Evidence-Based Practice for the Clinical Assessment of Psychogenic Nonepileptic Seizures: A Report From the American Neuropsychiatric Association Committee on Research
Publication: The Journal of Neuropsychiatry and Clinical Neurosciences
Abstract
The American Neuropsychiatric Association’s Committee on Research assigned the task of defining the most helpful clinical factors and tests in establishing the diagnosis of psychogenic nonepileptic seizures (PNES) during a neuropsychiatric assessment. A systematic review of the literature was conducted using three search engines and specified search terms for PNES and the predetermined clinical factors and diagnostic tests, followed by a selection process with specific criteria. Data extraction results from selected articles are presented for clinical factors (semiology, psychiatric comorbidities, medical comorbidities, psychological traits) and diagnostic tests (EEG, psychometric and neuropsychological measures, prolactin level, clinical neuroimaging, autonomic testing). Semiology with video EEG (vEEG) remains the most valuable tool to determine the diagnosis of PNES. With the exception of semiology, very few studies revealed the predictive value of a clinical factor for PNES, and such findings were isolated and not replicated in most cases. Induction techniques, especially when coupled with vEEG, can lead to a captured event, which then confirms the diagnosis. In the absence of a captured event, postevent prolactin level and personality assessment can support the diagnosis but need to be carefully contextualized with other clinical factors. A comprehensive clinical assessment in patients with suspected PNES can identify several clinical factors and may include a number of tests that can support the diagnosis of PNES. This is especially relevant when the gold standard of a captured event with typical semiology on vEEG cannot be obtained.
Psychogenic nonepileptic seizures (PNES) are the most common subtype of functional neurological disorder (FND; conversion disorder). They consist of paroxysmal alterations in motor, sensory, autonomic, or cognitive functions that are not associated with ictal epileptiform activity. Instead, PNES are neuropsychiatric symptoms thought to occur in the context of a complex array of biological and psychosocial vulnerabilities (1, 2).
An estimated 20%−30% of diagnosed epilepsy cases are actually misdiagnosed PNES (3). Early and correct diagnosis of PNES provides prompt access to treatment and reduces the risk of exposure to inappropriate and potentially harmful invasive diagnostic testing, injected medications, and intubation during emergency medical care. Unnecessary treatment with antiseizure medications may result in side effects and may exacerbate PNES symptoms (4). Furthermore, accurate diagnosis can be therapeutic in and of itself, as a small subset of newly presenting patients without other risk factors demonstrate a decrease in their PNES after communication of the diagnosis (5)
In 2013, the International League Against Epilepsy Nonepileptic Seizures Task Force published minimum requirements for the diagnosis of PNES (1). Diagnostic certainty in PNES is established at the highest level when a typical PNES episode is captured on video EEG (vEEG) with no ictal discharges and with semiology and history consistent with PNES (1). Lower levels of diagnostic certainty were established to provide guidance when the gold standard diagnostic evaluation cannot be performed due to clinical or resource limitations.
In addition to electrophysiological investigations, there are a number of clinical factors and ancillary tests that can provide additional support for the diagnosis of PNES. The American Neuropsychiatric Association (ANPA) Committee on Research set up the task of defining the clinical factors and tests that yield the most significance during a clinical neuropsychiatric assessment. Separate groups from the ANPA Committee on Research will report on clinical neuropsychiatric assessment in other phenotypes of FND and on evidence-based treatment in FND, including PNES.
Previous reviews have identified neuropsychiatric predisposing, precipitating, and perpetuating factors (6, 7) for the development and maintenance of PNES. Many demographic factors, such as age, sex, employment or disability, and health care utilization, are well characterized in PNES, and their evaluation should be included in every clinical assessment. PNESs are more frequent in women, usually start in the second or third decade of life, and can lead to chronic disability. Patients with PNES tend to have frequent health care contacts (7). Along with these established demographic characteristics, PNESs have been well described in men and in pediatric and geriatric populations.
In contrast to these predetermined demographic characteristics, the clinical factors and tests that we aimed to define as supportive in the diagnosis of PNES require further medical expertise to be interpreted and can be costly. Therefore, knowing how to prioritize evaluation of these clinical factors and tests has practical implications.
We systematically reviewed clinical factors and diagnostic tests based on the existing literature and our clinical experience. We aimed to define which clinical factors and diagnostic tests helped to increase confidence in a diagnosis of PNES.
Methods
A preliminary review of the existing literature, as well as the clinical expertise of the coauthors, helped to establish a list of categories of clinical factors and tests to be investigated. Search terms for PNES, which are presented in Appendix S1 in the online supplement , were then combined with search terms related to the clinical characteristics or tests. The selection of search terms for PNES was based on consensus among the multidisciplinary group of coauthors. Three search engines were used: PubMed, PsycINFO, and Cochrane. Abstracts published up until January 31, 2017, from the three search engines were reviewed. Abstract inclusion criteria were as follows: The study sample comprised individuals with PNES (with or without a control group), the clinical characteristic or test being analyzed was mentioned, and original research data were presented. Articles on mixed cohorts of individuals with PNES and other subtypes of FND were also included. Abstracts that were not available in English or in full text were excluded. An in-depth review of the selected full-text articles was conducted, and those articles that did not meet the inclusion criteria after full-text review were excluded as a result. In a few instances, the full-text review provided information about other publications that, if not already identified in the original search, were included in the final data extraction. The search method used for prolactin level, as an example, is presented in Figure S1 in the online supplement . Here, we summarize the findings for each clinical factor and test after data extraction and highlight the evidence supporting its use during a clinical assessment.
Results
Relevant articles included in our review are presented in the reference list in the online supplement . Only key articles or summary reviews are referenced in the Results section.
Semiology
By using PNES- and semiology-specific search terms, our search identified 507 abstracts. Data were extracted from 24 articles. All studies had an epileptic seizure (ES) control group, and seven also included a mixed (ES plus PNES) group. Table 1 summarizes the semiological signs that favor a diagnosis of PNES, the number of studies included, and the range of sensitivity and specificity for each sign.
| Suggestions and observations | PNES sign | Number of studies | Sensitivity (%) | Specificity (%) |
|---|---|---|---|---|
| During seizure | ||||
| Measure time | >2 minutes | 8 | 65 | 93 |
| Observe | ||||
| Ictal course | Waxing and waning | 3 | 36–90 | 96–100 |
| Synchrony of the limbs | Asynchronous | 5 | 43–96 | 82–100 |
| Pelvic movements | Pelvic thrusting | 8 | 8–60 | 88–100 |
| Body posture | Arc de cercle | 2 | 6–33 | 98–100 |
| Head movement | Side-to-side movements | 4 | 25–70 | 92–100 |
| Eyes | Eyes closed | 4 | 98–100 | |
| Eyelid fluttering | 5 | 0–19 | 88–100 | |
| Vocalizations | Ictal crying or weeping | 7 | 7–32 | 98–100 |
| Attempt to open the eyes | Forced closure | 1 | 100 | |
| Provide an item to be remembered | Recall is satisfactory | 3 | 50–88 | 94–100 |
| Test for responsiveness | Eye response or other people can alleviate seizure or preserved awareness | 4 | 0–83 | 77–100 |
| After seizure | ||||
| Observe the return of cognitive functions | Rapid recovery; no confusion | 4 | 15–73 | 38–85 |
| Observe breathing | No stertorous breathing | 2 | ||
| Shallow breathing | 1 | 3 | 98 | |
| Long duration of breathing (>94 seconds) | 1 | |||
| Test plantar reflex | No Babinski reflex | |||
| Check for urine loss | No urinary incontinence | 4 | ||
| Check the mouth and oral cavity | No oral ulceration | 1 |
In addition to the signs presented in Table 1, other semiological features are worth discussion. PNESs do not occur from sleep. One study showed that PNES occurred in an apparent behavior of sleep but with awake-pattern EEG (100% specificity, 56% sensitivity) (8). Presence of a teddy bear brought into the EEG unit predicted PNES with 88%−99% specificity and 5%−13% sensitivity (9, 10).
One study used the positive recall test under hypnosis when patients reported amnesia of the seizure. The test consists of inducing hypnosis and asking patients to think back to the last seizure and remember items from it. When subjects were able to reverse the amnesia and recall items from the seizure, the test was considered positive. This study showed 100% specificity for this test in PNES (11).
Although ESs tend to be shorter in duration on average, some ESs can be long and represent status epilepticus. Although pelvic thrusting is more frequent in PNES than ES, according to several studies, this finding has not been consistently replicated (12, 13), and pelvic thrusting can also occur in frontal ES. Despite its high specificity in PNES, an arched back position (arc de cercle) has also been reported in ES (14).
