Frequency-Band Coupling in Surface EEG Reflects Spiking Activity in Monkey Visual Cortex

Auditory evoked delta brushes involve stimulus-specific cortical networks in preterm infants

During the third trimester of gestation in humans, the auditory cortex displays spontaneous and auditory-evoked EEG patterns of intermittent local oscillatory activity nested in delta waves - delta brushes (DBs). To test whether the spatiotemporal dynamics of evoked DBs depends on stimulus type, we studied auditory evoked responses (AERs) to voice and "click" using 32-electrode EEG in 30 healthy neonates aged 30 to 38 post-menstrual weeks. Both stimuli elicited two peaks at approximately 250 ms and 600 ms, the second corresponding to the first principal components of the AER and the evoked DB. The DB showed stimulus-specific topography, temporal posterior and mid-temporal for "click", and mid-temporal and pre-central inferior for voice, and contained theta to gamma oscillations more widespread for the "click"response. Gamma oscillations increased with age. AERs predominated on the right but shifted toward the left with age for voice response. Auditory evoked DBs may therefore underlie specific auditory processing during fetal development.

Frontal midline theta power accounts for inter-individual differences in motor learning ability

Recent neurophysiological studies have demonstrated that frontal midline theta (FMT) activity plays a significant role in motor learning. One of the key challenges in motor learning is to understand the interindividual variability in learning proficiency rates, yet the underlying neural mechanisms remain unclear. To address this open question, this study recorded electroencephalogram activity from twenty-one healthy participants during a visuomotor tracking task to investigate whether modulation of FMT power and the theta phase synchronization across trials (theta phase consistency) during motor preparation could explain individual differences in learning proficiency. We found a significant positive correlation between increased FMT power during motor preparation and learning proficiency rates. Specifically, individuals with greater FMT power exhibited faster learning rates. In contrast, no significant correlation was observed between the consistency of the theta phase during motor preparation and learning proficiency. Together, these findings highlight that the FMT power, rather than phase synchrony, is closely associated with motor learning efficiency. This study provides a novel perspective for understanding the causes of individual differences in motor learning and further corroborates the previous evidence showing FMT power contributes to motor learning processes.

Motor learning leverages coordinated low-frequency cortico-basal ganglia activity to optimize motor preparation in humans with Parkinson's disease

Learning dexterous motor sequences is crucial to autonomy and quality of life but can be altered in Parkinson's disease (PD). Learning involves optimizing pre-movement planning (preplanning) of multiple sequence elements to reduce computational overhead during active movement. However, it is unclear which brain regions mediate preplanning or how this process evolves with learning. Recording cortico-basal ganglia field potentials during a multi-day typing task in four individuals with PD, we found evidence for network-wide multi-element preplanning that improved with learning, facilitated by functional connectivity. In both cortex and basal ganglia, pre-movement gamma (γ, 30-250 Hz) activity, historically linked to population spiking, distinguished between future action sequences and became increasingly predictive with learning. For motor cortex γ, this increase was tied to learning-related cross-frequency coupling led by cortically-driven network delta (δ, 0.5-4 Hz) synchrony. More generally, coordinated network δ supported a complex pattern of learning-driven cross-frequency couplings within and between cortex and basal ganglia, including striatal lead of cortical beta (β, 12-30 Hz) activity, reflecting the specialized roles of these brain regions in motor preparation. In contrast, impaired learning was characterized by practice-driven decreases in γ's predictive value, limited cross-frequency coupling and absent network δ synchrony, with network dynamics possibly altered by pathologically high inter-basal ganglia δ synchrony. These results suggest that cortically-led δ phase coordination optimized cortico-basal ganglia multi-element preplanning through enhanced recruitment of higher-frequency neural activity. Neurostimulation that enhances cortico-basal ganglia δ synchrony may thus hold potential for improving skilled fine motor control in PD.

Critical dynamics predicts cognitive performance and provides a common framework for heterogeneous mechanisms impacting cognition

The brain criticality hypothesis postulates that brain dynamics are set at a phase transition where information processing is optimized. Long-range temporal correlations (TCs) characterizing the dissipation of information within a signal have been shown to be a hallmark of brain criticality. However, the experimental link between cognitive performance, criticality, and thus TCs has remained elusive due to limitations in recording length and spatial and temporal resolution. In this study, we investigate multiday invasive EEG recordings of 104 persons with epilepsy (PwE) together with an extensive cognitive test battery. We show that short TCs predict cognitive impairment. Further, we show that heterogeneous factors, including interictal epileptiform discharges (IEDs), antiseizure medications (ASMs), and intermittent periods with slow-wave activity (SWSs), all act directly to perturb critical dynamics and thus cognition. Our work suggests critical dynamics to be the setpoint to measure optimal network function, thereby providing a unifying framework for the heterogeneous mechanisms impacting cognition in conditions like epilepsy.

Intravenous psilocybin induces dose-dependent changes in functional network organization in rat cortex

Psilocybin produces an altered state of consciousness in humans and is associated with complex spatiotemporal changes in cortical networks. Given the emphasis on rodent models for mechanistic studies, there is a need for characterization of the effect of psilocybin on cortex-wide network dynamics. Previous electroencephalographic studies of psychedelics in rodents have primarily used sparse electrode arrays with limited spatial resolution, precluding network level analysis, and have been restricted to lower gamma frequencies. Therefore, in this study, we used electroencephalographic recordings from 27 sites/electrodes across rat cortex (n = 6 male, 6 female) to characterize the effect of psilocybin (0.1, 1, and 10 mg/kg delivered over an hour) on brain network organization as inferred through changes in node degree (an index of network density) and connection strength (via weighted phase-lag index). The removal of aperiodic component from the electroencephalogram localized the primary oscillatory changes to theta (4-10 Hz), medium gamma (70-110 Hz), and high gamma (110-150 Hz) bands, which were used for the network analysis. Additionally, we determined the concurrent changes in theta-gamma phase-amplitude coupling. We report that psilocybin, in a dose-dependent manner, 1) disrupted theta-gamma coupling [p < 0.05], 2) increased frontal high gamma connectivity [p < 0.05] and posterior theta connectivity [p ≤ 0.049], and 3) increased frontal high gamma [p < 0.05] and posterior theta [p ≤ 0.046] network density. The behavioral activity and the medium gamma frontoparietal connectivity showed an inverted-U relationship with psilocybin dose. Our results suggest that high-frequency network organization, decoupled from local theta-phase, may be an important signature of psilocybin-induced non-ordinary state of consciousness.

