scholarly journals Frontoparietal network dynamics impairments in juvenile myoclonic epilepsy revealed by MEG energy landscape

2019 ◽  
Author(s):  
Dominik Krzemiński ◽  
Naoki Masuda ◽  
Khalid Hamandi ◽  
Krish D Singh ◽  
Bethany Routley ◽  
...  
Keyword(s):  
Resting State ◽  
Energy Landscape ◽  
Network Dynamics ◽  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy ◽  
Oscillatory Activity ◽  
Functional Networks ◽  
Occurrence Probability ◽  
Energy Landscapes ◽  
Frontoparietal Network

AbstractJuvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy affecting brain activity. It is unclear to what extent JME leads to abnormal network dynamics across functional networks. Here, we proposed a method to characterise network dynamics in MEG resting-state data, combining a pairwise maximum entropy model (pMEM) and the associated energy landscape analysis. Fifty-two JME patients and healthy controls underwent a resting-state MEG recording session. We fitted the pMEM to the oscillatory power envelopes in theta (4-7 Hz), alpha (8-13 Hz), beta (15-25 Hz) and gamma (30-60 Hz) bands in three source-localised resting-state networks: the frontoparietal network (FPN), the default mode network (DMN), and the sensorimotor network (SMN). The pMEM provided an accurate fit to the MEG oscillatory activity in both patient and control groups, and allowed estimation of the occurrence probability of each network state, with its regional activity and pairwise regional co-activation constrained by empirical data. We used energy values derived from the pMEM to depict an energy landscape of each network, with a higher energy state corresponding to a lower occurrence probability. When comparing the energy landscapes between groups, JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta and gamma-bands. Furthermore, numerical simulation of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of characteristic energy minima was shortened in JME patients. These network alterations were confirmed by a significant leave-one-out classification of individual participants based on a support vector machine employing the energy values of pMEM as features. Our findings suggested that JME patients had altered multi-stability in selective functional networks and frequency bands in the frontoparietal cortices.HighlightsAn energy landscape analysis characterises the dynamics of MEG oscillatory activityPatients with JME exhibit fewer local minima of the energy in their energy landscapesJME affects the network dynamics in the frontoparietal network.Energy landscape measures allow good single-patient classification.

scholarly journals A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG

2020 ◽  
Author(s):  
Marinho A Lopes ◽  
Dominik Krzemiński ◽  
Khalid Hamandi ◽  
Krish D. Singh ◽  
Naoki Masuda ◽  
...  
Keyword(s):  
Resting State ◽  
Classification Accuracy ◽  
Brain Activity ◽  
Juvenile Myoclonic Epilepsy ◽  
Brain Network ◽  
Myoclonic Epilepsy ◽  
Functional Networks ◽  
Normal Brain ◽  
Healthy Controls ◽  
Scalp Eeg

Objective Functional networks derived from resting-state scalp EEG from people with idiopathic (genetic) generalized epilepsy (IGE) have been shown to have an inherent higher propensity to generate seizures than those from healthy controls when assessed using the concept of brain network ictogenicity (BNI). Herein we test whether the BNI framework is applicable to resting-state MEG and whether it may achieve higher classification accuracy relative to previous studies using EEG. Methods The BNI framework consists in deriving a functional network from apparently normal brain activity, placing a mathematical model of ictogenicity into the network and then computing how often such network generates seizures in silico. We consider data from 26 people with juvenile myoclonic epilepsy (JME) and 26 healthy controls. Results We find that resting-state MEG functional networks from people with JME are characterized by a higher propensity to generate seizures (i.e. BNI) than those from healthy controls. We found a classification accuracy of 73%. Conclusions The BNI framework is applicable to MEG and capable of differentiating people with epilepsy from healthy controls. The observed classification accuracy is similar to previously achieved in scalp EEG. Significance The BNI framework may be applied to resting-state MEG to aid in epilepsy diagnosis.

