Histological and MRI markers of white matter damage in focal epilepsy

Epilepsy and demyelination: Towards a bidirectional relationship

Demyelination stands out as a prominent feature in individuals with specific types of epilepsy. Concurrently, individuals with demyelinating diseases, such as multiple sclerosis (MS) are at a greater risk of developing epilepsy compared to non-MS individuals. These bidirectional connections raise the question of whether both pathological conditions share common pathogenic mechanisms. This review focuses on the reciprocal relationship between epilepsy and demyelination diseases. We commence with an overview of the neurological basis of epilepsy and demyelination diseases, followed by an exploration of how our comprehension of these two disorders has evolved in tandem. Additionally, we discuss the potential pathogenic mechanisms contributing to the interactive relationship between these two diseases. A more nuanced understanding of the interplay between epilepsy and demyelination diseases has the potential to unveiling the molecular intricacies of their pathological relationships, paving the way for innovative directions in future clinical management and treatment strategies for these diseases.

Altered White-Matter Functional Network in Children with Idiopathic Generalized Epilepsy

RATIONALE AND OBJECTIVES

The white matter (WM) functional network changes offers insights into the potential pathological mechanisms of certain diseases, the alterations of WM functional network in idiopathic generalized epilepsy (IGE) remain unclear. We aimed to explore the topological characteristics changes of WM functional network in childhood IGE using resting-state functional Magnetic resonance imaging (MRI) and T1-weighted images.

METHODS

A total of 84 children (42 IGE and 42 matched healthy controls) were included in this study. Functional and structural MRI data were acquired to construct a WM functional network. Group differences in the global and regional topological characteristics were assessed by graph theory and the correlations with clinical and neuropsychological scores were analyzed. A support vector machine algorithm model was employed to classify individuals with IGE using WM functional connectivity as features, and the model's accuracy was evaluated using leave-one-out cross-validation.

RESULTS

In IGE group, at the network level, the WM functional network exhibited increased assortativity; at the nodal level, 17 nodes presented nodal disturbances in WM functional network, and nodal disturbances of 11 nodes were correlated with cognitive performance scores, disease duration and age of onset. The classification model achieved the 72.6% accuracy, 0.746 area under the curve, 69.1% sensitivity, 76.2% specificity.

CONCLUSION

Our study demonstrated that the WM functional network topological properties changes in childhood IGE, which were associated with cognitive function, and WM functional network may help clinical classification for childhood IGE. These findings provide novel information for understanding the pathogenesis of IGE and suggest that the WM function network might be qualified as potential biomarkers.

Structural and functional changes in the default mode network in drug-resistant epilepsy

PURPOSE

To investigate brain network properties and connectivity abnormalities of the default mode network (DMN) in drug-resistant epilepsy (DRE). The study was based on probabilistic fiber tracking and functional connectivity (FC) analysis, to explore the structural and functional connectivity patterns change between frontal lobe epilepsy (FLE) and temporal lobe epilepsy (TLE).

METHODS

A total of 33 DRE patients (18 TLE and 15 FLE) and 30 healthy controls (HCs) were recruited. The volume fraction of the septal brain region of the DMN in DRE was calculated using FreeSurfer. The FC analysis was performed using Data Processing and Analysis for Brain Imaging in MATLAB. The structural connections between brain regions of the DMN were calculated based on probabilistic fiber tracking.

RESULTS

The left precuneus (PCUN) volumes in epilepsy groups were lower than that in HCs. Compared with FLE, TLE showed reduced FC between the left hippocampus (HIP) and PCUN/medial frontal gyrus, and between the right inferior parietal lobule (IPL) and right superior temporal gyrus. Compared with HCs, FLE showed increased FCs between the right IPL and occipital lobe, and between the left superior frontal gyrus (SFG) and bilateral superior temporal gyrus. In terms of structural connectivity, TLE exhibited increased connectivity strength between the left SFG and left PCUN, and showed reduced connection strength between the left HIP and left posterior cingulate gyrus/left PCUN, when compared with the FLE.

CONCLUSIONS

TLE and FLE patients showed structural and functional changes in the DMN. Compared with FLE patients, the TLE patients showed reduced structural and functional connection strengths between the left HIP and PCUN. These alterations in connection strengths holds promise for the identification of TLE and FLE.

Epilepsy plus blindness in microdeletion of GABRA1 and GABRG2 in mouse and human

OBJECTIVE

GABAA receptor subunit gene (GABR) mutations are significant causes of epilepsy, including syndromic epilepsy. This report for the first time, describes intractable epilepsy and blindness due to optic atrophy in our patient, who has a microdeletion of the GABRA1 and GABRG2 genes. We then characterized the molecular phenotypes and determined patho-mechanisms underlying the genotype-phenotype correlations in a mouse model who is haploinsufficient for both genes (Gabra1+/-/Gabrg2+/- mouse).

METHODS

Electroencephalography was conducted in both human and mice with the same gene loss. GABAA receptor expression was evaluated by biochemical and imaging approaches. Optic nerve atrophy was evaluated with fundus photography in human while electronic microscopy, visual evoked potential and electroretinography recordings were conducted in mice.

RESULTS

The patient has bilateral optical nerve atrophy. Mice displayed spontaneous seizures, reduced electroretinography oscillatory potential and reduced GABAA receptor α1, β2 and γ2 subunit expression in various brain regions. Electronic microscopy showed that mice also had optic nerve degeneration, as indicated by increased G-ratio, the ratio of the inner axonal diameter to the total outer diameter, suggesting impaired myelination of axons. More importantly, we identified that phenobarbital was the most effective anticonvulsant in mice and the patient's seizures were also controlled with phenobarbital after failing multiple anti-seizure drugs.

CONCLUSIONS

This study is the first report of haploinsufficiency of two GABR epilepsy genes and visual impairment due to altered axonal myelination and resultant optic nerve atrophy. The study suggests the far-reaching impact of GABR mutations and the translational significance of animal models with the same etiology.

Deep learning distinguishes connectomes from focal epilepsy patients and controls: feasibility and clinical implications

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.