Animacy processing by distributed and interconnected networks in the temporal cortex of monkeys

Animacy perception, the ability to discern living from non-living entities, is crucial for survival and social interaction, as it includes recognizing abstract concepts such as movement, purpose, and intentions. This process involves interpreting cues that may suggest the intentions or actions of others. It engages the temporal cortex (TC), particularly the superior temporal sulcus (STS) and the adjacent region of the inferior temporal cortex (ITC), as well as the dorsomedial prefrontal cortex (dmPFC). However, it remains unclear how animacy is dynamically encoded over time in these brain areas and whether its processing is distributed or localized. In this study, we addressed these questions by employing a symbolic categorization task involving animate and inanimate objects using natural movie stimuli. Simultaneously, electrocorticography were conducted in both the TC and dmPFC. Time-frequency analysis revealed region-specific frequency representations throughout the observation of the movies. Spatial searchlight decoding analysis demonstrated that animacy processing is represented in a distributed manner. Regions encoding animacy information were found to be dispersed across the fundus and lip of the STS, as well as in the ITC. Next, we examined whether these dispersed regions form functional networks. Independent component analysis revealed that the spatial distribution of the component with the most significant animacy information corresponded with the dispersed regions identified by the spatial decoding analysis. Furthermore, Granger causality analysis indicated that these regions exhibit frequency-specific directional functional connectivity, with a general trend of causal influence from the ITC to STS across multiple frequency bands. Notably, a prominent feedback flow in the alpha band from the ITC to both the ventral bank and fundus of the STS was identified. These findings suggest a distributed and functionally interconnected neural substrate for animacy processing across the STS and ITC.

Multi-level therapeutic actions of cannabidiol in ketamine-induced schizophrenia psychopathology in male rats

Repeated administration of ketamine (KET) has been used to model schizophrenia-like symptomatology in rodents, but the psychotomimetic neurobiological and neuroanatomical underpinnings remain elusive. In parallel, the unmet need for a better treatment of schizophrenia requires the development of novel therapeutic strategies. Cannabidiol (CBD), a major non-addictive phytocannabinoid has been linked to antipsychotic effects with unclear mechanistic basis. Therefore, this study aims to clarify the neurobiological substrate of repeated KET administration model and to evaluate CBD's antipsychotic potential and neurobiological basis. CBD-treated male rats with and without prior repeated KET administration underwent behavioral analyses, followed by multilevel analysis of different brain areas including dopaminergic and glutamatergic activity, synaptic signaling, as well as electrophysiological recordings for the assessment of corticohippocampal and corticostriatal network activity. Repeated KET model is characterized by schizophrenia-like symptomatology and alterations in glutamatergic and dopaminergic activity mainly in the PFC and the dorsomedial striatum (DMS), through a bi-directional pattern. These observations are accompanied by glutamatergic/GABAergic deviations paralleled to impaired function of parvalbumin- and cholecystokinin-positive interneurons, indicative of excitation/inhibition (E/I) imbalance. Moreover, CBD counteracted the schizophrenia-like behavioral phenotype as well as reverted prefrontal abnormalities and ventral hippocampal E/I deficits, while partially modulated dorsostriatal dysregulations. This study adds novel insights to our understanding of the KET-induced schizophrenia-related brain pathology, as well as the CBD antipsychotic action through a region-specific set of modulations in the corticohippocampal and costicostrtiatal circuitry of KET-induced profile contributing to the development of novel therapeutic strategies focused on the ECS and E/I imbalance restoration.

Improving working memory by electrical stimulation and cross-frequency coupling

Working memory (WM) is essential for the temporary storage and processing of information required for complex cognitive tasks and relies on neuronal theta and gamma oscillations. Given the limited capacity of WM, researchers have investigated various methods to improve it, including transcranial alternating current stimulation (tACS), which modulates brain activity at specific frequencies. One particularly promising approach is theta-gamma peak-coupled-tACS (TGCp-tACS), which simulates the natural interaction between theta and gamma oscillations that occurs during cognitive control in the brain. The aim of this study was to improve WM in healthy young adults with TGCp-tACS, focusing on both behavioral and neurophysiological outcomes. Thirty-one participants completed five WM tasks under both sham and verum stimulation conditions. Electroencephalography (EEG) recordings before and after stimulation showed that TGCp-tACS increased power spectral density (PSD) in the high-gamma region at the stimulation site, while PSD decreased in the theta and delta regions throughout the cortex. From a behavioral perspective, although no significant changes were observed in most tasks, there was a significant improvement in accuracy in the 14-item Sternberg task, indicating an improvement in phonological WM. In conclusion, TGCp-tACS has the potential to promote and improve the phonological component of WM. To fully realize the cognitive benefits, further research is needed to refine the stimulation parameters and account for individual differences, such as baseline cognitive status and hormonal factors.

Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

Spontaneous reactivation of brain activity from learning to a subsequent off-line period has been implicated as a neural mechanism underlying memory consolidation. However, similarities in brain activity may also emerge as a result of individual, trait-like characteristics. Here, we introduced a novel approach for analyzing continuous electroencephalography (EEG) data to investigate learning-induced changes as well as trait-like characteristics in brain activity underlying memory consolidation. Thirty-one healthy young adults performed a learning task, and their performance was retested after a short (∼1 h) delay. Consolidation of two distinct types of information (serial-order and probability) embedded in the task were tested to reveal similarities in functional networks that uniquely predict the changes in the respective memory performance. EEG was recorded during learning and pre- and post-learning rest periods. To investigate brain activity associated with consolidation, we quantified similarities in EEG functional connectivity between learning and pre-learning rest (baseline similarity) and learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes from baseline to post-learning similarity could indicate learning-induced changes, possibly spontaneous reactivation. Higher learning-induced changes in alpha frequency connectivity (8.5-9.5 Hz) were associated with better consolidation of serial-order information, particularly for long-range connections across central and parietal sites. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5-3 Hz) specifically for more local, short-range connections. Furthermore, there was a substantial overlap between the baseline and post-learning similarities and their associations with consolidation performance, suggesting robust (trait-like) differences in functional connectivity networks underlying memory processes.

Gamma amplitude-envelope correlations are strongly elevated within hyperexcitable networks in focal epilepsy

Methods to quantify cortical hyperexcitability are of enormous interest for mapping epileptic networks in patients with focal epilepsy. We hypothesize that, in the resting state, cortical hyperexcitability increases firing-rate correlations between neuronal populations within seizure onset zones (SOZs). This hypothesis predicts that in the gamma frequency band (40-200 Hz), amplitude envelope correlations (AECs), a relatively straightforward measure of functional connectivity, should be elevated within SOZs compared to other areas. To test this prediction, we analyzed archived samples of interictal electrocorticographic (ECoG) signals recorded from patients who became seizure-free after surgery targeting SOZs identified by multiday intracranial recordings. We show that in the gamma band, AECs between nodes within SOZs are markedly elevated relative to those elsewhere. AEC-based node strength, eigencentrality, and clustering coefficient are also robustly increased within the SOZ with maxima in the low-gamma band (permutation test Z-scores > 8) and yield moderate discriminability of the SOZ using ROC analysis (maximal mean AUC ~ 0.73). By contrast to AECs, phase locking values (PLVs), a measure of narrow-band phase coupling across sites, and PLV-based graph metrics discriminate the seizure onset nodes weakly. Our results suggest that gamma band AECs may provide a clinically useful marker of cortical hyperexcitability in focal epilepsy.

Spiking Laguerre Volterra networks-predicting neuronal activity from local field potentials

Objective.Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations.Approach.Here, we fill this gap by proposing novel spiking Laguerre-Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics.Main results.The proposed networks were applied on extracellular microelectrode recordings of Parkinson's Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features.Significance.These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.

