Studying resting state features in epileptic patients as compared to healthy controls

A number of factors, including sleep/wake states, antiepileptic drugs and localization of the epileptic focus, can influence whether patients with epilepsy will show IED in the EEG (Coito et al. 2019a;

Cook et al. 2016; Seeck et al. 2017). In clinical practice, especially if high density EEG is recorded, due to the limited amount of time (normally around 1 hour), the probability of collecting IEDs during

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one session is relatively low and therefore the need of implementing biomarkers based on task-free (resting) wakefulness without IEDs epochs is crucial.

As in Study 1, in Study 2 we computed the global efficiency of the network, as an indication of pathological integration of different brain areas. We found that patients (both temporal and extratemporal) had an increased global efficiency at the entire brain level as compared to healthy controls: also, in this case, as in the previous study, the increased in this network feature can be interpreted as a marker of pathology. The hypothesis that similar epileptic networks are active both in absence and presence of EEG-visible epileptic activity is supported by simultaneous EEG-fMRI (Iannotti et al. 2016) and intracranial EEG studies (Bettus et al. 2009).

EEG-based network analysis has shown directional information in resting state imaging (Coito et al.

2019b) and could reveal important features of resting state network changes in epilepsy. The network perspective, strongly dependent on the functional interaction between its nodes (Cataldi et al. 2013), is important to improve our understanding of the complex interplay between pathological areas and other brain networks during task-free conditions.

In our Study 2 we found specific alterations of directed connectivity within three resting state networks: the default mode network, the somatomotor network and ventral network in temporal lobe as well as in extra temporal lobe patients.

Previous studies in patients with temporal lobe epilepsy have shown reduced connectivity as compared to healthy controls in the posterior cingulate cortex (PCC) and the anterior cingulate cortex (ACC), regions that are known to be included in the DMN (Coito et al. 2016) but specifically the connectivity changes inside the different resting state network, and especially between DMN regions were not investigated (Coito et al. 2019b). The directionality of the information flow between the different default mode network nodes using fMRI Granger-causality were previously investigated (Deshpande et al. 2011; Jiao et al. 2011; Uddin et al. 2009; Yan and He 2011; Zhou et al. 2011) but due to the technical temporal limitation of the fMRI based-method, there has been a lack of temporal resolution.

Patients with temporal lobe epilepsy often show abnormal functional, structural and morphometric connectivity in the precentral cortex (part of the somatomotor network) (Garcia et al. 2017;

Lemkaddem et al. 2014; Trimmel et al. 2018) as well as functional impairments of the ventral attention network during “oddball paradigm” (Bocquillon et al. 2009) linked with reduced attentional performances (Fleck et al. 2002). Our findings in Study 2 of the same resting state

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networks’ alterations in both temporal and extra-temporal would indicate this as a marker of epilepsy.

2.1 Limitations and future perspectives to study patients’ resting state activity in EEG

The delineation of the different resting state networks has been primarily done in fMRI (Schaefer et al. 2018) where the spatial resolution is higher than in EEG: generally the spatial extent of these networks on the anatomical parcellation, based on the largest overlap, could have limited the grouping of the different nodes. Furthermore, we did not take into account the single patient variability of these networks: possibly the mapping of the regions based on fMRI individual resting state functional interactionswould have improve the access to these alterations. One network that was expected to be altered, especially in the temporal lobe patients, was the limbic network: this was the network that unfortunately was mainly affected by the previously described bias with only a few regions, and sometimes also with few solution points in each region, attributed to it and by low signal contribution coming from the medial temporal regions. The generation of new atlas of connectivity analysis based on ESI, taking into account both the functional activation on different resting state networks and the spatial limitations of EEG signal is now needed (Farahibozorg et al.

2018).

It is also important to notice that quite often in fMRI, different studies showed a decrease in functional connectivity and in global efficiency associated with pathology, while ic-EEG and MEG studies normally shown increased connectivity: this discrepancy is yet unexplained and may be linked with the ‘indirect’ signal (i.e. the BOLD) collected with the fMRI. It is also important to note that the functional connectivity describe by the two modalities is different: one often refers to correlation while the other to Granger causality. High value of these functional connectivity matrices carry different meaning (De Vico Fallani et al. 2014). Further investigation, based on the same method but on different modalities, on both EEG and fRMI data also collected simultaneously, could help shedding some lights on this.

As in the Study 1, in Study 2 the etiology, semiology and prognosis of the patients population (even with an increased in the actual number and even if gathered from the same group (Berg et al. 2010)) were still heterogeneous. This probably explain why we were not able to find a correlation between any clinical variable correlating and the connectivity analysis. Further studies including larger cohorts with several types of epilepsy, more neuropsychological and clinical variables are needed

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since previous studies showed distinct spatial patterns specifically for frontal lobe and temporal lobe epilepsy (Fahoum et al. 2012).