Elevated plasma neurofilament light and glial fibrillary acidic protein in epilepsy versus nonepileptic seizures and nonepileptic disorders.

OBJECTIVE: Research suggests that recurrent seizures may lead to neuronal injury. Neurofilament light chain protein (NfL) and glial fibrillary acidic protein (GFAP) levels increase in cerebrospinal fluid and blood in response to neuroaxonal damage, and they have been hypothesized as potential biomarkers for epilepsy. We examined plasma NfL and GFAP levels and their diagnostic utility in differentiating patients with epilepsy from those with psychogenic nonepileptic seizures (PNES) and other nonepileptic disorders. METHODS: We recruited consecutive adults admitted for video-electroencephalographic monitoring and formal neuropsychiatric assessment. NfL and GFAP levels were quantified and compared between different patient groups and an age-matched reference cohort (n = 1926) and correlated with clinical variables in patients with epilepsy. RESULTS: A total of 138 patients were included, of whom 104 were diagnosed with epilepsy, 22 with PNES, and 12 with other conditions. Plasma NfL and GFAP levels were elevated in patients with epilepsy compared to PNES, adjusted for age and sex (NfL p = .04, GFAP p = .04). A high proportion of patients with epilepsy (20%) had NfL levels above the 95th age-matched percentile compared to the reference cohort (5%). NfL levels above the 95th percentile of the reference cohort had a 95% positive predictive value for epilepsy. Patients with epilepsy who had NfL levels above the 95th percentile were younger than those with lower levels (37.5 vs. 43.8 years, p = .03). SIGNIFICANCE: An elevated NfL or GFAP level in an individual patient may support an underlying epilepsy diagnosis, particularly in younger adults, and cautions against a diagnosis of PNES alone. Further examination of the association between NfL and GFAP levels and specific epilepsy subtypes or seizure characteristics may provide valuable insights into disease heterogeneity and contribute to the refinement of diagnosis, understanding pathophysiological mechanisms, and formulating treatment approaches.

Vascular imaging for Stereoelectroencephalography: A safety and planning study.

BACKGROUND: Stereoelectroencephalography (SEEG) is a procedure used to localize the epileptogenic zone in patients with medically refractory epilepsy, involving the stereotactic implantation of electrodes into brain parenchyma. Magnetic Resonance Imaging (MRI), Digital Subtraction Angiography, and Computed Tomography have been used preoperatively to prevent Intracranial Hemorrhage (ICH) by identifying electrode-vessel conflicts (EVC's) on planned electrode trajectories. There is variation in the use of Digital Subtraction Angiography and non-invasive sequences for vascular planning. Digital Subtraction Angiography provides high spatial resolution, but carries risks of arterial dissection, groin and retroperitoneal hematoma, and a 0.5-1.9% risk of stroke. Our group has incorporated Intravenous Cone Beam Computed Tomography (CBCT A/V) Brain into our SEEG workflow, given its effective implementation in other neurosurgical domains. Primary aims include validating the safety of our CBCT A/V sequence for SEEG planning and determining if CBCT A/V is comparable to other modalities in detecting EVC's. Secondary aims include elucidating the relationship of conflicting vessel calibre with ICH incidence in SEEG using CBCT A/V imaging. METHODS: A single-center retrospective study was conducted of 20 patients who underwent preoperative CBCT A/V Brain and MRI Brain with gadolinium enhancement, encompassing 273 electrode implantations from August 2020 - July 2023. The incidence and grade of post-implant, post-explant symptomatic ICH and asymptomatic ICH was noted. The total number of EVC's identifiable on MRI and CBCT A/V was recorded, along with average diameter of conflicting vessels. RESULTS: Across 20 patients and 273 implanted electrodes, there were four ICH events, where two were symptomatic and two were asymptomatic. The mean diameter of EVC's across all patients was 1.4 mm (±0.5). A significant difference (P < 0.0001) was observed between the number of EVC's that CBCT A/V could identify (20) compared to MRI (6). Two EVC's were identified in the region of two symptomatic ICH's, with the mean diameter of these conflicted vessels being 1.5 mm (±0.4). The two symptomatic ICH-associated EVC's were observed on CBCT A/V but not MRI. CONCLUSIONS: In our series, CBCT A/V demonstrates an acceptable safety profile for SEEG planning compared to other imaging modalities. CBCT A/V identified significantly more EVC's compared to MRI, including those contributing to transient symptomatic intracranial hemorrhage. A conflicting vessel calibre of less than 1.2 mm on CBCT A/V did not contribute to ICH in our SEEG series.

