Altered Brain Networks In Patients with Psychogenic Non-Epileptic Seizures (PNES) Using Ultra High Field MRI

Background: Psychogenic Non-epileptic Seizures (PNES) are attacks that appear similar to epileptic attacks. However, they lack abnormal electrical discharges in the brain and have psychological underpinnings and causes. The gold standard of diagnosis is video-EEG which is not widely accessible, creating a poor prognosis for patients. Resting state functional magnetic resonance imaging can aid in the diagnosis and treatment of PNES by helping better understand brain networks in patients with PNES. This study examines brain networks in patients with PNES with a focus on the default mode network and salience network. Methods: Twelve patients with PNES between the ages of 18-56 and twelve age- and sex- matched healthy participants between the ages of 18-59 were recruited. Participants underwent 7T resting-state fMRI scanning. Independent Components Analysis (ICA) and whole brain functional connectivity making use of region of interest analysis (ROI) was used to study the default mode network and the salience network. Results: No Significant differences in functional connectivity between regions in the default mode network (DMN) as well as the salience network (SN) were found when comparing patients with PNES to healthy control participants. Conclusions: In the current study patients with PNES do not show altered connectivity between brain regions in the default mode network as well as the salience network. Limitations and future directions of the current study will be discussed

Neuroimaging studies in patients with psychogenic non-epileptic seizures: A systematic meta-review

Psychogenic Non-epileptic Seizures (PNES) are ‘medically unexplained’ seizure-like episodes which superficially resemble epileptic seizures but which are not caused by epileptiform discharges in the brain. While many experts see PNES disorder as a multifactorial biopsychosocial condition, little is known about the neurobiological processes which may predispose, precipitate and/or perpetuate PNES symptomology. This systematic meta-review advances our knowledge and understanding of the neurobiological correlates of PNES by providing an up-to-date assessment of neuroimaging studies performed on individuals with PNES. Although the results presented appear inconclusive, they are consistent with an association between structural and functional brain abnormalities and PNES. These findings have implications for the way in which we think about this “medically unexplained” disorder and how we communicate the diagnosis to patients. However, it is also evident that neuroimaging studies in this area suffer from a number of significant limitations and future larger studies will need to better address these if we are to improve our understanding of the neurobiological correlates of predisposition to and/or manifestation of PNES

Differentiating Epileptic from Psychogenic Nonepileptic EEG Signals using Time Frequency and Information Theoretic Measures of Connectivity

Differentiating psychogenic nonepileptic seizures from epileptic seizures is a difficult task that requires timely recording of psychogenic events using video electroencephalography (EEG). Interpretation of video EEG to distinguish epileptic features from signal artifacts is error prone and can lead to misdiagnosis of psychogenic seizures as epileptic seizures resulting in undue stress and ineffective treatment with antiepileptic drugs. In this study, an automated surface EEG analysis was implemented to investigate differences between patients classified as having psychogenic or epileptic seizures. Surface EEG signals were grouped corresponding to the anatomical lobes of the brain (frontal, parietal, temporal, and occipital) and central coronal plane of the skull. To determine if differences were present between psychogenic and epileptic groups, magnitude squared coherence (MSC) and cross approximate entropy (C-ApEn) were used as measures of neural connectivity. MSC was computed within each neural frequency band (delta: 0.5Hz-4Hz, theta: 4-8Hz, alpha: 8-13Hz, beta: 13-30Hz, and gamma: 30-100Hz) between all brain regions. C-ApEn was computed bidirectionally between all brain regions. Independent samples t-tests were used to compare groups. The statistical analysis revealed significant differences between psychogenic and epileptic groups for both connectivity measures with the psychogenic group showing higher average connectivity. Average MSC was found to be lower for the epileptic group between the frontal/central, parietal/central, and temporal/occipital regions in the delta band and between the temporal/occipital regions in the theta band. Average C-ApEn was found to be greater for the epileptic group between the frontal/parietal, parietal/frontal, parietal/occipital, and parietal/central region pairs. These results suggest that differences in neural connectivity exist between psychogenic and epileptic patient groups

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

Structural alterations in functional neurological disorder and related conditions: A software and hardware problem?

Functional neurological (conversion) disorder (FND) is a condition at the interface of neurology and psychiatry. A “software” vs. “hardware” analogy describes abnormal neurobiological mechanisms occurring in the context of intact macroscopic brain structure. While useful for explanatory and treatment models, this framework may require more nuanced considerations in the context of quantitative structural neuroimaging findings in FND. Moreover, high co-occurrence of FND and somatic symptom disorders (SSD) as defined in DSM-IV (somatization disorder, somatoform pain disorder, and undifferentiated somatoform disorder; referred to as SSD for brevity in this article) raises the possibility of a partially overlapping pathophysiology. In this systematic review, we use a transdiagnostic approach to review and appraise the structural neuroimaging literature in FND and SSD. While larger sample size studies are needed for definitive characterization, this article highlights that individuals with FND and SSD may exhibit sensorimotor, prefrontal, striatal-thalamic, paralimbic, and limbic structural alterations. The structural neuroimaging literature is contextualized within the neurobiology of stress-related neuroplasticity, gender differences, psychiatric comorbidities, and the greater spectrum of functional somatic disorders. Future directions that could accelerate the characterization of the pathophysiology of FND and DSM-5 SSD are outlined, including “disease staging” discussions to contextualize subgroups with or without structural changes. Emerging neuroimaging evidence suggests that some individuals with FND and SSD may have a “software” and “hardware” problem, although if structural alterations are present the neural mechanisms of functional disorders remain distinct from lesional neurological conditions. Furthermore, it remains unclear whether structural alterations relate to predisposing vulnerabilities or consequences of the disorder. Keywords: Conversion disorder, Psychogenic, Neuroimaging, MRI, Functional neurological disorder, Somatic symptom disorde

Emotion and motor function: a clinical and developmental perspective

The idea that emotions and physical actions are strongly intertwined has been widely accepted for quite some time. Yet surprisingly, in both the affective neuroscience and movement neuroscience literature, relatively little empirical attention has been paid to the (psycho)neurophysiological processes underpinning emotion-motor interactions. This body of work provides new insights into emotion-motor interactions by furthering our understanding of the temporal relationship between emotion and motor preparation and motor output during different stages of brain maturation as well as the neurobiological correlates of abnormal motor output in the form of non-epileptic seizures