Biology subjects

Cabral, J.

Publications and source records attributed to Cabral, J..

At least 19 recordsLinked to original sources

bioRxiv2026

How stimulation waveform shape affects collective oscillations in the brain networks

Brain oscillations emerge from nonlinear interactions across anatomically connected neural populations, alternating between transiently coordinated and desynchronised states. Transcranial alternating current stimulation (tACS) can modulate these dynamics, but most work has focused on frequency and amplitude, leaving waveform shape comparatively unexplored. Here we used a whole-brain model of delay-coupled Stuart-Landau oscillators constrained by empirical human structural connectivity to determine how sinusoidal, square, triangular, sawtooth and pulsed stimulation reshape spontaneous alpha-band activity. All waveforms were applied at the same frequency and amplitude to the posterior parieto-occipital regions. Network responses were quantified using the Kuramoto order parameter, spectral entropy and metastable oscillatory modes of transient alpha bursts. Sinusoidal and pulsed stimulation produced the strongest effects, increasing global synchrony while reducing metastability and spectral entropy, consistent with a transition from a fluctuation-rich regime to a coherent and spectrally concentrated state. These waveforms also transformed intermittent alpha bursts into prolonged or near-continuous oscillatory episodes. In contrast, square, triangular and sawtooth stimulation reduced synchrony while largely preserving metastability, producing weaker and more fragmented modulation. These findings identify waveform shape as a key determinant of rhythmic stimulation effects and a principled parameter for neuromodulation design.

neuroscience↗

bioRxiv2026

Functional connectome harmonics capture early brain organization and maturity in neonates

The functional organization of the human brain is established early, yet the ontogeny of its large-scale functional gradients remains unclear. Using resting-state fMRI data from 714 neonates in the Developing Human Connectome Project, we mapped neonatal brain gradients via functional connectome harmonics (FCH). We identified adult-like sensory-to-multimodal and cognitive gradient patterns present at birth. Applying three FCH-derived metrics--entropy, power, and energy--we found that power and energy were higher in term-born compared to preterm neonates, while entropy was elevated in preterms. These metrics predicted up to ~30% of postmenstrual age, indicating their biological relevance. Our findings reveal that the neonatal brain possesses a robust gradient architecture underpinning early functional organization, offering novel biomarkers for assessing brain maturity and the impact of prematurity on neurodevelopment.

neuroscience↗

bioRxiv2025

Increased striatal coupling one week after ischemic stroke revealed by ultrafast functional MRI

BackgroundNetwork reorganization following ischemic stroke is thought to play a role in recovery. Although cortico-cortical reorganization is widely established, changes in interhemispheric striatal connections following ischemia remain poorly understood, even when stroke occurs in motor areas. Given the importance of the striatum to motor function, we investigated network-level striatal coupling in stroke using ultrafast resting-state fMRI, which has recently been shown to facilitate the dissection of synchronous oscillatory activity better than its conventional [~]1 sec time-resolution counterparts. MethodsA cohort of (N=18) sedated rats were randomized and N=9 rats underwent unilateral photothrombotic ischemic lesioning in motor cortex. One week after the lesion, when plasticity and recovery are well established, all animals were scanned on a 9.4T MRI scanner using a cryogenic coil using an ultrafast resting-state functional MRI sequence with temporal resolution of 90 ms. Data were collected for 24 minutes, and spectral power, phase locking, and functional connectivity were quantified. Histology was performed to confirm lesion extent. ResultsWhile cortico-cortical power, connectivity and synchrony were diminished one week post-stroke as expected, we surprisingly found increased striato-striatal power, synchrony and functional connectivity in the stroked group compared with the control group. In stroked animals, the spectral power in the ultraslow oscillation frequency band (0.02-0.4 Hz) significantly increased in the striatum while decreasing in the cortex. When data were undersampled to "conventional" fMRI temporal resolution (900 ms), the striatal effects were lost, revealing the power of ultrafast fMRI approaches in unveiling such phenomena. ConclusionsIncreased striato-striatal coupling, in the form of increased synchrony, spectral power, and functional connectivity, was revealed by ultrafast resting-state fMRI, but not conventional temporal resolution resting-state fMRI. Our findings suggest more involvement of subcortical areas in network reorganization than previously thought.

neuroscience↗

bioRxiv2025

Physical exercise and brain network dynamics: reduction of frontoparietal-striatal connectivity following 1 hour of aerobic cycling

