Magnetic resonance imaging pattern learning in temporal lobe epilepsy: classification and prognostics

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

Functional connectome contractions in temporal lobe epilepsy: Microstructural underpinnings and predictors of surgical outcome

OBJECTIVE

Temporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults. Although it is commonly related to hippocampal pathology, increasing evidence suggests structural changes beyond the mesiotemporal lobe. Functional anomalies and their link to underlying structural alterations, however, remain incompletely understood.

METHODS

We studied 30 drug-resistant TLE patients and 57 healthy controls using multimodal magnetic resonance imaging (MRI) analyses. All patients had histologically verified hippocampal sclerosis and underwent postoperative imaging to outline the extent of their surgical resection. Our analysis leveraged a novel resting-state functional MRI framework that parameterizes functional connectivity distance, consolidating topological and physical properties of macroscale brain networks. Functional findings were integrated with morphological and microstructural metrics, and utility for surgical outcome prediction was assessed using machine learning techniques.

RESULTS

Compared to controls, TLE patients showed connectivity distance reductions in temporoinsular and prefrontal networks, indicating topological segregation of functional networks. Testing for morphological and microstructural associations, we observed that functional connectivity contractions occurred independently from TLE-related cortical atrophy but were mediated by microstructural changes in the underlying white matter. Following our imaging study, all patients underwent an anterior temporal lobectomy as a treatment of their seizures, and postsurgical seizure outcome was determined at a follow-up at least 1 year after surgery. Using a regularized supervised machine learning paradigm with fivefold cross-validation, we demonstrated that patient-specific functional anomalies predicted postsurgical seizure outcome with 76 ± 4% accuracy, outperforming classifiers operating on clinical and structural imaging features.

SIGNIFICANCE

Our findings suggest connectivity distance contractions as a macroscale substrate of TLE. Functional topological isolation may represent a microstructurally mediated network mechanism that tilts the balance toward epileptogenesis in affected networks and that may assist in patient-specific surgical prognostication.

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. This study proposed an ML framework based on coarse-to-fine feature selection. The proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. The method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. The experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. The identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.

A connectome-based mechanistic model of focal cortical dysplasia

Neuroimaging studies have consistently shown distributed brain anomalies in epilepsy syndromes associated with a focal structural lesion, particularly mesiotemporal sclerosis. Conversely, a system-level approach to focal cortical dysplasia has been rarely considered, likely due to methodological difficulties in addressing variable location and topography. Given the known heterogeneity in focal cortical dysplasia histopathology, we hypothesized that lesional connectivity consists of subtypes with distinct structural signatures. Furthermore, in light of mounting evidence for focal anomalies impacting whole-brain systems, we postulated that patterns of focal cortical dysplasia connectivity may exert differential downstream effects on global network topology. We studied a cohort of patients with histologically verified focal cortical dysplasia type II (n = 27), and age- and sex-matched healthy controls (n = 34). We subdivided each lesion into similarly sized parcels and computed their connectivity to large-scale canonical functional networks (or communities). We then dichotomized connectivity profiles of lesional parcels into those belonging to the same functional community as the focal cortical dysplasia (intra-community) and those adhering to other communities (inter-community). Applying hierarchical clustering to community-reconfigured connectome profiles identified three lesional classes with distinct patterns of functional connectivity: decreased intra- and inter-community connectivity, a selective decrease in intra-community connectivity, and increased intra- as well as inter-community connectivity. Hypo-connectivity classes were mainly composed of focal cortical dysplasia type IIB, while the hyperconnected lesions were type IIA. With respect to whole-brain networks, patients with hypoconnected focal cortical dysplasia and marked structural damage showed only mild imbalances, while those with hyperconnected subtle lesions had more pronounced topological alterations. Correcting for interictal epileptic discharges did not impact connectivity patterns. Multivariate structural equation analysis provided a mechanistic model of such complex, diverging interactions, whereby the focal cortical dysplasia structural makeup shapes its functional connectivity, which in turn modulates whole-brain network topology.

An empirical assessment of appearance descriptors applied to MRI for automated diagnosis of TLE and MCI

OBJECTIVE

Differential diagnosis of mild cognitive impairment MCI and temporal lobe epilepsy TLE is a debated issue, specifically because these conditions may coincide in the elderly population. We evaluate automated differential diagnosis based on characteristics derived from structural brain MRI of different brain regions.

