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.

Early cortico-muscular coherence and cortical network changes in Parkinson's patients treated with MRgFUS.

Introduction: To investigate cortical network changes using Magnetoencephalography (MEG) signals in Parkinson's disease (PD) patients undergoing Magnetic Resonance-guided Focused Ultrasound (MRgFUS) thalamotomy. Methods: We evaluated the MEG signals in 16 PD patients with drug-refractory tremor before and after 12-month from MRgFUS unilateral lesion of the ventralis intermediate nucleus (Vim) of the thalamus contralateral to the most affected body side. We recorded patients 24 h before (T0) and 24 h after MRgFUS (T1). We analyzed signal epochs recorded at rest and during the isometric extension of the hand contralateral to thalamotomy. We evaluated cortico-muscular coherence (CMC), the out-strength index from non-primary motor areas to the pre-central area and connectivity indexes, using generalized partial directed coherence. Statistical analysis was performed using RMANOVA and post hoct-tests. Results: Most changes found at T1 compared to T0 occurred in the beta band and included: (1) a re-adjustment of CMC distribution; (2) a reduced out-strength from non-primary motor areas toward the precentral area; (3) strongly reduced clustering coefficient values. These differences mainly occurred during motor activation and with few statistically significant changes at rest. Correlation analysis showed significant relationships between changes of out-strength and clustering coefficient in non-primary motor areas and the changes in clinical scores. Discussion: One day after MRgFUS thalamotomy, PD patients showed a topographically reordered CMC and decreased cortico-cortical flow, together with a reduced local connection between different nodes. These findings suggest that the reordered cortico-muscular and cortical-networks in the beta band may represent an early physiological readjustment related to MRgFUS Vim lesion.

Myoclonus: Differential diagnosis and current management.

Myoclonus classically presents as a brief (10-50 ms duration), non-rhythmic jerk movement. The etiology could vary considerably ranging from self-limited to chronic or even progressive disorders, the latter falling into encephalopathic pictures that need a prompt diagnosis. Beyond the etiological classification, others evaluate myoclonus' body distribution (i.e., clinical classification) or the location of the generator (i.e., neurophysiological classification); particularly, knowing the anatomical source of myoclonus gives inputs on the observable clinical patterns, such as EMG bursts duration or EEG correlate, and guides the therapeutic choices. Among all the chronic disorders, myoclonus often presents itself as a manifestation of epilepsy. In this context, myoclonus has many facets. Myoclonus occurs as one, or the only, seizure manifestation while it can also present as a peculiar type of movement disorder; moreover, its electroclinical features within specific genetically determined epileptic syndromes have seldom been investigated. In this review, following a meeting of recognized experts, we provide an up-to-date overview of the neurophysiology and nosology surrounding myoclonus. Through the dedicated exploration of epileptic syndromes, coupled with pragmatic guidance, we aim to furnish clinicians and researchers alike with practical advice for heightened diagnostic management and refined treatment strategies. PLAIN LANGUAGE SUMMARY: In this work, we described myoclonus, a movement characterized by brief, shock-like jerks. Myoclonus could be present in different diseases and its correct diagnosis helps treatment.

Anatomically compliant modes of variations: New tools for brain connectivity.

Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research. In this work, we propose to tackle this problem through Smooth Functional Principal Component Analysis, which enables to perform dimensional reduction and exploration of the variability in functional connectivity maps, complying with the formidably complicated anatomy of the grey matter volume. In particular, we analyse a population that includes controls and subjects affected by schizophrenia, starting from fMRI data acquired at rest and during a task-switching paradigm. For both sessions, we first identify the common modes of variation in the entire population. We hence explore whether the subjects' expressions along these common modes of variation differ between controls and pathological subjects. In each session, we find principal components that are significantly differently expressed in the healthy vs pathological subjects (with p-values < 0.001), highlighting clearly interpretable differences in the connectivity in the two subpopulations. For instance, the second and third principal components for the rest session capture the imbalance between the Default Mode and Executive Networks characterizing schizophrenia patients.

Cortico-muscular coherence and brain networks in familial adult myoclonic epilepsy and progressive myoclonic epilepsy.

OBJECTIVE: Familial Adult Myoclonic Epilepsy (FAME) presents with action-activated myoclonus, often associated with epilepsy, sharing various features with Progressive Myoclonic Epilepsy (PMEs), but with slower course and limited motor disability. We aimed our study to identify measures suitable to explain the different severity of FAME2 compared to EPM1, the most common PME, and to detect the signature of the distinctive brain networks. METHODS: We analyzed the EEG-EMG coherence (CMC) during segmental motor activity and indexes of connectivity in the two patient groups, and in healthy subjects (HS). We also investigated the regional and global properties of the network. RESULTS: In FAME2, differently from EPM1, we found a well-localized distribution of beta-CMC and increased betweenness-centrality (BC) on the sensorimotor region contralateral to the activated hand. In both patient groups, compared to HS, there was a decline in the network connectivity indexes in the beta and gamma band, which was more obvious in FAME2. CONCLUSIONS: In FAME2, better localized CMC and increased BC in comparison with EPM1 patients could counteract the severity and the spreading of the myoclonus. Decreased indexes of cortical integration were more severe in FAME2. SIGNIFICANCE: Our measures correlated with different motor disabilities and identified distinctive brain network impairments.

