Properties of functional brain networks correlate with frequency of psychogenic non-epileptic seizures

Predictive Processing and the Pathophysiology of Functional Neurological Disorder

The contemporary neuroscience understanding of the brain as an active inference organ supports that our conscious experiences, including sensorimotor perceptions, depend on the integration of probabilistic predictions with incoming sensory input across hierarchically organized levels. As in other systems, these complex processes are prone to error under certain circumstances, which may lead to alterations in their outcomes (i.e., variations in sensations and movements). Such variations are an important aspect of functional neurological disorder, a complex disorder at the interface of brain-mind-body interactions. Thus, predictive processing frameworks offer fundamental mechanistic insights into the pathophysiology of functional neurological disorder. In recent years, many of the aspects relevant to the neurobiology of functional neurological disorder - e.g., aberrant motor and sensory processes, symptom expectation, self-agency, and illness beliefs, as well as interoception, allostasis, and emotion - have been investigated through the lens of predictive processing frameworks. Here, we provide an overview of the current state of research on predictive processing and the pathophysiology of functional neurological disorder.

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

Classification of Epileptic and Psychogenic Nonepileptic Seizures via Time-Frequency Features of EEG Data

The majority of psychogenic nonepileptic seizures (PNESs) are brought on by psychogenic causes, but because their symptoms resemble those of epilepsy, they are frequently misdiagnosed. Although EEG signals are normal in PNES cases, electroencephalography (EEG) recordings alone are not sufficient to identify the illness. Hence, accurate diagnosis and effective treatment depend on long-term video EEG data and a complete patient history. Video EEG setup, however, is more expensive than using standard EEG equipment. To distinguish PNES signals from conventional epileptic seizure (ES) signals, it is crucial to develop methods solely based on EEG recordings. The proposed study presents a technique utilizing short-term EEG data for the classification of inter-PNES, PNES, and ES segments using time-frequency methods such as the Continuous Wavelet transform (CWT), Short-Time Fourier transform (STFT), CWT-based synchrosqueezed transform (WSST), and STFT-based SST (FSST), which provide high-resolution time-frequency representations (TFRs). TFRs of EEG segments are utilized to generate 13 joint TF (J-TF)-based features, four gray-level co-occurrence matrix (GLCM)-based features, and 16 higher-order joint TF moment (HOJ-Mom)-based features. These features are then employed in the classification procedure. Both three-class (inter-PNES versus PNES versus ES: ACC: 80.9%, SEN: 81.8%, and PRE: 84.7%) and two-class (Inter-PNES versus PNES: ACC: 88.2%, SEN: 87.2%, and PRE: 86.1%; PNES versus ES: ACC: 98.5%, SEN: 99.3%, and PRE: 98.9%) classification algorithms performed well, according to the experimental results. The STFT and FSST strategies surpass the CWT and WSST strategies in terms of classification accuracy, sensitivity, and precision. Moreover, the J-TF-based feature sets often perform better than the other two.

White Matter Structural Connectivity and Its Impact on Psychogenic Non-Epileptic Seizures: An Evidence-Based Review

Psychiatric non-epileptic seizure (PNES), also known as a form of functional neurological disorders (FND), is a common but still underrecognized disorder presenting seizure-like symptoms and no electrophysiological abnormality. Despite the significant burden of this disorder, the neurobiological mechanisms are not clearly understood, which hinders the development of better diagnosis and treatment. In the recent neuroimaging research on PNES, brain network analysis has become a relevant topic beyond conventional methodologies. The human brain is a highly intricate system of interconnected regions that collaborate to facilitate a wide range of cognitive and behavioral functions. White matter tracts, which are comprised of bundles of axonal fibers, are the primary means by which information is transmitted between different brain regions. As such, comprehending the organization and structure of the brain's white matter network is critical for gaining insight into its functional architecture. This review article aims to provide an overview of the brain mechanisms underlying PNES, with a special focus on analyzing brain networks.

