A recap of 2023's neuroimaging of epilepsy
We searched PubMed and listed several neuroimaging of epilepsy papers published in 2023. Please let us know if you would like to highlight a paper!
Current state and guidance on arterial spin labeling perfusion MRI in clinical neuroimaging
This article focuses on clinical applications of arterial spin labeling (ASL) and is part of a wider effort from the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group to update and expand on the recommendations provided in the 2015 ASL consensus paper. Although the 2015 consensus paper provided general guidelines for clinical applications of ASL MRI, there was a lack of guidance on disease-specific parameters. Since that time, the clinical availability and clinical demand for ASL MRI has increased. This position paper provides guidance on using ASL in specific clinical scenarios, including acute ischemic stroke and steno-occlusive disease, arteriovenous malformations and fistulas, brain tumors, neurodegenerative disease, seizures/epilepsy, and pediatric neuroradiology applications, focusing on disease-specific considerations for sequence optimization and interpretation. We present several neuroradiological applications in which ASL provides unique information essential for making the diagnosis. This guidance is intended for anyone interested in using ASL in a routine clinical setting (i.e., on a single-subject basis rather than in cohort studies) building on the previous ASL consensus review.
The application of deep learning models to evaluate connectome data is gaining interest in epilepsy research. Deep learning may be a useful initial tool to partition connectome data into network subsets for further analysis. Few prior works have used deep learning to examine structural connectomes from patients with focal epilepsy. We evaluated whether a deep learning model applied to whole-brain connectomes could classify 28 participants with focal epilepsy from 20 controls and identify nodal importance for each group. Participants with epilepsy were further grouped based on whether they had focal seizures that evolved into bilateral tonic-clonic seizures (17 with, 11 without). The trained neural network classified patients from controls with an accuracy of 72.92%, while the seizure subtype groups achieved a classification accuracy of 67.86%. In the patient subgroups, the nodes and edges deemed important for accurate classification were also clinically relevant, indicating the model’s interpretability. The current work expands the evidence for the potential of deep learning to extract relevant markers from clinical datasets. Our findings offer a rationale for further research interrogating structural connectomes to obtain features that can be biomarkers and aid the diagnosis of seizure subtypes.
Neurocysticercosis and epilepsy: Imaging and clinical characteristics
The ILAE Neuroimaging Task Force aimed to publish educational case reports highlighting basic aspects related to neuroimaging in epilepsy consistent with the educational mission of the ILAE. Neurocysticercosis (NCC) is highly endemic in resource-limited countries and increasingly more often seen in non-endemic regions due to migration. Cysts with larva of the tapeworm Taenia solium lodge in the brain and cause several neurological conditions, of which seizures are the most common. There is great heterogeneity in the clinical presentation of neurocysticercosis because cysts vary in number, larval stage, and location among patients. We here present two illustrative cases with different clinical features to highlight the varying severity of symptoms secondary to this parasitic infestation. We also present several examples of imaging characteristics of the disease at various stages, which emphasize the central role of neuroimaging in the diagnosis of neurocysticercosis.
Radiological identification of temporal lobe epilepsy (TLE) is crucial for diagnosis and treatment planning. TLE neuroimaging abnormalities are pervasive at the group level, but they can be subtle and difficult to identify by visual inspection of individual scans, prompting applications of artificial intelligence (AI) assisted technologies. We assessed the ability of a convolutional neural network (CNN) algorithm to classify TLE vs. patients with AD vs. healthy controls using T1-weighted magnetic resonance imaging (MRI) scans. We used feature visualization techniques to identify regions the CNN employed to differentiate disease types. We show the following classification results: healthy control accuracy = 81.54% (SD = 1.77%), precision = 0.81 (SD = 0.02), recall = 0.85 (SD = 0.03), and F1-score = 0.83 (SD = 0.02); TLE accuracy = 90.45% (SD = 1.59%), precision = 0.86 (SD = 0.03), recall = 0.86 (SD = 0.04), and F1-score = 0.85 (SD = 0.04); and AD accuracy = 88.52% (SD = 1.27%), precision = 0.64 (SD = 0.05), recall = 0.53 (SD = 0.07), and F1 score = 0.58 (0.05). The high accuracy in identification of TLE was remarkable, considering that only 47% of the cohort had deemed to be lesional based on MRI alone. Model predictions were also considerably better than random permutation classifications (p < 0.01) and were independent of age effects. AI (CNN deep learning) can classify and distinguish TLE, underscoring its potential utility for future computer-aided radiological assessments of epilepsy, especially for patients who do not exhibit easily identifiable TLE associated MRI features (e.g., hippocampal sclerosis).
Atypical intrinsic neural timescales in temporal lobe epilepsy
Temporal lobe epilepsy (TLE) is the most common pharmacoresistant epilepsy in adults. Here we profiled local neural function in TLE in vivo, building on prior evidence that has identified widespread structural alterations. Using resting-state functional magnetic resonance imaging (rs-fMRI), we mapped the whole-brain intrinsic neural timescales (INT), which reflect temporal hierarchies of neural processing. Parallel analysis of structural and diffusion MRI data examined associations with TLE-related structural compromise. Finally, we evaluated the clinical utility of INT. We studied 46 patients with TLE and 44 healthy controls from two independent sites, and mapped INT changes in patients relative to controls across hippocampal, subcortical, and neocortical regions. We examined region-specific associations to structural alterations and explored the effects of age and epilepsy duration. Supervised machine learning assessed the utility of INT for identifying patients with TLE vs controls and left- vs right-sided seizure onset. Relative to controls, TLE showed marked INT reductions across multiple regions bilaterally, indexing faster changing resting activity, with strongest effects in the ipsilateral medial and lateral temporal regions, and bilateral sensorimotor cortices as well as thalamus and hippocampus. Findings were similar, albeit with reduced effect sizes, when correcting for structural alterations. INT reductions in TLE increased with advancing disease duration, yet findings differed from the aging effects seen in controls. INT-derived classifiers discriminated patients vs controls (balanced accuracy, 5-fold: 76% ± 2.65%; cross-site, 72%–83%) and lateralized the focus in TLE (balanced accuracy, 5-fold: 96% ± 2.10%; cross-site, 95%–97%), with high accuracy and cross-site generalizability. Findings were consistent across both acquisition sites and robust when controlling for motion and several methodological confounds. Our findings demonstrate atypical macroscale function in TLE in a topography that extends beyond mesiotemporal epicenters. INT measurements can assist in TLE diagnosis, seizure focus lateralization, and monitoring of disease progression, which emphasizes promising clinical utility.
