On-demand EEG education through competition - A novel, app-based approach to learning to identify interictal epileptiform discharges.

Objective: Misinterpretation of EEGs harms patients, yet few resources exist to help trainees practice interpreting EEGs. We therefore sought to evaluate a novel educational tool to teach trainees how to identify interictal epileptiform discharges (IEDs) on EEG. Methods: We created a public EEG test within the iOS app DiagnosUs using a pool of 13,262 candidate IEDs. Users were shown a candidate IED on EEG and asked to rate it as epileptiform (IED) or not (non-IED). They were given immediate feedback based on a gold standard. Learning was analyzed using a parametric model. We additionally analyzed IED features that best correlated with expert ratings. Results: Our analysis included 901 participants. Users achieved a mean improvement of 13% over 1,000 questions and an ending accuracy of 81%. Users and experts appeared to rely on a similar set of IED morphologic features when analyzing candidate IEDs. We additionally identified particular types of candidate EEGs that remained challenging for most users even after substantial practice. Conclusions: Users improved in their ability to properly classify candidate IEDs through repeated exposure and immediate feedback. Significance: This app-based learning activity has great potential to be an effective supplemental tool to teach neurology trainees how to accurately identify IEDs on EEG.

Psychiatric changes after stereotactic laser amygdalohippocampotomy for medial temporal lobe epilepsy.

PURPOSE: Stereotactic laser amygdalohippocampotomy (SLAH) is a minimally invasive surgical treatment for drug-resistant temporal lobe epilepsy (TLE) that has comparable rates of seizure freedom to traditional open resective TLE surgery. The objective of this study was to determine psychiatric outcome (i.e., depression and anxiety changes, psychosis) after SLAH, to explore possible contributory factors to these changes, and to determine the prevalence of de novo psychopathology. METHODS: We explored mood and anxiety in 37 adult patients with TLE undergoing SLAH using the Beck psychiatric symptoms scales (i.e., Beck Depression Inventory-II [BDI-II] and Beck Anxiety Inventory [BAI]) preoperatively and 6 months following surgery. Multivariable regression analysis was conducted to identify predictors of worse depression or anxiety symptoms following SLAH. The prevalence of de novo psychopathology following SLAH was also determined. RESULTS: We found a significant decrease in BDI-II (mean decline from 16.3 to 10.9, p = 0.004) and BAI (mean decline from 13.3 to 9.0, p = 0.045) scores following SLAH at the group level. While the rate of resolution of depression (from 62% to 49%) did not achieve statistical significance (p = 0.13, McNemar's), the rate of resolution of anxiety (from 57% to 35%) was statistically significant (p = 0.03, McNemar's). The de novo rate of psychopathology (i.e., new onset depression or anxiety) following SLAH was 1 of 7 (14%). Using a metric of meaningful change rather than complete symptom resolution, 16 of 37 (43%) patients experienced improvement in depression and 6 of 37 (16%) experienced worsening. For anxiety, 14 of 37 (38%) experienced meaningful improvement and 8 of 37 (22%) experienced worsening. Baseline performance on the Beck Scales was the only factor contributing to outcome status. DISCUSSION: In one of the first studies to evaluate psychiatric outcomes after SLAH, we found promising overall trends toward stability or significant improvement in symptom burden at the group level for both depression and anxiety. There was also a significant improvement in clinical anxiety, though the decrease in clinical depression was not significant, likely owing to the limitations of sample size. SLAH may improve overall psychiatric symptoms, similarly to traditional resective TLE surgery, but de novo psychopathology and postoperative psychiatric morbidity remain significant issues, and larger samples are necessary to determine causal contributory factors.

Withdrawal of antiseizure medications after MRI-Guided laser interstitial thermal therapy in extra-temporal lobe epilepsy.

