Faculty Bibliography

OBJECTIVE:Visual assessment of magnetic resonance imaging (MRI) from the Human Epilepsy Project 1 (HEP1) found 18% of participants had atrophic brain changes relative to age without known etiology. Here, we identify the underlying factors related to brain volume differences in people with focal epilepsy enrolled in HEP1. METHODS:Enrollment data for participants with complete records and brain MRIs were analyzed, including 391 participants aged 12-60 years. HEP1 excluded developmental or cognitive delay with intelligence quotient <70, and participants reported any formal learning disability diagnoses, repeated grades, and remediation. Prediagnostic seizures were quantified by semiology, frequency, and duration. T1-weighted brain MRIs were analyzed using Sequence Adaptive Multimodal Segmentation (FreeSurfer v7.2), from which a brain tissue volume to intracranial volume ratio was derived and compared to clinically relevant participant characteristics. RESULTS:Brain tissue volume changes observable on visual analyses were quantified, and a brain tissue volume to intracranial volume ratio was derived to compare with clinically relevant variables. Learning difficulties were associated with decreased brain tissue volume to intracranial volume, with a ratio reduction of .005 for each learning difficulty reported (95% confidence interval [CI] = -.007 to -.002, p = .0003). Each 10-year increase in age at MRI was associated with a ratio reduction of .006 (95% CI = -.007 to -.005, p < .0001). For male participants, the ratio was .011 less than for female participants (95% CI = -.014 to -.007, p < .0001). There were no effects from seizures, employment, education, seizure semiology, or temporal lobe electroencephalographic abnormalities. SIGNIFICANCE/CONCLUSIONS:This study shows lower brain tissue volume to intracranial volume in people with newly treated focal epilepsy and learning difficulties, suggesting developmental factors are an important marker of brain pathology related to neuroanatomical changes in focal epilepsy. Like the general population, there were also independent associations between brain volume, age, and sex in the study population.

OBJECTIVE:Autoimmune encephalitis can be followed by treatment-resistant epilepsy. Understanding its predictors and mechanisms are crucial to future studies to improve autoimmune encephalitis outcomes. Our objective was to determine the clinical and imaging predictors of postencephalitic treatment-resistant epilepsy. METHODS:We performed a retrospective cohort study (2012-2017) of adults with autoimmune encephalitis, both antibody positive and seronegative but clinically definite or probable. We examined clinical and imaging (as defined by morphometric analysis) predictors of seizure freedom at long term follow-up. RESULTS:Of 37 subjects with adequate follow-up data (mean 4.3 yrs, SD 2.5), 21 (57 %) achieved seizure freedom after a mean time of 1 year (SD 2.3), and one third (13/37, 35 %) discontinued ASMs. Presence of mesial temporal hyperintensities on the initial MRI was the only independent predictor of ongoing seizures at last follow-up (OR 27.3, 95 %CI 2.48-299.5). Morphometric analysis of follow-up MRI scans (n = 20) did not reveal any statistically significant differences in hippocampal, opercular, and total brain volumes between patients with postencephalitic treatment-resistant epilepsy and those without. SIGNIFICANCE/CONCLUSIONS:Postencephalitic treatment-resistant epilepsy is a common complication of autoimmune encephalitis and is more likely to occur in those with mesial temporal hyperintensities on acute MRI. Volume loss in the hippocampal, opercular, and overall brain on follow-up MRI does not predict postencephalitic treatment-resistant epilepsy, so additional factors beyond structural changes may account for its development.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Mood, anxiety disorders, and suicidality are more frequent in people with epilepsy than in the general population. Yet, their prevalence and the types of mood and anxiety disorders associated with suicidality at the time of the epilepsy diagnosis are not established. We sought to answer these questions in patients with newly diagnosed focal epilepsy and to assess their association with suicidal ideation and attempts. METHODS:statistics, and logistic regression analyses. RESULTS:A total of 151 (43.5%) patients had a psychiatric diagnosis; 134 (38.6%) met the criteria for a mood and/or anxiety disorder, and 75 (21.6%) reported suicidal ideation with or without attempts. Mood (23.6%) and anxiety (27.4%) disorders had comparable prevalence rates, whereas both disorders occurred together in 43 patients (12.4%). Major depressive disorders (MDDs) had a slightly higher prevalence than bipolar disorders (BPDs) (9.5% vs 6.9%, respectively). Explanatory variables of suicidality included MDD, BPD, panic disorders, and agoraphobia, with BPD and panic disorders being the strongest variables, particularly for active suicidal ideation and suicidal attempts. DISCUSSION/CONCLUSIONS:In patients with newly diagnosed focal epilepsy, the prevalence of mood, anxiety disorders, and suicidality is higher than in the general population and comparable to those of patients with established epilepsy. Their recognition at the time of the initial epilepsy evaluation is of the essence.