The dynamics of competition and decision-making.

We examine the underlying cognitive mechanisms that govern how competitions play out over time. We used cognitive modeling to examine the dynamic effects of time remaining and relative performance (whether the person is winning or losing) on effort and strategy. In this experiment, participants completed a competitive decision-making task with varying time limits and starting scores, in a repeated-measures design. Participants were tasked with scoring more points than their computerized opponent during a certain time frame, gaining and losing points for correct and incorrect decisions, respectively. The results showed that as the competition deadline approached and as participants drew ahead of their opponent within a competition, they increased effort and became more cautious. Furthermore, the effect of relative score on effort and caution changed over the course of a competition as the deadline approached. These results highlight the importance of considering dynamics when working to understand how competitions unfold as well as the underlying cognitive mechanisms that give rise to the dynamic behavior.

Automated segmentation of epilepsy surgical resection cavities: Comparison of four methods to manual segmentation.

Accurate resection cavity segmentation on MRI is important for neuroimaging research involving epilepsy surgical outcomes. Manual segmentation, the gold standard, is highly labour intensive. Automated pipelines are an efficient potential solution; however, most have been developed for use following temporal epilepsy surgery. Our aim was to compare the accuracy of four automated segmentation pipelines following surgical resection in a mixed cohort of subjects following temporal or extra temporal epilepsy surgery. We identified 4 open-source automated segmentation pipelines. Epic-CHOP and ResectVol utilise SPM-12 within MATLAB, while Resseg and Deep Resection utilise 3D U-net convolutional neural networks. We manually segmented the resection cavity of 50 consecutive subjects who underwent epilepsy surgery (30 temporal, 20 extratemporal). We calculated Dice similarity coefficient (DSC) for each algorithm compared to the manual segmentation. No algorithm identified all resection cavities. ResectVol (n = 44, 88 %) and Epic-CHOP (n = 42, 84 %) were able to detect more resection cavities than Resseg (n = 22, 44 %, P < 0.001) and Deep Resection (n = 23, 46 %, P < 0.001). The SPM-based pipelines (Epic-CHOP and ResectVol) performed better than the deep learning-based pipelines in the overall and extratemporal surgery cohorts. In the temporal cohort, the SPM-based pipelines had higher detection rates, however there was no difference in the accuracy between methods. These pipelines could be applied to machine learning studies of outcome prediction to improve efficiency in pre-processing data, however human quality control is still required.

Association of Localizing 18F-FDG-PET Hypometabolism and Outcome Following Epilepsy Surgery: Systematic Review and Meta-Analysis.

BACKGROUND AND OBJECTIVES: Although commonly used in the evaluation of patients for epilepsy surgery, the association between the detection of localizing 18fluorine fluorodeoxyglucose PET (18F-FDG-PET) hypometabolism and epilepsy surgery outcome is uncertain. We conducted a systematic review and meta-analysis to determine whether localizing 18F-FDG-PET hypometabolism is associated with favorable outcome after epilepsy surgery. METHODS: A systematic literature search was undertaken. Eligible publications included evaluation with 18F-FDG-PET before epilepsy surgery, with ≥10 participants, and those that reported surgical outcome at ≥12 months. Random-effects meta-analysis was used to calculate the odds of achieving a favorable outcome, defined as Engel class I, International League Against Epilepsy class 1-2, or seizure-free, with localizing 18F-FDG-PET hypometabolism, defined as concordant with the epilepsy surgery resection zone. Meta-regression was used to characterize sources of heterogeneity. RESULTS: The database search identified 8,916 studies, of which 98 were included (total patients n = 4,104). Localizing 18F-FDG-PET hypometabolism was associated with favorable outcome after epilepsy surgery for all patients with odds ratio (OR) 2.68 (95% CI 2.08-3.45). Subgroup analysis yielded similar findings for those with (OR 2.64, 95% CI 1.54-4.52) and without epileptogenic lesion detected on MRI (OR 2.49, 95% CI 1.80-3.44). Concordance with EEG (OR 2.34, 95% CI 1.43-3.83), MRI (OR 1.69, 95% CI 1.19-2.40), and triple concordance with both (OR 2.20, 95% CI 1.32-3.64) was associated with higher odds of favorable outcome. By contrast, diffuse 18F-FDG-PET hypometabolism was associated with worse outcomes compared with focal hypometabolism (OR 0.34, 95% CI 0.22-0.54). DISCUSSION: Localizing 18F-FDG-PET hypometabolism is associated with favorable outcome after epilepsy surgery, irrespective of the presence of an epileptogenic lesion on MRI. The extent of 18F-FDG-PET hypometabolism provides additional information, with diffuse hypometabolism associated with worse surgical outcome than focal 18F-FDG-PET hypometabolism. These findings support the incorporation of 18F-FDG-PET into routine noninvasive investigations for patients being evaluated for epilepsy surgery to improve epileptogenic zone localization and to aid patient selection for surgery.