scholarly journals Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy

2020 ◽  
Vol 4 (2) ◽  
pp. 374-396
Author(s):  
Dominik Krzemiński ◽  
Naoki Masuda ◽  
Khalid Hamandi ◽  
Krish D. Singh ◽  
Bethany Routley ◽  
...  
Keyword(s):  
Resting State ◽  
Energy State ◽  
Energy Landscape ◽  
Juvenile Myoclonic Epilepsy ◽  
Occurrence Probability ◽  
Resting State Networks ◽  
Entropy Model ◽  
Network Activation ◽  
Statistical Regularities ◽  
Network States

Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy. It is yet unclear to what extent JME leads to abnormal network activation patterns. Here, we characterized statistical regularities in magnetoencephalograph (MEG) resting-state networks and their differences between JME patients and controls by combining a pairwise maximum entropy model (pMEM) and novel energy landscape analyses for MEG. First, we fitted the pMEM to the MEG oscillatory power in the front-oparietal network (FPN) and other resting-state networks, which provided a good estimation of the occurrence probability of network states. Then, we used energy values derived from the pMEM to depict an energy landscape, with a higher energy state corresponding to a lower occurrence probability. JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta, and gamma bands. Furthermore, simulations of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of energy minima was shortened in JME patients. These network alterations were highlighted by significant classification of individual participants employing energy values as multivariate features. Our findings suggested that JME patients had altered multistability in selective functional networks and frequency bands in the fronto-parietal cortices.

scholarly journals MEG energy landscape abnormalities in juvenile myoclonic epilepsy

2019 ◽  
Author(s):  
Dominik Krzeminski ◽  
Naoki Masuda ◽  
Khalid Hamandi ◽  
Krish Singh ◽  
Jiaxiang Zhang
Keyword(s):  
Energy Landscape ◽  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy

scholarly journals White-Matter Hyperintensity Load and Differences in Resting-State Network Connectivity Based on Mild Cognitive Impairment Subtype

2021 ◽  
Vol 13 ◽  
Author(s):  
Martina Vettore ◽  
Matteo De Marco ◽  
Claudia Pallucca ◽  
Matteo Bendini ◽  
Maurizio Gallucci ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Resting State ◽  
Large Scale ◽  
Network Connectivity ◽  
Functional Networks ◽  
Frontoparietal Network ◽  
Definition Of ◽  
The Right

“Mild cognitive impairment” (MCI) is a diagnosis characterised by deficits in episodic memory (aMCI) or in other non-memory domains (naMCI). Although the definition of subtypes is helpful in clinical classification, it provides little insight on the variability of neurofunctional mechanisms (i.e., resting-state brain networks) at the basis of symptoms. In particular, it is unknown whether the presence of a high load of white-matter hyperintensities (WMHs) has a comparable effect on these functional networks in aMCI and naMCI patients. This question was addressed in a cohort of 123 MCI patients who had completed an MRI protocol inclusive of T1-weighted, fluid-attenuated inversion recovery (FLAIR) and resting-state fMRI sequences. T1-weighted and FLAIR images were processed with the Lesion Segmentation Toolbox to quantify whole-brain WMH volumes. The CONN toolbox was used to preprocess all fMRI images and to run an independent component analysis for the identification of four large-scale haemodynamic networks of cognitive relevance (i.e., default-mode, salience, left frontoparietal, and right frontoparietal networks) and one control network (i.e., visual network). Patients were classified based on MCI subtype (i.e., aMCI vs. naMCI) and WMH burden (i.e., low vs. high). Maps of large-scale networks were then modelled as a function of the MCI subtype-by-WMH burden interaction. Beyond the main effects of MCI subtype and WMH burden, a significant interaction was found in the salience and left frontoparietal networks. Having a low WMH burden was significantly more associated with stronger salience-network connectivity in aMCI (than in naMCI) in the right insula, and with stronger left frontoparietal-network connectivity in the right frontoinsular cortex. Vice versa, having a low WMH burden was significantly more associated with left-frontoparietal network connectivity in naMCI (than in aMCI) in the left mediotemporal lobe. The association between WMH burden and strength of connectivity of resting-state functional networks differs between aMCI and naMCI patients. Although exploratory in nature, these findings indicate that clinical profiles reflect mechanistic interactions that may play a central role in the definition of diagnostic and prognostic statuses.