Electroencephalogram Connectivity for the Diagnosis of Psychogenic Non-epileptic Seizures

Psychogenic non-epileptic seizures (PNES) are attacks that resemble epilepsy but are not associated with epileptic brain activity and are regularly misdiagnosed. The current gold standard method of diagnosis is expensive and complex. Electroencephalogram (EEG) analysis with machine learning could improve this. A k-nearest neighbours (kNN) and support vector machine (SVM) were used to classify EEG connectivity measures from 48 patients with PNES and 29 patients with epilepsy. The synchronisation method - correlation or coherence - and the binarisation threshold were defined through experimentation. Ten network parameters were extracted from the synchronisation matrix. The broad, delta, theta, alpha, beta, gamma, and combined 'all' frequency bands were compared along with three feature selection methods: the full feature set (no selection), light gradient boosting machine (LGBM) and k-Best. Coherence was the highest performing synchronisation method and 0.6 was the best coherence threshold. The highest balanced accuracy was 89.74%, produced by combining all six frequency bands and selecting features with LGBM, classified by the SVM. This method returned a comparatively high accuracy but at a high computation cost. Future research should focus on identifying specific frequency bands and network parameters to reduce this cost. Clinical relevance - This study found that EEG connectivity and machine learning methods can be used to differentiate PNES from epilepsy using interictal recordings to a high accuracy. Thus, this method could be an effective tool in assisting clinicians in PNES diagnosis without a video- EEG recording of a habitual seizure.

A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls

Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects.

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

Quantitative markers extracted from resting-state electroencephalogram (EEG) reveal subtle neurophysiological dynamics which may provide useful information to support the diagnosis of seizure disorders. We performed a systematic review to summarize evidence on markers extracted from interictal, visually normal resting-state EEG in adults with idiopathic epilepsy or psychogenic nonepileptic seizures (PNES). Studies were selected from 5 databases and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 26 studies were identified, 19 focusing on people with epilepsy, 6 on people with PNES, and one comparing epilepsy and PNES directly. Results suggest that oscillations along the theta frequency (4-8 Hz) may have a relevant role in idiopathic epilepsy, whereas in PNES there was no evident trend. However, studies were subject to a number of methodological limitations potentially introducing bias. There was often a lack of appropriate reporting and high heterogeneity. Results were not appropriate for quantitative synthesis. We identify and discuss the challenges that must be addressed for valid resting-state EEG markers of epilepsy and PNES to be developed.

Brain connectivity abnormalities in patients with functional (psychogenic nonepileptic) seizures: A systematic review

OBJECTIVES

The aim of the current endeavor was to systematically review the existing evidence on brain connectivity abnormalities in patients with functional seizures (FS).

METHODS

This systematic review was prepared according to the instructions of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. MEDLINE (accessed from PubMed) and Scopus from inception to April 4, 2020 were systematically searched. The following search strategy was implemented and these key words (in the title/abstract) were used: "connectivity" OR "network" AND "psychogenic" OR "dissociative" OR "nonepileptic".

RESULTS

Through the search strategy, we could identify eighteen articles. These studies have applied various methodologies and they could identify a variety of brain connectivity abnormalities in people with FS. However, none of these studies provided a high level of evidence. They were all small studies (none had a sample size of more than 21 patients). In addition, most of the studies did not match their cases and their controls with respect to the psychiatric comorbidities and other significant confounders.

CONCLUSION

Abnormal functional connectivity between emotion processing areas of the brain with regions involved in executive control and cognitive performance, and the functional connections of the anterior cingulate cortex are of major interest and may be involved in the pathophysiology of FS. Pursuing the concept of brain connectivity abnormalities in patients with FS and comparing the findings with well-matched controls in well-designed studies may result in a breakthrough in identifying the exact neurobiological origin of FS.

Activation of Functional Brain Networks in Children With Psychogenic Non-epileptic Seizures

Objectives

Psychogenic non-epileptic seizures (PNES) have been hypothesized to emerge in the context of neural networks instability. To explore this hypothesis in children, we applied a graph theory approach to examine connectivity in neural networks in the resting-state EEG in 35 children with PNES, 31 children with other functional neurological symptoms (but no PNES), and 75 healthy controls.

Methods

The networks were extracted from Laplacian-transformed time series by a coherence connectivity estimation method.

Results

Children with PNES (vs. controls) showed widespread changes in network metrics: increased global efficiency (gamma and beta bands), increased local efficiency (gamma band), and increased modularity (gamma and alpha bands). Compared to controls, they also had higher levels of autonomic arousal (e.g., lower heart variability); more anxiety, depression, and stress on the Depression Anxiety and Stress Scales; and more adverse childhood experiences on the Early Life Stress Questionnaire. Increases in network metrics correlated with arousal. Children with other functional neurological symptoms (but no PNES) showed scattered and less pronounced changes in network metrics.