Many semiological signs have been assessed for their specificity in discriminating PNES from ES. The specificities are very high (between 70% and 100%), which indicates that they can be used with confidence in clinical practice. Sensitivity for most signs is low due to variable semiologies across patients with PNES. There is little evidence on their interrater reliability, but most of the signs reported here have been assessed in more than one study and should not be prone to major interrater variability, as they are simple to determine (eyes closed-open, duration of more than 2 minutes, etc.). Avbersek and Sisodiya (15) provided a summary of many of the semiological signs discussed in this review.
vEEG
Our search identified 771 abstracts using PNES and vEEG search terms. Nineteen articles reporting on the utility of vEEG in the assessment of PNES were selected based on our inclusion criteria.
Duration of vEEG.
In routine vEEG, retrospective studies show that routine 20- to 30-minute vEEGs have a low chance of capturing PNES (7%) (16). This chance doubles when recording time increases to 4 hours (16).
For continuous vEEG, in a series of 100 consecutive vEEGs, 77% of PNES patients had an event within 24 hours and 96% within 48 hours (17). A retrospective study (226 vEEG tests of <24 hours) showed that diagnosis could be confirmed in 50% of cases (18). Another retrospective study found a median latency to a first captured event of 7 hours (19). In this last study, 61% had their first PNES within 24 hours, and latency was dependent on semiology type: akinetic (4 hours), major motor (5 hours), minor motor (21 hours), and subjective-experiential (22 hours).
In summary, a routine 20-minute vEEG is generally not sufficient, but a 24- to 48-hour vEEG might be a sufficient time-window, as it will capture an event in 50%–96% of cases (17–19). vEEG may take place in an inpatient or ambulatory setting based on a number of factors such as frequency of events, availability of the test, health insurance coverage, need for antiepileptic drug removal, and need to capture several semiologies, including comorbid PNES and ES.
Interictal abnormalities.
Of 127 PNES patients, 32% had a history of abnormal epileptiform discharges in a previous EEG (20). Most of the abnormalities read as epileptiform were actually wicket spikes, hypnagogic hypersynchrony, hyperventilation-induced slowing, fluctuation of sharply contoured background, and fragmented alpha activity. Another study reported an abnormal EEG in 46% of cases and epileptiform abnormalities in 9% (21). These were regional or generalized sharp waves but not spike and wave complexes. Another cohort reported nonspecific EEG changes in 50% of PNES patients and epileptiform changes in 8% (22).
Induction techniques.
Awaiting spontaneous PNES during vEEG can delay diagnosis; therefore, induction techniques can be added. We found 313 abstracts and reviewed 49 studies that met our inclusion criteria.
Noninvasive induction techniques.
Noninvasive induction techniques that are part of routine EEG activation procedures (hyperventilation and photic stimulation) can be used to induce events. In 16 out of 19 (84%) of PNES patients, noninvasive techniques (hyperventilation-photic stimulation with suggestion that this might induce an event) induced an event in a mean time of 2.4 minutes (range, 0.5–9 minutes) during vEEG (25). This technique also works when coupled with short-term vEEG (1–2 hours): 66% of patients suspected of PNES had an event (26).
A handful of studies used hypnosis or the patch test. The latter consists of a patch soaked in a placebo liquid placed on the patient’s neck. Less frequently reported induction methods included tuning fork, sleep deprivation, head-up tilt test, anesthesia with propofol, compression of temple region, moist swab application, memory/stress, and torchlight stimulation. One study confirmed that noninvasive techniques have a similar yield to placebo injection: 84% (N=27/34 patients) versus 65% (N=13/20 patients), respectively (27).
Verbal suggestion (VS) alone or before or during the induction technique has been studied. Two studies showed that adding a VS before photic stimulation and hyperventilation induced more events during the activation procedure: 10%–33% in the no-VS group versus 38%–67% in the VS group had an event (28, 29). Noninvasive techniques suggested as potential triggers led to event capture in 38%–84% of cases (25, 28), which is equivalent to placebo injection.
A possible risk of induction is the appearance of an atypical event, which could contribute to misdiagnosis or questioning. When possible, an individual familiar with the patient should be present to corroborate similarity of the captured event to usually observed ones.
Multiple studies on induction reported near 100% specificity, high predictive value for diagnosis of PNES, and low to moderate sensitivity. Induction of PNES within vEEG monitoring using conventional activation procedures (hyperventilation and photic stimulation) has sufficient evidence to be used as part of the routine evaluation of suspected PNES patients. It is argued that the deception involved in placebo techniques can be ethically questionable given the possibility of rupturing provider-patient trust, negatively affecting treatment adherence, or compromising the patient’s autonomy (30). However, the importance of a definitive and timely diagnosis of PNES is argued to be most important by those with opposing viewpoints, as it reduces harm due to misdiagnosis and lack of treatment (31).
Summary.
In conclusion, long-term vEEG (72 hours) is the gold-standard test for diagnosis of PNES. A shorter version (<24 hours) coupled with activation procedures (photic stimulation and hyperventilation) might be feasible and less costly. Induction techniques do not necessarily have to involve placebo injection, which is invasive and raises ethical concerns. Interictal abnormalities are not rare (up to 50%), and even epileptiform discharges (up to 9%) can be seen in PNES, but should not lead to overinterpretation of a diagnosis of epilepsy. History and semiology should always contextualize clinical correlation with EEG.
Psychiatric Comorbidities
Our search identified 799 abstracts using PNES and psychiatric comorbidities search terms. Data were extracted from 76 articles after full-text review.
The diagnostic methods and prevalence findings for each psychiatric diagnosis are specified in Table 2. Prevalence of psychiatric comorbid conditions in PNES ranges between 53% and 100% (32, 33), with a median of three psychiatric diagnoses, versus one in ES (34). When comparing prevalence of specific psychiatric diagnoses in PNES against ES, findings showed no significant and reliable difference between the two groups in most studies for mood, psychotic, and eating disorders. For instance, eight studies that compared PNES to epilepsy found a higher prevalence of depressive disorders in PNES, but only one found the difference to be statistically significant (35). On the other hand, two studies found the frequency of depression to be higher in epilepsy compared with PNES (36, 37). One study showed psychotic disorders to be more common in PNES than in epilepsy (36), but another study showed psychosis as more frequent in epilepsy (38). Finally, when compared with epilepsy, rates of eating disorders were nonsignificantly higher in PNES based on one study (38) and nonsignificantly higher in epilepsy in another study (36). Therefore, the presence of depressive, psychotic, and eating disorders does not reliably favor a diagnosis of PNES, based on studies with limited samples and inconsistent results.
| Comorbidity and diagnostic measures | Population studied | Range of prevalence in PNES | Predictive value for PNES |
|---|---|---|---|
| Mood disorders (N=39) | |||
| Clinical interview (N=16), SCID-I (N=14), medical chart review (N=4), PHQ–9 cutoff score (N=1), CIDI (N=1), MINI (N=1), K-SADS-PL (N=1) | Control subjects included healthy individuals, patients with epilepsy, outpatient psychiatric subjects, and other FND subjects. Studies with pediatric patients (N=7). Study with post-TBI patient (N=1). | Depression (MDD or depression or depressive disorders): 0%–78% (N=31), dysthymic disorder: 7%–22%(N=5), mood disorder: 13%–85% (N=11), bipolar disorder: 2%–10% (N=4) | Mood disorder did not predict PNES. |
| Anxiety disorders (N=35) | |||
| Clinical interview (N=14), SCID-I (N=13), medical chart review (N=1), GAD–7 cut-off score (N=1), Revised Children’s Manifest Anxiety Scale cut-off score (N=1), K-SADS-PL (N=1), CIDI (N=1), MINI (N=1) | Control subjects included healthy individuals, patients with epilepsy, outpatient psychiatric subjects, and other FND subjects. Studies with pediatric patients (N=6). Study with post-TBI patient (N=1). | Anxiety (not specified in study): 4.5%–82.6% (N=24), GAD: 4.5%–30% (N=9), panic disorder: 2%–70% (N=9), OCD: 3.27%–9% (N=4), agoraphobia: 58% (N=1), specific/simple phobia: 2.9%–9% (N=4), social phobia/ social anxiety disorder: 2.9%–5.7% (N=2), separation anxiety: 2.9%–24% (N=2), school phobia (in pediatric study): 36.4% (N=1) | Anxiety disorders predicted PNES versus epilepsy (N=1). |
| Lifetime trauma (N=38) | |||
| Clinical interview (N=16), SCID (N=12), medical chart review (N=1), MINI (N=1), Trauma Questionnaire (N=7), unspecified (N=1) | Control subjects included healthy individuals, patients with epilepsy, and other FND subjects. Studies with pediatric patients (N=3). | Lifetime trauma: 9%–100% (N=25), lifetime sexual abuse: 0%–77% (N=24), lifetime physical abuse: 13.33%–78.8% (N=17), emotional/psychological abuse: 24.6%–60.6% (N=10), emotional neglect: 30%–42% (N=2), loss/bereavement: 18.7%–56.7% (N=3), combat-related trauma: 19% (N=1), medical trauma: range 8.3%–19.7% (N=2) | Trauma history was significantly related to a diagnosis of PNES: sensitivity 67%, specificity 86% (N=1). |
| PTSD (N=24) | |||
| Clinical interview (N=12), SCID-I (N=8), medical chart review (N=2), questionnaire (N=1), MINI (N=1) | Control subjects included healthy individuals and patients with epilepsy. Studies with pediatric patients (N=3). Study with post-TBI patient (N=1). | 7%–100% (N=24) | PPV was 85%, sensitivity was 58%, and specificity was 87% toward PNES (N=1). |
| Substance use disorders (N=18) | |||
| Clinical interview (N=7), SCID-I (N=5), medical chart review (N=4), questionnaire (N=1), K-SADS-PL (N=1) | Study with pediatric patients (N=1). | 0%–63.3% (N=18) | None reported. |
