Biology subjects

Nourski, K. V.

Publications and source records attributed to Nourski, K. V..

18 recordsLinked to original sources

bioRxiv2026

Network and hierarchical organization of intrinsic timescales in the human brain

Intrinsic neural timescales represent the characteristic duration over which information is maintained in neuronal circuits. Evidence suggests that neural timescales vary systematically across the cortical hierarchy, with shorter timescales in primary sensory areas and longer timescales in higher-order association regions. In previous studies, hierarchy has been defined categorically, anatomically, or from the principal gradient of resting-state fMRI functional connectivity derived using diffusion map embedding (DME). Here, we assign hierarchical position to individual human intracranial electroencephalography (iEEG) recording sites by projecting their MNI coordinates onto this embedding space, derived from Human Connectome Project resting-state fMRI data. We estimated neural timescales from resting-state iEEG recordings in adult neurosurgical patients (n=46, 25 female) by extracting the aperiodic component of the local field potential power spectrum using spectral parameterization. Timescales increased monotonically with hierarchical position and associated with two region of interest (ROI)-level measures of network topology derived from DME of participants iEEG functional connectivity: ROIs with stronger mean functional connectivity exhibited longer timescales, as did ROIs functioning as hubs, defined by proximity to the center of embedding space. Finally, timescales varied with sleep stage, with slowest values during NREM and fastest during wake and REM. The hierarchical gradient present during wake and N1 was no longer detected during REM, N2, and N3 sleep, driven by a selective increase in timescales at lower levels of the hierarchy. This work presents a novel metric of hierarchy that can be applied to iEEG data, establishes a direct link between neural timescales, cortical hierarchy, and network topology in human iEEG, and demonstrates that this hierarchical organization is dynamically modulated by brain state. Significance StatementThe brain processes information across multiple timescales simultaneously, with different cortical regions specialized for fast sensory processing or slow integration of context. We measured intrinsic neural timescales from human intracranial electrophysiological recordings and applied a novel method to locate each recording site along the cortical hierarchy. Timescales increased systematically from sensory to association areas, and were longest in network hubs, i.e., regions that are uniformly and widely connected to the rest of the brain. During sleep, this hierarchical organization was not detected, with sensory areas showing the largest increases in timescale. These findings advance our understanding of how the brains temporal organization is shaped by network architecture and modulated by brain state.

neuroscience↗

bioRxiv2026

Early reactivation of medial temporal lobe neurons during emergence from propofol anesthesia in neurosurgical patients

Although much is known about the molecular and cellular effects of general anesthetics, the neural mechanisms underlying loss and recovery of consciousness during anesthesia remain elusive. We provide the first report of human single neuron activity recorded throughout emergence from general anesthesia. Following cessation of propofol infusion, emergence was assessed as motor response to verbal command (RC). In most cells, firing rates increased during the course of emergence. Analysis of pooled firing rate changes indicated a shift towards criticality during emergence and attractor states during and immediately following anesthesia and immediately after RC. Changes in activity in some regions occurred prior to overt RC, with shortest latencies in the hippocampus, parahippocampal gyrus, and amygdala, followed by cingulate and then insular cortex. This work reveals that brain activity in medial temporal regions may presage restoration of responsiveness in humans following general anesthesia.

neuroscience↗

bioRxiv2025

Reading specific memories from human neurons before and after sleep

The ability to retrieve a single episode encountered just once is a hallmark of human intelligence and episodic memory[1]. Yet, decoding a specific memory from neuronal activity in the human brain remains a formidable challenge. Here, we develop a transformer neural network model[2, 3] trained on neuronal spikes from intracranial microelectrodes recorded during a single viewing of an audiovisual episode. Combining spikes throughout the brain via cross-channel attention[4], capable of discovering neural patterns spread across brain regions and timescales, individual participant models predict vocalization of specific concepts such as persons or places during out-of-distribution memory recall. Brain regions differentially contribute to memory decoding before and after sleep. Models trained using only medial temporal lobe (MTL) spikes significantly decode concepts before but not after sleep, while models trained using only frontal cortex (FC) spikes decode concepts after but not before sleep, with better FC decoding relating to increased non-REM sleep. These findings suggest a system-wide distribution of information across neural populations that transforms over wake/sleep cycles[5]. Such decoding of internally generated memories suggests a path towards brain-computer interfaces to treat episodic memory disorders through enhancement or muting of specific memories.

