Biology subjects

Ranjeva, J.-P.

Publications and source records attributed to Ranjeva, J.-P..

5 recordsLinked to original sources

bioRxiv2026

Higher-order interdependencies of the epileptogenic network in presurgical resting-state fMRI

Drug-resistant focal epilepsy (DRFE) is understood as a complex brain network disorder. As a result, conventional pair-wise functional connectivity may fail to capture the higher-order interactions between groups of epileptogenic regions. We applied multivariate information theory to stereotactic electroencephalography-defined networks in retrospective resting-state fMRI analysis to determine whether the overall strength and dominant type of interdependencies between three or more regions, represented by the S-information and O-information respectively, could offer clinically relevant insights prior to surgical intervention in DRFE. We found networks involving the epileptogenic zone to be mostly dominated by re-dundant information-sharing, which was predicted by stronger structural and functional connections between regions. Non-temporal lobe epilepsy patients demonstrated greater redundancy and stronger comparative interdependencies than temporal lobe epilepsy patients, in line with a more complex network architecture. Among non-temporal lobe cases with postoperative follow-up, not seizure-free patients displayed more positive O-information deviation scores than seizure-free patients, suggesting elevated redundancy in networks associated with poorer surgical outcomes. Our findings potentiate the S-information and O-information as complementary higher-order descriptors of the epileptogenic network within personalised presurgical assessments.

neuroscience↗

bioRxiv2022

Multi-scale structural alterations of basal ganglia in focal epilepsy as demonstrated by 7T MRI

Focal epilepsy is characterized by repeated spontaneous seizures that originate from cortical epileptogenic zone networks (EZN). More recently, analysis of intracerebral recordings showed that subcortical structures, and in particular the thalamus, play an important role in facilitating and/or propagating epileptic activity. This supports previously reported structural alterations of these structures. Nonetheless, between-patient differences in EZN (e.g., temporal vs. non-temporal lobe epilepsy) as well as other clinical features (e.g., number of epileptogenic regions) might impact the magnitude as well as spatial distribution of subcortical structural changes. Here we used 7 Tesla MRI T1 data to provide a comprehensive description of subcortical morphological (volume, tissue deformation, and shape) and longitudinal relaxation (T1) changes in focal epilepsy patients to evaluate the impact of the EZN and patient-specific clinical features. Our results showed widespread morphometric and T1 changes. Focusing on the thalamus, atrophy varied across nuclei but appeared most prominent for the TLE group and the ipsilateral side, while shortening of T1 was observed for the lateral thalamus, in particular. Multivariate analyses across thalamic nuclei and basal ganglia showed that volume acted as the dominant discriminator between patients and controls, while (posterolateral) thalamic T1 measures looked promising to further differentiate patients based on EZN. In particular, the observed differences in T1 changes between thalamic nuclei indicated differential involvement of thalamic nuclei based on EZN. Finally, the number of epileptogenic regions was found to best explain the observed variability between patients. To conclude, this work revealed multi-scale subcortical alterations in focal epilepsy as well as their dependence on several clinical characteristics. Our results provide a basis for further, in-depth investigations using (quantitative) MRI and SEEG data and warrant further personalization of intervention strategies, such as deep brain stimulation, for treating focal epilepsy patients.

neuroscience↗

bioRxiv2020

A Motion Correction Strategy for Multi-Contrast based 3D parametric imaging: Application to Inhomogeneous Magnetization Transfer (ihMT)

PurposeTo propose an efficient retrospective image-based method for motion correction of multi-contrast acquisitions with a low number of available images (MC-MoCo) and evaluate its use in 3D inhomogeneous Magnetization Transfer (ihMT) experiments in the human brain. MethodsA framework for motion correction, including image pre-processing enhancement and rigid registration to an iteratively improved target image, was developed. The proposed method was compared to Motion Correction with FMRIBs Linear Image Registration Tool (MCFLIRT) function in FSL over 13 subjects. Native (pre-correction) and residual (post-correction) motions were evaluated by means of markers positioned at well-defined anatomical regions over each image. ResultsBoth motion correction strategies significantly reduced inter-image misalignment, and the MC-MoCo method yielded significantly better results than MCFLIRT. ConclusionMC-MoCo is a high-performance method for motion correction of multi-contrast volumes as in 3D ihMT imaging.

neuroscience↗

bioRxiv2020

Modular slowing of resting-state dynamic Functional Connectivity as a marker of cognitive dysfunction induced by sleep deprivation

Dynamic Functional Connectivity (dFC) in the resting state (rs) is considered as a correlate of cognitive processing. Describing dFC as a flow across morphing connectivity configurations, our notion of dFC speed quantifies the rate at which FC networks evolve in time. Here we probe the hypothesis that variations of rs dFC speed and cognitive performance are selectively interrelated within specific functional subnetworks. In particular, we focus on Sleep Deprivation (SD) as a reversible model of cognitive dysfunction. We found that whole-brain level (global) dFC speed significantly slows down after 24h of SD. However, the reduction in global dFC speed does not correlate with variations of cognitive performance in individual tasks, which are subtle and highly heterogeneous. On the contrary, we found strong correlations between performance variations in individual tasks -including Rapid Visual Processing (RVP, assessing sustained visual attention)- and dFC speed quantified at the level of functional subnetworks of interest. Providing a compromise between classic static FC (no time) and global dFC (no space), modular dFC speed analyses allow quantifying a different speed of dFC reconfiguration independently for sub-networks overseeing different tasks. Importantly, we found that RVP performance robustly correlates with the modular dFC speed of a characteristic frontoparietal module. HighlightsO_LISleep Deprivation (SD) slows down the random walk in FC space implemented by Dynamic Functional Connectivity (dFC) at rest. C_LIO_LIWhole-brain level slowing of dFC speed does not selectively correlate with fine and taskspecific changes in performance C_LIO_LIWe quantify dFC speed separately for different link-based modules coordinated by distinct regional "meta-hubs" C_LIO_LIModular dFC speed variations capture subtle and task-specific variations of cognitive performance induced by SD. C_LI Author summaryWe interpreted dynamic Functional Connectivity (dFC) as a random walk in the space of possible FC networks performed with a quantifiable "speed". Here, we analyze a fMRI dataset in which subjects are scanned and cognitively tested both before and after Sleep Deprivation (SD), used as a reversible model of cognitive dysfunction. While global dFC speed slows down after a sleepless night, it is not a sufficiently sensitive metric to correlate with fine and specific cognitive performance changes. To boost the capacity of dFC speed analyses to account for fine and specific cognitive decline, we introduce the notion of modular dFC speed. Capitalizing on an edge-centric measure of functional connectivity, which we call Meta-Connectivity, we isolate subgraphs of FC describing relatively independent random walks (dFC modules) and controlled by distinct "puppet masters" (meta-hubs). We then find that variations of the random walk speed of distinct dFC modules now selectively correlate with SD-induced variations of performance in the different tasks. This is in agreement with the fact that different subsystems - distributed but functionally distinct- oversee different tasks. The high sensitivity of modular dFC analyses bear promise of future applications to the early detection and longitudinal characterization of pathologies such as Alzheimers disease.

neuroscience↗

bioRxiv2019

Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG-frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG-frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG. The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond-range in all canonical frequencies of FCEEG to second-range of FCfMRI. Conversely, the second component additionally exposes linked but spatially divergent neuronal processing at the two timescales. This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals.

neuroscience↗