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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 involvement during seizures. Bilateral TLE (BiTLE) remains understudied, likely due to its complex underlying pathophysiology and heterogeneous clinical presentation.In this study, using a multicenter resting state functional MRI (rs-fMRI) dataset, 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 ranging from 0.10-0.50. Curves of network density versus 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=0.026), increased whole brain average clustering coefficient (p=0.035), and decreased whole brain average participation coefficient across a range of network densities (p=0.001). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p=0.016). Differences in network properties separate BiTLE and UTLE along the integration-segregation axis: 68% of patients with BiTLE were identified within the high segregation region, while only 41% of the UTLE patients were identified in the same region (p=0.042). Along the integration-segregation axis, UTLE patients with poor surgical outcomes were more similar to BiTLE than those with good surgical outcomes (p=0.72).Increased interictal whole brain network segregation, as measured by rs-fMRI, is specific to BiTLE, and may assist in non-invasively identifying this patient population prior to intracranial EEG or device implantation.

Background and ObjectivesChronic poststroke language impairment is typically worse in older individuals or those with large stroke lesions. However, there is unexplained variance that likely depends on intact tissue beyond the lesion. Brain age is an emerging concept, which is partially independent from chronologic age. Advanced brain age is associated with cognitive decline in healthy older adults; therefore, we aimed to investigate the relationship with stroke aphasia. We hypothesized that advanced brain age is a significant factor associated with chronic poststroke language impairments, above and beyond chronologic age, and lesion characteristics.MethodsThis cohort study retrospectively evaluated participants from the Predicting Outcomes of Language Rehabilitation in Aphasia clinical trial (NCT03416738), recruited through local advertisement in South Carolina (US). Primary inclusion criteria were left hemisphere stroke and chronic aphasia (≥12 months after stroke). Participants completed baseline behavioral testing including the Western Aphasia Battery–Revised (WAB-R), Philadelphia Naming Test (PNT), Pyramids and Palm Trees Test (PPTT), and Wechsler Adult Intelligence Scale Matrices subtest, before completing 6 weeks of language therapy. The PNT was repeated 1 month after therapy. We leveraged modern neuroimaging techniques to estimate brain age and computed a proportional difference between chronologic age and estimated brain age. Multiple linear regression models were used to evaluate the relationship between proportional brain age difference (PBAD) and behavior.ResultsParticipants (N = 93, 58 males and 35 females, average age = 61 years) had estimated brain ages ranging from 14 years younger to 23 years older than chronologic age. Advanced brain age predicted performance on semantic tasks (PPTT) and language tasks (WAB-R). For participants with advanced brain aging (n = 47), treatment gains (improvement on the PNT) were independently predicted by PBAD (T = −2.0474,p= 0.0468, 9% of variance explained).DiscussionThrough the application of modern neuroimaging techniques, advanced brain aging was associated with aphasia severity and performance on semantic tasks. Notably, therapy outcome scores were also associated with PBAD, albeit only among participants with advanced brain aging. These findings corroborate the importance of brain age as a determinant of poststroke recovery and underscore the importance of personalized health factors in determining recovery trajectories, which should be considered during the planning or implementation of therapeutic interventions.

Background: Idiopathic generalized epilepsy (IGE) is a collection of generalized nonlesional epileptic network disorders. Around 20-40% of patients with IGE are refractory to antiseizure medication, and mechanisms underlying refractoriness are poorly understood. Here, we characterize structural brain network alterations and determine whether network alterations differ between patients with refractory and nonrefractory IGE. Methods: Thirty-three patients with IGE (10 nonrefractory and 23 refractory) and 39 age- and sex-matched healthy controls were studied. Network nodes were segmented from T1-weighted images, while connections between these nodes (edges) were reconstructed from diffusion magnetic resonance imaging (MRI). Diffusion networks of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and streamline count (Count) were studied. Differences between all patients, refractory, nonrefractory, and control groups were computed using network-based statistics. Nodal volume differences between groups were computed using Cohen's d effect size calculation. Results: Patients had significantly decreased bihemispheric FA and Count networks and increased MD and RD networks compared with controls. Alterations in network architecture, with respect to controls, differed depending on treatment outcome, including predominant FA network alterations in refractory IGE and increased nodal volume in nonrefractory IGE. Diffusion MRI networks were not influenced by nodal volume. Discussion: Although a nonlesional disorder, patients with IGE have bihemispheric structural network alterations that may differ between patients with refractory and nonrefractory IGE. Given that distinct nodal volume and FA network alterations were observed between treatment outcome groups, a multifaceted network analysis may be useful for identifying imaging biomarkers of refractory IGE.