To evaluate in a real clinical scenario the impact of the ILAE-recommended “Harmonized neuroimaging of epilepsy structural sequences”- HARNESS protocol in patients affected by focal epilepsy. We prospectively enrolled focal epilepsy patients who underwent a structural brain MRI between 2020 and 2021 at Modena University Hospital. For all patients, MRIs were: (a) acquired according to the HARNESS-MRI protocol (H-MRI); (b) reviewed by the same neuroradiology team. MRI outcomes measures were: the number of positive (diagnostic) and negative MRI; the type of radiological diagnosis classified in: (1) Hippocampal Sclerosis; (2) Malformations of cortical development (MCD); (3) Vascular malformations; (4) Glial scars; (5) Low-grade epilepsy-associated tumors; (6) Dual pathology. For each patient we verified for previous MRI (without HARNESS protocol, noH-MRI) and the presence of clinical information in the MRI request form. Then the measured outcomes were reviewed and compared as appropriate. A total of 131 patients with H-MRI were included in the study. 100 patients out from this cohort had at least one previous noH-MRI scan. Of those, 92/100 were acquired at the same Hospital than H-MRI and 71/92 on a 3T scanner. The HARNESS protocol revealed 81 (62%) positive and 50 (38%) negative MRI, and MCD was the most common diagnosis (60%). Among the entire pool of 100 noH-MRI, 36 resulted positive with a significant difference (p < .001) compared to H-MRI. Similar findings were observed when accounting for the expert radiologists (H-MRI = 57 positive; noH-MRI = 33, p < .001) and the scanner field strength (H-MRI 43 = positive, noH-MRI = 23, p < .001), while clinical information were more present in H-MRI (p < .002). The adoption of a standardized and optimized MRI acquisition protocol together with adequate clinical information contribute to identify a higher number of potentially epileptogenic lesions (especially FCD) thus impacting concretely on the clinical management of patients with focal epilepsy.
Temporal lobe epilepsy (TLE) is one of the most common subtypes of focal epilepsy, with mesial temporal sclerosis (MTS) being a common radiological and histopathological finding. Accurate identification of MTS during presurgical evaluation confers an increased chance of good surgical outcome. Here we propose the use of glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) at 7 Tesla for mapping hippocampal glutamate distribution in epilepsy, allowing to differentiate lesional from non-lesional mesial TLE. We demonstrate that a directional asymmetry index, which quantifies the relative difference between GluCEST contrast in hippocampi ipsilateral and contralateral to the seizure onset zone, can differentiate between sclerotic and non-sclerotic hippocampi, even in instances where traditional presurgical MRI assessments did not provide evidence of sclerosis. Overall, our results suggest that hippocampal glutamate mapping through GluCEST imaging is a valuable addition to the presurgical epilepsy evaluation toolbox.
Multi-spectral diffusion MRI mega-analysis in genetic generalized epilepsy: Relation to outcomes
Genetic generalized epilepsy (GGE) is the most common form of generalized epilepsy. Although individual patients with GGE typically present without structural alterations, group differences have been demonstrated in GGE and some GGE subtypes like juvenile myoclonic epilepsy (GGE-JME). Previous studies usually involved only small cohorts from single centers and therefore could not assess imaging markers of multiple GGE subtypes. We performed a diffusion MRI mega-analysis in 192 participants consisting of 126 controls and 66 patients with GGE from four different cohorts and two different epilepsy centers. We applied whole-brain multi-site harmonization and analyzed fractional anisotropy (FA), as well as mean, radial and axial diffusivity (MD/RD/AD) to assess differences between controls, patients with GGE and the common GGE subtypes, i.e. GGE with generalized tonic-clonic seizures only (GGE-GTCS), GGE-JME and absence epilepsy (GGE-AE). We also analyzed relationships with patients' response to anti-seizure-medication (ASM). Relative to controls, we identified decreased anisotropy and increased RD in patients with GGE. We found no significant effects of disease duration, age of onset or seizure frequency on diffusion metrics. Patients with JME had increased MD and RD when compared to controls, while patients with GGE-GTCS showed decreased MD/AD when compared to controls. Compared to patients with GGE-AE, patients with GGE-GTCS had lower AD/MD. Compared to patients with GGE-GTCS, patients with GGE-JME had higher MD/RD and AD. Moreover, we found lower FA in patients with refractory when compared to patients with non-refractory GGE in the right cortico-spinal tract, but no significant differences in patients with active versus controlled epilepsy. We provide evidence that clinically defined GGE as a whole and GGE-subtypes harbor marked microstructural differences detectable with diffusion MRI. Moreover, we found an association between microstructural changes and treatment resistance. Our findings have important implications for future full-resolution multi-site studies when assessing GGE, its subtypes and ASM refractoriness.
Structural–functional coupling abnormalities in temporal lobe epilepsy
Nowadays, researchers are using advanced multimodal neuroimaging techniques to construct the brain network connectome to elucidate the complex relationship among the networks of brain functions and structure. The objective of this study was to evaluate the coupling of structural connectivity (SC) and functional connectivity (FC) in the entire brain of healthy controls (HCs), and to investigate modifications in SC–FC coupling in individuals suffering from temporal lobe epilepsy (TLE). Nowadays, researchers are using advanced multimodal neuroimaging techniques to construct the brain network connectome to elucidate the complex relationship among the networks of brain functions and structure. The objective of this study was to evaluate the coupling of structural connectivity (SC) and functional connectivity (FC) in the entire brain of healthy controls (HCs), and to investigate modifications in SC–FC coupling in individuals suffering from temporal lobe epilepsy (TLE). Nodes were divided into five clusters. Cluster 1 was primarily located in the limbic system (n = 9/27), whereas cluster 5 was mainly within the visual network (n = 12/29). By comparing average cluster SC–FC coupling in each cluster of the three groups, we identified marked discrepancies within the three cohorts in Cluster 3 (p = 0.001), Cluster 4 (p < 0.001), and Cluster 5 (p < 0.001). Post-hoc analysis revealed that the SC–FC coupling strengths in LTLE and RTLE were significantly lower than that in HCs in Cluster 3 (PL = 0.001/PR = 0.003), Cluster 4 (PL = 0.001/PR < 0.001), and Cluster 5 (PL < 0.001/PR < 0.001). We also observed that the within-cluster SC–FC coupling in cluster 5 of left- and right TLE was significantly lower than in HCs (PL = 0.0001, PR = 0.0005). The SC and FC are inconsistently coupled across the brain with spatial heterogeneity. In the fifth cluster with the highest degree of coupling in HCs, the average SC–FC coupling index of individuals with TLE was notably less than that of HCs, manifesting that brain regions with high coupling may be more delicate and prone to pathological disruption.
Volumetric gray matter findings in autonomic network regions of people with focal epilepsy
Voxel-based morphometry (VBM) studies of people with focal epilepsies revealed gray matter (GM) alterations in brain regions involved in cardiorespiratory regulation, which have been linked to the risk of sudden unexpected death in epilepsy (SUDEP). It remains unclear whether the type and localization of epileptogenic lesions influence the occurrence of such alterations. To test the hypothesis that VBM alterations of autonomic network regions are independent of epileptogenic lesions and that they reveal structural underpinnings of SUDEP risk, VBM was performed in 100 people with focal epilepsies without an epileptogenic lesion identifiable on MRI (mean age ± standard deviation = 35 ± 11 years, 56 female). The group was further stratified in high (sample size n = 29) and low risk of SUDEP (n = 71). GM volumes were compared between these two subgroups and to 100 matched controls. People with epilepsy displayed higher GM volume in both amygdalae and parahippocampal gyri and lower GM volume in the cerebellum and occipital (p<.05, familywise error corrected). There were no significant volumetric differences between high and low SUDEP risk subgroups. Our findings confirm that autonomic networks are structurally altered in people with focal epilepsy and they question VBM as a suitable method to show structural correlates of the SUDEP risk score.
Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics.
Single-photon emission computed tomography (SPECT) with the tracer 99mTc-HMPAO is a method to visualize the cerebral hyperperfusion during an epileptic seizure and thus localize the epileptogenic zone and seizure propagation. Subtraction of interictal from Ictal SPECT Co-registered to MRI (SISCOM) visualizes areas with relative increases in cerebral blood flow. The purpose of this retrospective study is to explore the added value of visualizing areas of hypoperfusion as well as hyperperfusion, so-called reversed SISCOM. Fifty-six patients operated for epilepsy who had been investigated with SISCOM were included in the analysis. The patients were divided into two groups based on seizure duration after tracer injection, above or below 30 s. The preoperative SISCOM description was compared to the area of resection and given a concordance score. The 56 SISCOM were recalculated visualizing also areas of hypoperfusion and again compared to the site of resection using the same scale of concordance. The reversed SISCOM were categorized into three subgroups: “Altered Conclusion,” “Confirmed Conclusion,” and “Adds Nothing.” If an area of hyperperfusion had an area of hypoperfusion in close proximity, it was re-interpreted as noise, thus possibly altering the conclusion. If the areas of hypoperfusion were in the opposite hemisphere it was interpreted as confirming factor. Further the concordance scores from conventional SISCOM and reversed SISCOM was compared to surgical outcome to explore the difference in sensitivity, positive predictive value (PPV), and odds ratio. In approximately half of the cases reversed SISCOM added additional value, meaning either altered the conclusion or confirmed the conclusion. The sensitivity, PPV, and odds ratio was also better in the subgroup of long, >30 s seizure duration after injection, and got worse in the group with short, <30 s seizure duration after injection. Adding reversed SISCOM performed better than conventional SISCOM at predicting good surgical outcome.
Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review
Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively treating and managing FCD. Magnetic resonance imaging (MRI)—one of the most commonly used non-invasive neuroimaging methods for evaluating the structure of the brain—is often implemented along with automatic methods to diagnose FCD. In this review, we define three categories for FCD identification based on MRI: visual, semi-automatic, and fully automatic methods. By conducting a systematic review following the PRISMA statement, we identified 65 relevant papers that have contributed to our understanding of automatic FCD identification techniques. The results of this review present a comprehensive overview of the current state-of-the-art in the field of automatic FCD identification and highlight the progress made and challenges ahead in developing reliable, efficient methods for automatic FCD diagnosis using MRI images. Future developments in this area will most likely lead to the integration of these automatic identification tools into medical image-viewing software, providing neurologists and radiologists with enhanced diagnostic capabilities. Moreover, new MRI sequences and higher-field-strength scanners will offer improved resolution and anatomical detail for precise FCD characterization. This review summarizes the current state of automatic FCD identification, thereby contributing to a deeper understanding and the advancement of FCD diagnosis and management.
[18F]FDG PET metabolic patterns in mesial temporal lobe epilepsy with different pathological types
To investigate [18F]FDG PET patterns of mesial temporal lobe epilepsy (MTLE) patients with distinct pathologic types and provide possible guidance for predicting long-term prognoses of patients undergoing epilepsy surgery. This was a retrospective review of MTLE patients who underwent anterior temporal lobectomy between 2016 and 2021. Patients were classified as having chronic inflammation and gliosis (gliosis, n = 44), hippocampal sclerosis (HS, n = 43), or focal cortical dysplasia plus HS (FCD-HS, n = 13) based on the postoperative pathological diagnosis. Metabolic patterns and the severity of metabolic abnormalities were investigated among MTLE patients and healthy controls (HCs). The standardized uptake value (SUV), SUV ratio (SUVr), and asymmetry index (AI) of regions of interest were applied to evaluate the severity of metabolic abnormalities. Imaging processing was performed with statistical parametric mapping (SPM12). With a mean follow-up of 2.8 years, the seizure freedom (Engel class IA) rates of gliosis, HS, and FCD-HS were 54.55%, 62.79%, and 69.23%, respectively. The patients in the gliosis group presented a metabolic pattern with a larger involvement of extratemporal areas, including the ipsilateral insula. SUV, SUVr, and AI in ROIs were decreased for patients in all three MTLE groups compared with those of HCs, but the differences among all three MTLE groups were not significant. MTLE patients with isolated gliosis had the worst prognosis and hypometabolism in the insula, but the degree of metabolic decrease did not differ from the other two groups. Hypometabolic regions should be prioritized for [18F]FDG PET presurgical evaluation rather than [18F]FDG uptake values.
Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy
Accurate localization of a seizure onset zone (SOZ) from independent components (IC) of resting-state functional magnetic resonance imaging (rs-fMRI) improves surgical outcomes in children with drug-resistant epilepsy (DRE). Automated IC sorting has limited success in identifying SOZ localizing ICs in adult normal rs-fMRI or uncategorized epilepsy. Children face unique challenges due to the developing brain and its associated surgical risks. This study proposes a novel SOZ localization algorithm (EPIK) for children with DRE. EPIK is developed in a phased approach, where fMRI noise-related biomarkers are used through high-fidelity image processing techniques to eliminate noise ICs. Then, the SOZ markers are used through a maximum likelihood-based classifier to determine SOZ localizing ICs. The performance of EPIK was evaluated on a unique pediatric DRE dataset (n = 52). A total of 24 children underwent surgical resection or ablation of an rs-fMRI identified SOZ, concurrently evaluated with an EEG and anatomical MRI. Two state-of-art techniques were used for comparison: (a) least squares support-vector machine and (b) convolutional neural networks. The performance was benchmarked against expert IC sorting and Engel outcomes for surgical SOZ resection or ablation. The analysis was stratified across age and sex. EPIK outperformed state-of-art techniques for SOZ localizing IC identification with a mean accuracy of 84.7% (4% higher), a precision of 74.1% (22% higher), a specificity of 81.9% (3.2% higher), and a sensitivity of 88.6% (16.5% higher). EPIK showed consistent performance across age and sex with the best performance in those < 5 years of age. It helped achieve a ~5-fold reduction in the number of ICs to be potentially analyzed during pre-surgical screening. Automated SOZ localization from rs-fMRI, validated against surgical outcomes, indicates the potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods, and is consistent across age and sex.