| Eating disorders (N=6) | |||
| Clinical interview (N=3), SCID-I (N=3) | 0%–39% (N=6) | None reported. | |
| Dissociative disorders (N=13) | |||
| Clinical interview (N=6), SCID-I (N=6), medical chart review (N=1) | Control subjects included healthy individuals, patients with epilepsy, outpatient psychiatric subjects, and other FND subjects. Study with pediatric patients (N=1). | 3%–91% (N=13); Psychogenic/dissociative amnesia: 3%–10% (N=2), psychogenic fugue: 0%–3.3% (N=2), depersonalization disorder: 0% (N=1), dissociative identity disorder: 22% (N=1), dissociative disorder NOS: 36.7%–59% (N=2) | None reported. |
| Psychotic disorders (N=14) | |||
| Clinical interview (N=6), SCID-I (7), medical chart review (N=1) | Studies with pediatric patients (N=3). | 2%–15% (N=14) | None reported. |
| Personality disorders (N=23) | |||
| Clinical interview (N=8), SCID-II (N=12), Questionnaire (N=3), medical chart review (N=1), unspecified (N=1) | Control subjects included healthy individuals, patients with epilepsy, outpatient psychiatric patients, and other FND subjects. Study with post-TBI patients (N=1). | Any personality disorder: 32%–83% (N=10), cluster A personality disorders: 0%–23% (N=5), cluster B personality disorders: 11%–69% (N=7): histrionic: 3%–62.5% (N=8); borderline: 4.5%–55% (N=13); narcissistic: 3% (N=1); antisocial: 2.2%–18% (N=6), cluster C personality disorders: 6%–37.1% (N=6): dependent: 6%–25% (2); avoidance: 0%–4.7% (N=2); obsessive-compulsive: 0%–16% (N=3) | Personality disorder had a 71.4% sensitivity and 74.3% specificity toward PNES diagnosis (N=1). |
a
Data for the number of studies are presented. CIDI=Composite International Diagnostic Interview; FND=functional neurological disorder; GAD–7=seven-item General Anxiety Disorder scale; K-SADS-PL=Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime; MDD=major depressive disorder; MINI=Mini-International Neuropsychiatric Interview; NOS=not otherwise specified; OCD=obsessive-compulsive disorder; PHQ–9=nine-item Patient Health Questionnaire; PPV=positive predictive value; PTSD=posttraumatic stress disorder; SCID=Structured Clinical Interview for DSM; TBI=traumatic brain injury.
Anxiety disorders, substance use disorders, posttraumatic stress disorder (PTSD), and cluster A or B personality disorders more consistently have higher prevalence rates in PNES compared with epilepsy, often (but not always) with a statistically significant difference. Prior exposure to traumatic experiences (aside from PTSD diagnosis) is also more common in PNES or mixed (PNES plus ES) patients compared with patients with ES (32). Characteristics described as the interictal personality of temporal lobe epilepsy were similarly elevated in patients with ES and those with PNES (39); therefore, this syndrome does not appear to be specific to ES.
History of all types of trauma combined and nonsexual trauma (7) and borderline personality disorder (40) are more frequent in PNES than other types of FND. Another study found that patients with PNES more frequently reported history of suicidality, psychiatric hospitalizations, and current PTSD compared with other FND motor phenotypes, but these predictors did not persist after a multivariate logistic regression analysis (41).
Besides comparative prevalence studies, some investigations specifically determined the predictive value of selected comorbid psychiatric conditions. In one study that compared 50 veterans with PNES to 37 with ES, PTSD conveyed a positive predictive value (PPV) of 85%, sensitivity of 58%, and specificity of 87% toward a PNES diagnosis (34). The presence of a personality disorder conferred 71.4% sensitivity and 74.3% specificity toward a PNES diagnosis in a study that included PNES, ES, and healthy subjects (42). Past traumatic experiences conferred a sensitivity of 67% and specificity of 86% toward a PNES diagnosis (43). Schramke et al. (35) performed a logistic regression analysis based on a retrospective chart review and established that a psychiatric diagnosis other than depression (including anxiety disorders and other diagnoses) is one the predictors of PNES group membership (versus ES), besides other clinical variables.
Although most studies have compared psychiatric diagnoses in PNES to epilepsy, the few studies that have compared psychiatric diagnoses in PNES to healthy control subjects have found exposure to traumatic experiences, PTSD, and personality disorders to be the most distinguishing features for PNES (44, 45).
Certain comorbidities may inform the choice or timing of treatment and prognosis. For example, prolonged exposure has been found to be an effective treatment in an uncontrolled trial for patients with comorbid PNES and PTSD (46). Presence of active substance use that can affect participation in therapy may require the clinician to prioritize substance use treatment before initiation of an evidence-based protocol. In terms of prognosis, higher degree of psychopathology is associated with poor outcomes in PNES (47).
Although the diagnosis of PNES cannot be established solely on the basis of any specific coexisting psychiatric disorder, some comorbidities—such as history of traumatic experiences, a diagnosis of PTSD and personality disorder—increase the likelihood of a diagnosis of PNES in individuals with seizure complaints. These findings are limited to a few isolated studies and therefore should be considered in the context of other clinical features. The screening of psychiatric comorbidities is a necessary step in all comprehensive neuropsychiatric evaluations of patients with suspected PNES, as their presence needs to be accounted for when formulating a treatment plan. However, psychiatric comorbidities are frequent in epilepsy, and clinicians should never assume a PNES diagnosis solely based on the presence of PTSD or personality disorders. Currently, there is limited evidence to support a differentiation of PNES from other FND phenotypes based on the presence of psychiatric comorbid diagnoses.
Medical Comorbidities
Our search identified 4,362 abstracts using PNES and known medical comorbidity search terms. After applying our inclusion and exclusion criteria, 62 full-text articles were included in the final data extraction. Some studies covered several comorbidities. Results from our findings are summarized in Table 3.
| Comorbidity and diagnostic measure | Population/sample studied | Range of prevalence (mean or %) | Predictive value for PNES |
|---|---|---|---|
| Asthma or allergies (N=6) | |||
| Self-report or multiple sources (N=4), psychiatric interview (N=2) | Epilepsy control subjects (N=4), Healthy control subjects (N=1), Psychosis control subjects (N=1) | Mean range of 1.3–1.93 allergies in PNES patients versus 0.7–1.0 in ES patients (N=2). | Statistically significant higher number of allergies in PNES patients versus all control groups (N=3). Odds ratio was 1.38–2.94 toward PNES for patients with asthma or polyallergy versus ES (N=2); odds ratio was 6.49 toward PNES for patients with 12 or more allergies (N=1). |
| Asthma: 29.8% in PNES versus 8.6% in psychosis (N=1). | |||
| Each additional allergy increased the likelihood of PNES by 2.98% (N=1). | |||
| Epilepsy (ES; N=23) | |||
| vEEG (N=22) | Epilepsy sample (N=12), | 3.4%–29% of ES adult samples (N=9). | None reported. |
| PNES sample (N=11), | 9.4%–50% of PNES adult samples (N=9). | ||
| Pediatric patients (N=4), | 40%–90.5% of PNES pediatric samples (N=2). 8.1%–24% of ES pediatric samples (N=2). | ||
| Vasovagal syncope (N=1) | |||
| Tilt table test (N=1) | 50% of psychogenic pseudosyncope patients with comorbid vasovagal syncope (N=1). | ||
| Learning disability (N=9) | |||
| Cognitive or educational assessment (N=4), medical records (N=2), psychiatric interview (N=1), genetic (N=2) | Pediatric epilepsy unit patients (N=2), Patients with learning disabilities (N=7) | 7.5%–68.4% (N=9). | None reported. |
| Chronic pain and fatigue (N=10) | |||
| Multiple sources, including self-report and medical records (N=10) | Epilepsy control subjects (N=5), Healthy control subjects (N=2), | 74%–75% of fibromyalgia patients with paroxysmal events had PNES (N=2). 8.8% of PNES patients had chronic pain or fibromyalgia (N=1). 67%–77% of PNES patients reported chronic pain (N=2). 61% of PNES patients reported headache (N=1). | Odds ratio was 2.25 toward PNES for patients with chronic pain versus ES (N=1). PNES patients were significantly more likely to report chronic pain, headaches, and migraines than ES patients and healthy control subjects (N=4). PPV for PNES of 76.9% for use of prescribed pain medication versus ES (N=1). PPV for PNES of 75% in chronic pain/fibromyalgia patients with intractable seizures versus ES (N=2). |
| Neurosurgery (N=5) | |||
| Medical records (N=5) | Epilepsy surgery patients (N=2), Mixed diagnosis neurosurgery patients (N=3) | De novo PNES following neurosurgery was 3.5%–4.6% (N=5). | Odds ratio was 2.89 toward PNES for patients with an IQ <80, following epilepsy surgery (N=1). |
| Traumatic head injury (N=11) | |||
| Multiple sources (N=10), MINI (N=1) | Healthy control subjects (N=2), Control subjects with mental health disorders (N=2), Veterans (N=2), Pediatric patients (N=1) | 24%–83% of PNES patients had a history of TBI (N=7). 49.3%–91% of PNES patients had mild TBI (N=4); 44% of pediatric PNES patients had TBI (N=1). 24%–58% of PNES was attributed directly to TBI (N=4). 33%–44% of war veterans with TBI were diagnosed with PNES (N=2). Statistically significant higher number of reported TBI for PNES patients compared with ES control subjects, as well as brain injury being classified as mild (N=2). | Odds ratio was 1.91–2.4 toward PNES for patients with TBI versus ES (N=2)b |
| Somatoform or MUS (N=5) | |||
| Multiple sources (N=5) | Functional weakness patients (N=1), Epilepsy control subjects (N=1), Patients who had a cryptogenic drop attack (N=1) | 7% of functional weakness patients had PNES (N=1). 28% of patients with cryptogenic drop attacks had PNES (N=1). 57.4%–70.8% of PNES patients had other MUS at diagnosis (N=2). | One disorder of interest (asthma, pain, MUS) gave a positive predictive value of 75.5% toward PNES. Increasing the number of disorders increased specificity at the expense of sensitivity versus ES control subjects (N=1). |
a
Data for the number of studies are presented. ES=epileptic seizures; MINI=Mini-International Neuropsychiatric Interview; MUS=medically unexplained symptoms; PPV=positive predictive value; TBI=traumatic brain injury; vEEG=video EEG.
