Biology subjects

Baillet, S.

Publications and source records attributed to Baillet, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Dissociable Anterior and Posterior Beta-Burst Dynamics Track Thought Disorder in Schizophrenia

BackgroundThought disorder in schizophrenia reflects disrupted internal model maintenance in predictive processing. Beta oscillations have been proposed to index top-down predictive signalling, but prior resting-state studies have focused on spectral power, overlooking transient burst dynamics that enable the stabilization and reinstatement of internal representations, especially in the default mode network (DMN). MethodsResting-state MEG was acquired from 25 patients (7 females) with schizophrenia and 25 matched controls (8 females) between May 2024 and October 2025. Beta bursts (15-30 Hz) were extracted from source-localized cortical time series, and burst rate, duration, power, and inter-regional synchrony were quantified. Associations with computationally-derived semantic and syntactic speech features were assessed using principal-component regression within patients. ResultsConventional beta power did not differ between groups after correction. In contrast, beta-burst dynamics showed significant group-by-anterior-posterior-position interactions for burst rate (p = 0.018), power (p = 0.0014) and duration (p < 0.001). Within the DMN, patients showed shorter inferior parietal burst duration (t(46.0) = -2.74, FDR-p = 0.0343) and increased burst synchrony within left prefrontal cortex (t(31.0) = 2.81, FDR-p = 0.0343) and between prefrontal cortices (t(29.8) = 2.73, FDR-p = 0.0423). Within patients, burst components predicted speech organization (R{superscript 2} = 0.47, F(3,19) = 5.66, p = 0.006), driven by the inferior parietal burst-duration component ({beta} = -0.55, p = 0.0036), but did not predict overall clinical severity. LimitationsThe modest patient sample, short resting-state recording, cross-sectional design, and lack of laminar specificity limit causal and mechanistic inference; burst-speech associations require replication. ConclusionSchizophrenia was associated with dissociable anterior and posterior beta-burst abnormalities that tracked objective speech markers of thought disorder. These findings suggest impaired hierarchical coordination of predictive beta signalling as a candidate mechanism linking intrinsic network dysfunction to disorganized thought.

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bioRxiv2026

Fiber-tract development contributes to functional specialization in the human hippocampus

Fiber-tracts exhibit distinct projection patterns along the anterior-posterior axis of the hippocampus, promoting a specialization in function. This specialization becomes increasingly pronounced throughout child development, with important implications for neurocognitive outcomes. Developmental changes in fiber-tract properties, including intra-axonal cross-sectional area and myelin content, may contribute to this anterior-posterior functional specialization. To test this, we developed a diffusion-MRI tractography pipeline to identify hippocampal fiber-tracts in single subjects, then examined whether age-related differences in total intra-axonal cross-sectional area and myelin content (T1w/T2w) could predict functional specialization in a large cross-sectional sample (N=539, aged 5-21 years). With age, we found that the cross-sectional area of short-range medial-temporal tracts, which primarily target the anterior/body of the hippocampus, exhibited rapid growth. Concomitantly, the cross-sectional area of long-range occipito-parietal tracts, which primarily target the posterior hippocampus, exhibited a modest pruning. Increases in myelin content were relatively homogenous across fiber-tracts. In support of our hypothesis, we found that the cross-sectional area of fiber-tracts significantly predicts the surface-area of an fMRI-defined posterior system; a sensitive marker of functional specialization in the hippocampus. Tracts targeting early visual cortex (V2, V3, V4) showed the strongest association, with statistical modeling indicating a mediating effect of early-visual tract development on the relation between age and functional specialization. These findings provide evidence consistent with a mechanism whereby anatomical neurodevelopment contributes to functional specialization in the human hippocampus.