METHODS

In 22 healthy controls, 19 patients with MCI, and 17 patients with TLE we used scale invariant feature transform (SIFT), local binary patterns (LBP), and wavelet-based features and investigate their predictive performance for MCI and TLE.

RESULTS

The classification based on SIFT features resulted in an accuracy of 81% of MCI vs. TLE and reasonable generalizability. Local binary patterns yielded satisfactory diagnostic performance with up to 94.74% sensitivity and 88.24% specificity in the right Thalamus for the distinction of MCI vs. TLE, but with limited generalizable. Wavelet features yielded similar results as LPB with 94.74% sensitivity and 82.35% specificity but generalize better.

SIGNIFICANCE

Features beyond volume analysis are a valid approach when applied to specific regions of the brain. Most significant information could be extracted from the thalamus, frontal gyri, and temporal regions, among others. These results suggest that analysis of changes of the central nervous system should not be limited to the most typical regions of interest such as the hippocampus and parahippocampal areas. Region-independent approaches can add considerable information for diagnosis. We emphasize the need to characterize generalizability in future studies, as our results demonstrate that not doing so can lead to overestimation of classification results.

LIMITATIONS

The data used within this study allows for separation of MCI and TLE subjects using a simple age threshold. While we present a strong indication that the presented method is age-invariant and therefore agnostic to this situation, new data would be needed for a rigorous empirical assessment of this findings.

Hubs disruption in mesial temporal lobe epilepsy. A resting-state fMRI study on a language-and-memory network

Mesial temporal lobe epilepsy (mTLE) affects the brain networks at several levels and patients suffering from mTLE experience cognitive impairment for language and memory. Considering the importance of language and memory reorganization in this condition, the present study explores changes of the embedded language‐and‐memory network (LMN) in terms of functional connectivity (FC) at rest, as measured with functional MRI. We also evaluate the cognitive efficiency of the reorganization, that is, whether or not the reorganizations support or allow the maintenance of optimal cognitive functioning despite the seizure‐related damage. Data from 37 patients presenting unifocal mTLE were analyzed and compared to 48 healthy volunteers in terms of LMN‐FC using two methods: pairwise correlations (region of interest [ROI]‐to‐ROI) and graph theory. The cognitive efficiency of the LMN‐FC reorganization was measured using correlations between FC parameters and language and memory scores. Our findings revealed a large perturbation of the LMN hubs in patients. We observed a hyperconnectivity of limbic areas near the dysfunctional hippocampus and mainly a hypoconnectivity for several cortical regions remote from the dysfunctional hippocampus. The loss of FC was more important in left mTLE (L‐mTLE) than in right (R‐mTLE) patients. The LMN‐FC reorganization may not be always compensatory and not always useful for patients as it may be associated with lower cognitive performance. We discuss the different connectivity patterns obtained and conclude that interpretation of FC changes in relation to neuropsychological scores is important to determine cognitive efficiency, suggesting the concept of “connectome” would gain to be associated with a “cognitome” concept.

Multidimensional Neuroanatomical Subtyping of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders with multiple biological etiologies and highly variable symptoms. Using a novel analytical framework that integrates cortex-wide MRI markers of vertical (i.e., thickness, tissue contrast) and horizontal (i.e., surface area, geodesic distance) cortical organization, we could show that a large multi-centric cohort of individuals with ASD falls into 3 distinctive anatomical subtypes (ASD-I: cortical thickening, increased surface area, tissue blurring; ASD-II: cortical thinning, decreased distance; ASD-III: increased distance). Bootstrap analysis indicated a high consistency of these biotypes across thousands of simulations, while analysis of behavioral phenotypes and resting-state fMRI showed differential symptom load (i.e., Autism Diagnostic Observation Schedule; ADOS) and instrinsic connectivity anomalies in communication and social-cognition networks. Notably, subtyping improved supervised learning approaches predicting ADOS score in single subjects, with significantly increased performance compared to a subtype-blind approach. The existence of different subtypes may reconcile previous results so far not converging on a consistent pattern of anatomical anomalies in autism, and possibly relate the presence of diverging corticogenic and maturational anomalies. The high accuracy for symptom severity prediction indicates benefits of MRI biotyping for personalized diagnostics and may guide the development of targeted therapeutic strategies.

