Nomograms to Predict Verbal Memory Decline After Temporal Lobe Resection in Adults With Epilepsy

Impact of white matter networks on risk for memory decline following resection versus ablation in temporal lobe epilepsy

BACKGROUND

With expanding neurosurgical options in epilepsy, it is important to characterise each options' risk for postoperative cognitive decline. Here, we characterise how patients' preoperative white matter (WM) networks relates to postoperative memory changes following different epilepsy surgeries.

METHODS

Eighty-nine patients with temporal lobe epilepsy with T1-weighted and diffusion-weighted imaging as well as preoperative and postoperative verbal memory scores (prose recall) underwent either anterior temporal lobectomy (ATL: n=38) or stereotactic laser amygdalohippocampotomy (SLAH; n=51). We computed laterality indices (ie, asymmetry) for volume of the hippocampus and fractional anisotropy (FA) of two deep WM tracts (uncinate fasciculus (UF) and inferior longitudinal fasciculus (ILF)).

RESULTS

Preoperatively, left-lateralised FA of the ILF was associated with higher prose recall (p<0.01). This pattern was not observed for the UF or hippocampus (ps>0.05). Postoperatively, right-lateralised FA of the UF was associated with less decline following left ATL (p<0.05) but not left SLAH (p>0.05), while right-lateralised hippocampal asymmetry was associated with less decline following both left ATL and SLAH (ps<0.05). After accounting for preoperative memory score, age of onset and hippocampal asymmetry, the association between UF and memory decline in left ATL remained significant (p<0.01).

CONCLUSIONS

Asymmetry of the hippocampus is an important predictor of risk for memory decline following both surgeries. However, asymmetry of UF integrity, which is only severed during ATL, is an important predictor of memory decline after ATL only. As surgical procedures and pre-surgical mapping evolve, understanding the role of frontal-temporal WM in memory networks could help to guide more targeted surgical approaches to mitigate cognitive decline.

Verbal memory network mapping in individual patients predicts postoperative functional impairments

Verbal memory decline is a significant concern following temporal lobe surgeries in patients with epilepsy, emphasizing the need for precision presurgical verbal memory mapping to optimize functional outcomes. However, the inter-individual variability in functional networks and brain function-structural dissociations pose challenges when relying solely on group-level atlases or anatomical landmarks for surgical guidance. Here, we aimed to develop and validate a personalized functional mapping technique for verbal memory using precision resting-state functional MRI (rs-fMRI) and neurosurgery. A total of 38 patients with refractory epilepsy scheduled for surgical interventions were enrolled and 28 patients were analyzed in the study. Baseline 30-min rs-fMRI scanning, verbal memory and language assessments were collected for each patient before surgery. Personalized verbal memory networks (PVMN) were delineated based on preoperative rs-fMRI data for each patient. The accuracy of PVMN was assessed by comparing post-operative functional impairments and the overlapping extent between PVMN and surgical lesions. A total of 14 out of 28 patients experienced clinically meaningful declines in verbal memory after surgery. The personalized network and the group-level atlas exhibited 100% and 75.0% accuracy in predicting postoperative verbal memory declines, respectively. Moreover, six patients with extra-temporal lesions that overlapped with PVMN showed selective impairments in verbal memory. Furthermore, the lesioned ratio of the personalized network rather than the group-level atlas was significantly correlated with postoperative declines in verbal memory (personalized networks: r = -0.39, p = .038; group-level atlas: r = -0.19, p = .332). In conclusion, our personalized functional mapping technique, using precision rs-fMRI, offers valuable insights into individual variability in the verbal memory network and holds promise in precision verbal memory network mapping in individuals.

Cortical Thickness Patterns of Cognitive Impairment Phenotypes in Drug-Resistant Temporal Lobe Epilepsy

OBJECTIVE

In temporal lobe epilepsy (TLE), a taxonomy classifying patients into 3 cognitive phenotypes has been adopted: minimally, focally, or multidomain cognitively impaired (CI). We examined gray matter (GM) thickness patterns of cognitive phenotypes in drug-resistant TLE and assessed potential use for predicting postsurgical cognitive outcomes.

METHODS

TLE patients undergoing presurgical evaluation were categorized into cognitive phenotypes. Network edge weights and distances were calculated using type III analysis of variance F-statistics from comparisons of GM regions within each TLE cognitive phenotype and age- and sex-matched healthy participants. In resected patients, logistic regression models (LRMs) based on network analysis results were used for prediction of postsurgical cognitive outcome.