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bioRxiv2026

Hierarchical Flows of Human Cortical Activity

Ongoing brain activity unfolds as structured spatiotemporal patterns across the cortex, yet quantifying the direction and strength of this propagation on the folded cortical sheet is challenging within and across individuals. We introduce geodesic cortical flow, a surface-based optical-flow framework that estimates millisecond-resolved surface-tangent propagation fields from source-imaged magnetoencephalography (MEG) data. In resting-state MEG from 608 healthy adults, spontaneous propagation was anisotropic and bidirectionally aligned with the principal unimodal-to-transmodal functional gradient: slow activity (1-13 Hz) was biased toward upstream propagation from sensory to association cortex, whereas beta activity (13-30 Hz) was biased toward downstream propagation in the opposite direction. Across adulthood, this balance shifted toward weaker upstream slow propagation and stronger downstream beta propagation. Propagation strength, indexed by kinetic energy of the cortical flow, followed a robust posterior-to-anterior gradient and, within frontoparietal cortex, higher kinetic energy was associated with better fluid intelligence after adjustment for age. Kinetic-energy dynamics further identified stable-state dwell times that tracked regional neuronal timescales. Together, these findings establish geodesic cortical flow as a geometry-informed framework for quantifying frequency-resolved cortical propagation and its variation across aging and cognition.

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bioRxiv2026

Distributed neurophysiological dynamics link perception, action, and language in schizophrenia

Schizophrenia is characterized by disturbances in both perception and expression that profoundly impair social functioning, yet these domains are typically studied in isolation. Predictive processing theories suggest that such symptoms may arise from abnormal updating of internal models, but the neural mechanisms linking perceptual and expressive dysfunction remain unclear. Using magnetoencephalography during multisensory perception and motor tasks, we tested whether beta-band activity (15-30 Hz), a neural signal implicated in predictive control, provides a shared substrate across domains. Compared to healthy individuals, patients with schizophrenia showed attenuated event-related modulation of beta activity, including weaker beta suppression during sensory processing and delayed or reduced post-movement beta rebound. These abnormalities were associated with a widened audiovisual temporal binding window, indicating atypical multisensory integration. Multivariate analyses further revealed that reduced beta modulation across sensory and motor systems covaried with impoverished semantic diversity, simplified syntactic structure in natural speech, and greater clinical symptom burden. Notably, beta abnormalities emerged as distributed latent patterns spanning sensory, motor, and frontotemporal regions. Together, these findings identify diminished event-related beta modulation as a common neural signature linking disrupted multisensory integration, action monitoring, and language organization in schizophrenia. We situate perceptual and expressive impairments within a unified framework of predictive dysfunction and advance a mechanistically grounded account that highlights beta dynamics as a promising target for future mechanistic and translational studies in psychosis. Significance StatementSchizophrenia disrupts both how people perceive the world and how they express their thoughts, yet these symptoms are usually studied separately. Using magnetoencephalography during multisensory and motor tasks, we identify a shared neural mechanism linking these domains: reduced event-related modulation of beta-band (15-30 Hz) activity, a signal implicated in predictive control. Compared to healthy individuals, patients showed diminished beta changes during sensory processing and movement completion, suggesting reduced flexibility in updating internal models. Importantly, this neural pattern covaried with abnormal audiovisual binding, disorganized natural speech, and greater clinical severity. These findings reveal distributed beta modulation as a cross-domain neural marker of predictive dysfunction, offering a unified framework for understanding perceptual and expressive disturbances in schizophrenia.

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bioRxiv2026

Unsupervised Representation Learning Generates Differentiable Neurophysiological Profiles

Human brain activity contains stable, individual-specific features that persist over months to years, forming neurophysiological profiles. Most model-based profiling approaches use participant labels or supervised objectives, making it difficult to determine whether successful differentiation reflects stable biology or exploitable idiosyncrasies. We introduce a participant-agnostic autoencoder framework that derives profiles from brief resting-state magnetoencephalography (MEG) segments using reconstruction as sole training objective. Discriminative profiles emerged from the learned latent space without participant labels. Within-session, autoencoder profiles reached 93.3% accuracy at 120 s, exceeding functional-connectivity, spectral, and contrastive baselines with recordings as short as 14 s when participant-specific anatomy was withheld from source reconstruction. Differentiation generalized above chance across recording sessions (between-session accuracy 49.5% for the pretrained autoencoder). Profiles also predicted age more accurately than baselines (r2=0.318), and the decoder enabled perturbation-based sensitivity analyses in spectral and connectivity spaces. This establishes participant-agnostic representation learning as a scalable and interpretable profiling.