In-scanner head motion systematically reduces estimated regional gray matter volumes obtained from structural brain MRI. Here, we investigate how head motion affects structural covariance networks that are derived from regional gray matter volumetric estimates. We acquired motion-affected and low-motion whole brain T1-weighted MRI in 29 healthy adult subjects and estimated relative regional gray matter volumes using a voxel-based morphometry approach. Structural covariance network analyses were undertaken while systematically increasing the number of included motion-affected scans. We demonstrate that the standard deviation in regional gray matter estimates increases as the number of motion-affected scans increases. This increases pairwise correlations between regions, a key determinant for construction of structural covariance networks. We further demonstrate that head motion systematically alters graph theoretic metrics derived from these networks. Finally, we present evidence that weighting correlations using image quality metrics can mitigate the effects of head motion. Our findings suggest that in-scanner head motion is a source of error that violates the assumption that structural covariance networks reflect neuroanatomical connectivity between brain regions. Results of structural covariance studies should be interpreted with caution, particularly when subject groups are likely to move their heads in the scanner.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Identification of an epileptogenic lesion on structural neuroimaging in individuals with focal epilepsy is important for management and treatment planning. The objective of this study was to determine the frequency of MRI-identified potentially epileptogenic structural abnormalities in a large multicenter study of adolescent and adult patients with newly diagnosed focal epilepsy. METHODS:Patients with a new diagnosis of focal epilepsy enrolled in the Human Epilepsy Project observational cohort study underwent 3-Tesla (3T) brain MRI using a standardized protocol. Imaging findings were classified as normal, abnormal, or incidental. Abnormal findings were classified as focal or diffuse, and as likely epilepsy-related or of unknown relationship to epilepsy. Fisher exact tests were performed to determine whether abnormal imaging or abnormality type was associated with clinical characteristics. RESULTS:418 participants were enrolled. 218 participants (59.3%) had no abnormalities detected, 149 (35.6%) had abnormal imaging, and 21 (5.0%) had incidental findings. 78 participants (18.7%) had abnormalities that were considered epilepsy-related and 71 (17.0%) had abnormalities of unknown relationship to epilepsy. Older participants were more likely to have imaging abnormalities, while participants with focal and epilepsy-related imaging abnormalities were younger than those without these abnormalities. 131 participants (31.3%) had a family history of epilepsy. Epilepsy-related abnormalities were not associated with participant sex, family history of epilepsy, or seizure type. DISCUSSION/CONCLUSIONS:We found that one in five patients with newly diagnosed focal epilepsy has an MRI finding that is likely causative and may alter treatment options. An additional one in five patients has abnormalities of unknown significance. This information is important for patient counseling, prognostication, and management.

