Biology subjects

Verschure, P.

Publications and source records attributed to Verschure, P..

5 recordsLinked to original sources

bioRxiv2022

Multi-modal and multi-model interrogation of large-scale functional brain networks

Current whole-brain models are generally tailored to the modelling of a particular modality of data (e.g., fMRI or MEG/EEG). Although different imaging modalities reflect different aspects of neural activity, we hypothesise that this activity arises from common network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct electromagnetic and metabolic features of brain activity to the dynamics on the brains macroscopic structural connectome. To jointly predict dynamical and functional connectivity features of distinct signal modalities, we consider two large-scale models generating local short-lived 40 Hz oscillations with various degrees of realism - namely Stuart Landau (SL) and Wilson and Cowan (WC) models. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC) and functional connectivity dynamics (FCD) to a comparable degree, by varying global coupling and mean conduction time delay. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD, highlighting the importance of balanced dynamics for the emergence of spatiotemporal patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.45 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns beyond the anatomical framework. Finally, we show that both models can generate MOMs with empirical-like properties. Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.

neuroscience↗

bioRxiv2022

Multiscale Effects of Excitatory-Inhibitory Homeostasis in Lesioned Cortical Networks: A Computational Study

Stroke-related disruptions in functional connectivity (FC) often spread beyond lesioned areas and, given the localized nature of lesions, it is unclear how the recovery of FC is orchestrated on a global scale. Since recovery is accompanied by long-term changes in excitability, we propose excitatory-inhibitory (E-I) homeostasis as a driving mechanism. We present a large-scale model of the neocortex, with synaptic scaling of local inhibition, showing how E-I homeostasis can drive the post-lesion restoration of FC and linking it to changes in excitability. We show that functional networks could reorganize to recover disrupted modularity and small-worldness, but not network dynamics, suggesting the need to consider forms of plasticity beyond synaptic scaling of inhibition. On average, we observed widespread increases in excitability, with the emergence of complex lesion-dependent patterns related to biomarkers of relevant side effects of stroke, such as epilepsy, depression and chronic pain. In summary, our results show that the effects of E-I homeostasis extend beyond local E-I balance, driving the restoration of global properties of FC, and relating to post-stroke symptomatology. Therefore, we suggest the framework of E-I homeostasis as a relevant theoretical foundation for the study of stroke recovery and for understanding the emergence of meaningful features of FC from local dynamics.

neuroscience↗

bioRxiv2019

The Degree Distribution of Human Brain Functional Connectivity is Generalized Pareto: A Multi-Scale Analysis

Are degree distributions of human brain functional connectivity networks heavy-tailed? Initial claims based on least-square fitting suggested that brain functional connectivity networks obey power law scaling in their degree distributions. This interpretation has been challenged on methodological grounds. Subsequently, estimators based on maximum-likelihood and non-parametric tests involving surrogate data have been proposed. No clear consensus has emerged as results especially depended on data resolution. To identify the underlying topological distribution of brain functional connectivity calls for a closer examination of the relationship between resolution and statistics of model fitting. In this study, we analyze high-resolution functional magnetic resonance imaging (fMRI) data from the Human Connectome Project to assess its degree distribution across resolutions. We consider resolutions from one thousand to eighty thousand regions of interest (ROIs) and test whether they follow a heavy or short-tailed distribution. We analyze power law, exponential, truncated power law, log-normal, Weibull and generalized Pareto probability distributions. Notably, the Generalized Pareto distribution is of particular interest since it interpolates between heavy-tailed and short-tailed distributions, and it provides a handle on estimating the tails heaviness or shortness directly from the data. Our results show that the statistics support the short-tailed limit of the generalized Pareto distribution, rather than a power law or any other heavy-tailed distribution. Working across resolutions of the data and performing cross-model comparisons, we further establish the overall robustness of the generalized Pareto model in explaining the data. Moreover, we account for earlier ambiguities by showing that down-sampling the data systematically affects statistical results. At lower resolutions models cannot easily be differentiated on statistical grounds while their plausibility consistently increases up to an upper bound. Indeed, more power law distributions are reported at low resolutions (5K) than at higher ones (50K or 80K). However, we show that these positive identifications at low resolutions fail cross-model comparisons and that down-sampling data introduces the risk of detecting spurious heavy-tailed distributions. This dependence of the statistics of degree distributions on sampling resolution has broader implications for neuroinformatic methodology, especially, when several analyses rely on down-sampled data, for instance, due to a choice of anatomical parcellations or measurement technique. Our findings that node degrees of human brain functional networks follow a short-tailed distribution have important implications for claims of brain organization and function. Our findings do not support common simplistic representations of the brain as a generic complex system with optimally efficient architecture and function, modeled with simple growth mechanisms. Instead these findings reflect a more nuanced picture of a biological system that has been shaped by longstanding and pervasive developmental and architectural constraints, including wiring-cost constraints on the centrality architecture of individual nodes.