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bioRxiv2026

Neural Signatures of Post-Decision Outcome Expectation and Evaluation in Human Sensorimotor Choice Behavior

The concept of embodied sensorimotor decision-making proposes that processes implicated in evaluating sensory inputs and selecting appropriate motor actions unfold partly in cortical regions traditionally associated with movement planning and execution. Reinforcement learning models emphasize the role of reward prediction error (RPE) in optimizing action selection based on decision outcome feedback. However, most evidence for the existence of RPE signals locates them in midline frontal and parietal cortex, and comes from tasks with externally manipulated reward probabilities that create artificial prediction errors. Whether RPE signals are expressed in human cortical motor areas during deterministic (non-probabilistic) tasks remains unclear, and would provide further support for embodied decision-making. We used magnetoencephalography (MEG) to study post-decision neural dynamics in a color discrimination task in selected cortical regions of interest (ROIs). Participants had to press buttons with their left or right index finger in response to checkerboard stimuli with different levels of color evidence for the correct choice. Outcomes were fully determined by participants choices. Delayed auditory feedback veridically indicated whether their hand choice was correct or not. We observed a robust beta-band (15-29 Hz) rebound after correct outcome feedback, strongest in ventral and dorsal premotor, anterior cingulate and superior parietal ROIs as well as occipital and auditory ROIs, and weakest in the primary motor and somatosensory ROIs. Critically, the rebound magnitude after correct feedback scaled inversely with color evidence strength and associated decision error rates. It was minimal in strong-evidence trials ([~]0.1% errors) and maximal in weak-evidence trials ([~]34% errors), resembling a context-sensitive positive RPE signal that was strongest when a correct outcome was least expected. Alpha-band (8-12 Hz) post-feedback rebound increases in weak evidence trials were not as strong as in the beta band and appeared mainly in occipital, superior parietal and posterior cingulate ROIs. After the decision but before feedback, both beta and alpha band power showed sensitivity to the level of sensory evidence on which the decisions had been based, with reduced post-movement rebound or enhanced suppression in trials with weak evidence--suggestive of internally generated outcome expectations. Pre-feedback alpha rebound suppression was strongest in occipital, superior parietal and posterior cingulate ROIs. Pre-feedback beta rebound suppression was not as strong. Together, these findings reveal distinct beta- and alpha-band dynamics that reflect internal pre-feedback outcome expectations and feedback-driven RPE-like outcome assessments. They support distributed cortical mechanisms, including premotor, parietal and cingulate regions, in reward expectation, outcome evaluation, and adaptive control, highlighting a role for motor and associative cortices in embodied decision-making, performance monitoring, and flexible behavior under uncertainty.

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bioRxiv2025

Lower Bound Estimates for Electrophysiological Power Dissipation in Human Gray Matter

The human brain is popularly described as remarkably energy efficient, with its metabolic power consumption estimated at approximately 20 watts (W). Biophysical models have partitioned this power budget across distinct cellular processes, but these theoretical estimates have yet to be empirically constrained using whole-brain electrophysiological data. Here, we used magnetoencephalography (MEG) source imaging of resting-state human brain activity to derive empirical lower-bound estimates of electrical power dissipation in cortical gray matter. We found that the total power dissipated by currents, primarily associated with post-synaptic potentials, ranges between 10-9 and 10-10 W, several orders of magnitude lower than prevailing metabolic estimates. Using finite element modeling (FEM), we observed that electrophysiological power dissipation is predominantly confined to gray matter. Additionally, spatial variations in MEG-derived power dissipation partially correlated with regional oxygen metabolism measured by PET, yet notable discrepancies emerged across large-scale functional brain networks. These results underscore a critical divergence between electrophysiological and metabolic indices of brain energy use, and highlight the need for more integrated biophysical models to bridge this gap and better characterize the physiological underpinnings of regional brain energetics.

