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•We used human intracranial recordings to investigate auditory conscious perception.•Significant activity for non-perceived sounds was limited to early auditory regions.•Perceived sounds triggered early increased activity in frontal eye fields and thalamus.•A wave of activity followed in frontoparietal association cortex for perceived sounds. Although recent work has made headway in understanding the neural temporospatial dynamics of conscious perception, much of that work has focused on visual paradigms. To determine whether there are shared mechanisms for perceptual consciousness across sensory modalities, here we test within the auditory domain. Participants completed an auditory threshold task while undergoing intracranial electroencephalography. Recordings from >2,800 grey matter electrodes were analyzed for broadband gamma power (a range which reflects local neural activity). For perceived trials, we find nearly simultaneous activity in early auditory regions, the right caudal middle frontal gyrus, and the non-auditory thalamus; followed by a wave of activity that sweeps through auditory association regions into parietal and frontal cortices. For not perceived trials, significant activity is restricted to early auditory regions. These findings show the cortical and subcortical networks involved in auditory perception are similar to those observed with vision, suggesting shared mechanisms for conscious perception.

Millions of people worldwide suffer motor or sensory impairment due to stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, diabetes, and motor neuron diseases such as ALS (amyotrophic lateral sclerosis). A brain-computer interface (BCI), which links the brain directly to a computer, offers a new way to study the brain and potentially restore impairments in patients living with these debilitating conditions. One of the challenges currently facing BCI technology, however, is to minimize surgical risk while maintaining efficacy. Minimally invasive techniques, such as stereoelectroencephalography (SEEG) have become more widely used in clinical applications in epilepsy patients since they can lead to fewer complications. SEEG depth electrodes also give access to sulcal and white matter areas of the brain but have not been widely studied in brain-computer interfaces. Here we show the first demonstration of decoding sulcal and subcortical activity related to both movement and tactile sensation in the human hand. Furthermore, we have compared decoding performance in SEEG-based depth recordings versus those obtained with electrocorticography electrodes (ECoG) placed on gyri. Initial poor decoding performance and the observation that most neural modulation patterns varied in amplitude trial-to-trial and were transient (significantly shorter than the sustained finger movements studied), led to the development of a feature selection method based on a repeatability metric using temporal correlation. An algorithm based on temporal correlation was developed to isolate features that consistently repeated (required for accurate decoding) and possessed information content related to movement or touch-related stimuli. We subsequently used these features, along with deep learning methods, to automatically classify various motor and sensory events for individual fingers with high accuracy. Repeating features were found in sulcal, gyral, and white matter areas and were predominantly phasic or phasic-tonic across a wide frequency range for both HD (high density) ECoG and SEEG recordings. These findings motivated the use of long short-term memory (LSTM) recurrent neural networks (RNNs) which are well-suited to handling transient input features. Combining temporal correlation-based feature selection with LSTM yielded decoding accuracies of up to 92.04 ± 1.51% for hand movements, up to 91.69 ± 0.49% for individual finger movements, and up to 83.49 ± 0.72% for focal tactile stimuli to individual finger pads while using a relatively small number of SEEG electrodes. These findings may lead to a new class of minimally invasive brain-computer interface systems in the future, increasing its applicability to a wide variety of conditions.

Memory consolidation during sleep is thought to depend on the precise temporal coordination of key electrophysiological events, including hippocampal sharp-wave ripples (SWRs), thalamocortical spindles, cortical high-frequency oscillations (CHFOs), and slow oscillations. Despite progress in animal models, the mechanistic interplay between these oscillations and behavioral memory outcomes remains poorly understood in humans. Intracranial EEG (iEEG) offers a unique opportunity to directly observe these dynamics with high spatial and temporal resolution, yet its application in the context of targeted memory reactivation (TMR) during natural sleep has been limited. This study investigated how auditory cues delivered during sleep influence neural signatures of memory consolidation—specifically cue-locked changes in hippocampal ripple activity, thalamo-cortical spindles, and cortical CHFOs—and whether these changes predict post-sleep memory performance. We aimed to identify electrophysiological markers that index a brain state receptive to memory reactivation and stabilization.