Conclusion

The results indicate that children with PNES present with increased activation of neural networks coupled with increased physiological arousal. While this shift in functional organization may confer a short-term adaptive advantage-one that facilitates neural communication and the child's capacity to respond self-protectively in the face of stressful life events-it may also have a significant biological cost. It may predispose the child's neural networks to periods of instability-presenting clinically as PNES-when the neural networks are faced with perturbations in energy flow or with additional demands.

Transient cortico-cortical disconnection during psychogenic nonepileptic seizures (PNES)

Psychogenic nonepileptic seizures (PNES) are paroxysmal clinical events that are often misdiagnosed as epileptic seizures, but which are not associated with electrographic discharge. Brain connectivity changes occurring during PNES are not known. We studied functional connectivity (Fc) in two patients with drug-resistant epilepsy, explored by stereotactic electroencephalography (EEG), in whom we recorded both epileptic seizures (ES) and PNES. Functional connectivity using pair-wise nonlinear correlation was computed between signals from seven brain areas: amygdala, hippocampus, lateral temporal cortex, anterior insula, orbitofrontal cortex, prefrontal cortex, and lateral parietal cortex. We assessed changes in global Fc during PNES in comparison with a background period. During PNES, a global decrease of Fc occurred between the different brain regions studied, compared with the interictal period. In both patients, decreased Fc was prominent in connections involving the anterior insula and parietal cortex. In conclusion, some PNES are associated with ictal functional disconnection between brain areas, particularly involving the parietal cortices and the anterior insula.

Memory and motor control in patients with psychogenic nonepileptic seizures

Psychogenic nonepileptic seizures (PNES) are of the most elusive phenomena in epileptology. Patients with PNES present episodes resembling epileptic seizures in their semiology yet lacking the underlying epileptic brain activity. These episodes are assumed to be related to psychological distress from past trauma, yet the underlying mechanism of this manifestation is still unknown. Using resting-state functional magnetic resonance imaging (fMRI), we investigated functional connectivity changes within and between large-scale brain networks in 9 patients with PNES, compared with a group of 13 age- and gender-matched healthy controls. Functional magnetic resonance imaging analyses identified functional connectivity disturbances between the medial temporal lobe (MTL) and the sensorimotor cortex and between the MTL and ventral attention networks in patients with PNES. Within network connectivity reduction was found within the visual network. Our findings suggest that PNES relate to changes in connectivity in between areas that are involved in memory processing and motor activity and attention control. These results may shed new light on the way by which traumatic memories may relate to PNES.

"Motoring in idle": The default mode and somatomotor networks are overactive in children and adolescents with functional neurological symptoms

Objective

Children and adolescents with functional neurological symptom disorder (FND) present with diverse neurological symptoms not explained by a disease process. Functional neurological symptoms have been conceptualized as somatoform dissociation, a disruption of the brain's intrinsic organization and reversion to a more primitive level of function. We used EEG to investigate neural function and functional brain organization in children/adolescents with FND.

Method

EEG was recorded in the resting eyes-open condition in 57 patients (aged 8.5-18 years) and 57 age- and sex-matched healthy controls. Using a topographical map, EEG power data were quantified for regions of interest that define the default mode network (DMN), salience network, and somatomotor network. Source localization was examined using low-resolution brain electromagnetic tomography (LORETA). The contributions of chronic pain and arousal as moderators of differences in EEG power were also examined.

Results

Children/adolescents with FND had excessive theta and delta power in electrode clusters corresponding to the DMN-both anteriorly (dorsomedial prefrontal cortex [dmFPC]) and posteriorly (posterior cingulate cortex [PCC], precuneus, and lateral parietal cortex)-and in the premotor/supplementary motor area (SMA) region. There was a trend toward increased theta and delta power in the salience network. LORETA showed activation across all three networks in all power bands and localized neural sources to the dorsal anterior cingulate cortex/dmPFC, mid cingulate cortex, PCC/precuneus, and SMA. Pain and arousal contributed to slow wave power increases in all three networks.

Conclusions

These findings suggest that children and adolescents with FND are characterized by overactivation of intrinsic resting brain networks involved in threat detection, energy regulation, and preparation for action.

