Cross-regional coordination of activity in the human brain during autobiographical self-referential processing.

For the human brain to operate, populations of neurons across anatomical structures must coordinate their activity within milliseconds. To date, our understanding of such interactions has remained limited. We recorded directly from the hippocampus (HPC), posteromedial cortex (PMC), ventromedial/orbital prefrontal cortex (OFC), and the anterior nuclei of the thalamus (ANT) during two experiments of autobiographical memory processing that are known from decades of neuroimaging work to coactivate these regions. In 31 patients implanted with intracranial electrodes, we found that the presentation of memory retrieval cues elicited a significant increase of low frequency (LF < 6 Hz) activity followed by cross-regional phase coherence of this LF activity before select populations of neurons within each of the four regions increased high-frequency (HF > 70 Hz) activity. The power of HF activity was modulated by memory content, and its onset followed a specific temporal order of ANT→HPC/PMC→OFC. Further, we probed cross-regional causal effective interactions with repeated electrical pulses and found that HPC stimulations cause the greatest increase in LF-phase coherence across all regions, whereas the stimulation of any region caused the greatest LF-phase coherence between that particular region and ANT. These observations support the role of the ANT in gating, and the HPC in synchronizing, the activity of cortical midline structures when humans retrieve self-relevant memories of their past. Our findings offer a fresh perspective, with high temporal fidelity, about the dynamic signaling and underlying causal connections among distant regions when the brain is actively involved in retrieving self-referential memories from the past.

Propofol disrupts alpha dynamics in functionally distinct thalamocortical networks during loss of consciousness.

During propofol-induced general anesthesia, alpha rhythms measured using electroencephalography undergo a striking shift from posterior to anterior, termed anteriorization, where the ubiquitous waking alpha is lost and a frontal alpha emerges. The functional significance of alpha anteriorization and the precise brain regions contributing to the phenomenon are a mystery. While posterior alpha is thought to be generated by thalamocortical circuits connecting nuclei of the sensory thalamus with their cortical partners, the thalamic origins of the propofol-induced alpha remain poorly understood. Here, we used human intracranial recordings to identify regions in sensory cortices where propofol attenuates a coherent alpha network, distinct from those in the frontal cortex where it amplifies coherent alpha and beta activities. We then performed diffusion tractography between these identified regions and individual thalamic nuclei to show that the opposing dynamics of anteriorization occur within two distinct thalamocortical networks. We found that propofol disrupted a posterior alpha network structurally connected with nuclei in the sensory and sensory associational regions of the thalamus. At the same time, propofol induced a coherent alpha oscillation within prefrontal cortical areas that were connected with thalamic nuclei involved in cognition, such as the mediodorsal nucleus. The cortical and thalamic anatomy involved, as well as their known functional roles, suggests multiple means by which propofol dismantles sensory and cognitive processes to achieve loss of consciousness.

Subspace partitioning in the human prefrontal cortex resolves cognitive interference.

The human prefrontal cortex (PFC) constitutes the structural basis underlying flexible cognitive control, where mixed-selective neural populations encode multiple task features to guide subsequent behavior. The mechanisms by which the brain simultaneously encodes multiple task-relevant variables while minimizing interference from task-irrelevant features remain unknown. Leveraging intracranial recordings from the human PFC, we first demonstrate that competition between coexisting representations of past and present task variables incurs a behavioral switch cost. Our results reveal that this interference between past and present states in the PFC is resolved through coding partitioning into distinct low-dimensional neural states; thereby strongly attenuating behavioral switch costs. In sum, these findings uncover a fundamental coding mechanism that constitutes a central building block of flexible cognitive control.

Regional variability in intracerebral properties of NREM to REM sleep transitions in humans.

