bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.08.01.668093

Robust and replicable effects of ageing on resting state brain electrophysiology measured with MEG.

Quinn, A. J.·Pitt, J.·Kohl, O.·Gohil, C.·van Es, M. W. J.·Nobre, A. C.·Woolrich, M. W.
View original source ↗Export metadata ↓

Abstract

Non-invasive recordings using brain electrophysiology provide credible insights into decline in neuronal functioning with age. New approaches are required to translate these results into compelling clinical metrics and meet the global challenge of preserving brain health in ageing. Changes in neuronal dynamics with ageing are observable in the power spectra of EEG and MEG recordings. Highly promising candidates for electrophysiological markers have been identified, but progress is hindered by substantial methodological variability across studies. This makes it challenging to establish a clear consensus on the frequency, location, and direction of any single reported effect within the larger body of research. We estimate a full-frequency whole-head profile of the ageing effect on eyes-closed resting-state MEG using the GLM-Spectrum. This data representation is easily sharable, facilitates meta-analyses and provides a framework for estimating statistical power of age effects for future study planning. We use this to show that the effect of age replicates across open-access MEG datasets and is robust to modelling of common covariates. Distinct components within the full frequency profile have different effect sizes, indicating that sample-size planning for ageing effects must consider the specific features of interest. The frequency profile of ageing is strongly robust to a range of common covariates and partially robust to modelling of grey matter volume. We establish that what seems a well-powered study may become underpowered when analyses target an age effect that is linearly separable from an age-relevant covariate such as grey matter volume. These results provide a pathway towards formal comparison and assessment of candidate markers for brain health in ageing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/668093v3_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@1a9c351org.highwire.dtl.DTLVardef@563837org.highwire.dtl.DTLVardef@84b898org.highwire.dtl.DTLVardef@1730472_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThere is a strong effect of ageing across the adult lifespan on neuronal oscillations across a range of frequencies and spatial locations. C_LIO_LIThe effect is replicable across multiple open-access datasets. C_LIO_LIThe effect is robust to a range of sources of inter-subject variability. C_LIO_LIThe age effect has heterogeneous effect size across space and frequency with implications for future study planning. C_LI

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Quinn, A. J., Pitt, J., Kohl, O., Gohil, C., van Es, M. W. J., Nobre, A. C., Woolrich, M. W.. 2025-08-01. Robust and replicable effects of ageing on resting state brain electrophysiology measured with MEG.. https://doi.org/10.1101/2025.08.01.668093

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

KEEP EXPLORING

Related preprints

bioRxiv2026

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↗
bioRxiv2026

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↗
bioRxiv2026

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↗