Biology subjects

Satzer, D.

Publications and source records attributed to Satzer, D..

9 recordsLinked to original sources

bioRxiv2026

Real-time detection of N2 and N3 sleep from scalp and intracranial EEG

Sleep rhythms such as slow oscillations and spindles arise predominantly during N2 and N3 sleep (N2+N3). Closed-loop stimulation targeting these rhythms is a promising strategy to enhance memory. Such stimulation requires online sleep stage classification. Conventional staging labels completed 30-s epochs using past and future data and cannot support online staging. We developed a causal tracker that, every 2 s, estimates the current stage from the preceding 10 s of scalp or intracranial EEG. We also evaluated a simulated rule that permits stimulation after three consecutive estimates indicating N2+N3 sleep. In six adults with drug-resistant epilepsy with simultaneous scalp EEG and stereo-EEG, real-time N2+N3 discrimination remained close to offline scoring of the same recordings (scalp AUC 0.953 versus 0.958). Thalamic channels alone reached AUC 0.934 and exceeded mesial temporal channels in every patient, and scalp plus thalamus performed best (0.967). The scalp tracker, retrained in 28 healthy adults, reached 0.976. When patients transitioned into N2+N3 sleep with stimulation initially disabled, the rule permitted stimulation within 5 min in 63-73% of transitions, with a median delay of 42-64 s among those detected. After leaving N2+N3, stimulation was disabled after a median of 19-22 s. The rule also switched on incorrectly 1.4-1.8 times per hour outside N2+N3 and remained active during 28-31% of REM in patients, compared with 2% in healthy adults. These findings show that both scalp and thalamic EEG can support real-time N2+N3 detection and inform when stimulation is permitted. Translationally, implanted thalamic leads, increasingly used for neuromodulation in epilepsy, could support sleep-stage-dependent stimulation without scalp EEG, while scalp EEG might be used for noninvasive neuromodulation. Closed-loop systems should be evaluated not only by sleep-stage classification accuracy but also by how reliably and promptly they enable and disable stimulation.

neuroscience↗

bioRxiv2026

Thalamic stereo-EEG detects sleep stages N2 and N3 with discrimination approaching scalp EEG

Sleep and epilepsy interact strongly during sleep stages N2 and N3, when synchronization of brain rhythms is increased. Sleep staging relies on scalp EEG, which is often absent or incomplete during stereo-EEG monitoring and unavailable from implanted devices, while thalamic leads are increasingly implanted for epilepsy neuromodulation. Given the role of the thalamus in spindle generation and slow oscillation dynamics, we hypothesized that thalamic channels would identify N2 and N3 (N2+N3) better than other intracranial sites. We analyzed 477 h of stereo-EEG with simultaneous scalp EEG from six adults, including 46 thalamic bipolar channels on 11 electrodes. Two classifiers were evaluated on identical channels: a random forest with hidden Markov decoding (RF + HMM) trained leave-one-participant-out, and the pretrained SleepSEEG algorithm developed without thalamic channels. The primary endpoint was discrimination of N2+N3 from other stages as classified from scalp EEG by a sleep expert. Among regions sampled in all participants, thalamic channels performed best (area under the curve [AUC] 0.94 with RF + HMM and 0.93 with SleepSEEG across whole recordings; 0.88 and 0.86 at night). With RF + HMM, thalamic channels outperformed mesial temporal and temporal neocortical channels in all six participants across whole recordings. The advantage persisted against channel-count-matched subsets and a white-matter control with both classifiers. Individual thalamic electrodes reached AUCs of 0.79-0.99 with RF + HMM (10 of 11 at least 0.89). These offline findings support thalamic N2+N3 detection and motivate real-time evaluation for sleep-targeted neuromodulation.

neuroscience↗

bioRxiv2026

Thalamic Nuclei Differentially Coordinate Propagation of Cortical Slow Oscillations

