Search bioRxiv⌕ Search

bioRxiv · 10.1101/2024.05.25.595917

Dynamic and topological properties of large-scale brain networks in rapid eye movement behavior disorder

Abstract

IntroductionThere is a lack of research in the existing literature when it comes to analyzing the dynamics of resting-state functional magnetic resonance imaging to understand the underlying mechanisms of isolated rapid eye movement sleep behavior disorder (iRBD). This study aims to contribute to our understanding of abnormalities in brain network dynamics in iRBD and their association with alpha-synucleinopathy. Additionally, I employed graph theoretical metrics to obtain a topological insight into the brain network of iRBD. MethodsResting-state fMRI data from 55 iRBD patients and 97 healthy controls (HCs) were utilized. A sliding window approach, functional connectivity analysis, and graph theory analysis were applied to the data. I calculated the mean, standard deviation, skewness, and kurtosis of the time series for both dynamic functional connectivity (dFC) and four graph metrics (clustering coefficient, global efficiency, assortativity coefficients, and eigenvector centrality). Subsequently, I compared the those metrices between iRBDs and HCs. Relationships between clinical scales and abnormal dFC were assessed using a general linear model. ResultsiRBD patients exhibited abnormal mean dFC, particularly in the default mode network, sensorimotor network, basal ganglia network, and cerebellum. Kurtosis of dFC revealed abnormalities between the middle temporal gyrus and cerebellum. Group differences were also observed in the mean eigenvector centrality of the precentral gyrus and thalamus. ConclusionThe mean of dFC identified impairments putatively in movement functions and various compensatory mechanisms. Moreover, mean eigenvector centrality revealed topological changes in motor-related network in iRBDs. The use of kurtosis as a potential index for extracting dynamic information may provide additional insights into pathophysiology in iRBDs.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

LI, Y., yoshinaga, k., Hanakawa, T.. 2024-05-26. Dynamic and topological properties of large-scale brain networks in rapid eye movement behavior disorder. https://doi.org/10.1101/2024.05.25.595917

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↗