Automated hippocampal segmentation in patients with epilepsy: available free online

Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference

Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics.

Quantitative susceptibility mapping identifies hippocampal and other subcortical grey matter tissue composition changes in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is associated with widespread brain alterations. Using quantitative susceptibility mapping (QSM) alongside transverse relaxation rate (R 2 * ), we investigated regional brain susceptibility changes in 36 patients with left-sided (LTLE) or right-sided TLE (RTLE) secondary to hippocampal sclerosis, and 27 healthy controls (HC). We compared three susceptibility calculation methods to ensure image quality. Correlations of susceptibility andR 2 * with age of epilepsy onset, frequency of focal-to-bilateral tonic-clonic seizures (FBTCS), and neuropsychological test scores were examined. Weak-harmonic QSM (WH-QSM) successfully reduced noise and removed residual background field artefacts. Significant susceptibility increases were identified in the left putamen in the RTLE group compared to the LTLE group, the right putamen and right thalamus in the RTLE group compared to HC, and a significant susceptibility decrease in the left hippocampus in LTLE versus HC. LTLE patients who underwent epilepsy surgery showed significantly lower left-versus-right hippocampal susceptibility. SignificantR 2 * changes were found between TLE and HC groups in the amygdala, putamen, thalamus, and in the hippocampus. Specifically, decreased R2 * was found in the left and right hippocampus in LTLE and RTLE, respectively, compared to HC. Susceptibility andR 2 * were significantly correlated with cognitive test scores in the hippocampus, globus pallidus, and thalamus. FBTCS frequency correlated positively with ipsilateral thalamic and contralateral putamen susceptibility and withR 2 * in bilateral globi pallidi. Age of onset was correlated with susceptibility in the hippocampus and putamen, and withR 2 * in the caudate. Susceptibility andR 2 * changes observed in TLE groups suggest selective loss of low-myelinated neurons alongside iron redistribution in the hippocampi, predominantly ipsilaterally, indicating QSM's sensitivity to local pathology. Increased susceptibility andR 2 * in the thalamus and putamen suggest increased iron content and reflect disease severity.

A devised thyroid segmentation with multi-stage modification based on Super-pixel U-Net under insufficient data

OBJECTIVES

The application of deep learning to medical image segmentation has received considerable attention. Nevertheless, when segmenting thyroid ultrasound images, it is difficult to achieve good segmentation results using deep learning methods because of the large number of nonthyroidal regions and insufficient training data.

METHODS

In this study, a Super-pixel U-Net, designed by adding a supplementary path to U-Net, was devised to boost the segmentation results of thyroids. The improved network can introduce more information into the network, boosting auxiliary segmentation results. A multi-stage modification is introduced in this method, which includes boundary segmentation, boundary repair, and auxiliary segmentation. To reduce the negative effects of non-thyroid regions in the segmentation, U-Net was utilized to obtain rough boundary outputs. Subsequently, another U-Net is trained to improve and repair the coverage of the boundary outputs. Super-pixel U-Net was applied in the third stage to assist in the segmentation of the thyroid more precisely. Finally, multidimensional indicators were used to compare the segmentation results of the proposed method with those of other comparison experiments.

DISCUSSION

The proposed method achieved an F1 Score of 0.9161 and an IoU of 0.9279. Furthermore, the proposed method also exhibits better performance in terms of shape similarity, with an average convexity of 0.9395. an average ratio of 0.9109, an average compactness of 0.8976, an average eccentricity of 0.9448, and an average rectangularity of 0.9289. The average area estimation indicator was 0.8857.

CONCLUSION

The proposed method exhibited superior performance, proving the improvements of the multi-stage modification and Super-pixel U-Net.

Shared functional network abnormality in patients with temporal lobe epilepsy and their siblings

AIM

Temporal lobe epilepsy is a neurological network disease in which genetics played a greater role than previously appreciated. This study aimed to explore shared functional network abnormalities in patients with sporadic temporal lobe epilepsy and their unaffected siblings.

METHODS

Fifty-eight patients with sporadic temporal lobe epilepsy, 13 unaffected siblings, and 30 healthy controls participated in this cross-sectional study. We examined the task-based whole-brain functional network topology and the effective functional connectivity between networks identified by group-independent component analysis.

RESULTS

We observed increased global efficiency, decreased clustering coefficiency, and decreased small-worldness in patients and siblings (p < 0.05, false discovery rate-corrected). The effective network connectivity from the ventral attention network to the limbic system was impaired (p < 0.001, false discovery rate-corrected). These features had higher prevalence in unaffected siblings than in normal population and was not correlated with disease burden. In addition, topological abnormalities had a high intraclass correlation between patients and their siblings.

CONCLUSION

Patients with temporal lobe epilepsy and their unaffected siblings showed shared topological functional disturbance and the effective functional network connectivity impairment. These abnormalities may contribute to the pathogenesis that promotes the susceptibility of seizures and language decline in temporal lobe epilepsy.

Lateralization of temporal lobe epileptic foci with automated chemical exchange saturation transfer measurements at 3 Tesla

BACKGROUND

Magnetic Resonance Imaging (MRI) is an indispensable tool for the diagnosis of temporal lobe epilepsy (TLE). However, about 30% of TLE patients show no lesion on structural MRI (sMRI-negative), posing a significant challenge for presurgical evaluation. This study aimed to investigate whether chemical exchange saturation transfer (CEST) MRI at 3 Tesla can lateralize the epileptic focus of TLE and study the metabolic contributors to the CEST signal measured.

METHODS

Forty TLE subjects (16 males and 24 females) were included in this study. An automated data analysis pipeline was established, including segmentation of the hippocampus and amygdala (HA), calculation of four CEST metrics and quantitative relaxation times (T₁ and T₂), and construction of prediction models by logistic regression. Furthermore, a modified two-stage Bloch-McConnell fitting method was developed to investigate the molecular imaging mechanism of 3 T CEST in identifying epileptic foci of TLE.

FINDINGS

The mean CEST ratio (CESTR) metric within 2.25-3.25 ppm in the HA was the most powerful index in predicting seizure laterality, with an area under the receiver-operating characteristic curve (AUC) of 0.84. And, the combination of T₂ and CESTR further increased the AUC to 0.92. Amine and guanidinium moieties were the two leading contributors to the CEST contrast between the epileptogenic HA and the normal HA.

INTERPRETATION

CEST at 3 Tesla is a powerful modality that can predict seizure laterality with high accuracy. This study can potentially facilitate the clinical translation of CEST MRI in identifying the epileptic foci of TLE or other localization-related epilepsies.

FUNDING

National Natural Science Foundation of China, Science Technology Department of Zhejiang Province, and Zhejiang University.

Cerebellar functional disruption and compensation in mesial temporal lobe epilepsy

Background

Cerebellar functional alterations are common in patients with mesial temporal lobe epilepsy (MTLE), which contribute to cognitive decline. This study aimed to deepen our knowledge of cerebellar functional alterations in patients with MTLE.

Methods

In this study, participants were recruited from an ongoing prospective cohort of 13 patients with left TLE (LTLE), 17 patients with right TLE (RTLE), and 30 healthy controls (HCs). Functional magnetic resonance imaging data were collected during a Chinese verbal fluency task. Group independent component (IC) analysis (group ICA) was applied to segment the cerebellum into six functionally separated networks. Functional connectivity was compared among cerebellar networks, cerebellar activation maps, and the centrality parameters of cerebellar regions. For cerebellar functional profiles with significant differences, we calculated their correlation with clinical features and neuropsychological scores.

Result

Compared to HCs and patients with LTLE, patients with RTLE had higher cerebellar functional connectivity between the default mode network (DMN) and the oculomotor network and lower cerebellar functional connectivity from the frontoparietal network (FPN) to the dorsal attention network (DAN) (p < 0.05, false discovery rate- (FDR-) corrected). Cerebellar degree centrality (DC) of the right lobule III was significantly higher in patients with LTLE compared to HC and patients with RTLE (p < 0.05, FDR-corrected). Higher cerebellar functional connectivity between the DMN and the oculomotor network, as well as lower cerebellar degree centrality of the right lobule III, was correlated with worse information test performance.

Conclusion

Cerebellar functional profiles were altered in MTLE and correlated with long-term memory in patients.

Hippocampal resection in temporal lobe epilepsy: Do we need to resect the tail?

INTRODUCTION

Anteromesial temporal lobe resection is the most common surgical technique used to treat drug-resistant mesial temporal lobe epilepsy, particularly when secondary to hippocampal sclerosis. Structural and functional imaging data suggest the importance of sparing the posterior hippocampus for minimising language and memory deficits. Recent work has challenged the view that maximal posterior hippocampal resection improves seizure outcome. This study was designed to assess whether resection of posterior hippocampal atrophy was associated with improved seizure outcome.

METHODS

Retrospective analysis of a prospective database of all anteromesial temporal lobe resections performed in individuals with hippocampal sclerosis at our epilepsy surgery centre, 2013-2021. Pre- and post-operative MRI were reviewed by 2 neurosurgical fellows to assess whether the atrophic segment, displayed by automated hippocampal morphometry, was resected, and ILAE seizure outcomes were collected at 1 year and last clinical follow-up. Data analysis used univariate and binary logistic regression.