A systematic review of the impact of brain tumours on risk of motor vehicle crashes.

PURPOSE: Brain tumours are associated with neurocognitive impairments that are important for safe driving. Driving is vital to maintaining patient autonomy, despite this there is limited research on driving capacity amongst patients with brain tumours. The purpose of this review is to examine MVC risk in patients with brain tumours to inform development of clearer driving guidelines. METHODS: A systematic review was performed using Medline and EMBASE. Observational studies were included. The outcome of interest was MVC or measured risk of MVC in patients with benign or malignant brain tumours. Descriptive analysis and synthesis without meta-analysis were used to summarise findings. A narrative review of driving guidelines from Australia, United Kingdom and Canada was completed. RESULTS: Three studies were included in this review. One cohort study, one cross-sectional study and one case-control study were included (19,135 participants) across United States and Finland. One study evaluated the incidence of MVC in brain tumour patients, revealing no difference in MVC rates. Two studies measured MVC risk using driving simulation and cognitive testing. Patients found at higher risk of MVC had greater degrees of memory and visual attention impairments. However, predictive patient and tumour characteristics of MVC risk were heterogeneous across studies. Overall, driving guidelines had clear recommendations on selected conditions like seizures but were vague surrounding neurocognitive deficits. CONCLUSION: Limited data exists regarding driving behaviour and MVC incidence in brain tumour patients. Existing guidelines inadequately address neurocognitive complexities in this group. Future studies evaluating real-world data is required to inform development of more applicable driving guidelines. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO 2023 CRD42023434608.

Identifying epilepsy surgery referral candidates with natural language processing in an Australian context.

OBJECTIVE: Epilepsy surgery is known to be underutilized. Machine learning-natural language processing (ML-NLP) may be able to assist with identifying patients suitable for referral for epilepsy surgery evaluation. METHODS: Data were collected from two tertiary hospitals for patients seen in neurology outpatients for whom the diagnosis of "epilepsy" was mentioned. Individual case note review was undertaken to characterize the nature of the diagnoses discussed in these notes, and whether those with epilepsy fulfilled prespecified criteria for epilepsy surgery workup (namely focal drug refractory epilepsy without contraindications). ML-NLP algorithms were then developed using fivefold cross-validation on the first free-text clinic note for each patient to identify these criteria. RESULTS: There were 457 notes included in the study, of which 250 patients had epilepsy. There were 37 (14.8%) individuals who fulfilled the prespecified criteria for epilepsy surgery referral without described contraindications, 32 (12.8%) of whom were not referred for epilepsy surgical evaluation in the given clinic visit. In the prediction of suitability for epilepsy surgery workup using the prespecified criteria, the tested models performed similarly. For example, the random forest model returned an area under the receiver operator characteristic curve of 0.97 (95% confidence interval 0.93-1.0) for this task, sensitivity of 1.0, and specificity of 0.93. SIGNIFICANCE: This study has shown that there are patients in tertiary hospitals in South Australia who fulfill prespecified criteria for epilepsy surgery evaluation who may not have been referred for such evaluation. ML-NLP may assist with the identification of patients suitable for such referral. PLAIN LANGUAGE SUMMARY: Epilepsy surgery is a beneficial treatment for selected individuals with drug-resistant epilepsy. However, it is vastly underutilized. One reason for this underutilization is a lack of prompt referral of possible epilepsy surgery candidates to comprehensive epilepsy centers. Natural language processing, coupled with machine learning, may be able to identify possible epilepsy surgery candidates through the analysis of unstructured clinic notes. This study, conducted in two tertiary hospitals in South Australia, demonstrated that there are individuals who fulfill criteria for epilepsy surgery evaluation referral but have not yet been referred. Machine learning-natural language processing demonstrates promising results in assisting with the identification of such suitable candidates in Australia.

