Correlation between autistic traits and brain functional connectivity in preschoolers with autism spectrum disorder: a resting state MEG study.

BACKGROUND: Neurophysiological studies recognized that Autism Spectrum Disorder (ASD) is associated with altered patterns of over- and under-connectivity. However, little is known about network organization in children with ASD in the early phases of development and its correlation with the severity of core autistic features. METHODS: The present study aimed at investigating the association between brain connectivity derived from MEG signals and severity of ASD traits measured with different diagnostic clinical scales, in a sample of 16 children with ASD aged 2 to 6 years. RESULTS: A significant correlation emerged between connectivity strength in cortical brain areas implicated in several resting state networks (Default mode, Central executive, Salience, Visual and Sensorimotor) and the severity of communication anomalies, social interaction problems, social affect problems, and repetitive behaviors. Seed analysis revealed that this pattern of correlation was mainly caused by global rather than local effects. CONCLUSIONS: The present evidence suggests that altered connectivity strength in several resting state networks is related to clinical features and may contribute to neurofunctional correlates of ASD. Future studies implementing the same method on a wider and stratified sample may further support functional connectivity as a possible biomarker of the condition.

Comparison of Resampling Techniques for Imbalanced Datasets in Machine Learning: Application to Epileptogenic Zone Localization From Interictal Intracranial EEG Recordings in Patients With Focal Epilepsy.

Aim: In neuroscience research, data are quite often characterized by an imbalanced distribution between the majority and minority classes, an issue that can limit or even worsen the prediction performance of machine learning methods. Different resampling procedures have been developed to face this problem and a lot of work has been done in comparing their effectiveness in different scenarios. Notably, the robustness of such techniques has been tested among a wide variety of different datasets, without considering the performance of each specific dataset. In this study, we compare the performances of different resampling procedures for the imbalanced domain in stereo-electroencephalography (SEEG) recordings of the patients with focal epilepsies who underwent surgery. Methods: We considered data obtained by network analysis of interictal SEEG recorded from 10 patients with drug-resistant focal epilepsies, for a supervised classification problem aimed at distinguishing between the epileptogenic and non-epileptogenic brain regions in interictal conditions. We investigated the effectiveness of five oversampling and five undersampling procedures, using 10 different machine learning classifiers. Moreover, six specific ensemble methods for the imbalanced domain were also tested. To compare the performances, Area under the ROC curve (AUC), F-measure, Geometric Mean, and Balanced Accuracy were considered. Results: Both the resampling procedures showed improved performances with respect to the original dataset. The oversampling procedure was found to be more sensitive to the type of classification method employed, with Adaptive Synthetic Sampling (ADASYN) exhibiting the best performances. All the undersampling approaches were more robust than the oversampling among the different classifiers, with Random Undersampling (RUS) exhibiting the best performance despite being the simplest and most basic classification method. Conclusions: The application of machine learning techniques that take into consideration the balance of features by resampling is beneficial and leads to more accurate localization of the epileptogenic zone from interictal periods. In addition, our results highlight the importance of the type of classification method that must be used together with the resampling to maximize the benefit to the outcome.

Different circuitry dysfunction in drug-naive patients with juvenile myoclonic epilepsy and juvenile absence epilepsy.

RATIONALE: Juvenile myoclonic epilepsy (JME) and juvenile absence epilepsy (JAE) are generalized epileptic syndromes presenting in the same age range. To explore whether uneven network dysfunctions may underlie the two different phenotypes, we examined drug-naive patients with JME and JAE at the time of their earliest presentation. METHODS: Patients were recruited based on typical JME (n = 23) or JAE (n = 18) presentation and compared with 16 age-matched healthy subjects (HS). We analyzed their awake EEG signals by Partial Directed Coherence and graph indexes. RESULTS: Out-density and betweenness centrality values were different between groups. With respect to both JAE and HS, JME showed unbalanced out-density and out-strength in alpha and beta bands on central regions and reduced alpha out-strength from fronto-polar to occipital regions, correlating with photosensitivity. With respect to HS, JAE showed enhanced alpha out-density and out-strength on fronto-polar regions. In gamma band, JAE showed reduced Global/Local Efficiency and Clustering Coefficient with respect to HS, while JME showed more scattered values. CONCLUSIONS: Our data suggest that regional network changes in alpha and beta bands underlie the different presentation distinguishing JME and JAE resulting in motor vs non-motor seizures characterizing these two syndromes. Conversely, impaired gamma-activity within the network seems to be a non-local marker of defective inhibition.

Impaired awareness in mesial temporal lobe epilepsy: Network analysis of foramen ovale and scalp EEG.