PURPOSE: This study investigated the success rate of antiseizure medications (ASMs) withdrawal following MRI Guided Laser Interstitial Thermal Therapy (MRg-LITT) for extra-temporal lobe epilepsy (ETLE), and identified predictors of seizure recurrence. METHODS: We retrospectively assessed 27 patients who underwent MRg-LITT for ETLE. Patients' demographics, disease characteristics, and post-surgical outcomes were evaluated for their potential to predict seizure recurrence associated with ASMs withdrawal. RESULTS: The median period of observation post MRg-LITT was 3 years (range 18 - 96 months) and the median period to initial ASMs reduction was 0.5 years (range 1-36 months). ASMs reduction was attempted in 17 patients (63%), 5 (29%) of whom had seizure recurrence after initial reduction. Nearly all patient who relapsed regained seizure control after reinstitution of their ASMs regimen. Pre-operative seizure frequency (p = 0.002) and occurrence of acute post-operative seizures (p = 0.01) were associated with increased risk for seizure recurrence post ASMs reduction. At the end of the observation period, 11% of patients were seizure free without drugs, 52% were seizure free with drugs and 37% still experienced seizures despite ASMs. Compared with pre-operative status, the number of ASMs was reduced in 41% of patients, unchanged in 55% of them and increased in only 4% of them. CONCLUSIONS: Successful MRg-LITT for ETLE allows for ASMs reduction in a significant portion of patients and complete ASMs withdrawal in a subset of them. Patients with higher pre-operative seizure frequency or occurrence of acute post operative seizures exhibit higher chances relapse post ASMs reduction.

A global survey on the attitudes of neurologists and psychiatrists about functional/psychogenic/dissociative/nonepileptic-seizures/attacks, in the search of its name.

PURPOSE: We conducted an observational study to investigate the opinions of neurologists and psychiatrists all around the world who are taking care of patients with seizures [epilepsy and functional seizures (FS)]. METHODS: Practicing neurologists and psychiatrists from around the world were invited to participate in an online survey. On 29th September 2022, an e-mail including a questionnaire was sent to the members of the International Research in Epilepsy (IR-Epil) Consortium. The study was closed on 1st March 2023. The survey, conducted in English, included questions about physicians' opinions about FS and anonymously collected data. RESULTS: In total, 1003 physicians from different regions of the world participated in the study. Both neurologists and psychiatrists identified "seizures" as their preferred term. Overall, the most preferred modifiers for "seizures" were "psychogenic" followed by "functional" by both groups. Most participants (57.9%) considered FS more difficult to treat compared to epilepsy. Both psychological and biological problems were considered as the underlying cause of FS by 61% of the respondents. Psychotherapy was considered the first treatment option for patients with FS (79.9%). CONCLUSION: Our study represents the first large-scale attempt of investigating physicians attitudes and opinions about a condition that is both frequent and clinically important. It shows that there is a broad spectrum of terms used by physicians to refer to FS. It also suggests that the biopsychosocial model has gained its status as a widely used framework to interpret and inform clinical practice on the management of patients.

Depression and anxiety in caregivers of patients with functional seizures.

OBJECTIVE: Contrary to patients, the psychological impact of functional seizures to caregivers has not been adequately investigated. This study aimed to evaluate the rates and determinants of depression and anxiety in caregivers of patients with functional seizures. METHODS: Patients with functional seizures and their caregivers completed surveys about demographic, disease-related, and psychosocial characteristics. Rates and determinants of depression and anxiety were evaluated using the Beck Depression and Anxiety Inventory scores as dependent variables and patient and caregiver characteristics as independent variables. RESULTS: Twenty-nine patients (76% female, mean age of 37 years) and their caregivers (59% female, mean age of 43 years) were recruited. Symptoms of anxiety and/or depression were present in 96% of patients (96% depression, 92% anxiety) and 59% of caregivers (52% depression, 50% anxiety). Specifically, 31% of caregivers manifested mild depression, 14% moderate depression, and 7% severe depression, whereas 48% were not depressed. Similarly, 14% of caregivers manifested mild anxiety, 29% moderate anxiety, and 7% severe anxiety, whereas 50% were not anxious. Patient and caregiver depression levels strongly correlated (r = .73, p < .0001). The presence of anxiety and depression in the caregiver was associated with male patient gender (p = .02), patient depression level (p = .002), the caregiver being a parent or sibling (p = .02), and caregiver burden (p = .0009). SIGNIFICANCE: Caregivers of patients with functional seizures experience high rates of anxiety and depression, explained by specific demographic and psychosocial factors that could act as intervention targets.

