Intracranial neural representation of phenomenal and access consciousness in the human brain.

After more than 30 years of extensive investigation, impressive progress has been made in identifying the neural correlates of consciousness (NCC). However, the functional role of spatiotemporally distinct consciousness-related neural activity in conscious perception is debated. An influential framework proposed that consciousness-related neural activities could be dissociated into two distinct processes: phenomenal and access consciousness. However, though hotly debated, its authenticity has not been examined in a single paradigm with more informative intracranial recordings. In the present study, we employed a visual awareness task and recorded the local field potential (LFP) of patients with electrodes implanted in cortical and subcortical regions. Overall, we found that the latency of visual awareness-related activity exhibited a bimodal distribution, and the recording sites with short and long latencies were largely separated in location, except in the lateral prefrontal cortex (lPFC). The mixture of short and long latencies in the lPFC indicates that it plays a critical role in linking phenomenal and access consciousness. However, the division between the two is not as simple as the central sulcus, as proposed previously. Moreover, in 4 patients with electrodes implanted in the bilateral prefrontal cortex, early awareness-related activity was confined to the contralateral side, while late awareness-related activity appeared on both sides. Finally, Granger causality analysis showed that awareness-related information flowed from the early sites to the late sites. These results provide the first LFP evidence of neural correlates of phenomenal and access consciousness, which sheds light on the spatiotemporal dynamics of NCC in the human brain.

The primate cortical LFP exhibits multiple spectral and temporal gradients and widespread task dependence during visual short-term memory.

Although cognitive functions are hypothesized to be mediated by synchronous neuronal interactions in multiple frequency bands among widely distributed cortical areas, we still lack a basic understanding of the distribution and task dependence of oscillatory activity across the cortical map. Here, we ask how the spectral and temporal properties of the local field potential (LFP) vary across the primate cerebral cortex, and how they are modulated during visual short-term memory. We measured the LFP from 55 cortical areas in two macaque monkeys while they performed a visual delayed match to sample task. Analysis of peak frequencies in the LFP power spectra reveals multiple discrete frequency bands between 3 and 80 Hz that differ between the two monkeys. The LFP power in each band, as well as the sample entropy, a measure of signal complexity, display distinct spatial gradients across the cortex, some of which correlate with reported spine counts in cortical pyramidal neurons. Cortical areas can be robustly decoded using a small number of spectral and temporal parameters, and significant task-dependent increases and decreases in spectral power occur in all cortical areas. These findings reveal pronounced, widespread, and spatially organized gradients in the spectral and temporal activity of cortical areas. Task-dependent changes in cortical activity are globally distributed, even for a simple cognitive task.NEW & NOTEWORTHY We recorded extracellular electrophysiological signals from roughly the breadth and depth of a cortical hemisphere in nonhuman primates (NHPs) performing a visual memory task. Analyses of the band-limited local field potential (LFP) power displayed widespread, frequency-dependent cortical gradients in spectral power. Using a machine learning classifier, these features allowed robust cortical area decoding. Further task dependence in LFP power were found to be widespread, indicating large-scale gradients of LFP activity, and task-related activity.

Reconstructing rodent brain signals during euthanasia with eigensystem realization algorithm (ERA).

We accurately reconstruct the Local Field Potential time series obtained from anesthetized and awake rats, both before and during CO 2 euthanasia. We apply the Eigensystem Realization Algorithm to identify an underlying linear dynamical system capable of generating the observed data. Time series exhibiting more intricate dynamics typically lead to systems of higher dimensions, offering a means to assess the complexity of the brain throughout various phases of the experiment. Our results indicate that anesthetized brains possess complexity levels similar to awake brains before CO 2 administration. This resemblance undergoes significant changes following euthanization, as signals from the awake brain display a more resilient complexity profile, implying a state of heightened neuronal activity or a last fight response during the euthanasia process. In contrast, anesthetized brains seem to enter a more subdued state early on. Our data-driven techniques can likely be applied to a broader range of electrophysiological recording modalities.

Neuronal functional connectivity is impaired in a layer dependent manner near chronically implanted intracortical microelectrodes in C57BL6 wildtype mice.