Deficits in executive functions (EF) are a common comorbidity among adolescents with epilepsy. EF deficits were previously correlated with altered connectivity of the fronto-parietal and cingulo-opercular neural networks. The current study investigated white matter integrity in adolescents with epilepsy (n = 29) relative to healthy controls (n = 19). Participants completed questionnaires, neuropsychological testing, and brain magnetic resonance imaging (MRI) that included diffusion tensor imaging (DTI) sequences. On BRIEF parent-report questionnaires, adolescents with epilepsy demonstrated lower working memory and planning abilities than healthy controls. Among adolescents with epilepsy, DTI measurements revealed lower fractional anisotropy (FA) within the right superior longitudinal fasciculus, forceps minor, and the superior frontal segment of the corpus callosum, and higher FA in the left uncinate fasciculus, compared to healthy controls. Better working memory ability in the epilepsy group was associated with higher FA in the superior frontal segment of the corpus callosum. Only in healthy controls, working memory and planning were positively associated with FA values in the left UF, forceps minor and the superior frontal segment of the corpus callosum. The current study complements previous functional studies on the same cohort and suggests that EF impairments among adolescents with epilepsy may be related to the altered anatomical organization of white matter tracts. Combining structural and functional data could potentially enrich the neuropsychological assessment of executive functioning in adolescents with epilepsy.
The cognitive profile of juvenile absence epilepsy (JAE) remains largely uncharacterized. This study aimed to: (1) elucidate the neuropsychological profile of JAE; (2) identify familial cognitive traits by investigating unaffected JAE siblings; (3) establish the clinical meaningfulness of JAE-associated cognitive traits; (4) determine whether cognitive traits across the idiopathic generalized epilepsy (IGE) spectrum are shared or syndrome-specific, by comparing JAE to juvenile myoclonic epilepsy (JME); and (5) identify relationships between cognitive abilities and clinical characteristics. We investigated 123 participants—23 patients with JAE, 16 unaffected siblings of JAE patients, 45 healthy controls, and 39 patients with JME—who underwent a comprehensive neuropsychological test battery including measures within four cognitive domains: attention/psychomotor speed, language, memory, and executive function. We correlated clinical measures with cognitive performance data to decode effects of age at onset and duration of epilepsy. Cognitive performance in individuals with JAE was reduced compared to controls across attention/psychomotor speed, language, and executive function domains; those with ongoing seizures additionally showed lower memory scores. Patients with JAE and their unaffected siblings had similar language impairment compared to controls. Individuals with JME had worse response inhibition than those with JAE. Across all patients, those with older age at onset had better attention/psychomotor speed performance. JAE is associated with wide-ranging cognitive difficulties that encompass domains reliant on frontal lobe processing, including language, attention, and executive function. JAE siblings share impairment with patients on linguistic measures, indicative of a familial trait. Executive function subdomains may be differentially affected across the IGE spectrum. Cognitive abilities are detrimentally modulated by an early age at seizure onset.
Extent of piriform cortex resection in children with temporal lobe epilepsy
A greater extent of resection of the temporal portion of the piriform cortex (PC) has been shown to be associated with higher likelihood of seizure freedom in adults undergoing anterior temporal lobe resection (ATLR) for drug-resistant temporal lobe epilepsy (TLE). There have been no such studies in children, therefore this study aimed to investigate this association in a pediatric cohort. A retrospective, neuroimaging cohort study of children with TLE who underwent ATLR between 2012 and 2021 was undertaken. The PC, hippocampal and amygdala volumes were measured on the preoperative and postoperative T1-weighted MRI. Using these volumes, the extent of resection per region was compared between the seizure-free and not seizure-free groups. In 50 children (median age 9.5 years) there was no significant difference between the extent of resection of the temporal PC in the seizure-free (median = 50%, n = 33/50) versus not seizure-free (median = 40%, n = 17/50) groups (p = 0.26). In a sub-group of 19 with ipsilateral hippocampal atrophy (quantitatively defined by ipsilateral-to-contralateral asymmetry), the median extent of temporal PC resection was greater in children who were seizure-free (53%) versus those not seizure-free (19%) (p = 0.009). This is the first study demonstrating that, in children with TLE and hippocampal atrophy, more extensive temporal PC resection is associated with a greater chance of seizure freedom—compatible with an adult series in which 85% of patients had hippocampal sclerosis. In a combined group of children with and without hippocampal atrophy, the extent of PC resection was not associated with seizure outcome, suggesting different epileptogenic networks within this cohort.
Deep learning in neuroimaging of epilepsy
In recent years, artificial intelligence, particularly deep learning (DL), has demonstrated utility in diverse areas of medicine. DL uses neural networks to automatically learn features from the raw data while this is not possible with conventional machine learning. It is helpful for the assessment of patients with epilepsy and whilst most published studies have been aimed at the automatic detection and prediction of seizures from electroencephalographic records, there is a growing number of investigations that use neuroimaging modalities (structural and functional magnetic resonance imaging, diffusion-weighted imaging and positron emission tomography) as input data. We review the application of DL to neuroimaging (sMRI, fMRI, DWI and PET) of focal epilepsy, specifically presurgical evaluation of drug-refractory epilepsy. First, a brief theoretical overview of artificial neural networks and deep learning is presented. Next, we review applications of deep learning to neuroimaging of epilepsy: diagnosis and lateralization, automated detection of lesion, presurgical evaluation and prediction of postsurgical outcome. Finally, the limitations, challenges and possible future directions in the application of these methods in the study of epilepsies are discussed. This approach could become an essential tool in clinical practice, particularly in the evaluation of images considered negative by visual inspection, in individualized treatments, and in the approach to epilepsy as a network disorder. However, greater multicenter collaboration is required to achieve the collection of sufficient data with the required quality together with the open access availability of the developed codes and tools.
To investigate how the presence/side of hippocampal sclerosis (HS) are related to the white matter structure of cingulum bundle (CB), arcuate fasciculus (AF), and inferior longitudinal fasciculus (ILF) in mesial temporal lobe epilepsy (MTLE). We acquired diffusion-weighted magnetic resonance imaging (MRI) from 86 healthy and 71 individuals with MTLE (22 righ-HS; right-HS, 34 left-HS; left-HS, and 15 nonlesional MTLE). We utilized two-tensor tractography and fiber clustering to compare fractional anisotropy (FA) of each side/tract between groups. Additionally, we examined the association between FA and nonverbal (WMS-R) and verbal (WMS-R, RAVLT codification) memory performance for MTLE individuals. White matter abnormalities depended on the side and presence of HS. The left-HS demonstrated widespread abnormalities for all tracts, the right-HS showed lower FA for ipsilateral tracts and the nonlesional MTLE group did not differ from healthy individuals. Results indicate no differences in verbal/nonverbal memory performance between the groups, but trend-level associations between higher FA of visual memory and the left CB (r = 0.286, P = 0.018), verbal memory (RAVLT) and -left CB (r = 0.335, P = 0.005), -right CB (r = 0.286, P = 0.016), and -left AF (r = 0.287, P = 0.017). Our results highlight that the presence and side of HS are crucial to understand the pathophysiology of MTLE. Specifically, left-sided HS seems to be related to widespread bilateral white matter abnormalities. Future longitudinal studies should focus on developing diagnostic and treatment strategies dependent on HS's presence/side.
