Directed functional connectivity during and without interictal spikes in left and right TLE patients using

In our first clinical study, we investigated the whole-brain dynamic pattern of interictal directed functional connectivity in left and right TLE patients (LTLE and RTLE, respectively) by combining EEG source imaging and time-varying Granger-causal modelling [205]. This is an important issue given that spikes are transient highly non-stationary events with fast propagation and their contribution to cognitive impairment is poorly understood. We could identify the main drivers and the network patterns of interictal epileptic activity in LTLE and RTLE. We further found that LTLE and RTLE have different network patterns: RTLE patients showed stronger connectivity targeting the contralateral hemisphere in comparison with LTLE patients, consistent with differences in neuropsychological impairments between the groups. This study offered an improved characterization of the dynamic behaviour of interictal spikes networks. The identification of the main drivers and connections of these networks is crucial to delineate the epileptogenic focus more accurately.

However, interictal spikes are not always visible during scalp EEG recordings. But even though, seizure relapse and behavioural impairments can be observed despite their absence. Therefore, a better characterisation of abnormal networks in the absence of interictal epileptiform discharges (IEDs) could have an important diagnostic and prognostic value. In our follow-up study [214], we investigated whether resting-state directed functional connectivity was altered in EEG epochs free of IEDs in LTLE and RTLE when compared to healthy controls, using Granger-causal modelling applied to EEG source signals. We found that resting-state EEG-based directed functional connectivity is altered in TLE patients even in the absence of IEDs and that these alterations are correlated with clinical and behavioural parameters. We showed that resting-state EEG directed connectivity in the absence of IED can provide an important understanding of abnormal brain activity in TLE and that the resting-state alterations compared to healthy controls could be an important biomarker of TLE. Indeed, in a follow-up study, we used these outflow values as features for a classification system (using Random Forests) and were able to obtain an accuracy of 90% for both diagnosing TLE (classifier: TLE vs healthy controls) and lateralizing TLE (classifier: LTLE vs RTLE). This demonstrates the potential of resting-state EEG-based directed functional connectivity for automatically diagnose and lateralize TLE.

We also started investigating whether directed functional connectivity of interictal spikes seen at both scalp and intracranial EEG revealed the same sources and connectivity pattern using simultaneous intracranial and high-density EEG recordings. Our preliminary results show that indeed both source reconstructed signals and connectivity patterns resemble the ones found with intracranial EEG.

Bellow we will go in further detail about the main findings in these clinical studies.

2.1. Dynamic connectivity during interictal spikes

The characterisation of the dynamics of interictal activity in the development of epileptic networks is important[186]. The emergence of spikes precedes the first seizure in animal models of epilepsy and may guide the development of aberrant neuronal circuits and the initiation of spontaneous seizures[56]. There is also evidence of spike-related cognitive impairments in epilepsy notably regarding memory maintenance and retrieval in the hippocampus of TLE patients[25].

The ipsilateral temporal lobe was the earliest significant driver during the spike in both groups. This suggests that EEG directed functional connectivity can correctly identify the origin of epileptic discharges in this group of patients with invasive validation. The other identified key drivers were also early active, with high or even peak activity before the spike peak, concordant with a previous study showing that ESI of the rising phase of the spike is better at localizing epileptic activity, while the source at the spike peak can be contaminated by propagation [86]. Ipsilateral medial temporal structures (hippocampus and amygdala) were also identified as significant drivers, although with less summed outflow than the temporal pole. This confirms the notion that medial temporal structures can be “seen” by the scalp EEG even if simultaneous or propagated activity in lateral temporal areas dominates in strength [12]. Furthermore, it shows that ESI directed functional connectivity can contribute for the correct localization of the epileptogenic zone by identifying the strongest drivers of information flow, showing thus its potential use in non-invasive presurgical evaluation. This approach could be specially helpful in difficult cases in which other non-invasive presurgical evaluation methods are not concordant.

2.2. Network changes in TLE: increased or decreased functional connectivity?

Many connectivity studies have reported that normal physiological networks can be disrupted in TLE [20, 21, 163]. In both studies, with or without interictal spikes, we showed the existence of regions outside the ipsilateral temporal lobe that had significant outflow changes, which supports previous evidence that impaired brain activity during epileptic activity is not spatially restricted to the epileptogenic focus.

Functional and structural studies showed that epileptic activity affect brain regions outside the seizure onset zone, namely the contralateral temporal lobe [24], the frontal lobe [215-217], and other extra-temporal regions [163].

However, interestingly, in the first study, we found that medio-limbic regions (medial temporal pole, hippocampus, amygdala, gyrus rectus) had an increased connectivity during interictal spikes compared with baseline periods without spikes [205], whereas in the second study, we found decreased connectivity when comparing baseline periods in patients (no IEDs) and healthy subjects, which suggests a complex pattern of dysfunction with a reduced baseline activity and pathological positive recruitment during IEDs.

Beyond temporal regions, in resting-state periods without spikes, we also found that patients had significantly reduced connectivity compared to healthy controls in several regions that are known to be included in the DMN, namely the posterior cingulate cortex (PCC) and the anterior cingulate cortex (ACC).

