Biology subjects

Cash, S.

Publications and source records attributed to Cash, S..

6 recordsLinked to original sources

bioRxiv2025

Decoding semantics from natural speech using human intracranial EEG

Brain-computer interfaces (BCIs) hold promise for restoring natural language production capabilities in patients with speech impairments, potentially enabling smooth conversation that conveys meaningful information via synthesized words. While considerable progress has been made in decoding phonetic features of speech, our ability to extract lexical semantic information (i.e. the meaning of individual words) from neural activity remains largely unexplored. Moreover, most existing BCI research has relied on controlled experimental paradigms rather than natural conversation, limiting our understanding of semantic decoding in ecological contexts. Here, we investigated the feasibility of decoding lexical semantic information from stereo-electroencephalography (sEEG) recordings in 14 participants during spontaneous conversation. Using multivariate pattern analysis, we were able to decode word level semantic features during language production with an average accuracy of 21% across all participants compared to a chance level of 10%. This semantic decoding remained robust across different semantic representations while maintaining specificity to semantic features. Further, we identified a distributed left-lateralized network spanning precentral gyrus, pars triangularis, and middle temporal cortex, with low-frequency oscillations showing stronger contributions. Together, our results establish the feasibility of extracting word meanings from neural activity during natural speech production and demonstrate the potential for decoding semantic content from unconstrained speech.

neuroscience↗

bioRxiv2020

Magnetic Temporal Interference For Noninvasive, High-resolution, and Localized Deep Brain Stimulation: Concept Validation

Non-invasive deep brain stimulation has been a major challenge in the field of neuroscience and brain stimulation in the past three decades. Current brain stimulation technologies suffer from such hurdles as the inability to do deep brain stimulation, poor spatial resolution, and invasiveness. Transcranial magnetic stimulation (TMS) technique, for instance, cannot target brain regions deeper than [~]2cm and has a poor spatial resolution, impacting a large area of the peripheral region and leading to various side effects. Implantable electrodes, even though effective for deep brain stimulation, are invasive and carry various drawbacks related to the surgery and site infection. In this paper, we propose a new concept that relies on temporal interference of two high- frequency magnetic fields generated by two electromagnetic coils. The neural system does not respond to each of these high-frequency magnetic fields alone because of the intrinsic low-pass filtering properties of the neural membrane. The peripheral areas of the brain are impacted only by the high-frequency magnetic fields that cannot stimulate the nerves, while the deep brain area where the two fields interfere experiences a magnetic field that contains a low-frequency envelope and therefore the nerves can be stimulated. This technique can noninvasively focus a magnetic or electric beam at any depth inside the brain with a high resolution, without impacting the peripheral regions.

bioengineering↗

bioRxiv2020

Ultra-compact Dual-band Smart NEMS Magnetoelectric Antennas for Simultaneous Wireless Energy Harvesting and Magnetic Field Sensing

Ultra-compact wireless implantable medical devices (IMDs) are in great demand for healthcare applications, in particular for neural recording and stimulation. Current implantable technologies based on miniaturized micro-coils suffer from low wireless power transfer efficiency (PTE) and are not always compliant with the specific absorption rate imposed by the Federal Communications Commission, particularly for deep brain implantation where field attenuation and tissue loss are significant. Moreover, current implantable devices are reliant on recordings of voltage or current. This has two major weaknesses: 1) the necessary direct contact between electrode and tissue degrades over time due to electrochemical fouling and tissue reactions, and 2) the necessity for differential recordings across space. Here, we report, for the first time, an ultra-compact dual-band smart nanoelectromechanical systems magnetoelectric (ME) antenna with a size of 250×174 µm2 that can efficiently perform wireless energy harvesting and sense ultra-small magnetic fields such as those arising from neural activities. The proposed smart ME antenna has a wireless PTE 1∼2 orders of magnitude higher than any other reported miniaturized micro-coil, allowing the wireless IMDs to be compliant with the specific absorption rate (SAR) limit and to operate under safe exposure of radio frequency energy. Furthermore, the magnetic sensing capability of the proposed smart ME antenna, with a limit of detection of 300∼500pT at > 200Hz, should allow the IMDs to record neural magnetic fields from the brain without requiring differential recording.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioengineering↗

bioRxiv2019

Interaction of Hippocampal Ripples and Cortical Slow Waves Leads to Coordinated Large-Scale Sleep Rhythm