Multi-scale structural alterations of the thalamus and basal ganglia in focal epilepsy using 7T MRI
Focal epilepsy is characterized by repeated spontaneous seizures that originate from cortical epileptogenic zone networks (EZN). Analysis of intracerebral recordings showed that subcortical structures, and in particular the thalamus, play an important role in seizure dynamics as well, supporting their structural alterations reported in the neuroimaging literature. Nonetheless, between-patient differences in EZN localization (e.g., temporal vs. non-temporal lobe epilepsy) as well as extension (i.e., number of epileptogenic regions) might impact the magnitude as well as spatial distribution of subcortical structural changes. Here we used 7 Tesla MRI T1 data to provide an unprecedented description of subcortical morphological (volume, tissue deformation, and shape) and longitudinal relaxation (T1) changes in focal epilepsy patients and evaluate the impact of the EZN and other patient-specific clinical features. Our results showed variable levels of atrophy across thalamic nuclei that appeared most prominent in the temporal lobe epilepsy group and the side ipsilateral to the EZN, while shortening of T1 was especially observed for the lateral thalamus. Multivariate analyses across thalamic nuclei and basal ganglia showed that volume acted as the dominant discriminator between patients and controls, while (posterolateral) thalamic T1 measures looked promising to further differentiate patients based on EZN localization. In particular, the observed differences in T1 changes between thalamic nuclei indicated differential involvement based on EZN localization. Finally, EZN extension was found to best explain the observed variability between patients. To conclude, this work revealed multi-scale subcortical alterations in focal epilepsy as well as their dependence on several clinical characteristics.
Effects of anterior temporal lobe resection on cortical morphology
Neuroimaging can capture brain restructuring after anterior temporal lobe resection (ATLR), a surgical procedure to treat drug-resistant temporal lobe epilepsy (TLE). Here, we examine the effects of this surgery on brain morphology measured in recently-proposed independent variables. We studied 101 individuals with TLE (55 left, 46 right onset) who underwent ATLR. For each individual we considered one pre-surgical MRI and one follow-up MRI 2–13 months after surgery. We used a surface-based method to locally compute traditional morphological variables, and the independent measures K, I, and S, where K measures white matter tension, I captures isometric scaling, and S contains the remaining information about cortical shape. A normative model trained on data from 924 healthy controls was used to debias the data and account for healthy ageing effects occurring during scans. A SurfStat random field theory clustering approach assessed changes across the cortex caused by ATLR. Compared to preoperative data, surgery had marked effects on all morphological measures. Ipsilateral effects were located in the orbitofrontal and inferior frontal gyri, the pre- and postcentral gyri and supramarginal gyrus, and the lateral occipital gyrus and lingual cortex. Contralateral effects were in the lateral occipital gyrus, and inferior frontal gyrus and frontal pole. The restructuring following ATLR is reflected in widespread morphological changes, mainly in regions near the resection, but also remotely in regions that are structurally connected to the anterior temporal lobe. The causes could include mechanical effects, Wallerian degeneration, or compensatory plasticity. The study of independent measures revealed additional effects compared to traditional measures.
Brain volumes and white matter diffusion across the adult lifespan in temporal lobe epilepsy
Typical aging is associated with gradual cognitive decline and changes in brain structure. The observation that cognitive performance in mesial temporal lobe epilepsy (TLE) patients diverges from controls early in life with subsequent decline running in parallel would suggest an initial insult but does not support accelerated decline secondary to seizures. Whether TLE patients demonstrate similar trajectories of age-related gray (GM) and white matter (WM) changes as compared to healthy controls remains uncertain. 3D T1-weighted and diffusion tensor images were acquired at a single site in 170 TLE patients (aged 23–74 years) with MRI signs of unilateral hippocampal sclerosis (HS, 77 right) and 111 healthy controls (aged 26–80 years). Global brain (GM, WM, total brain, and cerebrospinal fluid) and regional volumes (ipsi- and contralateral hippocampi), and fractional anisotropy (FA) of 10 tracts (three portions of corpus callosum, inferior longitudinal, inferior fronto-occipital and uncinate fasciculi, body of fornix, dorsal and parahippocampal-cingulum, and corticospinal tract) were compared between groups as a function of age. There were significant reductions of global brain and hippocampi volumes (greatest ipsilateral to HS), and FA of all 10 tracts in TLE versus controls. For TLE patients, regression lines run in parallel to those from controls for brain volumes and FA (for all tracts except the parahippocampal-cingulum and corticospinal tract) versus age across the adult lifespan. These results imply a developmental hindrance occurring earlier in life (likely in childhood/neurodevelopmental stages) rather than accelerated atrophy/degeneration of most brain structures herein analyzed in patients with TLE.
A new frontier in diagnostic radiology is the inclusion of machine-assisted support tools that facilitate the identification of subtle lesions often not visible to the human eye. Structural neuroimaging plays an essential role in the identification of lesions in patients with epilepsy, which often coincide with the seizure focus. In this study, we explored the potential for a convolutional neural network (CNN) to determine lateralization of seizure onset in patients with epilepsy using T1-weighted structural MRI scans as input. Using a dataset of 359 patients with temporal lobe epilepsy (TLE) from 7 surgical centers, we tested whether a CNN based on T1-weighted images could classify seizure laterality concordant with clinical team consensus. This CNN was compared with a randomized model (comparison with chance) and a hippocampal volume logistic regression (comparison with current clinically available measures). Furthermore, we leveraged a CNN feature visualization technique to identify regions used to classify patients. Across 100 runs, the CNN model was concordant with clinician lateralization on average 78% (SD = 5.1%) of runs with the best-performing model achieving 89% concordance. The CNN outperformed the randomized model (average concordance of 51.7%) on 100% of runs with an average improvement of 26.2% and outperformed the hippocampal volume model (average concordance of 71.7%) on 85% of runs with an average improvement of 6.25%. Feature visualization maps revealed that in addition to the medial temporal lobe, regions in the lateral temporal lobe, cingulate, and precentral gyrus aided in classification. These extratemporal lobe features underscore the importance of whole-brain models to highlight areas worthy of clinician scrutiny during temporal lobe epilepsy lateralization. This proof-of-concept study illustrates that a CNN applied to structural MRI data can visually aid clinician-led localization of epileptogenic zone and identify extrahippocampal regions that may require additional radiologic attention. This study provides Class II evidence that in patients with drug-resistant unilateral temporal lobe epilepsy, a convolutional neural network algorithm derived from T1-weighted MRI can correctly classify seizure laterality.