RESULTS

A total of 124 patients (63 females, mean age ± standard deviation [SD] = 36.0 ± 12.0 years) and 117 healthy controls (63 females, mean age ± SD = 36.1 ± 12.0 years) were analyzed. In the multidomain CI group (n = 66, 53.2%), 28 GM regions were significantly thinner compared to healthy controls. Focally impaired patients (n = 37, 29.8%) showed 13 regions, whereas minimally impaired patients (n = 21, 16.9%) had 2 significantly thinner GM regions. Regions affected in both multidomain and focally impaired patients included the anterior cingulate cortex, medial prefrontal cortex, medial temporal, and lateral temporal regions. In 69 (35 females, mean age ± SD = 33.6 ± 18.0 years) patients who underwent surgery, LRMs based on network-identified GM regions predicted postsurgical verbal memory worsening with a receiver operating curve area under the curve of 0.70 ± 0.15.

INTERPRETATION

A differential pattern of GM thickness can be found across different cognitive phenotypes in TLE. Including magnetic resonance imaging with clinical measures associated with cognitive profiles has potential in predicting postsurgical cognitive outcomes in drug-resistant TLE. ANN NEUROL 2024;95:984-997.

Fully automatic segmentation of brain lacunas resulting from resective surgery using a 3D deep learning model

The rapid and constant development of deep learning (DL) strategies is pushing forward the quality of object segmentation in images from diverse fields of interest. In particular, these algorithms can be very helpful in delineating brain abnormalities (lesions, tumors, lacunas, etc), enabling the extraction of information such as volume and location, that can inform doctors or feed predictive models. Here, we describe ResectVol DL, a fully automatic tool developed to segment resective lacunas in brain images of patients with epilepsy. ResectVol DL relies on the nnU-Net framework that leverages the 3D U-Net deep learning architecture. T1-weighted MRI datasets from 120 patients (57 women; 31.5 ± 15.9 years old at surgery) were used to train (n=78) and test (n=48) our tool. Manual segmentations were carried out by five different raters and were considered as ground truth for performance assessment. We compared ResectVol DL with two other fully automatic methods: ResectVol 1.1.2 and DeepResection, using the Dice similarity coefficient (DSC), Pearson's correlation coefficient, and relative difference to manual segmentation. ResectVol DL presented the highest median DSC (0.92 vs. 0.78 and 0.90), the highest correlation coefficient (0.99 vs. 0.63 and 0.94), and the lowest median relative difference (9 vs. 44 and 12 %). Overall, we demonstrate that ResectVol DL accurately segments brain lacunas, which has the potential to assist in the development of predictive models for postoperative cognitive and seizure outcomes.

Predictive models of epilepsy outcomes

PURPOSE OF REVIEW

Multiple complex medical decisions are necessary in the course of a chronic disease like epilepsy. Predictive tools to assist physicians and patients in navigating this complexity have emerged as a necessity and are summarized in this review.

RECENT FINDINGS

Nomograms and online risk calculators are user-friendly and offer individualized predictions for outcomes ranging from safety of antiseizure medication withdrawal (accuracy 65-73%) to seizure-freedom, naming, mood, and language outcomes of resective epilepsy surgery (accuracy 72-81%). Improving their predictive performance is limited by the nomograms' inability to ingest complex data inputs. Conversely, machine learning offers the potential of multimodal and expansive model inputs achieving human-expert level accuracy in automated scalp electroencephalogram (EEG) interpretation but lagging in predictive performance or requiring validation for other applications.

SUMMARY

Good to excellent predictive models are now available to guide medical and surgical epilepsy decision-making with nomograms offering individualized predictions and user-friendly tools, and machine learning approaches offering the potential of improved performance. Future research is necessary to bridge the two approaches for optimal translation to clinical care.

Early Identification of Candidates for Epilepsy Surgery: A Multicenter, Machine Learning, Prospective Validation Study

BACKGROUND AND OBJECTIVES

Epilepsy surgery is often delayed. We previously developed machine learning (ML) models to identify candidates for resective epilepsy surgery earlier in the disease course. In this study, we report the prospective validation.

