Biology subjects

Beyh, A.

Publications and source records attributed to Beyh, A..

14 recordsLinked to original sources

bioRxiv2026

Neuroscience in Conversation: Tracking Scientific Change Through an Open Seminar Archive

Scientific talks capture how researchers frame questions, debate ideas, and communicate emerging methods, providing a complementary record of scientific change to the published literature. We investigated whether an open neuroscience seminar archive could reveal shifting scientific attention and contain predictive information about subsequent discourse. We analysed 470 English-language transcripts from the Clinical Neuroanatomy Seminars YouTube channel, spanning 2010-2026 and approximately 2.6 million words, using longitudinal vocabulary analysis, unsupervised topic clustering, conceptual comparisons, and rolling temporal forecasting. The archive captured sustained discussion of language, white matter anatomy, connectivity, clinical neuroscience, and neurobiological mechanisms, alongside changes in their relative prominence. Network-related terminology became more prominent than localisation-related terminology, references to individual variability and computational approaches increased, and open science practices became more visible in scientific discussion. Beyond retrospective description, a regression model predicted keyword frequencies one and two years ahead with approximately 12% and 17% lower mean absolute error, respectively, than a persistence baseline. This modest gain is better explained by smoothing of year-to-year fluctuations than by genuine prediction: terms showing the strongest recent growth tended to fall back rather than continue rising. These findings suggest that spoken scientific discourse shows clear longitudinal change but offers limited evidence of information about subsequent shifts in attention. Interpretation remains grounded in the archive curated scope and changing content composition. By making nearly two decades of freely accessible neuroimaging education amenable to systematic analysis, this study connects open educational practice with the study of science itself. Open seminar archives can serve as reusable research resources, preserving the conversations through which scientific communities develop and revise their ideas.

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bioRxiv2026

Mapping reward processing circuits: the orbitofrontal-nucleus accumbens white matter pathways and their associations to psychopathological traits

Dysfunctions in reward circuits, including projections from the orbitofrontal cortex (OFC) to the nucleus accumbens (NAcc), have been implicated in psychopathology. The OFC is functionally divided into the medial OFC (mOFC), which processes rewards, and the lateral OFC (lOFC), which responds to non-reward or punishment stimuli. We investigated the structural connectivity of the lOFC-NAcc and mOFC-NAcc pathways and their associations with psychopathological traits using tractography-defined ROI analysis. Diffusion-weighted imaging data from 165 participants (aged 22 - 35, 53% male) in the Human Connectome Project (HCP) were analysed using spherical deconvolution tractography. MegaTrack, a novel tool, enabled tract segmentation via a single manual dissection mapped back to native space. Virtual dissection identified four bilateral pathways connecting the lOFC and mOFC to the NAcc, from which fractional anisotropy (FA) was extracted, and an lOFC/mOFC ratio was computed. Psychopathological traits were measured using HCP Achenbach Adult Self-Report scores. The lOFC-NAcc pathway showed positive associations with psychopathology and the mOFC-NAcc pathway negative associations. The lOFC/mOFC ratio emerged as a stronger predictor than individual pathways, with significant associations for depression, somatic problems, anti-social personality traits, and inattention. Effects were lateralized to the left hemisphere. These findings show that the lOFC-NAcc and mOFC-NAcc pathways can be structurally separated with tractography and display opposing correlations with psychopathological traits in a healthy population. Their ratio may index reward-processing dysfunction, and future research should test whether it is sensitive to dysfunction in clinical populations.