scholarly journals A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG

2021 ◽  
Vol 132 (4) ◽  
pp. 922-927
Author(s):  
Marinho A. Lopes ◽  
Dominik Krzemiński ◽  
Khalid Hamandi ◽  
Krish D. Singh ◽  
Naoki Masuda ◽  
...  
Keyword(s):  
Resting State ◽  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy

scholarly journals Altered Local Spontaneous Brain Activity in Juvenile Myoclonic Epilepsy: A Preliminary Resting-State fMRI Study

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Sisi Jiang ◽  
Cheng Luo ◽  
Zhixuan Liu ◽  
Changyue Hou ◽  
Pu Wang ◽  
...  
Keyword(s):  
Spontaneous Activity ◽  
Resting State ◽  
Age Of Onset ◽  
Brain Activity ◽  
Epileptiform Activity ◽  
Substantial Reduction ◽  
Resting State Fmri ◽  
Epileptic Activity ◽  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy

Purpose. The purpose of this study was to evaluate the regional synchronization of brain in patients with juvenile myoclonic epilepsy (JME).Methods. Resting-state fMRI data were acquired from twenty-one patients with JME and twenty-two healthy subjects. Regional homogeneity (ReHo) was used to analyze the spontaneous activity in whole brain. Two-samplet-test was performed to detect the ReHo difference between two groups. Correlations between the ReHo values and features of seizures were calculated further.Key Findings. Compared with healthy controls, patients showed significantly increased ReHo in bilateral thalami and motor-related cortex regions and a substantial reduction of ReHo in cerebellum and occipitoparietal lobe. In addition, greater ReHo value in the left paracentral lobule was linked to the older age of onset in patients.Significance. These findings implicated the abnormality of thalamomotor cortical network in JME which were associated with the genesis and propagation of epileptiform activity. Moreover, our study supported that the local brain spontaneous activity is a potential tool to investigate the epileptic activity and provided important insights into understanding the pathophysiological mechanisms of JME.

scholarly journals Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Ines R Violante ◽  
Lucia M Li ◽  
David W Carmichael ◽  
Romy Lorenz ◽  
Robert Leech ◽  
...  
Keyword(s):  
Working Memory ◽  
Cognitive Processing ◽  
Large Scale ◽  
Relative Phase ◽  
Memory Performance ◽  
Network Dynamics ◽  
Oscillatory Activity ◽  
Emergent Properties ◽  
Behavioral Performance ◽  
Frontoparietal Network

Cognitive functions such as working memory (WM) are emergent properties of large-scale network interactions. Synchronisation of oscillatory activity might contribute to WM by enabling the coordination of long-range processes. However, causal evidence for the way oscillatory activity shapes network dynamics and behavior in humans is limited. Here we applied transcranial alternating current stimulation (tACS) to exogenously modulate oscillatory activity in a right frontoparietal network that supports WM. Externally induced synchronization improved performance when cognitive demands were high. Simultaneously collected fMRI data reveals tACS effects dependent on the relative phase of the stimulation and the internal cognitive processing state. Specifically, synchronous tACS during the verbal WM task increased parietal activity, which correlated with behavioral performance. Furthermore, functional connectivity results indicate that the relative phase of frontoparietal stimulation influences information flow within the WM network. Overall, our findings demonstrate a link between behavioral performance in a demanding WM task and large-scale brain synchronization.

A systematic review of resting-state and task-based fmri in juvenile myoclonic epilepsy

2021 ◽  
Author(s):  
Hossein Sanjari Moghaddam ◽  
Ali Sanjari Moghaddam ◽  
Alireza Hasanzadeh ◽  
Zahra Sanatian ◽  
Amirreza Mafi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Resting State ◽  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy ◽  
And Task
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