Patients with movement disorders treated by deep brain stimulation do not always achieve successful therapeutic alleviation of motor symptoms, even in cases where surgery is without complications. Magnetic resonance imaging (MRI) offers methods to investigate structural brain-related factors that may be predictive for clinical motor outcomes. This review aimed to identify features which have been associated with variability in clinical post-operative motor outcomes in patients with Parkinson’s disease, dystonia, and essential tremor from structural MRI modalities. We performed a literature search for articles published between January 01, 2000, and April 01, 2022, and identified 5197 articles. Following screening through our inclusion criteria, we identified 60 total studies (39 = Parkinson’s disease, 11 = dystonia syndromes and 10 = essential tremor). The review captured a range of structural MRI methods and analysis techniques used to identify factors related to clinical post-operative motor outcomes from deep brain stimulation. Morphometric markers, including volume and cortical thickness were commonly identified in studies focused on patients with Parkinson’s disease and dystonia syndromes. Reduced metrics in basal ganglia, sensorimotor and frontal regions showed frequent associations with reduced motor outcomes. Increased structural connectivity to subcortical nuclei, sensorimotor and frontal regions were also associated with greater motor outcomes. In patients with tremor, increased structural connectivity to the cerebellum and cortical motor regions showed high prevalence across studies for greater clinical motor outcomes. In addition, we highlight conceptual issues for studies assessing clinical response with structural MRI and discuss future approaches towards optimising individualised therapeutic benefits. Although quantitative MRI markers are in their infancy for clinical purposes in movement disorder treatments, structural features obtained from MRI offer powerful potential to identify candidates who are more likely to benefit from deep brain stimulation and provide insight into the complexity of disorder pathophysiology.

Background and Objectives: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. Here we explore 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.Methods: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 to a randomized model (comparison to chance) and a hippocampal volume logistic regression (comparison to current clinically-available measures). Furthermore, we leveraged a CNN feature visualization technique to identify regions used to classify patients.Results: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.Discussion:These extra-temporal 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 extra-hippocampal regions that may require additional radiological attention.Classification of Evidence: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 MR images can correctly classify seizure laterality.

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.

BackgroundCervical dystonia is a movement disorder, characterised by involuntary head and neck muscle contractions. Although deep brain stimulation (DBS) of the globus pallidus internus (GPi) is an effective treatment option, motor outcomes can vary even when sufficient targeting accuracy is achieved. Increasing evidence supports a role of brainstem and cerebellum dysfunction in cervical dystonia pathogenesis.ObjectiveTo determine whether morphometry of brainstem and dentate nuclei, and DBS stimulatory overlap with cerebello-thalamic tracts modelled from normative connectivity, were related to DBS clinical motor outcomes.Methods27 patients with idiopathic cervical dystonia underwent bilateral targeting of the GPi. and were separated into suboptimal and optimal motor outcome groups. Dentate nuclei and brainstem volumes were quantified in association with clinical outcomes. A brainstem shape analysis was conducted and used as a seed to assess connectivity from a normative structural connectome. Patient-specific electrodes were modelled to quantify stimulatory overlap with the GPi and proximity to cerebellothalamic tracts.ResultsGPi implantation accuracy did not significantly differ between groups. Significantly reduced dentate nuclei and brainstem volumes were observed in patients with poorer clinical outcomes. Regional surface shape change of the brainstem was also observed in patients with poorer responses. Fibre tracking from this area intersected cerebellar, pallidal and cortical motor regions. Electrode field intersection with the non-decussating dentatorubrothalamic tract in the right, and in both hemispheres were also positively associated with clinical outcome.ConclusionsVariability in cerebellar and brainstem morphometry, and stimulation of non-decussating cerebello-thalamic pathways may contribute to the mediation of DBS motor outcomes.