The impact of cognitive resource constraints on goal prioritization.

Many decisions we face daily entail deliberation about how to coordinate resources shared between multiple, competing goals. When time permits, people appear to approach these goal prioritization problems by analytically considering all goal-relevant information to arrive at a prioritization decision. However, it is not yet clear if this normative strategy extends to situations characterized by resource constraints such as when deliberation time is scarce or cognitive load is high. We evaluated the questions of how limited deliberation time and cognitive load affect goal prioritization decisions across a series of experiments using a gamified experimental task, which required participants to make a series of interdependent goal prioritization decisions. We fit several candidate models to experimental data to identify decision strategy adaptations at the individual subject-level. Results indicated that participants tended to opt for a simple heuristic strategy when cognitive resources were constrained rather than making a general tradeoff between speed and accuracy (e.g., the type of tradeoff that would be predicted by evidence accumulation models). The most common heuristic strategy involved disproportionately weighing information about goal deadlines compared to other goal-relevant information such as the goal's difficulty and the goal's subjective value.

Impact of contrasting poultry exposures on human, poultry, and wastewater antibiotic resistomes in Bangladesh.

IMPORTANCE: Through the use of DNA sequencing, our study shows that there is no significant difference in the antibiotic resistance genes found in stool samples taken from individuals with high exposure to poultry routinely fed antibiotics and those without such exposure. This finding is significant as it suggests limited transmission of antibiotic resistance genes between poultry and humans in these circumstances. However, our research also demonstrates that commercially reared poultry are more likely to possess resistance genes to antibiotics commonly administered on medium-sized farms. Additionally, our study highlights the under-explored potential of wastewater as a source of various antibiotic resistance genes, some of which are clinically relevant.

Ex-SPECTing Success: predictors of successful SPECT radiotracer injection during pre-surgical video-EEG admissions.

OBJECTIVES: To determine predictors of successful ictal Single Photon Emission Computed Tomography (SPECT) injections during Epilepsy Monitoring Unit (EMU) admissions for patients undergoing presurgical evaluation for drug resistant focal epilepsy. METHODS: In this retrospective study, consecutive EMU admissions were analysed at a single centre between 2019-2021. All seizures that occurred during the admission were reviewed. 'Injectable seizures' occurred during hours when the radiotracer was available. EMU-level data were analysed to identify factors predictive of an EMU admission with a successful SPECT injection (successful admission). Seizure-level data were analysed to identify factors predictive of an 'injectable seizure' receiving a SPECT injection during the ictal phase (successful injection). A multivariate generalised linear model was used to identify predictive variables. RESULTS: 125 EMU admissions involving 103 patients (median 37 years, IQR27.0-45.5) were analysed. 38.8% of seizures that were eligible for SPECT (n=134) were successfully injected; this represented 17.4% of all seizures (n=298) that occurred during admission. Unsuccessful admissions were most commonly due to a lack of seizures during EMU-SPECT (19.3%) or no 'injectable seizures' (62.3%). Successful EMU-SPECT was associated with baseline seizure frequency >1 per week (95%CI 2.1-3.0, p <0.001) and focal PET hypometabolism (95%CI 2.0-3.7, p <0.001). On multivariate analysis, the only factor associated with successful injection was patients being able to indicate they were having a seizure to staff (95%CI 1.0-4.4, p=0.038). SIGNIFICANCE: Completing a successful ictal SPECT study remains challenging. Baseline seizure frequency of >1 per-week, a PET hypometabolic focus and a patient's ability to indicate seizure onset were identified as predictors of success. These findings may assist EMUs in optimising their SPECT protocols, patient selection, and resource allocation.

An international survey of SEEG cortical stimulation practices.

