Search bioRxiv⌕ Search

bioRxiv · 10.1101/2021.10.11.463988

Thalamic activity during scalp slow waves in humans

Abstract

Slow waves are major pacemakers of NREM sleep oscillations. While slow waves themselves are mainly generated by cortical neurons, it is not clear what role thalamic activity plays in the generation of some oscillations grouped by slow waves, and to what extent thalamic activity during slow waves is itself driven by corticothalamic inputs. To address this question, we simultaneously recorded both scalp EEG and local field potentials from six thalamic nuclei (bilateral anterior, mediodorsal and ventral anterior) in fifteen epileptic patients (age-range: 17-64 years, 7 females) undergoing Deep Brain Stimulation Protocol and assessed the temporal evolution of thalamic activity relative to scalp slow waves using time-frequency analysis. We found that thalamic activity in all six nuclei during scalp slow waves is highly similar to what is observed on the scalp itself. Slow wave downstates are characterized by delta, theta and alpha activity and followed by beta, high sigma and low sigma activity during subsequent upstates. Gamma activity in the thalamus is not significantly grouped by slow waves. Theta and alpha activity appeared first on the scalp, but sigma activity appeared first in the thalamus. These effects were largely independent from the scalp region in which SWs were detected and the precise identity of thalamic nuclei. Our results indicate that while small thalamocortical neuron assemblies may initiate cortical oscillations, especially in the sleep spindle range, the large-scale neuronal activity in the thalamus which is detected by field potentials is principally driven by global cortical activity, and thus it is highly similar to what is observed on the scalp.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Peter, U. P., Szalardy, O., Fabo, D., Eross, L., Bodizs, R.. 2021-10-12. Thalamic activity during scalp slow waves in humans. https://doi.org/10.1101/2021.10.11.463988

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Novel Tau Interaction Partners as Potential Biomarkers and Therapeutic Targets in Neurodegenerative Diseases

Alzheimers disease is a progressive neurodegenerative disorder leading to cognitive decline and ultimately death. One of the main hallmarks of this disease is the accumulation of pathological tau protein into neurofibrillary tangles. To better understand these pathological mechanisms, it is essential to characterize the tau protein interactome in health and disease. In this study, we performed a yeast two-hybrid screening using a human brain cDNA library, from which we identified 15 novel tau-interacting proteins that bind to full-length human tau, or its fragments: the N-terminus, microtubule-binding domain, or C-terminus. We subsequently validated these novel interactions using in vitro co-immunoprecipitation and colocalization assays with both physiological and pathological tau isoforms. Furthermore, their interactions with the human tau protein were analyzed using surface plasmon resonance. Finally, an in silico interactome analysis of these newly discovered tau-binding partners revealed several additional interactors that can link them to downstream processes critical to neurodegenerative pathways Our findings offer fresh insights into tau biology and highlight novel targets for therapeutic intervention and biomarker development.

neuroscience↗

Early visual cortex is not functionally necessary for temporal attention: A TMS study

Temporal attention enhances the selective processing of information at a specific point in time and improves perception. Previous neuroimaging studies investigating its neural mechanisms have either conflated temporal attention with temporal expectation or provided correlational evidence. Thus, whether early visual cortex is functionally necessary for temporal attention remains unknown. Here, we combined psychophysics with transcranial magnetic stimulation (TMS) to examine whether V1/V2 are functionally necessary and play a causal role in temporal attention. Observers performed an orientation-discrimination task in which two consecutive stimuli, T1 and T2, were presented in the periphery, preceded by a precue providing either valid or neutral temporal information. Double-pulse TMS was delivered under four conditions: at T1 (T1-TMS), at T2 (T2-TMS), at both T1 and T2 (both-TMS), or not at all (none-TMS), to assess its effects on temporal attention and the efficacy of the stimulation was verified by pupil size analysis. Behavioral results revealed robust temporal attention benefits on d', reaction time, and the Balanced Integration Score (BIS) across all four TMS conditions, indicating that the benefits of temporal attention were unaffected by occipital TMS. Furthermore, pupil dilation was largest in the both-TMS condition, followed by the T1-TMS and T2-TMS conditions, and smallest in the none-TMS condition, providing physiological verification of the stimulation protocol. Together, these findings reveal the first causal evidence that the early visual cortex is not functionally necessary for endogenous temporal attention.

neuroscience↗

Disentanglement of persistent and dynamic neural substrates of working memory

Competing theories of working memory have alternatively emphasized the importance of persistent and dynamic substrates of this cognitive function. Here, we directly tested these theories by computationally isolating the contributions of persistent and dynamic activity to working memory. We analyzed neuronal population activity recordings in macaques doing match/non-match working memory tasks. We first extracted signatures of persistent and dynamic activity through cue-specific temporal means, temporal principal components, and correlations of activity patterns across task epochs. We then generated synthetic data constrained by these signatures, and decoded cue stimuli from these data, separately from persistent and non-persistent neurons. We found that high decoding performance arose from a nuanced interplay of persistent and dynamic activity. Specifically, we could decode stimuli at empirical or above-chance levels across all tasks only from persistent neurons, but only after constraining correlations of activity patterns across epochs. In this way, we showed distinct roles for persistent and dynamic neuronal activity that help to unify two competing theories of working memory. More broadly, we also advanced computational controlled experiments as a principled framework for adjudicating between competing models of neuronal population activity.

neuroscience↗