Background Seizures can occur unpredictably in patients with acute encephalitis syndrome (AES), and many suffer from poor long-term neurological sequelae. Establishing factors associated with acute seizures risk and poor outcomes could support clinical care. We aimed to conduct regional and volumetric analysis of cerebral oedema on magnetic resonance imaging (MRI) in patients with AES. We assessed the relationship of brain oedema with acute seizure activity and long-term neurological outcome. Methods In a multi-centre cohort study, adults and children presenting with an AES were recruited in the UK. The clinical and brain MRI data were retrospectively reviewed. The outcomes variables were inpatient acute seizure activity and neurological disability at six-months post-discharge. A poor outcome was defined as a Glasgow outcome score (GOS) of 1–3. We quantified regional brain oedema on MRI through stereological examination of T2-weighted images using established methodology by independent and blinded assessors. Clinical and neuroimaging variables were analysed by multivariate logistic regression to assess for correlation with acute seizure activity and outcome. Results The study cohort comprised 69 patients (mean age 31.8 years; 53.6% female), of whom 41 (59.4%) had acute seizures as inpatients. A higher Glasgow coma scale (GCS) score on admission was a negative predictor of seizures (OR 0.61 [0.46–0.83], p = 0.001). Even correcting for GCS on admission, the presence of cortical oedema was a significant risk factor for acute seizure activity (OR 5.48 [1.62–18.51], p = 0.006) and greater volume of cerebral oedema in these cortical structures increased the risk of acute seizures (OR 1.90 [1.12–3.21], p = 0.017). At six-month post-discharge, 21 (30.4%) had a poor neurological outcome. Herpes simplex virus encephalitis was associated with higher risk of poor outcomes in univariate analysis (OR 3.92 [1.08–14.20], p = 0.038). When controlling for aetiology, increased volume of cerebral oedema was an independent risk factor for adverse neurological outcome at 6 months (OR 1.73 [1.06–2.83], p = 0.027). Conclusions Both the presence and degree of cerebral oedema on MRIs of patients with AES may help identify patients at risk of acute seizure activity and subsequent long-term morbidity.

Generalised epilepsy is thought to involve distributed brain networks. However, the molecular and cellular factors that render different brain regions more vulnerable to epileptogenesis remain largely unknown. We aimed to investigate epilepsy-related morphometric similarity network (MSN) abnormalities at the macroscale level and their relationships with microscale gene expressions at the microscale level.We compared the MSN of genetic generalised epilepsy with generalised tonic-clonic seizure patients (GGE-GTCS, n = 101) to demographically matched healthy controls (HC, n = 150). Cortical MSNs were estimated by combining seven morphometric features derived from structural magnetic resonance imaging for each individual. Regional gene expression profiles were derived from brain-wide microarray measurements provided by the Allen Human Brain Atlas.GGE-GTCS patients exhibited decreased regional MSNs in primary motor, prefrontal and temporal regions and increases in occipital, insular and posterior cingulate cortices, when compared with the HC. These case-control neuroimaging differences were validated using split-half analyses and were not affected by medication or drug response effects. When assessing associations with gene expression, genes associated with GGE-GTCS-related MSN differences were enriched in several biological processes, including 'synapse organisation', 'neurotransmitter transport' pathways and excitatory/inhibitory neuronal cell types. Collectively, the GGE-GTCS-related cortical vulnerabilities were associated with chromosomes 4, 5, 11 and 16 and were dispersed bottom-up at the cellular, pathway and disease levels, which contributed to epileptogenesis, suggesting diverse neurobiologically relevant enrichments in GGE-GTCS.By bridging the gaps between transcriptional signatures and in vivo neuroimaging, we highlighted the importance of using MSN abnormalities of the human brain in GGE-GTCS patients to investigate disease-relevant genes and biological processes.

It is well established that abnormal thalamocortical systems play an important role in the generation and maintenance of primary generalised seizures. However, it is currently unknown which thalamic nuclei and how nuclear‐specific thalamocortical functional connectivity are differentially impacted in patients with medically refractory and non‐refractory idiopathic generalised epilepsy (IGE). In the present study, we performed structural and resting‐state functional magnetic resonance imaging (MRI) in patients with refractory and non‐refractory IGE, segmented the thalamus into constituent nuclear regions using a probabilistic MRI segmentation method and determined thalamocortical functional connectivity using seed‐to‐voxel connectivity analyses. We report significant volume reduction of the left and right anterior thalamic nuclei only in patients with refractory IGE. Compared to healthy controls, patients with refractory and non‐refractory IGE had significant alterations of functional connectivity between the centromedian nucleus and cortex, but only patients with refractory IGE had altered cortical connectivity with the ventral lateral nuclear group. Patients with refractory IGE had significantly increased functional connectivity between the left and right ventral lateral posterior nuclei and cortical regions compared to patients with non‐refractory IGE. Cortical effects were predominantly located in the frontal lobe. Atrophy of the anterior thalamic nuclei and resting‐state functional hyperconnectivity between ventral lateral nuclei and cerebral cortex may be imaging markers of pharmacoresistance in patients with IGE. These structural and functional abnormalities fit well with the known importance of thalamocortical systems in the generation and maintenance of primary generalised seizures, and the increasing recognition of the importance of limbic pathways in IGE., We report that patients with idiopathic generalised epilepsy have different patterns of thalamic structural and functional connectivity alterations depending on whether they are refractory or not to anti‐seizure medication. Only refractory patients showed evidence of atrophy of the anterior (limbic) thalamic nuclei and increased functional connectivity between the ventral lateral posterior nuclei and cortex bilaterally.