Detection of differentiated changes in gray matter in children with progressive hydrocephalus and chronic compensated hydrocephalus using voxel-based morphometry and machine learning

Currently, no neuroimaging study has reported the detection of specific imaging biomarkers that distinguish the progressive hydrocephalus (PH) and chronic compensated hydrocephalus (CH). Our main focus is to evaluate the different structural changes in classifying the two types of hydrocephalus children. Twenty-two children with hydrocephalus (12 PHs and 10 CHs) and 30 age-matched healthy controls were enrolled and the T1-weighted imaging was collected in the study. A customized voxel-based morphometry (VBM) approach and support vector machine (SVM) were combined to investigate the structural changes and group classification. Comparing with the controls and CH, PH groups invariably showed a significant decrease of GM volume in the bilateral hippocampus/parahippocampus, insula, and motor-related areas. SVM applied to the GM volumes of bilateral hippocampus/parahippocampus, insula, and motor-related areas correctly identified hydrocephalus children from normal controls with a statistically significant accuracy of 88.46% (p ≤ .001). In addition, SVM applied to GM volumes of the same regions correctly identified PH from CH with a statistically significant accuracy of 77.27% (p ≤ .009). Using VBM analysis, we characterized and visualized the GM changes in children with hydrocephalus. Machine learning results further confirmed that a significant decrease of the bilateral hippocampus/parahippocampus, insula, and motor-related GM volume can serve as a specific neuroimaging index to distinguish the children with PH from the children with CH and controls at individual. The findings could help to aid the identification of individuals with PH in clinical practice.

Can machine learning algorithms accurately predict discharge to nonhome facility and early unplanned readmissions following spinal fusion? Analysis of a national surgical registry

OBJECTIVE

Nonhome discharge and unplanned readmissions represent important cost drivers following spinal fusion. The authors sought to utilize different machine learning algorithms to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion.

METHODS

The authors queried the 2012-2013 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) for patients undergoing cervical or lumbar spinal fusion. Outcomes assessed included discharge to nonhome facility and unplanned readmissions within 30 days after surgery. A total of 7 machine learning algorithms were evaluated. Predictive hierarchical clustering of procedure codes was used to increase model performance. Model performance was evaluated using overall accuracy and area under the receiver operating characteristic curve (AUC), as well as sensitivity, specificity, and positive and negative predictive values. These performance metrics were computed for both the imputed and unimputed (missing values dropped) datasets.

RESULTS

A total of 59,145 spinal fusion cases were analyzed. The incidence rates of discharge to nonhome facility and 30-day unplanned readmission were 12.6% and 4.5%, respectively. All classification algorithms showed excellent discrimination (AUC > 0.80, range 0.85-0.87) for predicting nonhome discharge. The generalized linear model showed comparable performance to other machine learning algorithms. By comparison, all models showed poorer predictive performance for unplanned readmission, with AUC ranging between 0.63 and 0.66. Better predictive performance was noted with models using imputed data.

CONCLUSIONS

In an analysis of patients undergoing spinal fusion, multiple machine learning algorithms were found to reliably predict nonhome discharge with modest performance noted for unplanned readmissions. These results provide early evidence regarding the feasibility of modern machine learning classifiers in predicting these outcomes and serve as possible clinical decision support tools to facilitate shared decision making.

Trajectories of brain remodeling in temporal lobe epilepsy

Temporal lobe epilepsy has been usually associated with progressive brain atrophy due to neuronal cell loss. However, recent animal models demonstrated a dual effect of epileptic seizures with initial enhancement of hippocampal neurogenesis followed by abnormal astrocyte proliferation and neurogenesis depletion in the chronic stage. Our aim was to test for the hypothesized bidirectional pattern of epilepsy-associated brain remodeling in the context of the presence and absence of mesial temporal lobe sclerosis. We acquired MRIs from a large cohort of mesial temporal lobe epilepsy patients with or without hippocampus sclerosis on radiological examination. The statistical analysis tested explicitly for common and differential brain patterns between the two patients' cohorts and healthy controls within the computational anatomy framework of voxel-based morphometry. The main effect of disease was associated with continuous hippocampus volume loss ipsilateral to the seizure onset zone in both temporal lobe epilepsy cohorts. The post hoc simple effects tests demonstrated bilateral hippocampus volume increase in the early epilepsy stages in patients without hippocampus sclerosis. Early age of onset and longer disease duration correlated with volume decrease in the ipsilateral hippocampus. Our findings of seizure-induced hippocampal remodeling are associated with specific patterns of mesial temporal lobe atrophy that are modulated by individual clinical phenotype features. Directionality of hippocampus volume changes strongly depends on the chronicity of disease. Specific anatomy differences represent a snapshot within a progressive continuum of seizure-induced structural remodeling.

Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review

Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN), logistic regression (LR), and support vector machines (SVM). ML may generate substantial improvements in neurosurgery. This systematic review assessed the current state of neurosurgical ML applications and the performance of algorithms applied. Our systematic search strategy yielded 6866 results, 70 of which met inclusion criteria. Performance statistics analyzed included area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Natural language processing (NLP) was used to model topics across the corpus and to identify keywords within surgical subspecialties. ML applications were heterogeneous. The densest cluster of studies focused on preoperative evaluation, planning, and outcome prediction in spine surgery. The main algorithms applied were NN, LR, and SVM. Input and output features varied widely and were listed to facilitate future research. The accuracy (F_(2,19) = 6.56, p < 0.01) and specificity (F_(2,16) = 5.57, p < 0.01) of NN, LR, and SVM differed significantly. NN algorithms demonstrated significantly higher accuracy than LR. SVM demonstrated significantly higher specificity than LR. We found no significant difference between NN, LR, and SVM AUC and sensitivity. NLP topic modeling reached maximum coherence at seven topics, which were defined by modeling approach, surgery type, and pathology themes. Keywords captured research foci within surgical domains. ML technology accurately predicts outcomes and facilitates clinical decision-making in neurosurgery. NNs frequently outperformed other algorithms on supervised learning tasks. This study identified gaps in the literature and opportunities for future neurosurgical ML research.

Temporal lobe epilepsy: Hippocampal pathology modulates connectome topology and controllability

OBJECTIVE

To assess whether hippocampal sclerosis (HS) severity is mirrored at the level of large-scale networks.

METHODS

We studied preoperative high-resolution anatomical and diffusion-weighted MRI of 44 temporal lobe epilepsy (TLE) patients with histopathologic diagnosis of HS (n = 25; TLE-HS) and isolated gliosis (n = 19; TLE-G) and 25 healthy controls. Hippocampal measurements included surface-based subfield mapping of atrophy and T2 hyperintensity indexing cell loss and gliosis, respectively. Whole-brain connectomes were generated via diffusion tractography and examined using graph theory along with a novel network control theory paradigm that simulates functional dynamics from structural network data.

RESULTS

Compared to controls, we observed markedly increased path length and decreased clustering in TLE-HS compared to controls, indicating lower global and local network efficiency, while TLE-G showed only subtle alterations. Similarly, network controllability was lower in TLE-HS only, suggesting limited range of functional dynamics. Hippocampal imaging markers were positively associated with macroscale network alterations, particularly in ipsilateral CA1-3. Systematic assessment across several networks revealed maximal changes in the hippocampal circuity. Findings were consistent when correcting for cortical thickness, suggesting independence from gray matter atrophy.

CONCLUSIONS

Severe HS is associated with marked remodeling of connectome topology and structurally governed functional dynamics in TLE, as opposed to isolated gliosis, which has negligible effects. Cell loss, particularly in CA1-3, may exert a cascading effect on brain-wide connectomes, underlining coupled disease processes across multiple scales.

Neuroimaging and connectomics of drug-resistant epilepsy at multiple scales: From focal lesions to macroscale networks

Epilepsy is among the most common chronic neurologic disorders, with 30%-40% of patients having seizures despite antiepileptic drug treatment. The advent of brain imaging and network analyses has greatly improved the understanding of this condition. In particular, developments in magnetic resonance imaging (MRI) have provided measures for the noninvasive characterization and detection of lesions causing epilepsy. MRI techniques can probe structural and functional connectivity, and network analyses have shaped our understanding of whole-brain anomalies associated with focal epilepsies. This review considers the progress made by neuroimaging and connectomics in the study of drug-resistant epilepsies due to focal substrates, particularly temporal lobe epilepsy related to mesiotemporal sclerosis and extratemporal lobe epilepsies associated with malformations of cortical development. In these disorders, there is evidence of widespread disturbances of structural and functional connectivity that may contribute to the clinical and cognitive prognosis of individual patients. It is hoped that studying the interplay between macroscale network anomalies and lesional profiles will improve our understanding of focal epilepsies and assist treatment choices.