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bioRxiv2026

Hemispheric Complementarity is Associated with Distinct Functional and Structural Brain Profiles

Hemispheric specialization is thought to follow a principle of complementarity, with homotopic functions such as language and attention lateralizing to opposite hemispheres, yet its consistency across individuals and relevance for cognition remain unclear. Using multimodal neuroimaging and twin-family data from 987 Human Connectome Project participants, we identified three sustained attentional lateralization phenotypes (right, left, bilateral) that were independent of handedness, differed by sex, and tracked broad shifts in brain organization, with bilateral individuals showing stronger intrahemispheric and homotopic connectivity. Examining attention and language revealed three multidomain profiles (crowded, mixed, split) reflecting whether functions colocalize within or are distributed across hemispheres. Mixed organization was linked to higher planum polare myelination, whereas crowded organization showed higher fiber density connectivity across dorsal attention-default network. Both phenotypes were heritable, yet neither predicted cognitive performance. These findings reveal a brain organization in which specialization emerges from flexible, heritable patterns linking function, structural connectivity, and myeloarchitecture.

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bioRxiv2026

Geometric constraints and cognitive inputs jointly shape emergent brain dynamics and topology

How the brain's physical geometry gives rise to its flexible functional repertoire remains a central question in neuroscience. Here, we trained three classes of recurrent neural networks (RNNs) on a working-memory task, forming a graded hierarchy of spatial constraints: Vanilla RNNs (no spatial constraints), Masked RNNs (projection constraints limiting where information enters and leaves the network), and biophysical RNNs (bioRNNs; projection constraints and spatial embedding of the networks' connectivity using the brain's inter-regional Euclidean geometry). We assessed how well each RNN class predicted empirical fMRI activity without exposing them to it during training. Our results showed that bioRNNs were the only networks to successfully predict empirical brain activity and to organize their dynamics into a spatial pattern that recapitulated the brain's principal hierarchy (the sensorimotor-association axis). Additionally, bioRNNs' ability to predict empirical brain activity emerged along a trajectory in which geometry was laid down first, then partly traded back as the task was mastered. Importantly, brain-like topological features emerged in bioRNNs as they increased their task proficiency while maintaining their ability to predict brain activity. Taken together, our results indicate that physical geometry and cognitive inputs play distinct, complementary roles: while geometry constrains the space of possible brain dynamics, cognitive inputs determine which dynamics are expressed. They also situate topology as the scaffold through which the physically embedded brain reconciles wiring costs and computational demands.

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bioRxiv2025

Individual Differences in Speech Monitoring: Functional and Structural Correlates of Delayed Auditory Feedback

Sensory feedback is essential for the fine-tuning of motor actions, and speech production is no exception. It depends on continuous self-monitoring to ensure that produced sounds match intended targets. Delaying auditory feedback (DAF) disrupts this alignment and impairs fluency, providing a powerful tool to investigate sensorimotor control. We combined functional and diffusion-weighted MRI in 31 participants performing a word-production task under delayed (DAF) and immediate (no-DAF) auditory feedback. While all participants slowed their speech under DAF, the extent of this effect varied across individuals and was quantified using a susceptibility index (SI). At the group level, DAF elicited increased activation in a right-lateralized network encompassing the superior temporal gyrus, supramarginal gyrus, inferior frontal gyrus, supplementary motor area, and left cerebellum. Incorporating individual differences revealed that higher susceptibility was associated with greater activation in left-hemisphere speech motor homologues and larger volume of the right long arcuate fasciculus, a white-matter pathway connecting auditory and motor speech regions. This pattern suggests that vulnerability reflects increased recruitment of neural resources and a stronger reliance on auditory-motor coupling. In contrast, resilience was associated with greater engagement of the bilateral angular gyrus and higher fiber density in the right posterior arcuate fasciculus, which connects auditory and somatosensory speech regions. This finding indicates that resilience is supported by a posterior circuit that efficiently integrates multi-modal sensory feedback. Together, these findings link functional dynamics with underlying structural connectivity to reveal how a right-lateralized network supports speech control, while accounting for individual differences in susceptibility to fluency disruption. Significance StatementFluent speech depends on the brains ability to monitor self-produced sounds and sensations from articulatory organs to adjust motor commands in real time. To uncover the neural basis of this process, we combined a fluency-disrupting paradigm, delayed auditory feedback (DAF), with functional and structural neuroimaging. This multimodal approach revealed that while DAF processing relies on a right-lateralized network, more susceptible individuals show enhanced recruitment of left-hemisphere monitoring regions. We also found that stronger white-matter connections in the right posterior arcuate fasciculus predict greater resilience and fluency. These findings provide an anatomically grounded account of how auditory and somatosensory feedback interact to support speech production, offering new insight into why some individuals are more susceptible to fluency breakdowns and related disorders.