neuroscience↗

bioRxiv2025

Dexmedetomidine produces more sleep-like brain activity compared to propofol

IntroductionDexmedetomidine is a selective 2-adrenergic agonist used as an anesthesia adjunct to produce a state of sleep-like sedation. However, how brain activity compares quantitatively during dexmedetomidine anesthesia to that during natural sleep, and thus just how "sleep-like" dexmedetomidine anesthesia is, remains unclear. Previously, we showed that the general anesthetic propofol is associated with changes in connectivity and cortical network structure comparable to those observed during sleep. Here, we compare the effects on brain activity of dexmedetomidine, propofol, and sleep quantitatively using intracranial encephalographic (iEEG) recordings in human research participants. MethodsiEEG recordings were obtained in 34 epilepsy patients being evaluated for potential seizure resection surgery. Band power and functional connectivity (alpha weighted phase lag index, gamma envelope correlations) and network entropy were measured in recordings during task-free ("resting state") periods just prior to surgery during anesthesia with either dexmedetomidine or propofol, and during overnight sleep. Anesthesia stage (wake, sedated, unresponsive) was determined using the Observers Assessment of Arousal and Sedation. Sleep was staged using standard polysomnography. ResultsAs expected, significant differences in delta power were observed during dexmedetomidine and propofol as well as during sleep. However, the magnitude of changes in delta power were smaller and regionally heterogeneous for propofol compared to dexmedetomidine and sleep. Functional connectivity changes were comparable between dexmedetomidine, propofol, and natural sleep. Significant changes in network entropy were observed for dexmedetomidine, propofol, and sleep, but changes were larger for propofol compared to dexmedetomidine and sleep. Quantitative comparisons between changes in delta power and network entropy suggest that unresponsiveness under dexmedetomidine produces a similar brain state to that observed during N2 sleep. ConclusionsWhile delta power, functional connectivity, and network entropy all showed changes during propofol, dexmedetomidine, and sleep, the magnitudes of these changes suggest that dexmedetomidine is more similar than propofol to sleep, specifically to N2 sleep.

neuroscience↗

bioRxiv2025

Human insula neurons respond to simple sounds during passive listening

The insula is critical for integrating sensory information from the body with that arising from the environment. Although previous studies suggest that posterior insula is sensitive to sounds, auditory response properties of insula neurons have not previously been reported. Here, we provide the first report of a population of human single neuron data from the insula and provide comparative data from the primary auditory cortex, recorded intracranially from human participants during passive listening. In each condition, more than 330 single neurons were recorded in 11 participants. Almost a third of neurons in posterior insula and a smaller subset in anterior insula responded to simple tones and clicks. Responsive neurons were distributed throughout posterior and anterior insula and showed preferred frequency tuning. Onset latencies in the insula were similar to those in the primary auditory cortex but response durations were significantly shorter. Overall, these data highlight that insula neurons respond to auditory stimuli even in non-behaviorally relevant contexts and suggest an important contribution of audition to the postulated integrative functions of insular cortex.

neuroscience↗

bioRxiv2024

Traveling waves shape neural population dynamics enabling predictions and internal model updating

The brain generates predictions based on statistical regularities in our environment. However, it is unclear how predictions are optimized through iterative interactions with the environment. Because traveling waves (TWs) propagate across the cortex shaping neural excitability, they can carry information to serve predictive processing. Using human intracranial recordings, we show that anterior-to-posterior alpha TWs correlated with prediction strength. Learning about priors altered neural state space trajectories, and how much it altered correlated with trial-by-trial prediction strength. Learning involved mismatches between predictions and sensory evidence triggering alpha-phase resets in lateral temporal cortex, accompanied by stronger alpha phase-high gamma amplitude coupling and high-gamma power. The mismatch initiated posterior-to-anterior alpha TWs and change in the subsequent trials state space trajectory, facilitating model updating. Our findings suggest a vital role of alpha TWs carrying both predictions to sensory cortex and mismatch signals to frontal cortex for trial-by-trial fine-tuning of predictive models.