METHODS

In this multicenter, prospective, longitudinal cohort study, random forest models were validated at a pediatric epilepsy center consisting of 2 hospitals and 14 outpatient neurology clinic sites and an adult epilepsy center with 2 hospitals and 27 outpatient neurology clinic sites. The models used neurology visit notes, EEG and MRI reports, visit patterns, hospitalizations, and medication, laboratory, and procedure orders to identify candidates for surgery. The models were trained on historical data up to May 10, 2019. Patients with an ICD-10 diagnosis of epilepsy who visited from May 11, 2019, to May 10, 2020, were screened by the algorithm and assigned surgical candidacy scores. The primary outcome was area under the curve (AUC), which was calculated by comparing scores from patients who underwent epilepsy surgery before November 10, 2020, against scores from nonsurgical patients. Nonsurgical patients' charts were reviewed to determine whether patients with high scores were more likely to be missed surgical candidates. Delay to surgery was defined as the time between the first visit that a surgical candidate was identified by the algorithm and the date of the surgery.

RESULTS

A total of 5,285 pediatric and 5,782 adult patients were included to train the ML algorithms. During the study period, 41 children and 23 adults underwent resective epilepsy surgery. In the pediatric cohort, AUC was 0.91 (95% CI 0.87-0.94), positive predictive value (PPV) was 0.08 (0.05-0.10), and negative predictive value (NPV) was 1.00 (0.99-1.00). In the adult cohort, AUC was 0.91 (0.86-0.97), PPV was 0.07 (0.04-0.11), and NPV was 1.00 (0.99-1.00). The models first identified patients at a median of 2.1 years (interquartile range [IQR]: 1.2-4.9 years, maximum: 11.1 years) before their surgery and 1.3 years (IQR: 0.3-4.0 years, maximum: 10.1 years) before their presurgical evaluations.

DISCUSSION

ML algorithms can identify surgical candidates earlier in the disease course. Even at specialized epilepsy centers, there is room to shorten the time to surgery.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that a machine learning algorithm can accurately distinguish patients with epilepsy who require resective surgery from those who do not.

The relationship between mood and anxiety and cognitive phenotypes in adults with pharmacoresistant temporal lobe epilepsy

OBJECTIVE

Patients with temporal lobe epilepsy (TLE) are often at a high risk for cognitive and psychiatric comorbidities. Several cognitive phenotypes have been identified in TLE, but it is unclear how phenotypes relate to psychiatric comorbidities, such as anxiety and depression. This observational study investigated the relationship between cognitive phenotypes and psychiatric symptomatology in TLE.

METHODS

A total of 826 adults (age = 40.3, 55% female) with pharmacoresistant TLE completed a neuropsychological evaluation that included at least two measures from five cognitive domains to derive International Classification of Cognitive Disorders in Epilepsy (IC-CoDE) cognitive phenotypes (i.e., intact, single-domain impairment, bi-domain impairment, generalized impairment). Participants also completed screening measures for depression and anxiety. Psychiatric history and medication data were extracted from electronic health records. Multivariable proportional odds logistic regression models examined the relationship between IC-CoDE phenotypes and psychiatric variables after controlling for relevant covariates.

RESULTS

Patients with elevated depressive symptoms had a greater odds of demonstrating increasingly worse cognitive phenotypes than patients without significant depressive symptomatology (odds ratio [OR] = 1.123-1.993, all corrected p's < .05). Number of psychotropic (OR = 1.584, p < .05) and anti-seizure medications (OR = 1.507, p < .001), use of anti-seizure medications with mood-worsening effects (OR = 1.748, p = .005), and history of a psychiatric diagnosis (OR = 1.928, p < .05) also increased the odds of a more severe cognitive phenotype, while anxiety symptoms were unrelated.

SIGNIFICANCE

This study demonstrates that psychiatric factors are not only associated with function in specific cognitive domains but also with the pattern and extent of deficits across cognitive domains. Results suggest that depressive symptoms and medications are strongly related to cognitive phenotype in adults with TLE and support the inclusion of these factors as diagnostic modifiers for cognitive phenotypes in future work. Longitudinal studies that incorporate neuroimaging findings are warranted to further our understanding of the complex relationships between cognition, mood, and seizures and to determine whether non-pharmacologic treatment of mood symptoms alters cognitive phenotype.

Preoperative white matter network organization and memory decline after epilepsy surgery

OBJECTIVE

Risk for memory decline is a common concern for individuals with temporal lobe epilepsy (TLE) undergoing surgery. Global and local network abnormalities are well documented in TLE. However, it is less known whether network abnormalities predict postsurgical memory decline. The authors examined the role of preoperative global and local white matter network organization and risk of postoperative memory decline in TLE.