OBJECTIVE: Cortical stimulation is an important component of stereoelectroencephalography (SEEG). Despite this, there is currently no standardized approach and significant heterogeneity in the literature regarding cortical stimulation practices. Via an international survey of SEEG clinicians, we sought to examine the spectrum of cortical stimulation practices to reveal areas of consensus and variability. METHODS: A 68-item questionnaire was developed to understand cortical stimulation practices including neurostimulation parameters, interpretation of epileptogenicity, functional and cognitive assessment and subsequent surgical decisions. Multiple recruitment pathways were pursued, with the questionnaire distributed directly to 183 clinicians. RESULTS: Responses were received from 56 clinicians across 17 countries with experience ranging from 2 to 60 years (M = 10.73, SD = 9.44). Neurostimulation parameters varied considerably, with maximum current ranging from 3 to 10 mA (M = 5.33, SD = 2.29) for 1 Hz and from 2 to 15 mA (M = 6.54, SD = 3.68) for 50 Hz stimulation. Charge density ranged from 8 to 200 µC/cm2 , with up to 43% of responders utilizing charge densities higher than recommended upper safety limits, i.e. 55 µC/cm2 . North American responders reported statistically significant higher maximum current (P < 0.001) for 1 Hz stimulation and lower pulse width for 1 and 50 Hz stimulation (P = 0.008, P < 0.001, respectively) compared to European responders. All clinicians evaluated language, speech, and motor function during cortical stimulation; in contrast, 42% assessed visuospatial or visual function, 29% memory, and 13% executive function. Striking differences were reported in approaches to assessment, classification of positive sites, and surgical decisions guided by cortical stimulation. Patterns of consistency were observed for interpretation of the localizing capacity of stimulated electroclinical seizures and auras, with habitual electroclinical seizures induced by 1 Hz stimulation considered the most localizing. SIGNIFICANCE: SEEG cortical stimulation practices differed vastly across clinicians internationally, highlighting the need for consensus-based clinical guidelines. In particular, an internationally standardized approach to assessment, classification, and functional prognostication will provide a common clinical and research framework for optimizing outcomes for people with drug-resistant epilepsy.

Identifying epilepsy surgery candidates with natural language processing: A systematic review.

INTRODUCTION: Epilepsy surgery is an underutilised, efficacious management strategy for selected individuals with drug-resistant epilepsy. Natural language processing (NLP) may aid in the identification of patients who are suitable to undergo evaluation for epilepsy surgery. The feasibility of this approach is yet to be determined. METHOD: In accordance with the PRISMA guidelines, a systematic review of the databases PubMed, EMBASE and Cochrane library was performed. This systematic review was prospectively registered on PROSPERO. RESULTS: 6 studies fulfilled inclusion criteria. The majority of included studies reported on datasets from only a single centre, with one study utilising data from two centres and one study six centres. The most commonly employed algorithms were support vector machines (5/6), with only one study utilising NLP strategies such as random forest models and gradient boosted machines. However, the results are promising, with all studies demonstrating moderate to high levels of performance in the identification of patients who may be suitable to undergo epilepsy surgery evaluation. Furthermore, multiple studies demonstrated that NLP could identify such patients 1-2 years prior to the treating clinicians instigating referral. However, no studies were identified that have evaluated the influence of implementing such algorithms on healthcare systems or patient outcomes. CONCLUSIONS: NLP is a promising approach to aid in the identification of patients that may be suitable to undergo epilepsy surgery evaluation. Further studies are required examining diverse datasets with additional analytical methodologies. Studies evaluating the impact of implementation of such algorithms would be beneficial.

Interruptions in healthcare: Modeling dynamic processes and effects at a team level.

Interruptions are associated with increases in medical errors amongst healthcare professionals, yet interventions to reduce interruptions have not been widely successful. While interruptions can be problematic for the interruptee, they may be necessary for the interrupter to maintain patient safety. To understand the emergent effects of interruptions within a dynamic environment, we develop a computational model that describes how nurses make decisions about interruptions and the effects those decisions have at a team level. Simulations reveal the dynamic interplay between urgency, task importance, the cost of being interrupted and team efficiency, depending on the consequences of clinical or procedural error, and shed light on the ways that the risks from interruptions can be better managed.

Abundance and phylogenetic distribution of eight key enzymes of the phosphorus biogeochemical cycle in grassland soils.

Grassland biomes provide valuable ecosystem services, including nutrient cycling. Organic phosphorus (Po) represents more than half of the total P in soils. Soil microorganisms release organic P through enzymatic processes, with alkaline phosphatases, acid phosphatases and phytases being the key P enzymes involved in the cycling of organic P. This study analysed 74 soil metagenomes from 17 different grassland biomes worldwide to evaluate the distribution and abundance of eight key P enzymes (PhoD, PhoX, PhoA, Nsap-A, Nsap-B, Nsap-C, BPP and CPhy) and their relationship with environmental factors. Our analyses showed that alkaline phosphatase phoD was the dataset's most abundant P-enzyme encoding genes, with a wide phylogenetic distribution. Followed by the acid phosphatases Nsap-A and Nsap-C showed similar abundance but a different distribution in their respective phylogenetic trees. Multivariate analyses revealed that pH, Tmax , SOC and soil moisture were associated with the abundance and diversity of all genes studied. PhoD and phoX genes strongly correlated with SOC and clay, and the phoX gene was more common in soils with low to medium SOC and neutral pH. In particular, P-enzyme genes tended to respond in a positively correlated manner among them, suggesting a complex relationship of abundance and diversity among them.