OBJECTIVE: We use co-registration of foramen-ovale and scalp-EEG to investigate network alterations in temporal-lobe epilepsy during focal seizures without (aura) or with impairment of awareness (SIA). METHODS: One aura and one SIA were selected from six patients. Temporal dynamic among 4 epochs, as well as the differences between aura and SIA, were analyzed through partial directed coherence and graph theory-based indices of centrality. RESULTS: Regarding the auras temporal evolution, fronto-parietal (FP) regions showed decreased connectivity with respect to the interictal period, in both epileptogenic (EH) and non-epileptogenic hemisphere (nEH). During SIAs, temporal dynamic showed more changes than auras: centrality of mesial temporal (mT) regions changes during all conditions, and nEH FP centrality showed the same dynamic trend of the aura (decreased centrality), until the last epoch, close to the impaired awareness, when showed increased centrality. Comparing SIA with aura, in proximity of impaired awareness, increased centrality was found in all the regions, except in nEH mT. CONCLUSIONS: Our findings suggested that the impairment of awareness is related to network alterations occurring first in neocortical regions and when awareness is still retained. SIGNIFICANCE: The analysis of 'hub' alteration can represent a suitable biomarker for scalp EEG-based prediction of awareness impairment.

EEG Assessment in Patients With Disorders of Consciousness: Aims, Advantages, Limits, and Pitfalls.

This study presents a brief review of literature exploring simple EEG-polygraphic examinations and procedures that can be carried out at a patient's bedside. These include EEG with a common electrode array and sleep evaluation. The review briefly discusses more complex analytical techniques, such as the application of advanced EEG signal processing methods developed by our research group, to define what type of consistent markers are suitable for clinical use or to better understand complex patient conditions. These advanced analytical techniques aim to detect relevant EEG-based markers that could be useful in evaluating patients and predicting outcomes. These data could contribute to future developments in research.

Network characteristics in benign epilepsy with centro-temporal spikes patients indicating defective connectivity during spindle sleep: A partial directed coherence study of EEG signals.

OBJECTIVE: To investigate the changes in EEG connectivity in children with the typical presentation of benign epilepsy with centro-temporal spikes (BECTS). METHODS: We compared awake and spindle-sleep EEG recordings obtained by a standard electrode array in patients with lateralised (10 Right, 9 Left-BECTS) or bilateral spikes (10 MF-BECTS) and in 17 age-matched controls. We analysed EEG activity using partial directed coherence, an estimator of connectivity based on the multivariate autoregressive models and calculated in- and out-degrees, strength, clustering coefficient and betweenness centrality. RESULTS: In comparison with the controls, the awake EEG recordings of the patients with lateralised BECTS showed a minimal increase in out-degrees on F4 and F3. The greater differences, found during sleep, included significant reductions in both in- and out-degrees and strength in all of the patient groups, but in T4 or T3 showing increased out-degrees and strength in Right and Left-BECTS. Betweenness centrality was significantly reduced on C3 and C4 in the patients with MF-BECTS. CONCLUSIONS: Our observations suggest that the main finding in BECTS patients is widely reduced local connectivity. SIGNIFICANCE: The network changes in BECTS can be interpreted as a permissive condition occurring in a developmental window that predisposes to seizure generation during spindle-sleep.

Altered resting state effective connectivity in long-standing vegetative state patients: an EEG study.

OBJECTIVE: Recent evidence mainly based on hemodynamic measures suggests that the impairment of functional connections between different brain areas may help to clarify the neuronal dysfunction occurring in patients with disorders of consciousness (DOC). The aim of this study was to evaluate effective EEG connectivity in a cohort of 18 patients in a chronic vegetative state (VS) observed years after the occurrence of hypoxic (eight) and traumatic or hemorrhagic brain insult. METHODS: we analysed the EEG signals recorded under resting conditions using a frequency domain linear index of connectivity (partial directed coherence: PDC) estimated from a multivariate autoregressive model. The results were compared with those obtained in ten healthy controls. RESULTS: Our findings indicated significant connectivity changes in EEG activities in delta and alpha bands. The VS patients showed a significant and widespread decrease in delta band connectivity, whereas the alpha activity was hyper-connected in the central and posterior cortical regions. CONCLUSION: These changes suggest the occurrence of severe circuitry derangements probably due to the loose control of the subcortical connections. The alpha hyper-synchronisation may be due to simplified networks mainly involving the short-range connections between intrinsically oscillatory cortical neurons that generate aberrant EEG alpha sources. This increased connectivity may be interpreted as a reduction in information capacity, implying an increasing prevalence of stereotypic activity patterns. SIGNIFICANCE: Our observations suggest a remarkable rearrangement of connectivity in patients with long-standing VS. We hypothesize that in persistent VS, after a first period characterized by a breakdown of cortical connectivity, neurodegenerative processes, largely independent from the type of initial insult, lead to cortex de-afferentation and to a severe reduction of possible cortical activity patterns and states.