Objective:Memory is a critical piece of the human experience and impairments in neural memory networks can have devastating consequences for the affected person. A subtype of memory, episodic memory generates context for the present based on past experience and allows us to make predictions about the future. Episodic memories become stable fixtures through long-term memory consolidation. It is believed that consolidation of episodic memory requires a dynamic interplay between connected hippocampal-cortical networks, mainly during sleep. Sleep oscillations, slow oscillations and thalamocortical spindles, coupled with hippocampal sharp wave ripples (SWR) is proposed to be mechanistically involved in establishing the crucial cortical-subcortical dialog. The current study aimed to determine alterations in typical sleep oscillations and oscillation coupling in patients with and without structural hippocampal damage and correlate them with neuropsychological measures believed to be sensitive to hippocampal dysfunction, i.e., Rey Auditory Verbal Learning Task (RAVLT) and Verbal Paired Associates (VPA-II).Participants and Methods:We used intracranial electroencephalography (iEEG) in 14 patients with epilepsy to directly record hippocampal and neocortical oscillations and neuropsychological measures obtained prior to implantation. Half of the participants were diagnosed with mesial temporal sclerosis (MTS) in the left hippocampus and healthy tissue in the right hippocampus. The other half did not have MTS and had either mesial temporal epilepsy without MTS or extra-temporal seizures. We analyzed hippocampal SWR output from both hippocampi and characterized neocortical slow oscillations and spindles and their coupling for each participant. We correlated electrophysiological data with behavioral results of neuropsychological testing in order to characterize the clinical relevance.Results:SWR analysis revealed significant differences in the frequency, t(7639) = 15.52, p>.001, p > .001), amplitude, t(7664) = -23.93, p > .001, and waveforms (p > .001) of SWR in the sclerotic versus healthy hippocampi. Patients with a sclerotic hippocampus but relatively preserved verbal memory scores (RAVLT, VPA-II) showed increased SWR amplitudes in the contralateral hippocampus compared to patients with low verbal memory scores. Additionally, we found differences between hemispheres in phase amplitude coupling of SWRs to spindles and SOs (p > 0.001). Results of our correlational analysis were variable and dependent upon additional factors, such as age of onset and diagnosis duration.Conclusions:Results from this work will aid in establishing a criterion for characterizing a relationship between subcortical and cortical oscillations as they relate to memory performance. Besides aiding our understanding of the neural mechanisms underpinning memory consolidation this will ideally help with developing neurophysiological biomarkers that may predict possible memory decline in resective or ablative neurosurgery absent of structural lesion. In addition, this work may potentially provide first evidence of a neurophysiological biomarker directly recorded from the human hippocampus to support possible reorganization of memory functioning in the non-sclerotic hippocampus.

Memory is a critical piece of the human experience. This is why damage or degradation to neural memory networks and related brain structures is so devastating; memory shapes what we understand to be our lives and 'world.' Episodic memory generates context for the present based on past experience and allows us to make predictions about the future. The hippocampus is a structure with a crucial role in episodic memory. On the other hand, long-term consolidation of episodic memories requires a series of highly connected networks that must work together to encode, store, and retrieve information. The precise orchestration of electrophysiological oscillations, or oscillation coupling, facilitates consolidation of memory traces in the brain, particularly in offline sleep states. Hallmark sleep oscillations, slow oscillations, and thalamocortical spindles, couple with hippocampal sharp wave ripples to establish a cortical-subcortical dialog necessary for long-term memory consolidation. The current study aims to explore the effect on behavior when there are alterations to typical sleep oscillations originating from the hippocampus (SWR). To address these questions, we employed intracranial electroencephalography (iEEG) to directly record hippocampal oscillations and neocortical oscillations from within the brain. Using this method allows us to gain a unique temporal and spatial perspective on electrophysiological biomarkers of memory not easily accessible with other methods. Results from this study uncovered evidence of SWR features specific to patients with left mesial temporal sclerosis and a higher degree of variability among patients with seizure foci not localized to either hippocampus.There were also notable findings related to the relationship between SWR features and performance on the Rey Auditory Verbal Learning Test (RAVLT). Taken together, this study provides a meaningful contribution to work examining the role of SWR in humans and their potential as a biomarker for long-term memory processes.