neuroscience↗

bioRxiv2023

Activity-induced gene expression in the human brain

Direct electrical stimulation (eSTIM) is widely used clinically, from neurosurgical mapping to therapeutic interventions for neurological and neuropsychiatric disorders1-10. Despite over a century of application, its molecular and cellular underpinnings remain unknown. Here, using state-of-the-art single-nuclei multiomic profiling, we map changes in cell-type-specific gene expression and chromatin accessibility in vivo in the human cortex following eSTIM of neurosurgery patients. eSTIM impacts a network of cells that extends beyond excitatory neurons to include inhibitory neurons, astrocytes, oligodendrocytes and microglia. We observed an upregulation of canonical immediate-early genes (IEGs: FOS, NPAS4, EGR4) in excitatory and inhibitory neurons and induction of cytokine-related genes CCL3 and CCL4 in microglia. The cross-species conservation of this gene signature, together with our examination of a cohort of both epilepsy and cancer patients, underscores the fundamental role of these changes in stimulation-driven plasticity while controlling for disease and environmental confounds. Our study of changes in chromatin accessibility reveals a common code that involves a cell-type specific signature of transcription factor binding motifs for members of the EGR family. By addressing these previously unexplored questions about activity-induced gene expression in vivo in the human brain, our findings challenge the long-standing neuron-centric view of eSTIM, highlighting the broader role of non-neuronal cells, including microglia, in mediating the impact of brain stimulation.

neuroscience↗

bioRxiv2023

Exercise modulates human hippocampal-cortical ripple dynamics

Physical exercise acutely improves hippocampus-dependent memory. Whereas animal studies have offered cellular- and synaptic-level accounts of these effects, human neuroimaging studies show that exercise improves hippocampal-cortical connectivity at the macroscale level. However, the neurophysiological basis of exercise-induced effects on hippocampal-cortical circuits remains unknown. Experimental evidence supports that hippocampal sharp wave-ripples (SWR) play a critical role in learning and memory. Coupling between SWRs in the hippocampus and neocortex may reflect modulations in inter-regional connectivity required by mnemonic processes. Here, we examine the hypothesis that exercise modulates hippocampal-cortical ripple dynamics in the human brain. We performed intracranial recordings in epilepsy patients undergoing pre-surgical evaluation, during awake resting state, before and after an exercise session. Exercise increased ripple rate in the hippocampus. Exercise also enhanced the coupling and phase-synchrony between cortical ripples in the limbic and the default mode (DM) cortical networks and hippocampal SWRs. Further, higher heart rate during exercise, reflecting exercise intensity, was related to a subsequent increase in resting state ripples across specific cortical networks including the DMN. These results offer the first direct evidence that a single exercise session elicits changes in ripple events, a well-established neurophysiological marker of mnemonic processing. The characterization and anatomical distribution of the described modulation points to hippocampal ripples as a potential mechanism by which exercise elicits its reported short-term effects in cognition.

neuroscience↗

bioRxiv2022

Improved high-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors

Multivariate autoregressive (MVAR) model estimation enables assessment of causal interactions in brain networks. However, accurately estimating MVAR models for high-dimensional electrophysiological recordings is challenging due to the extensive data requirements. Hence, the applicability of MVAR models for study of brain behavior over hundreds of recording sites has been very limited. Prior work has focused on different strategies for selecting a subset of important MVAR coefficients in the model and is motivated by the potential of MVAR models and the data requirements of conventional least-squares estimation algorithms. Here we propose incorporating prior information, such as fMRI, into MVAR model estimation using a weighted group LASSO regularization strategy. The proposed approach is shown to reduce data requirements by a factor of two relative to the recently proposed group LASSO method of Endemann et al. (2022) while resulting in models that are both more parsimonious and have higher fidelity to the ground truth. The effectiveness of the method is demonstrated using simulation studies of physiologically realistic MVAR models derived from iEEG data. The robustness of the approach to deviations between the conditions under which the prior information and iEEG data is obtained is illustrated using models from data collected in different sleep stages. This approach will allow accurate effective connectivity analyses over short time scales, facilitating investigations of causal interactions in the brain underlying perception and cognition during rapid transitions in behavioral state.

neuroscience↗

bioRxiv2022

Consciousness is indexed by analogous cortical reorganization during sleep and anesthesia

Although sleep and anesthesia are predicted to share common neural signatures of transitions into and out of unconsciousness, supportive evidence has been elusive. We identified these signatures using intracranial electroencephalography in neurosurgical patients. We applied diffusion map embedding to map cortical location into a space where proximity indicates functional similarity using a normalized connectivity ( diffusion) matrix, itself a rich source of information about network properties. During reduced consciousness, diffusion matrices exhibited decreased effective dimensionality, reflecting reduced network entropy. Furthermore, functional brain regions exhibited tighter clustering in embedding space with greater distances between regions, corresponding to decreased differentiation and functional integration. These changes were not region-specific, suggesting global network reorganization. These results strongly suggest common neural substrates for loss and recovery of consciousness during anesthesia and sleep, providing a systems-level mechanistic understanding within an intuitive geometric context and laying the foundation for evaluation of cortical state transitions in clinical settings.

