation in the subthalamic nucleus have suggested that circuit resonance may underlie the generation of spontaneous and stimulation-evoked beta oscillations in PD. Whether these stimulation-evoked, resonant oscillations are present across PD patients in the internal segment of the globus pallidus (GPi), a primary output nucleus in the BGTC circuit, is yet to be determined.

Methods

We characterized spontaneous and stimulation-evoked local field potentials (LFPs) in the GPi of four PD patients (five hemispheres) using deep brain stimulation (DBS) leads externalized after DBS implantation surgery.

Results

Our analyses show that low-frequency (2-4 Hz) stimulation in the GPi evoked long-latency (>50 ms) beta-band neural responses in the GPi in 4/5 hemispheres. We demonstrated that neural sources generating both stimulation-evoked and spontaneous beta oscillations were correlated in their frequency content and spatial localization.

Discussion

Our results support the hypothesis that the same neuronal population and resonance phenomenon in the BGTC circuit generates both spontaneous and evoked pallidal beta oscillations. These data also support the development of closed-loop control systems that modulate the GPi spontaneous oscillations across PD patients using beta band stimulation-evoked responses.

Tenuigenin promotes non-rapid eye movement sleep via the GABAA receptor and exerts somnogenic effect in a MPTP mouse model of Parkinson's disease

Sleep disturbances are commonly non-motor symptoms in Parkinson's diseases (PD). However, standard dopamine replacement therapies for the treatment of motor symptoms often prove inadequate in combating sleep disturbances. Previous studies conducted by our research group have reported the neuroprotective effects of tenuigenin, a natural extract from Polygala tenuifolia root, which has been traditionally employed in treating insomnia. The objective of this study was to investigate the potential of tenuigenin in modulating sleep-wake behaviors and elucidate the underlying mechanisms. We employed EEG/EMG recordings to evaluate the impact of tenuigenin on sleep-wake profiles. Furthermore, we utilized c-Fos immunostaining, whole-cell patch clamping and local field potentials (LFP) recording to explore the mechanisms involved in sleep-promoting effects of tenuigenin. Additionally, we examined the effects of tenuigenin on sleep-promoting in MPTP PD mice. Here, we found tenuigenin demonstrated a significant increase in NREM sleep and a reduction in sleep latency in mice, without altering the EEG power density. Moreover, tenuigenin increased c-Fos expression in the ventrolateral preoptic area (VLPO) and stimulated sleep-promoting neurons in VLPO. The sleep-promoting effects of tenuigenin were abolished when mice were pretreated with flumazenil, an antagonist at the benzodiazepine site of the GABAA receptor. Furthermore, tenuigenin was found to ameliorate sleep disturbances in MPTP-induced mice. The results suggesting that tenuigenin facilitated a type of NREM sleep comparable to physiological NREM sleep through interaction with the GABAA receptor. Additionally, tenuigenin demonstrated improvements in sleep disturbances in MPTP-induced PD mice, suggesting its potential as a sleep-promoting substance, particularly for PD patients experiencing sleep disturbances.

Bioadhesive polymer semiconductors and transistors for intimate biointerfaces

The use of bioelectronic devices relies on direct contact with soft biotissues. For transistor-type bioelectronic devices, the semiconductors that need to have direct interfacing with biotissues for effective signal transduction do not adhere well with wet tissues, thereby limiting the stability and conformability at the interface. We report a bioadhesive polymer semiconductor through a double-network structure formed by a bioadhesive brush polymer and a redox-active semiconducting polymer. The resulting semiconducting film can form rapid and strong adhesion with wet tissue surfaces together with high charge-carrier mobility of ~1 square centimeter per volt per second, high stretchability, and good biocompatibility. Further fabrication of a fully bioadhesive transistor sensor enabled us to produce high-quality and stable electrophysiological recordings on an isolated rat heart and in vivo rat muscles.