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bioRxiv2025

MegaTrack: a framework for the anatomically accurate and time-efficient virtual dissection and analysis of large-scale tractography datasets

The increasing prevalence of large-scale neuroimaging initiatives has created a need for efficient and anatomically accurate methods for analysing tractography data. Here, we present MegaTrack, a framework designed to combine the anatomical precision of manual dissections with the efficiency required for big data analysis. At its core, MegaTrack operates by normalising individual tractography datasets to a standard space, concatenating them into a single "mega" tractography dataset the user manually dissects before automatically extracting subject-specific tracts and metrics in native space. This approach permits identifying and extracting tracts not yet available in existing templates or automatic dissection tools. We validated this approach using multiple datasets and applications. Comparison with individual manual dissections showed high spatial agreement (weighted dice coefficient > 0.95) and strong correlations in tract-specific metrics (R2 > 0.9). In a longitudinal dataset, MegaTrack demonstrated improved reproducibility across time points and higher inter-rater reliability compared to manual dissections. In a motor neuron disease cohort, MegaTrack replicated known group differences in corticospinal tract microstructure with comparable sensitivity to manual dissections (p = 0.001) but with significantly reduced processing time. Finally, we showcase MegaTracks scalability by creating a comprehensive white matter atlas from 140 healthy adults and demonstrate its application for lesion analysis in stroke patients. This framework is accompanied by an interactive online tool that allows users to explore these atlases and perform customised lesion analyses. The MegaTrack framework is a time-efficient solution for the anatomically precise analysis of large tractography datasets, enabling the rapid simultaneous dissection of multiple subjects while preserving individual anatomical features. The frameworks ability to efficiently process large datasets makes it an excellent tool for generating training data for machine learning and deep learning algorithms in tractography analysis. This approach has broad applications in both research and clinical settings, from group comparisons to personalised lesion mapping, and can significantly contribute to the development of advanced automated tractography methods.

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bioRxiv2025

Inferring macroscopic intrinsic neural timescales using optimal control theory

The temporal evolution of brain activity relies on complex interactions within and between brain regions that are mediated by neurobiology and connectivity. To understand these interactions, many large-scale efforts have measured structural connectivity, neural activity, gene expression, and cognition across multiple modalities and species. However, data-driven discovery of large-scale activity models remains difficult owing to the lack of flexible quantitative frameworks for estimating the interplay between brain structure and function while preserving biophysical realism. Here, we provide such a framework by integrating network control theory (NCT) with automatic differentiation to estimate model parameters with greater biophysical realism from data. Specifically, we estimate the structural form of regional self-inhibition--a quantity that is experimentally difficult to measure--from MRI data. Next, we demonstrate that the resulting model-based self-inhibition parameters correlate significantly with regions intrinsic neural timescales (INTs), neurobiological measures of gene expression and cell-type densities, as well as behavioral measures of cognition. We demonstrate consistent results across multiple datasets and species. Finally, we demonstrate that our self-inhibition parameters enable the efficient control of brain dynamics from fewer brain regions. Taken together, our results provide a simple and flexible quantitative framework that more accurately captures the interplay between brain structure, function, and dynamics with greater biophysical realism.

