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

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.

Structural Changes in Brain Magnetic Resonance Imaging Associated With Psychogenic Non-epileptic Seizures: An Analytical Cross-Sectional Study

Background Psychogenic non-epileptic seizures (PNES) are often seen as indicators of poor motor and sensory function caused by psychological responses to stressful experiences. A seizure might trigger these reactions. The aim of our study was to assess the structural changes in brain MRI associated with psychogenic non-epileptic seizures. Methodology A retrospective analytical cross-sectional study at the Department of Medicine and Neurology, Ayub Teaching Hospital, Abbottabad, was conducted from October 2020 to September 2021. The medical records of patients with confirmed PNES were collected and retrospectively evaluated. Results Medical records and MRI scans were accessible for 52 patients with PNES; 10 patients were excluded from the study. The average age of the patients (standard deviation) was 34 (±9) years, and the average age at onset was 31.6 (±5.8) years. Based on the video-EEG recordings, 57.1% of patients (n=24) were classified as having broadly generalized motor seizures, 40% of patients (n=17) were classified as having predominantly akinetic seizures defined primarily by blank spells, and only one patient was classified as having focal motor seizures. Only three patients (7%) had a positive epilepsy family history. Twenty-four (47.6%) patients with brain MRI scans reported abnormal findings, while 22 (52.4%) had normal MRI findings. The majority of patients with abnormal MRIs had nonspecific white matter changes (50%), mesial temporal sclerosis (15%), and cysts (15%). In a statistical analysis, age at the beginning of PNES (p = 0.04), duration of PNES (p=0.01), concomitant epilepsy (p = 0.05), generalized motor seizures (p= 0.03), and focal motor seizures (p= 0.02) were strongly associated with abnormal brain MRI findings. Conclusion Research reveals that persons with PNES have a higher-than-average prevalence of anatomical brain abnormalities. The main takeaway is that these findings lend credence to the growing body of data suggesting that PNES may not be a medical mystery but rather a disorder with physical foundations in the brain. Important implications for diagnosing and treating PNES patients are discussed, as are the outcomes of earlier neuroimaging investigations of PNES. Studying the involvement of structural brain anomalies in the etiology of psychogenic non-epileptic seizures requires further well-designed multicenter studies with larger sample sizes and a consistent imaging approach (PNES). It is crucial to consider any confounding variables, such as co-occurring mental diseases, while designing this study.

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.

Regional brain atrophy and aberrant cortical folding relate to anxiety and depression in patients with traumatic brain injury and psychogenic nonepileptic seizures

OBJECTIVE

Psychogenic nonepileptic seizures (PNES) are characterized by multifocal and global abnormalities in brain function and connectivity. Only a few studies have examined neuroanatomic correlates of PNES. Traumatic brain injury (TBI) is reported in 83% of patients with PNES and may be a key component of PNES pathophysiology. In this study, we included patients with TBI preceding the onset of PNES (TBI-PNES) and TBI without PNES (TBI-only) to identify neuromorphometric abnormalities associated with PNES.

METHODS

Adults diagnosed with TBI-PNES (n = 62) or TBI-only (n = 59) completed psychological questionnaires and underwent 3-T magnetic resonance imaging. Imaging data were analyzed by voxel- and surface-based morphometry. Voxelwise general linear models computed group differences in gray matter volume, cortical thickness, sulcal depth, fractal dimension (FDf), and gyrification. Statistical models were assessed with permutation-based testing at 5000 iterations with the Threshold-Free Cluster Enhancement toolbox. Logarithmically scaled p-values corrected for multiple comparisons using familywise error were considered significant at p < .05. Post hoc analyses determined the association between structural and psychological measures (p < .05).

RESULTS

TBI-PNES participants demonstrated atrophy of the left inferior frontal gyrus and the right cerebellum VIII. Relative to TBI-only, TBI-PNES participants had decreased FDf in the right superior parietal gyrus and decreased sulcal depth in the left insular cortex. Significant clusters were positively correlated with global assessment of functioning scores, and demonstrated varying negative associations with measures of anxiety, depression, somatization, and global severity of symptoms.

SIGNIFICANCE

The diagnosis of PNES was associated with brain atrophy and reduced cortical folding in regions implicated in emotion processing, regulation, and response inhibition. Cortical folds primarily develop during the third trimester of pregnancy and remain relatively constant throughout the remainder of one's life. Thus, the observed aberrations in FDf and sulcal depth could originate early in development. The convergence of environmental, developmental, and neurobiological factors may coalesce to reflect the neuropathophysiological substrate of PNES.