Sleep slow oscillations (SOs) vary in the spatial extent of their cortical propagation, ranging from widespread Global events to spatially restricted Frontal events. These distinctions could have functional consequences for memory consolidation. It remains unknown whether individual thalamic nuclei are differentially engaged across SO propagation types, and whether thalamic activity before SO onset predicts subsequent propagation. We analyzed simultaneous scalp EEG and thalamic stereo-EEG from 24 full-night recordings in 6 epilepsy patients, sampling 11 thalamic nuclei and enabling direct characterization of nucleus-specific thalamocortical interactions during naturally occurring sleep slow oscillations. Cortical SOs were classified by propagation type and thalamocortical coupling was characterized via peri-event histograms, phase-locking analysis, waveform morphology comparisons, and pre-onset spectral and cross-frequency coupling features. All well-sampled thalamic nuclei showed broad temporal co-occurrence with prefrontal SOs, yet occupied distinct phase positions within the cortical SO cycle. Medial pulvinar (PuM) showed the strongest phase locking across subjects, with significant preference for the ascending post-trough phase. Among the examined nuclei, Frontal SOs were associated with significant phase locking in PuM, whereas Global SOs preferentially synchronized the centromedian nucleus near the cortical down-state trough. Thalamic waveform morphology differed systematically with propagation extent, with opposing effects between pulvinar and intralaminar nuclei. Pre-onset PuM activity showed suppressed alpha, sigma, and beta power and delta-phase cross-frequency coupling before Global SOs (all p < 0.05). The human thalamus engages NREM sleep SOs through nucleus-specific, propagation-sensitive dynamics rather than as a functionally uniform structure. Pre-onset PuM activity contains predictive signatures of cortical SO propagation extent. This finding has implications for closed-loop neuromodulation targeting specific SO states, although replication in larger cohorts is needed.

neuroscience↗

bioRxiv2026

Slow Oscillations Gate Interictal Spikes Across the Human Thalamocortical-Epileptogenic Network

BackgroundSlow oscillations (SOs; 0.5-1.5 Hz), a hallmark of non-rapid eye movement (NREM) sleep, are associated with a marked amplification of interictal epileptiform spike (IIS) activity in focal epilepsy. However, the network-level organization of this effect across the thalamocortical-epileptogenic system, and whether IIS-permissive SOs can be predicted from pre-onset brain states, remain unclear. MethodsWe analyzed simultaneous scalp EEG and stereo-EEG (SEEG) recordings from 6 patients with drug-resistant focal epilepsy across 24 full-day recording days, sampling prefrontal cortex (PFC), thalamus, and seizure onset zone (SOZ). SO-IIS coupling was characterized across vigilance states using peri-event and phase-based analyses, with a gamma-based validation step to reduce contamination by IIS-related slow potentials. Pre-onset phase-amplitude coupling (PAC) was compared between IIS-permissive and non-permissive SOs. ResultsSO-IIS coupling was observed across all regions, with the strongest and most temporally precise pre-trough IIS clustering in the SOZ (peak 4.4% in NREM), exceeding PFC (1.7%) and thalamic coupling. Thalamic coupling was preserved across wakefulness and NREM and was significant in 7/11 nuclei, with nucleus-specific phase preferences. SO morphological features, particularly up-slope and peak-to-peak amplitude at PFC contacts, predicted IIS occurrence in the SOZ. Pre-onset PAC differed significantly between permissive and non-permissive SOs across regions. ConclusionsSO-IIS coupling is a distributed, network-level phenomenon with region- and state-specific characteristics, and pre-onset PAC provides a predictive signature of IIS-permissive brain states. These findings support the feasibility of developing personalized, closed-loop neuromodulatory strategies targeting SO-gated IIS suppression in focal epilepsy.

neuroscience↗

bioRxiv2026

Visuospatial coding by theta oscillations in human hippocampus

The hippocampus has been proposed to support visual processing and perception, challenging longstanding accounts that emphasize navigation or declarative memory. A key prediction of visual-processing accounts is that the hippocampus should exhibit similar visuospatial coding properties to those of higher-order visual neocortical areas, such as sensitivity to the size of visual stimuli and contralateral visual field biases. We tested for these properties using intracranial EEG to measure hippocampal neural activity during a retinotopic mapping task. The hippocampus exhibited characteristic slow ([~]2 Hz) and fast ([~]8 Hz) theta oscillations throughout the task. Fast theta was responsive to the presence but not the amount of visual stimulation. In contrast, slow theta did not generally respond to stimulus presence but scaled with the size of the visual stimulus, consistent with larger receptive fields. Slow theta also showed a contralateral bias, an effect that was specific to the right hippocampus. None of these effects were attributable to microsaccades or performance of the concurrent vigilance task. These findings provide electrophysiological evidence for visual field coding by human hippocampus, supporting accounts of hippocampal function that emphasize its role atop the visual hierarchy. Visual processing of this kind may combine with self-motion, memory, and other signals to support the broader spatial and mnemonic functions with which hippocampal theta oscillations have long been associated.