Transitions between wake and sleep states show a progressive pattern underpinned by local sleep regulation. In contrast, little evidence is available on non-rapid eye movement (NREM) to rapid eye movement (REM) sleep boundaries, considered as mainly reflecting subcortical regulation. Using polysomnography (PSG) combined with stereoelectroencephalography (SEEG) in humans undergoing epilepsy presurgical evaluation, we explored the dynamics of NREM-to-REM transitions. PSG was used to visually score transitions and identify REM sleep features. SEEG-based local transitions were determined automatically with a machine learning algorithm using features validated for automatic intra-cranial sleep scoring (10.5281/zenodo.7410501). We analyzed 2988 channel-transitions from 29 patients. The average transition time from all intracerebral channels to the first visually marked REM sleep epoch was 8 s ± 1 min 58 s, with a great heterogeneity between brain areas. Transitions were observed first in the lateral occipital cortex, preceding scalp transition by 1 min 57 s ± 2 min 14 s (d = -0.83), and close to the first sawtooth wave marker. Regions with late transitions were the inferior frontal and orbital gyri (1 min 1 s ± 2 min 1 s, d = 0.43, and 1 min 1 s ± 2 min 5 s, d = 0.43, after scalp transition). Intracranial transitions were earlier than scalp transitions as the night advanced (last sleep cycle, d = -0.81). We show a reproducible gradual pattern of REM sleep initiation, suggesting the involvement of cortical mechanisms of regulation. This provides clues for understanding oneiric experiences occurring at the NREM/REM boundary.

Intracerebral Dynamics of Sleep Arousals: A Combined Scalp-Intracranial EEG Study.

As an intrinsic component of sleep architecture, sleep arousals represent an intermediate state between sleep and wakefulness and are important for sleep-wake regulation. They are defined in an all-or-none manner, whereas they actually present a wide range of scalp-electroencephalography (EEG) activity patterns. It is poorly understood how these arousals differ in their mechanisms. Stereo-EEG (SEEG) provides the unique opportunity to record intracranial activities in superficial and deep structures in humans. Using combined polysomnography and SEEG, we quantitatively categorized arousals during nonrapid eye movement sleep into slow wave (SW) and non-SW arousals based on whether they co-occurred with a scalp-EEG SW event. We then investigated their intracranial correlates in up to 26 brain regions from 26 patients (12 females). Across both arousal types, intracranial theta, alpha, sigma, and beta activities increased in up to 25 regions (p < 0.05; d = 0.06-0.63), while gamma and high-frequency (HF) activities decreased in up to 18 regions across the five brain lobes (p < 0.05; d = 0.06-0.44). Intracranial delta power widely increased across five lobes during SW arousals (p < 0.05 in 22 regions; d = 0.10-0.39), while it widely decreased during non-SW arousals (p < 0.05 in 19 regions; d = 0.10-0.30). Despite these main patterns, unique activities were observed locally in some regions such as the hippocampus and middle cingulate cortex, indicating spatial heterogeneity of arousal responses. Our results suggest that non-SW arousals correspond to a higher level of brain activation than SW arousals. The decrease in HF activities could potentially explain the absence of awareness and recollection during arousals.

Spatiotemporal Dynamics of Successive Activations across the Human Brain during Simple Arithmetic Processing.

Previous neuroimaging studies have offered unique insights about the spatial organization of activations and deactivations across the brain; however, these were not powered to explore the exact timing of events at the subsecond scale combined with a precise anatomical source of information at the level of individual brains. As a result, we know little about the order of engagement across different brain regions during a given cognitive task. Using experimental arithmetic tasks as a prototype for human-unique symbolic processing, we recorded directly across 10,076 brain sites in 85 human subjects (52% female) using the intracranial electroencephalography. Our data revealed a remarkably distributed change of activity in almost half of the sampled sites. In each activated brain region, we found juxtaposed neuronal populations preferentially responsive to either the target or control conditions, arranged in an anatomically orderly manner. Notably, an orderly successive activation of a set of brain regions-anatomically consistent across subjects-was observed in individual brains. The temporal order of activations across these sites was replicable across subjects and trials. Moreover, the degree of functional connectivity between the sites decreased as a function of temporal distance between regions, suggesting that the information is partially leaked or transformed along the processing chain. Our study complements prior imaging studies by providing hitherto unknown information about the timing of events in the brain during arithmetic processing. Such findings can be a basis for developing mechanistic computational models of human-specific cognitive symbolic systems.