Cortical microstructural gradients capture memory network reorganization in temporal lobe epilepsy
Temporal lobe epilepsy (TLE), one of the most common pharmaco-resistant epilepsies, is associated with pathology of paralimbic brain regions, particularly in the mesiotemporal lobe. Cognitive dysfunction in TLE is frequent, and particularly affects episodic memory. Crucially, these difficulties challenge the quality of life of patients, sometimes more than seizures, underscoring the need to assess neural processes of cognitive dysfunction in TLE to improve patient management. Our work harnessed a novel conceptual and analytical approach to assess spatial gradients of microstructural differentiation between cortical areas based on high-resolution MRI analysis. Gradients track region-to-region variations in intracortical lamination and myeloarchitecture, serving as a system-level measure of structural and functional reorganization. Comparing cortex-wide microstructural gradients between 21 patients and 35 healthy controls, we observed a reorganization of this gradient in TLE driven by reduced microstructural differentiation between paralimbic cortices and the remaining cortex with marked abnormalities in ipsilateral temporopolar and dorsolateral prefrontal regions. Findings were replicated in an independent cohort. Using an independent post-mortem dataset, we observed that in vivo findings reflected topographical variations in cortical cytoarchitecture. We indeed found that macroscale changes in microstructural differentiation in TLE reflected increased similarity of paralimbic and primary sensory/motor regions. Disease-related transcriptomics could furthermore show specificity of our findings to TLE over other common epilepsy syndromes. Finally, microstructural dedifferentiation was associated with cognitive network reorganization seen during an episodic memory functional MRI paradigm and correlated with interindividual differences in task accuracy. Collectively, our findings showing a pattern of reduced microarchitectural differentiation between paralimbic regions and the remaining cortex provide a structurally-grounded explanation for large-scale functional network reorganization and cognitive dysfunction characteristic of TLE.
The piriform cortex (PC) is located at the junction of the temporal and frontal lobes. It is involved physiologically in olfaction as well as memory and plays an important role in epilepsy. Its study at scale is held back by the absence of automatic segmentation methods on MRI. We devised a manual segmentation protocol for PC volumes, integrated those manually derived images into the Hammers Atlas Database (n = 30) and used an extensively validated method (multi-atlas propagation with enhanced registration, MAPER) for automatic PC segmentation. We applied automated PC volumetry to patients with unilateral temporal lobe epilepsy with hippocampal sclerosis (TLE; n = 174 including n = 58 controls) and to the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI; n = 151, of whom with mild cognitive impairment (MCI), n = 71; Alzheimer's disease (AD), n = 33; controls, n = 47). In controls, mean PC volume was 485 mm3 on the right and 461 mm3 on the left. Automatic and manual segmentations overlapped with a Jaccard coefficient (intersection/union) of ~0.5 and a mean absolute volume difference of ~22 mm3 in healthy controls, ~0.40/ ~28 mm3 in patients with TLE, and ~ 0.34/~29 mm3 in patients with AD. In patients with TLE, PC atrophy lateralised to the side of hippocampal sclerosis (p < .001). In patients with MCI and AD, PC volumes were lower than those of controls bilaterally (p < .001). Overall, we have validated automatic PC volumetry in healthy controls and two types of pathology. The novel finding of early atrophy of PC at the stage of MCI possibly adds a novel biomarker. PC volumetry can now be applied at scale.
Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe seizures. The purpose of this study was to leverage network neuroscience to better understand the interictal whole brain network of bilateral TLE (BiTLE). In this study, using a multicenter resting state functional magnetic resonance imaging (rs-fMRI) data set, we constructed whole-brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting-state, whole-brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities. Curves of network density vs each network metric were compared between groups. Finally, we derived a combined metric, which we term the “integration-segregation axis,” by combining whole-brain average clustering coefficient and global efficiency curves, and applying principal component analysis (PCA)–based dimensionality reduction. Compared to UTLE, BiTLE had decreased global efficiency (p = .031), and decreased whole brain average participation coefficient across a range of network densities (p = .019). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p = .020). Differences in network properties separate BiTLE and UTLE along the integration-segregation axis, with regions within the axis having a specificity of up to 0.87 for BiTLE. Along the integration-segregation axis, UTLE patients with poor surgical outcomes were distributed in the same regions as BiTLE, and network metrics confirmed similar patterns of increased segregation in both BiTLE and poor outcome UTLE. Increased interictal whole-brain network segregation, as measured by rs-fMRI, is specific to BiTLE, as well as poor surgical outcome UTLE, and may assist in non-invasively identifying this patient population prior to intracranial electroencephalography or device implantation.
Multimodal mapping of regional brain vulnerability to focal cortical dysplasia
Focal cortical dysplasia (FCD) type II is a highly epileptogenic developmental malformation and a common cause of surgically treated drug-resistant epilepsy. While clinical observations suggest frequent occurrence in the frontal lobe, mechanisms for such propensity remain unexplored. Here, we hypothesized that cortex-wide spatial associations of FCD distribution with cortical cytoarchitecture, gene expression and organizational axes may offer complementary insights into processes that predispose given cortical regions to harbour FCD. We mapped the cortex-wide MRI distribution of FCDs in 337 patients collected from 13 sites worldwide. We then determined its associations with (i) cytoarchitectural features using histological atlases by Von Economo and Koskinas and BigBrain; (ii) whole-brain gene expression and spatiotemporal dynamics from prenatal to adulthood stages using the Allen Human Brain Atlas and PsychENCODE BrainSpan; and (iii) macroscale developmental axes of cortical organization. FCD lesions were preferentially located in the prefrontal and fronto-limbic cortices typified by low neuron density, large soma and thick grey matter. Transcriptomic associations with FCD distribution uncovered a prenatal component related to neuroglial proliferation and differentiation, likely accounting for the dysplastic makeup, and a postnatal component related to synaptogenesis and circuit organization, possibly contributing to circuit-level hyperexcitability. FCD distribution showed a strong association with the anterior region of the antero-posterior axis derived from heritability analysis of interregional structural covariance of cortical thickness, but not with structural and functional hierarchical axes. Reliability of all results was confirmed through resampling techniques. Multimodal associations with cytoarchitecture, gene expression and axes of cortical organization indicate that prenatal neurogenesis and postnatal synaptogenesis may be key points of developmental vulnerability of the frontal lobe to FCD. Concordant with a causal role of atypical neuroglial proliferation and growth, our results indicate that FCD-vulnerable cortices display properties indicative of earlier termination of neurogenesis and initiation of cell growth. They also suggest a potential contribution of aberrant postnatal synaptogenesis and circuit development to FCD epileptogenicity.