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bioRxiv2025

sEEG-Suite: An Interactive Pipeline for Semi-Automated Contact Localization and Anatomical Labeling with Brainstorm

Stereoelectroencephalography (sEEG) is a critical tool for mapping epileptic networks in patients with drug-resistant epilepsy. Accurate localization and labeling of sEEG contacts are essential for identifying the seizure onset zone (SOZ) and ensuring optimal resective surgery. Traditional methods for localizing and labeling sEEG contacts rely on manual processing, which is prone to human error and variability. To address these challenges, we developed and integrated a semi-automatic sEEG contact localization and labeling pipeline within Brainstorm1, an open-source software platform for multimodal brain imaging analysis2-8, widely adopted in the neuroscience community with over 50,000 registered users and an active online user forum. The software has been supported by the National Institute of Health (NIH) for over two decades. The pipeline presented in this paper performs three key steps: (1) import and apply rigid co-registration of post-implantation Computed Tomography (CT) or post-CT with pre-implantation Magnetic Resonance Imaging (pre-MRI), (2) post-CT image segmentation and semi-automatic detection of sEEG contacts using GARDEL9, which has been integrated as a Brainstorm plugin, and (3) automatic anatomical labeling of contacts using standard and commonly used brain anatomy templates and atlases. Integrating this pipeline into Brainstorm brings the best of both worlds: GARDELs automation and Brainstorms multimodal data compatibility, and rich library of visualization and advanced analysis tools at both sensor and source level6. This sEEG-Suite tool facilitates reproducible research, supports clinical workflows, and accelerates sEEG-based investigations of invasive brain recordings.

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bioRxiv2024

The Lifespan Evolution of Individualized Neurophysiological Traits

How do neurophysiological traits that characterize individuals evolve across the lifespan? To address this question, we analyzed brief, task-free magnetoencephalographic recordings from over 1,000 individuals aged 4-89. We found that neurophysiological activity is significantly more similar between individuals in childhood than in adulthood, though periodic patterns of brain activity remain reliable markers of individuality across all ages. The cortical regions most critical for determining individuality shift across neurodevelopment and aging, with sensorimotor cortices becoming increasingly prominent in adulthood. These developmental changes in neurophysiology align closely with the expression of cortical genetic systems related to ion transport and neurotransmission, suggesting a growing influence of genetic factors on neurophysiological traits across the lifespan. Notably, this alignment peaks in late adolescence, a critical period when genetic factors significantly shape brain individuality. Overall, our findings highlight the role of sensorimotor regions in defining individual brain traits and reveal how genetic influences on these traits intensify with age. This study advances our understanding of the evolving biological foundations of inter-individual differences. Lay summaryThis study examines how brain activity reflects the development of individuality across a persons life. Using magnetoencephalography to capture brief recordings of spontaneous brain activity, the researchers distinguished between over 1,000 individuals, spanning ages 4 to 89. They found that the brain regions most associated with individuality change with age: sensory and motor regions become increasingly distinctive in early adulthood, highlighting their role in shaping a persons unique characteristics of brain activity. The study also revealed that changes in brain activity across different ages correspond to specific patterns of gene expression, shedding light on how genetics influence brain individuality. These findings deepen our understanding of the biological foundations of inter-individual differences and how it evolves over the lifespan.