b
A history of TBI increases the chances of PNES versus ES.
Obesity is a significant comorbidity in PNES compared with ES (body mass index of 30.5 versus 26.1, respectively; more than double the number of PNES patients were classified as obese) (48).
There are significant associations between PNES and asthma and allergies; the likelihood of a PNES diagnosis increased by 2.98% with each reported allergy, and the average number of reported allergies was significantly higher in PNES compared with ES (49).
Comorbid rates of ES and PNES (each in PNES and ES samples, respectively) in adult and pediatric populations are summarized in Table 3. One study of 46 patients with a mixed diagnosis (PNES plus ES) found that epilepsy antedated PNES in 70% of mixed cases and had simultaneous onset in 27.5% of cases (50). Walsh et al. (51) reported that 1% of patients referred to a syncope unit (for assessment of unexplained blackouts) were diagnosed with psychogenic pseudosyncope; 50% of these patients (N=7) had comorbid vasovagal syncope. Coexisting learning disability (LD) was difficult to specify, due to the mix of LD, adult, pediatric, and developmental disorders, with a wide range of LD comorbidity rates across studies.
De novo PNES following neurosurgery has been found in 3.5%–4.6% of postsurgical patients (52, 53), with an odds ratio of 2.89 for patients with an IQ below 80 after epilepsy surgery (52).
Traumatic brain injury (TBI) has a strong association with PNES. Between 24% and 83% of PNES patients report a history of TBI, most frequently classified as mild, and many times labeled by patients as the etiological factor for PNES (54, 55). Moderate and severe TBIs are well-established risk factors for posttraumatic epilepsy. One study that specifically examined the development of seizure episodes after moderate and severe TBIs found that one-third of patients had PNES versus two-thirds with epilepsy (56). Patients with moderate and severe TBI may be particularly at risk of misdiagnosis and deserve an unbiased examination of their seizure episodes, including consideration of a mixed (PNES plus ES) diagnosis.
Between 57.4% and 70.8% of PNES patients have other medically unexplained symptoms (MUS) at the time of the PNES diagnosis (5, 57). Many of these MUSs may represent other functional syndromes. Chronic pain and fatigue symptoms are common in PNES, with reported chronic pain providing an odds ratio of 2.25 toward PNES diagnosis (58). Use of prescribed pain medication conferred a PPV of 76.9% and historical fibromyalgia diagnosis a PPV of 75% toward a PNES diagnosis (59, 60). Having one disorder of interest (i.e., associated comorbidities such as asthma, pain, MUS) conferred a PPV of 75.5%, whereas increasing the number of disorders increased specificity (at the expense of sensitivity) (61).
Seizure complaints in the context of other functional disorders raise suspicion for PNES. However, the functional etiology of other symptoms may not always be previously known. An unbiased evaluation of other physical complaints, including a history and elemental neurological examination when indicated, can help establish their functional nature, thereby increasing confidence in the PNES diagnosis as it relates to the seizure complaint. A review of neurological signs suggestive of FND is beyond the scope of this manuscript, and readers are referred to other literature on the topic (62).
The reviewed evidence suggests a strong association between PNES and various medical comorbidities. The positive identification of comorbidities during an initial assessment could be used to increase the confidence in a diagnosis of PNES, as well as to inform overlapping theoretical model and treatment strategies.
Psychological Traits
Patients with PNES share certain psychological traits and vulnerabilities. We identified abstracts discussing psychological traits associated with PNES (alexithymia: 29; emotion dysregulation: 12; avoidance: 36; linguistic characteristics: 19; theory of mind: 1; somatization: 71; dissociation: 203). Table 4 summarizes our findings and specifies the final number of studies from which data were extracted for each psychological trait.
| Trait and diagnostic measure | Population studied | Range of prevalence in PNES | Conclusions and predictive value |
|---|---|---|---|
| Alexithymia (N=10) | |||
| Toronto Alexithymia Scale (N=9), Pediatric Alexithymia Scale (N=1) | Control subjects included healthy individuals, patients with epilepsy, and functional motor symptoms. Study with pediatric patients (N=1). | 30.0%–90.5% (N=8) | Scores were elevated in PNES versus healthy control subjects (N=4). PNES versus epilepsy could not be distinguished (N=6). Scores were significantly associated with PNES versus epilepsy (N=1). |
| Emotion dysregulation (N=10) | |||
| Difficulties in Emotion Regulation Scale (N=4), Dimensional Assessment of Personality Pathology–Basic Questionnaire (N=2), EPS–25 (N=1), Semistructured interview (N=1) | Control subjects included healthy individuals, patients with epilepsy, organic neurological disorders (including multiple sclerosis, epilepsy, myasthenia gravis and Guillain-Barre syndrome), and trauma-exposed seizure-free individuals with either low or high PTSD levels. | Not reported | Scores were elevated among PNES patients compared with healthy control subjects (N=4). Scores were elevated among PNES patients compared with epilepsy patients and patients with other organic neurological disorders (N=3). Scores for PNES patients were similar to those for individuals with high PTSD levels (N=2). |
| Avoidance tendencies (N=9) | |||
| Ways of Coping–Escape-Avoidance subscale (N=3), Approach Avoidance task (N=1), Multidimensional Experiential Avoidance Questionnaire (N=1), Fear Questionnaire (N=1), Attachment Style Questionnaire (N=1), EPS–25 (N=1) | Control subjects included healthy individuals and patients with epilepsy. Study with mixed age group (≥14 years old) (N=1). | Not reported | Avoidance scores were higher for PNES patients compared with healthy control subjects (N=5). Avoidance scores were higher for PNES patients compared with epilepsy patients (N=2). n A regression model using somatic symptoms and experiential avoidance could predict individuals with PNES versus epilepsy with 84.0% sensitivity and 83.3% specificity (N=1). |
| Linguistic characteristics (N=7) | |||
| CA (N=4), CA with diagnostic scoring aid (N=3) | Control subjects included patients with epilepsy. Study with pediatric patients (N=1). | Not reported | Linguistics correctly predicted >80% of the eventual v-EEG-supported diagnoses (N=4). |
| Somatization (N=9) | |||
| Screening for Somatoform Disorders–2 (N=3), Minnesota Multiphasic Personality Inventory (N=2), Somatoform Dissociation Questionnaire (N=1), Patient Health Questionnaire–15 (N=1), Childhood Somatization Inventory (N=1), Self-reported allergies (N=1) | Control subjects included healthy subjects, patients with epilepsy, and mixed PNES plus epilepsy. Study with pediatric patients (N=1). Study with mixed age group (≥16 years old) (N=1). | 60.0%–63.4% (N=2) | Scores were elevated among PNES patients compared with healthy subjects (N=3). Scores were higher for PNES patients compared with patients with epilepsy (N=8). A regression model using somatization, dissociation, and general psychopathology scores identified patients with PNES with a sensitivity of 79.4%; somatization score was the strongest single differentiating factor in this model (N=1). |
| Dissociation (N=26) | |||
| Dissociative Experiences Scale (N=13) | Control subjects included healthy individuals, patients with epilepsy, and mixed PNES plus epilepsy. | Not reported | Dissociation scores were higher for PNES patients compared with patients with epilepsy (N=8) or with no difference (N=5)b |
a
Data for the number of studies are presented. CA=conversation analysis; EPS–25=25-item Emotional Processing Scale. PTSD=posttraumatic stress disorder; vEEG=video EEG.
b
Dissociation scores were higher or no different for PNES patients compared with patients with epilepsy.