Protruding cantilever microelectrode array to monitor the inner electrical activity of cerebral organoids

Stem cell-derived cerebral organoids are artificially grown miniature organ-like structures mimicking embryonic brain architecture. They are composed of multiple neural cell types with 3D cell layer organization exhibiting local field potential. Measuring the extracellular electrical activity by means of conventional planar microelectrode arrays is particularly challenging due to the 3D architecture of organoids. In order to monitor the intra-organoid electrical activity of thick spheroid-shaped samples, we developed long protruding microelectrode arrays able to penetrate the inner regions of cerebral organoids to measure the local potential of neurons within the organoids. A new microfabrication process has been developed which, thanks to the relaxation of internal stresses of a stack of materials deposited over a sacrificial layer, allows one to build a protruding cantilever microelectrode array placed at the apex of beams which rise vertically, over two hundred microns. These slender beams inserted deeply into the organoids give access to the recording of local field potential from neurons buried inside the organoid. This novel device shall provide valuable tools to study neural functions in greater detail.

Platelets and hemostatic proteins are co-localized with chronic neuroinflammation surrounding implanted intracortical microelectrodes

Intracortical microelectrodes induce vascular injury upon insertion into the cortex. As blood vessels rupture, blood proteins and blood-derived cells (including platelets) are introduced into the 'immune privileged' brain tissues at higher-than-normal levels, passing through the damaged blood-brain barrier. Blood proteins adhere to implant surfaces, increasing the likelihood of cellular recognition leading to activation of immune and inflammatory cells. Persistent neuroinflammation is a major contributing factor to declining microelectrode recording performance. We investigated the spatial and temporal relationship of blood proteins fibrinogen and von Willebrand Factor (vWF), platelets, and type IV collagen, in relation to glial scarring markers for microglia and astrocytes following implantation of non-functional multi-shank silicon microelectrode probes into rats. Together with type IV collagen, fibrinogen and vWF augment platelet recruitment, activation, and aggregation. Our main results indicate blood proteins participating in hemostasis (fibrinogen and vWF) persisted at the microelectrode interface for up to 8-weeks after implantation. Further, type IV collagen and platelets surrounded the probe interface with similar spatial and temporal trends as vWF and fibrinogen. In addition to prolonged blood-brain barrier instability, specific blood and extracellular matrix proteins may play a role in promoting the inflammatory activation of platelets and recruitment to the microelectrode interface. STATEMENT OF SIGNIFICANCE: Implanted microelectrodes have substantial potential for restoring function to people with paralysis and amputation by providing signals that feed into natural control algorithms that drive prosthetic devices. Unfortunately, these microelectrodes do not display robust performance over time. Persistent neuroinflammation is widely thought to be a primary contributor to the devices' progressive decline in performance. Our manuscript reports on the highly local and persistent accumulation of platelets and hemostatic blood proteins around the microelectrode interface of brain implants. To our knowledge neuroinflammation driven by cellular and non-cellular responses associated with hemostasis and coagulation has not been rigorously quantified elsewhere. Our findings identify potential targets for therapeutic intervention and a better understanding of the driving mechanisms to neuroinflammation in the brain.

Sub-anesthetic doses of ketamine increase single cell entrainment in the rat auditory cortex during auditory steady-state response

BACKGROUND

Understanding the effects of the N-methyl-D-aspartate receptor (NMDA-R) antagonist ketamine on brain function is of considerable interest due to the discovery of its fast-acting antidepressant properties. It is well known that gamma oscillations are increased when ketamine is administered to rodents and humans, and increases in the auditory steady-state response (ASSR) have also been observed.

AIMS

To elucidate the cellular substrate of the increase in network activity and synchrony observed by sub-anesthetic doses of ketamine, the aim was to investigate spike timing and regularity and determine how this is affected by the animal's motor state.

METHODS

Single unit activity and local field potentials from the auditory cortex of awake, freely moving rats were recorded with microelectrode arrays during an ASSR paradigm.

