Sociodemographic and clinical risk factors for suicidal ideation and suicide attempt in functional/dissociative seizures and epilepsy: a large cohort study.

BACKGROUND: People with functional/dissociative seizures (FDS) are at elevated suicidality risk. OBJECTIVE: To identify risk factors for suicidality in FDS or epilepsy. METHODS: Retrospective cohort study from the UK's largest tertiary mental healthcare provider, with linked national admission data from the Hospital Episode Statistics. Participants were 2383 people with a primary or secondary diagnosis of FDS or epilepsy attending between 01 January 2007 and 18 June 2021. Outcomes were a first report of suicidal ideation and a first hospital admission for suicide attempt (International Classification of Diseases, version 10: X60-X84). Demographic and clinical risk factors were assessed using multivariable bias-reduced binomial-response generalised linear models. FINDINGS: In both groups, ethnic minorities had significantly reduced odds of hospitalisation following suicide attempt (OR: 0.45-0.49). Disorder-specific risk factors were gender, age and comorbidity profile. In FDS, both genders had similar suicidality risk; younger age was a risk factor for both outcomes (OR: 0.16-1.91). A diagnosis of depression or personality disorders was associated with higher odds of suicidal ideation (OR: 1.91-3.01). In epilepsy, females had higher odds of suicide attempt-related hospitalisation (OR: 1.64). Age had a quadratic association with both outcomes (OR: 0.88-1.06). A substance abuse disorder was associated with higher suicidal ideation (OR: 2.67). Developmental disorders lowered the risk (OR: 0.16-0.24). CONCLUSIONS: This is the first study systematically reporting risk factors for suicidality in people with FDS. Results for the large epilepsy cohort complement previous studies and will be useful in future meta-analyses. CLINICAL IMPLICATIONS: Risk factors identified will help identify higher-risk groups in clinical settings.

Limited clinical validity of univariate resting-state EEG markers for classifying seizure disorders.

Differentiating between epilepsy and psychogenic non-epileptic seizures presents a considerable challenge in clinical practice, resulting in frequent misdiagnosis, unnecessary treatment and long diagnostic delays. Quantitative markers extracted from resting-state EEG may reveal subtle neurophysiological differences that are diagnostically relevant. Two observational, retrospective diagnostic accuracy studies were performed to test the clinical validity of univariate resting-state EEG markers for the differential diagnosis of epilepsy and psychogenic non-epileptic seizures. Clinical EEG data were collected for 179 quasi-consecutive patients (age > 18) with a suspected diagnosis of epilepsy or psychogenic non-epileptic seizures who were medication-naïve at the time of EEG; 148 age- and gender-matched patients subsequently received a diagnosis from specialist clinicians and were included in the analyses. Study 1 is a hypothesis-driven study testing the ability of theta power and peak alpha frequency to classify people with epilepsy and people with psychogenic non-epileptic seizures, with an advanced machine learning pipeline. The next study (Study 2) is data-driven; a high number of quantitative EEG features are extracted and a similar machine learning approach as Study 1 assesses whether previously unexplored univariate EEG measures show promise as diagnostic markers. The results of Study 1 suggest that EEG markers that were previously identified as promising diagnostic indicators (i.e. theta power and peak alpha frequency) have limited clinical validity for the classification of epilepsy and psychogenic non-epileptic seizures (mean accuracy: 48%). The results of Study 2 indicate that identifying univariate markers that show good correlation with a categorical diagnostic label is challenging (mean accuracy: 45-60%). This is due to a considerable overlap in neurophysiological features between the diagnostic classes considered in this study, and to the presence of more dominant EEG dynamics such as alterations due to temporal proximity to epileptiform discharges. Markers that were identified in the context of previous epilepsy research using visually normal resting-state EEG were found to have limited clinical validity for the classification task of distinguishing between people with epilepsy and people with psychogenic non-epileptic seizures. A search for alternative diagnostic markers uncovered the challenges involved and generated recommendations for further research.

Computerised cognitive assessment in patients with traumatic brain injury: an observational study of feasibility and sensitivity relative to established clinical scales.