High frequency oscillations in human memory and cognition: a neurophysiological substrate of engrams?

Despite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected in the human mesial temporal lobe and neocortical intracranial recordings spanning gamma/epsilon (60-150 Hz), ripple (80-250 Hz) and higher frequency ranges. Separate from other non-oscillatory activities, these brief electrophysiological oscillations of distinct duration, frequency and amplitude are thought to be generated by coordinated spiking of neuronal ensembles within volumes as small as a single cortical column. Although the exact origins, mechanisms and physiological roles in health and disease remain elusive, they have been associated with human memory consolidation and cognitive processing. Recent studies suggest their involvement in encoding and recall of episodic memory with a possible role in the formation and reactivation of memory traces. High frequency oscillations are detected during encoding, throughout maintenance, and right before recall of remembered items, meeting a basic definition for an engram activity. The temporal coordination of high frequency oscillations reactivated across cortical and subcortical neural networks is ideally suited for integrating multimodal memory representations, which can be replayed and consolidated during states of wakefulness and sleep. High frequency oscillations have been shown to reflect coordinated bursts of neuronal assembly firing and offer a promising substrate for tracking and modulation of the hypothetical electrophysiological engram.

Unravelling the origin of the reward positivity: a human intracranial event-related brain potential study.

The reward positivity (RewP) is an event-related brain potential (ERP) component that emerges approximately 250 to 350 milliseconds (ms) after receiving reward-related feedback stimuli and is believed to be important for reinforcement learning and reward processing. Although numerous localization studies have indicated that the anterior cingulate cortex (ACC) is the neural generator of this component, other studies have identified sources outside of the ACC, fuelling a debate about its origin. Because the results of EEG and MEG source localization studies are severely limited by the inverse problem, we addressed this question by leveraging the high spatial and temporal resolution of intracranial EEG. We predicted that we would identify a neural generator of the RewP in the caudal ACC. We recorded intracranial EEG in 19 refractory epilepsy patients who underwent invasive video-EEG monitoring at Ghent University Hospital, Belgium. Participants engaged in the virtual T-maze task (vTMT), a trial-and-error task known to elicit a canonical RewP, while scalp and intracranial EEG were simultaneously recorded. The RewP was identified using a difference wave approach for both scalp and intracranial EEG. The data were aggregated across participants to create a virtual "meta-participant" that contained all the recorded intracranial ERPs (iERPs) with respect to their intracranial contact locations. We used both a hypothesis-driven (focused on ACC) and exploratory (whole-brain analysis) approach to segment the brain into regions of interest (ROI). For each ROI, we evaluated the degree to which the time course of the absolute current density (ACD) activity mirrored the time course of the RewP, and confirmed the statistical significance of the results using permutation analysis. The grand average waveform of the scalp data revealed a RewP at 309 ms after reward feedback with a frontocentral scalp distribution, consistent with the identification of this component as the RewP. The meta-participant contained iERPs recorded from 582 intracranial contacts in total. The ACD activity of the aggregated iERPs were most similar to the RewP in left caudal ACC, left dorsolateral prefrontal cortex, left frontomedial cortex, and left white matter, with the highest score attributed to caudal ACC, as predicted. To our knowledge, this is the first study that uses intracranial EEG aggregated across multiple human epilepsy patients and current source density analysis to identify the neural generator(s) of the RewP. These results provide direct evidence that the ACC is a neural generator of the RewP.

Epileptic activity on foramen ovale electrodes is associated with sleep and tau pathology in Alzheimer's disease.

