Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in ...advancing our understanding of brain processes in the epilepsies. Smaller‐scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well‐established by the ENIGMA Consortium, ENIGMA‐Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event‐based modeling analysis. We explore age of onset‐ and duration‐related features, as well as phenomena‐specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA‐Epilepsy.
Although some studies have shown neuroimaging and neuropsychological alterations in post-COVID-19 patients, fewer combined neuroimaging and neuropsychology evaluations of individuals who presented a ...mild acute infection. Here we investigated cognitive dysfunction and brain changes in a group of mildly infected individuals. We conducted a cross-sectional study of 97 consecutive subjects (median age of 41 years) without current or history of psychiatric symptoms (including anxiety and depression) after a mild infection, with a median of 79 days (and mean of 97 days) after diagnosis of COVID-19. We performed semi-structured interviews, neurological examinations, 3T-MRI scans, and neuropsychological assessments. For MRI analyses, we included a group of non-infected 77 controls. The MRI study included white matter (WM) investigation with diffusion tensor images (DTI) and functional connectivity with resting-state functional MRI (RS-fMRI). The patients reported memory loss (36%), fatigue (31%) and headache (29%). The quantitative analyses confirmed symptoms of fatigue (83% of participants), excessive somnolence (35%), impaired phonemic verbal fluency (21%), impaired verbal categorical fluency (13%) and impaired logical memory immediate recall (16%). The WM analyses with DTI revealed higher axial diffusivity values in post-infected patients compared to controls. Compared to controls, there were no significant differences in the functional connectivity of the posterior cingulum cortex. There were no significant correlations between neuropsychological scores and neuroimaging features (including DTI and RS-fMRI). Our results suggest persistent cognitive impairment and subtle white matter abnormalities in individuals mildly infected without anxiety or depression symptoms. The longitudinal analyses will clarify whether these alterations are temporary or permanent.
Background and purpose
The genetics of late seizure or epilepsy secondary to traumatic brain injury (TBI) or stroke are poorly understood. We undertook a systematic review to test the association of ...single‐nucleotide polymorphisms (SNPs) with the risk of post‐traumatic epilepsy (PTE) and post‐stroke epilepsy (PSE).
Methods
We followed methods from our prespecified protocol on PROSPERO to identify indexed articles for this systematic review. We collated the association statistics from the included articles to assess the association of SNPs with the risk of epilepsy amongst TBI or stroke patients. We assessed study quality using the Q‐Genie tool. We report odds ratios (OR) and hazard ratios with 95% confidence intervals (CIs).
Results
The literature search yielded 420 articles. We included 16 studies in our systematic review, of which seven were of poor quality. We examined published data on 127 SNPs from 32 genes identified in PTE and PSE patients. Eleven SNPs were associated with a significantly increased risk of PTE. Three SNPs, TRMP6 rs2274924, ALDH2 rs671, and CD40 ‐1C/T, were significantly associated with an increased risk of PSE, while two, AT1R rs12721273 and rs55707609, were significantly associated with reduced risk. The meta‐analysis for the association of the APOE ɛ4 with PTE was nonsignificant (OR 1.8, CI 0.6–5.6).
Conclusions
The current evidence on the association of genetic polymorphisms in epilepsy secondary to TBI or stroke is of low quality and lacks validation. A collaborative effort to pool genetic data linked to epileptogenesis in stroke and TBI patients is warranted.
Epilepsy is more than seizures and includes a high risk of comorbidities and psychological disorders, leading to poor quality of life (QOL). Earlier studies have showed a sedentary lifestyle in ...people with epilepsy (PWE), which could contribute to poorer health and psychological problems. The purpose of the present study was to compare habits of physical activity (PA), aerobic capacity, and QOL between PWE and healthy controls in order to identify the necessity of intervention of habits and information on physical exercise (PE) and to better understand the importance of PE for PWE. The study included 38 patients with temporal lobe epilepsy and 20 normal controls. Both groups answered the WHOQOL-Bref, which assesses the level of QOL, and IPAQ to evaluate the level of PA. In addition, they were submitted to a treadmill maximal cardiopulmonary effort test to identify physical capacity. The continuous variables were compared between groups by t-test and a general linear model, and the frequencies were compared by Chi-Square test through SPSS software. There was no difference in the level of PA between groups by questionnaire evaluation. However, there were significant differences in overall QOL, physical health, and level of PA in relation to work and physical capacity between groups; controls demonstrated better scores than PWE. Controls presented better physical capacity than PWE by cardiopulmonary effort test. According to intra-group analyses, PWE who were physically active had better QOL than inactive PWE. The study concluded that questionnaires about PE may not be the best instrument of evaluation, as demonstrated by the discrepancy of results compared to the validated objective cardiopulmonary evaluation of level of PA and physical capacity in this study.
•Machine learning and artificial intelligence have gained popularity for medical applications.•We applied support vector machine (SV) and deep learning (DL) in termporal lobe epilepsy ...(TLE)•Structural and diffusion-based models showed similar classification accuracies.•Diffusion-based models to diagnose TLE performed better or similar compared to models to lateralize TLE.•Models for patients with hippocampal sclerosis were more accurate than models that stratified non-lesional patients.
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.