Objective.This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near chronically implanted microelectrodes. 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 the FBR affects 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 single-shank, 16-channel,100µm site-spacing Michigan-style microelectrodes (3 mm length, 703µm2 site area) that span all cortical layers and the hippocampal CA1 region. Sex balanced C57BL6 wildtype mice (11-13 weeks old) received perpendicularly implanted microelectrode in left primary visual cortex. Electrophysiological recordings were performed during both spontaneous activity and visual sensory stimulation. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of local field potential (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.

Supramolecular Sequential Light-Harvesting Systems for Constructing White LED Device and Latent Fingerprint Imaging.

The fabrication of supramolecular light-harvesting systems (LHS) with sequential energy transfer is of significance in utilizing light energy. In this study, we report the non-covalent self-assembly of a sequential LHS by pillar[5]arene-based host-guest interaction in water and its applications in white light-emitting diode (LED) device and latent fingerprint imaging. The host-guest complex WP5 ⊃ ${ \supset }$ G self-assembles into nanoparticles in water and shows enhanced aggregation-induced emission (AIE) effect. The nanoparticles can be further used to construct sequential LHS with fluorescent dyes 4,7-di(2-thienyl)-benzo[2,1,3]thiadiazole (DBT) and sulforhodamine 101 (SR101). Impressively, the system shows white-light emission when the molar ratio of WP5 ⊃ ${ \supset }$ G/DBT/SR101 is 1100/2/16. The material can be coated on a LED bulb to achieve white-light emission. In addition, the sequential LHS exhibit multicolor fluorescence including red emission, which have been successfully applied to high-resolution imaging of latent fingerprints. Therefore, we demonstrated a general strategy for the construction of sequential LHS in water based on macrocyclic host-guest interaction and explored its multi-functional applications in white-light LED device and imaging of latent fingerprints, which will promote future development and application of supramolecular LHSs.

The electrophysiology of lexical prediction of emoji and text.

As emoji often appear naturally alongside text in utterances, they provide a way to study how prediction unfolds in multimodal sentences in direct comparison to unimodal sentences. In this experiment, participants (N = 40) read sentences in which the sentence-final noun appeared in either word form or emoji form, a between-subjects manipulation. The experiment featured both high constraint sentences and low constraint sentences to examine how the lexical processing of emoji interacts with prediction processes in sentence comprehension. Two well-established ERP components linked to lexical processing and prediction - the N400 and the Late Frontal Positivity - are investigated for sentence-final words and emoji to assess whether, to what extent, and in what linguistic contexts emoji are processed like words. Results indicate that the expected effects, namely an N400 effect to an implausible lexical item compared to a plausible one and an LFP effect to an unexpected lexical item compared to an expected one, emerged for both words and emoji. This paper discusses the similarities and differences between the stimulus types and constraint conditions, contextualized within theories of linguistic prediction, ERP components, and a multimodal lexicon.

Decoding Typical Flight States Based on Neural Signals from the Midbrain Motor Nuclei of Pigeons.

BACKGROUND: Exploring the neural encoding mechanism and decoding of motion state switching during flight can advance our knowledge of avian behavior control and contribute to the development of avian robots. However, limited acquisition equipment and neural signal quality have posed challenges, thus we understand little about the neural mechanisms of avian flight. METHODS: We used chronically implanted micro-electrode arrays to record the local field potentials (LFPs) in the formation reticularis medialis mesencephali (FRM) of pigeons during various motion states in their natural outdoor flight. Subsequently, coherence-based functional connectivity networks under different bands were constructed and the topological features were extracted. Finally, we used a support vector machine model to decode different flight states. RESULTS: Our findings indicate that the gamma band (80-150 Hz) in the FRM exhibits significant power for identifying different states in pigeons. Specifically, the avian brain transmitted flight related information more efficiently during the accelerated take-off or decelerated landing states, compared with the uniform flight and baseline states. Finally, we achieved a best average accuracy of 0.86 using the connectivity features in the 80-150 Hz band and 0.89 using the fused features for state decoding. CONCLUSIONS: Our results open up possibilities for further research into the neural mechanism of avian flight and contribute to the understanding of flight behavior control in birds.