Both sleep alterations and epileptiform activity are associated with the accumulation of amyloid-ß and tau pathology and are currently investigated for potential therapeutic interventions in Alzheimer's disease (AD). However, a bidirectional intertwining relation between sleep and neuronal hyperexcitability might modulate the effects of AD pathology on the corresponding associations. To investigate this, we performed multiple day simultaneous foramen ovale (FO) plus scalp EEG and polysomnography (PSG) recordings and acquired 18F-MK6240 tau PET-MR in three patients in the prodromal stage of AD and in two patients with mild and moderate dementia due to AD, respectively. As an eligibility criterion for the present study, subjects either had a history of a recent seizure (n = 2) or subclinical epileptiform activity (SEA) on a previous scalp EEG taken in a research context (n = 3). The 18F-MK6240 standard uptake value ratio (SUVR) and asymmetry index (AI) were calculated in a priori defined volumes of interest (VOIs). Linear mixed effects models were used to study associations between interictal epileptiform discharges (IEDs), PSG parameters and 18F-MK6240 SUVR. Epileptiform activity was bilateral but asymmetrically present on FO electrodes in all patients and ≥ 95% of IEDs were not visible on scalp EEG. In one patient two focal seizures were detected on FO electrodes, both without visual scalp EEG correlate. We observed lateralized periodic discharges, brief potentially ictal rhythmic discharges and lateralized rhythmic delta activity on FO electrodes in four patients. Unlike scalp EEG, intracranial electrodes showed a lateralization of epileptiform activity. Although the amount of IEDs on intracranial electrodes was not associated to the 18F-MK6240 SUVR binding in different VOIs, there was a congruent asymmetry of the 18F-MK6240 binding towards the most epileptic hemisphere for the mesial (P = 0.007) and lateral temporal cortex (P = 0.006). IEDs on intracranial electrodes were most abundant during slow wave sleep (SWS) (92/h) and N2 (81/h), followed by N1 (33/h) and least frequent during wakefulness (17/h) and REM sleep (9/h). The extent of IEDs during sleep was not reflected in the relative time in each sleep stage spent (REM% (P = 0.415), N1% (P = 0.668), N2% (P = 0.442), SWS% (P = 0.988)), and not associated with the arousal index (P = 0.317), apnea-hypopnea index (P = 0.846) or oxygen desaturation index (P = 0.746). Together, our observations suggest a multi-directional interaction between sleep, epileptiform activity and tau pathology in AD.

Predictable and unpredictable deviance detection in the human hippocampus and amygdala.

Our brains extract structure from the environment and form predictions given past experience. Predictive circuits have been identified in wide-spread cortical regions. However, the contribution of medial temporal structures in predictions remains under-explored. The hippocampus underlies sequence detection and is sensitive to novel stimuli, sufficient to gain access to memory, while the amygdala to novelty. Yet, their electrophysiological profiles in detecting predictable and unpredictable deviant auditory events remain unknown. Here, we hypothesized that the hippocampus would be sensitive to predictability, while the amygdala to unexpected deviance. We presented epileptic patients undergoing presurgical monitoring with standard and deviant sounds, in predictable or unpredictable contexts. Onsets of auditory responses and unpredictable deviance effects were detected earlier in the temporal cortex compared with the amygdala and hippocampus. Deviance effects in 1-20 Hz local field potentials were detected in the lateral temporal cortex, irrespective of predictability. The amygdala showed stronger deviance in the unpredictable context. Low-frequency deviance responses in the hippocampus (1-8 Hz) were observed in the predictable but not in the unpredictable context. Our results reveal a distributed network underlying the generation of auditory predictions and suggest that the neural basis of sensory predictions and prediction error signals needs to be extended.

Functional and anatomical connectivity predict brain stimulation's mnemonic effects.

Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered the stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

Frequency-specific directed connectivity between the hippocampus and parietal cortex during verbal and spatial episodic memory: an intracranial EEG replication.