Cerebellar, limbic, and midbrain volume alterations in sudden unexpected death in epilepsy

OBJECTIVE

The processes underlying sudden unexpected death in epilepsy (SUDEP) remain elusive, but centrally mediated cardiovascular or respiratory collapse is suspected. Volume changes in brain areas mediating recovery from extreme cardiorespiratory challenges may indicate failure mechanisms and allow prospective identification of SUDEP risk.

METHODS

We retrospectively imaged SUDEP cases (n = 25), patients comparable for age, sex, epilepsy syndrome, localization, and disease duration who were high-risk (n = 25) or low-risk (n = 23), and age- and sex-matched healthy controls (n = 25) with identical high-resolution T1-weighted scans. Regional gray matter volume, determined by voxel-based morphometry, and segmentation-derived structure sizes were compared across groups, controlling for total intracranial volume, age, and sex.

RESULTS

Substantial bilateral gray matter loss appeared in SUDEP cases in the medial and lateral cerebellum. This was less prominent in high-risk subjects and absent in low-risk subjects. The periaqueductal gray, left posterior and medial thalamus, left hippocampus, and bilateral posterior cingulate also showed volume loss in SUDEP. High-risk subjects showed left thalamic volume reductions to a lesser extent. Bilateral amygdala, entorhinal, and parahippocampal volumes increased in SUDEP and high-risk patients, with the subcallosal cortex enlarged in SUDEP only. Disease duration correlated negatively with parahippocampal volume. Volumes of the bilateral anterior insula and midbrain in SUDEP cases were larger the closer to SUDEP from magnetic resonance imaging.

SIGNIFICANCE

SUDEP victims show significant tissue loss in areas essential for cardiorespiratory recovery and enhanced volumes in areas that trigger hypotension or impede respiratory patterning. Those changes may shed light on SUDEP pathogenesis and prospectively detect patterns identifying those at risk.

Neuroimaging of Sudden Unexpected Death in Epilepsy (SUDEP): Insights From Structural and Resting-State Functional MRI Studies

The elusive nature of sudden unexpected death in epilepsy (SUDEP) has led to investigations of mechanisms and identification of biomarkers of this fatal scenario that constitutes the leading cause of premature death in epilepsy. In this short review, we compile evidence from structural and functional neuroimaging that demonstrates alterations to brain structures and networks involved in central autonomic and respiratory control in SUDEP and those at elevated risk. These findings suggest that compromised central control of vital regulatory processes may contribute to SUDEP. Both structural changes and dysfunctional interactions indicate potential mechanisms underlying the fatal event; contributions to individual risk prediction will require further study. The nature and sites of functional disruptions suggest potential non-invasive interventions to overcome failing processes.

Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning

The National Institutes of Health-sponsored Epilepsy Connectome Project aims to characterize connectivity changes in temporal lobe epilepsy (TLE) patients. The magnetic resonance imaging protocol follows that used in the Human Connectome Project, and includes 20 min of resting-state functional magnetic resonance imaging acquired at 3T using 8-band multiband imaging. Glasser parcellation atlas was combined with the FreeSurfer subcortical regions to generate resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuations (ALFFs), and fractional ALFF measures. Seven different frequency ranges such as Slow-5 (0.01-0.027 Hz) and Slow-4 (0.027-0.073 Hz) were selected to compute these measures. The goal was to train machine learning classification models to discriminate TLE patients from healthy controls, and to determine which combination of the resting state measure and frequency range produced the best classification model. The samples included age- and gender-matched groups of 60 TLE patients and 59 healthy controls. Three traditional machine learning models were trained: support vector machine, linear discriminant analysis, and naive Bayes classifier. The highest classification accuracy was obtained using RSFC measures in the Slow-4 + 5 band (0.01-0.073 Hz) as features. Leave-one-out cross-validation accuracies were ∼83%, with receiver operating characteristic area-under-the-curve reaching close to 90%. Increased connectivity from right area posterior 9-46v in TLE patients contributed to the high accuracies. With increased sample sizes in the near future, better machine learning models will be trained not only to aid the diagnosis of TLE, but also as a tool to understand this brain disorder.