Thalamic feedback shapes brain responses evoked by cortical stimulation in mice and humans

Cortical stimulation with single pulses is a common technique in clinical practice and research. However, we still do not understand the extent to which it engages subcortical circuits which contribute to the associated evoked potentials (EPs). Here we find that cortical stimulation generates remarkably similar EPs in humans and mice, with a late component similarly modulated by the subject's behavioral state. We optogenetically dissect the underlying circuit in mice, demonstrating that the late component of these EPs is caused by a thalamic hyperpolarization and rebound. The magnitude of this late component correlates with the bursting frequency and synchronicity of thalamic neurons, modulated by the subject's behavioral state. A simulation of the thalamo-cortical circuit highlights that both intrinsic thalamic currents as well as cortical and thalamic GABAergic neurons contribute to this response profile. We conclude that the cortical stimulation engages cortico-thalamo-cortical circuits highly preserved across different species and stimulation modalities.

Psilocybin induces dose-dependent changes in functional network organization in rat cortex

Psilocybin produces an altered state of consciousness in humans and is associated with complex spatiotemporal changes in brain networks. Given the emphasis on rodent models for mechanistic studies, there is a need for characterization of the effect of psilocybin on brain-wide network dynamics. Previous rodent studies of psychedelics, using electroencephalogram, have primarily been done with sparse electrode arrays that offered limited spatial resolution precluding network level analysis, and have been restricted to lower gamma frequencies. Therefore, in the study, we used electroencephalographic recordings from 27 sites (electrodes) across rat cortex (n=6 male, 6 female) to characterize the effect of psilocybin (0.1 mg/kg, 1 mg/kg, and 10 mg/kg delivered over an hour) on network organization as inferred through changes in node degree (index of network density) and connection strength (weighted phase-lag index). The removal of aperiodic component from the electroencephalogram localized the primary oscillatory changes to theta (4-10 Hz), medium gamma (70-110 Hz), and high gamma (110-150 Hz) bands, which were used for the network analysis. Additionally, we determined the concurrent changes in theta-gamma phase-amplitude coupling. We report that psilocybin, in a dose-dependent manner, 1) disrupted theta-gamma coupling [p<0.05], 2) increased frontal high gamma connectivity [p<0.05] and posterior theta connectivity [p≤0.049], and 3) increased frontal high gamma [p<0.05] and posterior theta [p≤0.046] network density. The medium gamma frontoparietal connectivity showed a nonlinear relationship with psilocybin dose. Our results suggest that high-frequency network organization, decoupled from local theta-phase, may be an important signature of psilocybin-induced non-ordinary state of consciousness.

Gamma Oscillations as a Biomarker of Neural Circuit Function in Psychosis: Where Are We, and Where Do We Go from Here?

This chapter is a selective and critical review of the literature on gamma oscillations in schizophrenia and related studies in other relevant fields that pertain to the hypothesis that abnormal gamma oscillations underlie symptoms of psychosis in individuals with schizophrenia. These gamma abnormalities result from deficient recurrent inhibition, in which parvalbumin-expressing, fast-spiking inhibitory interneurons do not receive sufficient excitation from N-methyl-D-aspartate receptors, resulting in a loss of phasic control over pyramidal cell spiking and impairment of gamma generation. The evidence for this hypothesis is critically reviewed, focusing on studies in the areas of visual feature binding, auditory steady-state response, and spontaneous gamma activity. The current state of the field is discussed, and recommendations for future directions are presented.

Single-pulse electrical stimulation artifact removal using the novel matching pursuit-based artifact reconstruction and removal method (MPARRM)

Objective.Single-pulse electrical stimulation (SPES) has been widely used to probe effective connectivity. However, analysis of the neural response is often confounded by stimulation artifacts. We developed a novel matching pursuit-based artifact reconstruction and removal method (MPARRM) capable of removing artifacts from stimulation-artifact-affected electrophysiological signals.Approach.To validate MPARRM across a wide range of potential stimulation artifact types, we performed a bench-top experiment in which we suspended electrodes in a saline solution to generate 110 types of real-world stimulation artifacts. We then added the generated stimulation artifacts to ground truth signals (stereoelectroencephalography signals from nine human subjects recorded during a receptive speech task), applied MPARRM to the combined signal, and compared the resultant denoised signal with the ground truth signal. We further applied MPARRM to artifact-affected neural signals recorded from the hippocampus while performing SPES on the ipsilateral basolateral amygdala in nine human subjects.Main results.MPARRM could remove stimulation artifacts without introducing spectral leakage or temporal spread. It accommodated variable stimulation parameters and recovered the early response to SPES within a wide range of frequency bands. Specifically, in the early response period (5-10 ms following stimulation onset), we found that the broadband gamma power (70-170 Hz) of the denoised signal was highly correlated with the ground truth signal (R=0.98±0.02, Pearson), and the broadband gamma activity of the denoised signal faithfully revealed the responses to the auditory stimuli within the ground truth signal with94%±1.47%sensitivity and99%±1.01%specificity. We further found that MPARRM could reveal the expected temporal progression of broadband gamma activity along the anterior-posterior axis of the hippocampus in response to the ipsilateral amygdala stimulation.Significance.MPARRM could faithfully remove SPES artifacts without confounding the electrophysiological signal components, especially during the early-response period. This method can facilitate the understanding of the neural response mechanisms of SPES.

Neuronal connected burst cascades bridge macroscale adaptive signatures across arousal states

The human brain displays a rich repertoire of states that emerge from the microscopic interactions of cortical and subcortical neurons. Difficulties inherent within large-scale simultaneous neuronal recording limit our ability to link biophysical processes at the microscale to emergent macroscopic brain states. Here we introduce a microscale biophysical network model of layer-5 pyramidal neurons that display graded coarse-sampled dynamics matching those observed in macroscale electrophysiological recordings from macaques and humans. We invert our model to identify the neuronal spike and burst dynamics that differentiate unconscious, dreaming, and awake arousal states and provide insights into their functional signatures. We further show that neuromodulatory arousal can mediate different modes of neuronal dynamics around a low-dimensional energy landscape, which in turn changes the response of the model to external stimuli. Our results highlight the promise of multiscale modelling to bridge theories of consciousness across spatiotemporal scales.

Validation of transparent and flexible neural implants for simultaneous electrophysiology, functional imaging, and optogenetics

The combination of electrophysiology and neuroimaging methods allows the simultaneous measurement of electrical activity signals with calcium dynamics from single neurons to neuronal networks across distinct brain regions in vivo. While traditional electrophysiological techniques are limited by photo-induced artefacts and optical occlusion for neuroimaging, different types of transparent neural implants have been proposed to resolve these issues. However, reproducing proposed solutions is often challenging and it remains unclear which approach offers the best properties for long-term chronic multimodal recordings. We therefore created a streamlined fabrication process to produce, and directly compare, two types of transparent surface micro-electrocorticography (μECoG) implants: nano-mesh gold structures (m-μECoGs) versus a combination of solid gold interconnects and PEDOT:PSS-based electrodes (pp-μECoGs). Both implants allowed simultaneous multimodal recordings but pp-μECoGs offered the best overall electrical, electrochemical, and optical properties with negligible photo-induced artefacts to light wavelengths of interest. Showing functional chronic stability for up to four months, pp-μECoGs also allowed the simultaneous functional mapping of electrical and calcium neural signals upon visual and tactile stimuli during widefield imaging. Moreover, recordings during two-photon imaging showed no visible signal attenuation and enabled the correlation of network dynamics across brain regions to individual neurons located directly below the transparent electrical contacts.

