bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.10.20.683478

Resting-state Functional Connections with the Hippocampus and with the Caudate Nucleus Predict Working Memory Performance in Multiple Sclerosis

Abstract

Episodic memory (EM) dysfunction is a common symptom of multiple sclerosis (MS). A related process, working memory (WM), supports long-term EM and is often also impaired. Functional connectivity (FC) between the hippocampus and caudate nucleus supports EM performance. However, this connectivitys specific influences on WM performance in MS is relatively unknown. Resting-state FC (RSFC) possesses clinical utility, including predicting cognitive impairments before they are captured by assessment. The present study tested whether RSFC with the hippocampus and caudate could predict WM performance in people with MS and in healthy individuals (HC). In a secondary analysis of 78 participants (42 MS, 36 HC), RSFC between the hippocampus, caudate nucleus, and other regions was quantified. These connections predictive influence on WM performance was then examined using a global WM performance measure for a subset with available neuropsychological data (26 MS, 15 HC). Across the entire sample (N=78), MS participants displayed stronger coupling between the right hippocampus and left dorsomedial prefrontal cortex, compared to HC participants. Within MS participants, stronger coupling between the left hippocampus (LHipp) and left ventral anterior cingulate (LvACC), and between the left caudate (LCaud) and right insula, were observed. Stronger decoupling between the LHipp and left supramarginal gyrus (LSMG) also emerged. Stronger LHipp-LvACC connectivity predicted worse WM performance, whereas stronger LHipp-LSMG connectivity and LCaud-RInsula connectivity each predicted better performance. The hippocampal connections were also inversely correlated. Findings identify a caudate circuit and potential hippocampal network whose aberrant, intrinsic activity could serve as neural markers for WM dysfunction in MS.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Cagna, C. J., Dobryakova, E., Sucich, K., Tong, T. T., Sandry, J.. 2025-10-22. Resting-state Functional Connections with the Hippocampus and with the Caudate Nucleus Predict Working Memory Performance in Multiple Sclerosis. https://doi.org/10.1101/2025.10.20.683478

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↗