Hippocampus-parietal cortex circuits are thought to play a crucial role in memory and attention, but their neural basis remains poorly understood. We employed intracranial intracranial electroencephalography (iEEG) to investigate the neurophysiological underpinning of these circuits across three memory tasks spanning verbal and spatial domains. We uncovered a consistent pattern of higher causal directed connectivity from the hippocampus to both lateral parietal cortex (supramarginal and angular gyrus) and medial parietal cortex (posterior cingulate cortex) in the delta-theta band during memory encoding and recall. This connectivity was independent of activation or suppression states in the hippocampus or parietal cortex. Crucially, directed connectivity from the supramarginal gyrus to the hippocampus was enhanced in participants with higher memory recall, highlighting its behavioral significance. Our findings align with the attention-to-memory model, which posits that attention directs cognitive resources toward pertinent information during memory formation. The robustness of these results was demonstrated through Bayesian replication analysis of the memory encoding and recall periods across the three tasks. Our study sheds light on the neural basis of casual signaling within hippocampus-parietal circuits, broadening our understanding of their critical roles in human cognition.

Recurrent Hippocampo-neocortical sleep-state divergence in humans.

Sleep is assumed to be a unitary, global state in humans and most other animals that is coordinated by executive centers in the brain stem, hypothalamus, and basal forebrain. However, the common observation of unihemispheric sleep in birds and marine mammals, as well as the recently discovered nonpathological regional sleep in rodents, calls into question whether the whole human brain might also typically exhibit different states between brain areas at the same time. We analyzed sleep states independently from simultaneously recorded hippocampal depth electrodes and cortical scalp electrodes in eight human subjects who were implanted with depth electrodes for pharmacologically intractable epilepsy evaluation. We found that the neocortex and hippocampus could be in nonsimultaneous states, on average, one-third of the night and that the hippocampus often led in asynchronous state transitions. Nonsimultaneous bout lengths varied from 30 s to over 30 min. These results call into question the conclusions of studies, across phylogeny, that measure only surface cortical state but seek to assess the functions and drivers of sleep states throughout the brain.

Hippocampal ripples signal contextually mediated episodic recall.

High-frequency oscillatory events, termed ripples, represent synchrony of neural activity in the brain. Recent evidence suggests that medial temporal lobe (MTL) ripples support memory retrieval. However, it is unclear if ripples signal the reinstatement of episodic memories. Analyzing electrophysiological MTL recordings from 245 neurosurgical participants performing episodic recall tasks, we find that the rate of hippocampal ripples rises just prior to the free recall of recently formed memories. This prerecall ripple effect (PRE) is stronger in the CA1 and CA3/dentate gyrus (CA3/DG) subfields of the hippocampus than the neighboring MTL regions entorhinal and parahippocampal cortex. PRE is also stronger prior to the retrieval of temporally and semantically clustered, as compared with unclustered, recalls, indicating the involvement of ripples in contextual reinstatement, which is a hallmark of episodic memory.

Intrinsic Neural Timescales in the Temporal Lobe Support an Auditory Processing Hierarchy.

During rest, intrinsic neural dynamics manifest at multiple timescales, which progressively increase along visual and somatosensory hierarchies. Theoretically, intrinsic timescales are thought to facilitate processing of external stimuli at multiple stages. However, direct links between timescales at rest and sensory processing, as well as translation to the auditory system are lacking. Here, we measured intracranial EEG in 11 human patients with epilepsy (4 women), while listening to pure tones. We show that, in the auditory network, intrinsic neural timescales progressively increase, while the spectral exponent flattens, from temporal to entorhinal cortex, hippocampus, and amygdala. Within the neocortex, intrinsic timescales exhibit spatial gradients that follow the temporal lobe anatomy. Crucially, intrinsic timescales at baseline can explain the latency of auditory responses: as intrinsic timescales increase, so do the single-electrode response onset and peak latencies. Our results suggest that the human auditory network exhibits a repertoire of intrinsic neural dynamics, which manifest in cortical gradients with millimeter resolution and may provide a variety of temporal windows to support auditory processing.SIGNIFICANCE STATEMENT Endogenous neural dynamics are often characterized by their intrinsic timescales. These are thought to facilitate processing of external stimuli. However, a direct link between intrinsic timing at rest and sensory processing is missing. Here, with intracranial EEG, we show that intrinsic timescales progressively increase from temporal to entorhinal cortex, hippocampus, and amygdala. Intrinsic timescales at baseline can explain the variability in the timing of intracranial EEG responses to sounds: cortical electrodes with fast timescales also show fast- and short-lasting responses to auditory stimuli, which progressively increase in the hippocampus and amygdala. Our results suggest that a hierarchy of neural dynamics in the temporal lobe manifests across cortical and limbic structures and can explain the temporal richness of auditory responses.