Comparative analysis of multifaceted neural effects associated with varying endogenous cognitive load

Contemporary neuroscience has firmly established that mental state variation concurs with changes in neural dynamic activity in a complex way that a one-to-one mapping cannot describe. To explore the scenario of the multifaceted changes in neural dynamics associated with simple mental state variation, we took cognitive load - a common cognitive manipulation in psychology - as a venue to characterize how multiple neural dynamic features are simultaneously altered by the manipulation and how their sensitivity differs. Electroencephalogram was collected from 152 participants performing stimulus-free tasks with different demands. The results show that task demand alters wide-ranging neural dynamic features, including band-specific oscillations across broad frequency bands, scale-free dynamics, and cross-frequency phase-amplitude coupling. The scale-free dynamics outperformed others in indexing cognitive load variation. This study demonstrates a complex relationship between cognitive dynamics and neural dynamics, which points to a necessity to integrate multifaceted neural dynamic features when studying mind-brain relationship in the future.

Brain organoids, consciousness, ethics and moral status

Advances in the field of human stem cells are often a source of public and ethical controversy. Researchers must frequently balance diverse societal perspectives on questions of morality with the pursuit of medical therapeutics and innovation. Recent developments in brain organoids make this challenge even more acute. Brain organoids are a new class of brain surrogate generated from human pluripotent stem cells (hPSCs). They have gained traction as a model for studying the intricacies of the human brain by using advancements in stem cell biology to recapitulate aspects of the developing human brain in vitro. However, recent observation of neural oscillations spontaneously emerging from these organoids raises the question of whether brain organoids are or could become conscious. At the same time, brain organoids offer a potentially unique opportunity to scientifically understand consciousness. To address these issues, experimental biologists, philosophers, and ethicists united to discuss the possibility of consciousness in human brain organoids and the consequent ethical and moral implications.

Characterizing endogenous delta oscillations in human MEG

Rhythmic activity in the delta frequency range (0.5-3 Hz) is a prominent feature of brain dynamics. Here, we examined whether spontaneous delta oscillations, as found in invasive recordings in awake animals, can be observed in non-invasive recordings performed in humans with magnetoencephalography (MEG). In humans, delta activity is commonly reported when processing rhythmic sensory inputs, with direct relationships to behaviour. However, rhythmic brain dynamics observed during rhythmic sensory stimulation cannot be interpreted as an endogenous oscillation. To test for endogenous delta oscillations we analysed human MEG data during rest. For comparison, we additionally analysed two conditions in which participants engaged in spontaneous finger tapping and silent counting, arguing that internally rhythmic behaviours could incite an otherwise silent neural oscillator. A novel set of analysis steps allowed us to show narrow spectral peaks in the delta frequency range in rest, and during overt and covert rhythmic activity. Additional analyses in the time domain revealed that only the resting state condition warranted an interpretation of these peaks as endogenously periodic neural dynamics. In sum, this work shows that using advanced signal processing techniques, it is possible to observe endogenous delta oscillations in non-invasive recordings of human brain dynamics.

Respiration organizes gamma synchrony in the prefronto-thalamic network

Multiple cognitive operations are associated with the emergence of gamma oscillations in the medial prefrontal cortex (mPFC) although little is known about the mechanisms that control this rhythm. Using local field potential recordings from cats, we show that periodic bursts of gamma recur with 1 Hz regularity in the wake mPFC and are locked to the exhalation phase of the respiratory cycle. Respiration organizes long-range coherence in the gamma band between the mPFC and the nucleus reuniens the thalamus (Reu), linking the prefrontal cortex and the hippocampus. In vivo intracellular recordings of the mouse thalamus reveal that respiration timing is propagated by synaptic activity in Reu and likely underlies the emergence of gamma bursts in the prefrontal cortex. Our findings highlight breathing as an important substrate for long-range neuronal synchronization across the prefrontal circuit, a key network for cognitive operations.

Spatial and temporal correlations in human cortex are inherently linked and predicted by functional hierarchy, vigilance state as well as antiepileptic drug load

The ability of neural circuits to integrate information over time and across different cortical areas is believed an essential ingredient for information processing in the brain. Temporal and spatial correlations in cortex dynamics have independently been shown to capture these integration properties in task-dependent ways. A fundamental question remains if temporal and spatial integration properties are linked and what internal and external factors shape these correlations. Previous research on spatio-temporal correlations has been limited in duration and coverage, thus providing only an incomplete picture of their interdependence and variability. Here, we use long-term invasive EEG data to comprehensively map temporal and spatial correlations according to cortical topography, vigilance state and drug dependence over extended periods of time. We show that temporal and spatial correlations in cortical networks are intimately linked, decline under antiepileptic drug action, and break down during slow-wave sleep. Further, we report temporal correlations in human electrophysiology signals to increase with the functional hierarchy in cortex. Systematic investigation of a neural network model suggests that these dynamical features may arise when dynamics are poised near a critical point. Our results provide mechanistic and functional links between specific measurable changes in the network dynamics relevant for characterizing the brain's changing information processing capabilities.

The contribution of gamma bursting to spontaneous gamma activity in schizophrenia

Increased spontaneous gamma (30-100 Hz) activity (SGA) has been reported in the auditory cortex in schizophrenia. This phenomenon has been correlated with psychotic symptoms such as auditory hallucinations and could reflect the dysfunction of NMDA receptors on parvalbumin-expressing inhibitory interneurons. Previous findings are from time-averaged spectra, so it is unknown whether increased spontaneous gamma occurs at a constant level, or rather in bursts. To better understand the dynamical nature of spontaneous gamma activity in schizophrenia, here we examined the contribution of gamma bursting and the slope of the EEG spectrum to this phenomenon. The main results from this data set were previously reported. Participants were 24 healthy control participants (HC) and 24 matched participants with schizophrenia (SZ). The data were from EEG recordings during auditory steady-state stimulation, which were localized to bilateral pairs of dipoles in auditory cortex. Time-frequency analysis was performed using Morlet wavelets. Oscillation bursts in the gamma range were defined as periods during which power exceeded 2 standard deviations above the trial-wide average value for at least one cycle. We extracted the burst parameters power, count, and area, as well as non-burst trial power and spectral slope. Gamma burst power and non-burst trial power were greater in SZ than HC, but burst count and area did not differ. Spectral slope was less negative in SZ than HC. Regression modeling found that gamma burst power alone best predicted SGA for both HC and SZ (> = 90% of variance), while spectral slope made a small contribution and non-burst trial power did not influence SGA. Increased SGA in the auditory cortex in schizophrenia is accounted for by increased power within gamma bursts, rather than a tonic increase in gamma-range activity, or a shift in spectral slope. Further research will be necessary to determine if these measures reflect different network mechanisms. We propose that increased gamma burst power is the main component of increased SGA in SZ and could reflect abnormally increased plasticity in cortical circuits due to enhanced plasticity of synapses on parvalbumin-expressing inhibitory interneurons. Thus, increased gamma burst power may be involved in producing psychotic symptoms and cognitive dysfunction.