RESULTS

Sixty consecutive eligible patients were identified of whom 70% were seizure free (ILAE Class 1 & 2) at one year. There was no statistically significant difference in seizure freedom outcomes in patients who had complete resection of atrophic posterior hippocampus or not (Fisher's Exact test statistic 0.69, not significant at p < .05) both at one year, and at last clinical follow-up. In the multivariate analysis only a history of status epilepticus (OR=0.2, 95%CI:0.042-0.955, p = .04) at one year, and pre-operative psychiatric disorder (OR=0.145, 95%CI:0.036-0.588, p = .007) at last clinical follow-up, were associated with a reduced chance of seizure freedom.

SIGNIFICANCE

Our data suggest that seizure freedom is not associated with whether or not posterior hippocampal atrophy is resected. This challenges the traditional surgical dogma of maximal posterior hippocampal resection in anteromesial temporal lobe resections and is a step further optimising this surgical procedure to maximise seizure freedom and minimise associated language and memory deficits.

Volumetric analysis of the piriform cortex in temporal lobe epilepsy

The piriform cortex, at the confluence of the temporal and frontal lobes, generates seizures in response to chemical convulsants and electrical stimulation. Resection of more than 50% of the piriform cortex in anterior temporal lobe resection for refractory temporal lobe epilepsy (TLE) was associated with a 16-fold higher chance of seizure freedom. The objectives of the current study were to implement a robust protocol to measure piriform cortex volumes and to quantify the correlation of these volumes with clinical characteristics of TLE. Sixty individuals with unilateral TLE (33 left) and 20 healthy controls had volumetric analysis of left and right piriform cortex and hippocampi. A protocol for segmenting and measuring the volumes of the piriform cortices was implemented, with good inter-rater and test-retest reliability. The right piriform cortex volume was consistently larger than the left piriform cortex in both healthy controls and patients with TLE. In controls, the mean volume of the right piriform cortex was 17.7% larger than the left, and the right piriform cortex extended a mean of 6 mm (Range: -4 to 12) more anteriorly than the left. This asymmetry was also seen in left and right TLE. In TLE patients overall, the piriform cortices were not significantly smaller than in controls. Hippocampal sclerosis was associated with decreased ipsilateral and contralateral piriform cortex volumes. The piriform cortex volumes, both ipsilateral and contralateral to the epileptic temporal lobe, were smaller with a longer duration of epilepsy. There was no significant association between piriform cortex volumes and the frequency of focal seizures with impaired awareness or the number of anti-seizure medications taken. Implementation of robust segmentation will enable consistent neurosurgical resection in anterior temporal lobe surgery for refractory TLE..

Volumetric and structural connectivity abnormalities co-localise in TLE

Patients with temporal lobe epilepsy (TLE) exhibit both volumetric and structural connectivity abnormalities relative to healthy controls. How these abnormalities inter-relate and their mechanisms are unclear. We computed grey matter volumetric changes and white matter structural connectivity abnormalities in 144 patients with unilateral TLE and 96 healthy controls. Regional volumes were calculated using T1-weighted MRI, while structural connectivity was derived using white matter fibre tractography from diffusion-weighted MRI. For each regional volume and each connection strength, we calculated the effect size between patient and control groups in a group-level analysis. We then applied hierarchical regression to investigate the relationship between volumetric and structural connectivity abnormalities in individuals. Additionally, we quantified whether abnormalities co-localised within individual patients by computing Dice similarity scores. In TLE, white matter connectivity abnormalities were greater when joining two grey matter regions with abnormal volumes. Similarly, grey matter volumetric abnormalities were greater when joined by abnormal white matter connections. The extent of volumetric and connectivity abnormalities related to epilepsy duration, but co-localisation did not. Co-localisation was primarily driven by neighbouring abnormalities in the ipsilateral hemisphere. Overall, volumetric and structural connectivity abnormalities were related in TLE. Our results suggest that shared mechanisms may underlie changes in both volume and connectivity alterations in patients with TLE.

Disorganization of language and working memory systems in frontal versus temporal lobe epilepsy

Cognitive impairment is a common comorbidity of epilepsy and adversely impacts people with both frontal lobe (FLE) and temporal lobe (TLE) epilepsy. While its neural substrates have been investigated extensively in TLE, functional imaging studies in FLE are scarce. In this study, we profiled the neural processes underlying cognitive impairment in FLE and directly compared FLE and TLE to establish commonalities and differences. We investigated 172 adult participants (56 with FLE, 64 with TLE and 52 controls) using neuropsychological tests and four functional MRI tasks probing expressive language (verbal fluency, verb generation) and working memory (verbal and visuo-spatial). Patient groups were comparable in disease duration and anti-seizure medication load. We devised a multiscale approach to map brain activation and deactivation during cognition and track reorganization in FLE and TLE. Voxel-based analyses were complemented with profiling of task effects across established motifs of functional brain organization: (i) canonical resting-state functional systems; and (ii) the principal functional connectivity gradient, which encodes a continuous transition of regional connectivity profiles, anchoring lower-level sensory and transmodal brain areas at the opposite ends of a spectrum. We show that cognitive impairment in FLE is associated with reduced activation across attentional and executive systems, as well as reduced deactivation of the default mode system, indicative of a large-scale disorganization of task-related recruitment. The imaging signatures of dysfunction in FLE are broadly similar to those in TLE, but some patterns are syndrome-specific: altered default-mode deactivation is more prominent in FLE, while impaired recruitment of posterior language areas during a task with semantic demands is more marked in TLE. Functional abnormalities in FLE and TLE appear overall modulated by disease load. On balance, our study elucidates neural processes underlying language and working memory impairment in FLE, identifies shared and syndrome-specific alterations in the two most common focal epilepsies and sheds light on system behaviour that may be amenable to future remediation strategies.

The Learning Curve and Inter-Observer Variability in Contouring the Hippocampus under the Hippocampal Sparing Guidelines of Radiation Therapy Oncology Group 0933

Hippocampal-sparing brain radiotherapy (HS-BRT) in cancer patients results in preservation of neurocognitive function after brain RT which can contribute to patients’ quality of life (QoL). The crucial element in HS-BRT treatment planning is appropriate contouring of the hippocampus. Ten doctors delineated the left and right hippocampus (LH and RH, respectively) on 10 patients’ virtual axial images of brain CT fused with T1-enhanced MRI (1 mm) according to the RTOG 0933 atlas recommendations. Variations in the spatial localization of the structure were described in three directions: right–left (X), cranio-caudal (Y), and forward–backward (Z). Discrepancies concerned three-dimensional localization, shape, volume and size of the hippocampus. The largest differences were observed in the first three delineated cases which were characterized by larger hippocampal volumes than the remaining seven cases. The volumes of LH of more than half of hippocampus contours were marginally bigger than those of RH. Most differences in delineation of the hippocampus were observed in the area of the posterior horn of the lateral ventricle. Conversely, a large number of hippocampal contours overlapped near the brainstem and the anterior horn of the lateral ventricle. The most problematic area of hippocampal contouring is the posterior horn of the lateral ventricle. Training in the manual contouring of the hippocampus during HS-BRT treatment planning under the supervision of experienced radiation oncologists is necessary to achieve optimal outcomes. This would result in superior outcomes of HS-BRT treatment and improvement in QoL of patients compared to without HS-BRT procedure. Correct delineation of the hippocampus is problematic. This study demonstrates difficulties in HS-BRT treatment planning and highlights critical points during hippocampus delineation.

A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI

Purpose

Hippocampal volumetry is an important biomarker to quantify atrophy in patients with mesial temporal lobe epilepsy. We investigate the sensitivity of automated segmentation methods to support radiological assessments of hippocampal sclerosis (HS). Results from FreeSurfer and FSL-FIRST are contrasted to a deep learning (DL)-based segmentation method.

Materials and Methods

We used T1-weighted MRI scans from 105 patients with epilepsy and 354 healthy controls. FreeSurfer, FSL, and a DL-based method were applied for brain anatomy segmentation. We calculated effect sizes (Cohen's d) between left/right HS and healthy controls based on the asymmetry of hippocampal volumes. Additionally, we derived 14 shape features from the segmentations and determined the most discriminating feature to identify patients with hippocampal sclerosis by a support vector machine (SVM).

Results

Deep learning-based segmentation of the hippocampus was the most sensitive to detecting HS. The effect sizes of the volume asymmetries were larger with the DL-based segmentations (HS left d= −4.2, right = 4.2) than with FreeSurfer (left= −3.1, right = 3.7) and FSL (left= −2.3, right = 2.5). For the classification based on the shape features, the surface-to-volume ratio was identified as the most important feature. Its absolute asymmetry yielded a higher area under the curve (AUC) for the deep learning-based segmentation (AUC = 0.87) than for FreeSurfer (0.85) and FSL (0.78) to dichotomize HS from other epilepsy cases. The robustness estimated from repeated scans was statistically significantly higher with DL than all other methods.

Conclusion

Our findings suggest that deep learning-based segmentation methods yield a higher sensitivity to quantify hippocampal sclerosis than atlas-based methods and derived shape features are more robust. We propose an increased asymmetry in the surface-to-volume ratio of the hippocampus as an easy-to-interpret quantitative imaging biomarker for HS.

Perspectives on Understanding Aberrant Brain Networks in Epilepsy

Epilepsy is a neurological disorder affecting approximately 70 million people worldwide. It is characterized by seizures that are complex aberrant dynamical events typically treated with drugs and surgery. Unfortunately, not all patients become seizure-free, and there is an opportunity for novel approaches to treat epilepsy using a network view of the brain. The traditional seizure focus theory presumed that seizures originated within a discrete cortical area with subsequent recruitment of adjacent cortices with seizure progression. However, a more recent view challenges this concept, suggesting that epilepsy is a network disease, and both focal and generalized seizures arise from aberrant activity in a distributed network. Changes in the anatomical configuration or widespread neural activities spanning lobes and hemispheres could make the brain more susceptible to seizures. In this perspective paper, we summarize the current state of knowledge, address several important challenges that could further improve our understanding of the human brain in epilepsy, and invite novel studies addressing these challenges.