BackgroundPrevious neuroimaging studies reported functional and structural impairments of the upper basal ganglia (specifically, the striatum) in idiopathic generalised epilepsy (IGE). However, these studies often overlook lower basal ganglia structures located in and adjacent to the midbrain due to poor contrast on clinically acquired volumetric T1-weighted scans. Here, we acquired 3D isotropic T1-weighted, T2-weighted and resting state functional MR images to investigate differences in volume, estimated myelin content and functional connectivity in three midbrain and midbrain-adjacent structures in patients with IGE: the substantia nigra (SN), subthalamic nuclei (SubTN) and red nuclei (RN).MethodsA total of 33 patients with IGE (23 refractory, 10 non-refractory) and 39 age and sex matched healthy controls underwent MR imaging. The SN, SubTN and RN were automatically segmented from T2-weighted images. Estimated median myelin content for each structure was determined using a previously described T1-weighted/T2-weighted ratio method. Functional connectivity analysis between midbrain structures and the rest of the brain was performed using seed-based resting state fMRI analysis and compared between participant groups using the CONN toolbox running in SPM12 (pFDRResultsAn increased volume of the right RN was found in patients with IGE relative to healthy controls. Structural volumes of the right SubTN differed between patients with non-refractory and refractory IGE. No myelin alterations of midbrain structures were found in patients with IGE or between treatment outcome groups. Functional connectivity alterations were found for all midbrain regions in patients with IGE, including significantly decreased functional connectivity between the left SN and the thalamus compared to controls, and significantly increased functional connectivity observed between the right SubTN and the left superior frontal gyrus in patients with IGE relative to controls. Connectivity alterations specific to patients with non-refractory IGE were also found.ConclusionWe report volumetric and functional connectivity alterations of midbrain and lower basal ganglia structures in patients with IGE. We postulate that an increased structural volume of the RN in patients is due to increased iron deposition that impacts on T2-weighted contrast. These findings are consistent with previous experiential work demonstrating pathophysiological abnormalities of the lower basal ganglia in animal models of generalised epilepsy.

ObjectiveTo determine whether surgery in patients with mesial temporal lobe epilepsy (mTLE) is associated with reduced brain-predicted age as a neural marker overall brain health, we compared brain-predicted and chronologic age difference (brain age gap estimation [BrainAGE]) in patients before and after surgery with healthy controls.MethodsWe acquired 3D T1-weighted MRI scans for 48 patients with mTLE before and after temporal lobe surgery to estimate brain age using a gaussian processes regression model. We examined BrainAGE before and after surgery controlling for brain volume change, comparing patients to 37 age- and sex-matched controls.ResultsPreoperatively, patients showed an increased BrainAGE of more than 7 years compared to controls. However, surgery was associated with a mean BrainAGE reduction of 5 years irrespective of whether or not surgery resulted in complete seizure freedom. We observed a lateralization effect as patients with left mTLE had BrainAGE values that more closely resembled control group values following surgery.ConclusionsOur findings suggest that while morphologic brain alterations linked to accelerated aging have been observed in mTLE, surgery may be associated with changes that reverse such alterations in some patients. This work highlights the advantages of resective surgery on overall brain health in patients with refractory focal epilepsy.

Epilepsy, characterised by a predisposition towards unprovoked seizures, is one of the most common neurological disorders globally. Whilst 60-70% of individuals diagnosed with epilepsy will gain seizure control through anti-seizure medication, the mechanisms underlying seizure persistence are unclear. Intractability can significantly degrade a patient’s quality of life amongst other things; the use of predictive modelling of epilepsy outcomes in deciding on treatment therefore offers a tangible benefit to the patient. Early indicators of pharmacoresistance may discourage certain treatment options, and save time in what has been indicated to be a critical stage for newly-diagnosed epilepsy. Primarily, this paper aims to evaluate existing predictive models to identify demographic, clinical, physiological (e.g. EEG), and neuroimaging (e.g. MRI) factors that may be predictive of treatment outcomes in newly-diagnosed epilepsy. Two electronic databases, MEDLINE and EMBASE, will be searched with terms related to prognosis in newly-diagnosed epilepsy, and identified studies will be included for review if they have combined at least two demographic, clinical, neuroimaging, and/or physiological factors to predict treatment outcome in people with newly-diagnosed epilepsy. Papers will be screened by two independent reviewers via titles, abstracts and then full text against the inclusion criteria for eligibility. Data will be extracted by reviewers using standardised forms, assessed for risk of bias using the PROBAST tool and synthesised narratively. If considered appropriate the authors will carry out a meta-analysis on the available data.Prospero registration number– CRD42022329936