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bioRxiv2025

Zero-phase-delay connectivity increases the reliability, concordance with structure, and prognostic ability of functional connectivity metrics

Neural populations synchronise their activity with either zero-phase-delay (activity in interacting regions occurs simultaneously) or a phase-delay (activity in one region follows the other). In electroencephalography and magnetoencephalography functional connectivity analyses, artefactual connectivity can also occur with zero-phase-delay. To minimise artefact, contemporary analyses typically exclude all zero-phase-delay interactions. However, the extent to which true interactions are resultingly lost - and the impact this has on the performance of functional connectivity metrics as biomarkers - remains unknown. Here, we show that most cortico-cortical functional connectivity occurs with zero- or near-zero phase-delay, even where such connectivity is unlikely to be artefactual. Including, rather than excluding, zero-phase-delay connectivity increases the reliability, convergence with neurobiology (structure-function concordance, homotopic interhemispheric connectivity, and age-related connectivity changes), and prognostic ability of functional connectivity metrics. We find that excluding zero-phase-delay connections penalises functional connectivity strength between the strongest structurally connected regions: stronger structural connections lead to functional connections with phase-delays closer to zero, mediated by a shorter signal propagation time. Our findings challenge generally accepted assumptions that zero-phase-exclusive methods are superior to zero-phase-inclusive methods.

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bioRxiv2024

Benchmarking Stroke Outcome Prediction through Comprehensive Data Analysis - NeuralCup 2023

Stroke remains a leading cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. To address these challenges, we established the NeuralCup consortium to benchmark predictive models of stroke outcome through a collaborative, data-driven approach. This study presents findings from 15 international teams who used a comprehensive dataset including clinical and imaging data, to identify and compare predictors of motor, cognitive, and emotional outcomes one year post-stroke. Our analyses integrated traditional statistical approaches and novel machine learning algorithms to uncover optimal recipes for predicting each domain. The differences in these optimal recipes reflect distinct brain mechanisms in response to different tasks. Key predictors across all domains included infarct characteristics, T1-weighted MRI sequences, and demographic factors. Additionally, integrating FLAIR imaging and white matter tract analysis significantly improved the prediction of cognitive and motor outcomes, respectively. These findings support a multifaceted approach to stroke outcome prediction, underscoring the potential of collaborative data science to develop personalized care strategies that enhance recovery and quality of life for stroke survivors. To encourage further model development and validation, we provide access to the training dataset at http://neuralcup.bcblab.com

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bioRxiv2023

Perceptual axioms are irreconcilable with Euclidean geometry

There are different definitions of axioms, but the one that seems to have general approval is that axioms are statements whose truths are universally accepted but cannot be proven; they are the foundation from which further propositional truths are derived. Previous attempts, led by David Hilbert, to show that all of mathematics can be built into an axiomatic system that is complete and consistent failed when Kurt Godel proved that there will always be statements which are known to be true but can never be proven within the same axiomatic system. But Godel and his followers took no account of brain mechanisms that generate and mediate logic. In this largely theoretical paper, but backed by previous experiments and our new ones reported below, we show that in the case of so-called "optical illusions" there exists a significant and irreconcilable difference between their visual perception and their description according to Euclidean geometry; when participants are asked to adjust, from an initial randomised state, the perceptual geometric axioms to conform to the Euclidean description, the two never match, although the degree of mismatch varies between individuals. These results provide evidence that perceptual axioms, or statements known to be perceptually true, cannot be described mathematically. Thus the logic of the visual perceptual system is irreconcilable with the cognitive (mathematical) system and cannot be updated even when knowledge of the difference between the two is available. Hence no one brain reality is more "objective" than any other.

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bioRxiv2023

Neural patterns of conscious visual awareness in the Riddoch syndrome

The Riddoch syndrome is one in which patients blinded by lesions to their primary visual cortex can consciously perceive visual motion in their blind field, an ability that correlates with activity in motion area V5. Our assessment of the characteristics of this syndrome in patient ST, using multimodal MRI, showed that: 1. STs V5 is intact, receives direct subcortical input, and decodable neural patterns emerge in it only during the conscious perception of visual motion; 2. moving stimuli activate medial visual areas but, unless associated with decodable V5 activity, they remain unperceived; 3. STs high confidence ratings when discriminating motion at chance levels, is associated with inferior frontal gyrus activity. Finally, we report that STs Riddoch Syndrome results in hallucinatory motion with hippocampal activity as a correlate. Our results shed new light on perceptual experiences associated with this syndrome and on the neural determinants of conscious visual experience.