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.

Psychiatric Disorders in Children and Adolescents With Psychogenic Nonepileptic Seizures

OBJECTIVE

Knowledge regarding psychiatric disorders in children and adolescents with psychogenic nonepileptic seizures (PNES) is limited. This study outlines the spectrum and risk of psychiatric disorders in childhood-onset PNES.

METHODS

We performed a nationwide matched cohort study of children and adolescents with PNES 5 to 17 years of age at the time of diagnosis between January 1, 1996, and December 31, 2014. Two matched comparison groups were included: children and adolescents with epilepsy (ES) and children and adolescents without PNES or epilepsy, called healthy controls (HC). Outcomes were prevalent psychiatric disorders before index (i.e., date of diagnosis or corresponding date for HC) and incident psychiatric disorders 2 years after index. Relative risks (RRs) were calculated and adjusted for potential confounders.

RESULTS

We included 384 children and adolescents with validated PNES, 1,152 with ES, and 1,920 HC. Among the cases of PNES, 153 (39.8%) had prevalent psychiatric disorders and 150 (39.1%) had incident psychiatric disorders. Compared to the ES and HC groups, children and adolescents with PNES had elevated risks of both prevalent psychiatric disorders (adjusted RR_PNES/ES 1.87, 95% confidence interval [CI] 1.59-2.21, adjusted RR_PNES/HC 5.54, 95% CI 4.50-6.81) and incident psychiatric disorders (adjusted RR_PNES/ES 2.33, 95% CI 1.92-2.83, adjusted RR_PNES/HC 8.37, 95% CI 6.31-11.11). A wide spectrum of specific psychiatric disorders displayed elevated RRs.

CONCLUSIONS

Children and adolescents with PNES are at higher risk of a wide range of psychiatric disorders compared to children and adolescents with ES and HC. A careful psychiatric evaluation is warranted to optimize and individualize treatment.

Quantitative EEG Findings in Patients With Psychogenic Nonepileptic Seizures

Objective. Psychogenic nonepileptic seizures (PNES), is one of the clinical manifestations of conversion disorder that epileptiform discharges do not accompany. Factors capable of increasing susceptibility to these seizures have not been adequately investigated yet. This study aims to investigate the quantitative electroencephalography (QEEG) findings for PNES by evaluating the resting EEG spectral power changes during the periods between seizures. Methods. Thirty-nine patients (29 females, 10 males) diagnosed with PNES (group 1) and 47 patients (23 females, 24 males) without any psychiatric diagnosis (group 2) were included in the study. The patients underwent a psychiatric examination at their first visit, were diagnosed and their EEGs were recorded. Using fast Fourier transformation (FFT), spectral power analysis was calculated for delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (15-30 Hz), high-beta (25-30 Hz), gamma-1 (31-40 Hz), gamma-2 (41-50 Hz), and gamma (30-80 Hz) frequency bands. Results. Six separate EEG band power, namely (C3-high beta, C3-gamma, C3-gamma-1, C3-gamma-2, P3-gamma, P3 gamma-1), were found to be higher in the patients diagnosed with PNES than in the control group. Conclusion. Our findings show that PNES correlate with high-frequency oscillations on central motor and somatosensory cortices.

Neuroimaging in Functional Neurological Disorder: State of the Field and Research Agenda

Functional neurological disorder (FND) was of great interest to early clinical neuroscience leaders. During the 20th century, neurology and psychiatry grew apart - leaving FND a borderland condition. Fortunately, a renaissance has occurred in the last two decades, fostered by increased recognition that FND is prevalent and diagnosed using "rule-in" examination signs. The parallel use of scientific tools to bridge brain structure - function relationships has helped refine an integrated biopsychosocial framework through which to conceptualize FND. In particular, a growing number of quality neuroimaging studies using a variety of methodologies have shed light on the emerging pathophysiology of FND. This renewed scientific interest has occurred in parallel with enhanced interdisciplinary collaborations, as illustrated by new care models combining psychological and physical therapies and the creation of a new multidisciplinary FND society supporting knowledge dissemination in the field. Within this context, this article summarizes the output of the first International FND Neuroimaging Workgroup meeting, held virtually, on June 17th, 2020 to appraise the state of neuroimaging research in the field and to catalyze large-scale collaborations. We first briefly summarize neural circuit models of FND, and then detail the research approaches used to date in FND within core content areas: cohort characterization; control group considerations; task-based functional neuroimaging; resting-state networks; structural neuroimaging; biomarkers of symptom severity and risk of illness; and predictors of treatment response and prognosis. Lastly, we outline a neuroimaging-focused research agenda to elucidate the pathophysiology of FND and aid the development of novel biologically and psychologically-informed treatments.