RESULTS

Ketamine administration yielded a significant increase in ASSR power and phase locking, both significantly modulated by motor activity. Before drug administration, putative fast-spiking interneurons (FSIs) were significantly more entrained to the stimulus than putative pyramidal neurons (PYRs). The degree of entrainment significantly increased at lower doses of ketamine (3 and 10 mg/kg for FSIs, 10 mg/kg for PYRs). At the highest dose (30 mg/kg), a strong increase in tonic firing of PYRs was observed.

CONCLUSIONS

These findings suggest an involvement of FSIs in the increased network synchrony and provide a possible cellular explanation for the well-documented effects of ketamine-induced increase in power and synchronicity during ASSR. The results support the importance to evaluate different motor states separately for more translational preclinical research.

Uncorrelated bilateral cortical input becomes timed across hippocampal subfields for long waves whereas gamma waves are largely ipsilateral

The role of interhemispheric connections along successive segments of cortico-hippocampal circuits is poorly understood. We aimed to obtain a global picture of spontaneous transfer of activity during non-theta states across several nodes of the bilateral circuit in anesthetized rats. Spatial discrimination techniques applied to bilateral laminar field potentials (FP) across the CA1/Dentate Gyrus provided simultaneous left and right readouts in five FP generators that reflect activity in specific hippocampal afferents and associative pathways. We used a battery of correlation and coherence analyses to extract complementary aspects at different time scales and frequency bands. FP generators exhibited varying bilateral correlation that was high in CA1 and low in the Dentate Gyrus. The submillisecond delays indicate coordination but not support for synaptic dependence of one side on another. The time and frequency characteristics of bilateral coupling were specific to each generator. The Schaffer generator was strongly bilaterally coherent for both sharp waves and gamma waves, although the latter maintained poor amplitude co-variation. The lacunosum-moleculare generator was composed of up to three spatially overlapping activities, and globally maintained high bilateral coherence for long but not short (gamma) waves. These two CA1 generators showed no ipsilateral relationship in any frequency band. In the Dentate Gyrus, strong bilateral coherence was observed only for input from the medial entorhinal areas, while those from the lateral entorhinal areas were largely asymmetric, for both alpha and gamma waves. Granger causality testing showed strong bidirectional relationships between all homonymous bilateral generators except the lateral entorhinal input and a local generator in the Dentate Gyrus. It also revealed few significant relationships between ipsilateral generators, most notably the anticipation of lateral entorhinal cortex toward all others. Thus, with the notable exception of the lateral entorhinal areas, there is a marked interhemispheric coherence primarily for slow envelopes of activity, but not for pulse-like gamma waves, except in the Schafer segment. The results are consistent with essentially different streams of activity entering from and returning to the cortex on each side, with slow waves reflecting times of increased activity exchange between hemispheres and fast waves generally reflecting ipsilateral processing.

A 'Total Unique Variation Analysis' for Brain-Machine Interfaces

When designing a fully implantable brain-machine interface (BMI), the primary aim is to detect as much neural information as possible with as few channels as possible. In this paper, we present a total unique variance analysis (TUVA) for evaluating the signal unique to each channel that cannot be predicted by linear combination of signals on other channels. TUVA is a statistical method for determining the total unique variance in multidimensional data, ordering channels from most to least informative, to aid in the design of maximally-efficacious BMIs. We demonstrate how this method can be applied to the design of BMIs by comparing TUVA values computed for simulated lead-field maps for high-channel-count electrocorticography (ECoG) with values computed for recordings in the interictal period in the context of surgery planning for epileptic resection.Clinical Relevance- This paper introduces a new statistical method for comparison of neural interface designs, focused on quantifying recording efficiency by minimizing channel crosstalk, which may help improve the risk-benefit profile of invasive neural recording.

Design of a Configurable 16-Electrode Sense and Stimulation Neuromodulation System

Electrical sensing and stimulation of nervous system activity is a valuable tool to investigate neural activity both in vivo and in vitro. A general system capable of supporting users across a variety of use cases would be valuable for the field of neuroscience. We propose a new system capable of supporting a variety of experimental cases including low and high impedance electrodes with stimulation amplitudes up to multiple mA. The system is designed to support sampling frequencies up to 26 kHz and to maximize stimulation flexibility with an electrically isolated system.