neuroscience↗

bioRxiv2022

Functional geometry of auditory cortical resting state networks derived from intracranial electrophysiology

Understanding central auditory processing critically depends on defining underlying auditory cortical networks and their relationship to the rest of the brain. We addressed these questions using resting state functional connectivity derived from human intracranial electroencephalography. Mapping recording sites into a low-dimensional space where proximity represents functional similarity revealed a hierarchical organization. At fine scale, a group of auditory cortical regions excluded several higher order auditory areas and segregated maximally from prefrontal cortex. On mesoscale, the proximity of limbic structures to auditory cortex suggested a limbic stream that parallels the classically described ventral and dorsal auditory processing streams. Identities of global hubs in anterior temporal and cingulate cortex depended on frequency band, consistent with diverse roles in semantic and cognitive processing. On a macro scale, observed hemispheric asymmetries were not specific for speech and language networks. This approach can be applied to multivariate brain data with respect to development, behavior, and disorders. BlurbWe describe the organization of human neocortex on multiple spatial scalesbased on resting state intracranial electrophysiology. We focus on cortical regions involved in auditory processing and examine inter-regional hierarchical relationships, network topology, and hemispheric lateralization. This work introduces a powerful analytical tool to examine mechanisms of altered arousal states, brain development, and neuropsychiatric disorders.

neuroscience↗

bioRxiv2021

Multivariate autoregressive model estimation for high dimensional intracranial electrophysiological data

Fundamental to elucidating the functional organization of the brain is the assessment of causal interactions between different brain regions. Multivariate autoregressive (MVAR) modeling techniques applied to multisite electrophysiological recordings are a promising avenue for identifying such causal links. They estimate the degree to which past activity in one or more brain regions is predictive of another regions present activity, while simultaneously accounting for the mediating effects of other regions. Including in the model as many mediating variables as possible has the benefit of drastically reducing the odds of detecting spurious causal connectivity. However, effective bounds on the number of MVAR model coefficients that can be estimated reliably from limited data make exploiting the potential of MVAR models challenging. Here, we utilize well-established dimensionality-reduction techniques to fit MVAR models to human intracranial data from {bsim}100 - 200 recording sites spanning dozens of anatomically and functionally distinct cortical regions. First, we show that high dimensional MVAR models can be successfully estimated from long segments of data and yield plausible connectivity profiles. Next, we use these models to generate synthetic data with known ground-truth connectivity to explore the utility of applying principal component analysis and group least absolute shrinkage and selection operator (LASSO) to reduce the number of parameters (connections) during model fitting to shorter data segments. We show that group LASSO is highly effective for recovering ground truth connectivity in the limited data regime, capturing important features of connectivity for high-dimensional models with as little as 10 s of data. The methods presented here have broad applicability to the analysis of high-dimensional time series data in neuroscience, facilitating the elucidation of the neural basis of sensation, cognition, and arousal.

neuroscience↗

bioRxiv2020

Oscillatory correlates of auditory working memory examined with human electrocorticography

This work examines how sounds are held in auditory working memory (AWM) in humans by examining oscillatory local field potentials (LFPs) in candidate brain regions. Previous fMRI studies by our group demonstrated blood oxygenation level-dependent (BOLD) response increases during maintenance in auditory cortex, inferior frontal cortex and the hippocampus using a paradigm with a delay period greater than 10s. The relationship between such BOLD changes and ensemble activity in different frequency bands is complex, and the long delay period raised the possibility that long-term memory mechanisms were engaged. Here we assessed LFPs in different frequency bands in six subjects with recordings from all candidate brain regions using a paradigm with a short delay period of 3 s. Sustained delay activity was demonstrated in all areas, with different patterns in the different areas. Enhancement in low frequency (delta) power and suppression across higher frequencies (beta/gamma) were demonstrated in primary auditory cortex in medial Heschls gyrus (HG) whilst non-primary cortex showed patterns of enhancement and suppression that altered at different levels of the auditory hierarchy from lateral HG to superior- and middle-temporal gyrus. Inferior frontal cortex showed increasing suppression with increasing frequency. The hippocampus and parahippocampal gyrus showed low frequency increases and high frequency decreases in oscillatory activity. The work demonstrates sustained activity patterns that can only be explained by AWM maintenance, with prominent low-frequency increases in medial temporal lobe regions.