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.

Skin conductance response and emotional response in women with psychogenic non-epileptic seizures

PURPOSE

Recent etiopathogenic models place emotional dysregulation at the core of psychogenic nonepileptic seizure (PNES). Our purpose was to assess physiological, cognitive, and behavioral emotional responses of PNES patients.

METHODS

This study compared three types of emotional responses to visual emotional stimuli between 34 female PNES group and 34 matched healthy controls: physiological response measured by skin conductance response (SCR) (rate, amplitude and latency) and heart rate deceleration; cognitive response measured by valence and arousal elicited by the images; and behavioural response measured by latency of ratings. The groups were characterized on psychiatric comorbidities, traumatic history, alexithymia, and dissociation.

RESULTS

Compared to controls, PNES group displayed lower SCR for all images (p = 0.038), shorter amplitude of heart rate deceleration (p = 0.024) and faster arousal rating for all images (p = 0.019), but no difference on cognitive rating of images. Within-groups analyses showed only in PNES subjects increased rate (+19.35%, p = 0.046) SCR for negative stimuli with strong arousal compared to negative with low arousal. PNES physiological response (SCR and heart rate deceleration) was negatively correlated to dissociation tendency (r=-0.48, p = 0.0083) and alexithymia (r=-0.44, p = 0.012)). For cognitive response, no correlation was found.

CONCLUSION

These results are in favour of a lower physiological emotional response but with an over-reactivity at behavioral level contrasting with similar cognitive assessment. For strong aversive stimuli, PNES might present a trend to overreact at physiological and behavioural levels. Our results suggest that dissociation and difficulty in describing feelings are associated with an altered physiological response in PNES women only.

Psychogenic Non-epileptic Seizures and Pseudo-Refractory Epilepsy, a Management Challenge

Psychogenic nonepileptic seizures (PNES) are neurobehavioral conditions positioned in a gray zone, not infrequently a no-man land, that lies in the intersection between Neurology and Psychiatry. According to the DSM 5, PNES are a subgroup of conversion disorders (CD), while the ICD 10 classifies PNES as dissociative disorders. The incidence of PNES is estimated to be in the range of 1.4-4.9/100,000/year, and the prevalence range is between 2 and 33 per 100,000. The International League Against Epilepsy (ILAE) has identified PNES as one of the 10 most critical neuropsychiatric conditions associated with epilepsy. Comorbidity between epilepsy and PNES, a condition leading to "dual diagnosis," is a serious diagnostic and therapeutic challenge for clinicians. The lack of prompt identification of PNES in epileptic patients can lead to potentially harmful increases in the dosage of anti-seizure drugs (ASD) as well as erroneous diagnoses of refractory epilepsy. Hence, pseudo-refractory epilepsy is the other critical side of the PNES coin as one out of four to five patients admitted to video-EEG monitoring units with a diagnosis of pharmaco-resistant epilepsy is later found to suffer from non-epileptic events. The majority of these events are of psychogenic origin. Thus, the diagnostic differentiation between pseudo and true refractory epilepsy is essential to prevent actions that lead to unnecessary treatments and ASD-related side effects as well as produce a negative impact on the patient's quality of life. In this article, we review and discuss recent evidence related to the neurobiology of PNES. We also provide an overview of the classifications and diagnostic steps that are employed in PNES management and dwell on the concept of pseudo-resistant epilepsy.

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.

Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity

The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience.

"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.

Psychogenic non-epileptic seizures in children and adolescents: Part I - Diagnostic formulations

Psychogenic non-epileptic seizures (PNES) are a nonspecific, umbrella category that is used to collect together a range of atypical neurophysiological responses to emotional distress, physiological stressors and danger. Because PNES mimic epileptic seizures, children and adolescents with PNES usually present to neurologists or to epilepsy monitoring units. After a comprehensive neurological evaluation and a diagnosis of PNES, the patient is referred to mental health services for treatment. This study documents the diagnostic formulations - the clinical formulations about the probable neurophysiological mechanisms - that were constructed for 60 consecutive children and adolescents with PNES who were referred to our Mind-Body Rehabilitation Programme for treatment. As a heuristic framework, we used a contemporary reworking of Janet's dissociation model: PNES occur in the context of a destabilized neural system and reflect a release of prewired motor programmes following a functional failure in cognitive-emotional executive control circuitry. Using this framework, we clustered the 60 patients into six different subgroups: (1) dissociative PNES (23/60; 38%), (2) dissociative PNES triggered by hyperventilation (32/60; 53%), (3) innate defence responses presenting as PNES (6/60; 10%), (4) PNES triggered by vocal cord adduction (1/60; 2%), (5) PNES triggered by activation of the valsalva manoeuvre (1/60; 1.5%) and (6) PNES triggered by reflex activation of the vagus (2/60; 3%). As described in the companion article, these diagnostic formulations were used, in turn, both to inform the explanations of PNES that we gave to families and to design clinical interventions for helping the children and adolescents gain control of their PNES.