Rhythmic cortical myoclonus in patients with 6Q22.1 deletion.

DNA deletions involving 6q22.1 region result in developmental encephalopathy (DE), often associated with movement disorders and epilepsy. The phenotype is attributed to the loss of the NUS1 gene included in the deleted region. Here we report three patients with 6q22.1 deletions of variable length all showing developmental delay, and rhythmic cortical myoclonus. Two patients had generalized seizures beginning in infancy. Myoclonic jerks had polygraphic features consistent with a cortical origin, also supported by cortico-muscular coherence analysis displaying a significant peak around 20 Hz contralateral to activated segment. Deletions in 6q22.1 region, similarly to NUS1 loss-of-function mutations, give rise to DE and cortical myoclonus via a haploinsufficiency mechanism. A phenotype of progressive myoclonic epilepsy (PME) may also occur.

Analyzing data in complicated 3D domains: Smoothing, semiparametric regression, and functional principal component analysis.

In this work, we introduce a family of methods for the analysis of data observed at locations scattered in three-dimensional (3D) domains, with possibly complicated shapes. The proposed family of methods includes smoothing, regression, and functional principal component analysis for functional signals defined over (possibly nonconvex) 3D domains, appropriately complying with the nontrivial shape of the domain. This constitutes an important advance with respect to the literature, because the available methods to analyze data observed in 3D domains rely on Euclidean distances, which are inappropriate when the shape of the domain influences the phenomenon under study. The common building block of the proposed methods is a nonparametric regression model with differential regularization. We derive the asymptotic properties of the methods and show, through simulation studies, that they are superior to the available alternatives for the analysis of data in 3D domains, even when considering domains with simple shapes. We finally illustrate an application to a neurosciences study, with neuroimaging signals from functional magnetic resonance imaging, measuring neural activity in the gray matter, a nonconvex volume with a highly complicated structure.

MRI-guided focused-ultrasound thalamotomy in essential tremor: Immediate and delayed changes in cortico-muscular coherence and cortico-cortical out-strength.

OBJECTIVE: Drug-resistant essential tremor (ET) can be treated by Magnetic-Resonance-guided Focused-Ultrasound (MRgFUS) targeted to thalamic ventralis-intermediate nucleus (ViM). We are presenting the results obtained in ET patients by evaluating the cortico-muscular coherence (CMC) and the out-strength among cortical areas. METHODS: We recorded MEG-EMG signals in 16 patients with predominant tremor on the right upper limb. The examination was performed the day before MRgFUS (T0) treatment, 24 hours (T1), and 3-months (T2) after lesioning the left ViM. Normalized CMC (nCMC) and cortico-cortical out-strength among cortical areas were assessed during isometric extension of the right hand. RESULTS: According to the Essential Tremor Rating Assessment Scale, 13 of 16 patients were considered responders. At T1, in the beta-band, nCMC increased in the left hemisphere, namely in the areas directly involved in motor functions. At T2, the nCMC in non-motor areas decreased and the out-strength from other examined cortical areas toward the left motor-area decreased. CONCLUSIONS: In patients positively responding to MRgFUS, the CMC increased in the motor-area of the treated hemisphere immediately after the treatment, while the reorganization of CMC and cortico-cortical out-strength toward the cortical motor area occurred with a delay. SIGNIFICANCE: The effective treatment with MRgFUS corresponds with a readjustment of the CMC and of the communication between cortical areas.

Entropy Metrics Correlating with Higher Residual Functioning in Patients with Chronic Disorders of Consciousness.

To test the ability of different entropy measures to classify patients with different conditions of chronic disorder of consciousness, we applied the Lempel-Ziv complexity, the amplitude coalition entropy (ACE), and the synchrony coalition entropy (SCE) to the EEG signals recorded in 32 patients, clinically evaluated using the coma recovery scale revised (CRS-R). All the entropy measures indicated that differences found in the theta and alpha bands can distinguish patients in a minimal consciousness state (MCS) with respect to those in a vegetative state/unresponsive wakefulness state (VS/UWS). These differences were significant comparing the entropy measure performed on the anterior region of the left hemisphere and midline region. The values of theta-alpha entropy positively correlated with those of the CRS-R scores. Among the entropy measures, ACE most often highlighted significant differences. The higher values found in MCS were for the less impaired patients, according to their CRS-R, suggest that the preservation of signal entropy on the anterior region of the dominant hemisphere correlates with better preservation of consciousness, even in chronic conditions.