Processing speed in temporal lobe epilepsy. A scoping review

BACKGROUND

Impaired processing speed (PS) can affect patients with temporal lobe epilepsy (TLE). However, it is usually considered a nonspecific clinical feature and is not measured, but this raises lexical and methodological problems. This review aims to evaluate the existing terminology and assessment methods of PS in patients with TLE.

METHODS

A scoping review was conducted based on the extended guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis. The electronic literature search was conducted on Medline-PubMed, American Psychological Association-PsycINFO, Elton Bryson Stephens Company, and Google Scholar, using the keywords "temporal lobe epilepsy" and "speed" or "slowing" plus "processing," "cognitive," "psychomotor," or "mental." Peer-reviewed articles published before December 2022 were analyzed if they were in English, including patients older than 14 years and at least one neuropsychological measure, reported original research focused on PS and had the selected keywords in the title, keywords, and abstract.

RESULTS

Seven articles published between December 2004 and September 2021 were selected. The terms "processing speed," "psychomotor speed," and "information processing speed," based on similar theoretical constructs, were the most frequently used. Assessment methods included non-computerized or paper-and-pencil tests (WAIS-III Digit Symbol and Symbol Search subtests, Purdue Pegboard and Grooved Pegboard Tests, Trail Making Test and Stroop Color-Word Test) and computerized tests (Sternberg Memory Scanning Test, Pattern Comparison Processing Speed, Computerized Visual Searching). In some studies, impairment was associated with white and gray matter damage in the brain, independent of clinical and treatment variables.

CONCLUSION

Clinical research on TLE has focused inconsistently on PS. Different evaluation terms and methods have been used while referring to similar theoretical constructs. These findings highlight a gap between the clinical importance of PS and its assessment. Studies are needed to share terms and tools among clinical centers and clarify the position of PS in the TLE phenotype.

Multi-Scale Deep Learning of Clinically Acquired Multi-Modal MRI Improves the Localization of Seizure Onset Zone in Children With Drug-Resistant Epilepsy

The present study investigates the effectiveness of a deep learning neural network for non-invasively localizing the seizure onset zone (SOZ) using multi-modal MRI data that are clinically acquired from children with drug-resistant epilepsy. A cortical parcellation was applied to localize the SOZ in cortical nodes of the epileptogenic hemisphere. At each node, the laminar surface analysis was followed to sample 1) the relative intensity of gray matter and white matter in multi-modal MRI and 2) the neighboring white matter connectivity using diffusion tractography edge strengths. A cross-validation was employed to train and test all layers of a multi-scale residual neural network (msResNet) that can classify SOZ node in an end-to-end fashion. A prediction probability of a given node belonging to the SOZ class was proposed as a non-invasive MRI marker of seizure onset likelihood. In an independent validation cohort, the proposed MRI marker provided a very large effect size of Cohen's d = 1.21 between SOZ and non-SOZ, and classified SOZ with a balanced accuracy of 0.75 in lesional and 0.67 in non-lesional MRI groups. The subsequent multi-variate logistic regression found the incorporation of the proposed MRI marker into interictal intracranial EEG (iEEG) markers further improves the differentiation between the epileptogenic focus (defined as SOZ resected during surgery) and non-epileptogenic sites (i.e., non-SOZ sites preserved during surgery) up to 15 % in non-lesional MRI group, suggesting that the proposed MRI marker could improve the localization of epileptogenic foci for successful pediatric epilepsy surgery.

That’s a Wrap: Could Controlling Activity-Regulated Myelination Prevent Absence Seizures?

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The role of the temporal pole in temporal lobe epilepsy: A diffusion kurtosis imaging study

This study aimed to evaluate the use of diffusion kurtosis imaging (DKI) to detect microstructural abnormalities within the temporal pole (TP) and its temporopolar cortex in temporal lobe epilepsy (TLE) patients. DKI quantitative maps were obtained from fourteen lesional TLE and ten non-lesional TLE patients, along with twenty-three healthy controls. Data collected included mean (MK); radial (RK) and axial kurtosis (AK); mean diffusivity (MD) and axonal water fraction (AWF). Automated fiber quantification (AFQ) was used to quantify DKI measurements along the inferior longitudinal (ILF) and uncinate fasciculus (Unc). ILF and Unc tract profiles were compared between groups and tested for correlation with disease duration. To characterize temporopolar cortex microstructure, DKI maps were sampled at varying depths from superficial white matter (WM) towards the pial surface. Patients were separated according to the temporal lobe ipsilateral to seizure onset and their AFQ results were used as input for statistical analyses. Significant differences were observed between lesional TLE and controls, towards the most temporopolar segment of ILF and Unc proximal to the TP within the ipsilateral temporal lobe in left TLE patients for MK, RK, AWF and MD. No significant changes were observed with DKI maps in the non-lesional TLE group. DKI measurements correlated with disease duration, mostly towards the temporopolar segments of the WM bundles. Stronger differences in MK, RK and AWF within the temporopolar cortex were observed in the lesional TLE and noticeable differences (except for MD) in non-lesional TLE groups compared to controls. This study demonstrates that DKI has potential to detect subtle microstructural alterations within the temporopolar segments of the ILF and Unc and the connected temporopolar cortex in TLE patients including non-lesional TLE subjects. This could aid our understanding of the extrahippocampal areas, more specifically the temporal pole role in seizure generation in TLE and might inform surgical planning, leading to better seizure outcomes.

High-Frequency Oscillations and Epileptogenic Network

Epilepsy is a network disease caused by aberrant neocortical large-scale connectivity spanning regions on the scale of several centimeters. High-frequency oscillations, characterized by the 80-600 Hz signals in electroencephalography, have been proven to be a promising biomarker of epilepsy that can be used in assessing the severity and susceptibility of epilepsy as well as the location of the epileptogenic zone. However, the presence of a high-frequency oscillation network remains a topic of debate as high-frequency oscillations have been previously thought to be incapable of propagation, and the relationship between high-frequency oscillations and the epileptogenic network has rarely been discussed. Some recent studies reported that high-frequency oscillations may behave like networks that are closely relevant to the epileptogenic network. Pathological highfrequency oscillations are network-driven phenomena and elucidate epileptogenic network development; high-frequency oscillations show different characteristics coincident with the epileptogenic network dynamics, and cross-frequency coupling between high-frequency oscillations and other signals may mediate the generation and propagation of abnormal discharges across the network.

DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization from Resting-State fMRI Connectivity

OBJECTIVE

Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up and therapeutic planning in medication refractory epilepsy. In this paper, we present the first deep learning approach to localize the EZ based on resting-state fMRI (rs-fMRI) data.

METHODS

Our network, called DeepEZ, uses a cascade of graph convolutions that emphasize signal propagation along expected anatomical pathways. We also integrate domain-specific information, such as an asymmetry term on the predicted EZ and a learned subject-specific bias to mitigate environmental confounds.

RESULTS

We validate DeepEZ on rs-fMRI collected from 14 patients with focal epilepsy at the University of Wisconsin Madison. Using cross validation, we demonstrate that DeepEZ achieves consistently high EZ localization performance (Accuracy: 0.88 ± 0.03; AUC: 0.73 ± 0.03) that far outstripped any of the baseline methods. This performance is notable given the variability in EZ locations and scanner type across the cohort.

CONCLUSION

Our results highlight the promise of using DeepEZ as an accurate and noninvasive therapeutic planning tool for medication refractory epilepsy.

SIGNIFICANCE

While prior work in EZ localization focused on identifying localized aberrant signatures, there is growing evidence that epileptic seizures affect inter-regional connectivity in the brain. DeepEZ allows clinicians to harness this information from noninvasive imaging that can easily be integrated into the existing clinical workflow.

The Role of MRI in the Treatment of Drug-Resistant Focal Epilepsy

BACKGROUND

Epilepsy is a prevalent chronic condition affecting about 50 million people worldwide. A third of patients with focal epilepsy suffer from seizures unresponsive to medication. Uncontrolled seizures damage the brain, are associated with cognitive decline, and have negative impact on well-being. For these patients, the surgical resection of the brain region that gives rise to seizures is the most effective treatment.

SUMMARY

Magnetic resonance imaging (MRI) plays a central role in detecting epileptogenic brain lesions. In this review, we critically discuss advances in neuroimaging acquisition, analytical post-acquisition techniques, and machine leaning methods for the detection of epileptogenic lesions, prediction of clinical outcomes, and identification of disease subtypes.

KEY MESSAGE

MRI is a mandatory investigation for diagnosis and treatment of epilepsy, particularly when surgery is being considered. Continuous progress in imaging techniques, combined with machine learning, will continue to push the boundaries of lesion visibility and provide increasingly precise predictors of clinical outcomes. Current efforts aiming at strengthening the competences of epileptologists in neuroimaging will ultimately reduce the need for invasive diagnostics.

High b-value diffusion tractography: Abnormal axonal network organization associated with medication-refractory epilepsy

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.

Relationship of white matter hyperintensities with clinical features of seizures in patients with epilepsy

BACKGROUND

Although epilepsy is primarily known as a cortical disorder, there is growing body of research demonstrating white matter alterations in patients with epilepsy.

OBJECTIVE

To investigate the prevalence of white matter hyperintensities (WMH) and its association with seizure characteristics in patients with epilepsy.

METHODS

The prevalence of WMH in 94 patients with epilepsy and 41 healthy controls were compared. Within the patient sample, the relationship between the presence of WMH and type of epilepsy, frequency of seizures, duration of disease and the number of antiepileptic medications were investigated.

RESULTS

The mean age and sex were not different between patients and healthy controls (p>0.2). WMH was present in 27.7% of patients and in 14.6% of healthy controls. Diagnosis of epilepsy was independently associated with the presence of WMH (ß=3.09, 95%CI 1.06-9.0, p=0.039). Patients with focal epilepsy had higher prevalence of WMH (35.5%) than patients with generalized epilepsy (14.7%). The presence of WMH was associated with older age but not with seizure characteristics.

CONCLUSIONS

WMH is more common in patients with focal epilepsy than healthy controls. The presence of WMH is associated with older age, but not with seizure characteristics.

White matter structural connectivity as a biomarker for detecting juvenile myoclonic epilepsy by transferred deep convolutional neural networks with varying transfer rates

Objective. By detecting abnormal white matter changes, diffusion magnetic resonance imaging (MRI) contributes to the detection of juvenile myoclonic epilepsy (JME). In addition, deep learning has greatly improved the detection performance of various brain disorders. However, there is almost no previous study effectively detecting JME by a deep learning approach with diffusion MRI.Approach. In this study, the white matter structural connectivity was generated by tracking the white matter fibers in detail based on Q-ball imaging and neurite orientation dispersion and density imaging. Four advanced deep convolutional neural networks (CNNs) were deployed by using the transfer learning approach, in which the transfer rate searching strategy was proposed to achieve the best detection performance.Main results. Our results showed: (a) Compared to normal control, the white matter' neurite density of JME was significantly decreased. The most significantly abnormal fiber tracts between the two groups were found to be cortico-cortical connection tracts. (b) The proposed transfer rate searching approach contributed to find each CNN's best performance, in which the best JME detection accuracy of 92.2% was achieved by using the Inception_resnet_v2 network with a 16% transfer rate.Significance. The results revealed: (a) Through detection of the abnormal white matter changes, the white matter structural connectivity can be used as a useful biomarker for detecting JME, which helps to characterize the pathophysiology of epilepsy. (b) The proposed transfer rate, as a new hyperparameter, promotes the CNNs transfer learning performance in detecting JME.

Detecting microstructural deviations in individuals with deep diffusion MRI tractometry

Most diffusion magnetic resonance imaging studies of disease rely on statistical comparisons between large groups of patients and healthy participants to infer altered tissue states in the brain; however, clinical heterogeneity can greatly challenge their discriminative power. There is currently an unmet need to move away from the current approach of group-wise comparisons to methods with the sensitivity to detect altered tissue states at the individual level. This would ultimately enable the early detection and interpretation of microstructural abnormalities in individual patients, an important step towards personalized medicine in translational imaging. To this end, Detect was developed to advance diffusion magnetic resonance imaging tractometry towards single-patient analysis. By operating on the manifold of white-matter pathways and learning normative microstructural features, our framework captures idiosyncrasies in patterns along white-matter pathways. Our approach paves the way from traditional group-based comparisons to true personalized radiology, taking microstructural imaging from the bench to the bedside.