Detection of Hippocampal Subfield Asymmetry at 7T With Automated Segmentation in Epilepsy Patients With Normal Clinical Strength MRIs

While the etiology of hippocampal sclerosis (HS) in epilepsy patients remains unknown, distinct phenotypes of hippocampal subfield atrophy have been associated with different clinical presentations and surgical outcomes. The advent of novel techniques including ultra-high field 7T magnetic resonance imaging (MRI) and automated subfield volumetry have further enabled detection of hippocampal pathology in patients with epilepsy, however, studies combining both 7T MRI and automated segmentation in epilepsy patients with normal-appearing clinical MRI are limited. In this study, we present a novel application of the automated segmentation of hippocampal subfields (ASHS) software to determine subfield volumes of the CA1, CA2/3, CA4/DG, and the subiculum using ultra high-field 7T MRI scans, including T1-weighted MP2RAGE and T2-TSE sequences, in 27 patients with either mesial temporal lobe epilepsy (mTLE) or neocortical epilepsy (NE) compared to age and gender matched healthy controls. We found that 7T improved visualization of structural abnormalities not otherwise seen on clinical strength MRIs in patients with unilateral mTLE. Additionally, our automated segmentation algorithm was able to detect structural differences in volume and asymmetry across hippocampal subfields in unilateral mTLE patients compared to controls. Specifically, amongst unilateral mTLE patients with longer disease durations, volume loss was observed in the ipsilateral CA1 and CA2/3 subfields and contralateral CA1. There were no differences in subfield volumes in patients with NE compared to controls. We report the first application of 7T with automated segmentation to characterize the relationship between disease duration burden and asymmetry across specific hippocampal subfields in this population. Disease duration was found to have a statistically significant positive relationship with subfield asymmetry within the unilateral mTLE cohort. These findings highlight the ability of 7T MRI and automated segmentation to provide novel qualitative and quantitative information in epilepsy patients who are otherwise MRI-negative at clinical field strengths.

[Imaging in the presurgical evaluation of epilepsy]

Während zwei Drittel der PatientInnen mit Epilepsie durch Medikamente anfallsfrei werden, ist die Erkrankung bei 30 % pharmakoresistent. Bei pharmakoresistenter fokaler Epilepsie bietet die Epilepsiechirurgie eine etwa 65 %ige Chance auf Anfallsfreiheit. Vorab muss der Anfallsfokus exakt eingegrenzt werden, wofür bildgebende Methoden unverzichtbar sind. In den letzten Jahren hat sich in der Prächirurgie der Anteil von PatientInnen mit unauffälliger konventioneller Magnetresonanztomographie (MRT) erhöht. Allerdings konnte die Sensitivität der MRT durch spezielle Aufnahmesequenzen und Techniken der Postprozessierung gesteigert werden. Die Quellenlokalisation des Signals von Elektro- und Magnetenzephalographie (EEG und MEG) verortet den Ursprung iktaler und interiktaler epileptischer Aktivität im Gehirn. Nuklearmedizinische Untersuchungen wie die interiktale Positronen-Emissions-Tomographie (PET) und die iktale Einzelphotonen-Emissionscomputertomographie (SPECT) detektieren chronische oder akute anfallsbezogene Veränderungen des Hirnmetabolismus und können auch bei nichtlokalisierendem MRT auf den epileptogenen Fokus hinweisen. Alle Befunde zusammengenommen werden zur Planung eventueller invasiver EEG-Ableitungen und letztlich der chirurgischen Operation eingesetzt. Konkordante Befunde sind mit besseren chirurgischen Ergebnissen assoziiert und zeigen auch im Langzeitverlauf signifikant höhere Anfallsfreiheitsraten.

Application value of molecular imaging technology in epilepsy

Epilepsy is a common neurological disease with various seizure types, complicated etiologies, and unclear mechanisms. Its diagnosis mainly relies on clinical history, but an electroencephalogram is also a crucial auxiliary examination. Recently, brain imaging technology has gained increasing attention in the diagnosis of epilepsy, and conventional magnetic resonance imaging can detect epileptic foci in some patients with epilepsy. However, the results of brain magnetic resonance imaging are normal in some patients. New molecular imaging has gradually developed in recent years and has been applied in the diagnosis of epilepsy, leading to enhanced lesion detection rates. However, the application of these technologies in epilepsy patients with negative brain magnetic resonance must be clarified. Thus, we reviewed the relevant literature and summarized the information to improve the understanding of the molecular imaging application value of epilepsy.

Making the Invisible Visible: Advanced Neuroimaging Techniques in Focal Epilepsy

It has been a clinically important, long-standing challenge to accurately localize epileptogenic focus in drug-resistant focal epilepsy because more intensive intervention to the detected focus, including resection neurosurgery, can provide significant seizure reduction. In addition to neurophysiological examinations, neuroimaging plays a crucial role in the detection of focus by providing morphological and neuroanatomical information. On the other hand, epileptogenic lesions in the brain may sometimes show only subtle or even invisible abnormalities on conventional MRI sequences, and thus, efforts have been made for better visualization and improved detection of the focus lesions. Recent advance in neuroimaging has been attracting attention because of the potentials to better visualize the epileptogenic lesions as well as provide novel information about the pathophysiology of epilepsy. While the progress of newer neuroimaging techniques, including the non-Gaussian diffusion model and arterial spin labeling, could non-invasively detect decreased neurite parameters or hypoperfusion within the focus lesions, advances in analytic technology may also provide usefulness for both focus detection and understanding of epilepsy. There has been an increasing number of clinical and experimental applications of machine learning and network analysis in the field of epilepsy. This review article will shed light on recent advances in neuroimaging for focal epilepsy, including both technical progress of images and newer analytical methodologies and discuss about the potential usefulness in clinical practice.

Diagnostic Performance of MRI Volumetry in Epilepsy Patients With Hippocampal Sclerosis Supported Through a Random Forest Automatic Classification Algorithm

Introduction: Several methods offer free volumetry services for MR data that adequately quantify volume differences in the hippocampus and its subregions. These methods are frequently used to assist in clinical diagnosis of suspected hippocampal sclerosis in temporal lobe epilepsy. A strong association between severity of histopathological anomalies and hippocampal volumes was reported using MR volumetry with a higher diagnostic yield than visual examination alone. Interpretation of volumetry results is challenging due to inherent methodological differences and to the reported variability of hippocampal volume. Furthermore, normal morphometric differences are recognized in diverse populations that may need consideration. To address this concern, we highlighted procedural discrepancies including atlas definition and computation of total intracranial volume that may impact volumetry results. We aimed to quantify diagnostic performance and to propose reference values for hippocampal volume from two well-established techniques: FreeSurfer v.06 and volBrain-HIPS.

Methods: Volumetry measures were calculated using clinical T1 MRI from a local population of 61 healthy controls and 57 epilepsy patients with confirmed unilateral hippocampal sclerosis. We further validated the results by a state-of-the-art machine learning classification algorithm (Random Forest) computing accuracy and feature relevance to distinguish between patients and controls. This validation process was performed using the FreeSurfer dataset alone, considering morphometric values not only from the hippocampus but also from additional non-hippocampal brain regions that could be potentially relevant for group classification. Mean reference values and 95% confidence intervals were calculated for left and right hippocampi along with hippocampal asymmetry degree to test diagnostic accuracy.

Results: Both methods showed excellent classification performance (AUC:> 0.914) with noticeable differences in absolute (cm³) and normalized volumes. Hippocampal asymmetry was the most accurate discriminator from all estimates (AUC:1~0.97). Similar results were achieved in the validation test with an automatic classifier (AUC:>0.960), disclosing hippocampal structures as the most relevant features for group differentiation among other brain regions.

Conclusion: We calculated reference volumetry values from two commonly used methods to accurately identify patients with temporal epilepsy and hippocampal sclerosis. Validation with an automatic classifier confirmed the principal role of the hippocampus and its subregions for diagnosis.

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.

Resection of piriform cortex predicts seizure freedom in temporal lobe epilepsy

Objective

Transsylvian selective amygdalo‐hippocampectomy (tsSAHE) represents a generally recognized surgical procedure for drug‐resistant mesial temporal lobe epilepsy (mTLE). Although postoperative seizure freedom can be achieved in about 70% of tsSAHE, there is a considerable amount of patients with persisting postoperative seizures. This might partly be explained by differing extents of resection of various tsSAHE target volumes. In this study we analyzed the resected proportions of hippocampus, amygdala as well as piriform cortex in regard of postoperative seizure outcome.

Methods

Between 2012 and 2017, 82 of 103 patients with mTLE who underwent tsSAHE at the authors’ institution were included in the analysis. Resected proportions of hippocampus, amygdala and temporal piriform cortex as target structures of tsSAHE were volumetrically assessed and stratified according to favorable (International League Against Epilepsy (ILAE) class 1) and unfavorable (ILAE class 2–6) seizure outcome.

Results

Patients with favorable seizure outcome revealed a significantly larger proportion of resected temporal piriform cortex volumes compared to patients with unfavorable seizure outcome (median resected proportional volumes were 51% (IQR 42–61) versus (vs.) 13 (IQR 11–18), P = 0.0001). Resected proportions of hippocampus and amygdala did not significantly differ for these groups (hippocampus: 81% (IQR 73–88) vs. 80% (IQR 74–92) (P = 0.7); amygdala: 100% (IQR 100–100) vs. 100% (IQR 100–100) (P = 0.7)).