Multimodal Data and Machine Learning for Detecting Specific Biomarkers in Pediatric Epilepsy Patients With Generalized Tonic-Clonic Seizures

Previous neuroimaging studies of epilepsy with generalized tonic-clonic seizures (GTCS) focus mainly on adults. However, the neural mechanisms that underline this type of epilepsy remain unclear, especially for children. The aim of the present study was to detect the effect of epilepsy on brains of children with GTCS and to investigate whether the changes in the brain can be used to discriminate between epileptic children and healthy children at the level of the individual. To achieve this purpose, we measured gray matter (GM) volume and fractional amplitude of low-frequency fluctuation (fALFF) differences on multimodel magnetic resonance imaging in 14 children with GTCS and 30 age- and gender-matched healthy controls. The patients showed GM volume reduction and a fALFF increase in the thalamus, hippocampus, temporal and other deep nuclei. A significant decrease of fALFF was mainly found in the default mode network (DMN). In addition, epileptic duration was significantly negatively related to the GM volumes and significantly positively related to the fALFF value of right thalamus. A support vector machine (SVM) applied to the GM volume of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 74.42% (P < 0.002). A SVM applied to the fALFF of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 83.72% (P < 0.002). The consistent neuroimaging results indicated that the right thalamus plays an important role in reflecting the chronic damaging effect of GTCS epilepsy in children. The length of time of a child's epileptic history was correlated with greater GM volume reduction and a fALFF increase in the right thalamus. GM volumes and fALFF values in the right thalamus can identify children with GTCS from the healthy controls with high accuracy and at an individual subject level. These results are likely to be valuable in explaining the clinical problems and understanding the brain abnormalities underlying this disorder.

Microstructure-Informed Connectomics: Enriching Large-Scale Descriptions of Healthy and Diseased Brains

Rapid advances in neuroimaging and network science have produced powerful tools and measures to appreciate human brain organization at multiple spatial and temporal scales. It is now possible to obtain increasingly meaningful representations of whole-brain structural and functional brain networks and to formally assess macroscale principles of network topology. In addition to its utility in characterizing healthy brain organization, individual variability, and life span-related changes, there is high promise of network neuroscience for the conceptualization and, ultimately, management of brain disorders. In the current review, we argue for a science of the human brain that, while strongly embracing macroscale connectomics, also recommends awareness of brain properties derived from meso- and microscale resolutions. Such features include MRI markers of tissue microstructure, local functional properties, as well as information from nonimaging domains, including cellular, genetic, or chemical data. Integrating these measures with connectome models promises to better define the individual elements that constitute large-scale networks, and clarify the notion of connection strength among them. By enriching the description of large-scale networks, this approach may improve our understanding of fundamental principles of healthy brain organization. Notably, it may also better define the substrate of prevalent brain disorders, including stroke, autism, as well as drug-resistant epilepsies that are each characterized by intriguing interactions between local anomalies and network-level perturbations.

A challenge of predicting seizure frequency in temporal lobe epilepsy using neuroanatomical features

The pathological and clinical characteristics of temporal lobe epilepsy (TLE) tend to be affected by epileptic seizures, specifically represented by seizure lateralization and frequency. Although the lateralization of the epileptogenic zone can be clarified in terms of neuroanatomical damage, there have been conflicting findings on the relationship between seizure frequency and neuroanatomical damage. In this study we sought to examine the relationship in the framework of machine learning-based predictive modeling. We acquired 60 grey matter (GM) anatomical features from structural MRI data and 46 white matter (WM) anatomical features from diffusion-weighted MRI data for 42 TLE patients and 45 healthy controls and applied the random forests method to the neuroanatomical features. We demonstrated that, whereas the neuroanatomical features were promising markers for the discrimination of the TLE patients from the healthy controls, the separation between the TLE patients with low and high seizure frequency on the basis of the neuroanatomical features was challenging. When we applied feature selection techniques for the construction of the predictive models, a greater number of features were selected as being relevant to the distinction of the TLE patients from the healthy controls than to the classification of the TLE patients according to seizure frequency. Furthermore, we adopted model-based clustering analysis and showed that seizure frequency-based subgroups were not matched well with neuroanatomy-based subgroups in the TLE patients. We propose that the challenge of predicting seizure frequency using neuroanatomical features could be attributable to considerable inter-individual variability in neuroanatomical damage among seizure frequency-based subgroups.