Two pervasive traits described in PNES are alexithymia and emotion dysregulation. Alexithymia refers to the inability to identify and describe emotion in oneself. Emotion dysregulation refers to the difficulty of adaptively managing and responding to one’s emotions. Alexithymia and emotion dysregulation both score higher in patients with PNES than in healthy control subjects. However, score comparisons between PNES and ES show mixed results (63–65). These findings may be explained by the presence of two described subtypes of PNES subjects: one characterized by higher levels of psychopathology, somatization, alexithymia, and emotion dysregulation when compared with patients with ES, and the other with comparatively normal levels of alexithymia and emotion regulation (63). Another level of heterogeneity within PNES is demonstrated by patients’ emotion regulation profiles: the underregulator subtype is characterized by emotional reactivity, poor arousal tolerance, and difficulty controlling affect; the overregulator subtype is characterized by emotional avoidance, excessively controlled behavior, and a tendency toward somatization (66).
Avoidance behaviors (physical or introspective) can represent a coping strategy to deal with conflict or stress. Patients with PNES show increased avoidance tendencies to social threat cues and employ escape-avoidance and distancing coping strategies more than healthy and ES control subjects (67). Experiential avoidance, an attempt to suppress unwanted internal experiences, also differentiates PNES from ES (68).
Theory of mind is the ability to infer and attribute mental states, a trait in which PNES patients have shown deficits compared with healthy control subjects, according to one study (69). Subjects with PNES describe their seizure experiences qualitatively differently from those with ES. Patients with ES tend to voluntarily provide details about subjective seizure symptoms, whereas those with PNES focus on circumstances around their seizures, exhibit focusing resistance (difficulty in focusing on seizure descriptions), rarely make reference to attempted seizure suppression, describe events in terms of what did not happen, and report reduced ictal consciousness. Making use of distinct conversation profiles through conversation analysis (a qualitative assessment of the conversation and interaction between the patient and the clinician) has been shown to reliably differentiate subjects with PNES and ES in both adults and children (70).
Somatization is characterized by the tendency to communicate and experience psychological distress in the form of physical symptoms with no discernible physical cause; it is considered a defense mechanism (71). Somatization is common in PNES (72) and can help differentiate PNES from ES (63), PTSD (73), and insomnia (74). Reuber et al. (75) found that somatization scores were the strongest single differentiating factor in a model that also included dissociation and general psychopathology scores and identified patients with PNES with a sensitivity of 79.4%.
Dissociative tendencies are highly prevalent in PNES. Most studies using the Dissociative Experiences Scale showed significantly higher dissociative scores in PNES compared with ES (76), although in a few studies the difference was nonsignificant (77).
Dimaro et al. (68) demonstrated that a model using somatic symptoms and experiential avoidance could predict diagnosis by correctly classifying patients with PNES or ES with 84.0% sensitivity and 83.3% specificity. From a prognostic standpoint, higher scores in measures of dissociation and more somatic complaints are associated with poor outcomes in PNES (47).
There are no guidelines supporting the clinical use of psychometric measures to identify specific traits of interest in patients with PNES. However, clinical identification of the most distinguishing psychological traits, such as avoidance, somatization, and dissociation, may provide supporting information during an assessment and may inform therapeutic targets.
Psychometric and Neuropsychological Measures
The Minnesota Multiphasic Personality Inventory 2 (MMPI-2) and the Personality Assessment Inventory (PAI) have been widely used to assess personality profiles in patients with PNES. Our search identified 63 abstracts using PNES and MMPI search terms. Data were extracted from 40 studies using MMPI or MMPI-2. In 2008 a new version of the MMPI-2, the MMPI-2 Restructured Form (MMPI-2-RF), was published; to date, nine studies using the MMPI-2-RF in PNES have been published. Both the MMPI and the MMPI-2 were found to be useful in differentiating between PNES and ES in the vast majority of studies. Use of the MMPI-2-RF scales did not show significant improvement over the original clinical scales. The presence of a “conversion V” profile (elevations in the Hypochondriasis and Hysteria scales relative to the Depression scale) allowed correct classification of PNES versus ES in about 70%−75% of cases (66). Our search also identified 10 abstracts using PNES and PAI search terms. Data were extracted from seven articles. Available information suggests that the PAI yields similar results to the MMPI versions, with the advantage that the PAI is less time-consuming to administer. Based on available data, personality testing such as the MMPI-2, MMPI-2-RF, and PAI should be considered during the assessment of patients with suspected PNES, with certain personality profiles favoring diagnosis of PNES.
Our search identified 347 abstracts using PNES and cognitive search terms. Data were extracted from 51 articles. By 2002, there were 23 published studies on cognitive function in patients with PNES (78), which were incorporated into a subsequent review in 2010 (79). The results of the few studies published since 2010 mainly replicate previous findings that show a lack of significant differences on neuropsychological testing performance between patients with PNES and ES. When differences were found, they tended to favor (better performance) patients with PNES; however, such differences were limited in magnitude and inconsistent across cognitive domains and were never clearly able to reliably predict group placement (PNES or ES) for individual patients (66). Therefore, cognitive testing as currently applied does not reliably differentiate patients with PNES from patients with ES. Specifically, the results of the studies published to date do not confirm the hypothesis that patients with ES perform more poorly on neuropsychological tests than patients with PNES. When differences are detected, they have to be interpreted by taking into consideration other factors, including the influence of associated psychological states and performance validity measures (which determine whether patients are sufficiently engaged in cognitive testing so that results reflect their actual ability level). Performance validity failure rates in PNES tend to be higher (at 28% or greater) compared with epilepsy (8%) and the general medical population (8%) (66).
Cognitive impairment has been documented in patients with PNES with a profile that shows general dysfunction across multiple domains. There is no consistent pattern of focal impairment in PNES, as opposed to focal epilepsy. Many factors are thought to affect cognition in PNES, including medication effects, motivation, hypo- or hypervigilant states, dissociation, sleep disturbance, chronic pain, and mood and anxiety disorders (80).
The assessment of subjective phenomena, including ictal consciousness, is a promising area of clinical research. Our search identified 125 abstracts using PNES and consciousness search terms. Data were extracted from three articles that discussed measurement instruments of consciousness. The Ictal Consciousness Inventory (ICI) is a self-report psychometric instrument developed to measure ictal consciousness along the dimensions of level (responsiveness) and content (subjective experiences) of consciousness. Ali et al. (81) found that patients with PNES had significantly higher ICI scores than patients with ES, in terms of both general awareness and responsiveness and vividness of subjective experiences. Although it is a promising additional tool, more studies are necessary to incorporate the ICI into routine clinical evaluations.
Clinical Neuroimaging
For clinical neuroimaging, 433 abstracts were identified. From those, 47 articles had relevant clinical neuroimaging data that were reviewed and from which data were extracted. Several studies reported on CT or MRI brain findings in the evaluation of PNES. Abnormalities in PNES generally ranged from 0% to 50% (13, 82). Rates of MRI abnormalities have been compared between PNES and ES, and some studies described prevalent abnormalities. In PNES, the frequency of MRI abnormalities ranges from 0% to 40%, with nonspecific white matter lesions being common (83, 84). Rates of abnormal scans in patients with mixed presentations (PNES plus ES) are higher at 39.1%−77.9% (83, 85). At the cohort level, patients with PNES consistently showed lower abnormality rates compared with ES, although the frequency of MRI abnormalities may not differ between mixed (PNES plus ES) and ES. The presence of focal MRI abnormalities is not indicative for ES versus PNES, as patients with PNES may display mesial temporal sclerosis (86) and other focal cortical lesions (87). Rates of neuroimaging abnormalities in children with PNES may be lower than adults (88).
Single photon emission tomography (SPECT) is an adjunctive test for PNES. Although patients with PNES in small case series show largely normal interictal and postictal SPECT scans, abnormalities in both acquisitions have been reported (89). However, those with PNES generally exhibited unchanged postictal versus interictal SPECT scans (90).
Hypometabolism on interictal fluorodeoxyglucose positron emission tomography (FDG-PET) can be found in some patients with PNES but is generally more commonly identified in those with temporal and frontal lobe ES. One study reported that a normal FDG-PET had a sensitivity, specificity, and diagnostic accuracy for PNES versus ES of more than 90% (91). Data-driven machine learning approaches are promising but require more research.
In summary, neuroimaging abnormalities including nonspecific white matter lesions and multifocal pathology are common in PNES; however, the presence of focal abnormalities should raise suspicion for ES. In cases where vEEG does not provide definitive diagnosis, adjunct tests including postictal versus interictal SPECT or interictal FDG-PET can be considered in the appropriate clinical context.