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bioRxiv2024

Stimulus-dependent delay of perceptual filling-in by microsaccades

Perception is a function of both stimulus features and active sensory sampling. The illusion of perceptual filling-in occurs when eye gaze is kept still: visual boundary perception may fail, causing adjacent visual features to remarkably merge into one uniform visual surface. Microsaccades-small, involuntary eye movements during gaze fixation-counteract perceptual filling-in, but the mechanisms underlying this process are not well understood. We investigated whether microsaccade efficacy for preventing filling-in depends on two boundary properties, color contrast and retinal eccentricity (distance from gaze center). Twenty-one human participants (male and female) fixated on a point until they experienced filling-in between two isoluminant colored surfaces. We found that increased color contrast independently extends the duration before filling-in but does not alter the impact of individual microsaccades. Conversely, lower eccentricity delayed filling-in only by increasing microsaccade efficacy. We propose that microsaccades facilitate stable boundary perception via a transient retinal motion signal that scales with eccentricity but is invariant to boundary contrast. These results shed light on how incessant eye movements integrate with ongoing stimulus processing to stabilize perceptual detail, with implications for visual rehabilitation and the optimization of visual presentations in virtual and augmented reality environments. Significance StatementTo perceive, sense organs actively sample the environment--for example, by touching, sniffing, or moving the eyes. Visual sampling persists even when gaze is fixed on a single point: involuntary microsaccades continuously move the eye in small jumps. We investigated a previously documented observation that microsaccades prevent illusory fading of perceived visual boundaries during fixation. We discovered that despite being connected, microsaccades and fading are sensitive to different stimulus features. Boundaries separating surfaces with more distinct colors inherently took longer to fade. Boundaries closer to the center of vision also took longer to fade, but only because microsaccades were more effective. These findings reveal new insight into how pervasive sensory sampling delivers a stable and detailed perceptual experience.

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bioRxiv2024

Model selection for spectral parameterization

Neurophysiological brain activity comprises rhythmic (periodic) and arrhythmic (aperiodic) signal elements, which are increasingly studied in relation to behavioral traits and clinical symptoms. Current methods for spectral parameterization of neural recordings rely on user-dependent parameter selection, which includes selecting the correct maximum number of spectral peaks to fit to the spectrum. This challenges the replicability and robustness of findings. Here, we introduce a data-driven model-selection procedure for determining the appropriate number of oscillatory peaks to fit to neural power spectra, based on the Bayesian Information Criterion (BIC). We present extensive tests of the approach with ground-truth and empirical magnetoencephalography recordings. Data-driven model selection enhances both the specificity and sensitivity of spectral decompositions. Overall, the proposed spectral decomposition with data-driven model selection reduces reliance on user-defined maximum number of peaks settings, enabling more robust, reproducible, and interpretable spectral parameterizations. Lay summaryBrain activity is composed of rhythmic patterns that repeat over time and arrhythmic elements that are less structured. Recent advances in brain signal analysis have improved our ability to distinguish between these two types of components, enhancing our understanding of brain signals. However, current methods require users to adjust several parameters manually to obtain their results. The outcomes of the analyses, therefore, depend on each users decisions and expertise. To improve the replicability of research findings, the authors propose a revised method to streamline the analysis of brain signal contents. They developed a new algorithm that defines the parameters of the analytical pipeline informed by the data. The effectiveness of this method is demonstrated with both synthesized and real-world data. The approach is made available to all researchers as a free, open-source app, observing best practices for neuroscience research.

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bioRxiv2024

Genetic Foundations of Neurophysiological and Behavioural Variability Across the Lifespan

Neurophysiological brain activity shapes cognitive functions and individual traits. Here, we investigated the extent to which individual neurophysiological properties are genetically determined and how these adult traits align with cortical gene expression patterns across development. Using task-free magnetoencephalography in monozygotic and dizygotic twins, as well as unrelated individuals, we found that neurophysiological traits were significantly more similar between monozygotic twins, indicating a genetic influence, although individual-specific variability remained predominant. These heritable brain dynamics were predominantly associated with genes involved in neurotransmission, expressed along a topographical gradient that mirrors psychological functions, including attention, planning, and emotional processes. Furthermore, the cortical expression patterns of genes associated with individual differentiation aligned most strongly with gene expression profiles observed during adulthood in previously published longitudinal datasets. These findings underscore a persistent genetic influence on neurophysiological activity, supporting individual cognitive and behavioral variability.