Altered Functional Brain Connectomes between Sporadic and Familial Parkinson's Patients

Familial Parkinson's disease (PD) is often caused by mutation of a certain gene, while sporadic PD is associated with variants of genes which can influence the susceptibility to PD. The goal of this study was to investigate the difference between the two forms of PD in terms of brain abnormalities using resting-state functional MRI and graph theory. Thirty-one familial PD patients and 36 sporadic PD patients underwent resting-state functional MRI scanning. Frequency-dependent functional connectivity was calculated for each subject using wavelet-based correlations of BOLD signal over 246 brain regions from Brainnetome Atlas. Graph theoretical analysis was then performed to analyze the topology of the functional network, and functional connectome differences were identified with a network-based statistical approach. Our results revealed a frequency-specific (0.016 and 0.031 Hz) connectome difference between familial and sporadic forms of PD, as indicated by an increase in assortativity and decrease in the nodal strength in the left medial amygdala of the familial PD group. In addition, the familial PD patients also showed a distinctive functional network between the left medial amygdala and regions related to retrieval of motion information. The present study indicates that the medial amygdala might be most vulnerable to both sporadic and familial PD. Our findings provide some new insights into disrupted resting-state functional connectomes between sporadic PD and familial PD.

The respiratory control of carbon dioxide in children and adolescents referred for treatment of psychogenic non-epileptic seizures

Psychogenic non-epileptic seizures (PNES) are a common problem in paediatric neurology and psychiatry that can best be understood as atypical responses to threat. Threats activate the body for action by mediating increases in arousal, respiration, and motor readiness. In previous studies, a range of cardiac, endocrine, brain-based, attention-bias, and behavioral measures have been used to demonstrate increases in arousal, vigilance, and motor readiness in patients with PNES. The current study uses respiratory measures to assess both the motor readiness of the respiratory system and the respiratory regulation of CO₂. Baseline respiratory rates during clinical assessment and arterial CO₂ levels during the hyperventilation component of routine video electroencephalogram were documented in 60 children and adolescents referred for treatment of PNES and in 50 controls. Patients showed elevated baseline respiratory rates [t(78) = 3.34, p = .001], with 36/52 (69%) of patients [vs. 11/28 (39%) controls] falling above the 75th percentile (χ² = 6.7343; df = 1; p = .009). Twenty-eight (47%) of patients [vs. 4/50 (8%) controls] showed a skewed hyperventilation-challenge profile—baseline PCO₂ <36 mmHg, a trough PCO₂ ≤ 20 mmHg, or a final PCO₂ <36 mmHg after 15 min of recovery—signaling difficulties with CO₂ regulation (χ² = 19.77; df = 1; p < .001). Children and adolescents with PNES present in a state of readiness-for-action characterized by high arousal coupled with activation of the respiratory motor system, increases in ventilation, and a hyperventilation-challenge profile shifted downward from homeostatic range. Breathing interventions that target arousal, decrease respiratory rate, and normalize ventilation and arterial CO₂ may help patients shift brain–body state and avert PNES episodes.

Modeling the network dynamics of pulse-coupled neurons

We derive a mean-field approximation for the macroscopic dynamics of large networks of pulse-coupled theta neurons in order to study the effects of different network degree distributions and degree correlations (assortativity). Using the ansatz of Ott and Antonsen [Chaos 18, 037113 (2008)], we obtain a reduced system of ordinary differential equations describing the mean-field dynamics, with significantly lower dimensionality compared with the complete set of dynamical equations for the system. We find that, for sufficiently large networks and degrees, the dynamical behavior of the reduced system agrees well with that of the full network. This dimensional reduction allows for an efficient characterization of system phase transitions and attractors. For networks with tightly peaked degree distributions, the macroscopic behavior closely resembles that of fully connected networks previously studied by others. In contrast, networks with highly skewed degree distributions exhibit different macroscopic dynamics due to the emergence of degree dependent behavior of different oscillators. For nonassortative networks (i.e., networks without degree correlations), we observe the presence of a synchronously firing phase that can be suppressed by the presence of either assortativity or disassortativity in the network. We show that the results derived here can be used to analyze the effects of network topology on macroscopic behavior in neuronal networks in a computationally efficient fashion.

Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?

The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer’s Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals.

Biological underpinnings of psychogenic nonepileptic seizures: directions for future research

Psychogenic nonepileptic seizures (PNES) are relatively common occurrences in epilepsy centers, but their pathophysiology is still poorly understood. Research that elucidates the pathophysiology of PNES, including their neurobiological basis and biomarkers, may have important clinical implications. The literature provides some evidence that genetic factors, intrinsic factors, and environmental factors probably play a significant role as the biological underpinnings of PNES. Researchers may be able to learn more about the pathophysiology of PNES by investigating the effects of each of these factors on functional and structural brain connectivity.