Impact of epilepsy surgery on quality of life: Systematic review and meta-analysis.

Improved quality of life (QoL) is an important outcome goal following epilepsy surgery. This study aims to quantify change in QoL for adults with drug-resistant epilepsy (DRE) who undergo epilepsy surgery, and to explore clinicodemographic factors associated with these changes. We conducted a systematic review and meta-analysis using Medline, Embase, and Cochrane Central Register of Controlled Trials. All studies reporting pre- and post-epilepsy surgery QoL scores in adults with DRE via validated instruments were included. Meta-analysis assessed the postsurgery change in QoL. Meta-regression assessed the effect of postoperative seizure outcomes on postoperative QoL as well as change in pre- and postoperative QoL scores. A total of 3774 titles and abstracts were reviewed, and ultimately 16 studies, comprising 1182 unique patients, were included. Quality of Life in Epilepsy Inventory-31 item (QOLIE-31) meta-analysis included six studies, and QOLIE-89 meta-analysis included four studies. Postoperative change in raw score was 20.5 for QOLIE-31 (95% confidence interval [CI] = 10.9-30.1, I2 = 95.5) and 12.1 for QOLIE-89 (95% CI = 8.0-16.1, I2 = 55.0%). This corresponds to clinically meaningful QOL improvements. Meta-regression demonstrated a higher postoperative QOLIE-31 score as well as change in pre- and postoperative QOLIE-31 score among studies of cohorts with higher proportions of patients with favorable seizure outcomes. At an individual study level, preoperative absence of mood disorders, better preoperative cognition, fewer trials of antiseizure medications before surgery, high levels of conscientiousness and openness to experience at the baseline, engagement in paid employment before and after surgery, and not being on antidepressants following surgery were associated with improved postoperative QoL. This study demonstrates the potential for epilepsy surgery to provide clinically meaningful improvements in QoL, as well as identifies clinicodemographic factors associated with this outcome. Limitations include substantial heterogeneity between individual studies and high risk of bias.

Arable soil nitrogen dynamics reflect organic inputs via the extended composite phenotype.

Achieving food security requires resilient agricultural systems with improved nutrient-use efficiency, optimized water and nutrient storage in soils, and reduced gaseous emissions. Success relies on understanding coupled nitrogen and carbon metabolism in soils, their associated influences on soil structure and the processes controlling nitrogen transformations at scales relevant to microbial activity. Here we show that the influence of organic matter on arable soil nitrogen transformations can be decoded by integrating metagenomic data with soil structural parameters. Our approach provides a mechanistic explanation of why organic matter is effective in reducing nitrous oxide losses while supporting system resilience. The relationship between organic carbon, soil-connected porosity and flow rates at scales relevant to microbes suggests that important increases in nutrient-use efficiency could be achieved at lower organic carbon stocks than currently envisaged.

Using cognitive modeling to examine the effects of competition on strategy and effort in races and tournaments.

We investigated the effects of two types of competition, races and tournaments (as well as an individual challenge and a do-your-best condition), on two different aspects of performance: effort and strategy. In our experiment, 100 undergraduate participants completed a simple cognitive task under four experimental conditions (in a repeated-measures design) based on different types of competitions and challenges. We used the Linear Ballistic Accumulator to quantify the effects of competition on strategy and effort. The results reveal that competition produced changes in strategy rather than effort, and that trait competitiveness had minimal impact on how people responded to competition. This suggests individuals are more likely to adjust their strategy in competitions, and the uncertainty created by different competition types influences the direction of these strategy adjustments.

Intrinsic and secondary epileptogenicity in focal cortical dysplasia type II.