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bioRxiv2023

An adversarial collaboration to critically evaluate theories of consciousness

Different theories explain how subjective experience arises from brain activity1,2. These theories have independently accrued evidence, yet, confirmation bias and dependence on design choices hamper progress in the field3. Here, we present an open science adversarial collaboration which directly juxtaposes Integrated Information Theory (IIT)4,5 and Global Neuronal Workspace Theory (GNWT)6-10, employing a theory-neutral consortium approach11,12. We investigate neural correlates of the content and duration of visual experience. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes, and their interpretation12. 256 human subjects viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography, and electrocorticography. We find information about conscious content in visual, ventro-temporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results confirm some predictions of IIT and GNWT, while substantially challenging both theories: for IIT, a lack of sustained synchronization within posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in prefrontal cortex. Beyond challenging the theories themselves, we present an alternative approach to advance cognitive neuroscience through a principled, theory-driven, collaborative effort. We highlight the challenges to change peoples mind 13 and the need for a quantitative framework integrating evidence for systematic theory testing and building.

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bioRxiv2023

Evaluation of abstract rule-based associations in the human premotor cortex during passive observation

Decision-making often manifests in behavior, typically yielding overt motor actions. This complex process requires the registration of sensory information with ones internal representation of the current context, before a categorical judgment of the most appropriate motor behavior can be issued. The construct concept of embodied decision-making encapsulates this sequence of complex processes, whereby behaviorally salient information from the environment is represented in an abstracted space of potential motor actions rather than only in an abstract cognitive "decision" space. Theoretical foundations and some empirical evidence account for support the involvement of premotor cortical circuits in embodied cognitive functions. Animal models show that premotor circuits participate in the registration and evaluation of actions performed by peers in social situations, that is, prior to controlling ones voluntary movements guided by arbitrary stimulus-response rules. However, such evidence from human data is currently limited. Here we used time-resolved magnetoencephalography imaging to characterize activations of the premotor cortex as human participants observed arbitrary, non-biological visual stimuli that either respected or violated a simple stimulus-response association rule. The participants had learned this rule previously, either actively, by performing a motor task (active learning), or passively, by observing a computer perform the same task (passive learning). We discovered that the human premotor cortex is activated during the passive observation of the correct execution of a sequence of events according to a rule learned previously. Premotor activation also differs when the subjects observe incorrect stimulus sequences. These premotor effects are present even when the observed events are of a non-motor, abstract nature, and even when the stimulus-response association rule was learned via passive observations of a computer agent performing the task, without requiring overt motor actions from the human participant. We found evidence of these phenomena by tracking cortical beta-band signaling in temporal alignment with the observation of task events and behavior. We conclude that premotor cortical circuits that are typically engaged during voluntary motor behavior are also involved in the interpretation of events of a non-ecological, unfamiliar nature but related to a learned abstract rule. As such, the present study provides the first evidence of neurophysiological processes of embodied decision-making in human premotor circuits when the observed events do not involve motor actions of a third party.

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bioRxiv2023

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6 800 timeseries features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is colocalized with multiple micro-architectural features, including genomic gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

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bioRxiv2022

Exogenous attention interferes with endogenous attention processing via lateralized alpha power

Current theories of attention differentiate exogenous (bottom-up) from endogenous (top-down) orienting of visuospatial attention. While both forms of attentional processing engage different processes, endogenous and exogenous attention are thought to share processing resources, as shown by recent empirical evidence of their functional interactions. Here, we aim to uncover the neurobiological basis of how salient events that drive exogenous attention disrupts endogenous attention processes. We hypothesize that interference from exogenous attention over endogenous attention involves alpha-band activity, a neural marker of visuospatial attention. To test this hypothesis, we contrast the effects of endogenous attention across two experimental conditions: a single cueing condition where endogenous attention is engaged in isolation, and a double cueing condition where endogenous attention is concurrently engaged with exogenous attention. Our results are consistent with previous work and show that the concurrent engagement of exogenous attention interferes with endogenous attention processes. Importantly, we evaluate our main hypothesis using a moderated mediation model. We found that changes in alpha-band activity mediate the relationship between endogenous attention and its benefits on task performance, and that the interference of exogenous attention on endogenous attention occurs via the moderation of this indirect effect. Altogether, our results substantiate a model of attention, whereby top-down and bottom-up attentional processes compete for shared neurophysiological resources. This model accounts for the observed patterns of interference between exogenous and endogenous attention. Significance StatementScientists differentiate top-down and bottom-up visuospatial attention processes. While bottom-up attention is rapidly engaged by emerging demands from the environment, top-down attention reflects slow voluntary shifts of attention. Several lines of research substantiate the idea that top-down and bottom-up attentional processes involve distinct brain systems. An increasing number of studies, however, argue that both attention systems share brain processing resources. The current study examines how salient visual events that engage bottom-up processes interfere with top-down processes. Using neurophysiological recordings and multivariate pattern classification techniques, the researchers show that both top-down and bottom-up attention processes do share brain resources expressed via alpha-band neurophysiological activity (8-12 Hz). The results further demonstrate that the interference patterns observed over brain activity in the alpha-band between both attention systems explains, in part, the interference between top-down and bottom-up attention at the behavioural level. The authors conclude by proposing a model of visuospatial attention whereby the dynamics between both attention systems are determined by their competition for limited brain processing resources.

