Cortical connectivity in fronto-temporal focal epilepsy from EEG analysis: A study via graph theory

Vecchio, Fabrizio; Miraglia, Francesca; Curcio, Giuseppe; Marca, Giacomo Della; Vollono, Catello; Mazzucchi, Edoardo; Bramantı, Placido; Rossini, Paolo Maria

doi:10.1016/j.clinph.2014.09.019

Cited by 64 publications

(49 citation statements)

References 73 publications

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“…Event-related potentials ( Figure 4a ) after substitutive electrical (n = 4, estimated power 0.75, Figure 4—figure supplement 2 ) and natural mechanical stimulation (n = 4, estimated power 0.79, Figure 4—figure supplement 3 ) conditions did not reveal any statistical difference (Montecarlo statistics with cluster correction for multiple comparisons). Furthermore, a network graph analysis approach ( Vecchio et al, 2015a ) revealed a lateralized EEG frequency modulation that was evoked both by electrical and mechanical stimuli ( Figure 4b ). Indeed, the primary sensorimotor areas in the hemisphere contralateral to the stimulus presented a significant reduction (3-way ANOVA followed by Duncan’s multiple range test, F(1,6) = 6.48, p<0.05, comparison to the ipsilateral hemisphere) in the clustering coefficient following the incoming sensory stimulus, regardless of its tactile or substitutive nature ( Figure 4c ).…”

Section: Resultsmentioning

confidence: 99%

Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans

Oddo

Raspopović

Artoni

et al. 2016

eLife

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Restoration of touch after hand amputation is a desirable feature of ideal prostheses. Here, we show that texture discrimination can be artificially provided in human subjects by implementing a neuromorphic real-time mechano-neuro-transduction (MNT), which emulates to some extent the firing dynamics of SA1 cutaneous afferents. The MNT process was used to modulate the temporal pattern of electrical spikes delivered to the human median nerve via percutaneous microstimulation in four intact subjects and via implanted intrafascicular stimulation in one transradial amputee. Both approaches allowed the subjects to reliably discriminate spatial coarseness of surfaces as confirmed also by a hybrid neural model of the median nerve. Moreover, MNT-evoked EEG activity showed physiologically plausible responses that were superimposable in time and topography to the ones elicited by a natural mechanical tactile stimulation. These findings can open up novel opportunities for sensory restoration in the next generation of neuro-prosthetic hands.DOI: http://dx.doi.org/10.7554/eLife.09148.001

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Section: Resultsmentioning

confidence: 99%

Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans

Oddo

Raspopović

Artoni

et al. 2016

eLife

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242

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“…We selected four methods that have been widely used to estimate functional brain connectivity from electrophysiological signals (local field potentials, depth-EEG or EEG/MEG) (see (Wendling et al 2009) for review). These measures were chosen to cover the main families of connectivity methods (linear and nonlinear regression, phase synchronization and mutual information).…”

Section: B Connectivity Measuresmentioning

confidence: 99%

“…In line with previous cognitive studies (Astolfi et al 2007;Babiloni et al 2005;Betti et al 2013;Bola and Sabel 2015;David et al 2003;David et al 2002;de Pasquale et al 2010;Hassan et al 2015a;Hassan et al 2014;Hassan and Wendling 2015;Hipp et al 2011;Hoechstetter et al 2004;Liljeström et al 2015;Schoffelen and Gross 2009), the basic principle is to estimate functional connectivity at the level of brain sources reconstructed from scalp signals. These methods, referred to as "source connectivity" were applied to both interictal EEG (Coito et al 2015;Song et al 2013;Vecchio et al 2014) and MEG signals (Dai et al 2012;Malinowska et al 2014) as well as to EEG signals recorded during seizures (Ding et al 2007;Jiruska et al 2013;Lu et al 2012) or resting states (Adebimpe et al 2016;Coito et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Identification of Interictal Epileptic Networks from Dense-EEG

et al. 2016

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Epilepsy is a network disease. The epileptic network usually involves spatially distributed brain regions. In this context, noninvasive M/EEG source connectivity is an emerging technique to identify functional brain networks at cortical level from noninvasive recordings. In this paper, we analyze the effect of the two key factors involved in EEG source connectivity processing: (i) the algorithm used in the solution of the EEG inverse problem and (ii) the method used in the estimation of the functional connectivity. We evaluate four inverse solutions algorithms (dSPM, wMNE, sLORETA and cMEM) and four connectivity measures (r , h, PLV, and MI) on data simulated from a combined biophysical/physiological model to generate realistic interictal epileptic spikes reflected in scalp EEG. We use a new network-based similarity index to compare between the network identified by each of the inverse/connectivity combination and the original network generated in the model. The method will be also applied on real data recorded from one epileptic patient who underwent a full presurgical evaluation for drug-resistant focal epilepsy. In simulated data, results revealed that the selection of the inverse/connectivity combination has a significant impact on the identified networks. Results suggested that nonlinear methods (nonlinear correlation coefficient, phase synchronization and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The wMNE inverse solution showed higher performance than dSPM, cMEM and sLORETA. In real data, the combination (wMNE/PLV) led to a very good matching between the interictal epileptic network identified from noninvasive EEG recordings and the network obtained from connectivity analysis of intracerebral EEG recordings. These results suggest that source connectivity method, when appropriately configured, is able to extract highly relevant diagnostic information about networks involved in interictal epileptic spikes from non-invasive dense-EEG data.

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“…Several research groups (Sporns and Zwi, 2004, Stam and Reijneveld, 2007, De Vico et al, 2007, He et al, 2007, de Haan et al, 2009a, Rubinov and Sporns, 2010, Vecchio et al, 2014a, Vecchio et al, 2015b, Miraglia et al, 2017) have recently engaged themselves with brain functional dataset analysis by graph theory applications. These applications are made with different methodological approaches and on different kinds of datasets.…”

Section: Introductionmentioning

confidence: 99%

Connectome: Graph theory application in functional brain network architecture

Vecchio

Miraglia²,

Rossini³

2017

Clinical Neurophysiology Practice

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HighlightsNetwork science and graph theory applications can help in understanding how human cognitive functions are linked to neuronal network structure.The present review focuses on pivotal recent studies regarding graph theory application on functional dynamic connectivity investigated by electroencephalographic (EEG) analysis.Graph analysis applications represent an interesting probe to analyze the distinctive features of real life by focusing on functional connectivity networks.Application of graph theory to patient data might provide more insight into the pathophysiological processes underlying brain disconnection.Graph theory might aid in monitoring the impact of eventual pharmacological and rehabilitative treatments.

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Cortical connectivity in fronto-temporal focal epilepsy from EEG analysis: A study via graph theory

Cited by 64 publications

References 73 publications

Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans

Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans

Identification of Interictal Epileptic Networks from Dense-EEG

Connectome: Graph theory application in functional brain network architecture

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