Brain functional connectivity in individuals with psychogenic nonepileptic seizures (PNES): An application of graph theory

Nodal degree centrality in the default mode-like network of the TgF344-AD Alzheimer's disease rat model as a measure of early network alterations

This study investigates brain network alterations in the default mode-like network (DMLN) at early stages of disease progression in a rat model of Alzheimer's disease (AD) with application in the development of early diagnostic biomarkers of AD in translational studies. Thirteen male TgF344-AD (TG) rats, and eleven male wild-types (WT) littermates underwent longitudinal resting-state fMRI at the age of 4 and 6 months (pre and early-plaque stages of AD). Alterations in connectivity within DMLN were characterized by calculating the nodal degree (ND), a graph theoretical measure of centrality. The ND values of the left CA2 subregion of the hippocampus was found to be significantly lower in the 4-month-old TG cohort compared to the age-matched WT littermates. Moreover, a lower ND value (hypo-connectivity) was observed in the right prelimbic cortex (prL) and basal forebrain in the 6-month-old TG cohort, compared to the same age WT cohort. Indeed, the ND pattern in the DMLN in both TG and WT cohorts showed significant differences across the two time points that represent pre-plaque and early plaque stages of disease progression. Our findings indicate that lower nodal degree (hypo-connectivity) in the left CA2 in the pre-plaque stage of AD and hypo-connectivity between the basal forebrain and the DMLN regions in the early-plaque stage demonstrated differences in comparison to healthy controls. These results suggest that a graph-theoretical measure such as the nodal degree, can characterize brain networks and improve our insights into the mechanisms underlying Alzheimer's disease.

An update on psychogenic nonepileptic seizures

PURPOSE OF REVIEW

The understanding of psychogenic nonepileptic seizures (PNES) has advanced steadily over recent decades. This update summarizes new insights from the last three years.

RECENT FINDINGS

The process of diagnosing PNES has shifted from the exclusion of epilepsy to one based on the recognition of typical clinical features. While the diagnosis cannot rely on any single feature in isolation, a range of semiological features characterising PNES are now recognised and a number of studies hint at the potential for machine learning and AI to improve the diagnostic process. Advances in data processing and analysis may also help to make sense of the heterogeneity of PNES populations demonstrated by recent studies focussing on aetiology and patient subgroups. It is now clear that PNES are associated with high rates of mental and physical comorbidities and premature death, highlighting that they are only one manifestation of a complex disorder extending beyond the nervous system and the seizures themselves.

SUMMARY

PNES are now understood as a manifestation of dysfunction in interacting brain networks. This understanding provides an explanation for the psychopathological and semiological heterogeneity of PNES patient populations. New insights into medical comorbidities and increased rates of premature death call for more research into associated pathological processes outside the nervous system.

Structural and functional changes associated with functional/dissociative seizures: A review of the literature

INTRODUCTION

The term 'functional/dissociative seizures (FDS)' refers to a paroxysmal, transient clinical manifestation that may include motor, sensory, vegetative, psychological and cognitive signs, similar to the manifestations observed in epileptic seizures. In recent years, there has been an increase of literature in the field of brain imaging research on functional neurological disorders and, more specifically, on FDS. However, most of the studies have been carried out on limited samples. We propose an update of this review work by performing a systematic review of studies performed since 2017 in the field of neuroimaging in patients with FDS.

METHODS

We conducted a systematic review of the literature using the PRISMA methodology and reproduced most of the methodological elements of the latest systematic literature review.

RESULTS

Our work over the last five years has identified 14 articles. It is still difficult to isolate a distinct structure or network specifically involved in the mechanism of FDS. However, certain structures are recurrently involved in imaging studies, notably the amygdala, the orbitofrontal cortex, and the anterior cingulate cortex.

CONCLUSION

The contribution of neuroimaging may allow a more precise explanation of the disorder for patients, avoiding the stigma frequently associated with this diagnosis. as with other 'conversion' phenomena which have traditionally been considered only as 'medically unexplained'. In the longer term and beyond a better understanding of the physiopathology of the disorder, the challenge of this neuroimaging work would be to identify specific imaging biomarkers for a diagnosis of FDS.

