Biology subjects

Hadid, V.

Publications and source records attributed to Hadid, V..

4 recordsLinked to original sources

bioRxiv2026

Shaped by meaning, weighted by reliability: New insights into multisensory integration

In everyday perception, sensory information is rarely interpreted in isolation. Instead, observers generate expectations about what is likely to occur next, using contextual information to guide decisions, particularly when sensory input is weak, ambiguous, or uncertain. These expectations can facilitate behaviour when they align with incoming evidence, but impose costs when they conflict, shaping how information from different senses contributes to behaviour. However, it remains unclear what kind of multisensory process is engaged when meaning guides decisions, and whether multisensory facilitation and non-independence reflect a single integrative mechanism or distinct routes shaped by context. Here, we examined how semantic context, visual clarity, and the relationship between auditory and visual signals jointly shape behaviour in a semantic correspondence task using dynamic, naturalistic events. Participants judged whether a target matched a preceding concept prime, establishing an expectation at the level of meaning. Targets were auditory-only, visual-only (clear or blurry), or audiovisual (AV), with AV components conveying either the same or different meanings across modalities. AV facilitation was not a general consequence of stimulus redundancy. When the prime accurately predicted the target and auditory and visual signals conveyed the same meaning, degrading visual input increased the contribution of auditory information, leading to faster responses and violations of the race-model inequality. By contrast, when the prime indicated the target but auditory and visual signals conveyed competing meanings, AV stimulation produced robust behavioural costs relative to vision. When the prime did not match the target, congruent and incongruent audiovisual events produced comparable effects, and race-model violations were observed even when auditory and visual signals encoded different concepts, indicating convergence at the level of the response rather than shared perceptual content. Finally, exploratory clustering revealed two participant subgroups with comparable overall response speed but systematically different multisensory profiles when semantic expectations did not allow reliable anticipation of the upcoming event. Together, these findings show that multisensory effects in semantic decisions are shaped by meaning and arise through two functionally distinct routes: one in which reduced sensory reliability promotes joint use of congruent audiovisual information, and another in which perceptually different signals converge on the same response when semantic expectations are not met. HighlightsO_LISemantic expectations shape how multisensory information is used to guide responses. C_LIO_LIReduced sensory reliability enables multisensory facilitation when signals convey the same meaning. C_LIO_LIMultisensory effects arise when sensory signals support the same response. C_LIO_LIIndividuals adopt distinct strategies for resolving uncertainty in multisensory decisions. C_LI

neuroscience↗

bioRxiv2025

Uncovering executive function profiles within interindividual variability: A data driven clustering exploration of design fluency in school-aged children

This study applied unsupervised machine learning to performances on a design fluency task, to identify distinct executive function (EF) profiles among 113 neurotypical Canadian children (62% female, 74% White, aged 7-13). By tracking design, repetition, and strategy production every minute for five minutes, the results revealed two profiles: Profile A generated fewer designs but exhibited a more stable production pattern across time. Profile B, initially more productive, showed a steeper increase in repetition errors and a decline in design production. These findings demonstrate how EF interindividual variability in neurotypical children can be captured through naturally emerging performance patterns, highlighting the value of temporal analysis in differentiating executive functioning profiles.

neuroscience↗

bioRxiv2022

Class imbalance should not throw you off balance: Choosing classifiers and performance metrics for brain decoding with imbalanced data

Machine learning (ML) is increasingly used in cognitive, computational and clinical neuroscience. The reliable and efficient application of ML requires a sound understanding of its subtleties and limitations. Training ML models on datasets with imbalanced classes is a particularly common problem, and it can have severe consequences if not adequately addressed. With the neuroscience ML user in mind, this paper provides a didactic assessment of the class imbalance problem and illustrates its impact through systematic manipulation of data imbalance ratios in (i) simulated data and (ii) brain data recorded with electroencephalography (EEG) and magnetoencephalography (MEG). Our results illustrate how the widely-used Accuracy (Acc) metric, which measures the overall proportion of successful predictions, yields misleadingly high performances, as class imbalance increases. Because Acc weights the per-class ratios of correct predictions proportionally to class size, it largely disregards the performance on the minority class. A binary classification model that learns to systematically vote for the majority class will yield an artificially high decoding accuracy that directly reflects the imbalance between the two classes, rather than any genuine generalizable ability to discriminate between them. We show that other evaluation metrics such as the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), and the less common Balanced Accuracy (BAcc) metric - defined as the arithmetic mean between sensitivity and specificity, provide more reliable performance evaluations for imbalanced data. Our findings also highlight the robustness of Random Forest (RF), and the benefits of using stratified cross-validation and hyperprameter optimization to tackle data imbalance. Critically, for neuroscience ML applications that seek to minimize overall classification error, we recommend the routine use of BAcc, which in the specific case of balanced data is equivalent to using standard Acc, and readily extends to multi-class settings. Importantly, we present a list of recommendations for dealing with imbalanced data, as well as open-source code to allow the neuroscience community to replicate and extend our observations and explore alternative approaches to coping with imbalanced data.

neuroscience↗

bioRxiv2019

NeuroPycon: A free Python toolbox for fast multi-modal and reproducible brain connectivity pipelines

Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses. Importantly, it provides shareable parameter files to facilitate replication of all analysis steps. NeuroPycon is based on the NiPype framework which facilitates data analyses by wrapping many commonly-used neuroimaging software tools into a common Python environment. In other words, rather than being a brain imaging software with is own implementation of standard algorithms for brain signal processing, NeuroPycon seamlessly integrates existing packages (coded in python, Matlab or other languages) into a unified python framework. Importantly, thanks to the multi-threaded processing and computational efficiency afforded by NiPype, NeuroPycon provides an easy option for fast parallel processing, which critical when handling large sets of multi-dimensional brain data. Moreover, its flexible design allows users to easily configure analysis pipelines by connecting distinct nodes to each other. Each node can be a Python-wrapped module, a user-defined function or a well-established tool (e.g. MNE-Python for MEG analysis, Radatools for graph theoretical metrics, etc.). Last but not least, the ability to use NeuroPycon parameter files to fully describe any pipeline is an important feature for reproducibility, as they can be shared and used for easy replication by others. The current implementation of NeuroPycon contains two complementary packages: The first, called ephypype, includes pipelines for electrophysiology analysis and a command-line interface for on the fly pipeline creation. Current implementations allow for MEG/EEG data import, pre-processing and cleaning by automatic removal of ocular and cardiac artefacts, in addition to sensor or source-level connectivity analyses. The second package, called graphpype, is designed to investigate functional connectivity via a wide range of graph-theoretical metrics, including modular partitions. The present article describes the philosophy, architecture, and functionalities of the toolkit and provides illustrative examples through interactive notebooks. NeuroPycon is available for download via github (https://github.com/neuropycon) and the two principal packages are documented online (https://neuropycon.github.io/ephypype/index.html. and https://neuropycon.github.io/graphpype/index.html). Future developments include fusion of multi-modal data (eg. MEG and fMRI or intracranial EEG and fMRI). We hope that the release of NeuroPycon will attract many users and new contributors, and facilitate the efforts of our community towards open source tool sharing and development, as well as scientific reproducibility.

neuroscience↗