Modulation of Spectral Representation and Connectivity Patterns in Response to Visual Narrative in the Human Brain

The regional brain networks and the underlying neurophysiological mechanisms subserving the cognition of visual narrative in humans have largely been studied with non-invasive brain recording. In this study, we specifically investigated how regional and cross-regional cortical activities support visual narrative interpretation using intracranial stereotactic electroencephalograms recordings from thirteen human subjects (6 females, and 7 males). Widely distributed recording sites across the brain were sampled while subjects were explicitly instructed to observe images from fables presented in “sequential” order, and a set of images drawn from multiple fables presented in “scrambled” order. Broadband activity mainly within the frontal and temporal lobes were found to encode if a presented image is part of a visual narrative (sequential) or random image set (scrambled). Moreover, the temporal lobe exhibits strong activation in response to visual narratives while the frontal lobe is more engaged when contextually novel stimuli are presented. We also investigated the dynamics of interregional interactions between visual narratives and contextually novel series of images. Interestingly, the interregional connectivity is also altered between sequential and scrambled sequences. Together, these results suggest that both changes in regional neuronal activity and cross-regional interactions subserve visual narrative and contextual novelty processing.

Functional neuronal circuitry and oscillatory dynamics in human brain organoids

Human brain organoids replicate much of the cellular diversity and developmental anatomy of the human brain. However, the physiology of neuronal circuits within organoids remains under-explored. With high-density CMOS microelectrode arrays and shank electrodes, we captured spontaneous extracellular activity from brain organoids derived from human induced pluripotent stem cells. We inferred functional connectivity from spike timing, revealing a large number of weak connections within a skeleton of significantly fewer strong connections. A benzodiazepine increased the uniformity of firing patterns and decreased the relative fraction of weakly connected edges. Our analysis of the local field potential demonstrate that brain organoids contain neuronal assemblies of sufficient size and functional connectivity to co-activate and generate field potentials from their collective transmembrane currents that phase-lock to spiking activity. These results point to the potential of brain organoids for the study of neuropsychiatric diseases, drug action, and the effects of external stimuli upon neuronal networks.

Automated intraoperative central sulcus localization and somatotopic mapping using median nerve stimulation

OBJECTIVE

Accurate identification of functional cortical regions is essential in neurological resection. The central sulcus (CS) is an important landmark that delineates functional cortical regions. Median nerve stimulation (MNS) is a standard procedure to identify the position of the CS intraoperatively. In this paper, we introduce an automated procedure that uses MNS to rapidly localize the CS and create functional somatotopic maps.

APPROACH

We recorded electrocorticographic signals from 13 patients who underwent MNS in the course of an awake craniotomy. We analyzed these signals to develop an automated procedure that determines the location of the CS and that also produces functional somatotopic maps.

MAIN RESULTS

The comparison between our automated method and visual inspection performed by the neurosurgeon shows that our procedure has a high sensitivity (89%) in identifying the CS. Further, we found substantial concordance between the functional somatotopic maps generated by our method and passive functional mapping (92% sensitivity).

SIGNIFICANCE

Our automated MNS-based method can rapidly localize the CS and create functional somatotopic maps without imposing additional burden on the clinical procedure. With additional development and validation, our method may lead to a diagnostic tool that guides neurosurgeon and reduces postoperative morbidity in patients undergoing resective brain surgery.

Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model's fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.

Neuronal Oscillatory Signatures in the Developing Mouse Visual Cortex After Short-Term Monocular Deprivation

Development and maturation in cortical networks depend on neuronal activity. For stabilization and pruning of connections, synchronized oscillations play a crucial role. A fundamental mechanism that enables coordinated activity during brain functioning is formed of synchronized neuronal oscillations in low- (delta and theta) and high- (gamma) frequency bands. The relationship between neural synchrony, cognition, and the perceptual process has been widely studied, but any possible role of neural synchrony in amblyopia has been less explored. We hypothesized that monocular deprivation (MD) during early postnatal life would lead to changes in neuronal activity that would be demonstrated by changes in phase-amplitude coupling (PAC) and altered power in specific oscillatory frequency. Our results demonstrate that functional connectivity in the visual cortex is altered by MD during adolescence. The amplitude of high-frequency oscillations is modulated by the phase of low-frequency oscillations. Demonstration of enhanced delta–gamma and theta–gamma PAC indicates that our results are relevant for a broad range of nested oscillatory markers. These markers are inherent to neuronal processing and are consistent with the hypothesized increase in the intrinsic coupling that arises from neural oscillatory phase alignment. Our results reveal distinct frequency bands exhibit altered power and coherence variations modulated by experience-driven plasticity.

Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex

Sensory perception and memory recall generate different conscious experiences. Although externally and internally driven neural activities signifying the same perceptual content overlap in the sensory cortex, their distribution in the prefrontal cortex (PFC), an area implicated in both perception and memory, remains elusive. Here, we test whether the local spatial configurations and frequencies of neural oscillations driven by perception and memory recall overlap in the macaque PFC using high-density electrocorticography and multivariate pattern analysis. We find that dynamically changing oscillatory signals distributed across the PFC in the delta-, theta-, alpha-, and beta-band ranges carry significant, but mutually different, information predicting the same feature of memory-recalled internal targets and passively perceived external objects. These findings suggest that the frequency-specific distribution of oscillatory neural signals in the PFC serves cortical signatures responsible for distinguishing between different types of cognition driven by external perception and internal memory.

Working Memory and Transcranial-Alternating Current Stimulation-State of the Art: Findings, Missing, and Challenges

Working memory (WM) is a cognitive process that involves maintaining and manipulating information for a short period of time. WM is central to many cognitive processes and declines rapidly with age. Deficits in WM are seen in older adults and in patients with dementia, schizophrenia, major depression, mild cognitive impairment, Alzheimer's disease, etc. The frontal, parietal, and occipital cortices are significantly involved in WM processing and all brain oscillations are implicated in tackling WM tasks, particularly theta and gamma bands. The theta/gamma neural code hypothesis assumes that retained memory items are recorded via theta-nested gamma cycles. Neuronal oscillations can be manipulated by sensory, invasive- and non-invasive brain stimulations. Transcranial alternating-current stimulation (tACS) and repetitive transcranial magnetic stimulation (rTMS) are frequency-tuned non-invasive brain stimulation (NIBS) techniques that have been used to entrain endogenous oscillations in a frequency-specific manner. Compared to rTMS, tACS demonstrates superior cost, tolerability, portability, and safety profile, making it an attractive potential tool for improving cognitive performance. Although cognitive research with tACS is still in its infancy compared to rTMS, a number of studies have shown a promising WM enhancement effect, especially in the elderly and patients with cognitive deficits. This review focuses on the various methods and outcomes of tACS on WM in healthy and unhealthy human adults and highlights the established findings, unknowns, challenges, and perspectives important for translating laboratory tACS into realistic clinical settings. This will allow researchers to identify gaps in the literature and develop frequency-tuned tACS protocols with promising safety and efficacy outcomes. Therefore, research efforts in this direction should help to consider frequency-tuned tACS as a non-pharmacological tool of cognitive rehabilitation in physiological aging and patients with cognitive deficits.

Sensory representation of visual stimuli in the coupling of low-frequency phase to spike times

Neural synchronization has been engaged in several brain mechanisms. Previous studies have shown that the interaction between the time of spiking activity and phase of local field potentials (LFPs) plays a key role in many cognitive functions. However, the potential role of this spike-LFP phase coupling (SPC) in neural coding is not fully understood. Here, we sought to investigate the role of this SPC for encoding the sensory properties of visual stimuli. To this end, we measured SPC strength in the preferred and anti-preferred motion directions of stimulus presented inside the receptive field of middle temporal (MT) neurons. We found a selective response in terms of SPC strength for different directions of motion. Remarkably, this selective function is inverted with respect to the spiking activity. In other words, the least SPC occurs for the preferred direction (based on the spike rate), and vice versa; the strongest SPC is induced in the anti-preferred direction. Altogether, these findings imply that the neural system may use spike-LFP phase coupling in the primate visual cortex to encode sensory information.