neuroscience↗

bioRxiv2026

Predictability of Sleep Slow Oscillation Emergence and Spatial Extent from Pre-Onset Neural Dynamics

Slow oscillations (SOs; [~]0.5-1.5 Hz) are a hallmark of non-rapid eye movement (NREM) sleep and are known to support memory consolidation and large-scale cortical communication. Although their instantaneous dynamics are well characterized, the neural processes that precede SO initiation--and whether they predict the spatial extent of the upcoming event--remain largely unknown, despite their potential utility for anticipatory closed-loop intervention. Using high-density EEG from 29 healthy adults, we examined neural activity in the 2-s interval preceding the SO trough. Analyses focused on distinct SO subtypes defined by their spatiotemporal properties: Global, Frontal, and Local. We specifically focused on Global and Frontal events, both of which originate frontally but differ in their propagation. We quantified instantaneous spectral power, time-frequency dynamics, phase-amplitude coupling (PAC), and amplitude-amplitude coupling. Across all analyses, theta-band power (4-8 Hz) emerged as the earliest and most robust predictor of SO initiation, remaining informative after restricting analyses to temporally isolated SOs to reduce potential residual-aftereffect confounds from preceding SOs. Theta power exhibited a sustained rise beginning nearly two seconds before the SO trough ([~]1.6 s before onset). Theta power increases were absent in surrogate epochs and reliably differentiated Global from Frontal SOs with moderate-to-strong effect sizes (Cohens d = 0.45-0.77), demonstrating the robustness of theta power as a physiological signature. Mechanistically, Global SOs were preceded by enhanced delta-theta PAC and broad low-frequency synchronization, whereas Frontal SOs were preceded by elevated theta/alpha-to-beta/low-gamma coupling, reflecting a state of locally enhanced coupling at frequencies higher than the SO range, which appears to restrict propagation. A simple logistic regression classifier using only pre-onset theta power achieved >95% accuracy in distinguishing SOs from surrogate events and differentiated Global from Frontal SOs with [~]83% multiclass accuracy, showing further sensitivity improvements when delta power was included. These findings demonstrate that isolated SOs are preceded by structured network dynamics that are tied to their spatial extent. Thus, theta activity can be used to predict SO occurrence and might be leveraged in closed-loop neuromodulation.

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bioRxiv2023

Tessellation Of Artificial Touch Via Microstimulation Of Human Somatosensory Cortex

When we interact with objects, we rely on signals from the hand that convey information about the object and our interaction with it. A basic feature of these interactions, the locations of contacts between the hand and object, is often only available via the sense of touch. Information about locations of contact between a brain-controlled bionic hand and an object can be signaled via intracortical microstimulation (ICMS) of somatosensory cortex (S1), which evokes touch sensations that are localized to a specific patch of skin. To provide intuitive location information, tactile sensors on the robotic hand drive ICMS through electrodes that evoke sensations at skin locations matching sensor locations. This approach requires that ICMS-evoked sensations be focal, stable, and distributed over the hand. To systematically investigate the localization of ICMS-evoked sensations, we analyzed the projected fields (PFs) of ICMS-evoked sensations - their location and spatial extent - from reports obtained over multiple years from three participants implanted with microelectrode arrays in S1. First, we found that PFs vary widely in their size across electrodes, are highly stable within electrode, are distributed over large swaths of each participants hand, and increase in size as the amplitude or frequency of ICMS increases. Second, while PF locations match the locations of the receptive fields (RFs) of the neurons near the stimulating electrode, PFs tend to be subsumed by the corresponding RFs. Third, multi-channel stimulation gives rise to a PF that reflects the conjunction of the PFs of the component channels. By stimulating through electrodes with largely overlapping PFs, then, we can evoke a sensation that is experienced primarily at the intersection of the component PFs. To assess the functional consequence of this phenomenon, we implemented multi-channel ICMS-based feedback in a bionic hand and demonstrated that the resulting sensations are more localizable than are those evoked via single-channel ICMS.