Circulating glial fibrillary acidic protein and ubiquitin carboxy-terminal hydrolase-L1 as markers of neuronal damage in children with epileptic seizures

BACKGROUND

Epilepsy is a common neurological disease that has a negative impact on physical, social, and cognitive function. Seizure-induced neuronal injury is one of the suggested mechanisms of epilepsy complications. We aimed to evaluate the circulating level of glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase-L1 (UCH-L1) as markers of neuronal damage in children with epilepsy and its relation to epilepsy characteristics.

STUDY DESIGN

METHODS: This case control study included 30 children with epilepsy and 30 healthy children as a control group. Seizure severity was determined based on Chalfont score. Serum level of GFAP and UCH-L1were measured, and their associations with epilepsy characteristics were investigated.

RESULTS

Circulating levels of GFAP and UCH-L1 were significantly higher in children with epilepsy than in controls (17.440 ± 6.74 and 5.700 ± 1.64 vs 7.06 ± 3.30 and 1.81 ± 0.23, respectively) especially in those with generalized and active seizures. GFAP and UCH-L1 were significantly correlated to the severity of seizures in the previous 6 months. Elevated GFAP level was a predictor for active seizures (OR 1.841, 95%CI 1.043-3.250, P = 0.035).

CONCLUSION

Circulating GFAP and UCH-L1 expression is increased in children with epilepsy especially those with active seizures.

SIGNIFICANCE

GFAP and UCH-L 1may serve as peripheral biomarkers for neuronal damage in children with epilepsy that can be used to monitor disease progression and severity for early identification of those with drug-resistant epilepsy and those who are in need for epilepsy surgery.

Fiber ball white matter modeling in focal 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.

Emerging Trends in Neuroimaging of Epilepsy

Neuroimaging techniques, particularly magnetic resonance imaging, yield increasingly sophisticated markers of brain structure and function. Combined with ongoing developments in machine learning, these methods refine our abilities to detect subtle epileptogenic lesions and develop reliable prognostics.

A hypothesis for the role of axon demyelination in seizure generation

Loss of myelin and altered oligodendrocyte distribution in the cerebral cortex are commonly observed both in postsurgical tissue derived from different focal epilepsies (such as focal cortical dysplasias and tuberous sclerosis) and in animal models of focal epilepsy. Moreover, seizures are a frequent symptom in demyelinating diseases, such as multiple sclerosis, and in animal models of demyelination and oligodendrocyte dysfunction. Finally, the excessive activity reported in demyelinated axons may promote hyperexcitability. We hypothesize that the extracellular potassium rise generated during epileptiform activity may be amplified by the presence of axons without appropriate myelin coating and by alterations in oligodendrocyte function. This process could facilitate the triggering of recurrent spontaneous seizures in areas of altered myelination and could result in further demyelination, thus promoting epileptogenesis.

Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy

OBJECTIVE

Our objective was to identify whether the whole-brain structural network alterations in patients with temporal lobe epilepsy (TLE) and focal to bilateral tonic-clonic seizures (FBTCS) differ from alterations in patients without FBTCS.

METHODS

We dichotomized a cohort of 83 drug-resistant patients with TLE into those with and without FBTCS and compared each group to 29 healthy controls. For each subject, we used diffusion-weighted magnetic resonance imaging to construct whole-brain structural networks. First, we measured the extent of alterations by performing FBTCS-negative (FBTCS-) versus control and FBTCS-positive (FBTCS+) versus control comparisons, thereby delineating altered subnetworks of the whole-brain structural network. Second, by standardizing each patient's networks using control networks, we measured the subject-specific abnormality at every brain region in the network, thereby quantifying the spatial localization and the amount of abnormality in every patient.

RESULTS

Both FBTCS+ and FBTCS- patient groups had altered subnetworks with reduced fractional anisotropy and increased mean diffusivity compared to controls. The altered subnetwork in FBTCS+ patients was more widespread than in FBTCS- patients (441 connections altered at t > 3, p < .001 in FBTCS+ compared to 21 connections altered at t > 3, p = .01 in FBTCS-). Significantly greater abnormalities-aggregated over the entire brain network as well as assessed at the resolution of individual brain areas-were present in FBTCS+ patients (p < .001, d = .82, 95% confidence interval = .32-1.3). In contrast, the fewer abnormalities present in FBTCS- patients were mainly localized to the temporal and frontal areas.

SIGNIFICANCE

The whole-brain structural network is altered to a greater and more widespread extent in patients with TLE and FBTCS. We suggest that these abnormal networks may serve as an underlying structural basis or consequence of the greater seizure spread observed in FBTCS.

Structural Brain Network Abnormalities and the Probability of Seizure Recurrence After Epilepsy Surgery

OBJECTIVE

We assessed preoperative structural brain networks and clinical characteristics of patients with drug-resistant temporal lobe epilepsy (TLE) to identify correlates of postsurgical seizure recurrences.

METHODS

We examined data from 51 patients with TLE who underwent anterior temporal lobe resection (ATLR) and 29 healthy controls. For each patient, using the preoperative structural, diffusion, and postoperative structural MRI, we generated 2 networks: presurgery network and surgically spared network. Standardizing these networks with respect to controls, we determined the number of abnormal nodes before surgery and expected to be spared by surgery. We incorporated these 2 abnormality measures and 13 commonly acquired clinical data from each patient into a robust machine learning framework to estimate patient-specific chances of seizures persisting after surgery.

RESULTS

Patients with more abnormal nodes had a lower chance of complete seizure freedom at 1 year and, even if seizure-free at 1 year, were more likely to relapse within 5 years. The number of abnormal nodes was greater and their locations more widespread in the surgically spared networks of patients with poor outcome than in patients with good outcome. We achieved an area under the curve of 0.84 ± 0.06 and specificity of 0.89 ± 0.09 in predicting unsuccessful seizure outcomes (International League Against Epilepsy [ILAE] 3-5) as opposed to complete seizure freedom (ILAE 1) at 1 year. Moreover, the model-predicted likelihood of seizure relapse was significantly correlated with the grade of surgical outcome at year 1 and associated with relapses up to 5 years after surgery.