Laminar microcircuitry of visual cortex producing attention-associated electric fields

Cognitive operations are widely studied by measuring electric fields through EEG and ECoG. However, despite their widespread use, the neural circuitry giving rise to these signals remains unknown because the functional architecture of cortical columns producing attention-associated electric fields has not been explored. Here, we detail the laminar cortical circuitry underlying an attention-associated electric field measured over posterior regions of the brain in humans and monkeys. First, we identified visual cortical area V4 as one plausible contributor to this attention-associated electric field through inverse modeling of cranial EEG in macaque monkeys performing a visual attention task. Next, we performed laminar neurophysiological recordings on the prelunate gyrus and identified the electric-field-producing dipoles as synaptic activity in distinct cortical layers of area V4. Specifically, activation in the extragranular layers of cortex resulted in the generation of the attention-associated dipole. Feature selectivity of a given cortical column determined the overall contribution to this electric field. Columns selective for the attended feature contributed more to the electric field than columns selective for a different feature. Last, the laminar profile of synaptic activity generated by V4 was sufficient to produce an attention-associated signal measurable outside of the column. These findings suggest that the top-down recipient cortical layers produce an attention-associated electric field that can be measured extracortically with the relative contribution of each column depending upon the underlying functional architecture.

Spectral Pattern Similarity Analysis: Tutorial and Application in Developmental Cognitive Neuroscience

The human brain encodes information in neural activation patterns. While standard approaches to analyzing neural data focus on brain (de-)activation (e.g., regarding the location, timing, or magnitude of neural responses), multivariate neural pattern similarity analyses target the informational content represented by neural activity. In adults, a number of representational properties have been identified that are linked to cognitive performance, in particular the stability, distinctiveness, and specificity of neural patterns. However, although growing cognitive abilities across childhood suggest advancements in representational quality, developmental studies still rarely utilize information-based pattern similarity approaches, especially in electroencephalography (EEG) research. Here, we provide a comprehensive methodological introduction and step-by-step tutorial for pattern similarity analysis of spectral (frequency-resolved) EEG data including a publicly available pipeline and sample dataset with data from children and adults. We discuss computation of single-subject pattern similarities and their statistical comparison at the within-person to the between-group level as well as the illustration and interpretation of the results. This tutorial targets both novice and more experienced EEG researchers and aims to facilitate the usage of spectral pattern similarity analyses, making these methodologies more readily accessible for (developmental) cognitive neuroscientists.

Holo-Hilbert spectral-based noise removal method for EEG high-frequency bands

Simultaneous EEG-fMRI is a growing and promising field, as it has great potential to further our understanding of the spatiotemporal dynamics of brain function in health and disease. In particular, there is much interest in understanding the fMRI correlates of brain activity in the gamma band (> 30 Hz), as these frequencies are thought to be associated with cognitive processes involving perception, attention, and memory, as well as with disorders such as schizophrenia and autism. However, progress in this area has been limited due to issues such as MR-induced artifacts in EEG recordings, which seem to be more problematic for gamma frequencies. This paper presents a noise removal method for the gamma band of EEG that is based on the Holo-Hilbert spectral analysis (HHSA), but with a new implementation strategy. HHSA uses a nested empirical mode decomposition (EMD) to identify amplitude and frequency modulations (AM and FM, respectively) by averaging over frequencies with high and significant powers. Our method examines gamma band by applying two layers of EMD to the FM and AM components, removing components with very low power based on the power-instantaneous frequency spectrum, and subsequently reconstructs the denoised gamma-band signal from the remaining components. Simulations demonstrate that our proposed method efficiently reduces artifacts while preserving the original gamma signal which is especially critical for simultaneous EEG/fMRI studies.

Six-dimensional dynamic tractography atlas of language connectivity in the developing brain

During a verbal conversation, our brain moves through a series of complex linguistic processing stages: sound decoding, semantic comprehension, retrieval of semantically coherent words, and overt production of speech outputs. Each process is thought to be supported by a network consisting of local and long-range connections bridging between major cortical areas. Both temporal and extratemporal lobe regions have functional compartments responsible for distinct language domains, including the perception and production of phonological and semantic components. This study provides quantitative evidence of how directly connected inter-lobar neocortical networks support distinct stages of linguistic processing across brain development. Novel six-dimensional tractography was used to intuitively visualize the strength and temporal dynamics of direct inter-lobar effective connectivity between cortical areas activated during each linguistic processing stage. We analysed 3401 non-epileptic intracranial electrode sites from 37 children with focal epilepsy (aged 5-20 years) who underwent extra-operative electrocorticography recording. Principal component analysis of auditory naming-related high-gamma modulations determined the relative involvement of each cortical area during each linguistic processing stage. To quantify direct effective connectivity, we delivered single-pulse electrical stimulation to 488 temporal and 1581 extratemporal lobe sites and measured the early cortico-cortical spectral responses at distant electrodes. Mixed model analyses determined the effects of naming-related high-gamma co-augmentation between connecting regions, age, and cerebral hemisphere on the strength of effective connectivity independent of epilepsy-related factors. Direct effective connectivity was strongest between extratemporal and temporal lobe site pairs, which were simultaneously activated between sentence offset and verbal response onset (i.e. response preparation period); this connectivity was approximately twice more robust than that with temporal lobe sites activated during stimulus listening or overt response. Conversely, extratemporal lobe sites activated during overt response were equally connected with temporal lobe language sites. Older age was associated with increased strength of inter-lobar effective connectivity especially between those activated during response preparation. The arcuate fasciculus supported approximately two-thirds of the direct effective connectivity pathways from temporal to extratemporal auditory language-related areas but only up to half of those in the opposite direction. The uncinate fasciculus consisted of <2% of those in the temporal-to-extratemporal direction and up to 6% of those in the opposite direction. We, for the first time, provided an atlas which quantifies and animates the strength, dynamics, and direction specificity of inter-lobar neural communications between language areas via the white matter pathways. Language-related effective connectivity may be strengthened in an age-dependent manner even after the age of 5.

Predictive Feedback, Early Sensory Representations, and Fast Responses to Predicted Stimuli Depend on NMDA Receptors

Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior (e.g., hearing a siren, we expect to see an ambulance and quickly make way). While there are theoretical and computational frameworks for prediction, the circuit and receptor-level mechanisms are unclear. Using high-density EEG, Bayesian modeling, and machine learning, we show that inferred "causal" relationships between stimuli and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audiovisual delayed match-to-sample task which elicited predictions. Predictive β feedback activated sensory representations in advance of predicted stimuli. Low-dose ketamine, an NMDAR blocker, but not the control drug dexmedetomidine, perturbed behavioral indices of predictions, their representation in higher-order cortex, feedback to posterior cortex, and pre-activation of sensory templates in higher-order sensory cortex. This study suggests that predictions depend on alpha activity in higher-order cortex, β feedback, and NMDARs, and ketamine blocks access to learned predictive information.SIGNIFICANCE STATEMENT We learn the statistical regularities around us, creating associations between sensory stimuli. These associations can be exploited by generating predictions, which enable fast and efficient behavior. When predictions are perturbed, it can negatively influence perception and even contribute to psychiatric disorders, such as schizophrenia. Here we show that the frontal lobe generates predictions and sends them to posterior brain areas, to activate representations of predicted sensory stimuli before their appearance. Oscillations in neural activity (α and β waves) are vital for these predictive mechanisms. The drug ketamine blocks predictions and the underlying mechanisms. This suggests that the generation of predictions in the frontal lobe, and the feedback pre-activating sensory representations in advance of stimuli, depend on NMDARs.