The effect of responsive neurostimulation (RNS) on neuropsychiatric and psychosocial outcomes in drug-resistant epilepsy.

OBJECTIVE: The impact of responsive neurostimulation (RNS) on neuropsychiatric and psychosocial outcomes has not been extensively evaluated outside of the original clinical trials and post-approval studies. The goal of this study was to ascertain the potential real-world effects of RNS on cognitive, psychiatric, and quality of life (QOL) outcomes in relation to seizure outcomes by examining 50 patients undergoing RNS implantation for drug-resistant epilepsy (DRE). METHODS: We performed a retrospective review of all patients treated at our institution with RNS for DRE with at least 12 months of follow-up. In addition to baseline demographic and disease-related characteristics, we collected cognitive (Full-Scale Intelligence Quotient, Verbal Comprehension, and Perceptual Reasoning Index), psychiatric (Beck Depression and Anxiety Inventory Scores), and QOL (QOLIE-31) outcomes at 6 and 12 months after RNS implantation and correlated them with seizure outcomes. RESULTS: Fifty patients (median age 39.5 years, 64% female) were treated with RNS for DRE in our institution from 2005 to 2020. Of the 37 of them who had well-documented pre and post-implantation seizure diaries, the 6-month median seizure frequency reduction was 88%, the response rate (50% or greater seizure frequency reduction) was 78%, and 32% of patients were free of disabling seizures in this timeframe. There was no statistically significant difference at a group level in any of the evaluated cognitive, psychiatric, and QOL outcomes at 6 and 12 months post-implantation compared to the pre-implantation baseline, irrespective of seizure outcomes, although a subset of patients experienced a decline in mood or cognitive variables. SIGNIFICANCE: Responsive neurostimulation does not appear to have a statistically significant negative or positive impact on neuropsychiatric and psychosocial status at the group level. We observed significant variability in outcome, with a minority of patients experiencing worse behavioral outcomes, which seemed related to RNS implantation. Careful outcome monitoring is required to identify the subset of patients experiencing a poor response and to make appropriate adjustments in care.

Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation.

BACKGROUND AND OBJECTIVES: Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as "ictal-interictal-injury continuum" (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns. METHODS: We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes. RESULTS: SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and "other" classes, SPaRCNet exceeds the following percentages of 20 experts-ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively. DISCUSSION: SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

Normal variants and artifacts: Importance in EEG interpretation.

Overinterpretation of EEG is an important contributor to the misdiagnosis of epilepsy. For the EEG to have a high diagnostic value and high specificity, it is critical to recognize waveforms that can be mistaken for abnormal patterns. This article describes artifacts, normal rhythms, and normal patterns that are prone to being misinterpreted as abnormal. Artifacts are potentials generated outside the brain. They are divided into physiologic and extraphysiologic. Physiologic artifacts arise from the body and include EMG, eyes, various movements, EKG, pulse, and sweat. Some physiologic artifacts can be useful for interpretation such as EMG and eye movements. Extraphysiologic artifacts arise from outside the body, and in turn can be divided into the environments (electrodes, equipment, and cellphones) and devices within the body (pacemakers and neurostimulators). Normal rhythms can be divided into awake patterns (alpha rhythm and its variants, mu rhythm, lambda waves, posterior slow waves of youth, HV-induced slowing, photic driving, and photomyogenic response) and sleep patterns (POSTS, vertex waves, spindles, K complexes, sleep-related hypersynchrony, and frontal arousal rhythm). Breach can affect both awake and sleep rhythms. Normal variants or variants of uncertain clinical significance include variants that may have been considered abnormal in the early days of EEG but are now considered normal. These include wicket spikes and wicket rhythms (the most common normal pattern overread as epileptiform), small sharp spikes (aka benign epileptiform transients of sleep), rhythmic midtemporal theta of drowsiness (aka psychomotor variant), Cigánek rhythm (aka midline theta), 6 Hz phantom spike-wave, 14 and 6 Hz positive spikes, subclinical rhythmic epileptiform discharges of adults (SREDA), slow-fused transients, occipital spikes of blindness, and temporal slowing of the elderly. Correctly identifying artifacts and normal patterns can help avoid overinterpretation and misdiagnosis. This is an educational review paper addressing a learning objective of the International League Against Epilepsy (ILAE) curriculum.