Physical exercise is beneficial for metabolic health and cognitive performance. In addition, exercise can be highly pleasurable and and serves as an effective stress reliever. Extant studies have highlighted frontal, parietal, and subcortical brain changes following acute bouts of exercise. However, slower, slightly delayed brain correlates after exercise at the network level have not been studied. Therefore, this studys objective was to investigate the changes in dynamic functional connectivity after 60 minutes of exercise in healthy males. Here we measured a 6-minute resting state fMRI in 24 young males at baseline and after a 60-minute cycling exercise challenge. Apart from routine preprocessing, the data were denoised with FSL-FIX and modeled with i) leading eigenvector dynamics analysis (LEiDA) to probe whole brain network dynamics and ii) with group independent component analysis (ICA) and dual regression to quantify static brain connectivity. The within subject statistical tests compared baseline to post-exercise conditions. We found that a striato-fronto-parietal network is destabilized after exercise, as indicated by a lower probability of occurrence in dynamic analysis through LEiDA. The brain areas in the network include the bilateral caudate, putamen and pallidum as well as middle orbitofrontal, frontal operculum, frontal trigonum and inferior parietal cortices. No differences between baseline and post exercise conditions were found in the dual regression of the group ICA components. We conclude that 60 minutes of cycling causes a prolonged effect in brain network dynamics, reducing synchronization between the striatum and frontoparietal networks with respect to baseline. This provides insights into the network-level neural correlates of aerobic exercise, which may be directly linked with the stress relieving effects of physical exercise.

neuroscience↗

bioRxiv2025

Spectral patterns of MEG oscillatory coupling emerge from meta-stable dynamics with small coupling delays

Functional connectivity (FC) is a fundamental mechanism of neural communication, connecting distinct oscillating populations. Oscillatory networks exhibit heterogeneity across frequencies and coupling modes whose origins are not well understood, but have been suggested to involve a complex interplay of critical-like dynamics and structure-function coupling. We here utilized structural connectivity (SC) to tune a whole-brain computational model of delay-coupled damped oscillators near a Hopf bifurcation to match oscillations and FC as observed in resting-state magnetoencephalography (MEG) data. We assessed two forms of oscillation-based FC from empirical and model data, namely phase synchronization (PS) and amplitude coupling (AC). We found that both oscillations and FC best matched with empirical observations in a meta-stable regime which was characterized by small delays, realistic oscillation lifetimes, and FC with intermediate strength and high variability. How well MEG FC patterns were matched by the model varied between frequency bands and best fits were observed for high-alpha and beta-band networks. These fits could partially, but not fully, be explained by correlations with SC, implicating that both structure-function coupling and critical-like metastable dynamics underlie empirical FC, and the contributions of these mechanisms varies between different frequency bands in MEG data.

neuroscience↗

bioRxiv2025

Anellovirus protein coded by ORF2/3 recruits host cell replication andhomologous recombination machinery during replication

Anelloviridae is a family of single-stranded DNA viruses that are thought to be non-pathogenic and commensal. Despite their ubiquitous presence in human populations, little is known about the anellovirus mechanism of replication in host cells. We identified the protein coded by ORF2/3 as necessary and sufficient to initiate replication from the minimal origin of replication for viruses of both the Beta- and Alphatorquevirus genera. Supporting this observation, we identified components of the polymerase alpha and BTR complexes as interacting with the viral replication initiation protein (Rip) during DNA replication, suggesting a recombination-dependent mechanism of replication that uses host cell machinery to mediate dissolution of replication intermediates. Furthermore, we mapped a 92-bp minimal origin of replication sequence for the Betatorquevirus genus comprised of an AT-rich stretch and a portion of the GC-rich region. Altogether, this study provides a first insight into the mechanism by which anelloviruses manipulate host cell machinery to facilitate viral genome replication and represents a significant step forward in understanding the complex processes underlying anellovirus replication and persistent infection of these important commensal viruses.