neuroscience↗

bioRxiv2020

Differential causal involvement of human auditory and frontal cortices in vocal motor control

Speech motor control requires integration of sensory and motor information. Bidirectional communication between frontal and auditory cortices is crucial for speech production, self-monitoring and motor control. We used cortical direct electrical stimulation (DES) to functionally dissect audio-motor interactions underlying speech production and motor control. Eleven neurosurgical patients performed a visually cued vocal task in which a short auditory feedback perturbation was introduced during vocalization. We evaluated the effect of DES on vocal initiation, voice fundamental frequency (F0) and feedback-dependent motor control. DES of frontal sites modulated vocal onset latencies. Stimulation of different inferior frontal gyrus sites elicited either shortening or prolongation of vocal latencies. DES distinctly modulated voice F0 at different vocalization stages. Frontal and temporal areas played an important role in setting voice F0 in the first 250 ms of an utterance, while Heschls gyrus was involved later when auditory input is available for self-monitoring. Vocal responses to pitch-shifted auditory feedback were mostly reduced by DES of non-core auditory cortices. Overall, we demonstrate that vocal planning and initiation are driven by frontal cortices, while feedback-dependent control relies predominantly on non-core auditory cortices. Our findings represent direct evidence of the role played by different auditory and frontal regions in vocal motor control.

neuroscience↗

bioRxiv2020

Common Fronto-temporal Effective Connectivity in Humans and Monkeys

Cognitive pathways supporting human language and declarative memory are thought to have uniquely evolutionarily differentiated in our species. However, cross-species comparisons are missing on site-specific effective connectivity between regions important for cognition. We harnessed a new approach using functional imaging to visualize the impact of direct electrical brain stimulation in human neurosurgery patients. Applying the same approach with macaque monkeys, we found remarkably comparable patterns of effective connectivity between auditory cortex and ventro-lateral prefrontal cortex (vlPFC) and parahippocampal cortex in both species. Moreover, in humans electrical tractography revealed rapid evoked potentials in vlPFC from stimulating auditory cortex and speech sounds drove vlPFC, consistent with prior evidence in monkeys of direct projections from auditory cortex to vocalization responsive regions in vlPFC. The results identify a common effective connectivity signature that from auditory cortex is equally direct to vlPFC and indirect to the hippocampus (via parahippocampal cortex) in human and nonhuman primates. HighlightsO_LIPrivileged human auditory to inferior frontal connectivity, linked to monkeys C_LIO_LICommon auditory to parahippocampal effective connectivity in both species C_LIO_LIGreater lateralization in human effective connectivity, more symmetrical in monkeys C_LIO_LIHuman fronto-temporal network function rooted in evolutionarily conserved signature C_LI eTOC short summaryFunctional connectivity between regions crucial for language and declarative memory is thought to have substantially differentiated in humans. Using a new technique to similarly visualize directional effective connectivity in humans and monkeys, we found remarkably comparable connectivity patterns in both species between fronto-temporal regions crucial for cognition.

neuroscience↗

bioRxiv2019

Cortical functional connectivity indexes arousal state during sleep and anesthesia

Disruption of cortical connectivity likely contributes to loss of consciousness (LOC) during both sleep and general anesthesia, but the degree of overlap in the underlying mechanisms is unclear. Both sleep and anesthesia comprise states of varying levels of arousal and consciousness, including states of largely maintained consciousness (sleep: N1, REM; anesthesia: sedated but responsive) as well as states of substantially reduced consciousness (sleep: N2/N3; anesthesia: unresponsive). Here, we tested the hypotheses that (1) cortical connectivity will reflect clear changes when transitioning into states of reduced consciousness, and (2) these changes are similar for arousal states of comparable levels of consciousness during sleep and anesthesia. Using intracranial recordings from five neurosurgical patients, we compared resting state cortical functional connectivity (as measured by weighted phase lag index) in the same subjects across arousal states during natural sleep [wake (WS), N1, N2, N3, REM] and propofol anesthesia [pre-drug wake (WA), sedated/responsive (S) and unresponsive (U)]. In wake states WS and WA, alpha-band connectivity within and between temporal, parietal and occipital regions was dominant. This pattern was largely unchanged in N1, REM and S. Transitions into states of reduced consciousness N2, N3 and U were characterized by dramatic and strikingly similar changes in connectivity, with dominant connections shifting to frontal cortex. We suggest that shifts from temporo-parieto-occipital to frontal cortical connectivity may reflect impaired sensory processing in states of reduced consciousness. The data indicate that functional connectivity can serve as a biomarker of arousal state and suggest common mechanisms of LOC in sleep and anesthesia.

