Search bioRxivSearch

bioRxiv · 10.1101/708602

Alpha rhythms reveal when, where and how memories are retrieved

Abstract

Our memories for past experiences can range from vague recognition to full-blown recall of associated details. Neuroimaging research has tried to understand the brain mechanisms underlying qualitatively different memories for decades (Yonelinas, 2002). On the one hand, Electroencephalography (EEG) has shown that recall signals unfold a few hundred milliseconds after simple recognition and are hallmarked by sustained voltage deflections over left posterior sensors (Herron, 2007; Johansson & Mecklinger, 2003; Mecklinger, Rosburg, & Johansson, 2016; Rugg & Curran, 2007). However, sensor-based analyses only provide limited insights into the supporting brain networks. On the other hand, functional magnetic resonance imaging (fMRI) has revealed a core recollection network centred on posterior parietal and medial temporal lobe (MTL) regions (Hayama, Vilberg, & Rugg, 2012; Johnson, Suzuki, & Rugg, 2013; King, de Chastelaine, Elward, Wang, & Rugg, 2015; Rugg, Johnson, & Uncapher, 2015; Rugg & Vilberg, 2013; Thakral, Benoit, & Schacter, 2017). However, due to the relatively poor time resolution of fMRI, the temporal dynamics of these regions during retrieval remain largely unknown. In order to overcome these modality-specific limitations, we here used Magnetoencephalography (MEG) in a verbal episodic memory paradigm assessing correct rejection (CR) of lures, item recognition (IR) of old words and associative recall (AR) of paired target words. We found that power decreases in the alpha frequency band (10-12 Hz) systematically track different mnemonic outcomes in both time and space: Over left posterior sensors, alpha power decreased in a stepwise fashion from 500 ms onward, first from CR to IR and then from IR to AR. When projecting alpha power into source space, the core recollection network known from fMRI studies emerged, including posterior parietal cortex (PPC) and hippocampus. While PPC showed a linear change across conditions, hippocampal effects were specific to recall. Critically, the hippocampal recall effect emerged [~]200 ms before the PPC recall effect, suggesting a bottom-up recall signal from hippocampus to PPC. Our data thus link engagement of the core recollection network to the temporal dynamics of episodic memory and suggest that alpha rhythms constitute a fundamental oscillatory mechanism revealing when, where and how our memories are retrieved.\n\nHighlightsO_LIAlpha rhythms distinguish between different retrieval outcomes\nC_LIO_LIAlpha power time courses track item recognition and associative recall\nC_LIO_LISource alpha power decreases track the fMRI core recollection network\nC_LIO_LIHippocampal recall signal precedes parietal signal\nC_LI

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Martin-Buro, M. C., Wimber, M., Henson, R., Staresina, B.. 2019-07-22. Alpha rhythms reveal when, where and how memories are retrieved. https://doi.org/10.1101/708602

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