bioRxiv Science⌕ Search

bioRxiv · 10.64898/2026.08.27.747371

AVOCODO: An open-source multimodal annotation platform for developmental EEG

Abstract

Behavioral annotation of synchronized video recordings is an essential step in developmental electroencephalography (EEG) research, supporting both the identification of behavior-related artifacts and the investigation of brain-behavior relationships. Existing annotation workflows, however, are often fragmented: proprietary EEG software provides limited flexibility for behavioral coding, whereas dedicated behavioral annotation platforms typically lack native integration with EEG data. We developed AVOCODO (Audio/VideO CODing Optimization), an open-source MATLAB-based software platform that integrates synchronized behavioral annotation directly into the EEG workflow. AVOCODO reads native EGI MFF recordings, synchronizes embedded video with EEG, visualizes the audio spectrogram to facilitate precise annotation of vocalizations, and writes user-defined behavioral events directly back into the original MFF recording as native EEG event markers while simultaneously exporting annotations as CSV files. The software supports fully customizable behavioral coding schemes, optional EEG visualization for quality control, and reloading of previously annotated recordings for review and inter-rater verification. Since its initial development in 2024, AVOCODO has been applied internally across five developmental EEG studies involving approximately 500 pediatric participants and more than 3,000 EEG recordings. By bridging behavioral annotation and EEG preprocessing within a unified open-source workflow, AVOCODO has the potential to improve the efficiency, reproducibility, and scalability of behavioral annotation in developmental EEG research.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

An, W. W.. 2026-09-01. AVOCODO: An open-source multimodal annotation platform for developmental EEG. https://doi.org/10.64898/2026.08.27.747371

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

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related preprints

Network analysis reveals converging molecular mechanisms in genetic epilepsies

Background: Rare genetic epilepsies are often difficult to diagnose and treat. They can arise from changes in many different genes, and the molecular basis linking these changes to epilepsy remains poorly understood. This complexity makes it difficult to select effective treatments and develop new therapies, leaving many patients with poorly controlled seizures and reduced quality of life Methods: To address this gap, we first identified epilepsy-associated genes and conducted a network-based analysis of biomolecular interactions in genetic epilepsy to identify converging pathways with high translational relevance and potential therapeutic targets. To run the analysis, we constructed a human brain-specific molecular interaction network by integrating all human pathways from the WikiPathways database. HotNet2 was then applied to identify highly perturbed clusters through evidence-based weight diffusion of EAGs in the human brain network. Results: We identified 2,496 epilepsy-associated genes (EAGs) from multiple resources. These genes encode coding for metabolite interconversion enzymes (20%), transporters (15%), and protein-modifying enzymes (11%). Furthermore, the network analysis identified 19 subnetworks with more than two nodes and 125 unique genes. Gene enrichment analysis revealed convergence of processes including action potential, synaptic function, nervous system development, TOR signaling, cilium organization, DNA repair, and cell fate specification. We identified 44 first-order neighbors connecting the subnetworks. By integrating drug-target interactions from DrugBank using CyTargetLinker, we highlighted potential therapeutic targets within the S4 subnetwork, associated with cerebral cortex development, as well as CREB1, a connector between subnetworks. Conclusion: Network analysis organized heterogeneous epilepsy-associated genes into biologically related subnetworks and generated hypotheses for further research.

bioinformatics↗

GaussF: An Unbiased Gaussian Framework for High-Precision Transcript Quantification with a Programmable Self-Benchmark Model

Short-read RNA sequencing has revolutionized transcriptome analysis, but accurate isoform-level quantification faces substantial challenges from overlapping sequences among complex isoforms and inherent sequencing biases, leading to uneven coverage. Current alignment-based and alignment-free methods, while valuable, largely rely on empirical, data-derived factors for bias correction and isoform resolution. This dependency limits their capacity to fully capture the intricate spectrum of sequencing data. We introduce GaussF, a novel alignment-free methodology specifically designed for precise isoform quantification by directly addressing these biases and the isoform recognition problem without relying on empirical data. GaussF is predicated on the observation that k-mer counts categorized by GC content across a natural transcript exhibit an intrinsic Gaussian distribution. It establishes a transcript-specific self-benchmarking workflow utilizing a theoretical Gaussian cumulative distribution function (CDF). Crucially, the parameters defining this CDF are derived independently of any empirical sequencing data. This parameter-fixed theoretical CDF serves as an unbiased benchmark, capturing only the genuine contribution of abundance while systematically excluding all bias-related data. To improve isoform resolution despite shared regions, GaussF characterizes isoforms through identifying minimal-shared regions and restructures the k-mer reference accordingly. This approach simplifies complexity and enhances accuracy. Rigorous validation using artificial and real RNA samples, alongside comparative analysis with leading quantification tools like Kallisto, Salmon, Cufflinks, StringTie, and RSEM, demonstrates GaussF's superior performance in accurate isoform detection and quantification. GaussF is available on GitHub and PyPI, representing a significant advancement in RNA-seq analysis.

bioinformatics↗

Predicting Differential Protein-Protein Interactions

Motivation Analysis of context-specific protein-protein interactions (PPIs) provides deeper insight into protein function, revealing how cellular pathway components interact in certain perturbation conditions. However, identifying such interactions experimentally often requires combining affinity purification with mass spectrometry, proving costly and time-consuming. On the other hand, it has been shown that an increase in protein abundance does not always correspond to an increase in protein interaction, making it an unsuitable surrogate for interaction studies. Results To address this, a random forest model was created using PPI-specific MINT embeddings and condition-specific mass spectrometry protein abundance information to predict differential interactions across two experimental conditions. The model was trained and tested on validated context-specific PPIs from eight distinct cell-lines, demonstrating proficiency in predicting differential PPIs for both novel proteins and cell lines. To our knowledge, we provide the first computational model that predicts differential PPIs between experimental conditions. Availability and implementation The code is available on GitHub and backed up on Zenodo (DOI: 10.5281/zenodo.23758127). The model is freely available on Zenodo (DOI: 10.5281/zenodo.23757927).

bioinformatics↗