Autonomic Nervous System Function
We identified 136 abstracts and data were extracted from 20 original articles. In the majority of studies, ES showed greater autonomic nervous system change than PNES in all stages of a seizure (92, 93). Heart rate was used to assess for autonomic change in most studies. There was evidence of change in ictal and postictal heart rate in individuals with ES, whereas PNES patients had a lower baseline heart rate and little to no change during similar periods. Heart rate change in patients with PNES resolved quickly following the seizure event, whereas patients with ES took longer to recover (93, 94). It is suggested that ESs generate a higher absolute sympathetic tone than PNES (95).
Other studies report changes in autonomic response associated with PNES. In one study, patients with PNES experienced preictal increase in sympathetic functioning followed by an increase in parasympathetic functioning during ictal and postictal phases (96). Another study reported that PNES patients showed a lower resting vagal tone, lower parasympathetic tone, and higher sympathetic tone than healthy control subjects (97). A small number of studies reported no differences in autonomic change between ES and PNES (97, 98). One study reported PNES patients experienced more autonomic symptoms during their seizures than patients with a mixed (PNES plus ES) diagnosis (36).
PNESs are associated with psychological comorbidities, such as PTSD and borderline personality disorder, that may affect autonomic nervous system biomarkers for PNES. Ictal motor activity, which differs between patients, may also influence heart rate variability. Such variability limits the use of autonomic nervous system responses as a reliable measurement to differentiate between ES and PNES.
Prolactin Level
Seventy-eight abstracts mentioned PNES and prolactin level. Seventeen original articles were included in the final data extraction.
Prolactin levels consistently show a rise in concentration following generalized ES and are a fairly reliable marker of ES (99). Increase in prolactin levels mostly occurs after generalized seizures and to a lesser extent after other types of ES (100). The prolactin level tends to remain unchanged following PNES (99–102). Evidence shows that prolactin levels’ predictive value for PNES is modest and has at times yielded both false positives and false negatives (103, 104). Results differ as to the reliability and predictive power of the prolactin level to differentiate between ES and PNES. Seven studies reported that prolactin levels help discriminate between seizures, five produced varied results, and six reported that the predictive nature of prolactin levels was unreliable.
In summary, the majority of studies reported that the prolactin level is helpful in differentiating between generalized ES and PNES but is lacking in reliability for diagnosis of PNES. Consistent with the recommendations by the American Academy of Neurology (105), the prolactin level may be helpful as an adjunct to current diagnostic methods but is not recommended on its own. Studies varied in their method of prolactin level measurement, number of repeated measurements, and threshold for a significant increase of prolactin levels.
Differential Diagnoses
Our search identified 239 abstracts discussing differential diagnosis in PNES. Fourteen full-text articles were reviewed for inclusion. These articles highlighted the negative implications of a delayed diagnosis, as summarized in the introduction. Medical conditions that should be considered in the differential diagnosis of PNES are summarized in Table S1 in the online supplement .
Diagnostic Formulation
We identified 64 abstracts and examined 16 articles on diagnostic formulation in PNES. The biopsychosocial formulation is the conceptualization model most commonly used for PNES; it accounts for heterogeneous presentations across individuals and across the lifespan and can fit a variety of etiological frameworks, which therefore informs treatment.
The biopsychosocial formulation states that an accumulation of individual predisposing, precipitating, and perpetuating factors leads to PNES. These factors are involved in the development of a reflexive way of maladaptive coping occurring in the context of past traumatic experiences, current life challenges, and physical health issues. Cultural influences and stressors also affect presentations of PNES. An example of a biopsychosocial/predisposing, precipitating, and perpetuating formulation is presented in Table S2 in the online supplement .
Conclusions
This systematic review identifies the clinical factors and diagnostic tests that provide the most helpful information to establish the diagnosis of PNES. Clinicians can use these findings to prioritize their clinical assessments in patients with suspected PNES. Although many of these findings may extrapolate to patients with other phenotypes of FND, our systematic review was specifically focused on PNES.
During an initial assessment, inquiry about and (ideally) observation of the seizure semiology retains the highest yield. Other physiological paroxysms should be considered in the differential diagnosis based on clinical presentation. Although not required for the diagnosis of PNES, other clinical factors can lend support and inform the diagnostic formulation. In no particular order, the clinical factors with the highest yield are history of traumatic experiences, PTSD, personality disorders, and medical comorbidities (especially multiple allergies, pain syndromes, history of mild TBI, neurological history, and other functional and somatic symptom disorders). Clinical determination of certain psychological traits, especially avoidance, somatization, and dissociation (based on clinical observation and ideally psychometric measures) may also help support the diagnosis of PNES. Presence and severity of many of these psychiatric and medical comorbidities and psychological traits need to be considered when developing a treatment plan.
In terms of diagnostic tests, electrophysiological evaluations, specifically vEEG capturing the typical episode, establish the highest level of diagnostic certainty. In ambiguous cases where the gold standard could not be obtained, adjunctive tests can be considered. Induction techniques using conventional activation procedures, especially during vEEG, postictal prolactin level, and standardized personality assessments can provide supportive information toward a diagnosis of PNES. These results should always be carefully contextualized. Overinterpretation of EEG abnormalities that do not match the described semiology can carry a risk of misdiagnosis.
The clinical evaluation of patients with suspected PNES primarily relies on a comprehensive neuropsychiatric assessment. When the diagnosis cannot be established at the highest level of certainty, additional information from clinical factors and certain diagnostic tests can increase the level of confidence in the diagnosis.
Footnote
Drs. Baslet and Bajestan share first authorship of this study.
Supplementary Material
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References
1.
LaFrance WC Jr, Baker GA, Duncan R, et al: Minimum requirements for the diagnosis of psychogenic nonepileptic seizures: a staged approach: a report from the International League Against Epilepsy Nonepileptic Seizures Task Force. Epilepsia 2013; 54:2005–2018
2.
American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, 5th ed. Washington, DC, American Psychiatric Publishing, 2013
3.
Benbadis SR: Nonepileptic behavioral disorders: diagnosis and treatment. Continuum (Minneap Minn) 2013; 19(3 Epilepsy):715–729
4.
Oto M, Espie C, Pelosi A, et al: The safety of antiepileptic drug withdrawal in patients with non-epileptic seizures. J Neurol Neurosurg Psychiatry 2005; 76:1682–1685
5.
Duncan R, Razvi S, Mulhern S: Newly presenting psychogenic nonepileptic seizures: incidence, population characteristics, and early outcome from a prospective audit of a first seizure clinic. Epilepsy Behav 2011; 20:308–311
6.
Baslet G, Seshadri A, Bermeo-Ovalle A, et al: Psychogenic non-epileptic seizures: an updated primer. Psychosomatics 2016; 57:1–17
7.
Reuber M, Howlett S, Khan A, et al: Non-epileptic seizures and other functional neurological symptoms: predisposing, precipitating, and perpetuating factors. Psychosomatics 2007; 48:230–238
8.
Benbadis SR, Lancman ME, King LM, et al: Preictal pseudosleep: a new finding in psychogenic seizures. Neurology 1996; 47:63–67
9.
Cervenka MC, Lesser R, Tran TT, et al: Does the teddy bear sign predict psychogenic nonepileptic seizures? Epilepsy Behav 2013; 28:217–220
10.
Burneo JG, Martin R, Powell T, et al: Teddy bears: an observational finding in patients with non-epileptic events. Neurology 2003; 61:714–715
11.
Kuyk J, Spinhoven P, van Dyck R: Hypnotic recall: a positive criterion in the differential diagnosis between epileptic and pseudoepileptic seizures. Epilepsia 1999; 40:485–491
12.
Saygi S, Katz A, Marks DA, et al: Frontal lobe partial seizures and psychogenic seizures: comparison of clinical and ictal characteristics. Neurology 1992; 42:1274–1277
13.
Devinsky O, Sanchez-Villaseñor F, Vazquez B, et al: Clinical profile of patients with epileptic and nonepileptic seizures. Neurology 1996; 46:1530–1533
14.
Pestana Knight EM, Grinenko O, Bulacio J, et al: Arc de cercle as a manifestation of focal epileptic seizures. Neurol Clin Pract 2018; 8:354–356
15.
Avbersek A, Sisodiya S: Does the primary literature provide support for clinical signs used to distinguish psychogenic nonepileptic seizures from epileptic seizures? J Neurol Neurosurg Psychiatry 2010; 81:719–725
16.
Modur PN, Rigdon B: Diagnostic yield of sequential routine EEG and extended outpatient video-EEG monitoring. Clin Neurophysiol 2008; 119:190–196
17.
Parra J, Kanner AM, Iriarte J, et al: When should induction protocols be used in the diagnostic evaluation of patients with paroxysmal events? Epilepsia 1998; 39:863–867
18.
Bettini L, Croquelois A, Maeder-Ingvar M, et al: Diagnostic yield of short-term video-EEG monitoring for epilepsy and PNESs: a European assessment. Epilepsy Behav 2014; 39:55–58
19.