Concurrent- and After-Effects of Medial Temporal Lobe Stimulation on Directed Information Flow to and from Prefrontal and Parietal Cortices during Memory Formation.

Electrical stimulation of the medial temporal lobe (MTL) has the potential to uncover causal circuit mechanisms underlying memory function. However, little is known about how MTL stimulation alters information flow with frontoparietal cortical regions implicated in episodic memory. We used intracranial EEG recordings from humans (14 participants, 10 females) to investigate how MTL stimulation alters directed information flow between MTL and PFC and between MTL and posterior parietal cortex (PPC). Participants performed a verbal episodic memory task during which they were presented with words and asked to recall them after a delay of ∼20 s; 50 Hz stimulation was applied to MTL electrodes on selected trials during memory encoding. Directed information flow was examined using phase transfer entropy. Behaviorally, we observed that MTL stimulation reduced memory recall. MTL stimulation decreased top-down PFC→MTL directed information flow during both memory encoding and subsequent memory recall, revealing aftereffects more than 20 s after end of stimulation. Stimulation suppressed top-down PFC→MTL influences to a greater extent than PPC→MTL. Finally, MTL→PFC information flow on stimulation trials was significantly lower for successful, compared with unsuccessful, memory recall; in contrast, MTL→ventral PPC information flow was higher for successful, compared with unsuccessful, memory recall. Together, these results demonstrate that the effects of MTL stimulation are behaviorally, regionally, and directionally specific, that MTL stimulation selectively impairs directional signaling with PFC, and that causal MTL-ventral PPC circuits support successful memory recall. Findings provide new insights into dynamic casual circuits underling episodic memory and their modulation by MTL stimulation.SIGNIFICANCE STATEMENT The medial temporal lobe (MTL) and its interactions with prefrontal and parietal cortices (PFC and PPC) play a critical role in human memory. Dysfunctional MTL-PFC and MTL-PPC circuits are prominent in psychiatric and neurologic disorders, including Alzheimer's disease and schizophrenia. Brain stimulation has emerged as a potential mechanism for enhancing memory and cognitive functions, but the underlying neurophysiological mechanisms and dynamic causal circuitry underlying bottom-up and top-down signaling involving the MTL are unknown. Here, we use intracranial EEG recordings to investigate the effects of MTL stimulation on causal signaling in key episodic memory circuits linking the MTL with PFC and PPC. Our findings have implications for translational applications aimed at realizing the promise of brain stimulation-based treatment of memory disorders.

Oscillatory mechanisms of intrinsic human brain networks.

Non-invasive neuroimaging has revealed specific network-based resting-state dynamics in the human brain, yet the underlying neurophysiological mechanism remains unclear. We employed intracranial electroencephalography to characterize local field potentials within the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) in 42 participants. We identified stronger within-network phase coherence at low frequencies (θ and α band) within the DMN, and at high frequencies (γ band) within the FPN. Hidden Markov modeling indicated that the DMN exhibited preferential low frequency phase coupling. Phase-amplitude coupling (PAC) analysis revealed that the low-frequency phase in the DMN modulated the high-frequency amplitude envelopes of the FPN, suggesting frequency-dependent characterizations of intrinsic brain networks at rest. These findings provide intracranial electrophysiological evidence in support of the network model for intrinsic organization of human brain and shed light on the way brain networks communicate at rest.