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.

Analyzing the Loss and the Recovery of Consciousness: Functional Connectivity Patterns and Changes in Heart Rate Variability During Propofol-Induced Anesthesia.

The analysis of the central and the autonomic nervous systems (CNS, ANS) activities during general anesthesia (GA) provides fundamental information for the study of neural processes that support alterations of the consciousness level. In the present pilot study, we analyzed EEG signals and the heart rate (HR) variability (HRV) in a sample of 11 patients undergoing spinal surgery to investigate their CNS and ANS activities during GA obtained with propofol administration. Data were analyzed during different stages of GA: baseline, the first period of anesthetic induction, the period before the loss of consciousness, the first period after propofol discontinuation, and the period before the recovery of consciousness (ROC). In EEG spectral analysis, we found a decrease in posterior alpha and beta power in all cortical areas observed, except the occipital ones, and an increase in delta power, mainly during the induction phase. In EEG connectivity analysis, we found a significant increase of local efficiency index in alpha and delta bands between baseline and loss of consciousness as well as between baseline and ROC in delta band only and a significant reduction of the characteristic path length in alpha band between the baseline and ROC. Moreover, connectivity results showed that in the alpha band there was mainly a progressive increase in the number and in the strength of incoming connections in the frontal region, while in the beta band the parietal region showed mainly a significant increase in the number and in the strength of outcoming connections values. The HRV analysis showed that the induction of anesthesia with propofol was associated with a progressive decrease in complexity and a consequent increase in the regularity indexes and that the anesthetic procedure determined bradycardia which was accompanied by an increase in cardiac sympathetic modulation and a decrease in cardiac parasympathetic modulation during the induction. Overall, the results of this pilot study showed as propofol-induced anesthesia caused modifications on EEG signal, leading to a "rebalance" between long and short-range cortical connections, and had a direct effect on the cardiac system. Our data suggest interesting perspectives for the interactions between the central and autonomic nervous systems for the modulation of the consciousness level.

Towards the Automatic Localization of the Irritative Zone Through Magnetic Source Imaging.

The present work aims at validating a Bayesian multi-dipole modeling algorithm (SESAME) in the clinical scenario consisting of localizing the generators of single interictal epileptiform discharges from resting state magnetoencephalographic recordings. We use the results of Equivalent Current Dipole fitting, performed by an expert user, as a benchmark, and compare the results of SESAME with those of two widely used source localization methods, RAP-MUSIC and wMNE. In addition, we investigate the relation between post-surgical outcome and concordance of the surgical plan with the cerebral lobes singled out by the methods. Unlike dipole fitting, the tested algorithms do not rely on any subjective channel selection and thus contribute towards making source localization more unbiased and automatic. We show that the two dipolar methods, SESAME and RAP-MUSIC, generally agree with dipole fitting in terms of identified cerebral lobes and that the results of the former are closer to the fitted equivalent current dipoles than those of the latter. In addition, for all the tested methods and particularly for SESAME, concordance with surgical plan is a good predictor of seizure freedom while discordance is not a good predictor of poor post-surgical outcome. The results suggest that the dipolar methods, especially SESAME, represent a reliable and more objective alternative to manual dipole fitting for clinical applications in the field of epilepsy surgery.

Identifying the epileptogenic zone by four non-invasive imaging techniques versus stereo-EEG in MRI-negative pre-surgery epilepsy patients.

OBJECTIVE: We evaluated four imaging techniques, i.e. Electroencephalography (EEG)-functional Magnetic Resonance Imaging (MRI) (EEG-fMRI), High-resolution EEG (HR-EEG), Magnetoencephalography (MEG) and 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (PET), for the identification of the epileptogenic zone (EZ) in 41 patients with negative MRI, candidate to neurosurgery. METHODS: For each technique, results were compared to the Stereo-EEG. Diagnostic measures were calculated with respect to the post-surgical outcome, either for all the patients (39/41, two patients excluded) and for the subgroup of patients with the EZ involving more than one lobe (20/41). RESULTS: When considered individually, each functional technique showed accuracy values ranging 54,6%-63,2%, having PET, MEG and HR-EEG higher sensitivity, and EEG-fMRI higher specificity. In patients with multilobar epileptogenic zone, functional techniques achieved the best accuracies (up to 80%) when three techniques, including EEG-fMRI, were considered together. CONCLUSIONS: The study highlights the accuracy of a combination of functional imaging techniques in the identification of EZ in MRI negative focal epilepsy. The best diagnostic yield was obtained if the combination of PET, MEG (or HR-EEG as alternative), EEG-fMRI were considered together. SIGNIFICANCE: The functional imaging techniques may improve the presurgical workup of MRI negative focal epilepsy, if epileptogenic zone involves more than one lobe.