Amygdala subfield and prefrontal cortex abnormalities in patients with functional seizures.

BACKGROUND: Functional seizures (FS) are paroxysmal episodes, resembling epileptic seizures, but without underlying epileptic abnormality. The aetiology and neuroanatomic associations are incompletely understood. Recent brain imaging data indicate cerebral changes, however, without clarifying possible pathophysiology. In the present study, we specifically investigated the neuroanatomic changes in subregions of the amygdala and hippocampus in FS. METHODS: T1 MRI scans of 37 female patients with FS and 37 age-matched female seizure naïve controls (SNC) were analyzed retrospectively in FreeSurfer version 7.1. Seizure naïve controls included patients with depression and anxiety disorders. The analysis included whole-brain cortical thickness, subcortical volumes, and subfields of the amygdala and hippocampus. Group comparisons were carried out using multivariable linear models. RESULTS: The FS and SNC groups did not differ in the whole hippocampus and amygdala volumes. However, patients had a significant reduction of the right lateral amygdala volume (p = 0.00041), an increase of the right central amygdala, (p = 0.037), and thinning of the left superior frontal gyrus (p = 0.024). Additional findings in patients were increased volumes of the right medial amygdala (p = 0.031), left anterior amygdala (p = 0.017), and left dentate gyrus of the hippocampus (p = 0.035). CONCLUSIONS: The observations from the amygdala and hippocampus segmentation affirm that there are neuroanatomic associations of FS. The pattern of these changes aligned with some of the cerebral changes described in chronic stress conditions and depression. The pattern of detected changes further study, and may, after validation, provide biomarkers for diagnosis and treatment.

Clinical MRI morphological analysis of functional seizures compared to seizure-naïve and psychiatric controls.

PURPOSE: Functional seizures (FS), also known as psychogenic nonepileptic seizures (PNES), are physical manifestations of acute or chronic psychological distress. Functional and structural neuroimaging have identified objective signs of this disorder. We evaluated whether magnetic resonance imaging (MRI) morphometry differed between patients with FS and clinically relevant comparison populations. METHODS: Quality-screened clinical-grade MRIs were acquired from 666 patients from 2006 to 2020. Morphometric features were quantified with FreeSurfer v6. Mixed-effects linear regression compared the volume, thickness, and surface area within 201 regions-of-interest for 90 patients with FS, compared to seizure-naïve patients with depression (n = 243), anxiety (n = 68), and obsessive-compulsive disorder (OCD, n = 41), respectively, and to other seizure-naïve controls with similar quality MRIs, accounting for the influence of multiple confounds including depression and anxiety based on chart review. These comparison populations were obtained through review of clinical records plus research studies obtained on similar scanners. RESULTS: After Bonferroni-Holm correction, patients with FS compared with seizure-naïve controls exhibited thinner bilateral superior temporal cortex (left 0.053 mm, p = 0.014; right 0.071 mm, p = 0.00006), thicker left lateral occipital cortex (0.052 mm, p = 0.0035), and greater left cerebellar white-matter volume (1085 mm3, p = 0.0065). These findings were not accounted for by lower MRI quality in patients with FS. CONCLUSIONS: These results reinforce prior indications of structural neuroimaging correlates of FS and, in particular, distinguish brain morphology in FS from that in depression, anxiety, and OCD. Future work may entail comparisons with other psychiatric disorders including bipolar and schizophrenia, as well as exploration of brain structural heterogeneity within FS.

Phenotyping of Review-of-Systems Responses to Differentiate Functional Seizures From Epilepsy.