neuroscience↗

bioRxiv2019

Challenging the boundaries of the physical self: purely distal cues in the environment impact body ownership

The unique ability to identify ones own body and experience it as ones own is fundamental in goal-oriented behavior and survival. However, the mechanisms underlying the so-called body ownership are yet not fully understood. The plasticity of body ownership has been studied using two experimental methods or their variations. Specifically, the Rubber Hand Illusion (RHI), where the tactile stimuli are externally generated, or the moving RHI which implies self-initiated movements. Grounded in these paradigms, evidence has demonstrated that body ownership is a product of bottom-up reception of self- and externally-generated multisensory information and top-down comparison between the predicted and the actual sensory stimuli. Crucially, provided the design of the current paradigms, where one of the manipulated cues always involves the processing of a proximal modality sensing the body or its surface (e.g., touch), the contribution of sensory signals which pertain to the environment remain elusive. Here we propose that, as any robust percept, body ownership depends on the integration and prediction of all the sensory stimuli, and therefore it will depend on the consistency of purely distal sensory signals pertaining to the environment. To test our hypothesis, we create an embodied goal-oriented task and manipulate the predictability of the surrounding environment by changing the congruency of purely distal multisensory cues while preserving bodily and action-driven signals entirely predictable. Our results empirically reveal that the way we represent our body is contingent upon all the sensory stimuli including purely distal and action-independent signals which pertain to the environment.

neuroscience↗

bioRxiv2018

Self beyond the body: task-relevant distal cues modulate performance and body ownership

The understanding of Body Ownership (BO) largely relies on the Rubber Hand Illusion (RHI) where synchronous stroking of real and Rubber Hands (RH) leads to an illusion of ownership of RH provided physical, anatomical, postural and spatial plausibility of the two body-parts. RHI also occurs during visuomotor synchrony, in particular, when the visual feedback of virtual arm movements follows the trajectory of the instantiated motor command. Hence BO seems to result from a bottom-up integration of afferent and efferent proximal multisensory evidence, and top-down prediction of both externally and self-generated signals, which occurs when the predictions about upcoming sensory signals are accurate. In motor control, the differential processing of predicted and actual sensory consequences of self-generated actions is addressed by, the so-called, Forward Model (FM). Based on an efference copy or corollary discharge, FM issues predictions about the sensory consequences of motor commands and compares them with the actual outcome. The discrepancies (Sensory Prediction Errors, SPEs) are used to correct the action on the consecutive trial and provide new estimates of the current state of the body and the environment. Here, we propose that BO might be computed by FMs, and therefore, it might depend on their consistency, specifically, in contexts where the sensory feedback is self-generated. Crucially, to reduce SPE, FMs integrate both proximal (proprioceptive) and distal (vision, audition) sensory cues relevant to the task. Thus, if BO depends on the consistency of FMs, it would be compromised by the incongruency of not only proximal but also distal cues. To test our hypothesis, we devised an embodied VR-based task where action outcomes were signaled by distinct auditory cues. By manipulating the cues with respect to their spatiotemporal congruency and valence, we show that distal feedback which violates predictions about action outcomes compromises both BO and performance. These results demonstrate that BO is influenced by not only efferent and afferent cues which pertain to the body itself but also those arising outside of the body and suggest that in goal-oriented tasks BO might result from a computation of FM.

neuroscience↗