OBJECTIVE: Favorable seizure outcome is reported following resection of bottom-of-sulcus dysplasia (BOSD). We assessed the distribution of epileptogenicity and dysplasia in and around BOSD to better understand this clinical outcome and the optimal surgical approach. METHODS: We studied 27 children and adolescents with magnetic resonance imaging (MRI)-positive BOSD who underwent epilepsy surgery; 85% became seizure-free postresection (median = 5.0 years follow-up). All patients had resection of the dysplastic sulcus, and 11 had additional resection of the gyral crown (GC) or adjacent gyri (AG). Markers of epileptogenicity were relative cortical hypometabolism on preoperative 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET), and spiking, ripples, fast ripples, spike-high-frequency oscillation cross-rate, and phase amplitude coupling (PAC) on preresection and postresection electrocorticography (ECoG), all analyzed at the bottom-of-sulcus (BOS), top-of-sulcus (TOS), GC, and AG. Markers of dysplasia were increased cortical thickness on preoperative MRI, and dysmorphic neuron density and variant allele frequency of somatic MTOR mutations in resected tissue, analyzed at similar locations. RESULTS: Relative cortical metabolism was significantly reduced and ECoG markers were significantly increased at the BOS compared to other regions. Apart from spiking and PAC, which were greater at the TOS compared to the GC, there were no significant differences in PET and other ECoG markers between the TOS, GC, and AG, suggesting a cutoff of epileptogenicity at the TOS rather than a tapering gradient on the cortical surface. MRI and tissue markers of dysplasia were all maximal in the BOS, reduced in the TOS, and mostly absent in the GC. Spiking and PAC reduced significantly over the GC after resection of the dysplastic sulcus. SIGNIFICANCE: These findings support the concept that dysplasia and intrinsic epileptogenicity are mostly limited to the dysplastic sulcus in BOSD and support resection or ablation confined to the MRI-visible lesion as a first-line surgical approach. 18 F-FDG PET and ECoG abnormalities in surrounding cortex seem to be secondary phenomena.

Evidence accumulation modelling in the wild: understanding safety-critical decisions.

Evidence accumulation models (EAMs) are a class of computational cognitive model used to understand the latent cognitive processes that underlie human decisions and response times (RTs). They have seen widespread application in cognitive psychology and neuroscience. However, historically, the application of these models was limited to simple decision tasks. Recently, researchers have applied these models to gain insight into the cognitive processes that underlie observed behaviour in applied domains, such as air-traffic control (ATC), driving, forensic and medical image discrimination, and maritime surveillance. Here, we discuss how this modelling approach helps researchers understand how the cognitive system adapts to task demands and interventions, such as task automation. We also discuss future directions and argue for wider adoption of cognitive modelling in Human Factors research.

Predictive models for starting antiseizure medication withdrawal following epilepsy surgery in adults.

More than half of adults with epilepsy undergoing resective epilepsy surgery achieve long-term seizure freedom and might consider withdrawing antiseizure medications. We aimed to identify predictors of seizure recurrence after starting postoperative antiseizure medication withdrawal and develop and validate predictive models. We performed an international multicentre observational cohort study in nine tertiary epilepsy referral centres. We included 850 adults who started antiseizure medication withdrawal following resective epilepsy surgery and were free of seizures other than focal non-motor aware seizures before starting antiseizure medication withdrawal. We developed a model predicting recurrent seizures, other than focal non-motor aware seizures, using Cox proportional hazards regression in a derivation cohort (n = 231). Independent predictors of seizure recurrence, other than focal non-motor aware seizures, following the start of antiseizure medication withdrawal were focal non-motor aware seizures after surgery and before withdrawal [adjusted hazard ratio (aHR) 5.5, 95% confidence interval (CI) 2.7-11.1], history of focal to bilateral tonic-clonic seizures before surgery (aHR 1.6, 95% CI 0.9-2.8), time from surgery to the start of antiseizure medication withdrawal (aHR 0.9, 95% CI 0.8-0.9) and number of antiseizure medications at time of surgery (aHR 1.2, 95% CI 0.9-1.6). Model discrimination showed a concordance statistic of 0.67 (95% CI 0.63-0.71) in the external validation cohorts (n = 500). A secondary model predicting recurrence of any seizures (including focal non-motor aware seizures) was developed and validated in a subgroup that did not have focal non-motor aware seizures before withdrawal (n = 639), showing a concordance statistic of 0.68 (95% CI 0.64-0.72). Calibration plots indicated high agreement of predicted and observed outcomes for both models. We show that simple algorithms, available as graphical nomograms and online tools (predictepilepsy.github.io), can provide probabilities of seizure outcomes after starting postoperative antiseizure medication withdrawal. These multicentre-validated models may assist clinicians when discussing antiseizure medication withdrawal after surgery with their patients.