Background and ObjectivesTo determine the association between surgical lesions of distinct gray and white structures and connections with favorable postoperative seizure outcomes.MethodsPatients with drug-resistant temporal lobe epilepsy (TLE) from 3 epilepsy centers were included. We employed a voxel-based and connectome-based mapping approach to determine the association between favorable outcomes and surgery-induced temporal lesions. Analyses were conducted controlling for multiple confounders, including total surgical resection/ablation volume, hippocampal volumes, side of surgery, and site where the patient was treated.ResultsThe cohort included 113 patients with TLE (54 women; 86 right-handed; mean age at seizure onset 16.5 years [SD 11.9]; 54.9% left) who were 61.1% free of disabling seizures (Engel Class 1) at follow-up. Postoperative seizure freedom in TLE was associated with (1) surgical lesions that targeted the hippocampus as well as the amygdala–piriform cortex complex and entorhinal cortices; (2) disconnection of temporal, frontal, and limbic regions through loss of white matter tracts within the uncinate fasciculus, anterior commissure, and fornix; and (3) functional disconnection of the frontal (superior and middle frontal gyri, orbitofrontal region) and temporal (superior and middle pole) lobes.DiscussionBetter postoperative seizure freedom is associated with surgical lesions of specific structures and connections throughout the temporal lobes. These findings shed light on the key components of epileptogenic networks in TLE and constitute a promising source of new evidence for future improvements in surgical interventions.Classification of EvidenceThis study provides Class II evidence that for patients with TLE, postoperative seizure freedom is associated with surgical lesions of specific temporal lobe structures and connections.

Objective Medial temporal lobe epilepsy (TLE) is the most common form of medication-resistant focal epilepsy in adults. Despite removal of medial temporal structures, over a third of patients continue to have disabling seizures post-operatively. Seizure refractoriness implies that extra-medial regions are capable of influencing the brain network and generating seizures. We tested whether abnormalities of structural network integration could be associated with surgical outcomes. Methods Presurgical magnetic resonance images from 121 patients with drug-resistant TLE across three independent epilepsy centers were used to train feed-forward neural network models based on tissue volume or graph-theory measures from whole-brain diffusion tensor imaging structural connectomes. An independent dataset of 47 patients with TLE from three other epilepsy centers was used to assess the predictive values of each model and regional anatomical contributions towards surgical treatment results. Results The receiver-operating characteristic (ROC) area under the curve (AUC) based on regional betweenness centrality was 0.88, significantly higher than a random model or models based on gray matter volumes, degree, strength, and clustering coefficient. Nodes most strongly contributing to the predictive models involved the bilateral parahippocampal gyri, as well as the superior temporal gyri. Interpretation Network integration in the medial and lateral temporal regions was related to surgical outcomes. Patients with abnormally integrated structural network nodes were less likely to achieve seizure freedom. These findings are in line with previous observations related to network abnormalities in TLE and expand on the notion of underlying aberrant plasticity. Our findings provide additional information on the mechanisms of surgical refractoriness. This article is protected by copyright. All rights reserved.

Neuromodulation is an increasingly utilized therapy for the treatment of people with drug-resistant epilepsy. To date, the most common and effective target has been the thalamus, which is known to play a key role in multiple forms of epilepsy. Neuroimaging has facilitated rapid developments in the understanding of functional targets, surgical and programming techniques, and the effects of thalamic stimulation. In this review, the role of neuroimaging in neuromodulation is explored. First, the structural and functional changes of the thalamus in common epilepsy syndromes are discussed as the rationale for neuromodulation of the thalamus. Next, methods for imaging different thalamic nuclei are presented, as well as rationale for the need of direct surgical targeting rather than reliance on traditional stereotactic coordinates. Lastly, we discuss the potential role of neuroimaging in assessing the effects of thalamic stimulation and as a potential biomarker for neuromodulation outcomes.

Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of temporal lobe epilepsy and may inform future discovery and validation of complementary MRI biomarkers in temporal lobe epilepsy.