CONCLUSION

Node abnormality offers a personalized, noninvasive marker that can be combined with clinical data to better estimate the chances of seizure freedom at 1 year and subsequent relapse up to 5 years after ATLR.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that node abnormality predicts postsurgical seizure recurrence.

Intact microstructure of the right corticostriatal pathway predicts creative ability in healthy adults

INTRODUCTION

Creativity is one of the most complex functions of the human brain. The corticostriatal pathways have been implicated in creative thinking, yet few studies have addressed the microstructural underpinnings of creative ability, especially those related to the corticostriatal dopaminergic circuitry. We hypothesized that performance in creativity tests can be predicted based on diffusion metrics of the corticostriatal pathways and basal ganglia.

METHODS

A total of 37 healthy adults were included. Neuropsychological tests of creativity, including the alternative uses task (AUT), test of creative imagery abilities (TCIA), remote associates test (RAT), and creative achievement questionnaire (CAQ), as well as diffusion MRI data were acquired for each participant.

RESULTS

We demonstrated an independent effect of TCIA originality and TCIA transformativeness subscores, and RAT score in predicting the mean diffusivity (MD), mean axial diffusivity (AD), mean fractional anisotropy (FA), and mean generalized FA of the right corticostriatal pathway. We also observed independent effects of AUT elaboration subscore in predicting the AD of the right substantia nigra, and radial diffusivity (RD) of the right globus pallidus.

CONCLUSION

Our results put a further spin on the "creative right brain" notion and question the presence of high-creative and low-creative networks in the brain.

NODDI in clinical research

Diffusion MRI (dMRI) has proven to be a useful imaging approach for both clinical diagnosis and research investigating the microstructures of nervous tissues, and it has helped us to better understand the neurophysiological mechanisms of many diseases. Though diffusion tensor imaging (DTI) has long been the default tool to analyze dMRI data in clinical research, acquisition with stronger diffusion weightings beyond the DTI regimen is now possible with modern clinical scanners, potentially enabling even more detailed characterization of tissue microstructures. To take advantage of such data, neurite orientation dispersion and density imaging (NODDI) has been proposed as a way to relate the dMRI signal to tissue features via biophysically inspired modeling. The number of reports demonstrating the potential clinical utility of NODDI is rapidly increasing. At the same time, the pitfalls and limitations of NODDI, and general challenges in microstructure modeling, are becoming increasingly recognized by clinicians. dMRI microstructure modeling is a rapidly evolving field with great promise, where people from different scientific backgrounds, such as physics, medicine, biology, neuroscience, and statistics, are collaborating to build novel tools that contribute to improving human healthcare. Here, we review the applications of NODDI in clinical research and discuss future perspectives for investigations toward the implementation of dMRI microstructure imaging in clinical practice.

MRI and pathology correlations in the medulla in sudden unexpected death in epilepsy (SUDEP): a postmortem study

AIMS

Sudden unexpected death in epilepsy (SUDEP) likely arises as a result of autonomic dysfunction around the time of a seizure. In vivo MRI studies report volume reduction in the medulla and other brainstem autonomic regions. Our aim, in a pathology series, is to correlate regional quantitative features on 9.4T MRI with pathology measures in medullary regions.

METHODS

Forty-seven medullae from 18 SUDEP, 18 nonepilepsy controls and 11 epilepsy controls were studied. In 16 cases, representing all three groups, ex vivo 9.4T MRI of the brainstem was carried out. Five regions of interest (ROI) were delineated, including the reticular formation zone (RtZ), and actual and relative volumes (RV), as well as T1, T2, T2* and magnetization transfer ratio (MTR) measurements were evaluated on MRI. On serial sections, actual and RV estimates using Cavalieri stereological method and immunolabelling indices for myelin basic protein, synaptophysin and Microtubule associated protein 2 (MAP2) were carried out in similar ROI.

RESULTS

Lower relative RtZ volumes in the rostral medulla but higher actual volumes in the caudal medulla were observed in SUDEP (P < 0.05). No differences between groups for T1, T2, T2* and MTR values in any region was seen but a positive correlation between T1 values and MAP2 labelling index in RtZ (P < 0.05). Significantly lower MAP2 LI were noted in the rostral medulla RtZ in epilepsy cases (P < 0.05).

CONCLUSIONS

Rostro-caudal alterations of medullary volume in SUDEP localize with regions containing respiratory regulatory nuclei. They may represent seizure-related alterations, relevant to the pathophysiology of SUDEP.

Automated Detection of Juvenile Myoclonic Epilepsy using CNN based Transfer Learning in Diffusion MRI

Epilepsy is one of the largest neurological diseases in the world, and juvenile myoclonic epilepsy (JME) usually occurs in adolescents, giving patients tremendous burdens during growth, which really needs the early diagnosis. Advanced diffusion magnetic resonance imaging (MRI) could detect the subtle changes of the white matter, which could be a non-invasive early diagnosis biomarker for JME. Transfer learning can solve the problem of insufficient clinical samples, which could avoid overfitting and achieve a better detection effect. However, there is almost no research to detect JME combined with diffusion MRI and transfer learning. In this study, two advanced diffusion MRI methods, high angle resolved diffusion imaging (HARDI) and neurite orientation dispersion and density imaging (NODDI), were used to generate the connectivity matrix which can describe tiny changes in white matter. And three advanced convolutional neural networks (CNN) based transfer learning were applied to detect JME. A total of 30 participants (15 JME patients and 15 normal controls) were analyzed. Among the three CNN models, Inception_resnet_v2 based transfer learning is better at detecting JME than Inception_v3 and Inception_v4, indicating that the "short cut" connection can improve the ability to detect JME. Inception_resnet_v2 achieved to detect JME with the accuracy of 75.2% and the AUC of 0.839. The results support that diffusion MRI and CNN based transfer learning have the potential to improve the automated detection of JME.