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bioRxiv2022

Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks

The relationship between structural and functional connectivity in the brain is a key question in connectomics. Here we quantify patterns of structure-function coupling across the neocortex, by comparing structural connectivity estimated using diffusion MRI with functional connectivity estimated using both neurophysiological (MEG-based) and haemodynamic (fMRI-based) recordings. We find that structure-function coupling is heterogeneous across brain regions and frequency bands. The link between structural and functional connectivity is generally stronger in multiple MEG frequency bands compared to resting state fMRI. Structure-function coupling is greater in slower and intermediate frequency bands compared to faster frequency bands. We also find that structure-function coupling systematically follows the archetypal sensorimotor-association hierarchy, as well as patterns of laminar differentiation, peaking in granular layer IV. Finally, structure-function coupling is better explained using structure-informed inter-regional communication metrics than using structural connectivity alone. Collectively, these results place neurophysiological and haemodynamic structure-function relationships in a common frame of reference and provide a starting point for a multi-modal understanding of structure-function coupling in the brain.

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bioRxiv2022

Evidence for shared resources in visuospatial attention: Endogenous attention misleads the decoding of exogenous attention in alpha rhythms

Prevailing accounts of visuospatial attention differentiate exogenous (involuntary shifts) from endogenous (voluntary control) orienting of attention. While these two forms of attentional processes are functionally separable, their interactions have been at the center of ongoing debates for more than two decades. One hypothesis is that exogenous and endogenous attention interfere because they share processing resources. Here, we confirm that endogenous attention alters exogenous attention processing, and examine the role of alpha-band neurophysiological activity in such interference events. We contrast the effects of exogenous attention across two experimental conditions: a single-cueing condition where exogenous attention is engaged alone, and a double-cueing condition where exogenous attention is concurrently engaged with endogenous attention. Our results show that the engagement of endogenous attention alters the emergence of exogenous attention across cue-related and target-related brain processes. Importantly, we also report that classifiers trained to decode exogenous attention from the power and phase of alpha-band brain activity in the single-cueing condition fail to do so in the doublecueing condition, where endogenous attention is also engaged. Taken together, our observations challenge the idea that exogenous attention operates independently from top-down processes and demonstrate that both forms of attention orienting engage shared brain processes, which constrain their interactions. Significance StatementVisuospatial attention is often dichotomized into top-down and bottom-up components: Top-down attention reflects slow voluntary shifts of attention orienting, while bottom-up attention is recruited by emerging demands from the environment. A large body of previous findings support the view that these two forms of attention orienting are functionally separable, with some interactions. The current study examines such interactions between top-down and bottom-up attention. Using electroencephalography (EEG) and multivariate pattern classification techniques, the researchers show that top-down attention interferes with the brain activity patterns of bottom-up attention. Moreover, machine learning classifiers trained to detect bottom-up attention based on brain activity in the alpha band (8-12 Hz), a marker of visuospatial attention, fail systematically when top-down attention is also engaged. The authors therefore conclude that both forms of visuospatial orienting are supported by overlapping processes that share brain resources.

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