MRI-based deep learning can discriminate between temporal lobe epilepsy, Alzheimer's disease, and healthy controls.

BACKGROUND: Radiological identification of temporal lobe epilepsy (TLE) is crucial for diagnosis and treatment planning. TLE neuroimaging abnormalities are pervasive at the group level, but they can be subtle and difficult to identify by visual inspection of individual scans, prompting applications of artificial intelligence (AI) assisted technologies. METHOD: We assessed the ability of a convolutional neural network (CNN) algorithm to classify TLE vs. patients with AD vs. healthy controls using T1-weighted magnetic resonance imaging (MRI) scans. We used feature visualization techniques to identify regions the CNN employed to differentiate disease types. RESULTS: We show the following classification results: healthy control accuracy = 81.54% (SD = 1.77%), precision = 0.81 (SD = 0.02), recall = 0.85 (SD = 0.03), and F1-score = 0.83 (SD = 0.02); TLE accuracy = 90.45% (SD = 1.59%), precision = 0.86 (SD = 0.03), recall = 0.86 (SD = 0.04), and F1-score = 0.85 (SD = 0.04); and AD accuracy = 88.52% (SD = 1.27%), precision = 0.64 (SD = 0.05), recall = 0.53 (SD = 0.07), and F1 score = 0.58 (0.05). The high accuracy in identification of TLE was remarkable, considering that only 47% of the cohort had deemed to be lesional based on MRI alone. Model predictions were also considerably better than random permutation classifications (p < 0.01) and were independent of age effects. CONCLUSIONS: AI (CNN deep learning) can classify and distinguish TLE, underscoring its potential utility for future computer-aided radiological assessments of epilepsy, especially for patients who do not exhibit easily identifiable TLE associated MRI features (e.g., hippocampal sclerosis).

In people with temporal lobe epilepsy, seizures start in a particular part of the brain positioned behind the ears called the temporal lobe. It is difficult for a doctor to detect that a person has temporal lobe epilepsy using brain scans. In this study, we developed a computer model that was able to identify people with temporal lobe epilepsy from scans of their brain. This computer model could be used to help doctors identify temporal lobe epilepsy from brain scans in the future.

Quality of life and functional limitations in persons with epilepsy.

OBJECTIVE: Epilepsy can reduce quality of life (QOL), functionality, and social participation, but these effects have not been adequately quantified in large, population-based, controlled studies. We sought to evaluate the impact of epilepsy on patients' QOL and employment outcomes. METHODS: In this cross-sectional study we used nationally representative, pooled data from the Medical Expenditure Panel Survey (MEPS) household component files for 2010-2018. MEPS is a population-based survey of U.S. community-dwelling persons. We included respondents with condition file records for epilepsy. We also analyzed respondents with records for seizure. The primary outcomes were short form-12 physical and mental health scores. Secondary outcomes included self-rated health status, employment status, educational attainment, school/household/work limitations, and missed workdays. We compared these outcomes between persons with epilepsy (PWE) and age- and gender-matched controls. RESULTS: We identified 1078 people with epilepsy, 2344 seizure cases, and 3422 cases of either condition (persons with epilepsy and/or seizures). Epilepsy was associated with a decrease of - 4.0 (95% CI: -5.1 to -2.8) points in SF-12 physical health scores and - 3.1 (95% CI: -4.2 to -1.9) in SF-12 mental health scores. Epilepsy was also associated with decreases in the likelihood of reporting good/very good/excellent health status (-13.3 [95% CI: -16.1 to -10.4] percentage points). Epilepsy was also associated with adverse employment-related outcomes. Specifically, PWE were 17.9 (95% CI: 14.3-21.4) percentage points more likely to report that they had work or household limitations. The associations between outcomes and epilepsy were, in most cases, larger than those between outcomes and other common, chronic conditions. SIGNIFICANCE: Epilepsy is associated with worse quality of life and employment-related outcomes. Interventions should aim to improve functioning and patients' ability to maintain employment.