Thalamocortical structural connectivity abnormalities in drug-resistant generalized epilepsy: A diffusion tensor imaging study

BACKGROUND AND PURPOSE

Epilepsy is one of the most common diseases of the nervous system. Approximately one-third of epilepsy cases are drug-resistant, among which generalized-onset seizures are very common. The present study aimed to analyze abnormalities of the thalamocortical fiber pathways in each hemisphere of the brains of patients with drug-resistant generalized epilepsy.

MATERIALS AND METHODS

The thalamocortical structural pathways were identified by diffusion tensor imaging (DTI) in 15 patients with drug-resistant generalized epilepsy and 16 gender/age-matched controls. The thalami of both groups were parcellated into subregions according to the local thalamocortical connectivity pattern. DTI measures of thalamocortical connections were compared between the two groups.

RESULTS

Probabilistic tractography analyses showed that fractional anisotropy of thalamocortical pathways in patients with epilepsy decreased significantly, and the radial diffusivity of the left thalamus pathways with homolateral motor and parietal-occipital cortical regions in the drug-resistant epilepsy group increased significantly. In addition to the right thalamus pathway and prefrontal cortical region, fractional anisotropy of all other pathways was inversely correlated with disease duration.

CONCLUSION

The results provide evidence indicating widespread bilateral abnormalities in the thalamocortical pathways in epilepsy patients and imply that the degree of abnormality in the pathway increases with the disease duration.

Microstructural changes and internet addiction behaviour: A preliminary diffusion MRI study

Internet addiction (IA) is a major health problem and is associated with comorbidities like insomnia and depression. These consequences frequently confound neuroanatomical correlates of IA in those suffering from it. We enrolled a number of 123 healthy native German-speaking adults (53 male, mean age: 36.8 ± 18.86) from the Leipzig Study for Mind-Body-Emotion Interactions (LEMON) database, for whom diffusion MRI data, internet addiction test, brief self-control scale (SCS), coping orientations to problems experienced (COPE), and depression scores were available. DMRI connectometry was used to investigate white matter microstructural correlates of the severity of internet addiction identified through IAT, in a group of healty young individuals. A multiple regression model was adopted with age, gender, SCS total score, COPE total score, and BDI-sum as covariates to track white matter fibers in which connectivity was associated with IAT. The connectometry analysis identified a direct correlation between connectivity in the splenium of corpus callosum (CC), parts of bilateral corticospinal tracts (CST), and bilateral arcuate fasciculi (AF) (FDR = 0.0023001), and an inverse correlation of the connectivity in the genu of CC and right fornix (FDR = 0.047138), with the IAT score in healthy adults. We suggest connectivity in the CC and CST as well as fornix and AF to be considered as microstructural biomarkers of predisposition to IA in healthy population.

Distinctive Patterns of Seizure-Related White Matter Alterations in Right and Left Temporal Lobe Epilepsy

Background: We hypothesized that right and left temporal lobe epilepsy (RTLE and LTLE, respectively) have distinctive spatial patterns of white matter (WM) changes that can be differentiated and interpreted with the use of multiple diffusion parameters. We compared the global microstructure of fiber bundles with regard to WM alterations in both RTLE and LTLE, addressing some of the methodological issues of previous studies. Methods: Diffusion tensor imaging data from 17 patients with RTLE (age: 40.7 ± 10.4), 15 patients with LTLE (age: 37.3 ± 10.4), and 15 controls (age: 34.8 ± 11.2) were used in the study. WM integrity was quantified by fractional anisotropy (FA), mean diffusivity (MD), longitudinal diffusivity (LD), and radial diffusivity (RD). The diffusion parameters were compared between the groups in tracts representing the core of the fiber bundles. The volumes of hippocampi and amygdala were subsequently compared across the groups, while the data were adjusted for the effect of hippocampal sclerosis. Results: Significantly reduced FA and increased MD, LD, and RD were found bilaterally over widespread brain regions in RTLE. An increase in MD and RD values was observed in widespread WM fiber bundles ipsilaterally in LTLE, largely overlapping with regions where FA was lower, while no increase in LD was observed. We also found a difference between the LTLE and RTLE groups for the right hippocampal volume (with and without adjustment for HS), whereas no significant volume differences were found between patients and controls. Conclusions: It appears that patients with RTLE exhibit a more widespread pattern of WM alterations that extend far beyond the temporal lobe in both ipsilateral and contralateral hemisphere; furthermore, these changes seem to reflect more severe damage related to chronic degeneration. Conversely, more restrained changes in the LTLE may imply a pattern of less severe axonal damage, more restricted to ipsilateral hemisphere. Comprehensive finding of more prominent hippocampal atrophy in the RTLE raises an interesting issue of seizure-induced implications on gray matter and WM microstructure that may not necessarily mean a straightforward causal relationship. Further correlations of diffusion-derived metrics with neuropsychological and functional imaging measures may provide complementary information on underlying WM abnormalities with regard to functional hemispheric specialization.

Lower myelin-water content of the frontal lobe in childhood absence epilepsy

OBJECTIVE

The frontal lobe in childhood absence epilepsy (CAE) might be affected due to the suggested involvement of the frontal lobe during absence seizures and reports on attentional deficits. Previously, subtle white matter abnormalities have been reported in CAE. However, the impact of one of the most characteristic components of the white matter, the myelin content, remains underdetermined. Therefore, this study investigated whether the myelin content in frontal areas is adversely affected in CAE compared to controls.

METHODS

Seventeen children with childhood absence epilepsy (mean age ± standard deviation [SD], 9.2 ± 2.1 years) and 15 age- and sex-matched controls (mean age ± SD, 9.8 ± 1.8 years) underwent neuropsychological assessment and a magnetic resonance imaging (MRI) examination. T2 relaxometry scans were used to distinguish myelin-water from tissue water and to determine the myelin-water fraction (MWF) in the frontal, temporal, parietal, occipital, and insular lobes. A linear regression model including age and sex as covariates was used to investigate group differences. Furthermore, the relationship of MWF with cognitive performance and epilepsy characteristics was determined.