A reduced connectivity in between medio-temporal regions and between those and other regions of the DMN, such as the PCC, has also been shown by fMRI studies during interictal periods in unilateral TLE patients [158, 164-166], however they did not specifically take into account the existence or not of IEDs during these periods. Here, we showed that in the absence of interictal spikes detected on the scalp EEG, ipsilateral regions in LTLE and contralateral regions in RTLE had decreased connectivity compared to controls. This diminished connectivity could reflect the chronic abnormal brain function in these patients, in which interictal and ictal activity represent recurrent transient paroxystic exacerbations of the disease. This was also corroborated by a negative correlation between disease duration and summed outflow in certain regions, mainly medio-limbic, showing that longer disease duration are associated with increased network impairments in TLE. This decreased connectivity is likely to be related to the structural degeneration observed in epilepsy [166, 218], namely in the ipsilateral medial temporal lobe but also in the regions to which they are structurally connected.

However, during interictal spikes, and similarly to MEG and intracranial EEG studies, we found disease-related connectivity increases in opposition to fMRI studies reporting decrease of functional connectivity of hemodynamic signals [20]. This discrepancy between electrophysiological and hemodynamic correlations is currently unexplained.

2.3. Altered network pattern in TLE

During interictal spikes, we found that the strongest drivers statistically significant from baseline periods were in the ipsilateral temporal lobe in both patients groups, concordant with invasive validation of the epileptogenic zone, and that the strongest outflows were from the ipsilateral medial temporal pole to the contralateral and extratemporal regions. Concordantly, in the follow-up study during periods without IEDs, we found an altered driving pattern compared to healthy controls in the absence of IEDs in both groups: the strongest outflows in both patient groups were from the ipsilateral hippocampus, while in controls these were from the PCC towards the medial temporal and frontal lobes. This corroborates the importance of the hippocampus in TLE. Indeed, the hippocampus is known as a core region for spike generation and seizure onset in TLE. Our results indicate that, even in periods with no scalp detectable IED, the strongest information transfer still originates from the ipsilateral hippocampus, and not from the PCC as in healthy subjects. This dysconnectivity pattern suggests that TLE patients could be identified in IED-free recordings using connectivity analysis.

In this context, we used these resting-state (no IEDs) outflow values to build a classification system based on two two-class Random Forests (RF) classifiers to diagnose and lateralise TLE: TLE vs healthy controls (diagnosis) and LTLE vs RTLE (lateralization). This was the first study to automatically diagnose and lateralize TLE based on electrophysiological directed functional connectivity. The high accuracy achieved (90%) in both diagnosis and lateralization demonstrates the potential of resting-state EEG-based directed

contribution for the search of new TLE biomarkers. Due to their low cost, EEG-based measures could be widely implemented to help clinicians in the diagnosis of TLE.

2.4. LTLE versus RTLE

Functional and structural asymmetry of the two hemispheres has long been recognized [219].

During interictal spikes, we found more contralateral involvement in RTLE than in LTLE, in concordance with the neuropsychological assessment: in LTLE, the main drivers were in the ipsilateral hemisphere while in RTLE, the main drivers were in both ipsilateral and contralateral areas, and moreover, the strongest connections from these main drivers were mainly towards the ipsilateral hemisphere in LTLE while in RTLE they were mainly towards contralateral regions. Consistently, other studies demonstrated more contralateral hemisphere impairment in RTLE compared to LTLE using fMRI [4], magnetic resonance spectroscopy (hippocampus) [6] and volumetry (subcortical structures) [5, 220].

In periods without pathological events, we have also interestingly found that more contralateral structures in RTLE had decreased summed outflow, while in LTLE there were mainly ipsilateral regions showing an impaired summed outflow when compared to controls. This was corroborated by the fact that longer disease durations were associated with a more reduced driving from contralateral medio-limbic regions in RTLE.

This difference might be due to more structural abnormalities in RTLE compared to LTLE [6, 220], which might explain why RTLE patients present more cognitive deficits attributed to contralateral temporal dysfunction [4, 205]. Thus, long disease durations might affect brain structures more bilaterally in RTLE than in LTLE.

2.5. Intracranial correlates of scalp EEG-based functional connectivity

Scalp EEG-based functional connectivity analyses hold many advantages as compared to intracranial EEG analyses, as mentioned in section 3.4, namely the fact that no invasive recordings are needed, the possibility to study whole-brain functional networks and a healthy control group can be used for statistical comparison with the patients' results. However, intracranial EEG measures the neuronal activity within the cortex without attenuation by the several head tissues, and therefore the signals have a higher SNR than scalp EEG signals. Therefore, simultaneous intracranial and scalp EEG recordings are important to confirm the reliability of scalp EEG analysis by comparing the ESI and connectivity results with the ones obtained from intracranial EEG signals recorded at the same time.

Thus, we started by investigating whether directed functional connectivity of interictal spikes seen at both scalp and intracranial EEG revealed the same sources and connectivity pattern using simultaneous intracranial and high-density EEG recordings. We found that source reconstruction from high-density EEG was able to retrieve the strongest source identified by intracranial EEG, which is a further evidence of the reliability of ESI during interictal spikes to non-invasively determine the epileptogenic zone. We then found also that connectivity analyses depicted the same structures as the strongest drivers of epileptic activity as well as connectivity patterns in both datasets. This is still a preliminary study in only 1 patient and the results have to be further confirmed in a larger cohort of patients.