IntroductionA first unprovoked seizure is a common presentation, reliably identifying those that will have recurrent seizures is a challenge. This study will be the first to explore the combined utility of serum biomarkers, quantitative electroencephalogram (EEG) and quantitative MRI to predict seizure recurrence. This will inform patient stratification for counselling and the inclusion of high-risk patients in clinical trials of disease-modifying agents in early epilepsy.Methods and analysis100 patients with first unprovoked seizure will be recruited from a tertiary neuroscience centre and baseline assessments will include structural MRI, EEG and a blood sample. As part of a nested pilot study, a subset of 40 patients will have advanced MRI sequences performed that are usually reserved for patients with refractory chronic epilepsy. The remaining 60 patients will have standard clinical MRI sequences. Patients will be followed up every 6 months for a 24-month period to assess seizure recurrence. Connectivity and network-based analyses of EEG and MRI data will be carried out and examined in relation to seizure recurrence. Patient outcomes will also be investigated with respect to analysis of high-mobility group box-1 from blood serum samples.Ethics and disseminationThis study was approved by North East—Tyne & Wear South Research Ethics Committee (20/NE/0078) and funded by an Association of British Neurologists and Guarantors of Brain clinical research training fellowship. Findings will be presented at national and international meetings published in peer-reviewed journals.Trial registration numberNIHR Clinical Research Network's (CRN) Central Portfolio Management System (CPMS)—44976.

Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes.

Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem co-expression patterns of epilepsy risk genes. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1,328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-genetic signatures could guide diagnosis, and ultimately, tailor therapeutic approaches to specific epilepsy syndromes.

BackgroundMagnetic resonance imaging (MRI) of the brain could be a key diagnostic and research tool for understanding the neuropsychiatric complications of COVID-19. For maximum impact, multi-modal MRI protocols will be needed to measure the effects of SARS-CoV2 infection on the brain by diverse potentially pathogenic mechanisms, and with high reliability across multiple sites and scanner manufacturers.MethodsA multi-modal brain MRI protocol comprising sequences for T1-weighted MRI, T2-FLAIR, diffusion MRI (dMRI), resting-state functional MRI (fMRI), susceptibility-weighted imaging (swMRI) and arterial spin labelling (ASL) was defined in close approximation to prior UK Biobank (UKB) and C-MORE protocols for Siemens 3T systems. We iteratively defined a comparable set of sequences for General Electric (GE) 3T systems. To assess multi-site feasibility and between-site variability of this protocol, N=8 healthy participants were each scanned at 4 UK sites: 3 using Siemens PRISMA scanners (Cambridge, Liverpool, Oxford) and 1 using a GE scanner (King’s College London). Over 2,000 Imaging Derived Phenotypes (IDPs) measuring both data quality and regional image properties of interest were automatically estimated by customised UKB image processing pipelines. Components of variance and intra-class correlations were estimated for each IDP by linear mixed effects models and benchmarked by comparison to repeated measurements of the same IDPs from UKB participants.ResultsIntra-class correlations for many IDPs indicated good-to-excellent between-site reliability. First considering only data from the Siemens sites, between-site reliability generally matched the high levels of test-retest reliability of the same IDPs estimated in repeated, within-site, within-subject scans from UK Biobank. Inclusion of the GE site resulted in good-to-excellent reliability for many IDPs, but there were significant between-site differences in mean and scaling, and reduced ICCs, for some classes of IDP, especially T1 contrast and some dMRI-derived measures. We also identified high reliability of quantitative susceptibility mapping (QSM) IDPs derived from swMRI images, multi-network ICA-based IDPs from resting-state fMRI, and olfactory bulb structure IDPs from T1, T2-FLAIR and dMRI data.ConclusionThese results give confidence that large, multi-site MRI datasets can be collected reliably at different sites across the diverse range of MRI modalities and IDPs that could be mechanistically informative in COVID brain research. We discuss limitations of the study and strategies for further harmonization of data collected from sites using scanners supplied by different manufacturers. These protocols have already been adopted for MRI assessments of post-COVID patients in the UK as part of the COVID-CNS consortium.

Temporal lobe epilepsy is associated with MRI findings reflecting underlying mesial temporal sclerosis. Identifying these MRI features is critical for the diagnosis and management of temporal lobe epilepsy. To date, this process relies on visual assessment by highly trained human experts (e.g. neuroradiologists, epileptologists). Artificial intelligence is increasingly recognized as a promising aid in the radiological evaluation of neurological diseases, yet its applications in temporal lobe epilepsy have been limited. Here, we applied a convolutional neural network to assess the classification accuracy of temporal lobe epilepsy based on structural MRI. We demonstrate that convoluted neural networks can achieve high accuracy in the identification of unilateral temporal lobe epilepsy cases even when the MRI had been originally interpreted as normal by experts. We show that accuracy can be potentiated by employing smoothed grey matter maps and a direct acyclic graphs approach. We further discuss the foundations for the development of computer-aided tools to assist with the diagnosis of epilepsy.