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

Multi-scale neural decoding and analysis

Objective.

Complex spatiotemporal neural activity encodes rich information related to behavior and cognition. Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code. Multi-modal techniques can overcome tradeoffs in the spatial and temporal resolution of a single modality to reveal deeper and more comprehensive understanding of system-level neural mechanisms. Uncovering multi-scale dynamics is essential for a mechanistic understanding of brain function and for harnessing neuroscientific insights to develop more effective clinical treatment.

Approach.

We discuss conventional methodologies used for characterizing neural activity at different scales and review contemporary examples of how these approaches have been combined. Then we present our case for integrating activity across multiple scales to benefit from the combined strengths of each approach and elucidate a more holistic understanding of neural processes.

Main results.

We examine various combinations of neural activity at different scales and analytical techniques that can be used to integrate or illuminate information across scales, as well the technologies that enable such exciting studies. We conclude with challenges facing future multi-scale studies, and a discussion of the power and potential of these approaches.

Significance.

This roadmap will lead the readers toward a broad range of multi-scale neural decoding techniques and their benefits over single-modality analyses. This Review article highlights the importance of multi-scale analyses for systematically interrogating complex spatiotemporal mechanisms underlying cognition and behavior.

Alpha and high gamma phase amplitude coupling during motor imagery and weighted cross-frequency coupling to extract discriminative cross-frequency patterns

Motor imagery modulates specific neural oscillations like actual movement does. Representatively, suppression of the alpha power (e.g., event-related desynchronization [ERD]) is the typical pattern of motor imagery in the motor cortex. However, in addition to this amplitude-based feature, the coupling across frequencies includes important information about the brain functions and the existence of such complex information has been reported in various invasive studies. Yet, the interaction across multiple frequencies during motor imagery processing is still unclear and has not been widely studied, particularly concerning the non-invasive signals. In this study, we provide empirical evidence of the comodulation between the phase of alpha rhythm and the amplitude of high gamma rhythm during the motor imagery process. We used electroencephalography (EEG) in our investigation during the imagination of left- or right-hand movement recorded from 52 healthy subjects, and quantified the ERD of alpha and phase-amplitude coupling (PAC) which is a relative change of modulation index to the base line period (before the cue). As a result, we found that the coupling between the phase of alpha (8-12 Hz) and the amplitude of high gamma (70-120 Hz) and this PAC decreases during motor imagery and then rebounds to the baseline like alpha ERD (r = 0.29 to 0.42). This correlation between PAC and ERD was particularly stronger in the ipsilateral area. In addition, trials that demonstrated higher alpha power during the ready period (before the cue) showed a larger ERD during motor imagery and similarly, trials with higher modulation index during the ready period yielded a greater decrease in PAC during imagery. In the classification analysis, we found that the effective phase frequency that showed better decoding accuracy in left and right-hand imagery, varied across subjects. Motivated by result, we proposed a weighted cross-frequency coupling (WCFC) method that extracts the maximal discriminative feature by combining band power and CFC. In the evaluation, WCFC with only two electrodes yielded a performance comparable to the conventional algorithm with 64 electrodes in classifying left and right-hand motor imagery. These results indicate that the phase-amplitude frequency plays an important role in motor imagery, and that optimizing this frequency ranges is crucial for extracting information features to decode the motor imagery types.

Origins of 1/f-like tissue oxygenation fluctuations in the murine cortex

The concentration of oxygen in the brain spontaneously fluctuates, and the distribution of power in these fluctuations has a 1/f-like spectra, where the power present at low frequencies of the power spectrum is orders of magnitude higher than at higher frequencies. Though these oscillations have been interpreted as being driven by neural activity, the origin of these 1/f-like oscillations is not well understood. Here, to gain insight of the origin of the 1/f-like oxygen fluctuations, we investigated the dynamics of tissue oxygenation and neural activity in awake behaving mice. We found that oxygen signal recorded from the cortex of mice had 1/f-like spectra. However, band-limited power in the local field potential did not show corresponding 1/f-like fluctuations. When local neural activity was suppressed, the 1/f-like fluctuations in oxygen concentration persisted. Two-photon measurements of erythrocyte spacing fluctuations and mathematical modeling show that stochastic fluctuations in erythrocyte flow could underlie 1/f-like dynamics in oxygenation. These results suggest that the discrete nature of erythrocytes and their irregular flow, rather than fluctuations in neural activity, could drive 1/f-like fluctuations in tissue oxygenation.

Within-subject reaction time variability: Role of cortical networks and underlying neurophysiological mechanisms

Variations in reaction time are a ubiquitous characteristic of human behavior. Extensively documented, they have been successfully modeled using parameters of the subject or the task, but the neural basis of behavioral reaction time that varies within the same subject and the same task has been minimally studied. In this paper, we investigate behavioral reaction time variance using 28 datasets of direct cortical recordings in humans who engaged in four different types of simple sensory-motor reaction time tasks. Using a previously described technique that can identify the onset of population-level cortical activity and a novel functional connectivity algorithm described herein, we show that the cumulative latency difference of population-level neural activity across the task-related cortical network can explain up to 41% of the trial-by-trial variance in reaction time. Furthermore, we show that reaction time variance may primarily be due to the latencies in specific brain regions and demonstrate that behavioral latency variance is accumulated across the whole task-related cortical network. Our results suggest that population-level neural activity monotonically increases prior to movement execution, and that trial-by-trial changes in that increase are, in part, accounted for by inhibitory activity indexed by low-frequency oscillations. This pre-movement neural activity explains 19% of the measured variance in neural latencies in our data. Thus, our study provides a mechanistic explanation for a sizable fraction of behavioral reaction time when the subject's task is the same from trial to trial.

Sensory feedback-dependent coding of arm position in local field potentials of the posterior parietal cortex

Although multisensory integration is crucial for sensorimotor function, it is unclear how visual and proprioceptive sensory cues are combined in the brain during motor behaviors. Here we characterized the effects of multisensory interactions on local field potential (LFP) activity obtained from the superior parietal lobule (SPL) as non-human primates performed a reaching task with either unimodal (proprioceptive) or bimodal (visual-proprioceptive) sensory feedback. Based on previous analyses of spiking activity, we hypothesized that evoked LFP responses would be tuned to arm location but would be suppressed on bimodal trials, relative to unimodal trials. We also expected to see a substantial number of recording sites with enhanced beta band spectral power for only one set of feedback conditions (e.g. unimodal or bimodal), as was previously observed for spiking activity. We found that evoked activity and beta band power were tuned to arm location at many individual sites, though this tuning often differed between unimodal and bimodal trials. Across the population, both evoked and beta activity were consistent with feedback-dependent tuning to arm location, while beta band activity also showed evidence of response suppression on bimodal trials. The results suggest that multisensory interactions can alter the tuning and gain of arm position-related LFP activity in the SPL.