Identification of the Epileptogenic Zone from Stereo-EEG Signals: A Connectivity-Graph Theory Approach.

In the context of focal drug-resistant epilepsies, the surgical resection of the epileptogenic zone (EZ), the cortical region responsible for the onset, early seizures organization, and propagation, may be the only therapeutic option for reducing or suppressing seizures. The rather high rate of failure in epilepsy surgery of extra-temporal epilepsies highlights that the precise identification of the EZ, mandatory objective to achieve seizure freedom, is still an unsolved problem that requires more sophisticated methods of investigation. Despite the wide range of non-invasive investigations, intracranial stereo-EEG (SEEG) recordings still represent, in many patients, the gold standard for the EZ identification. In this contest, the EZ localization is still based on visual analysis of SEEG, inevitably affected by the drawback of subjectivity and strongly time-consuming. Over the last years, considerable efforts have been made to develop advanced signal analysis techniques able to improve the identification of the EZ. Particular attention has been paid to those methods aimed at quantifying and characterizing the interactions and causal relationships between neuronal populations, since is nowadays well assumed that epileptic phenomena are associated with abnormal changes in brain synchronization mechanisms, and initial evidence has shown the suitability of this approach for the EZ localization. The aim of this review is to provide an overview of the different EEG signal processing methods applied to study connectivity between distinct brain cortical regions, namely in focal epilepsies. In addition, with the aim of localizing the EZ, the approach based on graph theory will be described, since the study of the topological properties of the networks has strongly improved the study of brain connectivity mechanisms.

From brain topography to brain topology: relevance of graph theory to functional neuroscience.

Although several brain regions show significant specialization, higher functions such as cross-modal information integration, abstract reasoning and conscious awareness are viewed as emerging from interactions across distributed functional networks. Analytical approaches capable of capturing the properties of such networks can therefore enhance our ability to make inferences from functional MRI, electroencephalography and magnetoencephalography data. Graph theory is a branch of mathematics that focuses on the formal modelling of networks and offers a wide range of theoretical tools to quantify specific features of network architecture (topology) that can provide information complementing the anatomical localization of areas responding to given stimuli or tasks (topography). Explicit modelling of the architecture of axonal connections and interactions among areas can furthermore reveal peculiar topological properties that are conserved across diverse biological networks, and highly sensitive to disease states. The field is evolving rapidly, partly fuelled by computational developments that enable the study of connectivity at fine anatomical detail and the simultaneous interactions among multiple regions. Recent publications in this area have shown that graph-based modelling can enhance our ability to draw causal inferences from functional MRI experiments, and support the early detection of disconnection and the modelling of pathology spread in neurodegenerative disease, particularly Alzheimer's disease. Furthermore, neurophysiological studies have shown that network topology has a profound link to epileptogenesis and that connectivity indices derived from graph models aid in modelling the onset and spread of seizures. Graph-based analyses may therefore significantly help understand the bases of a range of neurological conditions. This review is designed to provide an overview of graph-based analyses of brain connectivity and their relevance to disease aimed principally at general neuroscientists and clinicians.

Epileptogenic networks of type II focal cortical dysplasia: a stereo-EEG study.

In the context of focal and drug-resistant epilepsy, surgical resection of the epileptogenic zone may be the only therapeutic option for reducing or suppressing seizures. In many such patients, intracranial stereo-EEG recordings remain the gold standard for the epilepsy surgery work-up. Assessing the extent of the epileptogenic zone and its organisation is a crucial objective, and requires advanced methods of signal processing. Over the last ten years, considerable efforts have been made to develop signal analysis techniques for characterising the connectivity between spatially distributed regions. The aim of this study was to evaluate the changes in dynamic connectivity pattern under inter-ictal, pre-ictal and ictal conditions using signals derived from stereo-EEG recordings of 10 patients with Taylor-type focal cortical dysplasia. A causal linear multivariate method - partial directed coherence - and indices derived from graph theory were used to characterise the synchronisation property of the lesional zone (corresponding to the epileptogenic zone in our patients) and to distinguish it from other regions involved in ictal activity or not. The results show that a significantly different connectivity pattern (mainly in the gamma band) distinguishes the epileptogenic zone from other cortical regions not only during the ictal event, but also during the inter- and pre-ictal periods. This indicates that the lesional nodes play a leading role in generating and propagating ictal EEG activity by acting as the hubs of the epileptic network originating and sustaining seizures. Our findings also indicate that the cortical regions beyond the dysplasia involved in the ictal activity essentially act as "secondary" generators of synchronous activity. The leading role of the lesional zone may account for the good post-surgical outcome of patients with type II focal cortical dysplasia as resecting the dysplasia removes the epileptogenic zone responsible for seizure organisation. Furthermore, our findings strongly suggest that advanced signal processing techniques aimed at studying synchronisation and characterising brain networks could substantially improve the pre-surgical evaluation of patients with focal epilepsy, even in cases without an associated anatomically detectable lesion.