Attention deficit hyperactivity disorder in patients with seizures: Functional seizures vs. epilepsy

BACKGROUND

We investigated the rates of positive screening for attention deficit-hyperactivity disorder (ADHD) in adults with seizures [i.e., focal epilepsy vs. idiopathic generalized epilepsy (IGE) vs. functional seizures (FS)]. We hypothesized that the rates of positive screening for ADHD are different between these three groups of patients.

METHODS

This was a cross sectional study. Patients, 19 to 55 years of age, with a diagnosis of IGE, focal epilepsy or FS were investigated at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from September 2022 until January 2023 and during their follow-up visits. We used the validated Persian version of Adult ADHD Self-Report Scale (ASRS v1.1)15 to investigate and screen for ADHD in these patients.

RESULTS

Forty patients with focal epilepsy, 40 with IGE, and 40 with FS were included. Attention deficit-hyperactivity disorder (ADHD) screening was positive in 35% of patients with FS, in 30% of those with focal epilepsy (compared with FS, p = 0.633), and in 10% of patients with IGE (compared with FS, p = 0.007).

CONCLUSION

Adult patients with functional seizures and those with focal epilepsy are at a high risk of self-reporting experiences that could be characteristic of ADHD. Screening tools [e.g., Adult ADHD Self-Report Scale (ASRS v1.1)] are useful to help clinicians address seizure comorbidities such as ADHD. However, a clinical diagnosis of ADHD should be ascertained in a patient with positive screening.

A systems biology approach for discovering the cellular and molecular aspects of psychogenic non-epileptic seizure

Objectives

Psychogenic non-epileptic seizure (PNES) is the most common non-epileptic disorder in patients referring to epilepsy centers. Contrary to common beliefs about the disease's harmlessness, the death rate of PNES patients is similar to drug-resistant epilepsy. Meanwhile, the molecular pathomechanism of PNES is unknown with very limited related research. Thus, the aim of this in silico study was to find different proteins and hormones associated with PNES via a systems biology approach.

Methods

Different bioinformatics databases and literature review were used to find proteins associated with PNES. The protein-hormone interaction network of PNES was constructed to discover its most influential compartments. The pathways associated with PNES pathomechanism were found by enrichment analysis of the identified proteins. Besides, the relationship between PNES-related molecules and psychiatric diseases was discovered, and the brain regions that could express altered levels of blood proteins were discovered.

Results

Eight genes and three hormones were found associated with PNES through the review process. Proopiomelanocortin (POMC), neuropeptide Y (NPY), cortisol, norepinephrine, and brain-derived neurotrophic factor (BDNF) were identified to have a high impact on the disease pathogenesis network. Moreover, activation of Janus kinase-signaling transducer and activator of transcription (JAK-STAT) and JAK, as well as signaling of growth hormone receptor, phosphatidylinositol 3-kinase /protein kinase B (PI3K/AKT), and neurotrophin were found associated with PNES molecular mechanism. Several psychiatric diseases such as depression, schizophrenia, and alcohol-related disorders were shown to be associated with PNES predominantly through signaling molecules.

Significance

This study was the first to gather the biochemicals associated with PNES. Multiple components and pathways and several psychiatric diseases associated with PNES, and some brain regions that could be altered during PNES were suggested, which should be confirmed in further studies. Altogether, these findings could be used in future molecular research on PNES patients.

Alternation in functional connectivity within default mode network after psychodynamic psychotherapy in borderline personality disorder

BACKGROUND

Borderline personality disorder (BPD) is characterized by impairments in emotion regulation, impulse control, and interpersonal and social functioning along with a deficit in emotional awareness and empathy. In this study, we investigated whether functional connectivity (FC) within the default mode network (DMN) is affected by 1-year psychodynamic psychotherapy in patients with BPD.

METHODS

Nine BPD patients filled out the demography, Interpersonal Reactive Index (IRI), Toronto Alexithymia Scale 20 (TAS 20), the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST), and the Borderline Evaluation Severity over Time (BEST) questionnaire. The BPD group (9F) and the control group (9F) had a mean ± SD age of 28.2 ± 5.3 years and 30.4 ± 6.1 years, respectively. BPD subjects underwent longitudinal resting-state fMRI before psychodynamic psychotherapy and then every 4 months for a year after initiating psychotherapy. FC in DMN was characterized by calculating the nodal degree, a measure of centrality in the graph theory.