RESULTS

The frontal lobe revealed a significantly lower myelin-water content in children with CAE compared to controls over the developmental age range of 6-12 years (5.7 ± 1.0% vs 6.6 ± 1.1%, P = 0.02). This association was not found for any of the other four lobes (P > 0.10). No significant relation was found between myelin-water content and cognitive performance or epilepsy characteristics.

SIGNIFICANCE

The lower frontal myelin-water content of children with CAE in comparison with healthy controls probably reflects an altered neurodevelopmental aspect in CAE, of which the underlying mechanisms still need to be unraveled.

White matter correlates of disease duration in patients with temporal lobe epilepsy: updated review of literature

BACKGROUND

Medial temporal lobe epilepsy (mTLE) has been associated with widespread white mater (WM) alternations in addition to mesial temporal sclerosis (MTS). Herein, we aimed to investigate the correlation between disease duration and WM structural abnormalities in mTLE using diffusion MRI (DMRI) connectometry approach.

METHOD

DMRI connectometry was conducted on 24 patients with mTLE. A multiple regression model was used to investigate white matter tracts with microstructural correlates to disease duration, controlling for age and sex. DMRI data were processed in the MNI space using q-space diffeomorphic reconstruction to obtain the spin distribution function (SDF). The SDF values were converted to quantitative anisotropy (QA) and used in further analyses.

RESULTS

Connectometry analysis identified impaired white matter QA of the following fibers to be correlated with disease duration: bilateral retrosplenial cingulum, bilateral fornix, right inferior longitudinal fasciculus (ILF), and genu of corpus callosum (CC) (FDR = 0.009).

CONCLUSION

Our results were obtained from DMRI connectometry, which indicates the connectivity and the level of diffusion in nerve fibers rather just the direction of diffusion. Compared to previous studies investigating the correlation between duration of epilepsy and white matter integrity in mTLE patients, we detected broader and somewhat different associations in midline structures and component of limbic system. However, further studies with larger sample sizes are required to elucidate previous and current results.

Cognitive slowing and its underlying neurobiology in temporal lobe epilepsy

Cognitive slowing is a known but comparatively under-investigated neuropsychological complication of the epilepsies in relation to other known cognitive comorbidities such as memory, executive function and language. Here we focus on a novel metric of processing speed, characterize its relative salience compared to other cognitive difficulties in epilepsy, and explore its underlying neurobiological correlates. Research participants included 55 patients with temporal lobe epilepsy (TLE) and 58 healthy controls from the Epilepsy Connectome Project (ECP) who were administered a battery of tests yielding 14 neuropsychological measures, including selected tests from the NIH Toolbox-Cognitive Battery, and underwent 3T MRI and resting state fMRI. TLE patients exhibited a pattern of generalized cognitive impairment with very few lateralized abnormalities. Using the neuropsychological measures, machine learning (Support Vector Machine binary classification model) classified the TLE and control groups with 74% accuracy with processing speed (NIH Toolbox Pattern Comparison Processing Speed Test) the best predictor. In TLE, slower processing speed was associated predominantly with decreased local gyrification in regions including the rostral and caudal middle frontal gyrus, inferior precentral cortex, insula, inferior parietal cortex (angular and supramarginal gyri), lateral occipital cortex, rostral anterior cingulate, and medial orbital frontal regions, as well as three small regions of the temporal lobe. Slower processing speed was also associated with decreased connectivity between the primary visual cortices in both hemispheres and the left supplementary motor area, as well as between the right parieto-occipital sulcus and right middle insular area. Overall, slowed processing speed is an important cognitive comorbidity of TLE associated with altered brain structure and connectivity.

Symmetric tract-based spatial statistics of patients with left versus right mesial temporal lobe epilepsy with hippocampal sclerosis

This study aims to investigate the diffusion metrics of left versus right temporal lobe epilepsy in a well-defined subgroup of patients with mesial temporal lobe epilepsy (mTLE) because of unilateral hippocampal sclerosis while taking into account interhemispheric differences. Eighteen patients with TLE [nine left temporal lobe epilepsy (LTLE) and nine right temporal lobe epilepsy (RTLE)] and a norm group of 36 nonepileptic individuals were scanned with a multiband accelerated diffusion tensor imaging protocol at 3T. The scalar diffusion tensor parameters fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) and, after projection on a symmetric skeleton, their hemispheric difference (dFA, dMD, and dRD) were analyzed using tract-based spatial statistics. In the cluster with significantly (P<0.008) different dFA, dMD, and dRD between right TLE and left TLE, the hemispheric difference in the mean scalar indices (dmFA, dmMD, and dmRD) was assessed and tested for differences using a one-way analysis of variance and for correlation with patient age, seizure onset, or duration of epilepsy using Pearson's correlation. Patients with LTLE showed lower dFA, higher dMD, and higher dRD (P<0.008) compared with patients with RTLE in a cluster including parts of the uncinated and inferior longitudinal fasciculus and the inferior fronto-occipital fasciculus. dmFA, dmMD, and dmRD differed significantly between groups (P<10, corrected) and showed no correlation with patient age, seizure onset, or duration of epilepsy. The exclusion of bilateral interindividual variance through the calculation of the hemispheric difference of the diffusion metrics by the symmetric variant of tract-based spatial statistics allows for a sensitive differentiation of LTLE and RTLE with unilateral hippocampal sclerosis.

Imaging biomarkers of epileptogenecity after traumatic brain injury - Preclinical frontiers

Posttraumatic epilepsy (PTE) is a major neurodegenerative disease accounting for 20% of symptomatic epilepsy cases. A long latent phase offers a potential window for prophylactic treatment strategies to prevent epilepsy onset, provided that the patients at risk can be identified. Some promising imaging biomarker candidates for posttraumatic epileptogenesis have been identified, but more are required to provide the specificity and sensitivity for accurate prediction. Experimental models and preclinical longitudinal, multimodal imaging studies allow follow-up of complex cascade of events initiated by traumatic brain injury, as well as monitoring of treatment effects. Preclinical imaging data from the posttraumatic brain are rich in information, yet examination of their specific relevance to epilepsy is lacking. Accumulating evidence from ongoing preclinical studies in TBI support insight into processes involved in epileptogenesis, e.g. inflammation and changes in functional and structural brain-wide connectivity. These efforts are likely to produce both new biomarkers and treatment targets for PTE.