Interpretation

These results strongly suggest temporal piriform cortex to constitute a key target resection volume to achieve seizure freedom following tsSAHE.

Voxel-Based Morphometry-from Hype to Hope. A Study on Hippocampal Atrophy in Mesial Temporal Lobe Epilepsy

BACKGROUND AND PURPOSE

Automated volumetry of the hippocampus is considered useful to assist the diagnosis of hippocampal sclerosis in temporal lobe epilepsy. However, voxel-based morphometry is rarely used for individual subjects because of high rates of false-positives. We investigated whether an approach with high dimensional warping to the template and nonparametric statistics would be useful to detect hippocampal atrophy in patients with hippocampal sclerosis.

MATERIALS AND METHODS

We performed single-subject voxel-based morphometry with nonparametric statistics within the framework of Statistical Parametric Mapping to compare MRI from 26 well-characterized patients with temporal lobe epilepsy individually against a group of 110 healthy controls. The following statistical threshold was used: P < .05 corrected for multiple comparisons with family-wise error over the region of interest right and left hippocampus.

RESULTS

The sensitivity for the detection of atrophy related to hippocampal sclerosis was 0.92 (95% CI, 0.67-0.99) for the right hippocampus and 0.60 (0.31-0.83) for the left, and the specificity for volume changes was 0.98 (0.93-0.99). All clusters of decreased hippocampal volumes were correctly lateralized to the seizure focus. Hippocampal volume decrease was in accordance with neuronal cell loss on histology reports.

CONCLUSIONS

Nonparametric voxel-based morphometry is sensitive and specific for hippocampal atrophy in patients with mesial temporal lobe epilepsy and may be useful in clinical practice.

Thalamus and focal to bilateral seizures: A multiscale cognitive imaging study

OBJECTIVE

To investigate the functional correlates of recurrent secondarily generalized seizures in temporal lobe epilepsy (TLE) using task-based fMRI as a framework to test for epilepsy-specific network rearrangements. Because the thalamus modulates propagation of temporal lobe onset seizures and promotes cortical synchronization during cognition, we hypothesized that occurrence of secondarily generalized seizures, i.e., focal to bilateral tonic-clonic seizures (FBTCS), would relate to thalamic dysfunction, altered connectivity, and whole-brain network centrality.

METHODS

FBTCS occur in a third of patients with TLE and are a major determinant of disease severity. In this cross-sectional study, we analyzed 113 patients with drug-resistant TLE (55 left/58 right), who performed a verbal fluency fMRI task that elicited robust thalamic activation. Thirty-three patients (29%) had experienced at least one FBTCS in the year preceding the investigation. We compared patients with TLE-FBTCS to those without FBTCS via a multiscale approach, entailing analysis of statistical parametric mapping (SPM) 12-derived measures of activation, task-modulated thalamic functional connectivity (psychophysiologic interaction), and graph-theoretical metrics of centrality.

RESULTS

Individuals with TLE-FBTCS had less task-related activation of bilateral thalamus, with left-sided emphasis, and left hippocampus than those without FBTCS. In TLE-FBTCS, we also found greater task-related thalamotemporal and thalamomotor connectivity, and higher thalamic degree and betweenness centrality. Receiver operating characteristic curves, based on a combined thalamic functional marker, accurately discriminated individuals with and without FBTCS.

CONCLUSIONS

In TLE-FBTCS, impaired task-related thalamic recruitment coexists with enhanced thalamotemporal connectivity and whole-brain thalamic network embedding. Altered thalamic functional profiles are proposed as imaging biomarkers of active secondary generalization.

Clinical evaluation of automated quantitative MRI reports for assessment of hippocampal sclerosis

OBJECTIVES

Hippocampal sclerosis (HS) is a common cause of temporal lobe epilepsy. Neuroradiological practice relies on visual assessment, but quantification of HS imaging biomarkers-hippocampal volume loss and T2 elevation-could improve detection. We tested whether quantitative measures, contextualised with normative data, improve rater accuracy and confidence.

METHODS

Quantitative reports (QReports) were generated for 43 individuals with epilepsy (mean age ± SD 40.0 ± 14.8 years, 22 men; 15 histologically unilateral HS; 5 bilateral; 23 MR-negative). Normative data was generated from 111 healthy individuals (age 40.0 ± 12.8 years, 52 men). Nine raters with different experience (neuroradiologists, trainees, and image analysts) assessed subjects' imaging with and without QReports. Raters assigned imaging normal, right, left, or bilateral HS. Confidence was rated on a 5-point scale.

RESULTS

Correct designation (normal/abnormal) was high and showed further trend-level improvement with QReports, from 87.5 to 92.5% (p = 0.07, effect size d = 0.69). Largest magnitude improvement (84.5 to 93.8%) was for image analysts (d = 0.87). For bilateral HS, QReports significantly improved overall accuracy, from 74.4 to 91.1% (p = 0.042, d = 0.7). Agreement with the correct diagnosis (kappa) tended to increase from 0.74 ('fair') to 0.86 ('excellent') with the report (p = 0.06, d = 0.81). Confidence increased when correctly assessing scans with the QReport (p < 0.001, η²_p = 0.945).

CONCLUSIONS

QReports of HS imaging biomarkers can improve rater accuracy and confidence, particularly in challenging bilateral cases. Improvements were seen across all raters, with large effect sizes, greatest for image analysts. These findings may have positive implications for clinical radiology services and justify further validation in larger groups.

KEY POINTS

• Quantification of imaging biomarkers for hippocampal sclerosis-volume loss and raised T2 signal-could improve clinical radiological detection in challenging cases. • Quantitative reports for individual patients, contextualised with normative reference data, improved diagnostic accuracy and confidence in a group of nine raters, in particular for bilateral HS cases. • We present a pre-use clinical validation of an automated imaging assessment tool to assist clinical radiology reporting of hippocampal sclerosis, which improves detection accuracy.

Hippocampal Shape Is Associated with Memory Deficits in Temporal Lobe Epilepsy

Objective

Cognitive problems, especially disturbances in episodic memory, and hippocampal sclerosis are common in temporal lobe epilepsy (TLE), but little is known about the relationship of hippocampal morphology with memory. We aimed to relate hippocampal surface‐shape patterns to verbal and visual learning.

Methods

We analyzed hippocampal surface shapes on high‐resolution magnetic resonance images and the Adult Memory and Information Processing Battery in 145 unilateral refractory TLE patients undergoing epilepsy surgery, a validation set of 55 unilateral refractory TLE patients, and 39 age‐ and sex‐matched healthy volunteers.

Results

Both left TLE (LTLE) and right TLE (RTLE) patients had lower verbal (LTLE 44 ± 11; RTLE 45 ± 10) and visual learning (LTLE 34 ± 8, RTLE 30 ± 8) scores than healthy controls (verbal 58 ± 8, visual 39 ± 6; p < 0.001). Verbal learning was more impaired the greater the atrophy of the left superolateral hippocampal head. In contrast, visual memory was worse with greater bilateral inferomedial hippocampal atrophy. Postsurgical verbal memory decline was more common in LTLE than in RTLE (reliable change index in LTLE 27% vs RTLE 7%, p = 0.006), whereas there were no differences in postsurgical visual memory decline between those groups. Preoperative atrophy of the left hippocampal tail predicted postsurgical verbal memory decline.

Interpretation

Memory deficits in TLE are associated with specific morphological alterations of the hippocampus, which could help stratify TLE patients into those at high versus low risk of presurgical or postsurgical memory deficits. This knowledge could improve planning and prognosis of selective epilepsy surgery and neuropsychological counseling in TLE. ANN NEUROL 2020 ANN NEUROL 2020;88:170–182

Shared hippocampal abnormalities in sporadic temporal lobe epilepsy patients and their siblings

OBJECTIVE

To examine the shared familial contribution to hippocampal and extrahippocampal morphological abnormalities in patients with sporadic temporal lobe epilepsy (TLE) and their unaffected siblings.

METHODS

We collected clinical, electrophysiological, and T1-weighted magnetic resonance imaging (MRI) data of 18 sporadic patients with TLE without lesions other than hippocampal sclerosis (12 right, 6 left), their 18 unaffected full siblings, and 18 matched healthy volunteers. We compared between-group differences in cortical thickness and volumes of five subcortical areas (hippocampus, amygdala, thalamus, putamen, and pallidum). We determined the subregional extent of hippocampal abnormalities using surface shape analysis. All our imaging results were corrected for multiple comparisons using random field theory.

RESULTS

We detected smaller hippocampal volumes in patients (right TLE: median right hippocampus 1.92 mL, interquartile range [IQR] 1.39-2.62, P < .001; left TLE: left hippocampus 2.05 mL, IQR 1.99-2.33, P = .01) and their unaffected siblings (right hippocampus 2.65 mL, IQR 2.32-2.80, P < .001; left hippocampus 2.39 mL, IQR 2.18-2.53, P < .001) compared to healthy controls (right hippocampus 2.94 mL, IQR 2.77-3.24; left hippocampus 2.71 mL, IQR 2.37-2.89). Surface shape analysis showed that patients with TLE had bilateral subregional atrophy in both hippocampi (right > left). Similar but less-pronounced subregional atrophy was detected in the right hippocampus of unaffected siblings. Patients with TLE had reduced cortical thickness in bilateral premotor/prefrontal cortices and the right precentral gyrus. Siblings did not show abnormalities in cortical or subcortical areas other than the hippocampus.