Weakened functional connectivity in patients with psychogenic non-epileptic seizures (PNES) converges on basal ganglia

BACKGROUND

Psychogenic non-epileptic seizures (PNES) are involuntary paroxysmal events that are unaccompanied by epileptiform EEG discharges. We hypothesised that PNES are a disorder of distributed brain networks resulting from their functional disconnection.The disconnection may underlie a dissociation mechanism that weakens the influence of unconsciously presented traumatising information but exerts maladaptive effects leading to episodic failures of behavioural control manifested by psychogenic 'seizures'.

METHODS

To test this hypothesis, we compared functional connectivity (FC) derived from resting state high-density EEGs of 18 patients with PNES and 18 age-matched and gender-matched controls. To this end, the EEGs were transformed into source space using the local autoregressive average inverse solution. FC was estimated with a multivariate measure of lagged synchronisation in the θ, α and β frequency bands for 66 brain sites clustered into 18 regions. A multiple comparison permutation test was applied to deduce significant between-group differences in inter-regional and intraregional FC.

RESULTS

The significant effect of PNES-a decrease in lagged FC between the basal ganglia and limbic, prefrontal, temporal, parietal and occipital regions-was found in the α band.

CONCLUSION

We believe that this finding reveals a possible neurobiological substrate of PNES, which explains both attenuation of the effect of potentially disturbing mental representations and the occurrence of PNES episodes. By improving understanding of the aetiology of this condition, our results suggest a potential refinement of diagnostic criteria and management principles.

Neurophysiologic studies of functional neurologic disorders

Functional neurologic disorders are largely genuine and represent conversion disorders, where the dysfunction is unconscious, but there are some that are factitious, where the abnormality is feigned and conscious. Malingering, which can have the same manifestations, is similarly feigned, but not considered a genuine disease. There are no good methods for differentiating these three entities at the present time. Physiologic studies of functional weakness and sensory loss reveal normal functioning of primary motor and sensory cortex, but abnormalities of premotor cortex and association cortices. This suggests a top-down influence creating the dysfunction. Studies of functional tremor and myoclonus show that these disorders utilize normal voluntary motor structures to produce the involuntary movements, again suggesting a higher-level abnormality. Agency is abnormal and studies shows that dysfunction of the temporoparietal junction may be a correlate. The limbic system is overactive and might initiate involuntary movements, but the mechanism for this is not known. The limbic system would then be the source of top-down dysfunction. It can be speculated that the involuntary movements are involuntary due to lack of proper feedforward signaling.

Neurobiological origin of psychogenic nonepileptic seizures: A review of imaging studies

Psychogenic nonepileptic seizures (PNES) are relatively common occurrences in epilepsy centers. Pathophysiology of PNES is still poorly understood. In this paper, the evidence for a neurobiological origin of PNES will be reviewed. Recent evidence suggests altered functional and structural brain connectivity as an underlying pathophysiological mechanism in patients with PNES. Pursuing the concept of connectome in patients with PNES and comparing the findings with healthy individuals may result in a breakthrough in identifying the exact neurobiological origin of PNES. Finding the neurobiological bases and identification of biomarkers of PNES will potentially have important clinical implications in formulating better diagnostic and therapeutic approaches for affected patients.

Resiliency of EEG-Based Brain Functional Networks

Applying tools available in network science and graph theory to study brain networks has opened a new era in understanding brain mechanisms. Brain functional networks extracted from EEG time series have been frequently studied in health and diseases. In this manuscript, we studied failure resiliency of EEG-based brain functional networks. The network structures were extracted by analysing EEG time series obtained from 30 healthy subjects in resting state eyes-closed conditions. As the network structure was extracted, we measured a number of metrics related to their resiliency. In general, the brain networks showed worse resilient behaviour as compared to corresponding random networks with the same degree sequences. Brain networks had higher vulnerability than the random ones (P < 0.05), indicating that their global efficiency (i.e., communicability between the regions) is more affected by removing the important nodes. Furthermore, the breakdown happened as a result of cascaded failures in brain networks was severer (i.e., less nodes survived) as compared to randomized versions (P < 0.05). These results suggest that real EEG-based networks have not been evolved to possess optimal resiliency against failures.