Are brain MRI abnormalities associated with the semiology of functional seizures?

PURPOSE: To investigate whether radiologically apparent brain magnetic resonance imaging (MRI) abnormalities are associated with the functional seizure (FS) semiology. METHODS: All patients with a diagnosis of FS at the epilepsy centers at Shiraz University of Medical Sciences, Iran; Aichi Medical University Hospital, Japan; University of Michigan, USA; University of California, Los Angeles, USA; Emory University School of Medicine, USA; and Hospital el Cruce, Argentina, were studied. RESULTS: One hundred patients were included; 77 (77%) had motor functional seizures. Lobar location of brain abnormality did not have an association with the semiology (p = .83). There was no significant difference between ictal behaviors in patients with frontal or parietal lesions compared to those with temporal or occipital lesions. CONCLUSION: There were no associations between functional seizure ictal behaviors and locations of the radiologically apparent brain MRI abnormalities. Further studies are needed to evaluate the underpinnings of varying behaviors in FS.

Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs.

BACKGROUND AND OBJECTIVES: The validity of brain monitoring using electroencephalography (EEG), particularly to guide care in patients with acute or critical illness, requires that experts can reliably identify seizures and other potentially harmful rhythmic and periodic brain activity, collectively referred to as "ictal-interictal-injury continuum" (IIIC). Previous interrater reliability (IRR) studies are limited by small samples and selection bias. This study was conducted to assess the reliability of experts in identifying IIIC. METHODS: This prospective analysis included 30 experts with subspecialty clinical neurophysiology training from 18 institutions. Experts independently scored varying numbers of ten-second EEG segments as "seizure (SZ)," "lateralized periodic discharges (LPDs)," "generalized periodic discharges (GPDs)," "lateralized rhythmic delta activity (LRDA)," "generalized rhythmic delta activity (GRDA)," or "other." EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 and 2020. Primary outcome measures were pairwise IRR (average percent agreement [PA] between pairs of experts) and majority IRR (average PA with group consensus) for each class and beyond chance agreement (κ). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability. RESULTS: Among 2,711 EEGs, 49% were from women, and the median (IQR) age was 55 (41) years. In total, experts scored 50,697 EEG segments; the median [range] number scored by each expert was 6,287.5 [1,002, 45,267]. Overall pairwise IRR was moderate (PA 52%, κ 42%), and majority IRR was substantial (PA 65%, κ 61%). Noise-bias analysis demonstrated that a single underlying receiver operating curve can account for most variation in experts' false-positive vs true-positive characteristics (median [range] of variance explained ([Formula: see text]): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics ([Formula: see text]: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise but rather to variation in decision thresholds. DISCUSSION: Our results provide precise estimates of expert reliability from a large and diverse sample and a parsimonious theory to explain the origin of disagreements between experts. The results also establish a standard for how well an automated IIIC classifier must perform to match experts. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that an independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared with expert consensus.

NEUROSCIENCE AND ART: LIFE AND TIMES OF YANNOULIS CHALEPAS

The objective of this article is to highlight the bidirectional relationship between neuroscience and art in the life and times of the most preeminent sculptor in modern Greek history, Yannoulis Chalepas. Analysis of biographical sources and testimonies on the life and works of Yannoulis Chalepas was performed. Findings are discussed in relation to the neuropsychiatric maladies that he faced in his lifespan and their impact on his art. Yannoulis Chalepas' life and art are trichotomized in a charismatic, premorbid era (1851-1877), a prolonged, medieval, morbid period (1878-1917), and a transfigurative, post morbid era (1918-1938). The amalgamate of medical evidence suggests that Yannoulis Chalepas suffered from schizophrenia. That was reflected in his art through two distinct periods of artistic productivity and stylistic creativity. The bidirectional relationship between neuroscience and art in the history of humanity is also exemplified in the legacy of Yannoulis Chalepas. The borderland of artistic ingenuity with aberrant behavior, the misconceptions of neurocognitive disorders with psychosis along with their associated social stigma, the effect of artistic expression in the manifestation of psychiatric disease, as well as its healing and often transformative power are concepts that still tantalize equally scientists and artists around the globe.