Systems-level analysis of local field potentials reveals differential effects of lysergic acid diethylamide and ketamine on neuronal activity and functional connectivity

Psychedelic substances have in recent years attracted considerable interest as potential treatments for several psychiatric conditions, including depression, anxiety, and addiction. Imaging studies in humans point to a number of possible mechanisms underlying the acute effects of psychedelics, including changes in neuronal firing rates and excitability as well as alterations in functional connectivity between various brain nodes. In addition, animal studies using invasive recordings, have suggested synchronous high-frequency oscillations involving several brain regions as another key feature of the psychedelic brain state. To better understand how the imaging data might be related to high-resolution electrophysiological measurements, we have here analyzed the aperiodic part of the local field potential (LFP) in rodents treated with a classic psychedelic (LSD) or a dissociative anesthetic (ketamine). In addition, functional connectivity, as quantified by mutual information measures in the LFP time series, has been assessed with in and between different structures. Our data suggest that the altered brain states of LSD and ketamine are caused by different underlying mechanisms, where LFP power shifts indicate increased neuronal activity but reduced connectivity following ketamine, while LSD also leads to reduced connectivity but without an accompanying change in LFP broadband power.

Local field potentials in major depressive and obsessive-compulsive disorder: a frequency-based review

Objectives

The purpose of this paper is to provide a mini-review covering the recent progress in human and animal studies on local field potentials (LFPs) of major depressive disorder (MDD) and obsessive-compulsive disorder (OCD).

Materials and methods

PubMed and EMBASE were searched to identify related studies. Inclusion criteria were (1) reported the LFPs on OCD or MDD, (2) published in English, and (3) human or animal studies. Exclusion criteria were (1) review or meta-analysis or other literature types without original data and (2) conference abstract without full text. Descriptive synthesis of data was performed.

Results

Eight studies on LFPs of OCD containing 22 patients and 32 rats were included: seven were observational studies with no controls, and one animal study included a randomized and controlled phase. Ten studies on LFPs of MDD containing 71 patients and 52 rats were included: seven were observational studies with no controls, one study with control, and two animal studies included a randomized and controlled phase.

Conclusion

The available studies revealed that different frequency bands were associated with specific symptoms. Low frequency activity seemed to be closely related to OCD symptoms, whereas LFPs findings in patients with MDD were more complicated. However, limitations of recent studies restrict the drawing of definite conclusions. Combined with other measures such as Electroencephalogram, Electrocorticography, or Magnetoencephalography and long-term recordings in various physiological states (rest state, sleep state, task state) could help to improve the understanding of potential mechanisms.

Large-Area Field Potential Imaging Having Single Neuron Resolution Using 236 880 Electrodes CMOS-MEA Technology

The electrophysiological technology having a high spatiotemporal resolution at the single-cell level and noninvasive measurements of large areas provide insights on underlying neuronal function. Here, a complementary metal-oxide semiconductor (CMOS)-microelectrode array (MEA) is used that uses 236 880 electrodes each with an electrode size of 11.22 × 11.22 µm and 236 880 covering a wide area of 5.5 × 5.9 mm in presenting a detailed and single-cell-level neural activity analysis platform for brain slices, human iPS cell-derived cortical networks, peripheral neurons, and human brain organoids. Propagation pattern characteristics between brain regions changes the synaptic propagation into compounds based on single-cell time-series patterns, classification based on single DRG neuron firing patterns and compound responses, axonal conduction characteristics and changes to anticancer drugs, and network activities and transition to compounds in brain organoids are extracted. This detailed analysis of neural activity at the single-cell level using the CMOS-MEA provides a new understanding of the basic mechanisms of brain circuits in vitro and ex vivo, on human neurological diseases for drug discovery, and compound toxicity assessment.

