Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2022.09.21.508847

Efficient estimation of time-dependent functional connectivity using Structural Connectivity constraints

Hernandez, H.·Araya, D.·Gonzalez, L. L.·Roman, C.·Trujillo-Barreto, N.·Guevara, P.·El-Deredy, W.
View original source ↗Export metadata ↓

Abstract

Multivariate autoregressive models [MAR] allows estimating effective brain connectivity by considering both power and phase fluctuations of the signals involved. A MAR models brain activity in one region as a linear combination of past activations in all other regions. A Hidden Markov model, HMM, whose states emisions are drawn from state-specific MARs, can then be used to model fast switching of effective brain connectivity over time. However, the large number of MAR parameters, impede the accurate and efficient estimation of such models from neuroimaging timeseries with limited length. We propose a new model for inferring time-dependent effective brain connectivity by using a sparse MAR parameterisation to model the states emisions of a Hidden Semi-Markov Model, HsMM-MAR-AC. The sparse MAR model parameters are restricted by Anatomical Connectivity information in two ways: direct effective connectivity between two regions is only considered if the corresponding structural link/connection exists; and the time-lag associated with each direct connection is computed based on the average fibre length between the two regions, such that only one lag per connection is estimated. We simulated ground truth time-dependent brain connectivity states by generating time-series of 4 to 10 minutes sampled at 5ms, from switching Resting State Networks with a reference structural connectivity, and evaluated the accuracy of the new model in recovering the simulated Brain connectivity States against different levels of connectivity thresholding above and below the reference. We show that even when restricting the MAR to half of the reference connections, the model was able to recover the number of brain states, the associated connectivity features and their dynamics, with as little data as 4 mins. More relaxed structural connectivity thresholds required longer data to estimate the model accurately, and became computationally unfeasible without anatomical restrictions. HsMM-MAR-AC offers an efficient algorithm for estimating time-depend Effective Connectivity (tdEC) from neuroimaging data, that exploits the advantages of MAR without identifiability problems, excessive demand on data collection, or unnecessary computational complexity.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hernandez, H., Araya, D., Gonzalez, L. L., Roman, C., Trujillo-Barreto, N., Guevara, P., El-Deredy, W.. 2022-09-22. Efficient estimation of time-dependent functional connectivity using Structural Connectivity constraints. https://doi.org/10.1101/2022.09.21.508847

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

KEEP EXPLORING

Related preprints

bioRxiv2026

StableProt: Structure-Aware Deep Learning for Protein Thermostability (Tm) and Environmental Adaptation (OGT) Prediction

Predicting protein melting temperature (Tm) and organismal optimal growth temperature (OGT) is essential for enzyme engineering, biocatalyst design, and metagenomic bioprospecting, yet computational prediction faces several fundamental bottlenecks: sequence-only representations miss tertiary packing; dual-task coupling induces destructive gradient conflict; natural database skew (>87% mesophiles) drives mesophilic collapse; and existing tools produce uncalibrated point predictions. To address these challenges, we present StableProt, a structure-aware framework that couples amino acid sequences with Foldseek 3Di structural tokens via a frozen SaProt backbone to capture 3D contacts. Disjoint heads condition thermodynamic stability on ecological context without gradient conflict, while targeted 14% mesophilic subsampling expands thermophile representation to eliminate mesophilic collapse. Calibrated ensemble modeling reduces calibration error to 1.92%, providing dependable 95% confidence intervals. Evaluating models across 10{degrees}C temperature intervals reveals severe baseline collapse at thermal extremes, whereas StableProt maintains robust accuracy across regimes. On ProThermDB and zero-shot FireProtDB, StableProt achieves state-of-the-art probabilistic accuracy (CRPS = 4.52{degrees}C and 8.71{degrees}C) while recovering 92.0% of hyperthermophiles ([≥]80{degrees}C), and in prospective validation, its calibrated intervals capture both enzyme stability (75.3% coverage) and host environmental growth temperatures (78.1% coverage). StableProt is freely accessible as an interactive web server at https://stableprot.dixitlab.org/.

bioinformatics↗
bioRxiv2026

RoDPAA: A Robust Dissimilarity-Based Principal Angle Analysis Framework for Multi-View Microbiome Data Integration

Multiview microbiome studies profile the same participants across body sites or molecular layers, but integration is hampered by sparse, compositional data, modest sample sizes and methods that recognize only exact sharing. We present RoDPAA (Robust Dissimilarity-based Principal Angle Analysis), a framework that represents each view through any data-appropriate dissimilarity, such as Bray--Curtis, without log-ratio transformation or likelihood assumptions. RoDPAA aligns view-specific signal subspaces through principal angles, captures complete and partial sharing, and tests by permutation how many components are shared, using only eigendecomposition and little tuning. In Gaussian simulations, RoDPAA matched JIVE and MOFA given true ranks, ran faster and alone recovered the shared dimension in every replicate. In two inflammatory bowel disease (IBD) cohorts with sparse, zero-inflated data, Bray--Curtis-based RoDPAA recovered shared structure that was stable to prevalence filtering, whereas log-ratio-based alternatives were not. Its leading microbiome--metabolome factors, learned without disease labels, were the only ones associated with IBD.

bioinformatics↗
bioRxiv2026

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