SIGNIFICANCE

Our results demonstrate a shared vulnerability of the hippocampus in both patients with TLE and their unaffected siblings, pointing to a contribution of familial factors to hippocampal atrophy. This neuroimaging trait could represent an endophenotype of TLE, which might precede the onset of epilepsy in some individuals.

Microstructural imaging in temporal lobe epilepsy: Diffusion imaging changes relate to reduced neurite density

PURPOSE

Previous imaging studies in patients with refractory temporal lobe epilepsy (TLE) have examined the spatial distribution of changes in imaging parameters such as diffusion tensor imaging (DTI) metrics and cortical thickness. Multi-compartment models offer greater specificity with parameters more directly related to known changes in TLE such as altered neuronal density and myelination. We studied the spatial distribution of conventional and novel metrics including neurite density derived from NODDI (Neurite Orientation Dispersion and Density Imaging) and myelin water fraction (MWF) derived from mcDESPOT (Multi-Compartment Driven Equilibrium Single Pulse Observation of T1/T2)] to infer the underlying neurobiology of changes in conventional metrics.

METHODS

20 patients with TLE and 20 matched controls underwent magnetic resonance imaging including a volumetric T1-weighted sequence, multi-shell diffusion from which DTI and NODDI metrics were derived and a protocol suitable for mcDESPOT fitting. Models of the grey matter-white matter and grey matter-CSF surfaces were automatically generated from the T1-weighted MRI. Conventional diffusion and novel metrics of neurite density and MWF were sampled from intracortical grey matter and subcortical white matter surfaces and cortical thickness was measured.

RESULTS

In intracortical grey matter, diffusivity was increased in the ipsilateral temporal and frontopolar cortices with more restricted areas of reduced neurite density. Diffusivity increases were largely related to reductions in neurite density, and to a lesser extent CSF partial volume effects, but not MWF. In subcortical white matter, widespread bilateral reductions in fractional anisotropy and increases in radial diffusivity were seen. These were primarily related to reduced neurite density, with an additional relationship to reduced MWF in the temporal pole and anterolateral temporal neocortex. Changes were greater with increasing epilepsy duration. Bilaterally reduced cortical thickness in the mesial temporal lobe and centroparietal cortices was unrelated to neurite density and MWF.

CONCLUSIONS

Diffusivity changes in grey and white matter are primarily related to reduced neurite density with an additional relationship to reduced MWF in the temporal pole. Neurite density may represent a more sensitive and specific biomarker of progressive neuronal damage in refractory TLE that deserves further study.

Hippocampal profiling: Localized magnetic resonance imaging volumetry and T2 relaxometry for hippocampal sclerosis

Objective

Hippocampal sclerosis (HS) is the most common cause of drug‐resistant temporal lobe epilepsy, and its accurate detection is important to guide epilepsy surgery. Radiological features of HS include hippocampal volume loss and increased T2 signal, which can both be quantified to help improve detection. In this work, we extend these quantitative methods to generate cross‐sectional area and T2 profiles along the hippocampal long axis to improve the localization of hippocampal abnormalities.

Methods

T1‐weighted and T2 relaxometry data from 69 HS patients (32 left, 32 right, 5 bilateral) and 111 healthy controls were acquired on a 3‐T magnetic resonance imaging (MRI) scanner. Automated hippocampal segmentation and T2 relaxometry were performed and used to calculate whole‐hippocampal volumes and to estimate quantitative T2 (qT2) values. By generating a group template from the controls, and aligning this so that the hippocampal long axes were along the anterior‐posterior axis, we were able to calculate hippocampal cross‐sectional area and qT2 by a slicewise method to localize any volume loss or T2 hyperintensity. Individual patient profiles were compared with normative data generated from the healthy controls.

Results

Profiling of hippocampal volumetric and qT2 data could be performed automatically and reproducibly. HS patients commonly showed widespread decreases in volume and increases in T2 along the length of the affected hippocampus, and focal changes may also be identified. Patterns of atrophy and T2 increase in the left hippocampus were similar between left, right, and bilateral HS. These profiles have potential to distinguish between sclerosis affecting volume and qT2 in the whole or parts of the hippocampus, and may aid the radiological diagnosis in uncertain cases or cases with subtle or focal abnormalities where standard whole‐hippocampal measurements yield normal values.

Significance

Hippocampal profiling of volumetry and qT2 values can help spatially localize hippocampal MRI abnormalities and work toward improved sensitivity of subtle focal lesions.

Neural basis of episodic memory in the intermediate term after medial temporal lobe resection

OBJECTIVE

How the brain supports intermediate-term preservation of memory in patients who have undergone unilateral medial temporal lobe resection (MTLR) has not yet been demonstrated. To understand the neural basis of episodic memory in the intermediate term after surgery for temporal lobe epilepsy (TLE), the authors investigated the relationship between the activation of the hippocampus (HIP) during successful memory encoding and individual memory capacity in patients who had undergone MTLR. They also compared hippocampal activation with other parameters, including structural volumes of the HIP, duration of illness, and age at seizure onset.

METHODS

Thirty-five adult patients who had undergone unilateral MTLR at least 1 year before recruiting and who had a favorable seizure outcome were enrolled (17 left MTLR, 18 right MTLR; mean follow-up 6.31 ± 2.72 years). All patients underwent a standardized neuropsychological examination of memory function and functional MRI scanning with a memory-encoding paradigm of words and figures. Activations of the HIP during successful memory encoding were calculated and compared with standard neuropsychological memory scores, hippocampal volumes, and other clinical variables.

RESULTS

Greater activation in the HIP contralateral to the side of the resection was related to higher postoperative memory scores and greater postoperative memory improvement than the preoperative baseline in both patient groups. Specifically, postoperative verbal memory performance was positively correlated with contralateral right hippocampal activation during word encoding in the left-sided surgery group. In contrast, postoperative visual memory performance was positively correlated with contralateral left hippocampal activation during figure encoding in the right-sided surgery group. Activation of the ipsilateral remnant HIP was not correlated with any memory scores or volumes of the HIP; however, it had a negative correlation with the seizure-onset age and positive correlation with the duration of illness in both patient groups.

CONCLUSIONS

For the first time, a neural basis that supports effective intermediate-term episodic memory after unilateral MTLR has been characterized. The results provide evidence that engagement of the HIP contralateral rather than ipsilateral to the side of resection is responsible for effective memory function in the intermediate term (> 1 year) after surgery in patients who have undergone left MTLR and right MTLR. Engagement of the material-specific contralesional HIP, verbal memory in the left-sided surgery group, and visual memory in the right-sided surgery group were observed.

Mesial temporal lobe epilepsy: Revisiting the relation of hippocampal volumetry with memory deficits

OBJECTIVE

Neuropsychological tests can infer the lateralization of the epileptogenic focus, associating verbal memory to mesial structures in the left temporal lobe and visual or nonverbal memory to the right side. High-field magnetic resonance imaging (MRI) with high-resolution protocols allows acquisitions suitable for advanced postprocessing with precise volumetry of brain structures, and functional MRI demonstrates evidence that epilepsy should be seen as a network pathology, involving several structures in the brain. Since the literature showing associations between the volumetry of brain structures in left and right mesial temporal lobe epilepsy (MTLE) and verbal and visual memory performance on neuropsychological tests is conflicting, we revisited these relationships, considering the hippocampal volumetry of patients with unilateral MTLE.

METHODS

Automatized hippocampal volumes were obtained using FreeSurfer software from MRI exams of 35 patients with unilateral MTLE and hippocampal atrophy and homolateral ictal onset zone defined by video electroencephalography concordant to the side of hippocampal volume reduction (15 on the left side). Verbal memory was assessed using the Rey Auditory-Verbal Learning Test (RAVLT), and visual memory tests employed the Rey-Osterrieth Complex Figure Test (ROCFT). The statistical analysis explored relationships between hippocampal volumetry, lateralization, and performance on memory tests.

RESULTS

In general, we observed deficits in both verbal and visual memory for patients with left and right hippocampal volume reduction. Patients with left hippocampal volume reduction had poorer performance on verbal memory tests compared with those with right hippocampal atrophy (t = -3.813, p < 0.001). Visual memory deficits were seen on both left and right MTLE without a statistically significant difference (t = 0.074, p = 0.942). The correlation between the Hippocampal Asymmetry Index (HAI) and visual and verbal Z-scores was significant only for visual Z-score in right MTLE (R = -0.45, p = 0.048).

CONCLUSIONS

Verbal memory deficit seems to be more consistent in patients with left hippocampal volume reduction. Although it had only a moderate correlation to HAI, visual memory deficit is suggested as a poorer indicator for right MTLE. Considering that verbal and visual memory deficits are seen on both right and left MTLE, MTLE should not be regarded as a unilateral, focal, or local insult but as a multifactorial and network pathology, possibly involving several brain structures.