molecular biology↗

bioRxiv2025

Disrupted salience network dynamics during the imagery of migraine attacks

Moderate to severe head pain is a hallmark of recurring migraine attacks. However, it is challenging to study patients during spontaneous attacks and most research on the brain mechanisms of pain in migraine patients has been limited to the processing of painful stimuli between attacks. Here, we hypothesize that the experience of a migraine attack extends beyond the response to painful stimuli and is associated with specific impairments of the salience network (SN), which integrates sensory, emotional and cognitive information in relation to salient stimuli. To test this hypothesis, we analysed the SN dynamics of a group of patients with episodic migraine in three distinct conditions: at rest during a spontaneous migraine attack (ictal phase); while performing an imagery task aiming to elicit the experience of a previous attack, during the interictal phase; and at rest, during the interictal phase. For comparison, we also studied a group of healthy controls in three matching conditions, including rest as well as an imagery task of a (non-migraine) head pain experience. We collected functional magnetic resonance imaging (fMRI) data and used a dynamic functional connectivity (dFC) analysis to examine the temporal features of the SN from a total of 78 samples. Compared to healthy controls, the SN had a significantly shorter lifetime in patients during the pain imagery task, but not during a migraine attack or interictal resting state. Our results support the disruption of the SN in migraine, and indicate that pain imagery may be a useful paradigm for isolating the emotional and cognitive aspects of pain and investigating SN dynamics.

neuroscience↗

bioRxiv2025

Frequency-Dependent Neurostimulation in Whole Brain Network models

BackgroundBrain stimulation at specific frequencies has been shown to have therapeutic effects for certain neurological disorders. However, it remains unclear how these oscillatory inputs interact with neuronal dynamics and what drives this frequency selectivity. Here, we propose that this is achieved by targeting specific brain circuits through frequency-selective network entrainment. MethodsTo demonstrate that this type of entrainment can occur in the brain connectome structure, we use a minimal physics-based model of coupled oscillators that preserves only the essential structural connectivity of real brain networks. Using this model, we test how periodic stimulation influences network oscillatory dynamics, identifying sub-networks that synchronize with specific stimulation frequencies. Furthermore, we validate the suitability of the model to reproduce well-known frequency-selective entrainment using visual and auditory periodic stimulation. ResultsOur model reproduces selective network entrainment, whereby distinct stimulation frequencies favor the synchrony of different sub-networks. Crucially, entrainment exhibits a physics-predicted inverse relationship with frequency: lower-frequency stimulation favors broader synchrony across the network, while higher-frequency inputs produce spatially confined effects. These patterns emerge purely from network structure and oscillatory physics, independent of biological details. ConclusionThis physics-based approach elucidates the fundamental mechanistic principles governing frequency-selective brain network stimulation. Our minimal model demonstrates that selective entrainment of specific subnetworks can be achieved through frequency selection alone, largely determined by network structure and oscillatory dynamics. These findings provide a theoretical foundation for understanding how network architecture determines stimulation selectivity, supporting the development of principled approaches to targeted neuromodulation. Author summaryIn this work, we explore how external rhythmic stimulation interacts with the brains own rhythms. We use a physics-based model that represents the brain as a network of interconnected oscillators, linked according to the human connectome. Through this model, we show that stimulation effects are not limited to the targeted region but spread across the network, depending on the stimulation frequency, its spatial location and the underlying network dynamics. Our results reveal that low-frequency rhythms promote large-scale synchrony, while high-frequency rhythms engage more localized areas, all emerging from the brains structural connections and communication delays. The simulated cortical activation exhibits a frequency-selective profile that aligns strongly with that estimated from sensory-evoked electroencephalographic activity. This agreement suggests that differences between individuals in how their brain networks operate may explain why stimulation affects each person differently. Our findings offer a mechanistic framework for designing personalized stimulation protocols, illustrating how computational modeling can guide the optimization of noninvasive brain stimulation in both research and clinical contexts.

bioengineering↗

bioRxiv2025

Altered brain-ventricle coupling modes over Alzheimer's disease progression detected with fMRI

The temporal coordination between fMRI signals in gray matter and ventricular spaces represents an emerging yet poorly understood dimension of brain physiology with potential relevance for neuroprotection. Here, we analyzed 2177 resting-state fMRI scans from 880 participants spanning the cognitive spectrum from normal aging to dementia, using a unified phase-coupling approach that incorporates fluctuations in both brain tissue and ventricular space. We identify distinct modes of Brain-Ventricle (BraVe) coupling occurring over time, and compare their temporal occupancy across cognitive status groups, neurodegenerative biomarkers and cognitive scores. In the most prevalent BraVe mode, signals across the entire brain evolve in anti-phase with ventricular signals, occurring less frequently with cognitive decline (p < 10-7, g > 0.3). In the remaining BraVe modes, specific cortical regions temporarily align with ventricular fluctuations, shaping canonical resting-state networks at the cortical level. Two BraVe modes overlapping with the Default Mode Network and the Frontoparietal Network, occur less frequently in dementia (p < 10-8, g > 0.35), with the latter correlating significantly with FDG-PET glucose metabolism (r = 0.20, p = 3.65x10-5). Conversely, a mode overlapping with the Limbic Network increases in occupancy with cognitive decline and associates with APOE {varepsilon}4 status (r = 0.11, p = 1.30x10-6). BraVe mode occupancy correlates more strongly and broadly with cognitive function than with disease-specific molecular biomarkers, with between 16 and 27 cognitive and functional variables per mode surviving strict Bonferroni correction, spanning global cognition, episodic memory, executive function, and everyday functioning. These findings reveal that the transient phase shifts underlying resting-state network dynamics are not arbitrary: when cortical subnetworks shift out of phase from the global brain signal, they align with ventricular signals, suggesting that resting-state functional connectivity and brain-ventricle coupling may be two perspectives on the same underlying physical process, one that is directly linked with cognitive function.

neuroscience↗

bioRxiv2024

Trehalose dimycolate inhibits phagosome maturation and promotes intracellular M. tuberculosis growth via noncanonical SNARE interaction

Mycobacterial cell envelopes are rich in unusual lipids and glycans that play key roles during infection and vaccination. The most abundant envelope glycolipid is trehalose dimycolate (TDM). TDM compromises the host response to mycobacterial species via multiple mechanisms, including inhibition of phagosome maturation. The molecular mechanism by which TDM inhibits phagosome maturation has been elusive. We find that a clickable, photoaffinity TDM probe recapitulates key phenotypes of native TDM in macrophage host cells and binds several host SNARE proteins, including VTI1B, STX8, and VAMP2. VTI1B and STX8 normally promote endosome fusion by forming a complex with VAMP8. However, in the presence of Mycobacterium tuberculosis, VTI1B and STX8 complex with VAMP2, which in turn decreases VAMP8 binding. VAMP2 acts together with mycolate structure to inhibit phagosome maturation and promotes intracellular M. tuberculosis replication. Thus one mechanism by which TDM constrains the innate immune response to M. tuberculosis is via non-canonical SNARE complexation. Significance StatementGlycolipids from the Mycobacterium tuberculosis cell envelope, particularly trehalose dimycolate (TDM), play major roles in subverting the immune response to this intracellular pathogen. How subversion occurs is often unclear because glycans and lipids are technically challenging to study in cells. We discovered that a TDM-mimicking chemical probe interacts with three host SNARE proteins, including two that regulate endosome fusion and one that does not. The presence of TDM or M. tuberculosis triggers abnormal binding of these SNAREs, which in turn inhibits the fusion of M. tuberculosis-containing phagosomes with lysosomes and promotes M. tuberculosis replication. Our work provides an unusual example of a bacterial pathogen restricting the immune response via glycolipid-SNARE interactions.

microbiology↗

bioRxiv2024

DySCo: a general framework for dynamic Functional Connectivity

1A crucial challenge in neuroscience involves characterising brain dynamics from high-dimensional brain recordings. Dynamic Functional Connectivity (dFC) is an analysis paradigm that aims to address this challenge. dFC consists of a time-varying matrix (dFC matrix) expressing how pairwise interactions across brain areas change with time. However, the main dFC approaches have been developed and applied mostly empirically, lacking a unifying theoretical framework, a general interpretation, and a common set of measures to quantify the dFC matrices properties. Moreover, the dFC field has been lacking ad-hoc algorithms to compute and process the matrices efficiently. This has prevented the field to show its full potential with high-dimensional datasets and/or real time applications. With this paper, we introduce the Dynamic Symmetric Connectivity Matrix analysis framework (DySCo), with its associated repository. DySCo is a unifying approach that allows the study of brain signals at different spatio-temporal scales, down to voxel level, that is computationally ultrafast. DySCo unifies in a single theoretical framework the most employed dFC matrices, which share a common mathematical structure. Doing so it allows: 1) A new interpretation of dFC that further justifies its use to capture the spatiotemporal patterns of data interactions in a form that is easily translatable across different imaging modalities. 2) The introduction of the the Recurrence Matrix EVD to compute and store the eigenvectors and eigenvalues of all types of dFC matrices in an efficent manner that is orders of magnitude faster than naive algorithms, and without loss of information. 3) To simply define quantities of interest for the dynamic analyses such as: the amount of connectivity (norm of a matrix) the similarity between matrices, their informational complexity. The methodology developed here is validated on both a synthetic dataset and a rest/N-back task experimental paradigm - the fMRI Human Connectome Project dataset. We demonstrate that all the measures proposed are highly sensitive to changes in brain configurations. To illustrate the computational efficiency of the DySCo toolbox, we perform the analysis at the voxel-level, a computationally very demanding task which is easily afforded by the RMEVD algorithm.

neuroscience↗

bioRxiv2024

A novel functional gene delivery platform based on a commensal human anellovirus demonstrates transduction in multiple tissue types

Anelloviridae is a family of non-enveloped viruses with negative-sense, circular, single-stranded deoxyribonucleic acid (ssDNA) genomes that infect vertebrates and are a ubiquitous component of the human virome. Human anelloviruses evade induction of humoral immune responses and appear to be non-pathogenic. These properties, in conjunction with their enormous genomic diversity and wide tissue distribution, make anelloviruses compelling candidates as vectors for next-generation genetic medicines. Here we report the first gene delivery vector system based on a human commensal virus. This Anellovector is based on a virus of the Betatorquevirus genus. Production is enabled by the development of the Self-Amplifying Trans-complementation of a Universal Recombinant aNellovector (SATURN) system, which relies on a self-replicating plasmid to provide viral proteins in trans that drive replication and capsid-dependent packaging of vector genomes. The SATURN system also utilizes a Cre-lox-based recombination mechanism to generate single unit-sized circular genomes inside the MOLT-4 production cell line. We demonstrate that the SATURN system can package a vector genome from a single betatorquevirus with capsids from multiple betatorquevirus species, supporting the feasibility of establishing a novel vector platform that takes advantage of the remarkable diversity of anelloviruses. The Anellovector demonstrated function in vitro in retinal pigment epithelial (RPE) cells. The Anellovector also demonstrated durable in vivo function in the mouse eye for 9 months after subretinal administration, and achieved comparable gene expression to dose-matched adeno-associated virus 9 (AAV9) when transduced by the intracerebroventricular (ICV) route of administration. To our knowledge, this is the first report of a functional anellovirus-based gene therapy vector. Anellovectors have great potential to deliver safe, redosable, and potent therapeutics, helping to expand the reach of programmable medicines.

bioengineering↗

bioRxiv2024

Emergence of multiple spontaneous coherent subnetworks from a single configuration of human connectome-coupled oscillators model

Large-scale brain models with biophysical or biophysically inspired parameters generate brain-like dynamics with multi-state metastability. Multi-state metastability reflects the capacity of the brain to transition between different network configurations and cognitive states in response to changing environments or tasks, thus relating to cognitive flexibility. To study this phenomenon, we used a Kuramoto network of oscillators corresponding to a human brain atlas of 90 nodes, each with an intrinsic frequency of 40 Hz. The networks nodes were interconnected based on the structural connectivity strengths and delays found in the human brain. We identified global coupling and delay scale parameters corresponding to maximum spectral entropy, a proxy for maximal multi-state metastability. At this point, we show that multiple coherent (functional) sub-networks spontaneously emerge across multiple oscillatory modes, and persist in time for periods between 140 and 4389 ms. Most nodes in the network exhibit broad frequency spectra away from their intrinsic frequency, and switch between modes, in a manner similar to that reported in empirical resting-state neuroimaging data. We suggest that the obtained dynamics at maximum metastability is a suitable model of the awake brain. Further, we show that global coupling and delay scale parameters away from maximum metastability yield dynamical features similar to other brain states such as sleep and anesthesia. Therefore, spectral entropy also correlates with wakefulness and the synchrony of functional networks.

neuroscience↗

bioRxiv2023

Harmonic decomposition of spacetime (HADES) framework characterises the spacetime hierarchy of the DMT brain state