INTRODUCTION/BACKGROUND:In-scanner head motion is a common cause of reduced image quality in neuroimaging, and causes systematic brain-wide changes in cortical thickness and volumetric estimates derived from structural MRI scans. There are few widely available methods for measuring head motion during structural MRI. Here, we train a deep learning predictive model to estimate changes in head pose using video obtained from an in-scanner eye tracker during an EPI-BOLD acquisition with participants undertaking deliberate in-scanner head movements. The predictive model was used to estimate head pose changes during structural MRI scans, and correlated with cortical thickness and subcortical volume estimates. METHODS:). We evaluated the utility of our technique by assessing the relationship between video-based head pose changes during structural MRI and (i) vertex-wise cortical thickness and (ii) subcortical volume estimates. RESULTS:Video-based head pose estimates were significantly correlated with ground truth head pose changes estimated from EPI-BOLD imaging in a hold-out dataset. We observed a general brain-wide overall reduction in cortical thickness with increased head motion, with some isolated regions showing increased cortical thickness estimates with increased motion. Subcortical volumes were generally reduced in motion affected scans. CONCLUSIONS:We trained a predictive model to estimate changes in head pose during structural MRI scans using in-scanner eye tracker video. The method is independent of individual image acquisition parameters and does not require markers to be to be fixed to the patient, suggesting it may be well suited to clinical imaging and research environments. Head pose changes estimated using our approach can be used as covariates for morphometric image analyses to improve the neurobiological validity of structural imaging studies of brain development and disease.

Image labeling using convolutional neural networks (CNNs) are a template-free alternative to traditional morphometric techniques. We trained a 3D deep CNN to label the hippocampus and amygdala on whole brain 700 μm isotropic 3D MP2RAGE MRI acquired at 7T. Manual labels of the hippocampus and amygdala were used to (i) train the predictive model and (ii) evaluate performance of the model when applied to new scans. Healthy controls and individuals with epilepsy were included in our analyses. Twenty-one healthy controls and sixteen individuals with epilepsy were included in the study. We utilized the recently developed DeepMedic software to train a CNN to label the hippocampus and amygdala based on manual labels. Performance was evaluated by measuring the dice similarity coefficient (DSC) between CNN-based and manual labels. A leave-one-out cross validation scheme was used. CNN-based and manual volume estimates were compared for the left and right hippocampus and amygdala in healthy controls and epilepsy cases. The CNN-based technique successfully labeled the hippocampus and amygdala in all cases. Mean DSC = 0.88 ± 0.03 for the hippocampus and 0.8 ± 0.06 for the amygdala. CNN-based labeling was independent of epilepsy diagnosis in our sample (p = .91). CNN-based volume estimates were highly correlated with manual volume estimates in epilepsy cases and controls. CNNs can label the hippocampus and amygdala on native sub-mm resolution MP2RAGE 7T MRI. Our findings suggest deep learning techniques can advance development of morphometric analysis techniques for high field strength, high spatial resolution brain MRI.

Identifying a structural brain lesion on MRI has important implications in epilepsy and is the most important correlate to seizure freedom after surgery in patients with drug-resistant focal onset epilepsy. However, at conventional magnetic field strengths (1.5 and 3T) only around 60-85% of MRI examinations reveal such lesions. Over the last decade, studies have demonstrated the added value of 7T MRI in patients with and without known epileptogenic lesions from 1.5 and/or 3T. However, translation of 7T MRI to clinical practice is still challenging, particularly in centers new to 7T, and there is a need for practical recommendations on targeted use of 7T MRI in the clinical management of patients with epilepsy. The 7T Epilepsy Task Force - an international group representing 21 7T MRI centers with experience from scanning over 2000 patients with epilepsy - would hereby like to share its experience with the neurology community regarding the appropriate clinical indications, patient selection and preparation, acquisition protocols and setup, technical challenges, and radiological guidelines for 7T MRI in epilepsy patients. This article mainly addresses structural imaging, but also presents multiple non-structural MRI techniques that benefit from 7T and hold promise as future directions in epilepsy. Answering to the increased availability of 7T MRI as an approved tool for diagnostic purposes, this article aims to give guidance on clinical 7T MRI epilepsy management by giving recommendations on referral, suitable 7T MRI protocols and image interpretation.

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.