neuroscience↗

bioRxiv2023

Biomimetic multi-channel microstimulation of somatosensory cortex conveys high resolution force feedback for bionic hands

AO_SCPLOWBSTRACTC_SCPLOWManual interactions with objects are supported by tactile signals from the hand. This tactile feedback can be restored in brain-controlled bionic hands via intracortical microstimulation (ICMS) of somatosensory cortex (S1). In ICMS-based tactile feedback, contact force can be signaled by modulating the stimulation intensity based on the output of force sensors on the bionic hand, which in turn modulates the perceived magnitude of the sensation. In the present study, we gauged the dynamic range and precision of ICMS-based force feedback in three human participants implanted with arrays of microelectrodes in S1. To this end, we measured the increases in sensation magnitude resulting from increases in ICMS amplitude and participants ability to distinguish between different intensity levels. We then assessed whether we could improve the fidelity of this feedback by implementing "biomimetic" ICMS-trains, designed to evoke patterns of neuronal activity that more closely mimic those in natural touch, and by delivering ICMS through multiple channels at once. We found that multi-channel biomimetic ICMS gives rise to stronger and more distinguishable sensations than does its single-channel counterpart. Finally, we implemented biomimetic multi-channel feedback in a bionic hand and had the participant perform a compliance discrimination task. We found that biomimetic multi-channel tactile feedback yielded improved discrimination over its single-channel linear counterpart. We conclude that multi-channel biomimetic ICMS conveys finely graded force feedback that more closely approximates the sensitivity conferred by natural touch.

neuroscience↗

bioRxiv2022

Microstimulation of human somatosensory cortex evokes task-dependent, spatially patterned responses in motor cortex

AO_SCPLOWBSTRACTC_SCPLOWMotor (M1) and somatosensory (S1) cortex play a critical role in motor control but the nature of the signaling between these structures is not known. To fill this gap, we recorded - in three human participants whose hands were paralyzed as a result of a spinal cord injury - the responses evoked in the hand and arm representations of primary motor cortex (M1) while we delivered ICMS to the somatosensory cortex (S1). We found that ICMS of S1 activated some M1 neurons at short, fixed latencies, locked to each pulse in a manner consistent with monosynaptic activation. However, most of the changes in M1 firing rates were much more variable in time, suggesting a more indirect effect of the stimulation. The spatial pattern of M1 activation varied systematically depending on the stimulating electrode: S1 electrodes that elicited percepts at a given hand location tended to activate M1 neurons with movement fields at the same location. However, the indirect effects of S1 ICMS on M1 were strongly context dependent, such that the magnitude and even sign relative to baseline varied across tasks. We tested the implications of these effects for brain-control of a virtual hand, in which ICMS was used to convey tactile feedback about object interactions. While ICMS-evoked activation of M1 disrupted decoder performance, this disruption could be minimized with biomimetic stimulation, which emphasizes contact transients at the onset and offset of grasp, reduces sustained stimulation, and has been shown to convey useful contact-related information. SO_SCPLOWIGNIFICANCEC_SCPLOWMotor (M1) and somatosensory (S1) cortex play a critical role in motor control but the nature of the signaling between these structures is not known. To fill this gap, we recorded from M1 while delivering intracortical microstimulation (ICMS) to S1 of three human participants, whose hands were paralyzed by spinal cord injury. We found that ICMS activates M1 and that the motor fields of activated M1 neurons match the sensory fields of the stimulated S1 electrodes. These findings have important implications for using ICMS to convey tactile feedback for brain-controlled bionic hands. Indeed, the ICMS-evoked M1 activity worsens control of the hand. Fortunately, this effect is minimized by using biomimetic tactile feedback, which emphasizes contact transients and reduces sustained ICMS.

neuroscience↗