bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/345181

The relationship between age, neural differentiation, and memory performance

Koen, J. D.·Hauck, N.·Rugg, M. D.
View original source ↗Export metadata ↓

Abstract

Healthy aging is associated with decreased neural selectivity (dedifferentiation) in category-selective cortical regions. This finding has prompted the suggestion that dedifferentiation contributes to age-related cognitive decline. Consistent with this possibility, dedifferentiation has been reported to negatively correlate with fluid intelligence in older adults. Here, we examined whether dedifferentiation is associated with performance in another cognitive domain - episodic memory - that is also highly vulnerable to aging. Given the proposed role of differentiation in age-related cognitive decline, we predicted there would be a stronger link between dedifferentiation and episodic memory performance in older than in younger adults. Young (18-30 yrs) and older (64-75 yrs) male and female humans underwent fMRI scanning while viewing images of objects and scenes prior to a subsequent recognition memory test. We computed a differentiation index in two regions-of-interest (ROIs): parahippocampal place area (PPA) and lateral occipital complex (LOC). This index quantified the selectivity of the BOLD response to an ROIs preferred versus non-preferred category (scenes for PPA, objects for LOC). The differentiation index in the PPA, but not the LOC, was lower in older than in younger adults. Additionally, the PPA differentiation index predicted recognition memory performance for the studied items. This relationship was independent of and not moderated by age. The PPA differentiation index also predicted performance on a latent fluency factor derived from a neuropsychological test battery; this relationship was also age invariant. These findings suggest that two independent factors, one associated with age, and the other with cognitive performance, drive neural differentiation.\n\nSignificance StatementAging is associated with neural dedifferentiation - reduced neural selectivity in category selective cortical brain regions - which has been proposed to mediate cognitive aging. Here, we examined whether neural differentiation is predictive of episodic memory performance, and whether the relationship is moderated by age. A neural differentiation index was estimated for scene-(PPA) and object-(LOC) selective cortical regions while participants studied images for a subsequent memory test. Age related reductions were observed for the PPA, but not the LOC, differentiation index. Importantly, the PPA differentiation index demonstrated age invariant correlations with subsequent memory performance and a fluency factor derived from a neuropsychological battery. Together, these findings suggest that neural differentiation is associated with two independent factors: age and cognitive performance.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

BibTeXRIS

Koen, J. D., Hauck, N., Rugg, M. D.. 2018-06-13. The relationship between age, neural differentiation, and memory performance. https://doi.org/10.1101/345181

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↗