Gray Matter and White Matter Abnormalities in Temporal Lobe Epilepsy Patients with and without Hippocampal Sclerosis

The presentation and distribution of gray matter (GM) and white matter (WM) abnormalities in temporal lobe epilepsy (TLE) have been widely studied. Here, we investigated the GM and WM abnormalities in TLE patients with and without hippocampal sclerosis (HS) in five groups of participants: healthy controls (HCs) (n = 28), right TLE patients with HS (n = 26), right TLE patients without HS (n = 30), left TLE patients with HS (n = 25), and left TLE patients without HS (n = 27). We performed a flexible factorial statistical test in a whole-brain voxel-based morphometry analysis to identify significant GM and WM abnormalities and analysis of variance of hippocampal and amygdala regions among the five groups using the FreeSurfer procedure. Furthermore, we conducted multiple regression analysis to assess regional GM and WM changes with disease duration. We observed significant ipsilateral mesiotemporal GM and WM volume reductions in TLE patients with HS compared with HCs. We also observed a slight GM amygdala swelling in right TLE patients without HS. The regression analysis revealed significant negative GM and WM changes with disease duration specifically in left TLE patients with HS. The observed GM and WM abnormalities may contribute to our understanding of the root of epilepsy mechanisms.

The impact of epilepsy surgery on the structural connectome and its relation to outcome

Background

Temporal lobe surgical resection brings seizure remission in up to 80% of patients, with long-term complete seizure freedom in 41%. However, it is unclear how surgery impacts on the structural white matter network, and how the network changes relate to seizure outcome.

Methods

We used white matter fibre tractography on preoperative diffusion MRI to generate a structural white matter network, and postoperative T1-weighted MRI to retrospectively infer the impact of surgical resection on this network. We then applied graph theory and machine learning to investigate the properties of change between the preoperative and predicted postoperative networks.

Results

Temporal lobe surgery had a modest impact on global network efficiency, despite the disruption caused. This was due to alternative shortest paths in the network leading to widespread increases in betweenness centrality post-surgery. Measurements of network change could retrospectively predict seizure outcomes with 79% accuracy and 65% specificity, which is twice as high as the empirical distribution. Fifteen connections which changed due to surgery were identified as useful for prediction of outcome, eight of which connected to the ipsilateral temporal pole.

Conclusion

Our results suggest that the use of network change metrics may have clinical value for predicting seizure outcome. This approach could be used to prospectively predict outcomes given a suggested resection mask using preoperative data only.

A Comparative Study of Feature Selection Methods for the Discriminative Analysis of Temporal Lobe Epilepsy

It is crucial to differentiate patients with temporal lobe epilepsy (TLE) from the healthy population and determine abnormal brain regions in TLE. The cortical features and changes can reveal the unique anatomical patterns of brain regions from structural magnetic resonance (MR) images. In this study, structural MR images from 41 patients with left TLE, 34 patients with right TLE, and 58 normal controls (NC) were acquired, and four kinds of cortical measures, namely cortical thickness, cortical surface area, gray matter volume (GMV), and mean curvature, were explored for discriminative analysis. Three feature selection methods including the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM), and the support vector machine-recursive feature elimination (SVM-RFE) were investigated to extract dominant features among the compared groups for classification using the support vector machine (SVM) classifier. The results showed that the SVM-RFE achieved the highest performance (most classifications with more than 84% accuracy), followed by the SCDRM, and the t-test. Especially, the surface area and GMV exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical measures were combined. Additionally, the dominant regions with higher classification weights were mainly located in the temporal and the frontal lobe, including the entorhinal cortex, rostral middle frontal, parahippocampal cortex, superior frontal, insula, and cuneus. This study concluded that the cortical features provided effective information for the recognition of abnormal anatomical patterns and the proposed methods had the potential to improve the clinical diagnosis of TLE.