Amygdala lesions are associated with improved mood after epilepsy surgery
Neuroimaging studies in healthy and clinical populations strongly associate the amygdala with emotion, especially negative emotions. The consequences of surgical resection of the amygdala on mood are not well characterized. We tested the hypothesis that amygdala resection would result in mood improvement. In this study, we evaluated a cohort of 52 individuals with medial temporal lobectomy for intractable epilepsy who had resections variably involving the amygdala. All individuals achieved good post-surgical seizure control and had pre- and post-surgery mood assessment with the Beck Depression Inventory (BDI) ratings. We manually segmented the surgical resection cavities and performed multivariate lesion-symptom mapping of change in BDI. Our results showed a significant improvement in average mood ratings from pre- to post-surgery across all patients. In partial support of our hypothesis, resection of the right amygdala was significantly associated with mood improvement (r = 0.5, p = 0.008). The lesion-symptom map also showed that resection of the right hippocampus and para-hippocampal gyrus was associated with worsened post-surgical mood. Future studies could evaluate this finding prospectively in larger samples while including other neuropsychological outcome measures.
Editorial: Advances in neuroimaging of epilepsy
Epilepsy is a multifaceted disorder that can have a variety of etiologic factors, including genetic and structural causes. Neuroimaging is an essential component of its diagnostic workup. Conventional MRI scans can reveal visible structural brain abnormalities that may be the cause of seizures in both common and rare forms of epilepsy. However, in some cases, even when the suspicion of an epileptogenic structural lesion is high, such as in patients being referred for epilepsy surgery, a conventional brain MRI may not reveal any abnormalities. Advanced neuroimaging techniques such as MRI fingerprinting, brain morphometry, functional MRI, and ultra-high field MRI can provide additional diagnostic information and can help identify subtle or cryptic lesions that may not be visible on a conventional brain MRI. This can aid in more accurate clinical assessments of individuals with epilepsy, or those with an unclear etiology. Advanced neuroimaging is a powerful tool to investigate neural networks underlying seizure generation, epilepsy-associated large-scale system reorganization, and substrates of cognitive comorbidities. Most advanced imaging techniques can be applied to study both groups and single subjects, providing working hypotheses that can subsequently be explored in larger studies. Comprehensive views can be further provided by multisite neuroimaging meta-analyses, that explore morphological and functional brain abnormalities shared by subjects with common epilepsies or specific syndromes.
Focal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common pathologies causing pharmacoresistant focal epilepsy. Resective neurosurgery yields high success rates, especially if the full extent of the lesion is correctly identified and completely removed. The visual assessment of magnetic resonance imaging does not pinpoint the FCD in 30%–50% of cases, and half of all patients with FCD are not amenable to epilepsy surgery, partly because the FCD could not be sufficiently localized. Computational approaches to FCD detection are an active area of research, benefitting from advancements in computer vision. Automatic FCD detection is a significant challenge and one of the first clinical grounds where the application of artificial intelligence may translate into an advance for patients' health. The emergence of new methods from the combination of health and computer sciences creates novel challenges. Imaging data need to be organized into structured, well-annotated datasets and combined with other clinical information, such as histopathological subtypes or neuroimaging characteristics. Algorithmic output, that is, model prediction, requires a technically correct evaluation with adequate metrics that are understandable and usable for clinicians. Publication of code and data is necessary to make research accessible and reproducible. This critical review introduces the field of automatic FCD detection, explaining underlying medical and technical concepts, highlighting its challenges and current limitations, and providing a perspective for a novel research environment.
Structural and functional neuroimaging studies often overlook lower basal ganglia structures located in and adjacent to the midbrain due to poor contrast on clinically acquired T1-weighted scans. Here, we acquired T1-weighted, T2-weighted, and resting-state fMRI scans to investigate differences in volume, estimated myelin content and functional connectivity of the substantia nigra (SN), subthalamic nuclei (SubTN) and red nuclei (RN) of the midbrain in IGE. Thirty-three patients with IGE (23 refractory, 10 non-refractory) and 39 age and sex-matched healthy controls underwent MR imaging. Midbrain structures were automatically segmented from T2-weighted images and structural volumes were calculated. The estimated myelin content for each structure was determined using a T1-weighted/T2-weighted ratio method. Resting-state functional connectivity analysis of midbrain structures (seed-based) was performed using the CONN toolbox. An increased volume of the right RN was found in IGE and structural volumes of the right SubTN differed between patients with non-refractory and refractory IGE. However, no volume findings survived corrections for multiple comparisons. No myelin alterations of midbrain structures were found for any subject groups. We found functional connectivity alterations including significantly decreased connectivity between the left SN and the thalamus and significantly increased connectivity between the right SubTN and the superior frontal gyrus in IGE. We report volumetric and functional connectivity alterations of the midbrain in patients with IGE. We postulate that potential increases in structural volumes are due to increased iron deposition that impacts T2-weighted contrast. These findings are consistent with previous studies demonstrating pathophysiological abnormalities of the lower basal ganglia in animal models of generalised epilepsy.
This study focuses on white matter alterations in pharmacoresistant epilepsy patients with no visible lesions in the temporal and frontal lobes on clinical MRI (i.e. MR-negative) with lesions confirmed by resective surgery. The aim of the study was to extend the knowledge about group-specific neuropathology in MR-negative epilepsy. We used the fixel-based analysis (FBA) that overcomes the limitations of traditional diffusion tensor image analysis, mainly within-voxel averaging of multiple crossing fibres. Group-wise comparisons of fixel parameters between healthy controls (N = 100) and: (1) frontal lobe epilepsy (FLE) patients (N = 9); (2) temporal lobe epilepsy (TLE) patients (N = 13) were performed. A significant decrease of the cross-section area of the fixels in the superior longitudinal fasciculus was observed in the FLE. Results in TLE reflected widespread atrophy of limbic, thalamic, and cortico-striatal connections and tracts directly connected to the temporal lobe (such as the anterior commissure, inferior fronto-occipital fasciculus, uncinate fasciculus, splenium of corpus callosum, and cingulum bundle). Alterations were also observed in extratemporal connections (brainstem connection, commissural fibres, and parts of the superior longitudinal fasciculus). To our knowledge, this is the first study to use an advanced FBA method not only on the datasets of MR-negative TLE patients, but also MR-negative FLE patients, uncovering new common tract-specific alterations on the group level.
Task-based functional magnetic resonance imaging (tfMRI) has developed as a common alternative in epilepsy surgery to the intracarotid amobarbital procedure, also known as the Wada procedure. Prior studies have implicated tfMRI as a comparable predictor of postsurgical cognitive outcomes. However, the predictive validity of tfMRI has not been established. This preregistered systematic review and meta-analysis (CRD42020183563) synthesizes the literature predicting postsurgical cognitive outcomes in temporal lobe epilepsy (TLE) using tfMRI. The PubMed and PsycINFO literature databases were queried for English-language articles published between January 1, 2009 and December 31, 2020 associating tfMRI laterality indices or symmetry of task activation with outcomes in TLE. Their references were reviewed for additional relevant literature, and unpublished data from our center were incorporated. Nineteen studies were included in the meta-analysis. tfMRI studies predicted postsurgical cognitive outcomes in left TLE (p = −.27, 95% confidence interval [CI] = −.32 to −.23) but not right TLE (p = −.02, 95% CI = −.08 to .03). Among studies of left TLE, language tfMRI studies were more robustly predictive of postsurgical cognitive outcomes (p = −.27, 95% CI = −.33 to −.20) than memory tfMRI studies (p = −.27, 95% CI = −.43 to −.11). Further moderation by cognitive outcome domain indicated language tfMRI predicted confrontation naming (p = −.32, 95% CI = −.41 to −.22) and verbal memory (p = −.26, 95% CI = −.35 to −.17) outcomes, whereas memory tfMRI forecasted only verbal memory outcomes (p = −.37, 95% CI = −.57 to −.18). Surgery type, birth sex, level of education, age at onset, disease duration, and hemispheric language dominance moderated study outcomes. Sensitivity analyses suggested the interval of postsurgical follow-up, and reporting and methodological practices influenced study outcomes as well. These findings intimate tfMRI is a modest predictor of outcomes in left TLE that should be considered in the context of a larger surgical workup.