Spindle oscillations in communicating axons within a reconstituted hippocampal formation are strongest in CA3 without thalamus.

Spindle-shaped waves of oscillations emerge in EEG scalp recordings during human and rodent non-REM sleep. The association of these 10-16 Hz oscillations with events during prior wakefulness suggests a role in memory consolidation. Human and rodent depth electrodes in the brain record strong spindles throughout the cortex and hippocampus, with possible origins in the thalamus. However, the source and targets of the spindle oscillations from the hippocampus are unclear. Here, we employed an in vitro reconstruction of four subregions of the hippocampal formation with separate microfluidic tunnels for single axon communication between subregions assembled on top of a microelectrode array. We recorded spontaneous 400-1000 ms long spindle waves at 10-16 Hz in single axons passing between subregions as well as from individual neurons in those subregions. Spindles were nested within slow waves. The highest amplitudes and most frequent occurrence suggest origins in CA3 neurons that send feed-forward axons into CA1 and feedback axons into DG. Spindles had 50-70% slower conduction velocities than spikes and were not phase-locked to spikes suggesting that spindle mechanisms are independent of action potentials. Therefore, consolidation of declarative-cognitive memories in the hippocampus may be separate from the more easily accessible consolidation of memories related to thalamic motor function.

Enabling High-Performance Hybrid Solid-State Batteries by Improving the Microstructure of Free-Standing LATP/LFP Composite Cathodes.

The phosphate lithium-ion conductor Li1.5Al0.5Ti1.5(PO4)3 (LATP) is an economically attractive solid electrolyte for the fabrication of safe and robust solid-state batteries, but high sintering temperatures pose a material engineering challenge for the fabrication of cell components. In particular, the high surface roughness of composite cathodes resulting from enhanced crystal growth is detrimental to their integration into cells with practical energy density. In this work, we demonstrate that efficient free-standing ceramic cathodes of LATP and LiFePO4 (LFP) can be produced by using a scalable tape casting process. This is achieved by adding 5 wt % of Li2WO4 (LWO) to the casting slurry and optimizing the fabrication process. LWO lowers the sintering temperature without affecting the phase composition of the materials, resulting in mechanically stable, electronically conductive, and free-standing cathodes with a smooth, homogeneous surface. The optimized cathode microstructure enables the deposition of a thin polymer separator attached to the Li metal anode to produce a cell with good volumetric and gravimetric energy densities of 289 Wh dm-3 and 180 Wh kg-1, respectively, on the cell level and Coulombic efficiency above 99% after 30 cycles at 30 °C.

Effects of Transcranial Ultrasound Stimulation on Blood Oxygen Metabolism and Brain Rhythms in Nitroglycerin-Induced Migraine Mice.

OBJECTIVES: In this study, we aimed to investigate the regulatory mechanism of transcranial ultrasound stimulation (TUS) on nitroglycerin-induced migraine in mice. MATERIALS AND METHODS: The experiment was divided into four groups, namely, the normal saline control group (n = 9), ultrasound stimulation control group (n = 6), nitroglycerin-induced migraine group (n = 9), and ultrasound stimulation group (n = 9). The behavior, blood oxygen metabolism, and brain rhythm distribution of the four groups were analyzed. RESULTS: We found that after TUS, the movement time and speed of mice with migraine are modulated to those of the control groups, and the number of head scratching and grooming events is significantly reduced. TUS increased the deoxygenated hemoglobin, and the power of the 4-to-40 Hz frequency band of local field potentials in the cortex of migraine mice. TUS also decreased the expression of plasma calcitonin gene-related peptide and cortical c-Fos protein. CONCLUSIONS: Ultrasound stimulation can regulate brain rhythm and blood oxygen metabolism and reduce migraine symptoms in mice. The regulatory mechanism may be related to reducing calcitonin gene-related peptide in blood vessels.

Physiological effects of dual target DBS in an individual with Parkinson's disease and a sensing-enabled pulse generator.