Background:
Although aquaporin-4 (AQP4) is widely expressed in the human brain cortex, lesions are rare in neuromyelitis optica (NMO) spectrum disorders (NMOSD). Recently, however, several studies ...have demonstrated occult structural brain atrophy in NMO.
Objective:
This study aims to investigate magnetic resonance imaging (MRI) patterns of gray matter (GM) and white matter (WM) abnormalities in patients with NMOSD and to assess the visual pathway integrity during disease duration correlation of the retinal nerve fiber layer (RNFL) and pericalcarine cortex thickness.
Methods:
Twenty-one patients with NMOSD and 34 matched healthy controls underwent both high-field MRI (3T) high-resolution T1-weighted and diffusion-tensor MRI. Voxel-based morphometry, cortical analyses (Freesurfer) and diffusion-tensor imaging (DTI) analyses (TBSS-FSL) were used to investigate brain abnormalities. In addition, RNFL measurement by optic-coherence tomography (OCT) was performed.
Results:
We demonstrate that NMOSD is associated with GM and WM atrophy, encompassing more frequently the motor, sensory and visual pathways, and that the extent of GM atrophy correlates with disease duration. Furthermore, we demonstrate for the first time a correlation between RNFL and pericalcarine cortical thickness, with cortical atrophy evolving over the course of disease.
Conclusions:
Our findings indicate a role for retrograde and anterograde neurodegeneration in GM atrophy in NMOSD. However, the presence atrophy encompassing almost all lobes suggests that additional pathomechanisms might also be involved.
Summary
Objective
Although altered large‐scale brain network organization in patients with temporal lobe epilepsy (TLE) has been shown using morphologic measurements such as cortical thickness, these ...studies, have not included critical subcortical structures (such as hippocampus and amygdala) and have had relatively small sample sizes. Here, we investigated differences in topological organization of the brain volumetric networks between patients with right TLE (RTLE) and left TLE (LTLE) with unilateral hippocampal atrophy.
Methods
We performed a cross‐sectional analysis of 86 LTLE patients, 70 RTLE patients, and 116 controls. RTLE and LTLE groups were balanced for gender (p = 0.64), seizure frequency (Mann‐Whitney U test, p = 0.94), age (p = 0.39), age of seizure onset (p = 0.21), and duration of disease (p = 0.69). Brain networks were constructed by thresholding correlation matrices of volumes from 80 cortical/subcortical regions (parcellated with Freesurfer v5.3 https://surfer.nmr.mgh.harvard.edu/) that were then analyzed using graph theoretical approaches.
Results
We identified reduced cortical/subcortical connectivity including bilateral hippocampus in both TLE groups, with the most significant interregional correlation increases occurring within the limbic system in LTLE and contralateral hemisphere in RTLE. Both TLE groups demonstrated less optimal topological organization, with decreased global efficiency and increased local efficiency and clustering coefficient. LTLE also displayed a more pronounced network disruption. Contrary to controls, hub nodes in both TLE groups were not distributed across whole brain, but rather found primarily in the paralimbic/limbic and temporal association cortices. Regions with increased centrality were concentrated in occipital lobes for LTLE and contralateral limbic/temporal areas for RTLE.
Significance
These findings provide first evidence of altered topological organization of the whole brain volumetric network in TLE, with disruption of the coordinated patterns of cortical/subcortical morphology.
The differentiation between ameloblastoma (AB) and odontogenic keratocyst (OKC) is essential for the formulation of the surgical plan, especially considering the biological behavior of these two ...pathological entities. Therefore, developing means to increase the accuracy of the diagnostic process is extremely important for a safe treatment. The aim of this study was to use magnetic resonance imaging (MRI) based on texture analysis (TA) as an aid in differentiating AB from OKC. This study comprised 18 patients; eight patients with AB and ten with OKC. All diagnoses were determined through incisional biopsy and later through histological examination of the surgical specimen. MRI was performed using a 3 T scanner with a neurovascular coil according to a specific protocol. All images were exported to segmentation software in which the volume of interest (VOI) was determined by a radiologist, who was blind to the histopathological results. Next, the textural parameters were computed by using the MATLAB software. Spearman's correlation coefficient was used to assess the correlation between texture parameters and the selected variables. Differences in TA parameters were compared between AB and OKC by using the Mann-Whitney test. Mann-Whitney test showed a statistically significant difference between AB and OKC for the parameters entropy (P = 0.033) and sum average (P = 0.033). MRI texture analysis has the potential to discriminate between AB and OKC as a noninvasive method. MRI texture analysis can be an additional tool to differentiate ameloblastoma from odontogenic keratocyst.
Objective
Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large ...multicenter cross‐sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE‐HS) correlate with clinical features.
Methods
We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1‐weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA‐Epilepsy consortium, comprising 804 people with MTLE‐HS and 1625 healthy controls from 25 centers. Features with a moderate case–control effect size (Cohen d ≥ .5) were used to train an event‐based model (EBM), which estimates a sequence of disease‐specific biomarker changes from cross‐sectional data and assigns a biomarker‐based fine‐grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance.
Results
In MTLE‐HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10−16), age at onset (ρ = −.18, p = 9.82 × 10−7), and ASM resistance (area under the curve = .59, p = .043, Mann–Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE‐HS with mild or nondetectable abnormality on T1W MRI.
Significance
From cross‐sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE‐HS subjects in other cohorts and help establish connections between imaging‐based progression staging and clinical features.
PDF