Theta oscillations represent collective dynamics of multineuronal membrane potentials of murine hippocampal pyramidal cells

Theta (θ) oscillations are one of the characteristic local field potentials (LFPs) in the hippocampus that emerge during spatial navigation, exploratory sniffing, and rapid eye movement sleep. LFPs are thought to summarize multineuronal events, including synaptic currents and action potentials. However, no in vivo study to date has directly interrelated θ oscillations with the membrane potentials (Vm) of multiple neurons, and it remains unclear whether LFPs can be predicted from multineuronal Vms. Here, we simultaneously patch-clamp up to three CA1 pyramidal neurons in awake or anesthetized mice and find that the temporal evolution of the power and frequency of θ oscillations in Vms (θ_Vms) are weakly but significantly correlate with LFP θ oscillations (θ_LFP) such that a deep neural network could predict the θ_LFP waveforms based on the θ_Vm traces of three neurons. Therefore, individual neurons are loosely interdependent to ensure freedom of activity, but they partially share information to collectively produce θ_LFP.

What multiplexing means for the interpretation of functional MRI data

Despite technology advances that have enabled routine acquisition of functional MRI data with sub-millimeter resolution, the inferences that cognitive neuroscientists must make to link fMRI data to behavior are complicated. Thus, a single dataset subjected to different analyses can be interpreted in different ways. This article presents two optical analogies that can be useful for framing fMRI analyses in a way that allows for multiple interpretations of fMRI data to be valid simultaneously without undermining each other. The first is reflection: when an object is reflected in a mirrored surface, it appears as if the reflected object is sharing space with the mirrored object, but of course it is not. This analogy can be a good guide for interpreting the fMRI signal, since even at sub-millimeter resolutions the signal is determined by a mixture of local and long-range neural computations. The second is refraction. If we view an object through a multi-faceted prism or gemstone, our view will change-sometimes dramatically-depending on our viewing angle. In the same way, interpretation of fMRI data (inference of underlying neuronal activity) can and should be different depending on the analysis approach. Rather than representing a weakness of the methodology, or the superiority of one approach over the other (for example, simple regression analysis versus multi-voxel pattern analysis), this is an expected consequence of how information is multiplexed in the neural networks of the brain: multiple streams of information are simultaneously present in each location. The fact that any one analysis typically shows only one view of the data also puts some parentheses around fMRI practitioners' constant search for ground truth against which to compare their data. By holding our interpretations lightly and understanding that many interpretations of the data can all be true at the same time, we do a better job of preparing ourselves to appreciate, and eventually understand, the complexity of the brain and the behavior it produces.

Uncovering the organization of neural circuits with Generalized Phase Locking Analysis

Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand mesoscopic "field" signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies. We show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA, used jointly with biophysical modeling, can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings.

Reconstruction and localization of auditory sources from intracerebral SEEG using independent component analysis

Stereo-electroencephalography (SEEG) is the surgical implantation of electrodes in the brain to better localize the epileptic network in pharmaco-resistant epileptic patients. This technique has exquisite spatial and temporal resolution. Still, the number and the position of the electrodes in the brain is limited and determined by the semiology and/or preliminary non-invasive examinations, leading to a large number of unexplored brain structures in each patient. Here, we propose a new approach to reconstruct the activity of non-sampled structures in SEEG, based on independent component analysis (ICA) and dipole source localization. We have tested this approach with an auditory stimulation dataset in ten patients. The activity directly recorded from the auditory cortex served as ground truth and was compared to the ICA applied on all non-auditory electrodes. Our results show that the activity from the auditory cortex can be reconstructed at the single trial level from contacts as far as ∼40 mm from the source. Importantly, this reconstructed activity is localized via dipole fitting in the proximity of the original source. In addition, we show that the size of the confidence interval of the dipole fitting is a good indicator of the reliability of the result, which depends on the geometry of the SEEG implantation. Overall, our approach allows reconstructing the activity of structures far from the electrode locations, partially overcoming the spatial sampling limitation of intracerebral recordings.