Novel Imaging Biomarkers for Huntington's Disease and Other Hereditary Choreas

PURPOSE OF THE REVIEW

Imaging biomarkers for neurodegenerative disorders are primarily developed with the goal to aid diagnosis, to monitor disease progression, and to assess the efficacy of disease-modifying therapies in support to clinical outcomes that may either show limited sensitivity or need extended time for their evaluation. This article will review the most recent concepts and findings in the field of neuroimaging applied to Huntington's disease and Huntington-like syndromes. Emphasis will be given to the discussion of potential pharmacodynamic biomarkers for clinical trials in Huntington's disease (HD) and of neuroimaging tools that can be used as diagnostic biomarkers in HD-like syndromes.

RECENT FINDINGS

Several magnetic resonance (MR) and positron emission tomography (PET) molecular imaging tools have been identified as potential pharmacodynamic biomarkers and others are in the pipeline after preclinical validation. MRI and 18F-fluorodeoxyglucose PET can be considered useful supportive diagnostic tools for the differentiation of other HD-like syndromes. New trials in HD have the primary goal to lower mutant huntingtin (mHTT) protein levels in the brain in order to reduce or alter the progression of the disease. MR and PET molecular imaging markers have been developed as tools to monitor disease progression and to evaluate treatment outcomes of disease-modifying trials in HD. These markers could be used alone or in combination for detecting structural and pharmacodynamic changes potentially associated with the lowering of mHTT.

Safety and efficacy of stereoelectroencephalography in pediatric focal epilepsy: a single-center experience

OBJECTIVE

Patients with medically refractory localization-related epilepsy (LRE) may be candidates for surgical intervention if the seizure onset zone (SOZ) can be well localized. Stereoelectroencephalography (SEEG) offers an attractive alternative to subdural grid and strip electrode implantation for seizure lateralization and localization; yet there are few series reporting the safety and efficacy of SEEG in pediatric patients.

METHODS

The authors review their initial 3-year consecutive experience with SEEG in pediatric patients with LRE. SEEG coverage, SOZ localization, complications, and preliminary seizure outcomes following subsequent surgical treatments are assessed.

RESULTS

Twenty-five pediatric patients underwent 30 SEEG implantations, with a total of 342 electrodes placed. Ten had prior resections or ablations. Seven had no MRI abnormalities, and 8 had multiple lesions on MRI. Based on preimplantation hypotheses, 7 investigations were extratemporal (ET), 1 was only temporal-limbic (TL), and 22 were combined ET/TL investigations. Fourteen patients underwent bilateral investigations. On average, patients were monitored for 8 days postimplant (range 3-19 days). Nearly all patients were discharged home on the day following electrode explantation. There were no major complications. Minor complications included 1 electrode deflection into the subdural space, resulting in a minor asymptomatic extraaxial hemorrhage; and 1 in-house and 1 delayed electrode superficial scalp infection, both treated with local wound care and oral antibiotics. SEEG localized the hypothetical SOZ in 23 of 25 patients (92%). To date, 18 patients have undergone definitive surgical intervention. In 2 patients, SEEG localized the SOZ near eloquent cortex and subdural grids were used to further delineate the seizure focus relative to mapped motor function just prior to resection. At last follow-up (average 21 months), 8 of 15 patients with at least 6 months of follow-up (53%) were Engel class I, and an additional 6 patients (40%) were Engel class II or III. Only 1 patient was Engel class IV.

CONCLUSIONS

SEEG is a safe and effective technique for invasive SOZ localization in medically refractory LRE in the pediatric population. SEEG permits bilateral and multilobar investigations while avoiding large craniotomies. It is conducive to deep, 3D, and perilesional investigations, particularly in cases of prior resections. Patients who are not found to have focally localizable seizures are spared craniotomies.

Abnormal neurite density and orientation dispersion in unilateral temporal lobe epilepsy detected by advanced diffusion imaging

Background

Despite recent advances in diffusion MRI (dMRI), there is still limited information on neurite orientation dispersion and density imaging (NODDI) in temporal lobe epilepsy (TLE). This study aimed to demonstrate neurite density and dispersion in TLE with and without hippocampal sclerosis (HS) using whole-brain voxel-wise analyses.

Material and methods

We recruited 33 patients with unilateral TLE (16 left, 17 right), including 14 patients with HS (TLE-HS) and 19 MRI-negative ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET)-positive patients (MRI-/PET+ TLE). The NODDI toolbox calculated the intracellular volume fraction (ICVF) and orientation dispersion index (ODI). Conventional dMRI metrics, that is, fractional anisotropy (FA) and mean diffusivity (MD), were also estimated. After spatial normalization, all dMRI parameters (ICVF, ODI, FA, and MD) of the patients were compared with those of age- and sex-matched healthy controls using Statistical Parametric Mapping 12 (SPM12). As a complementary analysis, we added an atlas-based region of interest (ROI) analysis of relevant white matter tracts using tract-based spatial statistics.

Results

We found decreased neurite density mainly in the ipsilateral temporal areas of both right and left TLE, with the right TLE showing more severe and widespread abnormalities. In addition, etiology-specific analyses revealed a localized reduction in ICVF (i.e., neurite density) in the ipsilateral temporal pole in MRI-/PET+ TLE, whereas TLE-HS presented greater abnormalities, including FA and MD, in addition to a localized hippocampal reduction in ODI. The results of the atlas-based ROI analysis were consistent with the results of the SPM12 analysis.

Conclusion

NODDI may provide clinically relevant information as well as novel insights into the field of TLE. Particularly, in MRI-/PET+ TLE, neurite density imaging may have higher sensitivity than other dMRI parameters. The results may also contribute to better understanding of the pathophysiology of TLE with HS.

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We examined temporal lobe epilepsy (TLE) with or without hippocampal sclerosis (HS).

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Neurite orientation dispersion and density imaging (NODDI) was used.

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Ipsilateral reduction of neurite density was found in MRI-negative PET-positive TLE.

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More extensive abnormalities were presented in TLE with HS.

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NODDI may provide clinical relevance and novel insights into the field of TLE.