Neural representations of perceptual decision formation that are abstracted from specific motor requirements have previously been identified in humans using non-invasive electrophysiology; however, it is currently unclear where these originate in the brain. Here we capitalized on the high spatiotemporal precision of intracranial EEG to localize such abstract decision signals. Participants undergoing invasive electrophysiological monitoring for epilepsy were asked to judge the direction of random-dot stimuli and respond either with a speeded button press ( N  = 24), or vocally, after a randomized delay ( N  = 12). We found a widely distributed motor-independent network of regions where high-frequency activity exhibited key characteristics consistent with evidence accumulation, including a gradual buildup that was modulated by the strength of the sensory evidence, and an amplitude that predicted participants’ choice accuracy and response time. Our findings offer a new view on the brain networks governing human decision-making. Using intracranial EEG in human participants, the authors identify a functionally distinct set of brain regions which exhibited characteristic signatures of decision formation independently of the motor action associated with the choice.

The discrete events of our narrative experience are organized by the neural substrate that underlies episodic memory. This narrative process is segmented into discrete units by event boundaries. This permits a replay process that acts to consolidate each event into a narrative memory. High frequency oscillations (HFOs) are a potential mechanism for synchronizing neural activity during these processes. Here, we use intracranial recordings from participants viewing and freely recalling a naturalistic stimulus. We show that hippocampal HFOs increase following event boundaries and that coincident hippocampal-cortical HFOs (co-HFOs) occur in cortical regions previously shown to underlie event segmentation (inferior parietal, precuneus, lateral occipital, inferior frontal cortices). We also show that event-specific patterns of co-HFOs that occur during event viewing re-occur following the subsequent three event boundaries (in decaying fashion) and also during recall. This is consistent with models that support replay as a mechanism for memory consolidation. Hence, HFOs may coordinate activity across brain regions serving widespread event segmentation, encode naturalistic memory, and bind representations to assemble memory of a coherent, continuous experience.

Natural vision is an active sensing process that entails frequent eye movements to sample the environment. Nonetheless vision is often studied using passive viewing with eye position held constant. Using closed-loop eye-tracking, with saccade-contingent stimulation and simultaneous intracranial recordings in surgical epilepsy patients, we tested the critical role of eye movement signals during natural visual processing in the hippocampus and hippocampal-amygdala circuit. Prior work shows that saccades elicit phase reset of ongoing neural excitability fluctuations across a broad array of cortical and subcortical areas. Here we show that saccade-related reset systematically modulates neuronal ensemble responses to visual input, enables phase-coding of information across the saccade-fixation cycle and modulates network connectivity between hippocampus and amygdala. The saccade-fixation cycle thus emerges as a fundamental sampling unit, organizing a range of neural operations including input representation, network connectivity and information coding.Competing Interest StatementThe authors have declared no competing interest.

Episodic memory function is predicated upon the precise coordination between the hippocampus and widespread cortical regions. However, our understanding of the neural mechanisms involved in this process is incomplete. In this study, human subjects undergoing intracranial electroencephalography (iEEG) monitoring performed a list learning task. We show sharp-wave ripple (SWR)-locked reactivation of specific semantic processing regions during free recall. This cortical activation consists of both broadband high frequency (non-oscillatory) and cortical ripple (oscillatory) activity. SWRs and cortical ripples in the anterior temporal lobe, a major semantic hub, co-occur and increase in rate prior to recall. Coincident hippocampal-ATL ripples are associated with a greater increase in cortical reactivation, show specificity in location based on recall content, and are preceded by cortical theta oscillations. These findings may represent a reactivation of hippocampus and cortical semantic regions orchestrated by an interplay between hippocampal SWRs, cortical ripples, and theta oscillations.

Neural representations of perceptual decision formation that are abstracted from specific motor requirements have previously been identified in humans using non-invasive electrophysiology, however, it is currently unclear where these originate in the brain. Here, we capitalized on the high spatiotemporal precision of intracranial EEG to localize such abstract decision signals. Presurgical epilepsy patients judged the direction of random-dot stimuli and responded either with a speeded button press (N=23), or vocally, after a randomized delay (N=11). We found a widely distributed motor-independent network of regions where high-frequency activity exhibited key characteristics consistent with evidence accumulation, including a gradual build-up that was modulated by the strength of the sensory evidence, and an amplitude that predicted subjects’ choice accuracy and response time. Our findings offer a new view on the brain networks governing human decision making.