Enhanced frontocentral EEG connectivity in photosensitive generalized epilepsies: a partial directed coherence study.

PURPOSE: Photosensitive epilepsy (PSE) is the most common form of reflex epilepsy presenting with electroencephalography (EEG) paroxysms elicited by intermittent photic stimulation (IPS). To investigate whether the neuronal network undergoes dynamic changes before and during the transition to an EEG epileptic discharge, we estimated EEG connectivity patterns in photosensitive (PS) patients with idiopathic generalized epilepsy. METHODS: EEG signals were evaluated under resting conditions and during 14 Hz IPS, a frequency that consistently induces photoparoxysmal responses (PPRs) in PS patients. Partial directed coherence (PDC), a linear measure of effective connectivity based on multivariate autoregressive models, was used in 10 PS patients and 10 controls. Anterior versus posterior (F3, F4, C3, C4, and P3, P4, O1, O2) and interhemispheric connectivity patterns (F4, C4, P4, O2, and F3, C3, P3, O1) were estimated with focus on beta and gamma band activity. KEY FINDINGS: PDC analysis revealed an enhanced connectivity pattern in terms of both the number and strength of outflow connections in the PS patient group. Under resting condition, the greater connectivity in the PS patients occurred in the beta band, whereas it mainly involved the gamma band during IPS (i.e., the frequencies ranging from 40-60 Hz that include the higher harmonics of the stimulus frequency). Both at rest and during IPS, the differences between the PS patients and controls were due primarily to clearly increased connectivity involving the anterior cortical regions. SIGNIFICANCE: Our findings indicate that PS patients are characterised by abnormal EEG hyperconnectivity, primarily involving the anterior cortical regions under resting conditions and during IPS. This suggests that, even if the occipital cortical regions are the recipient zone of the stimulus and probably hyperexcitable, the anterior cortical areas are prominently involved in generating the hypersynchronization underlying the spike-and wave discharges elicited by IPS.

EEG-EMG coherence estimated using time-varying autoregressive models in movement-activated myoclonus in patients with progressive myoclonic epilepsies.

We aimed this study at verifying the appropriateness of bivariate time-varying autoregressive models in detecting EEG-EMG relationships and identifying the characteristics of myoclonus-related EEG changes in patients with two forms of progressive myoclonus epilepsy (PME). Our results indicate that TVAR analysis was able to detect the presence of prominent peaks of EEG-EMG coherence between the EMG and contralateral frontocentral EEG derivation in all patients, revealing differences in time-frequency spectral profiles associated to the two different forms of PMEs, possibly correlated with the severity of myoclonus.

Partial Directed Coherence estimated on Stereo-EEG signals in patients with Taylor's type focal cortical dysplasia.

The study was aimed at evaluating the changes in dynamical connectivity, between interictal, preictal and ictal condition, among signals derived from StereoEEG recordings in patients with Taylor's type focal cortical dysplasia (FCD type-II), by means of Partial Directed Coherence and indexes derived from graph theory. Results showed that seizures are characterized by an increased synchronization, mainly within the regions involved in the generation of the epileptogenic activity. Our findings reveal that the proposed procedure can be considered a suitable techinque to properly identify the pathological synchronization mechanisms underlying seizure generation and to support the identification of the epileptogenic zone.

Simultaneous EEG-fMRI in patients with Unverricht-Lundborg disease: event-related desynchronization/synchronization and hemodynamic response analysis.

We performed simultaneous acquisition of EEG-fMRI in seven patients with Unverricht-Lundborg disease (ULD) and in six healthy controls using self-paced finger extension as a motor task. The event-related desynchronization/synchronization (ERD/ERS) analysis showed a greater and more diffuse alpha desynchronization in central regions and a strongly reduced post-movement beta-ERS in patients compared with controls, suggesting a significant dysfunction of the mechanisms regulating active movement and movement end. The event-related hemodynamic response obtained from fMRI showed delayed BOLD peak latency in the contralateral primary motor area suggesting a less efficient activity of the neuronal populations driving fine movements, which are specifically impaired in ULD.