INTRODUCTION: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) or globus pallidus (GP) is an established therapy for Parkinson's disease (PD). Novel DBS devices can record local field potential (LFP) physiomarkers from the STN or GP. While beta (13-30 Hz) and gamma (40-90 Hz) STN and GP LFP oscillations correlate with PD motor severity and with therapeutic effects of treatments, STN-GP interactions in electrophysiology in patients with PD are not well characterized. METHODS: Simultaneous bilateral STN and GP LFPs were recorded in a patient with PD who received bilateral STN-DBS and GP-DBS. Power spectra in each target and STN-GP coherence were assessed in various ON- and OFF-levodopa and DBS states, both at rest and with voluntary movement. RESULTS: OFF-levodopa and OFF-DBS, beta peaks were present at bilateral STN and GP, coincident with prominent STN-GP beta coherence. Levodopa and dual-target-DBS (simultaneous STN-DBS and GP-DBS) completely suppressed STN-GP coherence. Finely-tuned gamma (FTG) activity at half the stimulation frequency (62.5 Hz) was seen in the STN during GP-DBS at rest. To assess the effects of movement on FTG activity, we recorded LFPs during instructed movement. We observed FTG activity in bilateral GP and bilateral STN during contralateral body movements while on GP-DBS and ON-levodopa. No FTG was seen with STN-DBS or dual-target-DBS. CONCLUSION: Dual-target-DBS and levodopa suppressed STN-GP coherence. FTG throughout the basal ganglia was induced by GP-DBS in the presence of levodopa and movement. This bilateral STN-FTG and GP-FTG corresponded with the least severe bradykinesia state, suggesting a pro-kinetic role for FTG.

Event detection and classification from multimodal time series with application to neural data.

The detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series. Neuroscience experiments, for example, can simultaneously record multimodal neural signals such as local field potentials (LFPs), which can be modeled as Gaussian, and neuronal spikes, which can be modeled as point processes. Currently, no method exists for event detection from such multimodal data, and as such our objective in this work is to develop a method to meet this need. Here we address this challenge by developing the multimodal event detector (MED) algorithm which simultaneously estimates event times and classes. To do this, we write a multimodal likelihood function for Gaussian and point-process observations and derive the associated maximum likelihood estimator of simultaneous event times and classes. We additionally introduce a cross-modal scaling parameter to account for model mismatch in real datasets. We validate this method in extensive simulations as well as in a neural spike-LFP dataset recorded during an eye-movement task, where the events of interest are eye movements with unknown times and directions. We show that the MED can successfully detect eye movement onset and classify eye movement direction. Further, the MED successfully combines information across data modalities, with multimodal performance exceeding unimodal performance. This method can facilitate applications such as the discovery of latent events in multimodal neural population activity and the development of brain-computer interfaces for naturalistic settings without constrained tasks or prior knowledge of event times.

Anxiety control by astrocytes in the lateral habenula.

The potential role of astrocytes in lateral habenula (LHb) in modulating anxiety was explored in this study. The habenula are a pair of small nuclei located above the thalamus, known for their involvement in punishment avoidance and anxiety. Herein, we observed an increase in theta-band oscillations of local field potentials (LFPs) in the LHb when mice were exposed to anxiety-inducing environments. Electrical stimulation of LHb at theta-band frequency promoted anxiety-like behavior. Calcium (Ca2+) levels and pH in the cytosol of astrocytes and local brain blood volume changes were studied in mice expressing either a Ca2+ or a pH sensor protein specifically in astrocytes and mScarlet fluorescent protein in the blood plasma using fiber photometry. An acidification response to anxiety was observed. Photoactivation of archaerhopsin-T (ArchT), an optogenetic tool that acts as an outward proton pump, results in intracellular alkalinization. Photostimulation of LHb in astrocyte-specific ArchT-expressing mice resulted in dissipation of theta-band LFP oscillation in an anxiogenic environment and suppression of anxiety-like behavior. These findings provide evidence that LHb astrocytes modulate anxiety and may offer a new target for treatment of anxiety disorders.

Beyond Constant Current: Origin of Pulse-Induced Activation in Phase-Transforming Battery Electrodes.

