Biology subjects

Warnke, P. C.

Publications and source records attributed to Warnke, P. C..

4 recordsLinked to original sources

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

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↗

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↗

bioRxiv2022

Electrophysiological Markers of Memory Consolidation in the Human Brain when Memories are Reactivated during Sleep

Human accomplishments depend on learning, and effective learning depends on consolidation. Consolidation is the process whereby new memories are gradually stored in an enduring way in the brain so that they can be available when needed. For factual or event knowledge, consolidation is thought to progress during sleep as well as during waking states, and to be mediated by interactions between hippocampal and neocortical networks. However, consolidation is difficult to observe directly, but rather is inferred through behavioral observations. Here, we investigated overnight memory change by measuring electrical activity in and near the hippocampus. Electroencephalographic (EEG) recordings were made in five patients from electrodes implanted to determine whether a surgical treatment could relieve their seizure disorders. One night, while each patient slept in a hospital monitoring room, we recorded electrophysiological responses to 10-20 specific sounds that were presented very quietly, to avoid arousal. Half of the sounds had been associated with objects and their precise spatial locations that patients learned before sleep. After sleep, we found systematic improvements in spatial recall, replicating prior results. We assume that when the sounds were presented during sleep, they reactivated and strengthened corresponding spatial memories. Notably, the sounds also elicited oscillatory intracranial EEG activity, including increases in theta, sigma, and gamma EEG bands. Gamma responses, in particular, were consistently associated with the degree of improvement in spatial memory exhibited after sleep. We thus conclude that this electrophysiological activity in the hippocampus and adjacent medial temporal cortex reflects sleep-based enhancement of memory storage. Significance StatementSleep contributes to memory consolidation, we presume, because memories are replayed during sleep. Understanding this aspect of consolidation can help with optimizing normal learning in many contexts, and with treating memory disorders and other diseases. Here, we systematically manipulated sleep-based processing using targeted memory reactivation; brief sounds coupled with pre-sleep learning were quietly presented again during sleep, producing (a) recall improvements for specific spatial memories associated with those sounds, and (b) physiological responses in the sleep EEG. Neural activity in the hippocampus and adjacent medial temporal cortex was thus found in association with memory consolidation during sleep. These findings advance understanding of consolidation by linking beneficial memory changes during sleep to both memory reactivation and specific patterns of brain activity.

neuroscience↗