Abnormal hippocampal structure and function in juvenile myoclonic epilepsy and unaffected siblings

Juvenile myoclonic epilepsy is the most common genetic generalized epilepsy syndrome, characterized by a complex polygenetic aetiology. Structural and functional MRI studies demonstrated mesial or lateral frontal cortical derangements and impaired fronto-cortico-subcortical connectivity in patients and their unaffected siblings. The presence of hippocampal abnormalities and associated memory deficits is controversial, and functional MRI studies in juvenile myoclonic epilepsy have not tested hippocampal activation. In this observational study, we implemented multi-modal MRI and neuropsychological data to investigate hippocampal structure and function in 37 patients with juvenile myoclonic epilepsy, 16 unaffected siblings and 20 healthy controls, comparable for age, gender, handedness and hemispheric dominance as assessed with language laterality indices. Automated hippocampal volumetry was complemented by validated qualitative and quantitative morphological criteria to detect hippocampal malrotation, assumed to represent a neurodevelopmental marker. Neuropsychological measures of verbal and visuo-spatial learning and an event-related verbal and visual memory functional MRI paradigm addressed mesiotemporal function. We detected a reduction of mean left hippocampal volume in patients and their siblings compared with controls (P < 0.01). Unilateral or bilateral hippocampal malrotation was identified in 51% of patients and 50% of siblings, against 15% of controls (P < 0.05). For bilateral hippocampi, quantitative markers of verticalization had significantly larger values in patients and siblings compared with controls (P < 0.05). In the patient subgroup, there was no relationship between structural measures and age at disease onset or degree of seizure control. No overt impairment of verbal and visual memory was identified with neuropsychological tests. Functional mapping highlighted atypical patterns of hippocampal activation, pointing to abnormal recruitment during verbal encoding in patients and their siblings [P < 0.05, familywise error (FWE)-corrected]. Subgroup analyses indicated distinct profiles of hypoactivation along the hippocampal long axis in juvenile myoclonic epilepsy patients with and without malrotation; patients with malrotation also exhibited reduced frontal recruitment for verbal memory, and more pronounced left posterior hippocampal involvement for visual memory. Linear models across the entire study cohort indicated significant associations between morphological markers of hippocampal positioning and hippocampal activation for verbal items (all P < 0.05, FWE-corrected). We demonstrate abnormalities of hippocampal volume, shape and positioning in patients with juvenile myoclonic epilepsy and their siblings, which are associated with reorganization of function and imply an underlying neurodevelopmental mechanism with expression during the prenatal stage. Co-segregation of abnormal hippocampal morphology in patients and their siblings is suggestive of a genetic imaging phenotype, independent of disease activity, and can be construed as a novel endophenotype of juvenile myoclonic epilepsy.

Association of Piriform Cortex Resection With Surgical Outcomes in Patients With Temporal Lobe Epilepsy

Importance

A functional area associated with the piriform cortex, termed area tempestas, has been implicated in animal studies as having a crucial role in modulating seizures, but similar evidence is limited in humans.

Objective

To assess whether removal of the piriform cortex is associated with postoperative seizure freedom in patients with temporal lobe epilepsy (TLE) as a proof-of-concept for the relevance of this area in human TLE.

Design, Setting, and Participants

This cohort study used voxel-based morphometry and volumetry to assess differences in structural magnetic resonance imaging (MRI) scans in consecutive patients with TLE who underwent epilepsy surgery in a single center from January 1, 2005, through December 31, 2013. Participants underwent presurgical and postsurgical structural MRI and had at least 2 years of postoperative follow-up (median, 5 years; range, 2-11 years). Patients with MRI of insufficient quality were excluded. Findings were validated in 2 independent cohorts from tertiary epilepsy surgery centers. Study follow-up was completed on September 23, 2016, and data were analyzed from September 24, 2016, through April 24, 2018.

Exposures

Standard anterior temporal lobe resection.

Main Outcomes and Measures

Long-term postoperative seizure freedom.

Results

In total, 107 patients with unilateral TLE (left-sided in 68; 63.6% women; median age, 37 years [interquartile range {IQR}, 30-45 years]) were included in the derivation cohort. Reduced postsurgical gray matter volumes were found in the ipsilateral piriform cortex in the postoperative seizure-free group (n = 46) compared with the non-seizure-free group (n = 61). A larger proportion of the piriform cortex was resected in the seizure-free compared with the non-seizure-free groups (median, 83% [IQR, 64%-91%] vs 52% [IQR, 32%-70%]; P < .001). The results were seen in left- and right-sided TLE and after adjusting for clinical variables, presurgical gray matter alterations, presurgical hippocampal volumes, and the proportion of white matter tract disconnection. Findings were externally validated in 2 independent cohorts (31 patients; left-sided TLE in 14; 54.8% women; median age, 41 years [IQR, 31-46 years]). The resected proportion of the piriform cortex was individually associated with seizure outcome after surgery (derivation cohort area under the curve, 0.80 [P < .001]; external validation cohorts area under the curve, 0.89 [P < .001]). Removal of at least half of the piriform cortex increased the odds of becoming seizure free by a factor of 16 (95% CI, 5-47; P < .001). Other mesiotemporal structures (ie, hippocampus, amygdala, and entorhinal cortex) and the overall resection volume were not associated with outcomes.

Conclusions and Relevance

These results support the importance of resecting the piriform cortex in neurosurgical treatment of TLE and suggest that this area has a key role in seizure generation.

Morphological and Advanced Imaging of Epilepsy: Beyond the Basics

The etiology of epilepsy is variable and sometimes multifactorial. Clinical course and response to treatment largely depend on the precise etiology of the seizures. Along with the electroencephalogram (EEG), neuroimaging techniques, in particular, magnetic resonance imaging (MRI), are the most important tools for determining the possible etiology of epilepsy. Over the last few years, there have been many developments in data acquisition and analysis for both morphological and functional neuroimaging of people suffering from this condition. These innovations have increased the detection of underlying structural pathologies, which have till recently been classified as "cryptogenic" epilepsy. Cryptogenic epilepsy is often refractory to anti-epileptic drug treatment. In drug-resistant patients with structural or consistent functional lesions related to the epilepsy syndrome, surgery is the only treatment that can offer a seizure-free outcome. The pre-operative detection of the underlying structural condition increases the odds of successful surgical treatment of pharmacoresistant epilepsy. This article provides a comprehensive overview of neuroimaging techniques in epilepsy, highlighting recent advances and innovations and summarizes frequent etiologies of epilepsy in order to improve the diagnosis and management of patients suffering from seizures, especially young patients and children.

Improved Detection of Subtle Mesial Temporal Sclerosis: Validation of a Commercially Available Software for Automated Segmentation of Hippocampal Volume

BACKGROUND AND PURPOSE

Identification of mesial temporal sclerosis is critical in the evaluation of individuals with temporal lobe epilepsy. Our aim was to assess the performance of FDA-approved software measures of hippocampal volume to identify mesial temporal sclerosis in patients with medically refractory temporal lobe epilepsy compared with the initial clinical interpretation of a neuroradiologist.

MATERIALS AND METHODS

Preoperative MRIs of 75 consecutive patients who underwent a temporal resection for temporal lobe epilepsy from 2011 to 2016 were retrospectively reviewed, and 71 were analyzed using Neuroreader, a commercially available automated segmentation and volumetric analysis package. Volume measures, including hippocampal volume as a percentage of total intracranial volume and the Neuroreader Index, were calculated. Radiologic interpretations of the MR imaging and pathology from subsequent resections were classified as either mesial temporal sclerosis or other, including normal findings. These measures of hippocampal volume were evaluated by receiver operating characteristic curves on the basis of pathologic confirmation of mesial temporal sclerosis in the resected temporal lobe. Sensitivity and specificity were calculated for each method and compared by means of the McNemar test using the optimal threshold as determined by the Youden J point.

RESULTS

Optimized thresholds of hippocampal percentage of a structural volume relative to total intracranial volume (<0.19%) and the Neuroreader Index (≤-3.8) were selected to optimize sensitivity and specificity (89%/71% and 89%/78%, respectively) for the identification of mesial temporal sclerosis in temporal lobe epilepsy compared with the initial clinical interpretation of the neuroradiologist (50% and 87%). Automated measures of hippocampal volume predicted mesial temporal sclerosis more accurately than radiologic interpretation (McNemar test, P < .0001).

CONCLUSIONS

Commercially available automated segmentation and volume analysis of the hippocampus accurately identifies mesial temporal sclerosis and performs significantly better than the interpretation of the radiologist.

Clinical validation of automated hippocampal segmentation in temporal lobe epilepsy

OBJECTIVE

To provide a multi-atlas framework for automated hippocampus segmentation in temporal lobe epilepsy (TLE) and clinically validate the results with respect to surgical lateralization and post-surgical outcome.

METHODS

We retrospectively identified 47 TLE patients who underwent surgical resection and 12 healthy controls. T1-weighted 3 T MRI scans were acquired for all subjects, and patients were identified by a neuroradiologist with regards to lateralization and degree of hippocampal sclerosis (HS). Automated segmentation was implemented through the Joint Label Fusion/Corrective Learning (JLF/CL) method. Gold standard lateralization was determined from the surgically resected side in Engel I (seizure-free) patients at the two-year timepoint. ROC curves were used to identify appropriate thresholds for hippocampal asymmetry ratios, which were then used to analyze JLF/CL lateralization.

RESULTS

The optimal template atlas based on subject images with varying appearances, from normal-appearing to severe HS, was demonstrated to be composed entirely of normal-appearing subjects, with good agreement between automated and manual segmentations. In applying this atlas to 26 surgically resected seizure-free patients at a two-year timepoint, JLF/CL lateralized seizure onset 92% of the time. In comparison, neuroradiology reads lateralized 65% of patients, but correctly lateralized seizure onset in these patients 100% of the time. When compared to lateralized neuroradiology reads, JLF/CL was in agreement and correctly lateralized all 17 patients. When compared to nonlateralized radiology reads, JLF/CL correctly lateralized 78% of the nine patients.