Mechanistic understanding of phase transformation dynamics during battery charging and discharging is crucial toward rationally improving intercalation electrodes. Most studies focus on constant-current conditions. However, in real battery operation, such as in electric vehicles during discharge, the current is rarely constant. In this work we study current pulsing in LiXFePO4 (LFP), a model and technologically important phase-transforming electrode. A current-pulse activation effect has been observed in LFP, which decreases the overpotential by up to ∼70% after a short, high-rate pulse. This effect persists for hours or even days. Using scanning transmission X-ray microscopy and operando X-ray diffraction, we link this long-lived activation effect to a pulse-induced electrode homogenization on both the intra- and interparticle length scales, i.e., within and between particles. Many-particle phase-field simulations explain how such pulse-induced homogeneity contributes to the decreased electrode overpotential. Specifically, we correlate the extent and duration of this activation to lithium surface diffusivity and the magnitude of the current pulse. This work directly links the transient electrode-level electrochemistry to the underlying phase transformation and explains the critical effect of current pulses on phase separation, with significant implication on both battery round-trip efficiency and cycle life. More broadly, the mechanisms revealed here likely extend to other phase-separating electrodes, such as graphite.

A machine learning approach for real-time cortical state estimation.

Objective.Cortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as 'cortical state'. Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation.Approach. We use unsupervised Gaussian mixture models to identify discrete, emergent clusters in spontaneous local field potential signals in cortex. We then extend our approach to a temporally-informed hidden semi-Markov model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments.Main results. Unsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data.Significance. To our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.

Oscillatory vs. non-oscillatory subthalamic beta activity in Parkinson's disease.

Parkinson's disease is characterized by exaggerated beta activity (13-35 Hz) in cortico-basal ganglia motor loops. Beta activity includes both periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and excitation/inhibition balance (i.e. non-oscillatory activity). However, the relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. We recorded, modelled and analysed subthalamic local field potentials in parkinsonian patients at rest while off or on medication. Autoregressive modelling with additive 1/f noise clarified the relationships between measures of beta activity in the time domain (i.e. amplitude and duration of beta bursts) or in the frequency domain (i.e. power and sharpness of the spectral peak) and oscillatory vs. non-oscillatory activity: burst duration and spectral sharpness are specifically sensitive to oscillatory activity, whereas burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Our experimental data confirmed the model predictions and assumptions. We subsequently analysed the effect of levodopa, obtaining strong-to-extreme Bayesian evidence that oscillatory beta activity is reduced in patients on vs. off medication, with moderate evidence for absence of modulation of the non-oscillatory component. Finally, specifically the oscillatory component of beta activity correlated with the rate of motor progression of the disease. Methodologically, these results provide an integrative understanding of beta-based biomarkers relevant for adaptive deep brain stimulation. Biologically, they suggest that primarily the oscillatory component of subthalamic beta activity is dopamine dependent and may play a role not only in the pathophysiology but also in the progression of Parkinson's disease. KEY POINTS: Beta activity in Parkinson's disease includes both true periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and synaptic balance (i.e. non-oscillatory activity). The relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. Burst duration and spectral sharpness are specifically sensitive to oscillatory activity, while burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Only the oscillatory component of subthalamic beta activity is dopamine-dependent. Stronger beta oscillatory activity correlates with faster motor progression of the disease.

Transcranial ultrasound stimulation selectively affects cortical neurovascular coupling across neuronal types and LFP frequency bands.

Previous studies have affirmed that transcranial ultrasound stimulation (TUS) can influence cortical neurovascular coupling across low-frequency (0-2 Hz)/high-frequency (160-200 Hz) neural oscillations and hemodynamics. Nevertheless, the selectivity of this coupling triggered by transcranial ultrasound stimulation for spike activity (> 300 Hz) and additional frequency bands (4-150 Hz) remains elusive. We applied transcranial ultrasound stimulation to mice visual cortex while simultaneously recording total hemoglobin concentration, spike activity, and local field potentials. Our findings include (1) a significant increase in coupling strength between spike firing rates of putative inhibitory neurons/putative excitatory neurons and total hemoglobin concentration post-transcranial ultrasound stimulation; (2) an ~ 2.1-fold higher Pearson correlation coefficient between putative inhibitory neurons and total hemoglobin concentration compared with putative excitatory neurons and total hemoglobin concentration (*P < 0.05); (3) a notably greater cross-correlation between putative inhibitory neurons and total hemoglobin concentration than that between putative excitatory neurons and total hemoglobin concentration (*P < 0.05); (4) an enhancement of Pearson correlation coefficient between the relative power of γ frequency band (30-80 Hz), hγ frequency band (80-150 Hz) and total hemoglobin concentration following transcranial ultrasound stimulation (*P < 0.05); and (5) strongest cross-correlation observed at negative delay for θ frequency band, and positive delay for α, ß, γ, hγ frequency bands. Collectively, these results demonstrate that cortical neurovascular coupling evoked by transcranial ultrasound stimulation exhibits selectivity concerning neuronal types and local field potential frequency bands.