METHODS

One hundred one individuals with TLE (n = 51 with left TLE and 50 with right TLE) underwent preoperative T1-weighted MRI, diffusion MRI, and neuropsychological memory testing in a prospective longitudinal study. Fifty-six age- and sex-matched controls completed the same protocol. Forty-four patients (22 with left TLE and 22 with right TLE) subsequently underwent temporal lobe surgery and postoperative memory testing. Preoperative structural connectomes were generated via diffusion tractography and analyzed using measures of global and local (i.e., medial temporal lobe [MTL]) network organization. Global metrics measured network integration and specialization. The local metric was calculated as an asymmetry of the mean local efficiency between the ipsilateral and contralateral MTLs (i.e., MTL network asymmetry).

RESULTS

Higher preoperative global network integration and specialization were associated with higher preoperative verbal memory function in patients with left TLE. Higher preoperative global network integration and specialization, as well as greater leftward MTL network asymmetry, predicted greater postoperative verbal memory decline for patients with left TLE. No significant effects were observed in right TLE. Accounting for preoperative memory score and hippocampal volume asymmetry, MTL network asymmetry uniquely explained 25%-33% of the variance in verbal memory decline for left TLE and outperformed hippocampal volume asymmetry and global network metrics. MTL network asymmetry alone produced good diagnostic classification of memory decline in left TLE (i.e., an area under the receiver operating characteristic curve of 0.80-0.84 and correct classification of 65%-76% of cases with cross-validation).

CONCLUSIONS

These preliminary data suggest that global white matter network disruption contributes to verbal memory impairment preoperatively and predicts postsurgical verbal memory outcomes in left TLE. However, a leftward asymmetry of MTL white matter network organization may confer the highest risk for verbal memory decline. Although this requires replication in a larger sample, the authors demonstrate the importance of characterizing preoperative local white matter network properties within the to-be-operated hemisphere and the reserve capacity of the contralateral MTL network, which may eventually be useful in presurgical planning.

Leveraging the resting brain to predict memory decline after temporal lobectomy

OBJECTIVE

Predicting memory morbidity after temporal lobectomy in patients with temporal lobe epilepsy (TLE) relies on indices of preoperative temporal lobe structural and functional integrity. However, epilepsy is increasingly considered a network disorder, and memory a network phenomenon. We assessed the utility of functional network measures to predict postoperative memory changes.

METHODS

Seventy-two adults with TLE (37 left/35 right) underwent preoperative resting-state functional magnetic resonance imaging and pre- and postoperative neuropsychological assessment. We compared functional connectivity throughout the memory network of each patient to a healthy control template (n = 19) to identify differences in global organization. A second metric indicated the degree of integration of the to-be-resected temporal lobe with the rest of the memory network. We included these measures in a linear regression model alongside standard clinical variables as predictors of memory change after surgery.

RESULTS

Left TLE patients with more atypical memory networks, and with greater functional integration of the to-be-resected region with the rest of the memory network preoperatively, experienced the greatest decline in verbal memory after surgery. Together, these two measures explained 44% of variance in verbal memory change, outperforming standard clinical and demographic variables. None of the variables examined was associated with visuospatial memory change in patients with right TLE.

SIGNIFICANCE

Resting-state connectivity provides valuable information concerning both the integrity of to-be-resected tissue and functional reserve across memory-relevant regions outside of the to-be-resected tissue. Intrinsic functional connectivity has the potential to be useful for clinical decision-making regarding memory outcomes in left TLE, and more work is needed to identify the factors responsible for differences seen in right TLE.

Interictal magnetoencephalography abnormalities to guide intracranial electrode implantation and predict surgical outcome