Temporal interference stimulation targets deep primate brain.

Temporal interference (TI) stimulation, a novel non-invasive stimulation strategy, has recently been shown to modulate neural activity in deep brain regions of living mice. Yet, it is uncertain if this method is applicable to larger brains and whether the electric field produced under traditional safety currents can penetrate deep regions as observed in mice. Despite recent model-based simulation studies offering positive evidence at both macro- and micro-scale levels, the absence of electrophysiological data from actual brains hinders comprehensive understanding and potential application of TI. This study aims to directly measure the spatiotemporal properties of the interfered electric field in the rhesus monkey brain and to validate the effects of TI on the human brain. Two monkeys were involved in the measurement, with implantation of several stereo-electroencephalography (SEEG) depth electrodes. TI stimulation was applied to anesthetized monkeys using two pairs of surface electrodes at differing stimulation parameters. Model-based simulations were also conducted and subsequently compared with actual recordings. Additionally, TI stimulation was administered to patients with motor disorders to validate its effects on motor symptoms. Through the integration of computational electric field simulation with empirical measurements, it was determined that the temporally interfering electric fields in the deep central regions are capable of attaining a magnitude sufficient to induce a subthreshold modulation effect on neural signals. Additionally, an improvement in movement disorders was observed as a result of TI stimulation. This study is the first to systematically measure the TI electric field in living non-human primates, offering empirical evidence that TI holds promise as a more focal and precise method for modulating neural activities in deep regions of a large brain. This advancement paves the way for future applications of TI in treating neuropsychiatric disorders.

The brain dynamics of visuospatial perspective-taking captured by intracranial EEG.

Visuospatial perspective-taking (VPT) is the ability to imagine a scene from a position different from the one used in self-perspective judgments (SPJ). We typically use VPT to understand how others see the environment. VPT requires overcoming the self-perspective, and impairments in this process are implicated in various brain disorders, such as schizophrenia and autism. However, the underlying brain areas of VPT are not well distinguished from SPJ-related ones and from domain-general responses to both perspectives. In addition, hierarchical processing theory suggests that domain-specific processes emerge over time from domain-general ones. It mainly focuses on the sensory system, but outside of it, support for this hypothesis is lacking. Therefore, we aimed to spatiotemporally distinguish brain responses domain-specific to VPT from the specific ones to self-perspective, and domain-general responses to both perspectives. In particular, we intended to test whether VPT- and SPJ specific responses begin later than the general ones. We recorded intracranial EEG data from 30 patients with epilepsy who performed a task requiring laterality judgments during VPT and SPJ, and analyzed the spatiotemporal features of responses in the broad gamma band (50-150 Hz). We found VPT-specific processing in a more extensive brain network than SPJ-specific processing. Their dynamics were similar, but both differed from the general responses, which began earlier and lasted longer. Our results anatomically distinguish VPT-specific from SPJ-specific processing. Furthermore, we temporally differentiate between domain-specific and domain-general processes both inside and outside the sensory system, which serves as a novel example of hierarchical processing.

Deep learning based decoding of single local field potential events.

How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. However, the brain is obviously a single-trial processor. Thus, we here demonstrate that an unsupervised machine learning approach can be used to extract meaningful information from electro-physiological recordings on a single-trial basis. We use an auto-encoder network to reduce the dimensions of single local field potential (LFP) events to create interpretable clusters of different neural activity patterns. Strikingly, certain LFP shapes correspond to latency differences in different recording channels. Hence, LFP shapes can be used to determine the direction of information flux in the cerebral cortex. Furthermore, after clustering, we decoded the cluster centroids to reverse-engineer the underlying prototypical LFP event shapes. To evaluate our approach, we applied it to both extra-cellular neural recordings in rodents, and intra-cranial EEG recordings in humans. Finally, we find that single channel LFP event shapes during spontaneous activity sample from the realm of possible stimulus evoked event shapes. A finding which so far has only been demonstrated for multi-channel population coding.