IntroductionHerpes simplex virus (HSV) encephalitis is a rare severe form of brain inflammation that commonly leaves survivors and their families with devastating long-term consequences. The virus particularly targets the temporal lobe of the brain causing debilitating problems in memory, especially verbal memory. It is postulated that immunomodulation with the corticosteroid, dexamethasone, could improve outcomes by reducing brain swelling. However, there are concerns (so far not observed) that such immunosuppression might facilitate increased viral replication with resultant worsening of disease. A previous trail closed early because of slow recruitment.MethodDexEnceph is a pragmatic multicentre, randomised, controlled, open-label, observer-blind trial to determine whether adults with HSV encephalitis who receive dexamethasone alongside standard antiviral treatment with aciclovir for have improved clinical outcomes compared with those who receive standard treatment alone. Overall, 90 patients with HSV encephalitis are being recruited from a target of 45 recruiting sites; patients are randomised 1:1 to the dexamethasone or control arms of the study. The primary outcome measured is verbal memory as assessed by the Weschler Memory Scale fourth edition Auditory Memory Index at 26 weeks after randomisation. Secondary outcomes are measured up to 72 weeks include additional neuropsychological, clinical and functional outcomes as well as comparison of neuroimaging findings. Patient safety monitoring occurs throughout and includes the detection of HSV DNA in cerebrospinal fluid 2 weeks after randomisation, which is indicative of ongoing viral replication. Innovative methods are being used to ensure recrutiment targets are met for this rare disease.DiscussionDexEnceph aims to be the first completed randomised controlled trial of corticosteroid therapy in HSV encephalitis. The results will provide evidence for future practice in managing adults with the condition and has the potential to improve outcomes .Ethics and disseminationThe trial has ethical approval from the UK National Research Ethics Committee (Liverpool Central, REF: 15/NW/0545, 10 August 2015). Protocol V.2.1, July 2019. The results will be published and presented as soon as possible on completion.Trial registration numbersISRCTN11774734, EUDRACT 2015-001609-16.

ObjectiveNeuroimaging research is providing insights into epilepsy as a disorder of brain connectivity linked to functional impairments which may have an identifiable genetic component. This case-control study aims to identify imbalances in a functional connectome dimension spanning from unimodal to transmodal networks and explore the potential genetic basis of such alterations in patients with newly diagnosed focal epilepsy (NDfE).MethodsWe used gradient-based analysis of resting-sate fMRI data comparing cortical gradient maps in patients with NDfE (n = 27) to age and sex-matched controls (n = 36). Using a brain-wide gene expression dataset, gene combinations associated with altered brain regions were then entered into an enrichment analysis.ResultsWe found an increased differentiation of connectivity profiles between unimodal and transmodal networks in NDfE, which was particularly pronounced in the patients with persistent seizures at 12-months follow-up (n=10). Differences corresponded to gradient score reductions in a visual network and increases in limbic and default mode systems which subserve higher-level cognition. Cortical difference maps were spatially correlated with regional expression of a weighted gene combination. These genes were enriched for disease and ontology terms and pathways previously associated with epilepsy and seizure susceptibility.InterpretationsLarge-scale functional hierarchy may be altered from in focal epilepsy from diagnosis and correlate with response to treatment. Combining functional neuroimaging and transcriptional data analysis may provide a framework for understanding the wide-ranging impairments associated with the disorder and mechanistic insight into how gene processes may drive alterations in brain function mediating the genetic risk of epilepsy.

Multicompartment diffusion magnetic resonance imaging (MRI) approaches are increasingly being applied to estimate intra‐axonal and extra‐axonal diffusion characteristics in the human brain. Fiber ball imaging (FBI) and its extension fiber ball white matter modeling (FBWM) are such recently described multicompartment approaches. However, these particular approaches have yet to be applied in clinical cohorts. The modeling of several diffusion parameters with interpretable biological meaning may offer the development of new, noninvasive biomarkers of pharmacoresistance in epilepsy. In the present study, we used FBI and FBWM to evaluate intra‐axonal and extra‐axonal diffusion properties of white matter tracts in patients with longstanding focal epilepsy. FBI/FBWM diffusion parameters were calculated along the length of 50 white matter tract bundles and statistically compared between patients with refractory epilepsy, nonrefractory epilepsy and controls. We report that patients with chronic epilepsy had a widespread distribution of extra‐axonal diffusivity relative to controls, particularly in circumscribed regions along white matter tracts projecting to cerebral cortex from thalamic, striatal, brainstem, and peduncular regions. Patients with refractory epilepsy had significantly greater markers of extra‐axonal diffusivity compared to those with nonrefractory epilepsy. The extra‐axonal diffusivity alterations in patients with epilepsy observed in the present study could be markers of neuroinflammatory processes or a reflection of reduced axonal density, both of which have been histologically demonstrated in focal epilepsy. FBI is a clinically feasible MRI approach that provides the basis for more interpretive conclusions about the microstructural environment of the brain and may represent a unique biomarker of pharmacoresistance in epilepsy., Bryant et al. used fiber ball imaging to measure intra‐axonal and extra‐axonal white matter diffusion in patients with longstanding focal epilepsy. It is reported that extra‐axonal diffusion is different between patients with refractory and nonrefractory epilepsy and may represent a novel imaging biomarker of pharmacoresistance.