SIGNIFICANCE

While a neuroradiologist's interpretation of MR imaging is a key, albeit imperfect, diagnostic tool for seizure localization in medically-refractory TLE patients, automated hippocampal segmentation may provide more efficient and accurate epileptic foci localization. These promising findings demonstrate the clinical utility of automated segmentation in the TLE MR imaging pipeline prior to surgical resection, and suggest that further investigation into JLF/CL-assisted MRI reading could improve clinical outcomes. Our JLF/CL software is publicly available at https://www.nitrc.org/projects/ashs/.

Automated T2 relaxometry of the hippocampus for temporal lobe epilepsy

Objective

Hippocampal sclerosis (HS), the most common cause of refractory temporal lobe epilepsy, is associated with hippocampal volume loss and increased T2 signal. These can be identified on quantitative imaging with hippocampal volumetry and T2 relaxometry. Although hippocampal segmentation for volumetry has been automated, T2 relaxometry currently involves subjective and time‐consuming manual delineation of regions of interest. In this work, we develop and validate an automated technique for hippocampal T2 relaxometry.

Methods

Fifty patients with unilateral or bilateral HS and 50 healthy controls underwent T₁‐weighted and dual‐echo fast recovery fast spin echo scans. Hippocampi were automatically segmented using a multi‐atlas–based segmentation algorithm (STEPS) and a template database. Voxelwise T2 maps were determined using a monoexponential fit. The hippocampal segmentations were registered to the T2 maps and eroded to reduce partial volume effect. Voxels with T2 >170 msec excluded to minimize cerebrospinal fluid (CSF) contamination. Manual determination of T2 values was performed twice in each subject. Twenty controls underwent repeat scans to assess interscan reproducibility.

Results

Hippocampal T2 values were reliably determined using the automated method. There was a significant ipsilateral increase in T2 values in HS (p < 0.001), and a smaller but significant contralateral increase. The combination of hippocampal volumes and T2 values separated the groups well. There was a strong correlation between automated and manual methods for hippocampal T2 measurement (0.917 left, 0.896 right, both p < 0.001). Interscan reproducibility was superior for automated compared to manual measurements.

Significance

Automated hippocampal segmentation can be reliably extended to the determination of hippocampal T2 values, and a combination of hippocampal volumes and T2 values can separate subjects with HS from healthy controls. There is good agreement with manual measurements, and the technique is more reproducible on repeat scans than manual measurement. This protocol can be readily introduced into a clinical workflow for the assessment of patients with focal epilepsy.

Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients

PURPOSE

Segmentation of the hippocampus from magnetic resonance (MR) images is a key task in the evaluation of mesial temporal lobe epilepsy (mTLE) patients. Several automated algorithms have been proposed although manual segmentation remains the benchmark. Choosing a reliable algorithm is problematic since structural definition pertaining to multiple edges, missing and fuzzy boundaries, and shape changes varies among mTLE subjects. Lack of statistical references and guidance for quantifying the reliability and reproducibility of automated techniques has further detracted from automated approaches. The purpose of this study was to develop a systematic and statistical approach using a large dataset for the evaluation of automated methods and establish a method that would achieve results better approximating those attained by manual tracing in the epileptogenic hippocampus.

METHODS

A template database of 195 (81 males, 114 females; age range 32-67 yr, mean 49.16 yr) MR images of mTLE patients was used in this study. Hippocampal segmentation was accomplished manually and by two well-known tools (FreeSurfer and hammer) and two previously published methods developed at their institution [Automatic brain structure segmentation (ABSS) and LocalInfo]. To establish which method was better performing for mTLE cases, several voxel-based, distance-based, and volume-based performance metrics were considered. Statistical validations of the results using automated techniques were compared with the results of benchmark manual segmentation. Extracted metrics were analyzed to find the method that provided a more similar result relative to the benchmark.

RESULTS

Among the four automated methods, ABSS generated the most accurate results. For this method, the Dice coefficient was 5.13%, 14.10%, and 16.67% higher, Hausdorff was 22.65%, 86.73%, and 69.58% lower, precision was 4.94%, -4.94%, and 12.35% higher, and the root mean square (RMS) was 19.05%, 61.90%, and 65.08% lower than LocalInfo, FreeSurfer, and hammer, respectively. The Bland-Altman similarity analysis revealed a low bias for the ABSS and LocalInfo techniques compared to the others.

CONCLUSIONS

The ABSS method for automated hippocampal segmentation outperformed other methods, best approximating what could be achieved by manual tracing. This study also shows that four categories of input data can cause automated segmentation methods to fail. They include incomplete studies, artifact, low signal-to-noise ratio, and inhomogeneity. Different scanner platforms and pulse sequences were considered as means by which to improve reliability of the automated methods. Other modifications were specially devised to enhance a particular method assessed in this study.

Estimating Intracranial Volume in Brain Research: An Evaluation of Methods

Intracranial volume (ICV) is a standard measure often used in morphometric analyses to correct for head size in brain studies. Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation across different subject groups in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and type of software most suitable for use in estimating the ICV measure. Four groups of 53 subjects are considered, including adult controls (AC, adults with Alzheimer's disease (AD), pediatric controls (PC) and group of pediatric epilepsy subjects (PE). Reference measurements were calculated for each subject by manually tracing intracranial cavity without sub-sampling. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (FreeSurfer Ver. 5.3.0, FSL Ver. 5.0, SPM8 and SPM12) were examined in their ability to automatically estimate ICV across the groups. Results on sub-sampling studies with a 95 % confidence showed that in order to keep the accuracy of the inter-leaved slice sampling protocol above 99 %, sampling period cannot exceed 20 mm for AC, 25 mm for PC, 15 mm for AD and 17 mm for the PE groups. The study assumes a priori knowledge about the population under study into the automated ICV estimation. Tuning of the parameters in FSL and the use of proper atlas in SPM showed significant reduction in the systematic bias and the error in ICV estimation via these automated tools. SPM12 with the use of pediatric template is found to be a more suitable candidate for PE group. SPM12 and FSL subjected to tuning are the more appropriate tools for the PC group. The random error is minimized for FS in AD group and SPM8 showed less systematic bias. Across the AC group, both SPM12 and FS performed well but SPM12 reported lesser amount of systematic bias.

Manual-Protocol Inspired Technique for Improving Automated MR Image Segmentation during Label Fusion

Recent advances in multi-atlas based algorithms address many of the previous limitations in model-based and probabilistic segmentation methods. However, at the label fusion stage, a majority of algorithms focus primarily on optimizing weight-maps associated with the atlas library based on a theoretical objective function that approximates the segmentation error. In contrast, we propose a novel method—Autocorrecting Walks over Localized Markov Random Fields (AWoL-MRF)—that aims at mimicking the sequential process of manual segmentation, which is the gold-standard for virtually all the segmentation methods. AWoL-MRF begins with a set of candidate labels generated by a multi-atlas segmentation pipeline as an initial label distribution and refines low confidence regions based on a localized Markov random field (L-MRF) model using a novel sequential inference process (walks). We show that AWoL-MRF produces state-of-the-art results with superior accuracy and robustness with a small atlas library compared to existing methods. We validate the proposed approach by performing hippocampal segmentations on three independent datasets: (1) Alzheimer's Disease Neuroimaging Database (ADNI); (2) First Episode Psychosis patient cohort; and (3) A cohort of preterm neonates scanned early in life and at term-equivalent age. We assess the improvement in the performance qualitatively as well as quantitatively by comparing AWoL-MRF with majority vote, STAPLE, and Joint Label Fusion methods. AWoL-MRF reaches a maximum accuracy of 0.881 (dataset 1), 0.897 (dataset 2), and 0.807 (dataset 3) based on Dice similarity coefficient metric, offering significant performance improvements with a smaller atlas library (< 10) over compared methods. We also evaluate the diagnostic utility of AWoL-MRF by analyzing the volume differences per disease category in the ADNI1: Complete Screening dataset. We have made the source code for AWoL-MRF public at: https://github.com/CobraLab/AWoL-MRF.

Brain imaging in the assessment for epilepsy surgery

Brain imaging has a crucial role in the presurgical assessment of patients with epilepsy. Structural imaging reveals most cerebral lesions underlying focal epilepsy. Advances in MRI acquisitions including diffusion-weighted imaging, post-acquisition image processing techniques, and quantification of imaging data are increasing the accuracy of lesion detection. Functional MRI can be used to identify areas of the cortex that are essential for language, motor function, and memory, and tractography can reveal white matter tracts that are vital for these functions, thus reducing the risk of epilepsy surgery causing new morbidities. PET, SPECT, simultaneous EEG and functional MRI, and electrical and magnetic source imaging can be used to infer the localisation of epileptic foci and assist in the design of intracranial EEG recording strategies. Progress in semi-automated methods to register imaging data into a common space is enabling the creation of multimodal three-dimensional patient-specific datasets. These techniques show promise for the demonstration of the complex relations between normal and abnormal structural and functional data and could be used to direct precise intracranial navigation and surgery for individual patients.

Quantitative Neuroimaging Software for Clinical Assessment of Hippocampal Volumes on MR Imaging

BACKGROUND

Multiple neurological disorders including Alzheimer's disease (AD), mesial temporal sclerosis, and mild traumatic brain injury manifest with volume loss on brain MRI. Subtle volume loss is particularly seen early in AD. While prior research has demonstrated the value of this additional information from quantitative neuroimaging, very few applications have been approved for clinical use. Here we describe a US FDA cleared software program, NeuroreaderTM, for assessment of clinical hippocampal volume on brain MRI.

OBJECTIVE

To present the validation of hippocampal volumetrics on a clinical software program.

