Search bioRxivSearch

bioRxiv · 10.1101/702928

Coordination of human hippocampal sharpwave-ripples during NREM sleep with cortical theta bursts, spindles, downstates and upstates

Abstract

In rodents, waking firing patterns replay in NREM sleep during hippocampal sharpwave-ripples (HC-SWR), correlated with neocortical graphoelements (NC-GE). NC-GE include theta-bursts, spindles, downstates and upstates. In humans, consolidation during sleep is correlated with scalp-recorded spindles and down-upstates, but HC-SWR cannot be recorded non-invasively. Here we show in humans of both sexes that HC-SWR are highly correlated with NC-GE during NREM, with significantly more related HC-SWR/NC-GE for downstates or upstates than theta-bursts or spindles, in N2 than N3, in posterior than anterior HC, in frontal than occipital cortex, and ipsilaterally than contralaterally. The preferences interacted, e.g. frontal spindles co-occurred frequently with posterior HC-SWR in N2. These preferred GE, stages and locations for HC-SWR/NC-GE interactions may index selective consolidation activity, although that was not tested in this study. SWR recorded in different HC regions seldom co-occurred, and were related to GE in different cortical areas, showing that HC-NC interact in multiple transient, widespread but discrete, networks. NC-GE tend to occur with consistent temporal relationships to HC-SWR, and to each other. Cortical theta-bursts usually precede HC-SWR, where they may help define cortical input triggering HC-SWR firing. HC-SWR often follow cortical downstate onsets, surrounded by locally-decreased broadband power, suggesting a mechanism synchronizing cortical, thalamic and hippocampal activities. Widespread cortical upstates and spindles follow HC-SWR, consistent with the hypothesized contribution by hippocampal firing during HC-SWR to cortical firing-patterns during upstates and spindles. Overall, our results describe how hippocampal and cortical oscillations are coordinated in humans during events that are critical for memory consolidation in rodents.\n\nSignificance StatementHippocampal sharpwave-ripples, essential for memory consolidation, mark when hippocampal neurons replay waking firing patterns. In rodents, cortical sleep waves coordinate the transfer of temporary hippocampal to permanent cortical memories, but their relationship with human HC-SWR remains unclear. We show that human hippocampal sharpwave-ripples co-occur with all varieties of cortical sleep waves, in all cortical regions, and in all stages of Non-REM sleep but with overall preferences for each of these. We found that sharpwave-ripples in different parts of the hippocampus usually occurred independently of each other, and preferentially interacted with different cortical areas. We found that sharpwave-ripples typically occur after certain types of cortical waves, and before others, suggesting how the cortico-hippocampo-cortical interaction may be organized in time and space.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jiang, X., Gonzalez-Martinez, J., Halgren, E.. 2019-07-15. Coordination of human hippocampal sharpwave-ripples during NREM sleep with cortical theta bursts, spindles, downstates and upstates. https://doi.org/10.1101/702928

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

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience