Biology subjects

David, O.

Publications and source records attributed to David, O..

14 recordsLinked to original sources

bioRxiv2026

Directed Human Structural Connectome Reveals Hierarchical Organization and Shapes Large-Scale Brain Dynamics

The human structural connectome, most commonly derived from diffusion-weighted imaging (DWI) and tractography, provides a macroscopic description of whole-brain wiring and serves as the structural foundation of network neuroscience, large-scale brain simulations, and personalized digital brain twins. However, tractography-derived connectomes are fundamentally limited by their inability to distinguish afferent from efferent connections, yielding networks that are undirected and therefore blind to the hierarchical organization imposed by the directionality of anatomical connections. In this study, we introduce a directed human structural connectome (DHSC) by transferring tracer-derived projection patterns from macaque to human using cross-species connectivity blueprints. Topological analysis of the DHSC manifests biological plausibility, a small-world network organization, and a directionality-based hierarchy, which offer the hierarchical organization of human brain networks. In the context of brain dynamics, the introduction of directionality reshapes the propagation and persistence of sensory inputs. DHSC also best captures the empirical spatiotemporal dynamics of stimulus-evoked brain activity. The findings demonstrate that anatomical directionality is a critical determinant of large-scale brain organization and dynamics. This provides evidence that directed connectome may offer potential advantages in large-scale simulations of the human brain. The resulting DHSC, along with all related analyses and data are openly available.

neuroscience↗

bioRxiv2026

The microstructure-weighted human connectome: network properties and structure-function correlations across spatial scales

Conventional connectome edge weights, such as number of streamlines (NOS) or diffusion tensor imaging (DTI) metrics, lack specificity to microstructural details which may hold relevance for macroscale brain organisation. Since biophysical diffusion modelling offers greater specificity to microstructure, we investigated whether parameters from the Standard Model of white matter diffusion provide informative alternatives for connectome weights - namely the intra-axonal signal fraction (f) and perpendicular extra-axonal diffusivity (DePerp). Using diffusion MRI data from healthy adults, we constructed structural networks at four parcellation scales, weighted by f, DePerp, NOS, fractional anisotropy (FA) and radial diffusivity (RD). While all weights reproduced expected small-world properties and hub structure, only DePerp and normalised NOS captured non-random properties of local organisation at all spatial scales. We then correlated each weighted connectome with resting-state fMRI functional connectivity and intracranial measurements of conduction velocity. At both the whole-brain and regional level, NOS gave strong coupling with fMRI functional connectivity. Connectomes weighted by DePerp, FA and RD gave weak but significant coupling with fMRI functional connectivity and conduction velocity. Notably, DePerp and RD gave highest consistency in regional structure-function coupling across spatial scales. Thus, connectome weights derived from DePerp capture meaningful aspects of brain network organisation with functional relevance.

neuroscience↗

bioRxiv2026

Directed Brain Connectomics Revealed by Bicommunity Structure

The brain is a complex, interconnected system in which structural wiring underpins the flow of information between functional units. After two decades of advances in connectomics, the asymmetry of edge structures in large-scale brain networks remains largely unexplored. Here, we build a directed connectome combining structure and function, and dissect its organization into bicommunities where sending and associated receiving sets of nodes, through clustering of directed-edge information, are fully acknowledged. Our findings reveal a primary directional axis from sensory to association cortices, indicative of bottom-up information flow. Validation with invasive electrophysiological measures demonstrates that these bicommunities capture an intermediate organizational scale at which connection asymmetry aligns across modalities. Finally, sets of distinct known anatomical fiber bundles and their functional mapping are recovered as specific bicommunities. This work identifies asymmetric pathways onto which neural computation can be expressed, and establishes directed connectomics as a novel framework for understanding brain organization.

neuroscience↗

bioRxiv2025

Non-invasive prediction of conduction velocities in the human brain from MRI-derived microstructure features at 7 Tesla

The conduction velocity of neuronal signals along axons is a key neurophysiological property that can be altered in various disease processes. While cortico-cortical evoked potentials (CCEPs) can be measured in presurgical assessment to provide information about conduction delay between a subset of brain regions, it is currently not possible to efficiently and systematically estimate conduction velocity in vivo across the whole brain. Given the established link between conduction velocity and axon morphology (most notably axon diameter but also myelination), mapping a reliable and quantitative metric linked to axon properties could fill the gap of inferring conduction velocity across the entire human brain. By integrating multiple MRI-derived microstructural measures - including axon radius, axonal water fraction, extra-axonal perpendicular diffusivity, and longitudinal relaxation time - and conduction velocity estimates obtained from a large database of CCEPs, we developed a whole-brain prediction model of conduction velocity. Our multivariate MRI-based model explained 29% of variance in neurophysiological conduction velocity, making it possible to partially predict whole-brain conduction velocity and delay matrices along connections for which no direct measurement is commonly available from epilepsy surgery investigations. This integrative MRI-based approach could provide a non-invasive framework for comprehensively characterising conduction delays in vivo across the human brain white matter.

neuroscience↗

bioRxiv2025

Local optogenetic control of genome editing and tumorigenesis in vivo using wireless implantable optoelectronics

Precise spatial regulation of site-specific DNA recombination (SSR) in vivo remains a challenge due to limited tunability of current platforms. Here, we present an optogenetic approach that overcome these limitations by employing engineered light-regulated recombinase E-LightR-Cre and tunable wireless implantable optoelectronic devices. E-LightR-Cre meets the key criteria for spatial regulation of SSR in vivo, showing no detectable activity in the dark, while demonstrating robust activation upon blue-light illumination. To achieve local E-LightR-Cre activation in murine lungs, we developed wireless, fully-implantable optoelectronic devices enabling focal illumination with no discernible organ damage. By modulating illumination intensity and duration, we can control the size of the activated area. Local expression of oncogenic KRas-G12D in a photoactivated subpopulation of cells in vitro revealed rapid reprogramming of the mutant expressing cells and their non-activated neighbors. Light-guided activation of E-LightR-Cre in mouse lungs resulted in focal expression of a reporter gene and allowed us to induce local formation of oncogenic lesions in vivo.

bioengineering↗

bioRxiv2024

Personalized cue-reactive delta-theta oscillations guide deep brain stimulation for opioid use disorder

Abstract / SummarySubstance use disorder (SUD) is a significant public health concern, with over 30% of the affected population not responding to available treatments. Severe SUD is characterized by drug-cue reactivity that has been reported to predict treatment-failure. We leveraged this pathophysiological feature to optimize deep brain stimulation (DBS) of the nucleus accumbens region (NAc) in an adult with SUD. A personalized drug cue-reactivity task was administered while recording NAc region electrophysiology from a lead externalized for clinical purposes. We identified a drug cue-evoked signal in the ventral NAc associated with intensification of opioid-related cravings, which attenuated subsequent to stimulation delivered to the same area. DBS was then programmed to engage this focal region, which resulted in sustained suppression of drug-related cravings. This finding heralds the potential for personalized strategies to optimize DBS for SUD.

neuroscience↗

bioRxiv2023

Intermodal consistency of whole-brain connectivity and signal propagation delays

Measuring the propagation of perturbations across the human brain and their transmission delays is critical for network neuroscience, but it is a challenging problem that still requires cross-validation approaches. Here, we compare results from a recently introduced, non-invasive technique of functional delays estimation from source-reconstructed electro/magnetoencephalography, to the corresponding findings from a large dataset of cortico-cortical evoked potentials estimated from intracerebral stimulations of epileptic pharmaco-resistant patients. The two methods yield significantly similar probabilistic connectivity maps and signal propagation delays. This similarity suggests a correspondence between the mechanisms underpinning the propagation of spontaneously generated scale-free perturbations (i.e. neuronal avalanches observed in resting state activity studied using magnetoencephalography) and the spreading of cortico-cortical evoked potentials. This manuscript provides evidence for the accuracy of a subject-specific estimate of functional delays obtained non-invasively from reconstructed sources. Conversely, our findings show that estimates obtained from externally-induced perturbations capture physiological activities. In conclusion, this manuscript constitutes a cross-validation between two different modalities. Importantly, the capability to measure delays non-invasively (as per MEG) paves the way for the inclusion of functional delays in personalized large-scale brain models as well as in diagnostic and prognostic algorithms.

neuroscience↗

bioRxiv2023

How Absence Seizures Impair Sensory Perception: Insights from Awake fMRI and Simulation Studies in Rats

In patients suffering absence epilepsy, recurring seizures can significantly decrease their quality of life and lead to yet untreatable comorbidities. Absence seizures are characterized by spike-and- wave discharges on the electroencephalogram associated with a transient alteration of consciousness. However, it is still unknown how the brain responds to external stimuli during and outside of seizures. This study aimed to investigate responsiveness to visual and somatosensory stimulation in GAERS, a well-established rat model for absence epilepsy. Animals were imaged under non-curarized awake state using a quiet, zero-echo-time, functional magnetic resonance imaging (fMRI) sequence. Sensory stimulations were applied during interictal and ictal periods. Whole brain hemodynamic responses were compared between these two states. Additionally, a mean-field simulation model was used to explain the changes of neural responsiveness to visual stimulation between states. During a seizure, whole-brain responses to both sensory stimulations were suppressed and spatially hindered. In the cortex, hemodynamic responses were negatively polarized during seizures, despite the application of a stimulus. The mean-field simulation revealed restricted propagation of activity due to stimulation and agreed well with fMRI findings. Results suggest that sensory processing is hindered or even suppressed by the occurrence of an absence seizure, potentially contributing to decreased responsiveness during this absence epileptic process.

neuroscience↗

bioRxiv2023

Comparison of hemodynamic response functions estimated from resting-state functional MRI and invasive electrophysiological recordings in rats

Resting-state functional MRI (rs-fMRI) is a popular technology that has enriched our understanding of brain and spinal cord functioning, including how different regions communicate (connectivity). But fMRI is an indirect measure of neural activity capturing blood hemodynamics. The hemodynamic response function (HRF) interfaces between the unmeasured neural activity and measured fMRI time series. The HRF is variable across brain regions and individuals, and is modulated by non-neural factors. Ignoring this HRF variability causes errors in FC estimates. Hence, it is crucial to reliably estimate the HRF from rs-fMRI data. Robust techniques have emerged to estimate the HRF from fMRI time series. Although such techniques have been validated non-invasively using simulated and empirical fMRI data, thorough invasive validation using simultaneous electrophysiological recordings, the gold standard, has been elusive. This report addresses this gap in the literature by comparing HRFs derived from invasive intracranial electroencephalogram recordings with HRFs estimated from simultaneously acquired fMRI data in six epileptic rats. We found that the HRF shape parameters (HRF amplitude, latency and width) were not significantly different (p>0.05) between ground truth and estimated HRFs. In the single pathological region, the HRF width was marginally significantly different (p=0.03). Our study provides preliminary invasive validation for the efficacy of the HRF estimation technique in reliably estimating the HRF non-invasively from rs-fMRI data directly. This has a notable impact on rs-fMRI connectivity studies, and we recommend that HRF deconvolution be performed to minimize HRF variability and improve connectivity estimates.

neuroscience↗

bioRxiv2022

Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation

Communication between gray matter regions underpins all facets of brain function. To date, progress in understanding large-scale neural communication has been hampered by the inability of current neuroimaging techniques to track signaling at whole-brain, high-spatiotemporal resolution. Here, we use 2.77 million intracranial EEG recordings, acquired following 29,055 single-pulse electrical stimulations in a total of 550 individuals, to study inter-areal communication in the human brain. We found that network communication models--computed on structural connectivity inferred from diffusion MRI--can explain the propagation of direct, focal electrical stimulation through white matter, measured at millisecond time scales. Building on this finding, we show that a parsimonious statistical model comprising structural, functional and spatial factors can accurately and robustly predict cortex-wide effects of brain stimulation (out-of-sample R2=54%). Our work contributes towards the biological validation of concepts in network neuroscience and provides insight into how white matter connectivity shapes inter-areal signaling. We anticipate that our findings will have implications for research on macroscale neural information processing and the design of brain stimulation paradigms.

neuroscience↗

bioRxiv2020

Large-scale study reveals regional fungicide applications as a major determinant of resistance evolution in the wheat pathogen Zymoseptoria tritici in France.

O_LIResearch rationale: In modern cropping systems, the near-universal use of plant protection products selects for resistance in pest populations. The emergence and evolution of this adaptive trait threaten treatment efficacy. We identified determinants of fungicide resistance evolution and quantified their effects at a large spatiotemporal scale. C_LIO_LIMethods: We focused on Zymoseptoria tritici, which causes leaf blotch in wheat. Phenotypes of qualitative or quantitative resistance to various fungicides were monitored annually, from 2004 to 2017, at about 70 sites throughout 22 regions of France (territorial units of 25 000km2 on average). We modelled changes in resistance frequency with regional anti-Septoria fungicide use, yield losses due to the disease and the regional area under organic wheat. C_LIO_LIKey results: The major driver of resistance dynamics was fungicide use at the regional scale. We estimated its effect on the increase in resistance and relative apparent fitness of each resistance phenotype. The predictions of the model replicated the spatiotemporal patterns of resistance observed in field populations (R2 from 0.56 to 0.82). C_LIO_LIMain conclusion: The evolution of fungicide resistance is mainly determined at the regional scale. This study therefore showed that collective management at the regional scale could effectively complete local actions. C_LI

evolutionary biology↗

bioRxiv2020

Opponent intracerebral signals for reward and punishment prediction errors in humans

