Network alterations in eating epilepsy during resting state and during eating using Magnetoencephalography

Published:November 12, 2022DOI:


      • Patients with eating epilepsy show a disrupted network during resting state. During eating, they tend towards a hyperconnected state with deviations from small worldness.
      • Patients with spontaneous seizures show a deviation from small worldness even in a resting state compared to those with pure reflex seizures.
      • Connectivity changes in resting state networks [Default Mode Network, Attention network, sensorimotor networks] were observed in resting state records of patients with eating epilepsy compared to controls.
      • Connectivity changes during eating involved the default mode network nodes, limbic cortex, and nodes participating in the physiological process of eating.



      Eating epilepsy presents various imaging and electrophysiological features along with various seizure triggers. As such, network changes in eating epilepsy have not been comprehensively explored. This study was conducted to illustrate resting state network changes in eating epilepsy and to study the changes in network configurations during eating.


      Magnetoencephalography recordings of nineteen patients with drug-resistant eating epilepsy were compared with healthy controls during resting state. A subgroup of nine patients and 12 controls had MEG recordings during eating. Network changes were analyzed using phase lag index across 5 frequency bands [delta, theta, alpha, beta, and gamma] using clustering coefficient (CC), betweenness centrality (BC), path length (PL), modularity (Q), and small worldness (SW).


      During the resting state, PL was decreased in patients with epilepsy in the delta, theta, and gamma band. Q was lower in patients with epilepsy in the beta and gamma bands. During eating, in patients with epilepsy, PL and SW were increased in all frequency bands, and Q was decreased in the beta band and increased in the rest of the frequency bands. Patients with mixed types of seizures showed higher PL in all bands except alpha, higher Q in all bands, and higher SW in the alpha and beta bands. Node-wise changes in CC and BC implicated changes in DMN and ‘eating’ networks.


      Reflex Eating epilepsy presents with a hyperconnected network that exacerbates during eating. The cause of seizure onset and loss of consciousness in eating epilepsy might be due to aberrant network interaction between the regions of the brain involved with eating, such as the sensorimotor cortex, lateral parietal cortex, and insula with the limbic cortex and default mode network across multiple frequency bands.


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        • Panayiotopoulos C.P.
        Reflex seizures and reflex epilepsies [Internet].
        (Available from:) Bladon Medical Publishing, 2005
      1. ’Eating epilepsy’--a reappraisal. – PubMed – NCBI [Internet]. [cited 2019 Mar 6]. Available from:

        • Patel M.
        • Satishchandra P.
        • Saini J.
        • Bharath R.D.
        • Sinha S.
        Eating epilepsy: phenotype, MRI, SPECT and video-EEG observations.
        Epilepsy Res. 2013; 107: 115-120
        • Hansen P.
        • Kringelbach M.
        • Salmelin R.
        MEG: An introduction to methods.
        Oxford University Press, 2010: 449
        • Ahlfors S.P.
        • Han J.
        • Belliveau J.W.
        • Hämäläinen M.S.
        Sensitivity of MEG and EEG to source orientation.
        Brain Topogr. 2010; 23: 227-232
        • Tadel F.
        • Baillet S.
        • Mosher J.C.
        • Pantazis D.
        • Leahy R.M.
        Brainstorm: a user-friendly application for MEG/EEG analysis [Internet].
        Comput Intelligence Neurosci. 2011; ([cited 2017 Oct 14]. Available from:
        • Fischl B.
        NeuroImage [Internet]. 2012; 62 ([cited 2020 Sep 19]. Available from: 774-781
        • Desikan R.S.
        • Ségonne F.
        • Fischl B.
        • Quinn B.T.
        • Dickerson B.C.
        • Blacker D.
        • et al.
        An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.
        NeuroImage. 2006; 31: 968-980
        • Gramfort A.
        • Papadopoulo T.
        • Olivi E.
        • Clerc M.
        OpenMEEG: opensource software for quasistatic bioelectromagnetics.
        Biomed Eng OnLine. 2010; 9: 45
        • Stam C.J.
        • Nolte G.
        • Daffertshofer A.
        Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources.
        Hum Brain Mapp. 2007; 28: 1178-1193
        • Hillebrand A.
        • Barnes G.R.
        • Bosboom J.L.
        • Berendse H.W.
        • Stam C.J.
        Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution.
        NeuroImage. 2012; 59: 3909-3921
        • van Wijk B.C.M.
        • Stam C.J.
        • Daffertshofer A.
        Comparing brain networks of different size and connectivity density using graph theory.
        PLoS ONE. 2010; 5: e13701
        • Bullmore E.
        • Sporns O.
        Complex brain networks: graph theoretical analysis of structural and functional systems.
        Nat Rev Neurosci [Internet]. 2009; 10 ([cited 2020 Sep 26]. Available from: 186-198
        • Fornito A.
        • Zalesky A.
        • Bullmore E.
        Fundamentals of brain network analysis.
        Academic Press, 2016: 496 p.
        • Benjamini Y.
        • Yekutieli D.
        The control of the false discovery rate in multiple testing under dependency.
        Ann Stat. 2001; 29: 1165-1188
      2. Fornito A, Zalesky A, Bullmore ET, editors. Chapter 5 - Centrality and Hubs. In: Fundamentals of Brain Network Analysis [Internet]. San Diego: Academic Press; 2016 [cited 2020 Dec 3]. p. 137–61. Available from:

        • Bolt T.
        • Nomi J.S.
        • Rubinov M.
        • Uddin L.Q.
        Correspondence between evoked and intrinsic functional brain network configurations.
        Hum Brain Mapp [Internet]. 2017; 38 ([cited 2022 Jun 25]. Available from: 1992-2007
        • Zhang X.
        • Yang Y.
        • Zhang M.H.
        • Zhong N.
        Network analysis of brain functional connectivity in mental arithmetic using task-evoked fMRI.
        in: Wang S. Yamamoto V. Su J. Yang Y. Jones E. Iasemidis L. Brain informatics. Springer International Publishing, Cham2018: 141-152 (Lecture Notes in Computer Science)
        • Gleichgerrcht E.
        • Kocher M.
        • Bonilha L.
        Connectomics and graph theory analyses: Novel insights into network abnormalities in epilepsy.
        Epilepsia [Internet]. 2015; 56 ([cited 2022 Jun 25]. Available from: 1660-1668
        • Bernhardt B.C.
        • Bonilha L.
        • Gross D.W.
        Network analysis for a network disorder: the emerging role of graph theory in the study of epilepsy.
        Epilepsy Behav [Internet]. 2015; 50 ([cited 2022 Jun 25]. Available from: 162-170
        • Fransson P.
        • Marrelec G.
        The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: evidence from a partial correlation network analysis.
        NeuroImage [Internet]. 2008; 42 (cited 2020 Oct 12]. Available from: 1178-1184
        • Hsiao F.J.
        • Yu H.Y.
        • Chen W.T.
        • Kwan S.Y.
        • Chen C.
        • Yen D.J.
        • et al.
        Increased intrinsic connectivity of the default mode network in temporal lobe epilepsy: evidence from resting-state MEG recordings.
        PLoS ONE [Internet]. 2015; 10 ([cited 2020 Sep 7]. Available from:
        • McGill M.L.
        • Devinsky O.
        • Kelly C.
        • Milham M.
        • Castellanos F.X.
        • Quinn B.T.
        • et al.
        Default mode network abnormalities in idiopathic generalized epilepsy.
        Epilepsy Behav [Internet]. 2012; 23 ([cited 2020 Oct 12]. Available from: 353-359
        • Sakurai K.
        • Takeda Y.
        • Tanaka N.
        • Kurita T.
        • Shiraishi H.
        • Takeuchi F.
        • et al.
        Generalized spike-wave discharges involve a default mode network in patients with juvenile absence epilepsy: a MEG study.
        Epilepsy Res. 2010; 89: 176-184
        • Rolls E.T.
        Taste, olfactory, and food reward value processing in the brain.
        Prog Neurobiol. 2015; 127–128: 64-90
        • Gottfried J.A.
        Central mechanisms of odour object perception.
        Nat Rev Neurosci. 2010; 11: 628-641
        • Small D.M.
        Taste representation in the human insula.
        Brain Struct Funct. 2010; 214: 551-561
        • Topolovec J.C.
        • Gati J.S.
        • Menon R.S.
        • Shoemaker J.K.
        • Cechetto D.F.
        Human cardiovascular and gustatory brainstem sites observed by functional magnetic resonance imaging.
        J Comp Neurol. 2004; 471: 446-461
        • Humbert I.A.
        • Robbins J.
        Normal swallowing and functional magnetic resonance imaging: a systematic review.
        Dysphagia. 2007; 22: 266-275
        • Canolty R.T.
        • Knight R.T.
        The functional role of cross-frequency coupling.
        Trends Cogn Sci. 2010; 14: 506-515
        • Lopes da Silva F.H.
        • Witter M.P.
        • Boeijinga P.H.
        • Lohman A.H.
        Anatomic organization and physiology of the limbic cortex.
        Physiol Rev. 1990; 70: 453-511
        • Cirignotta F.
        • Marcacci G.
        • Lugaresi E.
        Epileptic seizures precipitated by eating.
        Epilepsia. 1977; 18: 445-449