Intracranial EEG is the gold standard technique for epileptogenic zone localization but requires a preconceived hypothesis of the location of the epileptogenic tissue. This placement is guided by qualitative interpretations of seizure semiology, MRI, EEG and other imaging modalities, such as magnetoencephalography. Quantitative abnormality mapping using magnetoencephalography has recently been shown to have potential clinical value. We hypothesized that if quantifiable magnetoencephalography abnormalities were sampled by intracranial EEG, then patients' post-resection seizure outcome may be better. Thirty-two individuals with refractory neocortical epilepsy underwent magnetoencephalography and subsequent intracranial EEG recordings as part of presurgical evaluation. Eyes-closed resting-state interictal magnetoencephalography band power abnormality maps were derived from 70 healthy controls as a normative baseline. Magnetoencephalography abnormality maps were compared to intracranial EEG electrode implantation, with the spatial overlap of intracranial EEG electrode placement and cerebral magnetoencephalography abnormalities recorded. Finally, we assessed if the implantation of electrodes in abnormal tissue and subsequent resection of the strongest abnormalities determined by magnetoencephalography and intracranial EEG corresponded to surgical success. We used the area under the receiver operating characteristic curve as a measure of effect size. Intracranial electrodes were implanted in brain tissue with the most abnormal magnetoencephalography findings-in individuals that were seizure-free postoperatively (T = 3.9, P = 0.001) but not in those who did not become seizure-free. The overlap between magnetoencephalography abnormalities and electrode placement distinguished surgical outcome groups moderately well (area under the receiver operating characteristic curve = 0.68). In isolation, the resection of the strongest abnormalities as defined by magnetoencephalography and intracranial EEG separated surgical outcome groups well, area under the receiver operating characteristic curve = 0.71 and area under the receiver operating characteristic curve = 0.74, respectively. A model incorporating all three features separated surgical outcome groups best (area under the receiver operating characteristic curve = 0.80). Intracranial EEG is a key tool to delineate the epileptogenic zone and help render individuals seizure-free postoperatively. We showed that data-driven abnormality maps derived from resting-state magnetoencephalography recordings demonstrate clinical value and may help guide electrode placement in individuals with neocortical epilepsy. Additionally, our predictive model of postoperative seizure freedom, which leverages both magnetoencephalography and intracranial EEG recordings, could aid patient counselling of expected outcome.

Adult epilepsy

Epilepsy is a common medical condition that affects people of all ages, races, social classes, and geographical regions. Diagnosis of epilepsy remains clinical, and ancillary investigations (electroencephalography, imaging, etc) are of aid to determine the type, cause, and prognosis. Antiseizure medications represent the mainstay of epilepsy treatment: they aim to suppress seizures without adverse events, but they do not affect the underlying predisposition to generate seizures. Currently available antiseizure medications are effective in around two-thirds of patients with epilepsy. Neurosurgical resection is an effective strategy to reach seizure control in selected individuals with drug-resistant focal epilepsy. Non-pharmacological treatments such as palliative surgery (eg, corpus callosotomy), neuromodulation techniques (eg, vagus nerve stimulation), and dietary interventions represent therapeutic options for patients with drug-resistant epilepsy who are not suitable for resective brain surgery.

What are we really predicting with fMRI in epilepsy surgery?

While memory and language functional magnetic resonance imaging (fMRI) paradigms are becoming evermore refined, the measures of outcome they predict following epilepsy surgery tend to remain single scores on pencil and paper tests that were developed decades ago and have been repeatedly shown to bear little relation to patients' subjective reports of memory problems in the real world. The growing imbalance between the increasing sophistication of the predictive paradigms on the one hand and the vintage measures of the outcome on the other in the fMRI epilepsy surgery literature threatens the clinical relevance of studies employing these technologies. This paper examines some of the core principles of assessing neuropsychological outcomes following epilepsy surgery and explores how these may be adapted and applied in fMRI study designs to maximize the clinical relevance of these studies.

New insights for predicting surgery outcome in patients with temporal lobe epilepsy. A systematic review

Resective surgery is the treatment of choice for one-third of adult patients with focal, drug-resistant epilepsy. This procedure is associated with substantial clinical and cognitive risks. In clinical practice, there is no validated model for epilepsy surgery outcome prediction (ESOP). Meta-analyses on ESOP studies assessing prognostic factors report discrepancies in terms of study design. Our review aims to systematically investigate methodological and analytical aspects of studies predicting clinical and cognitive outcomes after temporal lobe epilepsy surgery. A systematic review of ESOP studies published between 2000 and 2022 from three databases (MEDLINE, Web of Science, and PsycINFO) was completed by following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. It yielded 4867 articles. Among them, 21 corresponded to our inclusion criteria and were therefore retained in the final review. The risk of bias was assessed using A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST). Data extracted from the 21 studies were analyzed using narrative synthesis and descriptive statistics. Our findings show an increase in the use of multimodal datasets and machine learning analyses in recent ESOP studies, although regression remained the most frequently used approach. We also identified a more frequent use of network notions in recent ESOP studies. Nevertheless, several methodological issues were noted, such as small sample sizes, lack of information on the follow-up period, variability in seizure outcome, and the definition of neuropsychological postoperative change. Of 21 studies, only one provided a clinical tool to anticipate the cognitive outcome after epilepsy surgery. We conclude that methodological issues should be overcome before we move towards more complete models to better predict clinical and cognitive outcomes after epilepsy surgery. Recommendations for future studies to harness the possibilities of multimodal datasets and data fusion, are provided. A stronger bridge between fundamental and clinical research may result in developing accessible clinical tools.