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bioRxiv2022

The neural determinants of abstract beauty

We have enquired into the neural activity which correlates with the experience of beauty aroused by abstract paintings consisting of arbitrary assemblies of lines and colours. During the brain imaging experiments, subjects rated abstract paintings according to aesthetic appeal. There was low agreement on the aesthetic classification of these paintings among participants. Univariate analyses revealed higher activity with higher declared aesthetic appeal in both the visual areas and the medial frontal cortex. Additionally, representational similarity analysis (RSA) revealed that the experience of beauty correlated with decodable patterns of activity in visual areas. These results are broadly similar to those obtained in previous studies on facial beauty. With abstract art, it was the involvement of visual areas implicated in the processing of lines and colours while with faces it was of visual areas implicated in the processing of faces. Both categories of aesthetic experience correlated with increased activity in medial frontal cortex. We conclude that the sensory areas participate in the selection of stimuli according to aesthetic appeal and that it is the co-operative activity between the sensory areas and the medial frontal cortex that is the basis for the experience of abstract visual beauty. Further, this co-operation is enabled by "experience dependent" functional connections, in the sense that currently the existence and high specificity of these connections can only be demonstrated during certain experiences.

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bioRxiv2022

Hypervoxels: A Multidimensional Framework For The Representation And Analysis Of Neuroimaging Data

Most neuroimaging modalities use regular grids of voxels to represent the three-dimensional space occupied by the brain. However, a regular 3D voxel grid does not reflect the anatomical and topological complexity represented by the brains white matter connections. In contrast, tractography reconstructions based on diffusion MRI provide a closer characterisation of the white matter pathways followed by the neuronal fibres interconnecting different brain regions. In this work, we introduce hypervoxels as a new methodological framework that combines the spatial encoding capabilities of multidimensional voxels with the anatomical and topological information found in tractography data. We provide a detailed description of the framework and evaluate the benefits of using hypervoxels by carrying out comparative voxel and hypervoxel cluster inference analyses on diffusion MRI data from a neuroimaging study on amyotrophic lateral sclerosis (ALS). Compared to the voxel analyses, the use of hypervoxels improved the detection of effects of interest in the data in terms of statistical significance levels and spatial distribution across white matter regions known to be affected in ALS. In these regions, the hypervoxel results also identified specific white matter pathways that resolve the anatomical ambiguity otherwise observed in the results from the voxel analyses. The observed increase in sensitivity and specificity can be explained by the superior ability of hypervoxel-based methods to represent and disentangle the anatomical overlap of white matter connections. Based on this premise, we expect that the use of hypervoxels should improve the analysis of neuroimaging data when the effects of interest under investigation are expected to be aligned along distinct but potentially overlapping white matter pathways.

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bioRxiv2021

The missing pathway in current visuospatial processing models

Visuospatial learning depends on the parahippocampal place area (PPA), a functionally heterogenous area which current visuospatial processing models place downstream from parietal cortex and only from area V4 of early visual cortex (EVC). However, evidence for anatomical connections between the PPA and other EVC areas is inconsistent, and these connections are not discussed in current models. Through a data-driven analysis based on diffusion MRI tractography, we present evidence that the PPA sits at the confluence of two white matter systems. The first conveys information from the retrosplenial complex to the anterior PPA and runs within the cingulum bundle. The second system connects all peripheral EVC areas to the posterior PPA and corresponds to the medial occipital longitudinal tract (MOLT), a novel white matter pathway distinct from the cingulum. Based on further functional connectivity analysis and meta-analytic data, we propose that the MOLT supports early stage encoding of visuospatial information by allowing direct reciprocal exchange between the PPA and EVC.

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