Whether maximizing rewards and minimizing punishments rely on distinct brain systems remains debated, inconsistent results coming from human neuroimaging and animal electrophysiology studies. Bridging the gap across species and techniques, we recorded intracerebral activity from twenty patients with epilepsy while they performed an instrumental learning task. We found that both reward and punishment prediction errors (PE), estimated from computational modeling of choice behavior, correlated positively with broadband gamma activity (BGA) in several brain regions. In all cases, BGA increased with both outcome (reward or punishment versus nothing) and surprise (how unexpected the outcome is). However, some regions (such as the ventromedial prefrontal and lateral orbitofrontal cortex) were more sensitive to reward PE, whereas others (such as the anterior insula and dorsolateral prefrontal cortex) were more sensitive to punishment PE. Thus, opponent systems in the human brain might mediate the repetition of rewarded choices and the avoidance of punished choices.

neuroscience↗

bioRxiv2019

Ictal recruitment of anterior nucleus of thalamus in human focal epilepsy

The thalamic nuclei play diverse roles in the initiation, propagation, and termination of temporal lobe seizures. The role of the anterior nucleus of the thalamus (ANT) - a node that is integral to the limbic network is unclear. The objective of this study was to characterize temporal and - spectral patterns of ANT ictal recruitment in drug-resistant temporal lobe epilepsy (TLE). We hypothesized that seizures localized to the temporolimbic network are likely to recruit ANT, and the odds of recruitment were higher in seizures that had altered consciousness. Ten patients undergoing stereo-electroencephalography (SEEG) were recruited prospectively to record field potentials from the ANT. Using epileptogenicity index and line length, we computed the number of seizures that recruited the ANT (recruitment ratio), the recruitment latencies between the ANT and the epileptogenic zone (EZ), and latency of ANT recruitment to clinical manifestation for seventy-nine seizures. We observed that seizures localized to mesial temporal subregions (hippocampus, amygdala, anterior cingulate) have a higher predilection for ANT recruitment, and the recruitment was faster (ranged 5-12 secs) and preceded clinical onset for seizures that impaired consciousness. Seizures that recruited ANT lasted significantly longer (t=1.795, p=0.005). Recruitment latency was inversely correlated to seizure duration (r=-0.78, p=0.004). Electrical stimulation of the EZ induced seizures, in which early recruitment of ANT was confirmed. Stimulation of ANT did not induce a seizure. Finally, we tested the hypothesis that spectral and entropy-based features extracted from thalamic field potentials can distinguish its state of ictal recruitment from other interictal states (including awake, sleep). For this, we employed classification machine learning that discriminated thalamic ictal state from other interictal states with high accuracy (92.8%) and precision (93.1%). Among the features, the emergence of the theta rhythm (4-8 Hz) maximally discriminated the endogenous ictal state from other interictal states of vigilance. These results prompt a mechanistic role for the ANT in the early organization and sustaining of seizures, and the possibility to serve as a target for therapeutic closed-loop stimulation in TLE.

neuroscience↗

bioRxiv2018

MEK/mTOR-dependent D1 dopamine receptor activation induces local protein synthesis via eEF2 dephosphorylation in neurons

Neuromodulators in general, and dopamine in particular, define brain and neuronal states in different ways including regulation of global and local mRNA translation. Yet, the signaling pathways underlying the effects of dopamine on mRNA translation are not clear. Here, using genetic, pharmacologic, biochemical, and imaging methods, we tested the hypothesis that dopamine regulates phosphorylation of the eukaryotic elongation factor 2 (eEF2). We found that activation of dopamine receptor D1 but not D2 leads to rapid dephosphorylation of eEF2 at Thr56 in cortical primary neuronal culture and in vivo in a time-dependent manner. Additionally, NMDA receptor, mTOR, and ERK pathways are upstream to the D1 receptor-dependent eEF2 dephosphorylation and essential for it. Furthermore, D1 receptor activation resulted in a major reduction in dendritic eEF2 phosphorylation levels together with a correlative increase in local mRNA translation. These results reveal the role of eEF2 in dopamine regulation of local mRNA translation in neurons.\n\nOne-sentence summaryD1 receptor activation increases protein synthesis in dendrites by inactivating eEF2K in an ERK2/mTOR-dependent manner.

neuroscience↗