bioRxiv Science⌕ Search

bioRxiv · 10.1101/2024.05.12.593795

Alterations in Causal Functional Brain Networks in Alzheimer's Disease: A resting-state fMRI study

Abstract

BackgroundAlterations in functional connectivity (FC) of the brain is known to predate the onset of clinical symptoms of Alzheimers disease (AD) by several decades. Identifying the altered functional brain networks in AD can help in its prognosis and diagnosis. ObjectiveFC analysis is predominantly correlational. However, correlation does not necessarily imply causation. This study aims to infer causal functional connectivity (CFC) from functional magnetic resonance imaging (fMRI) data and obtain the sub-networks of CFC that are altered in AD compared to cognitively normal (CN) subjects. MethodsWe used the recently developed Time-aware PC algorithm to infer CFC between brain regions. The CFC outcome was compared with correlation-based functional connectivity obtained by sparse partial correlation. Then, Network-based Statistics (NBS) was used to obtain CFC sub-networks that altered in AD subjects compared to healthy controls while correcting for multiple comparisons at 5% level of significance. ResultsOur findings identified causal brain networks involving the inferior frontal gyrus, superior temporal gyrus (temporal pole), middle temporal gyrus (temporal pole), and different lobes of the cerebellum to be significantly reduced in strength in AD compared to CN group (p-value = 0.0299; NBS corrected). In the sample dataset that has been analysed, no brain networks were found to exhibit significant increase in strength in AD compared to CN group at 5% level of significance with NBS correction. ConclusionsOur findings provide insights into disruptions in causal brain networks in AD. The corresponding brain regions are in agreement with published medical literature on brain regions impacted by AD. Our work establishes a methodology for finding causal brain networks that are affected by AD using TPC algorithm to compute subject-specific CFC and then using NBS for finding CFC subnetworks that alter between AD and CN groups. Larger datasets are expected to identify further subnetworks affected by AD.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Biswas, R., Sripada, S.. 2024-05-14. Alterations in Causal Functional Brain Networks in Alzheimer's Disease: A resting-state fMRI study. https://doi.org/10.1101/2024.05.12.593795

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↗