White matter network organization predicts memory decline after epilepsy surgery

The authors have withdrawn their manuscript owing to a substantial change in data analysis and findings/conclusions. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

A procedure for predicting, illustrating, communicating, and optimizing patient-centered outcomes of epilepsy surgery using nomograms and Bayes' theorem

Clinicians have an ethical obligation to obtain and convey relevant information about possible treatment outcomes in a manner that can be comprehended by patients. This contributes to the processes of informed consent and shared prospective decision-making. In epilepsy neurosurgery, there has historically been an emphasis on studying clinician-centered (e.g., seizure- and cognition-related) outcomes and using these data to inform recommendations and, by extension, to frame pre-surgical counseling with respect to patients' decisions about elective neurosurgery. In contrast, there is a relative dearth of available data related to patient-centered outcomes of epilepsy neurosurgery, such as functional (e.g., employment) status, and there is also a lack of methods to communicate these data to patients. Here, illustrated using a hypothetical case scenario, we present a potential solution to the latter of these problems using principles of evidence-based neuropsychology; published data on patient employment status before and after epilepsy neurosurgery; and Bayes' theorem. First, we reviewed existing literature on employment outcomes following epilepsy neurosurgery to identify and extract data relevant to our hypothetical patient, clinical question, and setting. Then, we used the base rate (prior probability) of post-surgical unemployment, contingency tables (to derive likelihood ratios), and Bayes' theorem to compute the conditional (posterior) probability of post-surgical employment status for our hypothetical patient scenario. Finally, we translated this information to an intuitive visual format (Bayesian nomogram) that can support evidence-based pre-surgical counseling. We propose that the application of our patient-centered decision-support process and visual aid will improve clinician-patient communication about prospective risks and benefits of epilepsy neurosurgery and will empower clinicians and patients to make informed decisions about whether or not to pursue elective neurosurgery with a greater degree of confidence and with more realistic and concrete expectations about possible outcomes. We further propose that clinicians and patients would benefit from incorporating this evidence-based framework into a broader sequence of function-focused epilepsy treatment that includes pre-surgical assessments and interventions ("prehabilitation"), neurosurgery, and post-surgical cognitive/vocational rehabilitation.

Machine Learning for Precision Epilepsy Surgery

Brain surgery offers the best chance of seizure-freedom for patients with focal drug-resistant epilepsy, but only 50% achieve sustained seizure-freedom. With the explosion of data collected during routine presurgical evaluations and recent advances in computational science, we now have a tremendous potential to achieve precision epilepsy surgery: a data-driven tailoring of surgical planning. This review highlights the clinical need, the relevant computational science focusing on machine learning, and discusses some specific applications in epilepsy surgery.

Cognitive decline and quality of life after resective epilepsy surgery

OBJECTIVES

The objectives of this study were to examine the association between cognitive decline and quality of life (QoL) change in a large sample of individuals with drug-resistant epilepsy who underwent resective surgery and to examine whether the association between cognitive decline and QoL is differentially affected by seizure classification outcome (Engel Class 1 vs. 2-4) or side of surgery (left vs. right hemisphere).

MATERIALS AND METHODS

The sample comprised 224 adults (ages ≥ 18) with drug-resistant focal epilepsy treated with resective surgery who underwent comprehensive pre-operative and post-operative evaluations including neuropsychological testing and the Quality of Life in Epilepsy Inventory - 31 between 1991 and 2020. Linear mixed-effects models were fit to examine subject-specific trajectories and assess the effects of time (pre- to post-operative), cognitive decline (number of measures that meaningfully declined), and the interaction between time and cognitive decline on pre- to post-operative change in QoL.