Perrin MW, Sahoo SK, Goodkin HP: Latency to first psychogenic nonepileptic seizure upon admission to inpatient EEG monitoring: evidence for semiological differences. Epilepsy Behav 2010; 19:32–35
20.
Benbadis SR, Tatum WO: Overintepretation of EEGs and misdiagnosis of epilepsy. J Clin Neurophysiol 2003; 20:42–44
21.
Nežádal T, Hovorka J, Herman E, et al: Psychogenic non-epileptic seizures: our video-EEG experience. Neurol Res 2011; 33:694–700
22.
Reuber M, Fernández G, Bauer J, et al: Interictal EEG abnormalities in patients with psychogenic nonepileptic seizures. Epilepsia 2002; 43:1013–1020
23.
Ribaï P, Tugendhaft P, Legros B: Usefulness of prolonged video-EEG monitoring and provocative procedure with saline injection for the diagnosis of non epileptic seizures of psychogenic origin. J Neurol 2006; 253:328–332
24.
Chen DK, Izadyar S, Collins RL, et al: Induction of psychogenic nonepileptic events: success rate influenced by prior induction exposure, ictal semiology, and psychological profiles. Epilepsia 2011; 52:1063–1070
25.
Benbadis SR, Johnson K, Anthony K, et al: Induction of psychogenic nonepileptic seizures without placebo. Neurology 2000; 55:1904–1905
26.
Benbadis SR, Siegrist K, Tatum WO, et al: Short-term outpatient EEG video with induction in the diagnosis of psychogenic seizures. Neurology 2004; 63:1728–1730
27.
Popkirov S, Grönheit W, Wellmer J: Hyperventilation and photic stimulation are useful additions to a placebo-based suggestive seizure induction protocol in patients with psychogenic nonepileptic seizures. Epilepsy Behav 2015; 46:88–90
28.
Hoepner R, Labudda K, Schoendienst M, et al: Informing patients about the impact of provocation methods increases the rate of psychogenic nonepileptic seizures during EEG recording. Epilepsy Behav 2013; 28:457–459
29.
McGonigal A, Oto M, Russell AJ, et al: Outpatient video EEG recording in the diagnosis of non-epileptic seizures: a randomised controlled trial of simple suggestion techniques. J Neurol Neurosurg Psychiatry 2002; 72:549–551
30.
Leeman BA: Provocative techniques should not be used for the diagnosis of psychogenic nonepileptic seizures. Epilepsy Behav 2009; 15:110–114, discussion 115–118
31.
Benbadis SR: Provocative techniques should be used for the diagnosis of psychogenic nonepileptic seizures. Epilepsy Behav 2009; 15:106–109, discussion 115–118
32.
Turner K, Piazzini A, Chiesa V, et al: Patients with epilepsy and patients with psychogenic non-epileptic seizures: video-EEG, clinical and neuropsychological evaluation. Seizure 2011; 20:706–710
33.
O’Brien FM, Fortune GM, Dicker P, et al: Psychiatric and neuropsychological profiles of people with psychogenic nonepileptic seizures. Epilepsy Behav 2015; 43:39–45
34.
Salinsky M, Evrard C, Storzbach D, et al: Psychiatric comorbidity in veterans with psychogenic seizures. Epilepsy Behav 2012; 25:345–349
35.
Schramke CJ, Kay KA, Valeriano JP, et al: Using patient history to distinguish between patients with non-epileptic and patients with epileptic events. Epilepsy Behav 2010; 19:478–482
36.
Galimberti CA, Ratti MT, Murelli R, et al: Patients with psychogenic nonepileptic seizures, alone or epilepsy-associated, share a psychological profile distinct from that of epilepsy patients. J Neurol 2003; 250:338–346
37.
Marchetti RL, Kurcgant D, Gallucci Neto J, et al: Evaluating patients with suspected nonepileptic psychogenic seizures. J Neuropsychiatry Clin Neurosci 2009; 21:292–298
38.
Scévola L, Teitelbaum J, Oddo S, et al: Psychiatric disorders in patients with psychogenic nonepileptic seizures and drug-resistant epilepsy: a study of an Argentine population. Epilepsy Behav 2013; 29:155–160
39.
Tremont G, Smith MM, Bauer L, et al: Comparison of personality characteristics on the bear-fedio inventory between patients with epilepsy and those with non-epileptic seizures. J Neuropsychiatry Clin Neurosci 2012; 24:47–52
40.
Stone J, Sharpe M, Binzer M: Motor conversion symptoms and pseudoseizures: a comparison of clinical characteristics. Psychosomatics 2004; 45:492–499
41.
Matin N, Young SS, Williams B, et al: Neuropsychiatric associations with gender, illness duration, work disability, and motor subtype in a U.S. functional neurological disorders clinic population. J Neuropsychiatry Clin Neurosci 2017; 29:375–382
42.
Direk N, Kulaksizoglu IB, Alpay K, et al: Using personality disorders to distinguish between patients with psychogenic nonepileptic seizures and those with epileptic seizures. Epilepsy Behav 2012; 23:138–141
43.
Arnold LM, Privitera MD: Psychopathology and trauma in epileptic and psychogenic seizure patients. Psychosomatics 1996; 37:438–443
44.
Pick S, Mellers JDC, Goldstein LH: Dissociation in patients with dissociative seizures: relationships with trauma and seizure symptoms. Psychol Med 2017; 47:1215–1229
45.
Reuber M, Pukrop R, Bauer J, et al: Multidimensional assessment of personality in patients with psychogenic non-epileptic seizures. J Neurol Neurosurg Psychiatry 2004; 75:743–748
46.
Myers L, Vaidya-Mathur U, Lancman M: Prolonged exposure therapy for the treatment of patients diagnosed with psychogenic non-epileptic seizures (PNES) and post-traumatic stress disorder (PTSD). Epilepsy Behav 2017; 66:86–92
47.
Durrant J, Rickards H, Cavanna AE: Prognosis and outcome predictors in psychogenic nonepileptic seizures. Epilepsy Res Treat 2011; 2011:274736
48.
Marquez AV, Farias ST, Apperson M, et al: Psychogenic nonepileptic seizures are associated with an increased risk of obesity. Epilepsy Behav 2004; 5:88–93
49.
Robbins NM, Larimer P, Bourgeois JA, et al: Number of patient-reported allergies helps distinguish epilepsy from psychogenic nonepileptic seizures. Epilepsy Behav 2016; 55:174–177
50.
Wissel BD, Dwivedi AK, Gaston TE, et al: Which patients with epilepsy are at risk for psychogenic nonepileptic seizures (PNES)? A multicenter case-control study. Epilepsy Behav 2016; 61:180–184
51.
Walsh KE, Baneck T, Page RL, et al: Psychogenic pseudosyncope: not always a diagnosis of exclusion. Pacing Clin Electrophysiol 2018; 41:480–486
52.
Asadi-Pooya AA, Asadollahi M, Tinker J, et al: Post-epilepsy surgery psychogenic nonepileptic seizures. Epilepsia 2016; 57:1691–1696
53.
Reuber M, Kral T, Kurthen M, et al: New-onset psychogenic seizures after intracranial neurosurgery. Acta Neurochir (Wien) 2002; 144:901–907, discussion 907
54.
Barry E, Krumholz A, Bergey GK, et al: Nonepileptic posttraumatic seizures. Epilepsia 1998; 39:427–431
55.
Mökleby K, Blomhoff S, Malt UF, et al: Psychiatric comorbidity and hostility in patients with psychogenic nonepileptic seizures compared with somatoform disorders and healthy controls. Epilepsia 2002; 43:193–198
56.
Hudak AM, Trivedi K, Harper CR, et al: Evaluation of seizure-like episodes in survivors of moderate and severe traumatic brain injury. J Head Trauma Rehabil 2004; 19:290–295
57.
McKenzie PS, Oto M, Graham CD, et al: Medically unexplained symptoms in patients with PNES: Do they explain poor employment outcome in patients with good seizure outcomes? Epilepsy Behav 2016; 59:9–12
58.
Elliott JO, Charyton C: Biopsychosocial predictors of psychogenic non-epileptic seizures. Epilepsy Res 2014; 108:1543–1553
59.
Gazzola DM, Carlson C, Rugino A, et al: Psychogenic nonepileptic seizures and chronic pain: a retrospective case-controlled study. Epilepsy Behav 2012; 25:662–665
60.
Benbadis SR: A spell in the epilepsy clinic and a history of “chronic pain” or “fibromyalgia” independently predict a diagnosis of psychogenic seizures. Epilepsy Behav 2005; 6:264–265
61.
Dixit R, Popescu A, Bagić A, et al: Medical comorbidities in patients with psychogenic nonepileptic spells (PNES) referred for video-EEG monitoring. Epilepsy Behav 2013; 28:137–140
62.
Daum C, Gheorghita F, Spatola M, et al: Interobserver agreement and validity of bedside “positive signs” for functional weakness, sensory and gait disorders in conversion disorder: a pilot study. J Neurol Neurosurg Psychiatry 2015; 86:425–430
63.