Objective: Although the populations of patients with functional seizures (FS) and epileptic seizures (ES) are extremely heterogeneous with multiple etiologies and phenotypes, patients with FS have increased somatic sensitivity and report more positive complaints on review-of-systems questionnaires (ROSQs). We evaluated if data-driven clustering and projection analysis could identify symptom phenotypes that could differentiate between patients with FS and ES.Methods: The dataset included all adult patients admitted from January 2006 to March 2020 for video-electroencephalography with available ROSQs (N = 877). Latent clusters and axes of variation in ROSQ responses were evaluated using multiple well-established methods. Leave-one-out cross-validation was used to evaluate if logistic regression using information could differentiate patients with FS from ES.Results: When evaluating individual symptom response and proportion of positive responses, the area under the receiver operating curve (AUC) was 62% (95% CI, 53%-69%) and 72% (CI, 65%-78%), respectively. The best AUC achieved by phenotyping methods was 74%. The patterns of clusters and components reflected properties of each analysis and did not correlate with assigned "system" from the ROSQ or other interpretations.Discussion: The overall proportion of positive responses was the most informative metric to differentiate patients with FS compared to ES. While both FS and ES are heterogeneous populations with multiple subgroups, these subgroups were not meaningfully identified based on ROSQ symptoms. The limited overall predictive accuracy and AUC suggests that, in absence of other supporting data, ROSQ responses in patients with FS and ES were not clinically useful for screening.

Title: Functional seizures across the adult lifespan: female sex, delay to diagnosis and disability.

OBJECTIVE: The clinical characteristics of functional seizures may vary based on age-of-onset or age-of-presentation. Description of age-related differences has focused on three categories: pediatric, young-adult, and older-adult. We evaluated how factors continuously varied based on age-of-presentation across the adult lifespan. METHODS: Based on cross-sectional data from 365 adult (18 to 88 years old) patients with documented diagnoses of functional seizures, we evaluated how the quantity and prevalence of specific ictal behaviors, historical factors, and comorbidities varied based on patient age-of-presentation using sequential weighted averages. RESULTS: Four factors changed prominently with age-of-presentation: female predominance decreased at two inflection points - ages 35 and 62; the prevalence of work disability was higher until age-of-presentation 30 then plateaued at 80%; there was greater delay to diagnosis in older patients; and comorbidities was higher with age-of-presentation, starting from early adulthood. The proportion of patients who presented with functional seizures decreased after 50. Ictal behavior did not substantially vary with age-of-presentation. CONCLUSION: The time from onset to diagnosis increased with age-of-presentation, which may be related to increased comorbidities and the misconception that FS do not start in older age. The female predominance decreased nonlinearly with age. By age 30, most patients' seizures already had substantial association with unemployment. These findings emphasize that patients can develop functional seizures at any age. The rapid development of disability relatively early in life, which then stays at a high prevalence rate, demonstrates the need for prompt referral for definitive diagnosis and treatment.

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.

Factors associated with delay to video-EEG in dissociative seizures.

PURPOSE: While certain clinical factors suggest a diagnosis of dissociative seizures (DS), otherwise known as functional or psychogenic nonepileptic seizures (PNES), ictal video-electroencephalography monitoring (VEM) is the gold standard for diagnosis. Diagnostic delays were associated with worse quality of life and more seizures, even after treatment. To understand why diagnoses were delayed, we evaluated which factors were associated with delay to VEM. METHODS: Using data from 341 consecutive patients with VEM-documented dissociative seizures, we used multivariate log-normal regression with recursive feature elimination (RFE) and multiple imputation of some missing data to evaluate which of 76 clinical factors were associated with time from first dissociative seizure to VEM. RESULTS: The mean delay to VEM was 8.4 years (median 3 years, IQR 1-10 years). In the RFE multivariate model, the factors associated with longer delay to VEM included more past antiseizure medications (0.19 log-years/medication, standard error (SE) 0.05), more medications for other medical conditions (0.06 log-years/medication, SE 0.03), history of physical abuse (0.75 log-years, SE 0.27), and more seizure types (0.36 log-years/type, SE 0.11). Factors associated with shorter delay included active employment or student status (-1.05 log-years, SE 0.21) and higher seizure frequency (0.14 log-years/log[seizure/month], SE 0.06). CONCLUSIONS: Patients with greater medical and seizure complexity had longer delays. Delays in multiple domains of healthcare can be common for victims of physical abuse. Unemployed and non-student patients may have had more barriers to access VEM. These results support earlier referral of complex cases to a comprehensive epilepsy center.

Epilepsy, dissociative seizures, and mixed: Associations with time to video-EEG.