RESULTS

The results indicated that patients with BPD present with aberrant DMN connectivity compared to healthy controls. Over a year of psychotherapy, the patients with BPD showed both FC changes (decreasing nodal degree in the dorsal anterior cingulate cortex and increasing in other cingulate cortex regions) and behavioral improvement in their symptoms and substance use. There was also a significant positive association between the decreased nodal degree in regions of the dorsal cingulate cortex and a decrease in the score of the TAS-20 indicating difficulty in identifying feelings after psychotherapy.

CONCLUSION

In BPD, there is altered FC within the DMN and disruption in self-processing and emotion regulation. Psychotherapy may modify the DMN connectivity and that modification is associated with positive changes in BPD emotional symptoms.

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.

Effective connectivity between emotional and motor brain regions in people with psychogenic nonepileptic seizures (PNES)

OBJECTIVE

To characterize the effective connectivity (EC) between the emotion and motor brain regions in patients with psychogenic nonepileptic seizures (PNES), based on resting-state spectral dynamic causal modeling (spDCM).

METHODS

Twenty-three patients with PNES and twenty-five healthy control (HC) subjects underwent resting-state fMRI scanning. The coupling parameters indicating the causal interactions between eight brain regions associated with emotion, executive control, and motion were estimated for both groups, using resting-state fMRI spDCM.

RESULTS

Compared to the HC subjects, in patients with PNES: (i) the left insula (INS) and left and right inferior frontal gyri (IFG) are more inhibited by the amygdala (AMYG), anterior cingulate cortex (ACC), and precentral gyrus (PCG); (ii) the left AMYG has greater inhibitory effects on the INS, IFG, dorsolateral prefrontal cortex (DLPFC), PCG, and supplementary motor area (SMA); (iii) the left ACC has more inhibitory effects on the INS and IFG; (iv) the right ACC is more inhibited by the INS and IFG, and has a less inhibitory effect on the SMA and PCG; and (v) the left caudate (CAU) had increased inhibitory effects on the AMYG and IFG and a more excitatory effect on the SMA.

CONCLUSION

Our results suggest that in patients with PNES, the emotion-processing regions have inhibitory effects on the executive control areas and motor regions. Our findings may provide further insight into the influence of emotional arousal on functional movements and the underlying mechanisms of involuntary movements during functional seizures. Furthermore, they may suggest that emotion regulation through cognitive behavioral psychotherapies can be a potentially effective treatment modality.

A minority of patients with functional seizures have abnormalities on neuroimaging

OBJECTIVE

Functional seizures often are managed incorrectly as a diagnosis of exclusion. However, a significant minority of patients with functional seizures may have abnormalities on neuroimaging that typically are associated with epilepsy, leading to diagnostic confusion. We evaluated the rate of epilepsy-associated findings on MRI, FDG-PET, and CT in patients with functional seizures.

METHODS

We studied radiologists' reports from neuroimages at our comprehensive epilepsy center from a consecutive series of patients diagnosed with functional seizures without comorbid epilepsy from 2006 to 2019. We summarized the MRI, FDG-PET, and CT results as follows: within normal limits, incidental findings, unrelated findings, non-specific abnormalities, post-operative study, epilepsy risk factors (ERF), borderline epilepsy-associated findings (EAF), and definitive EAF.

RESULTS

Of the 256 MRIs, 23% demonstrated ERF (5%), borderline EAF (8%), or definitive EAF (10%). The most common EAF was hippocampal sclerosis, with the majority of borderline EAF comprising hippocampal atrophy without T2 hyperintensity or vice versa. Of the 87 FDG-PETs, 26% demonstrated borderline EAF (17%) or definitive EAF (8%). Epilepsy-associated findings primarily included focal hypometabolism, especially of the temporal lobes, with borderline findings including subtle or questionable hypometabolism. Of the 51 CTs, only 2% had definitive EAF.

SIGNIFICANCE

This large case series provides further evidence that, while uncommon, EAF are seen in patients with functional seizures. A significant portion of these abnormal findings are borderline. The moderately high rate of these abnormalities may represent framing bias from the indication of the study being "seizures," the relative subtlety of EAF, or effects of antiseizure medications.

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.