Brown RJ, Bouska JF, Frow A, et al: Emotional dysregulation, alexithymia, and attachment in psychogenic nonepileptic seizures. Epilepsy Behav 2013; 29:178–183
64.
Novakova B, Howlett S, Baker R, et al: Emotion processing and psychogenic non-epileptic seizures: A cross-sectional comparison of patients and healthy controls. Seizure 2015; 29:4–10
65.
Bewley J, Murphy PN, Mallows J, et al: Does alexithymia differentiate between patients with nonepileptic seizures, patients with epilepsy, and nonpatient controls? Epilepsy Behav 2005; 7:430–437
66.
Willment K, Hill M, Baslet G, et al: Cognitive impairment and evaluation in psychogenic nonepileptic seizures: an integrated cognitive-emotional approach. Clin EEG Neurosci 2015; 46:42–53
67.
Goldstein LH, Drew C, Mellers J, et al: Dissociation, hypnotizability, coping styles and health locus of control: characteristics of pseudoseizure patients. Seizure 2000; 9:314–322
68.
Dimaro LV, Dawson DL, Roberts NA, et al: Anxiety and avoidance in psychogenic nonepileptic seizures: the role of implicit and explicit anxiety. Epilepsy Behav 2014; 33:77–86
69.
Schönenberg M, Jusyte A, Höhnle N, et al: Theory of mind abilities in patients with psychogenic nonepileptic seizures. Epilepsy Behav 2015; 53:20–24
70.
Cornaggia CM, Gugliotta SC, Magaudda A, et al: Conversation analysis in the differential diagnosis of Italian patients with epileptic or psychogenic non-epileptic seizures: a blind prospective study. Epilepsy Behav 2012; 25:598–604
71.
Brown RJ, Reuber M: Psychological and psychiatric aspects of psychogenic non-epileptic seizures (PNES): a systematic review. Clin Psychol Rev 2016; 45:157–182
72.
Reuber M, Pukrop R, Bauer J, et al: Outcome in psychogenic nonepileptic seizures: 1 to 10-year follow-up in 164 patients. Ann Neurol 2003; 53:305–311
73.
Roberts NA, Burleson MH, Weber DJ, et al: Emotion in psychogenic nonepileptic seizures: responses to affective pictures. Epilepsy Behav 2012; 24:107–115
74.
Bodde NM, Bartelet DC, Ploegmakers M, et al: MMPI-II personality profiles of patients with psychogenic nonepileptic seizures. Epilepsy Behav 2011; 20:674–680
75.
Reuber M, House AO, Pukrop R, et al: Somatization, dissociation and general psychopathology in patients with psychogenic non-epileptic seizures. Epilepsy Res 2003; 57:159–167
76.
Prueter C, Schultz-Venrath U, Rimpau W: Dissociative and associated psychopathological symptoms in patients with epilepsy, pseudoseizures, and both seizure forms. Epilepsia 2002; 43:188–192
77.
Alper K, Devinsky O, Perrine K, et al: Dissociation in epilepsy and conversion nonepileptic seizures. Epilepsia 1997; 38:991–997
78.
Cragar DE, Berry DTR, Fakhoury TA, et al: A review of diagnostic techniques in the differential diagnosis of epileptic and nonepileptic seizures. Neuropsychol Rev 2002; 12:31–64
79.
Dodrill CB: Use of neuropsychological and personality testing to identify adults with psychogenic nonepileptic seizures; in Gates and Rowan’s Nonepileptic Seizures, 3rd ed. Edited by Schachter SC, LaFrance WC Jr. Cambridge, United Kingdom, Cambridge University Press, 2010, pp 136–141
80.
Drane DL, Locke DEC: Mechanisms of possible neurocognitive dysfunction; in Psychogenic Nonepileptic Seizures: Toward the Integration of Care. Edited by Dworetzky BA, Baslet GC. New York, Oxford University Press, 2017, pp 86–105
81.
Ali F, Rickards H, Bagary M, et al: Ictal consciousness in epilepsy and nonepileptic attack disorder. Epilepsy Behav 2010; 19:522–525
82.
Drake ME Jr, Pakalnis A, Phillips BB: Neuropsychological and psychiatric correlates of intractable pseudoseizures. Seizure 1992; 1:11–13
83.
Rotge JY, Lambrecq V, Marchal C, et al.: Conversion disorder and coexisting nonepileptic seizures in patients with refractory seizures. EpilepsyBehav 2009; 16:350–352
84.
Kanner AM, Parra J, Frey M, et al: Psychiatric and neurologic predictors of psychogenic pseudoseizure outcome. Neurology 1999; 53:933–938
85.
Reuber M, Qurishi A, Bauer J, et al: Are there physical risk factors for psychogenic non-epileptic seizures in patients with epilepsy? Seizure 2003; 12:561–567
86.
Benbadis SR, Tatum WO 4th, Murtagh FR, et al: MRI evidence of mesial temporal sclerosis in patients with psychogenic nonepileptic seizures. Neurology 2000; 55:1061–1062
87.
Kipervasser S, Fried I, Korczyn AD, et al: Cortical lesions associated with transient neurological symptoms - not always a matter of cause and effect. Eur J Neurol 2003; 10:721–725
88.
Beach R, Reading R: The importance of acknowledging clinical uncertainty in the diagnosis of epilepsy and non-epileptic events. Arch Dis Child 2005; 90:1219–1222
89.
Varma AR, Moriarty J, Costa DC, et al: HMPAO SPECT in non-epileptic seizures: preliminary results. Acta Neurol Scand 1996; 94:88–92
90.
Biraben A, Taussig D, Bernard AM, et al.: Video-EEG and ictal SPECT in three patients with both epileptic and non-epileptic seizures. Epileptic Disord 1999; 1:51–55
91.
Swartz BE, Brown C, Mandelkern MA, et al.: The use of 2-deoxy-2-[18F]fluoro-D-glucose (FDG-PET) positron emission tomography in the routine diagnosis of epilepsy. Mol Imaging Biol 2002; 4:245–252
92.
Opherk C, Hirsch LJ: Ictal heart rate differentiates epileptic from non-epileptic seizures. Neurology 2002; 58:636–638
93.
Nousiainen U, Mervaala E, Ylinen A, et al: The importance of the electrocardiogram in ambulatory electroencephalographic recordings. Arch Neurol 1989; 46:1171–1174
94.
van der Kruijs SJM, Vonck KEJ, Langereis GR, et al: Autonomic nervous system functioning associated with psychogenic nonepileptic seizures: analysis of heart rate variability. Epilepsy Behav 2016; 54:14–19
95.
Jeppesen J, Beniczky S, Johansen P, et al: Comparing maximum autonomic activity of psychogenic non-epileptic seizures and epileptic seizures using heart rate variability. Seizure 2016; 37:13–19
96.
Kozlowska K, Palmer DM, Brown KJ, et al: Reduction of autonomic regulation in children and adolescents with conversion disorders. Psychosom Med 2015; 77:356–370
97.
Ponnusamy A, Marques JLB, Reuber M: Heart rate variability measures as biomarkers in patients with psychogenic nonepileptic seizures: potential and limitations. Epilepsy Behav 2011; 22:685–691
98.
Reinsberger C, Perez DL, Murphy MM, et al: Pre- and postictal, not ictal, heart rate distinguishes complex partial and psychogenic nonepileptic seizures. Epilepsy Behav 2012; 23:68–70
99.
Aydin S, Dag E, Ozkan Y, et al: Time-dependent changes in the serum levels of prolactin, nesfatin-1 and ghrelin as a marker of epileptic attacks young male patients. Peptides 2011; 32:1276–1280
100.
Collins WC, Lanigan O, Callaghan N: Plasma prolactin concentrations following epileptic and pseudoseizures. J Neurol Neurosurg Psychiatry 1983; 46:505–508
101.
Pritchard PB 3rd, Wannamaker BB, Sagel J, et al: Serum prolactin and cortisol levels in evaluation of pseudoepileptic seizures. Ann Neurol 1985; 18:87–89
102.
Fisher RS, Chan DW, Bare M, et al: Capillary prolactin measurement for diagnosis of seizures. Ann Neurol 1991; 29:187–190
103.
Anzola GP: Predictivity of plasma prolactin levels in differentiating epilepsy from pseudoseizures: a prospective study. Epilepsia 1993; 34:1044–1048
104.
Ehsan T, Fisher RS, Johns D, et al: Sensitivity and specificity of paired capillary prolactin measurement in diagnosis of seizures. J Epilepsy 1996; 9:101–105
105.
Chen DK, So YT, Fisher RS: Use of serum prolactin in diagnosing epileptic seizures: report of the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology. Neurology 2005; 65:668–675
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Received: 7 February 2020
Revision received: 30 March 2020
Accepted: 22 April 2020
Published online: 11 August 2020
Published in print: Winter 2021
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The authors report no financial relationships with commercial interests.
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