Structural alterations of the central nervous system are demonstrated in people with diabetes. We aimed to characterise areas of global and anatomical grey matter volumes using T1-weighted magnetic resonance (MR) brain imaging (3-T MR imaging machine) in participants from the UK Biobank dataset. Participants with diabetes (DM) (n=569) and people without diabetes (CON) (n=20,801) were analysed. The mean age of DM diagnosis was 49.5±15 years. Baseline demographic and anthropometric data are as follows: DM group included more men (DM: 65% vs. CON: 47%) but were of a similar age (DM: 57.6±7.1 vs. CON: 55±7.5 years). BMI (DM: 29.6±5.2 vs. CON: 26.5±4.2 kgm2; p Disclosure J. Burgess: None. S. S. Keller: None. I. N. Petropoulos: None. D. Selvarajah: None. S. Tesfaye: Advisory Panel; Self; Angellini, Bayer AG, Eva Pharma, Wörwag Pharma, Speaker's Bureau; Self; Abbott, AstraZeneca, Grunenthal Group, MSD Corporation, Novo Nordisk, Pfizer Inc. U. Alam: None.

Temporal lobe epilepsy (TLE), a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter pathology in TLE relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multi-site ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 TLE patients and 1,418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in TLE, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity, and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of TLE-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of TLE and may inform future discovery and validation of complementary MRI biomarkers in TLE.

Purpose Most techniques used for automatic segmentation of subcortical brain regions are developed for three-dimensional (3D) MR images. MRIs obtained in non-specialist hospitals may be non-isotropic and two-dimensional (2D). Automatic segmentation of 2D images may be challenging and represents a lost opportunity to perform quantitative image analysis. We determine the performance of a modified subcortical segmentation technique applied to 2D images in patients with idiopathic generalised epilepsy (IGE). Methods Volume estimates were derived from 2D (0.4 × 0.4 × 3 mm) and 3D (1 × 1x1mm) T1-weighted acquisitions in 31 patients with IGE and 39 healthy controls. 2D image segmentation was performed using a modified FSL FIRST (FMRIB Integrated Registration and Segmentation Tool) pipeline requiring additional image reorientation, cropping, interpolation and brain extraction prior to conventional FIRST segmentation. Consistency between segmentations was assessed using Dice coefficients and volumes across both approaches were compared between patients and controls. The influence of slice thickness on consistency was further assessed using 2D images with slice thickness increased to 6 mm. Results All average Dice coefficients showed excellent agreement between 2 and 3D images across subcortical structures (0.86–0.96). Most 2D volumes were consistently slightly lower compared to 3D volumes. 2D images with increased slice thickness showed lower agreement with 3D images with lower Dice coefficients (0.55–0.83). Significant volume reduction of the left and right thalamus and putamen was observed in patients relative to controls across 2D and 3D images. Conclusion Automated subcortical volume estimation of 2D images with a resolution of 0.4 × 0.4x3mm using a modified FIRST pipeline is consistent with volumes derived from 3D images, although this consistency decreases with an increased slice thickness. Thalamic and putamen atrophy has previously been reported in patients with IGE. Automated subcortical volume estimation from 2D images is feasible and most reliable at using in-plane acquisitions greater than 1 mm x 1 mm and provides an opportunity to perform quantitative image analysis studies in clinical trials.

Diffusion magnetic resonance imaging (dMRI) tractography has played a critical role in characterizing patterns of aberrant brain network reorganization among patients with epilepsy. However, the accuracy of dMRI tractography is hampered by the complex biophysical properties of white matter tissue. High b-value diffusion imaging overcomes this limitation by better isolating axonal pathways. In this study, we introduce tractography derived from fiber ball imaging (FBI), a high b-value approach which excludes non-axonal signals, to identify atypical neuronal networks in patients with epilepsy. Specifically, we compared network properties obtained from multiple diffusion tractography approaches (diffusion tensor imaging, diffusion kurtosis imaging, FBI) in order to assess the pathophysiological relevance of network rearrangement in medication-responsive vs. medication-refractory adults with focal epilepsy. We show that drug-resistant epilepsy is associated with increased global network segregation detected by FBI-based tractography. We propose exploring FBI as a clinically feasible alternative to quantify topological changes that could be used to track disease progression and inform on clinical outcomes.