Norepinephrine links astrocytic activity to regulation of cortical state

Cortical state, defined by population-level neuronal activity patterns, determines sensory perception. While arousal-associated neuromodulators-including norepinephrine (NE)-reduce cortical synchrony, how the cortex resynchronizes remains unknown. Furthermore, general mechanisms regulating cortical synchrony in the wake state are poorly understood. Using in vivo imaging and electrophysiology in mouse visual cortex, we describe a critical role for cortical astrocytes in circuit resynchronization. We characterize astrocytes' calcium responses to changes in behavioral arousal and NE, and show that astrocytes signal when arousal-driven neuronal activity is reduced and bi-hemispheric cortical synchrony is increased. Using in vivo pharmacology, we uncover a paradoxical, synchronizing response to Adra1a receptor stimulation. We reconcile these results by demonstrating that astrocyte-specific deletion of Adra1a enhances arousal-driven neuronal activity, while impairing arousal-related cortical synchrony. Our findings demonstrate that astrocytic NE signaling acts as a distinct neuromodulatory pathway, regulating cortical state and linking arousal-associated desynchrony to cortical circuit resynchronization.

Theoretical considerations and supporting evidence for the primary role of source geometry on field potential amplitude and spatial extent

Field potential (FP) recording is an accessible means to capture the shifts in the activity of neuron populations. However, the spatial and composite nature of these signals has largely been ignored, at least until it became technically possible to separate activities from co-activated sources in different structures or those that overlap in a volume. The pathway-specificity of mesoscopic sources has provided an anatomical reference that facilitates transcending from theoretical analysis to the exploration of real brain structures. We review computational and experimental findings that indicate how prioritizing the spatial geometry and density of sources, as opposed to the distance to the recording site, better defines the amplitudes and spatial reach of FPs. The role of geometry is enhanced by considering that zones of the active populations that act as sources or sinks of current may arrange differently with respect to each other, and have different geometry and densities. Thus, observations that seem counterintuitive in the scheme of distance-based logic alone can now be explained. For example, geometric factors explain why some structures produce FPs and others do not, why different FP motifs generated in the same structure extend far while others remain local, why factors like the size of an active population or the strong synchronicity of its neurons may fail to affect FPs, or why the rate of FP decay varies in different directions. These considerations are exemplified in large structures like the cortex and hippocampus, in which the role of geometrical elements and regional activation in shaping well-known FP oscillations generally go unnoticed. Discovering the geometry of the sources in play will decrease the risk of population or pathway misassignments based solely on the FP amplitude or temporal pattern.

Spatiotemporal spike-centered averaging reveals symmetry of temporal and spatial components of the spike-LFP relationship during human focal seizures

The electrographic manifestation of neural activity can reflect the relationship between the faster action potentials of individual neurons and the slower fluctuations of the local field potential (LFP). This relationship is typically examined in the temporal domain using the spike-triggered average. In this study, we add a spatial component to this relationship. Here we first derive a theoretical model of the spike-LFP relationship across a macroelectrode. This mathematical derivation showed a special symmetry in the spike-LFP relationship wherein a sinc function in the temporal domain predicts a sinc function in the spatial domain. We show that this theoretical result is observed in a real-world system by characterizing the spike-LFP relationship using microelectrode array (MEA) recordings of human focal seizures. To do this, we present a approach, termed the spatiotemporal spike-centered average (st-SCA), that allows for visualization of the spike-LFP relationship in both the temporal and spatial domains. We applied this method to 25 MEA recordings obtained from seven patients with pharmacoresistant focal epilepsy. Of the five patients with MEAs implanted in recruited territory, three exhibited spatiotemporal patterns consistent with a sinc function, and two exhibited spatiotemporal patterns resembling deep wells of excitation. These results suggest that in some cases characterization of the spike-LFP relationship in the temporal domain is sufficient to predict the underlying spatial pattern. Finally, we discuss the biological interpretation of these findings and propose that the sinc function may reflect the role of mid-range excitatory connections during seizure activity.