https://academic.oup.com/cercor/article/33/10/5774/6850566?login=false
Benign epilepsy with centrotemporal spikes (BECTS) is a common pediatric epilepsy syndrome that has been widely reported to show abnormal brain structure and function. However, the genetic mechanisms underlying structural and functional changes remain largely unknown. Based on the structural and resting-state functional magnetic resonance imaging data of 22 drug-naïve children with BECTS and 33 healthy controls, we conducted voxel-based morphology (VBM) and fractional amplitude of low-frequency fluctuation (fALFF) analyses to compare cortical morphology and spontaneous brain activity between the 2 groups. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging spatial correlation analyses were applied to explore gene expression profiles associated with gray matter volume (GMV) and fALFF changes in BECTS. VBM analysis demonstrated significantly increased GMV in the right brainstem and right middle cingulate gyrus in BECTS. Moreover, children with BECTS exhibited significantly increased fALFF in left temporal pole, while decreased fALFF in right thalamus and left precuneus. These brain structural and functional alterations were closely related to behavioral and cognitive deficits, and the fALFF-linked gene expression profiles were enriched in voltage-gated ion channel and synaptic activity as well as neuron projection. Our findings suggest that brain morphological and functional abnormalities in children with BECTS involve complex polygenic genetic mechanisms.
Arousal and salience network connectivity alterations in surgical temporal lobe epilepsy
It is poorly understood why patients with mesial temporal lobe epilepsy (TLE) have cognitive deficits and brain network changes that extend beyond the temporal lobe, including altered extratemporal intrinsic connectivity networks (ICNs). However, subcortical arousal structures project broadly to the neocortex, are affected by TLE, and thus may contribute to these widespread network effects. The authors’ objective was to examine functional connectivity (FC) patterns between subcortical arousal structures and neocortical ICNs, possible neurocognitive relationships, and FC changes after epilepsy surgery. The authors obtained resting-state functional magnetic resonance imaging (fMRI) in 50 adults with TLE and 50 controls. They compared nondirected FC (correlation) and directed FC (Granger causality laterality index) within the salience network, default mode network, and central executive network, as well as between subcortical arousal structures; these 3 ICNs were also compared between patients and controls. They also used an fMRI-based vigilance index to relate alertness to arousal center FC. Finally, fMRI was repeated in 29 patients > 12 months after temporal lobe resection. Nondirected FC within the salience (p = 0.042) and default mode (p = 0.0008) networks, but not the central executive network (p = 0.79), was decreased in patients in comparison with controls (t-tests, corrected). Nondirected FC between the salience network and subcortical arousal structures (nucleus basalis of Meynert, thalamic centromedian nucleus, and brainstem pedunculopontine nucleus) was reduced in patients in comparison with controls (p = 0.0028–0.015, t-tests, corrected), and some of these connectivity abnormalities were associated with lower processing speed index, verbal comprehension, and full-scale IQ. Interestingly, directed connectivity measures suggested a loss of top-down influence from the salience network to the arousal nuclei in patients. After resection, certain FC patterns between the arousal nuclei and salience network moved toward control values in the patients, suggesting that some postoperative recovery may be possible. Although an fMRI-based vigilance measure suggested that patients exhibited reduced alertness over time, FC abnormalities between the salience network and arousal structures were not influenced by the alertness levels during the scans. FC abnormalities between subcortical arousal structures and ICNs, such as the salience network, may be related to certain neurocognitive deficits in TLE patients. Although TLE patients demonstrated vigilance abnormalities, baseline FC perturbations between the arousal and salience networks are unlikely to be driven solely by alertness level, and some may improve after surgery. Examination of the arousal network and ICN disturbances may improve our understanding of the downstream clinical effects of TLE.
Mapping Lesion-Related Epilepsy to a Human Brain Network
It remains unclear why lesions in some locations cause epilepsy while others do not. Identifying the brain regions or networks associated with epilepsy by mapping these lesions could inform prognosis and guide interventions. To assess whether lesion locations associated with epilepsy map to specific brain regions and networks. This case-control study used lesion location and lesion network mapping to identify the brain regions and networks associated with epilepsy in a discovery data set of patients with poststroke epilepsy and control patients with stroke. Patients with stroke lesions and epilepsy (n = 76) or no epilepsy (n = 625) were included. Generalizability to other lesion types was assessed using 4 independent cohorts as validation data sets. The total numbers of patients across all datasets (both discovery and validation datasets) were 347 with epilepsy and 1126 without. Therapeutic relevance was assessed using deep brain stimulation sites that improve seizure control. Data were analyzed from September 2018 through December 2022. All shared patient data were analyzed and included; no patients were excluded. Epilepsy or no epilepsy. Lesion locations from 76 patients with poststroke epilepsy (39 [51%] male; mean [SD] age, 61.0 [14.6] years; mean [SD] follow-up, 6.7 [2.0] years) and 625 control patients with stroke (366 [59%] male; mean [SD] age, 62.0 [14.1] years; follow-up range, 3-12 months) were included in the discovery data set. Lesions associated with epilepsy occurred in multiple heterogenous locations spanning different lobes and vascular territories. However, these same lesion locations were part of a specific brain network defined by functional connectivity to the basal ganglia and cerebellum. Findings were validated in 4 independent cohorts including 772 patients with brain lesions (271 [35%] with epilepsy; 515 [67%] male; median [IQR] age, 60 [50-70] years; follow-up range, 3-35 years). Lesion connectivity to this brain network was associated with increased risk of epilepsy after stroke (odds ratio [OR], 2.82; 95% CI, 2.02-4.10; P < .001) and across different lesion types (OR, 2.85; 95% CI, 2.23-3.69; P < .001). Deep brain stimulation site connectivity to this same network was associated with improved seizure control (r, 0.63; P < .001) in 30 patients with drug-resistant epilepsy (21 [70%] male; median [IQR] age, 39 [32-46] years; median [IQR] follow-up, 24 [16-30] months). The findings in this study indicate that lesion-related epilepsy mapped to a human brain network, which could help identify patients at risk of epilepsy after a brain lesion and guide brain stimulation therapies.