The dialogue between cortex and hippocampus is known to be crucial for sleep dependent consolidation of long lasting memories. During slow wave sleep memory replay depends on slow oscillation (SO) and spindles in the (neo)cortex and sharp wave-ripple complexes (SWR) in the hippocampus, however, the mechanisms underlying interaction of these rhythms are poorly understood. Here, we examined the interaction between cortical SOs and hippocampal SWRs in a computational model of the hippocampo-cortico-thalamic network and compared the results with human intracranial recordings during sleep. We observed that ripple occurrence peaked following the onset of SO (Down-to-Up-state transition) and that cortical input to hippocampus was crucial to maintain this relationship. Ripples influenced the spatiotemporal structure of cortical SO and duration of the Up/Down-states. In particular, ripples were capable of synchronizing Up-to-Down state transition events across the cortical network. Slow waves had a tendency to initiate at cortical locations receiving hippocampal ripples, and these \"initiators\" were able to influence sequential reactivation within cortical Up states. We concluded that during slow wave sleep, hippocampus and neocortex maintain a complex interaction, where SOs bias the onset of ripples, while ripples influence the spatiotemporal pattern of SOs.

neuroscience↗

bioRxiv2019

The laminar profile of sleep spindles in humans

Sleep spindles are generated in thalamocortical, corticothalamic and possibly cortico-cortical circuits. Previous hypotheses suggested that slow and fast spindles or spindles with various spatial extent may be generated in different circuits with various cortical laminar innervation patterns. We used NREM sleep EEG data recorded from four human epileptic patients undergoing presurgical electrophysiological monitoring with subdural electrocorticographic grids (ECoG) and implanted laminar microelectrodes penetrating the cortex (IME). The position of IME electrodes within cortical layers was confirmed using postsurgical histological reconstructions. Many micro-domain spindles detected on the IME occurred only in one layer and were absent from the ECoG, but with increasing amplitude simultaneous detection in other layers and on the ECoG became more likely. Macro-domain spindles sufficiently large to be detected on the ECoG were in contrast usually accompanied by IME spindles. Neither micro-domain nor macro-domain spindle cortical profiles were strongly associated with sleep spindle frequency or globality. Multiple-unit and single-unit activity during spindles, however, was heterogeneous across spindle types, but also across layers and subjects. Our results indicate that extremely local spindles may occur in any cortical layer, but co-occurrence at other locations becomes likelier with increasing amplitude and the relatively large spindles detected on ECoG channels have a stereotypical laminar profile. We found no compelling evidence that different spindle types are associated with different laminar profiles, suggesting that they are generated in cortical and thalamic circuits with similar cortical innervation patterns. Local neuronal activity is a stronger candidate mechanism for driving functional differences between spindles subtypes.

neuroscience↗

bioRxiv2018

Theta bursts precede, and spindles follow, cortical and thalamic downstates in human NREM sleep

AbstractSince their discovery, slow oscillations have been observed to group spindles during non-REM sleep. Previous studies assert that the slow oscillation downstate (DS) is preceded by slow spindles (10-12Hz), and followed by fast spindles (12-16Hz). Here, using both direct transcortical recordings in patients with intractable epilepsy (n=10, 8 female), as well as scalp EEG recordings from a healthy cohort (n=3, 1 female), we find in multiple cortical areas that both slow and fast spindles follow the DS. Although discrete oscillations do precede DSs, they are theta bursts (TB) centered at 5-8Hz. TBs were more pronounced for DSs in NREM stage N2 compared with N3. TB with similar properties occur in the thalamus, but unlike spindles they have no clear temporal relationship with cortical TB. These differences in corticothalamic dynamics, as well as differences between spindles and theta in coupling high frequency content, are consistent with NREM theta having separate generative mechanisms from spindles. The final inhibitory cycle of the TB coincides with the DS peak, suggesting that in N2, TB may help trigger the DS. Since the transition to N1 is marked by the appearance of theta, and the transition to N2 by the appearance of DS and thus spindles, a role of TB in triggering DS could help explain the sequence of electrophysiological events characterizing sleep. Finally, the coordinated appearance of spindles and DSs are implicated in memory consolidation processes, and the current findings redefine their temporal coupling with theta during NREM sleep.\n\nSignificance StatementSleep is characterized by large slow waves which modulate brain activity. Prominent among these are downstates, periods of a few tenths of a second when most cells stop firing, and spindles, oscillations at about twelve times a second lasting for about a second. In this study, we provide the first detailed description of another kind of sleep wave: theta bursts, a brief oscillation at about six cycles per second. We show, recording during natural sleep directly from the human cortex and thalamus, as well as on the human scalp, that theta bursts precede, and spindles follow downstates. Theta bursts may help trigger downstates in some circumstances, and organize cortical and thalamic activity so that memories can be consolidated during sleep.

neuroscience↗