PURPOSE: Video-electroencephalographic monitoring (VEM) is a core component to the diagnosis and evaluation of epilepsy and dissociative seizures (DS)-also known as functional or psychogenic seizures-but VEM evaluation often occurs later than recommended. To understand why delays occur, we compared how patient-reported clinical factors were associated with time from first seizure to VEM (TVEM) in patients with epilepsy, DS or mixed. METHODS: We acquired data from 1245 consecutive patients with epilepsy, VEM-documented DS or mixed epilepsy and DS. We used multivariate log-normal regression with recursive feature elimination (RFE) to evaluate which of 76 clinical factors interacting with patients' diagnoses were associated with TVEM. RESULTS: The mean and median TVEM were 14.6 years and 10 years, respectively (IQR 3-23 years). In the multivariate RFE model, the factors associated with longer TVEM in all patients included unemployment and not student status, more antiseizure medications (current and past), concussion, and ictal behavior suggestive of temporal lobe epilepsy. Average TVEM was shorter for DS than epilepsy, particularly for patients with depression, anxiety, migraines, and eye closure. Average TVEM was longer specifically for patients with DS taking more medications, more seizure types, non-metastatic cancer, and with other psychiatric comorbidities. CONCLUSIONS: In all patients with seizures, trials of numerous antiseizure medications, unemployment and non-student status was associated with longer TVEM. These associations highlight a disconnect between International League Against Epilepsy practice parameters and observed referral patterns in epilepsy. In patients with dissociative seizures, some but not all factors classically associated with DS reduced TVEM.

Reliability of additional reported seizure manifestations to identify dissociative seizures.

PURPOSE: Descriptions of seizure manifestations (SM), or semiology, can help localize the symptomatogenic zone and subsequently included brain regions involved in epileptic seizures, as well as identify patients with dissociative seizures (DS). Patients and witnesses are not trained observers, so these descriptions may vary from expert review of seizure video recordings of seizures. To better understand how reported factors can help identify patients with DS or epileptic seizures (ES), we evaluated the associations between more than 30 SMs and diagnosis using standardized interviews. METHODS: Based on patient- and observer-reported data from 490 patients with diagnoses documented by video-electoencephalography, we compared the rate of each SM in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic seizure-like events (PSLE), mixed DS and ES, and inconclusive testing. RESULTS: In addition to SMs that we described in a prior manuscript, the following were associated with DS: light triggers, emotional stress trigger, pre-ictal and post-ictal headache, post-ictal muscle soreness, and ictal sensory symptoms. The following were associated with ES: triggered by missing medication, aura of déjà vu, and leftward eye deviation. There were numerous manifestations separately associated with mixed ES and DS. CONCLUSIONS: Reported SM can help identify patients with DS, but no manifestation is pathognomonic for either ES or DS. Patients with mixed ES and DS reported factors divergent from both ES-alone and DS-alone.

Objective score from initial interview identifies patients with probable dissociative seizures.

OBJECTIVE: To develop a Dissociative Seizures Likelihood Score (DSLS), which is a comprehensive, evidence-based tool using information available during the first outpatient visit to identify patients with "probable" dissociative seizures (DS) to allow early triage to more extensive diagnostic assessment. METHODS: Based on data from 1616 patients with video-electroencephalography (vEEG) confirmed diagnoses, we compared the clinical history from a single neurology interview of patients in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic nonepileptic seizure-like events (PSLE), mixed DS plus ES, and inconclusive monitoring. We used data-driven methods to determine the diagnostic utility of 76 features from retrospective chart review and applied this model to prospective interviews. RESULTS: The DSLS using recursive feature elimination (RFE) correctly identified 77% (95% confidence interval (CI), 74-80%) of prospective patients with either ES or DS, with a sensitivity of 74% and specificity of 84%. This accuracy was not significantly inferior than neurologists' impression (84%, 95% CI: 80-88%) and the kappa between neurologists' and the DSLS was 21% (95% CI: 1-41%). Only 3% of patients with DS were missed by both the fellows and our score (95% CI 0-11%). SIGNIFICANCE: The evidence-based DSLS establishes one method to reliably identify some patients with probable DS using clinical history. The DSLS supports and does not replace clinical decision making. While not all patients with DS can be identified by clinical history alone, these methods combined with clinical judgement could be used to identify patients who warrant further diagnostic assessment at a comprehensive epilepsy center.