RESULTS

Increases in QoL following resection were observed (B = -10.72 [SE = 1.22], p < .001; mean difference between time point 1 and time point 2 QoL rating = 8.11). There was also a main effect of cognitive decline on QoL (B = -.85 [SE = .27], p = .002). Follow-up analyses showed that the number of cognitive measures that declined was significantly associated with post-surgical QoL, (r = -.20 p = .003), but not pre-surgical QoL, (r = -.04 p = .594), and with pre-to post-surgery raw change in QoL score, (r = -.18 p = .009). A cognitive decline by time point interaction was observed, such that those who had greater cognitive decline had less improvement in overall QoL following resection (B = .72 [SE = .27], p = .009). Similar results were observed within the Engel Class 1 outcome subgroup. However, within the Engel Class 2-4 outcome subgroup, QoL improved following resection, but there was no main effect of cognitive decline or interaction between cognitive decline and time point on QoL change. There was no main effect of resection hemisphere on overall QoL, nor were there interactions with hemisphere by time, hemisphere by cognitive decline, or hemisphere by time by cognitive decline.

CONCLUSIONS

Quality of life improves following epilepsy surgery. Participants who had cognitive decline across a greater number of measures experienced less improvement in QoL post-operatively overall, but there was no clear pattern of domain-specific cognitive decline associated with change in QoL. Our results indicate that cognitive decline in a diffuse set of cognitive domains negatively influences post-operative QoL, particularly for those who experience good seizure outcomes (i.e., seizure freedom), regardless of the site or side of resection.

What's in Your Nomogram? Personalized Prognostication of Verbal Memory Decline after Temporal Lobe Resection in Adults With Epilepsy

OBJECTIVE

This study aims to develop and externally validate models to predict the probability of postoperative verbal memory decline in adults following temporal lobe resection (TLR) for epilepsy using easily accessible preoperative clinical predictors.

METHODS

Multivariable models were developed to predict delayed verbal memory outcome on 3 commonly used measures: Rey Auditory Verbal Learning Test (RAVLT), and Logical Memory (LM), and Verbal Paired Associates (VPA) subtests from Wechsler Memory Scale-Third Edition. Using Harrell's step-down procedure for variable selection, models were developed in 359 adults who underwent TLR at Cleveland Clinic and validated in 290 adults at 1 of 5 epilepsy surgery centers in the United States or Canada.

RESULTS

Twenty-nine percent of the development cohort and 26% of the validation cohort demonstrated significant decline on at least 1 verbal memory measure. Initial models had good-to-excellent predictive accuracy (calibration (c) statistic range = .77-.80) in identifying patients with memory decline; however, models slightly underestimated decline in the validation cohort. Model coefficients were updated using data from both cohorts to improve stability. The model for RAVLT included surgery side, baseline memory score, and hippocampal resection. The models for LM and VPA included surgery side, baseline score, and education. Updated model performance was good to excellent (RAVLT c = .81, LM c = .76, VPA c = .78). Model calibration was very good, indicating no systematic over- or under-estimation of risk.

CONCLUSIONS

Nomograms are provided in 2 easy-to-use formats to assist clinicians in estimating the probability of verbal memory decline in adults considering TLR for treatment of epilepsy.

Prediction of the clinical and naming status after anterior temporal lobe resection in patients with epilepsy

By assessing the cognitive capital, neuropsychological evaluation (NPE) plays a vital role in the perioperative workup of patients with refractory focal epilepsy. In this retrospective study, we used cutting-edge statistical approaches to examine a group of 47 patients with refractory temporal lobe epilepsy (TLE), who underwent standard anterior temporal lobectomy (ATL). Our objective was to determine whether NPE may represent a robust predictor of the postoperative status, two years after surgery. Specifically, based on pre- and postsurgical neuropsychological data, we estimated the sensitivity of cognitive indicators to predict and to disentangle phenotypes associated with more or less favorable outcomes. Engel (ENG) scores were used to assess clinical outcome, and picture naming (NAM) performance to estimate naming status. Two methods were applied: (a) machine learning (ML) to explore cognitive sensitivity to postoperative outcomes; and (b) graph theory (GT) to assess network properties reflecting favorable vs. less favorable phenotypes after surgery. Specific neuropsychological indices assessing language, memory, and executive functions can globally predict outcomes. Interestingly, preoperative cognitive networks associated with poor postsurgical outcome already exhibit an atypical, highly modular and less densely interconnected configuration. We provide statistical and clinical tools to anticipate the condition after surgery and achieve a more personalized clinical management. Our results also shed light on possible mechanisms put in place for cognitive adaptation after acute injury of central nervous system in relation with surgery.