Functional connectivity analysis in EEG source space: The choice of method

Functional connectivity (FC) is among the most informative features derived from EEG. However, the most straightforward sensor-space analysis of FC is unreliable owing to volume conductance effects. An alternative-source-space analysis of FC-is optimal for high- and mid-density EEG (hdEEG, mdEEG); however, it is questionable for widely used low-density EEG (ldEEG) because of inadequate surface sampling. Here, using simulations, we investigate the performance of the two source FC methods, the inverse-based source FC (ISFC) and the cortical partial coherence (CPC). To examine the effects of localization errors of the inverse method on the FC estimation, we simulated an oscillatory source with varying locations and SNRs. To compare the FC estimations by the two methods, we simulated two synchronized sources with varying between-source distance and SNR. The simulations were implemented for hdEEG, mdEEG, and ldEEG. We showed that the performance of both methods deteriorates for deep sources owing to their inaccurate localization and smoothing. The accuracy of both methods improves with the increasing between-source distance. The best ISFC performance was achieved using hd/mdEEG, while the best CPC performance was observed with ldEEG. In conclusion, with hdEEG, ISFC outperforms CPC and therefore should be the preferred method. In the studies based on ldEEG, the CPC is a method of choice.

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.

Prevalence and distribution of MRI abnormalities in patients with psychogenic nonepileptic events

INTRODUCTION

Both structural and functional abnormalities have been reported in patients with psychogenic nonepileptic events (PNEEs), although no truly consistent abnormalities have been found.

METHODS

We retrospectively identified patients discharged from our EMU with video-EEG diagnoses of epileptic seizures, PNEEs, epileptic seizures plus PNEEs, interictal epileptiform abnormalities only, and nondiagnostic admissions. We then collected brain MRI results for analysis.

RESULTS

We found significant brain MRI abnormalities in 33.8% of patients with PNEEs, clearly higher than the rate of brain MRI abnormalities in the healthy population. In addition, we found statistically significant differences in the locations of brain MRI abnormalities in patients with epileptic seizures (more frequently temporal) versus PNEEs (more frequently multifocal).

CONCLUSION

This multifocal nature of abnormalities in patients with psychogenic nonepileptic events may help to explain the underlying pathophysiology as it relates to psychiatric disorders which are so frequently comorbid with PNEEs.

Nonepileptic seizures: an updated review

Psychogenic nonepileptic seizures (PNES) are a functional neurological disorder/conversion disorder subtype, which are neurobehavioral conditions at the interface of neurology and psychiatry. Significant advancements over the past decade have been made in the diagnosis, management, and neurobiological understanding of PNES. This article reviews published PNES research focusing on semiologic features that distinguish PNES from epileptic seizures, consensus diagnostic criteria, the intersection of PNES and other comorbidities, neurobiological studies, evidence-based treatment interventions, and outcome studies. Epidemiology and healthcare utilization studies highlight a continued unmet medical need in the comprehensive care of PNES. Consensus guidelines for diagnostic certainty are based on clinical history, semiology of witnessed typical event(s), and EEG findings. While certain semiologic features may aid in the diagnosis of PNES, the gold standard remains capturing a typical event on video electroencephalography (EEG) showing the absence of epileptiform activity with history and semiology consistent with PNES. Medical-neurologic and psychiatric comorbidities are prevalent in PNES; these should be assessed in diagnostic evaluations and integrated into treatment interventions and prognostic considerations. Several studies, including a pilot, multicenter, randomized clinical trial, have now demonstrated that a cognitive behavioral therapy-informed psychotherapy is an efficacious treatment for PNES, and additional efforts are necessary to evaluate the utility of pharmacologic and other psychotherapy treatments. Neuroimaging studies, while requiring replication, suggest that PNES may occur in the context of alterations within and across sensorimotor, emotion regulation/processing, cognitive control, and multimodal integration brain systems. Future research could investigate similarities and differences between PNES and other somatic symptom disorders.

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.

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.