The human brain is a complex system, whose activity exhibits flexible and continuous reorganisation across space and time. The decomposition of whole-brain recordings into harmonic modes has revealed a repertoire of gradient-like activity patterns associated with distinct brain functions. However, the way these activity patterns are expressed over time with their changes in various brain states remains unclear. In this study, we develop the Harmonic Decomposition of Spacetime (HADES) framework that characterises how different harmonic modes defined in space are expressed over time, and, as a proof-of-principle, demonstrate the sensitivity and robustness of this approach to specific changes induced by the serotonergic psychedelic N,N-Dimethyltryptamine (DMT) in healthy participants. HADES demonstrates significant decreases in contributions across most low-frequency harmonic modes in the DMT-induced brain state. When normalizing the contributions by condition (DMT and non-DMT), we detect a decrease specifically in the second functional harmonic, which represents the uni- to transmodal functional hierarchy of the brain, supporting the hypothesis that functional hierarchy is changed in psychedelics. Moreover, HADES dynamic spacetime measures of fractional occupancy, life time and latent space provide a precise description of the significant changes of the spacetime hierarchical organization of brain activity in the psychedelic state.

neuroscience↗

bioRxiv2023

Design of effective personalised perturbation strategies for enhancing cognitive intervention in Alzheimer's disease

One of the potential and promising adjuvant therapies for Alzheimers disease is that of non-invasive transcranial neurostimulation to potentiate cognitive training interventions. Conceptually, this is achieved by driving brain dynamics towards an optimal state for an effective facilitation of cognitive training interventions. However, current neurostimulation protocols rely on experimental trial-and-error approaches that result in variability of symptom improvements and suboptimal progress. Here, we leveraged whole-brain computational modelling by assessing the regional susceptibility towards optimal brain dynamics from Alzheimers disease. In practice, we followed the three-part concept of Dynamic Sensitivity Analysis by first understanding empirical differences between healthy controls and patients with mild cognitive impairment and mild dementia due to Alzheimers Disease; secondly, by building computational models for all individuals in the mild cognitive impairment and mild dementia cohorts; and thirdly, by perturbing brain regions and assessing the impact on the recovery of brain dynamics to the healthy state (here defined in functional terms, summarised by a measure of metastability for the healthy group). By doing so, we show the importance of key regions, along the anterior-posterior medial line, in driving in-silico improvement of mild dementia and mild cognitive impairment groups. Moreover, this subset consists mainly of regions with high structural nodal degree. Overall, this in-silico perturbational approach could inform the design of stimulation strategies for re-establishing healthy brain dynamics, putatively facilitating effective cognitive interventions targeting the cognitive decline in Alzheimers disease.

neuroscience↗

bioRxiv2023

Maternal prenatal distress exposure negatively associates with the stability of neonatal frontoparietal network

Maternal prenatal distress (PD), frequently defined as in utero prenatal stress exposure (PSE) to the developing fetus, influences the developing brain and numerous associations between PSE and brain structure have been described both in neonates and in older children. Previous studies addressing PSE-linked alterations in neonates brain activity have focused on connectivity analyses from predefined seed regions, but the effects of PSE at the level of distributed functional networks remains unclear. In this study, we investigated the impact of prenatal distress on the spatial and temporal properties of functional networks detected in functional MRI data from 20 naturally sleeping, term-born (age 25.85 {+/-} 7.72 days, 11 males), healthy neonates. First, we performed group level independent component analysis (GICA) to evaluate an association between PD and the spatial configuration of the functional networks. Second, we search for an association with PD at the level of the stability of functional networks over time using leading eigenvector dynamics analysis (LEiDA). No statistically significant associations were detected at the spatial level for the GICA-derived networks. However, at the dynamic level, LEiDA revealed that maternal PD significantly decreased the stability of a frontoparietal network. These results imply that maternal PD may influence the stability of frontoparietal connections in neonatal brain network dynamics and adds to the cumulating evidence that frontal areas are especially sensitive to PSE.

neuroscience↗

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

A specific mesh-like organization of human septin octameric complex drives membrane reshaping and curvature sensitivity

Septins are cytoskeletal proteins interacting with the inner plasma membrane and other cytoskeletal partners. Being key in membrane remodeling processes, they often localize at specific micrometric curvatures. To analyze the behavior of human septins at the membrane, we have used a combination of methods to assay their ultrastructural organization, their curvature sensitivity as well as their role in membrane reshaping. In contrast to budding yeast septins, on membranes, human septins systematically organize into a two-layered mesh of orthogonal filaments instead of generating parallel sheets of filaments observed for budding yeast septins. This peculiar mesh organization is curvature sensitive and drives membrane reshaping as well. The observed membrane deformations together with the filamentous organization are recapitulated in a coarsegrained computed simulation to understand their mechanisms. Our results highlight the specificity of animal septins as opposed to fungal proteins.

biophysics↗