Time-dependent degree-degree correlations in epileptic brain networks: from assortative to dissortative mixing

We investigate the long-term evolution of degree-degree correlations (assortativity) in functional brain networks from epilepsy patients. Functional networks are derived from continuous multi-day, multi-channel electroencephalographic data, which capture a wide range of physiological and pathophysiological activities. In contrast to previous studies which all reported functional brain networks to be assortative on average, even in case of various neurological and neurodegenerative disorders, we observe large fluctuations in time-resolved degree-degree correlations ranging from assortative to dissortative mixing. Moreover, in some patients these fluctuations exhibit some periodic temporal structure which can be attributed, to a large extent, to daily rhythms. Relevant aspects of the epileptic process, particularly possible pre-seizure alterations, contribute marginally to the observed long-term fluctuations. Our findings suggest that physiological and pathophysiological activity may modify functional brain networks in a different and process-specific way. We evaluate factors that possibly influence the long-term evolution of degree-degree correlations.

Altered regional activity and inter-regional functional connectivity in psychogenic non-epileptic seizures

Although various imaging studies have focused on detecting the cerebral function underlying psychogenic non-epileptic seizures (PNES), the nature of PNES remains poorly understood. In this study, we combined the resting state fMRI with fractional amplitude of low-frequency fluctuations (fALFF) and functional connectivity based on the seed voxel linear correlation approach to examine the alterations of regional and inter-regional network cerebral functions in PNES. A total of 20 healthy controls and 18 patients were enrolled. The PNES patients showed significantly increased fALFF mainly in the dorsolateral prefrontal cortex (DLPFC), parietal cortices, and motor areas, as well as decreased fALFF in the triangular inferior frontal gyrus. Thus, our results add to literature suggesting abnormalities of neural synchrony in PNES. Moreover, PNES exhibited widespread inter-regional neural network deficits, including increased (DLPFC, sensorimotor, and limbic system) and decreased (ventrolateral prefrontal cortex) connectivity, indicating that changes in the regional cerebral function are related to remote inter-regional network deficits. Correlation analysis results revealed that the connectivity between supplementary motor area and anterior cingulate cortex correlated with the PNES frequency, further suggesting the skewed integration of synchronous activity could predispose to the occurrence of PNES. Our findings provided novel evidence to investigate the pathophysiological mechanisms of PNES.

Hemispheric asymmetry of electroencephalography-based functional brain networks

Electroencephalography (EEG)-based functional brain networks have been investigated frequently in health and disease. It has been shown that a number of graph theory metrics are disrupted in brain disorders. EEG-based brain networks are often studied in the whole-brain framework, where all the nodes are grouped into a single network. In this study, we studied the brain networks in two hemispheres and assessed whether there are any hemispheric-specific patterns in the properties of the networks. To this end, resting state closed-eyes EEGs from 44 healthy individuals were processed and the network structures were extracted separately for each hemisphere. We examined neurophysiologically meaningful graph theory metrics: global and local efficiency measures. The global efficiency did not show any hemispheric asymmetry, whereas the local connectivity showed rightward asymmetry for a range of intermediate density values for the constructed networks. Furthermore, the age of the participants showed significant direct correlations with the global efficiency of the left hemisphere, but only in the right hemisphere, with local connectivity. These results suggest that only local connectivity of EEG-based functional networks is associated with brain hemispheres.

New insights into psychogenic nonepileptic seizures 2011-2014

PURPOSE

There has been a rapid increase in the rate of publications about psychogenic nonepileptic seizures (PNES). This review summarises insights from the 50 most important original articles about PNES published since 2011 and describes the advances made in the understanding of PNES over the last 3 years.

METHOD

We carried out a systematic literature search of all English language publications about PNES published between October 2011 and October 2014 on Scopus, Ovid Medline and Web of Knowledge, and inspected all abstracts. Having excluded all review articles, case reports, conference abstracts, articles exploring PNES in children, and articles not actually focussing on PNES, we considered 150 papers for inclusion in this review. We assessed the quality of the identified studies and used expert judgement to identify the 50 most important publications from the review period and composed a narrative review based on these original papers.

