Abnormal directed connectivity of resting state networks in focal epilepsy

Carboni, Margherita; Stefano, Pia De; Vorderwülbecke, Bernd J.; Tourbier, Sébastien; Mullier, Emeline; Rubega, Maria; Momjian, Shahan; Schaller, Karl; Hagmann, Patric; Seeck, M.; Michel, Christoph M.; Mierlo, Pieter van; Vulliémoz, Serge

doi:10.1016/j.nicl.2020.102336

Cited by 30 publications

(32 citation statements)

References 52 publications

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“…In both TLE and extratemporal epilepsy, EEG resting-state network analysis showed increased efficiency in the somatosensory, ventral attention, and default mode networks. 74 Studying extratemporal epilepsy related to a spectrum of different malformations of cortical development, a prior study supported structural covariance network regularization and functional randomization, indicating modality-specific shifts in network integration and segregation. Despite diverging changes in structural and functional network topologies, both suggest alterations in hubness of local regions affecting overall network efficiency in these patients.…”

Section: Hubs In Common Epilepsy Syndromesmentioning

confidence: 83%

Epilepsy and brain network hubs

et al. 2022

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Epilepsy is a disorder of brain networks. A better understanding of structural and dynamic network properties may improve epilepsy diagnosis, treatment, and prognostics. Hubs are brain regions with high connectivity to other parts of the brain and are typically situated along the brain's most efficient communication pathways, supporting large-scale brain wiring and many higher order neural functions. The visualization and analysis of hubs offers a perspective on regional and global network organization and can provide novel insights into brain disorders and epilepsy. By notably supporting the interaction between various brain networks, hubs may be implicated in seizure spread and in epilepsy-related phenotypes. In this review, we will discuss the growing literature on atypical hub organization in common epilepsy syndromes, both related to neuroimaging of brain structure and function, and related to neurophysiological data from magneto-and electroencephalographic measures of neural dynamics. With studies increasingly exploring the clinical utility of network neuroscience approaches, we highlight the potential of hub mapping as a candidate biomarker of cognitive dysfunction and postsurgical seizure outcome. We will conclude the review with a discussion of current limitations and outlook for future research.

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Section: Hubs In Common Epilepsy Syndromesmentioning

confidence: 83%

Epilepsy and brain network hubs

et al. 2022

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“…We took the first component of the singular value decomposition computed over all the time-series of every source (independently of their orientation) belonging to a given ROI. This method was described in Rubega et al 2019 and used in several other studies (Carboni et al 2019, 2020; Damborská et al 2020). The singular value decomposition has a sign ambiguity, which can bias directed connectivity measures.…”

Section: Methodsmentioning

confidence: 99%

Phase-amplitude coupling and phase synchronization between medial temporal, frontal and posterior brain regions support episodic autobiographical memory recall

Roehri

eacutechet

Seeber

et al. 2021

Preprint

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Episodic autobiographical memory (EAM) is a complex cognitive function that emerges from the coordination of specific and distant brain regions. Specific brain rhythms, namely theta and gamma oscillations and their synchronization, are thought of as putative mechanisms enabling EAM. Yet, the mechanisms of inter-regional interaction in the EAM network remain unclear in humans at the whole brain level. To investigate this, we analyzed EEG recordings of participants instructed to retrieve autobiographical episodes. EEG recordings were projected in the source space, and time-courses of atlas-based brain regions-of-interest (ROIs) were derived. Directed phase synchrony in high theta (7-10 Hz) and gamma (30-80 Hz) bands and high theta-gamma phase-amplitude coupling were computed between each pair of ROIs. Using network-based statistics, a graph-theory method, we found statistically significant networks for each investigated mechanism. In the gamma band, two sub-networks were found, one between the posterior cingulate cortex (PCC) and the medial temporal lobe (MTL) and another within the medial frontal areas. In the high theta band, we found a PCC to ventromedial prefrontal cortex (vmPFC) network. In phase-amplitude coupling, we found the high theta phase of the left MTL biasing the gamma amplitude of posterior regions and the vmPFC. Other regions of the temporal lobe and the insula were also phase biasing the vmPFC. These findings suggest that EAM, rather than emerging from a single mechanism at a single frequency, involves precise spatio-temporal signatures mapping on distinct memory processes. We propose that the MTL orchestrates activity in vmPFC and PCC via precise phase-amplitude coupling, with vmPFC and PCC interaction via high theta phase synchrony and gamma synchronization contributing to bind information within the PCC-MTL sub-network or valuate the candidate memory within the medial frontal sub-network.

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“…CMP3 has been successfully employed in a number of methodological (Akselrod et al, 2021;Glomb, Mullier, et al, 2020;Glomb, Rué Queralt, et al, 2020;Pascucci et al, 2021;Rué-Queralt et al, 2021;Zheng et al, 2020), clinical (Carboni et al, 2019(Carboni et al, , 2020(Carboni et al, , 2022, and data (Pascucci et al, 2022a(Pascucci et al, , 2022b research articles. CMP3 is part of the BIDS Apps, and also part of ReproNim/containers, a DataLad dataset with a collection of 40 popular containerized neuroimaging research pipelines, which allows one to easily include it as a subdataset within DataLad-controlled BIDS datasets, and achieve fully reproducible analysis by running CMP3 directly with DataLad.…”

Section: Community Impactmentioning

confidence: 99%

Connectome Mapper 3: A Flexible and Open-Source Pipeline Software for Multiscale Multimodal Human Connectome Mapping

Tourbier¹,

Rué-Queralt²,

Glomb³

et al. 2022

JOSS

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Connectome Mapper 3 (CMP3) is an open-source processing pipeline software, written in Python 3, for multi-scale multi-modal connectome mapping of the human brain. It provides researchers with a unique workflow, implemented in accordance with the Brain Imaging Data Structure (BIDS) App framework (Gorgolewski et al., 2017), that leverages a number of widely adopted software tools to map a graph representation of the structural and functional connections between brain regions, the connectome, at multiple scales. The workflow can take any structural / diffusion / resting-state functional Magnetic Resonance Imaging (MRI) dataset structured according to the BIDS standard (Gorgolewski et al., 2016), and is intended to be run via its Docker and Singularity/Apptainer containerized versions. To improve its accessibility, CMP3 comes with a graphical user interface, which supports the user in all the steps involved in the configuration of the pipelines, the configuration and execution of the BIDS App, and the control of the output quality. CMP3 has been successfully employed in a number of research papers, and is currently being extended to electroencephalography, to offer a solid multi-modal framework to the community for the investigation of brain network function and organization at specific scales, as well as a map that links different spatial and temporal scales. CMP3 is available from the Python Package Index (PyPI), and the container images are available from DockerHub and Sylabs Cloud.

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Abnormal directed connectivity of resting state networks in focal epilepsy

Abstract: Highlights Abnormal brain network is visible in absence of epileptic activity on hd-EEG. Higher efficiency in patients vs. controls in absence of epileptic activity on hd-EEG. Resting state networks are altered in epileptic patients.

Cited by 30 publications

References 52 publications

Epilepsy and brain network hubs

Epilepsy and brain network hubs

Phase-amplitude coupling and phase synchronization between medial temporal, frontal and posterior brain regions support episodic autobiographical memory recall

Connectome Mapper 3: A Flexible and Open-Source Pipeline Software for Multiscale Multimodal Human Connectome Mapping

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