neuroscience↗

bioRxiv2019

A sound-sensitive source of alpha oscillations in human non-primary auditory cortex

The functional organization of human auditory cortex can be probed by characterizing responses to various classes of sound at different anatomical locations. Along with histological studies this approach has revealed a primary field in posteromedial Heschls gyrus (HG) with pronounced induced high-frequency (70-150 Hz) activity and short-latency responses that phase-lock to rapid transient sounds. Low-frequency neural oscillations are also relevant to stimulus processing and information flow, however their distribution within auditory cortex has not been established. Alpha activity (7-14 Hz) in particular has been associated with processes that may differentially engage earlier versus later levels of the cortical hierarchy, including functional inhibition and the communication of sensory predictions. These theories derive largely from the study of occipitoparietal sources readily detectable in scalp electroencephalography. To characterize the anatomical basis and functional significance of less accessible temporal-lobe alpha activity we analyzed responses to sentences in seven human adults (four female) with epilepsy who had been implanted with electrodes in superior temporal cortex. In contrast to primary cortex in posteromedial HG, a non-primary field in anterolateral HG was characterized by high spontaneous alpha activity that was strongly suppressed during auditory stimulation. Alpha-power suppression decreased with distance from anterolateral HG throughout superior temporal cortex, and was more pronounced for clear compared to degraded speech. This suppression could not be accounted for solely by a change in the slope of the power spectrum. The differential manifestation and stimulus-sensitivity of alpha oscillations across auditory fields should be accounted for in theories of their generation and function.\n\nSignificance StatementTo understand how auditory cortex is organized in support of perception, we recorded from patients implanted with electrodes for clinical reasons. This allowed measurement of activity in brain regions at different levels of sensory processing. Oscillations in the alpha range (7-14 Hz) have been associated with functions including sensory prediction and inhibition of regions handling irrelevant information, but their distribution within auditory cortex is not known. A key finding was that these oscillations dominated in one particular non-primary field, anterolateral Heschls gyrus, and were suppressed when subjects listened to sentences. These results build on our knowledge of the functional organization of auditory cortex and provide anatomical constraints on theories of the generation and function of alpha oscillations.

neuroscience↗

bioRxiv2018

Processing of Auditory Novelty Across the Cortical Hierarchy: An Intracranial Electrophysiology Study

Under the predictive coding hypothesis, specific spatiotemporal patterns of cortical activation are postulated to occur during sensory processing as expectations generate feedback predictions and prediction errors generate feedforward signals. Establishing experimental evidence for this information flow within cortical hierarchy has been difficult, especially in humans, due to spatial and temporal limitations of non-invasive measures of cortical activity. This study investigated cortical responses to auditory novelty using the local/global deviant paradigm, which engages the hierarchical network underlying auditory predictive coding over short ( local deviance; LD) and long ( global deviance; GD) time scales. Electrocorticographic responses to auditory stimuli were obtained in neurosurgical patients from regions of interest (ROIs) including auditory, auditory-related and prefrontal cortex. LD and GD effects were assayed in averaged evoked potential (AEP) and high gamma (70-150 Hz) signals, the former likely dominated by local synaptic currents and the latter largely reflecting local spiking activity. AEP LD effects were distributed across all ROIs, with greatest percentage of significant sites in core and non-core auditory cortex. High gamma LD effects were localized primarily to auditory cortex in the superior temporal plane and on the lateral surface of the superior temporal gyrus (STG). LD effects exhibited progressively longer latencies in core, non-core, auditory-related and prefrontal cortices, consistent with feedforward signaling. The spatial distribution of AEP GD effects overlapped that of LD effects, but high gamma GD effects were more restricted to non-core areas. High gamma GD effects had shortest latencies in STG and preceded AEP GD effects in most ROIs. This latency profile, along with the paucity of high gamma GD effects in the superior temporal plane, suggest that the STG plays a prominent role in initiating novelty detection signals over long time scales. Thus, the data demonstrate distinct patterns of information flow in human cortex associated with auditory novelty detection over multiple time scales.

neuroscience↗