Phase of neural oscillations as a reference frame for attention-based routing in visual cortex.

Selective attention allows the brain to efficiently process the image projected onto the retina, selectively focusing neural processing resources on behaviorally relevant visual information. While previous studies have documented the crucial role of the action potential rate of single neurons in relaying such information, little is known about how the activity of single neurons relative to their neighboring network contributes to the efficient representation of attended stimuli and transmission of this information to downstream areas. Here, we show in the dorsal visual pathway of monkeys (medial superior temporal area) that neurons fire spikes preferentially at a specific phase of the ongoing population beta (∼20 Hz) oscillations of the surrounding local network. This preferred spiking phase shifts towards a later phase when monkeys selectively attend towards (rather than away from) the receptive field of the neuron. This shift of the locking phase is positively correlated with the speed at which animals report a visual change. Furthermore, our computational modeling suggests that neural networks can manipulate the preferred phase of coupling by imposing differential synaptic delays on postsynaptic potentials. This distinction between the locking phase of neurons activated by the spatially attended stimulus vs. that of neurons activated by the unattended stimulus, may enable the neural system to discriminate relevant from irrelevant sensory inputs and consequently filter out distracting stimuli information by aligning the spikes which convey relevant/irrelevant information to distinct phases linked to periods of better/worse perceptual sensitivity for higher cortices. This strategy may be used to reserve the narrow windows of highest perceptual efficacy to the processing of the most behaviorally relevant information, ensuring highly efficient responses to attended sensory events.

Local field potentials and single unit dynamics in motor cortex of unconstrained macaques during different behavioral states.

Different sleep stages have been shown to be vital for a variety of brain functions, including learning, memory, and skill consolidation. However, our understanding of neural dynamics during sleep and the role of prominent LFP frequency bands remain incomplete. To elucidate such dynamics and differences between behavioral states we collected multichannel LFP and spike data in primary motor cortex of unconstrained macaques for up to 24 h using a head-fixed brain-computer interface (Neurochip3). Each 8-s bin of time was classified into awake-moving (Move), awake-resting (Rest), REM sleep (REM), or non-REM sleep (NREM) by using dimensionality reduction and clustering on the average spectral density and the acceleration of the head. LFP power showed high delta during NREM, high theta during REM, and high beta when the animal was awake. Cross-frequency phase-amplitude coupling typically showed higher coupling during NREM between all pairs of frequency bands. Two notable exceptions were high delta-high gamma and theta-high gamma coupling during Move, and high theta-beta coupling during REM. Single units showed decreased firing rate during NREM, though with increased short ISIs compared to other states. Spike-LFP synchrony showed high delta synchrony during Move, and higher coupling with all other frequency bands during NREM. These results altogether reveal potential roles and functions of different LFP bands that have previously been unexplored.

Leveraging Julia's automated differentiation and symbolic computation to increase spectral DCM flexibility and speed.

Using neuroimaging and electrophysiological data to infer neural parameter estimations from theoretical circuits requires solving the inverse problem. Here, we provide a new Julia language package designed to i) compose complex dynamical models in a simple and modular way with ModelingToolkit.jl, ii) implement parameter fitting based on spectral dynamic causal modeling (sDCM) using the Laplace approximation, analogous to MATLAB implementation in SPM12, and iii) leverage Julia's unique strengths to increase accuracy and speed by employing Automatic Differentiation during the fitting procedure. To illustrate the utility of our flexible modular approach, we provide a method to improve correction for fMRI scanner field strengths (1.5T, 3T, 7T) when fitting models to real data.