Background: Online technology could potentially revolutionise how patients are cognitively assessed and monitored. However, it remains unclear whether assessments conducted remotely can match established pen-and-paper neuropsychological tests in terms of sensitivity and specificity. Methods: This observational study aimed to optimise an online cognitive assessment for use in traumatic brain injury (TBI) clinics. The tertiary referral clinic in which this tool has been clinically implemented typically sees patients a minimum of 6 months post-injury in the chronic phase. Between March and August 2019, we conducted a cross-group, cross-device and factor analyses at the St. Mary's Hospital TBI clinic and major trauma wards at Imperial College NHS trust and St. George's Hospital in London (UK), to identify a battery of tasks that assess aspects of cognition affected by TBI. Between September 2019 and February 2020, we evaluated the online battery against standard face-to-face neuropsychological tests at the Imperial College London research centre. Canonical Correlation Analysis (CCA) determined the shared variance between the online battery and standard neuropsychological tests. Finally, between October 2020 and December 2021, the tests were integrated into a framework that automatically generates a results report where patients' performance is compared to a large normative dataset. We piloted this as a practical tool to be used under supervised and unsupervised conditions at the St. Mary's Hospital TBI clinic in London (UK). Findings: The online assessment discriminated processing-speed, visual-attention, working-memory, and executive-function deficits in TBI. CCA identified two significant modes indicating shared variance with standard neuropsychological tests (r = 0.86, p < 0.001 and r = 0.81, p = 0.02). Sensitivity to cognitive deficits after TBI was evident in the TBI clinic setting under supervised and unsupervised conditions (F (15,555) = 3.99; p < 0.001). Interpretation: Online cognitive assessment of TBI patients is feasible, sensitive, and efficient. When combined with normative sociodemographic models and autogenerated reports, it has the potential to transform cognitive assessment in the healthcare setting. Funding: This work was funded by a National Institute for Health Research (NIHR) Invention for Innovation (i4i) grant awarded to DJS and AH (II-LB-0715-20006).

Increased suicide attempt risk in people with epilepsy in the presence of concurrent psychogenic nonepileptic seizures.

OBJECTIVES: To test the hypothesis that people with concurrent diagnosis of epilepsy and psychogenic nonepileptic seizures (PNES) are at increased risk of attempting suicide as compared to people with epilepsy or PNES alone. To report on suicide rates. METHODS: Retrospective cohort study from the UK largest tertiary mental health care provider, with linked nationwide admission and mortality data from the Hospital Episode Statistics and Office for National Statistics. Participants were 2460 people with a primary or secondary diagnosis of epilepsy, PNES or concurrent epilepsy and PNES attending between 1 January 2007 and 18 June 2021. The primary outcome was a first hospital admission for suicide attempt (International Classification of Diseases, version 10 X60-X84). RESULTS: 9% of participants had at least one suicide attempt-related hospital admission. For people with concurrent diagnosis of epilepsy and PNES, the odds for suicide attempt-related admissions were 2.52 times the odds of people with epilepsy alone (OR 0.40; 95% CI 0.21 to 0.79; p=0.01). Odds were comparable between people with concurrent diagnosis and people with PNES alone (OR 0.75; 95% CI 0.41 to 1.48; p=0.40). Post hoc analyses revealed that the odds of people with PNES alone were 1.93 times the odds of people with epilepsy alone (OR 0.52; 95% CI 0.38 to 0.70; p<0.001). CONCLUSIONS: People with concurrent diagnosis of epilepsy and PNES or PNES alone have significantly increased odds of hospitalisation due to suicide attempt as compared to people with epilepsy alone (152% and 93% increase, respectively). These findings have direct implications for the clinical management of suicide risk in people with epilepsy.

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review.

Quantitative markers extracted from resting-state electroencephalogram (EEG) reveal subtle neurophysiological dynamics which may provide useful information to support the diagnosis of seizure disorders. We performed a systematic review to summarize evidence on markers extracted from interictal, visually normal resting-state EEG in adults with idiopathic epilepsy or psychogenic nonepileptic seizures (PNES). Studies were selected from 5 databases and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 26 studies were identified, 19 focusing on people with epilepsy, 6 on people with PNES, and one comparing epilepsy and PNES directly. Results suggest that oscillations along the theta frequency (4-8 Hz) may have a relevant role in idiopathic epilepsy, whereas in PNES there was no evident trend. However, studies were subject to a number of methodological limitations potentially introducing bias. There was often a lack of appropriate reporting and high heterogeneity. Results were not appropriate for quantitative synthesis. We identify and discuss the challenges that must be addressed for valid resting-state EEG markers of epilepsy and PNES to be developed.

Resting-state functional connectivity predicts the ability to adapt arm reaching in a robot-mediated force field.

Motor deficits are common outcomes of neurological conditions such as stroke. In order to design personalised motor rehabilitation programmes such as robot-assisted therapy, it would be advantageous to predict how a patient might respond to such treatment. Spontaneous neural activity has been observed to predict differences in the ability to learn a new motor behaviour in both healthy and stroke populations. This study investigated whether spontaneous resting-state functional connectivity could predict the degree of motor adaptation of right (dominant) upper limb reaching in response to a robot-mediated force field. Spontaneous neural activity was measured using resting-state electroencephalography (EEG) in healthy adults before a single session of motor adaptation. The degree of beta frequency (ß; 15-25 Hz) resting-state functional connectivity between contralateral electrodes overlying the left primary motor cortex (M1) and the anterior prefrontal cortex (aPFC) could predict the subsequent degree of motor adaptation. This result provides novel evidence for the functional significance of resting-state synchronization dynamics in predicting the degree of motor adaptation in a healthy sample. This study constitutes a promising first step towards the identification of patients who will likely gain most from using robot-mediated upper limb rehabilitation training based on simple measures of spontaneous neural activity.