RESULTS

Almost one half of the studies initially identified only provided Class 4 evidence. Recent work has provided more support for a biopsychosocial account of PNES. It has illustrated the heterogeneity of PNES, identifying varying and distinct psychological profiles of individuals with this disorder. These findings suggest that intervention needs to be flexible or adaptive if it is appropriately to target the different mechanisms which may give rise to PNES. Several educational and psychotherapeutic interventions for PNES have been described, but sufficiently powered randomised controlled trials are yet to be undertaken. Recent research using social, economic and quality of life indicators has provided further evidence of the societal and individual burden of PNES.

CONCLUSION

The research into PNES published over the last 3 years has deepened our understanding of the condition as a biopsychosocial disorder which is neither a "physical" nor a "psychological" condition. A number of small studies have demonstrated the potential of educational and psychotherapeutic treatments, but rigorous and sufficiently large trials still need to be conducted to determine the effectiveness of these interventions.

Uncinate fasciculus connectivity in patients with psychogenic nonepileptic seizures: A preliminary diffusion tensor tractography study

The amygdala, hippocampus, and medial prefrontal cortex are limbic brain regions connected by the uncinate fasciculus (UF) and implicated in emotion regulation. The aim of this study was to assess the connectivity characteristics of the UF in patients with psychogenic nonepileptic seizures (PNES) and matched healthy controls. We hypothesized that white matter connectivity of the UF in patients with PNES would differ from that in healthy controls. Eight patients with PNES and eight age- and sex-matched healthy controls underwent 3T MRI and 32-direction diffusion tensor imaging (DTI). Computation of DTI indices including fractional anisotropy (FA) and diffusion tensor tractography was performed. Two regions of interest were defined to manually trace the UF in each hemisphere for each subject. Fractional anisotropy and the number of reconstructed streamlines for the left and right hemispheres of the UF and the degree of asymmetry for each measure were compared between groups. Correlations between UF measures and clinical variables were also performed. Patients with PNES exhibited a significantly greater number of UF streamlines in the right hemisphere tract than in the left hemisphere (p=0.031), with such difference not observed in controls (p=0.81). This was reflected in a significant group difference in the asymmetry index (AI) for the number of streamlines, with more rightward asymmetry in patients with PNES (p=0.021). Average FA of the UF was similar between groups and between hemispheres for each group (all p>0.05). Age at illness onset was correlated with the AI for FA (r=-0.87; p=0.0045). Previously observed differences in emotion processing between controls and patients with PNES may be related to the differences in the rightward asymmetry in the number of UF streamlines in patients with PNES. Age at PNES onset appears to also have a role in the FA asymmetry of the UF. This is the first study to investigate the structural connectivity in these regions involved in emotional regulation in patients with PNES; further research is necessary to clarify the complex relationships between clinical measures and DTI characteristics.

Brain complex network analysis by means of resting state fMRI and graph analysis: will it be helpful in clinical epilepsy?

Functional magnetic resonance imaging (fMRI) has just completed 20 years of existence. It currently serves as a research tool in a broad range of human brain studies in normal and pathological conditions, as is the case of epilepsy. To date, most fMRI studies aimed at characterizing brain activity in response to various active paradigms. More recently, a number of strategies have been used to characterize the low-frequency oscillations of the ongoing fMRI signals when individuals are at rest. These datasets have been largely analyzed in the context of functional connectivity, which inspects the covariance of fMRI signals from different areas of the brain. In addition, resting state fMRI is progressively being used to evaluate complex network features of the brain. These strategies have been applied to a number of different problems in neuroscience, which include diseases such as Alzheimer's, schizophrenia, and epilepsy. Hence, we herein aimed at introducing the subject of complex network and how to use it for the analysis of fMRI data. This appears to be a promising strategy to be used in clinical epilepsy. Therefore, we also review the recent literature that has applied these ideas to the analysis of fMRI data in patients with epilepsy.

Assortative mixing in functional brain networks during epileptic seizures

We investigate assortativity of functional brain networks before, during, and after one-hundred epileptic seizures with different anatomical onset locations. We construct binary functional networks from multi-channel electroencephalographic data recorded from 60 epilepsy patients; and from time-resolved estimates of the assortativity coefficient, we conclude that positive degree-degree correlations are inherent to seizure dynamics. While seizures evolve, an increasing assortativity indicates a segregation of the underlying functional network into groups of brain regions that are only sparsely interconnected, if at all. Interestingly, assortativity decreases already prior to seizure end. Together with previous observations of characteristic temporal evolutions of global statistical properties and synchronizability of epileptic brain networks, our findings may help to gain deeper insights into the complicated dynamics underlying generation, propagation, and termination of seizures.