Electrophysiological signatures of reward learning in the rodent touchscreen-based Probabilistic Reward Task

Blunted reward learning and reward-related activation within the corticostriatal-midbrain circuitry have been implicated in the pathophysiology of anhedonia and depression. Unfortunately, the search for more efficacious interventions for anhedonic behaviors has been hampered by the use of vastly different preclinical and clinical assays. In a first step in addressing this gap, in the current study, we used event-related potentials and spectral analyses in conjunction with a touchscreen version of the rodent Probabilistic Reward Task (PRT) to identify the electrophysiological signatures of reward learning in rats. We trained 11 rats (5 females and 6 males) on the rodent touchscreen-based PRT and subsequently implanted them with deep electrodes in the anterior cingulate cortex (ACC) and nucleus accumbens (NAc) for local field potentials recordings during the PRT. Behaviorally, the expected responsivity-to-reward profile was observed. At the electrophysiological level, we identified a negative amplitude deflection 250-500 ms after feedback in the ACC and NAc electrodes, as well as power increase in feedback-locked delta (1-5 Hz) and alpha/beta (9-17 Hz) bands in both electrodes for rewarded trials. Using a reverse-translational approach, we identified electrophysiological signatures of reward learning in rats similar to those described in humans. These findings and approaches might provide a useful translational platform to efficiently evaluate novel therapeutics targeting anhedonia.

Intracerebral Electrophysiological Recordings to Understand the Neural Basis of Human Face Recognition

Understanding how the human brain recognizes faces is a primary scientific goal in cognitive neuroscience. Given the limitations of the monkey model of human face recognition, a key approach in this endeavor is the recording of electrophysiological activity with electrodes implanted inside the brain of human epileptic patients. However, this approach faces a number of challenges that must be overcome for meaningful scientific knowledge to emerge. Here we synthesize a 10 year research program combining the recording of intracerebral activity (StereoElectroEncephaloGraphy, SEEG) in the ventral occipito-temporal cortex (VOTC) of large samples of participants and fast periodic visual stimulation (FPVS), to objectively define, quantify, and characterize the neural basis of human face recognition. These large-scale studies reconcile the wide distribution of neural face recognition activity with its (right) hemispheric and regional specialization and extend face-selectivity to anterior regions of the VOTC, including the ventral anterior temporal lobe (VATL) typically affected by magnetic susceptibility artifacts in functional magnetic resonance imaging (fMRI). Clear spatial dissociations in category-selectivity between faces and other meaningful stimuli such as landmarks (houses, medial VOTC regions) or written words (left lateralized VOTC) are found, confirming and extending neuroimaging observations while supporting the validity of the clinical population tested to inform about normal brain function. The recognition of face identity - arguably the ultimate form of recognition for the human brain - beyond mere differences in physical features is essentially supported by selective populations of neurons in the right inferior occipital gyrus and the lateral portion of the middle and anterior fusiform gyrus. In addition, low-frequency and high-frequency broadband iEEG signals of face recognition appear to be largely concordant in the human association cortex. We conclude by outlining the challenges of this research program to understand the neural basis of human face recognition in the next 10 years.

Recording electrical activity from the brain of behaving octopus

Octopuses, which are among the most intelligent invertebrates,^1,2,3,4 have no skeleton and eight flexible arms whose sensory and motor activities are at once autonomous and coordinated by a complex central nervous system.^5,6,7,8 The octopus brain contains a very large number of neurons, organized into numerous distinct lobes, the functions of which have been proposed based largely on the results of lesioning experiments.^{9,10,11,12,13} In other species, linking brain activity to behavior is done by implanting electrodes and directly correlating electrical activity with observed animal behavior. However, because the octopus lacks any hard structure to which recording equipment can be anchored, and because it uses its eight flexible arms to remove any foreign object attached to the outside of its body, in vivo recording of electrical activity from untethered, behaving octopuses has thus far not been possible. Here, we describe a novel technique for inserting a portable data logger into the octopus and implanting electrodes into the vertical lobe system, such that brain activity can be recorded for up to 12 h from unanesthetized, untethered octopuses and can be synchronized with simultaneous video recordings of behavior. In the brain activity, we identified several distinct patterns that appeared consistently in all animals. While some resemble activity patterns in mammalian neural tissue, others, such as episodes of 2 Hz, large amplitude oscillations, have not been reported. By providing an experimental platform for recording brain activity in behaving octopuses, our study is a critical step toward understanding how the brain controls behavior in these remarkable animals.