Intrinsic excitation-inhibition imbalance affects medial prefrontal cortex differently in autistic men versus women

Excitation-inhibition (E:I) imbalance is theorized as an important pathophysiological mechanism in autism. Autism affects males more frequently than females and sex-related mechanisms (e.g., X-linked genes, androgen hormones) can influence E:I balance. This suggests that E:I imbalance may affect autism differently in males versus females. With a combination of in-silico modeling and in-vivo chemogenetic manipulations in mice, we first show that a time-series metric estimated from fMRI BOLD signal, the Hurst exponent (H), can be an index for underlying change in the synaptic E:I ratio. In autism we find that H is reduced, indicating increased excitation, in the medial prefrontal cortex (MPFC) of autistic males but not females. Increasingly intact MPFC H is also associated with heightened ability to behaviorally camouflage social-communicative difficulties, but only in autistic females. This work suggests that H in BOLD can index synaptic E:I ratio and that E:I imbalance affects autistic males and females differently.

Auditory Cortex Volume and Gamma Oscillation Abnormalities in Schizophrenia

We investigated whether the gray matter volume of primary auditory cortex (Heschl's gyrus [HG]) was associated with abnormal patterns of auditory γ activity in schizophrenia, namely impaired γ synchronization in the 40-Hz auditory steady-state response (ASSR) and increased spontaneous broadband γ power. (The γ data were previously reported in Hirano et al, JAMA Psychiatry, 2015;72:813-821). Participants were 24 healthy controls (HC) and 23 individuals with chronic schizophrenia (SZ). The ASSR was obtained from the electroencephalogram to click train stimulation at 20, 30, and 40 Hz rates. Dipole source localization of the ASSR was used to provide a spatial filter of auditory cortex activity, from which ASSR evoked power and phase locking factor (PLF), and induced γ power were computed. HG gray matter volume was derived from structural magnetic resonance imaging at 3 T with manually traced regions of interest. As expected, HG gray matter volume was reduced in SZ compared with HC. In SZ, left hemisphere ASSR PLF and induced γ power during the 40-Hz stimulation condition were positively and negatively correlated with left HG gray matter volume, respectively. These results provide evidence that cortical gray matter structure, possibly resulting from reduced synaptic connectivity at the microcircuit level, is related to impaired γ synchronization and increased spontaneous γ activity in schizophrenia.

Characterizing the orientation selectivity in V1 and V4 of macaques by quadratic phase coupling

OBJECTIVE

Orientation selectivity is one of the significant characteristics of neurons in the primary visual cortex (V1). Some neurons in extrastriate visual cortical areas also exhibit certain orientation selectivity. But it is still not well understood that how the orientation selectivity generates. Most previous studies about the orientation selectivity are based on the spike firing rate. However, the spikes are prone to be biased by the detection and sorting algorithms. Then, in this paper, the local field potential (LFP) is adopted to investigate the mechanism of orientation selectivity.

APPROACH

We used the quadratic phase coupling (QPC), which was calculated by wavelet bicoherence, to describe the characteristics of orientation selectivity available in V1 and V4. The raw wideband neural signals were recorded by two chronically implanted multi-electrode arrays, which were placed in V1 and V4 respectively in two macaques performing a selective visual attention task.

MAIN RESULTS

There is a strong correlation between the total bicoherence (TotalBic), which is a quantization for the overall QPC of frequency pairs in gamma band, and the grating orientation. Furthermore, the QPC distribution at the non-preferred orientation is mainly concentrated in the low frequencies (30-40 Hz) of gamma; while the QPC distribution at the preferred orientation concentrates in both the low frequencies and high frequencies (60-80 Hz) of gamma. In addition, the TotalBic of the gamma-band LFP between V1 and V4 varies with the grating orientations, indicating that the QPC is available in the feedforward link and the gamma-band LFP in V1 modulates the QPC in V4.

SIGNIFICANCE

The QPC reflects the orientations of the sinusoidal grating and describes the interaction of gamma-band LFP between different brain regions. Our results suggest that the QPC is an alternative avenue to explore the mechanism for generating orientation selectivity of visual neurons effectively.

Theta-Nested Gamma Oscillations in Next Generation Neural Mass Models

Theta-nested gamma oscillations have been reported in many areas of the brain and are believed to represent a fundamental mechanism to transfer information across spatial and temporal scales. In a series of recent experiments in vitro it has been possible to replicate with an optogenetic theta frequency stimulation several features of cross-frequency coupling (CFC) among theta and gamma rhythms observed in behaving animals. In order to reproduce the main findings of these experiments we have considered a new class of neural mass models able to reproduce exactly the macroscopic dynamics of spiking neural networks. In this framework, we have examined two set-ups able to support collective gamma oscillations: namely, the pyramidal interneuronal network gamma (PING) and the interneuronal network gamma (ING). In both set-ups we observe the emergence of theta-nested gamma oscillations by driving the system with a sinusoidal theta-forcing in proximity of a Hopf bifurcation. These mixed rhythms always display phase amplitude coupling. However, two different types of nested oscillations can be identified: one characterized by a perfect phase locking between theta and gamma rhythms, corresponding to an overall periodic behavior; another one where the locking is imperfect and the dynamics is quasi-periodic or even chaotic. From our analysis it emerges that the locked states are more frequent in the ING set-up. In agreement with the experiments, we find theta-nested gamma oscillations for forcing frequencies in the range [1:10] Hz, whose amplitudes grow proportionally to the forcing intensity and which are clearly modulated by the theta phase. Furthermore, analogously to the experiments, the gamma power and the frequency of the gamma-power peak increase with the forcing amplitude. At variance with experimental findings, the gamma-power peak does not shift to higher frequencies by increasing the theta frequency. This effect can be obtained, in our model, only by incrementing, at the same time, also the stimulation power. An effect achieved by increasing the amplitude either of the noise or of the forcing term proportionally to the theta frequency. On the basis of our analysis both the PING and the ING mechanism give rise to theta-nested gamma oscillations with almost identical features.

Antiepileptic drugs induce subcritical dynamics in human cortical networks

Cortical network functioning critically depends on finely tuned interactions to afford neuronal activity propagation over long distances while avoiding runaway excitation. This importance is highlighted by the pathological consequences and impaired performance resulting from aberrant network excitability in psychiatric and neurological diseases, such as epilepsy. Theory and experiment suggest that the control of activity propagation by network interactions can be adequately described by a branching process. This hypothesis is partially supported by strong evidence for balanced spatiotemporal dynamics observed in the cerebral cortex; however, evidence of a causal relationship between network interactions and cortex activity, as predicted by a branching process, is missing in humans. Here this cause-effect relationship is tested by monitoring cortex activity under systematic pharmacological reduction of cortical network interactions with antiepileptic drugs. This study reports that cortical activity cascades, presented by the propagating patterns of epileptic spikes, as well as temporal correlations decline precisely as predicted for a branching process. The results provide a missing link to the branching process theory of cortical network function with implications for understanding the foundations of cortical excitability and its monitoring in conditions like epilepsy.