METHOD

Subjects were drawn (n = 99) from the Alzheimer Disease Neuroimaging Initiative study. Volumetric brain MR imaging was acquired in both 1.5 T (n = 59) and 3.0 T (n = 40) scanners in participants with manual hippocampal segmentation. Fully automated hippocampal segmentation and measurement was done using a multiple atlas approach. The Dice Similarity Coefficient (DSC) measured the level of spatial overlap between NeuroreaderTM and gold standard manual segmentation from 0 to 1 with 0 denoting no overlap and 1 representing complete agreement. DSC comparisons between 1.5 T and 3.0 T scanners were done using standard independent samples T-tests.

RESULTS

In the bilateral hippocampus, mean DSC was 0.87 with a range of 0.78-0.91 (right hippocampus) and 0.76-0.91 (left hippocampus). Automated segmentation agreement with manual segmentation was essentially equivalent at 1.5 T (DSC = 0.879) versus 3.0 T (DSC = 0.872).

CONCLUSION

This work provides a description and validation of a software program that can be applied in measuring hippocampal volume, a biomarker that is frequently abnormal in AD and other neurological disorders.

Multiatlas-Based Segmentation Editing With Interaction-Guided Patch Selection and Label Fusion

We propose a novel multiatlas-based segmentation method to address the segmentation editing scenario, where an incomplete segmentation is given along with a set of existing reference label images (used as atlases). Unlike previous multiatlas-based methods, which depend solely on appearance features, we incorporate interaction-guided constraints to find appropriate atlas label patches in the reference label set and derive their weights for label fusion. Specifically, user interactions provided on the erroneous parts are first divided into multiple local combinations. For each combination, the atlas label patches well-matched with both interactions and the previous segmentation are identified. Then, the segmentation is updated through the voxelwise label fusion of selected atlas label patches with their weights derived from the distances of each underlying voxel to the interactions. Since the atlas label patches well-matched with different local combinations are used in the fusion step, our method can consider various local shape variations during the segmentation update, even with only limited atlas label images and user interactions. Besides, since our method does not depend on either image appearance or sophisticated learning steps, it can be easily applied to general editing problems. To demonstrate the generality of our method, we apply it to editing segmentations of CT prostate, CT brainstem, and MR hippocampus, respectively. Experimental results show that our method outperforms existing editing methods in all three datasets.

Structural imaging biomarkers of sudden unexpected death in epilepsy

The mechanisms underlying sudden unexpected death in epilepsy (SUDEP) remain unclear. Wandschneider et al. reveal increased amygdalo-hippocampal volume in cases of SUDEP and in individuals at high risk, compared to individuals at low risk and people without epilepsy. Findings are consistent with histopathological reports in sudden infant death syndrome.

Sudden unexpected death in epilepsy is a major cause of premature death in people with epilepsy. We aimed to assess whether structural changes potentially attributable to sudden death pathogenesis were present on magnetic resonance imaging in people who subsequently died of sudden unexpected death in epilepsy. In a retrospective, voxel-based analysis of T₁ volume scans, we compared grey matter volumes in 12 cases of sudden unexpected death in epilepsy (two definite, 10 probable; eight males), acquired 2 years [median, interquartile range (IQR) 2.8] before death [median (IQR) age at scanning 33.5 (22) years], with 34 people at high risk [age 30.5 (12); 19 males], 19 at low risk [age 30 (7.5); 12 males] of sudden death, and 15 healthy controls [age 37 (16); seven males]. At-risk subjects were defined based on risk factors of sudden unexpected death in epilepsy identified in a recent combined risk factor analysis. We identified increased grey matter volume in the right anterior hippocampus/amygdala and parahippocampus in sudden death cases and people at high risk, when compared to those at low risk and controls. Compared to controls, posterior thalamic grey matter volume, an area mediating oxygen regulation, was reduced in cases of sudden unexpected death in epilepsy and subjects at high risk. The extent of reduction correlated with disease duration in all subjects with epilepsy. Increased amygdalo-hippocampal grey matter volume with right-sided changes is consistent with histo-pathological findings reported in sudden infant death syndrome. We speculate that the right-sided predominance reflects asymmetric central influences on autonomic outflow, contributing to cardiac arrhythmia. Pulvinar damage may impair hypoxia regulation. The imaging findings in sudden unexpected death in epilepsy and people at high risk may be useful as a biomarker for risk-stratification in future studies.

Multi-atlas segmentation of biomedical images: A survey

Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al. (2004), Klein, et al. (2005), and Heckemann, et al. (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003-2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.

Quantitative MRI in refractory temporal lobe epilepsy: relationship with surgical outcomes

Medically intractable temporal lobe epilepsy (TLE) remains a serious health problem. Across treatment centers, up to 40% of patients with TLE will continue to experience persistent postoperative seizures at 2-year follow-up. It is unknown why such a large number of patients continue to experience seizures despite being suitable candidates for resective surgery. Preoperative quantitative MRI techniques may provide useful information on why some patients continue to experience disabling seizures, and may have the potential to develop prognostic markers of surgical outcome. In this article, we provide an overview of how quantitative MRI morphometric and diffusion tensor imaging (DTI) data have improved the understanding of brain structural alterations in patients with refractory TLE. We subsequently review the studies that have applied quantitative structural imaging techniques to identify the neuroanatomical factors that are most strongly related to a poor postoperative prognosis. In summary, quantitative imaging studies strongly suggest that TLE is a disorder affecting a network of neurobiological systems, characterized by multiple and inter-related limbic and extra-limbic network abnormalities. The relationship between brain alterations and postoperative outcome are less consistent, but there is emerging evidence suggesting that seizures are less likely to remit with surgery when presurgical abnormalities are observed in the connectivity supporting brain regions serving as network nodes located outside the resected temporal lobe. Future work, possibly harnessing the potential from multimodal imaging approaches, may further elucidate the etiology of persistent postoperative seizures in patients with refractory TLE. Furthermore, quantitative imaging techniques may be explored to provide individualized measures of postoperative seizure freedom outcome.

T2 mapping outperforms normalised FLAIR in identifying hippocampal sclerosis

RATIONALE

Qualitatively, FLAIR MR imaging is sensitive to the detection of hippocampal sclerosis (HS). Quantitative analysis of T2 maps provides a useful objective measure and increased sensitivity over visual inspection of T2-weighted scans. We aimed to determine whether quantification of normalised FLAIR is as sensitive as T2 mapping in detection of HS.

METHOD

Dual echo T2 and FLAIR MR images were retrospectively analysed in 27 patients with histologically confirmed HS and increased T2 signal in ipsilateral hippocampus and 14 healthy controls. Regions of interest were manually segmented in all hippocampi aiming to avoid inclusion of CSF. Hippocampal T2 values and measures of normalised FLAIR Signal Intensity (nFSI) were compared in healthy and sclerotic hippocampi.

RESULTS

HS was identified on T2 values with 100% sensitivity and 100% specificity. HS was identified on nFSI measures with 60% sensitivity and 93% specificity.

CONCLUSION

T2 mapping is superior to nFSI for identification of HS.

Automated versus manual hippocampal segmentation in preoperative and postoperative patients with epilepsy

OBJECTIVE

To compare manual and automated preoperative and postoperative hippocampal volume measurements in patients with intractable epilepsy.

METHODS

We studied 34 patients referred to the Clinical Epilepsy Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH) for evaluation of intractable epilepsy and 21 normal volunteers who received 1.5 or 3 T GE Signa magnetic resonance imaging (MRI) scans. Hippocampal volumes were traced manually on each slice and assembled into three-dimensional volumes by investigators who were blinded to other data. Automated volumetric measurements were obtained using FreeSurfer. Statistical analysis was performed with GraphPad Prism.

RESULTS

Automated hippocampal volumes were larger than manual volumes in both patients and normal volunteers (p < 0.05). Right to left hemisphere hippocampal ratio and percent of hippocampus resected did not differ significantly by segmentation method. It was not possible to obtain accurate total resection volumes with the automated method.

SIGNIFICANCE

Values such as side-to-side ratio and percent resected may be more directly translatable between manual and automated methods than absolute measures of volume. Accurate determination of resection volumes is important for studies of the effects of surgery on both seizure control and postoperative neuropsychological deficits. Our preliminary data suggest that FreeSurfer may provide an accurate and simple method for quantitating hippocampal resections. However, it may be less valuable for large or extratemporal resections, or when distortions of normal anatomy are present. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here.

Accurate and Fully Automatic Hippocampus Segmentation Using Subject-Specific 3D Optimal Local Maps Into a Hybrid Active Contour Model

Assessing the structural integrity of the hippocampus (HC) is an essential step toward prevention, diagnosis, and follow-up of various brain disorders due to the implication of the structural changes of the HC in those disorders. In this respect, the development of automatic segmentation methods that can accurately, reliably, and reproducibly segment the HC has attracted considerable attention over the past decades. This paper presents an innovative 3-D fully automatic method to be used on top of the multiatlas concept for the HC segmentation. The method is based on a subject-specific set of 3-D optimal local maps (OLMs) that locally control the influence of each energy term of a hybrid active contour model (ACM). The complete set of the OLMs for a set of training images is defined simultaneously via an optimization scheme. At the same time, the optimal ACM parameters are also calculated. Therefore, heuristic parameter fine-tuning is not required. Training OLMs are subsequently combined, by applying an extended multiatlas concept, to produce the OLMs that are anatomically more suitable to the test image. The proposed algorithm was tested on three different and publicly available data sets. Its accuracy was compared with that of state-of-the-art methods demonstrating the efficacy and robustness of the proposed method.