Stimulation-induced entrainment of hippocampal network activity: Identifying optimal input frequencies

The hippocampus contains rich oscillatory activity, with continuous ebbs and flows of rhythmic currents that constrain its ability to integrate inputs. During associative learning, the hippocampus must integrate inputs from a range of sources carrying information about events and the contexts in which they occur. Under these circumstances, temporal coordination of activity between sender and receiver is likely essential for successful communication. Previously, it has been shown that the coordination of rhythmic activity between the lateral entorhinal cortex (LEC) and the CA1 region of the hippocampus is tightly correlated with the onset of learning in an associative learning task. We aimed to examine whether rhythmic inputs from the LEC in specific frequency ranges were sufficient to enhance the temporal coordination of activity in downstream CA1. In urethane-anesthetized rats, we applied extracellular low-intensity alternating current stimulation across the length of the LEC. Using this method, we aimed to phase-bias ongoing neuronal activity in LEC at a range of different frequencies (from 1.25 to 55 Hz). Rhythmic stimulation of LEC at both 35 and 50 Hz increased the proportion of CA1 neurons significantly entrained to the phase of the applied stimulation current. A subset of stimulation frequencies modified CA1 spiking relationships to the phase of local ongoing CA1 oscillations, with each stimulation frequency exerting a unique influence upon downstream CA1, often in frequency ranges outside the target stimulation frequency. These results suggest there are optimal frequencies for LEC-CA1 communication, and that different profiles of LEC rhythms likely have distinct outcomes upon CA1 processing.

What neural oscillations can and cannot do for syntactic structure building

Understanding what someone says requires relating words in a sentence to one another as instructed by the grammatical rules of a language. In recent years, the neurophysiological basis for this process has become a prominent topic of discussion in cognitive neuroscience. Current proposals about the neural mechanisms of syntactic structure building converge on a key role for neural oscillations in this process, but they differ in terms of the exact function that is assigned to them. In this Perspective, we discuss two proposed functions for neural oscillations - chunking and multiscale information integration - and evaluate their merits and limitations taking into account a fundamentally hierarchical nature of syntactic representations in natural languages. We highlight insights that provide a tangible starting point for a neurocognitive model of syntactic structure building.

Assessment of a single trial impact on the amplitude of the averaged event related potentials

Widely used in neuroscience the averaging of event related potentials is based on the assumption that small responses to the investigated events are present in every trial but can be hidden under the random noise. This situation often takes place, especially in experiments performed at hierarchically lower levels of sensory systems. However, in the studies of higher order complex neuronal networks evoked responses might appear only under particular conditions and be absent otherwise. We encountered this problem studying a propagation of interoceptive information to the cortical areas in the sleep-wake cycle. Cortical responses to various visceral events were present during some periods of sleep, then disappeared for a while and restored again after a period of absence. Further investigation of the viscero-cortical communication required a method that would allow labeling the trials contributing to the averaged event related responses-"efficient trials," and separating them from the trials without any response. Here we describe a heuristic approach to solving this problem in the context of viscero-cortical interactions occurring during sleep. However, we think that the proposed technique can be applicable to any situation where neuronal processing of the same events is expected to be variable due to internal or external factors modulating neuronal activity. The method was first implemented as a script for Spike 2 program version 6.16 (CED). However, at present a functionally equivalent version of this algorithm is also available as Matlab code at https://github.com/george-fedorov/erp-correlations.