Paradigm Shifts in the Neuropsychology of Epilepsy

This article reviews the major paradigm shifts that have occurred in the area of the application of clinical and experimental neuropsychology to epilepsy and epilepsy surgery since the founding of the International Neuropsychological Society. The five paradigm shifts discussed include: 1) The neurobiology of cognitive disorders in epilepsy - expanding the landscape of syndrome-specific neuropsychological impairment; 2) pathways to comorbidities: bidirectional relationships and their clinical implications; 3) discovering quality of life: The concept, its quantification and applicability; 4) outcomes of epilepsy surgery: challenging conventional wisdom; and 5) Iatrogenic effects of treatment: cognitive and behavioral effects of antiepilepsy drugs. For each area we characterize the status of knowledge, the key developments that have occurred, and how they have altered our understanding of the epilepsies and their management. We conclude with a brief overview of where we believe the field will be headed in the next decade which includes changes in assessment paradigms, moving from characterization of comorbidities to interventions; increasing development of new measures, terminology and classification; increasing interest in neurodegenerative proteins; transitioning from clinical seizure features to modifiable risk factors; and neurobehavioral phenotypes. Overall, enormous progress has been made over the lifespan of the INS with promise of ongoing improvements in understanding of the cognitive and behavioral complications of the epilepsies and their treatment. (JINS, 2017, 23, 791-805).

Personalized structural image analysis in patients with temporal lobe epilepsy

Volumetric and morphometric studies have demonstrated structural abnormalities related to chronic epilepsies on a cohort- and population-based level. On a single-patient level, specific patterns of atrophy or cortical reorganization may be widespread and heterogeneous but represent potential targets for further personalized image analysis and surgical therapy. The goal of this study was to compare morphometric data analysis in 37 patients with temporal lobe epilepsies with expert-based image analysis, pre-informed by seizure semiology and ictal scalp EEG. Automated image analysis identified abnormalities exceeding expert-determined structural epileptogenic lesions in 86% of datasets. If EEG lateralization and expert MRI readings were congruent, automated analysis detected abnormalities consistent on a lobar and hemispheric level in 82% of datasets. However, in 25% of patients EEG lateralization and expert readings were inconsistent. Automated analysis localized to the site of resection in 60% of datasets in patients who underwent successful epilepsy surgery. Morphometric abnormalities beyond the mesiotemporal structures contributed to subtype characterisation. We conclude that subject-specific morphometric information is in agreement with expert image analysis and scalp EEG in the majority of cases. However, automated image analysis may provide non-invasive additional information in cases with equivocal radiological and neurophysiological findings.

Preferential susceptibility of limbic cortices to microstructural damage in temporal lobe epilepsy: A quantitative T1 mapping study

The majority of MRI studies in temporal lobe epilepsy (TLE) have utilized morphometry to map widespread cortical alterations. Morphological markers, such as cortical thickness or grey matter density, reflect combinations of biological events largely driven by overall cortical geometry rather than intracortical tissue properties. Because of its sensitivity to intracortical myelin, quantitative measurement of longitudinal relaxation time (qT₁) provides and an in vivo proxy for cortical microstructure. Here, we mapped the regional distribution of qT₁ in a consecutive cohort of 24 TLE patients and 20 healthy controls. Compared to controls, patients presented with a strictly ipsilateral distribution of qT₁ increases in temporopolar, parahippocampal and orbitofrontal cortices. Supervised statistical learning applied to qT₁ maps could lateralize the seizure focus in 92% of patients. Intracortical profiling of qT₁ along streamlines perpendicular to the cortical mantle revealed marked effects in upper levels that tapered off at the white matter interface. Findings remained robust after correction for cortical thickness and interface blurring, suggesting independence from previously reported morphological anomalies in this disorder. Mapping of qT₁ along hippocampal subfield surfaces revealed marked increases in anterior portions of the ipsilateral CA1-3 and DG that were also robust against correction for atrophy. Notably, in operated patients, qualitative histopathological analysis of myelin stains in resected hippocampal specimens confirmed disrupted internal architecture and fiber organization. Both hippocampal and neocortical qT₁ anomalies were more severe in patients with early disease onset. Finally, analysis of resting-state connectivity from regions of qT₁ increases revealed altered intrinsic functional network embedding in patients, particularly to prefrontal networks. Analysis of qT₁ suggests a preferential susceptibility of ipsilateral limbic cortices to microstructural damage, possibly related to disrupted myeloarchitecture. These alterations may reflect atypical neurodevelopment and affect the integrity of fronto-limbic functional networks.

Presurgical thalamic "hubness" predicts surgical outcome in temporal lobe epilepsy

OBJECTIVE

To characterize the presurgical brain functional architecture presented in patients with temporal lobe epilepsy (TLE) using graph theoretical measures of resting-state fMRI data and to test its association with surgical outcome.

METHODS

Fifty-six unilateral patients with TLE, who subsequently underwent anterior temporal lobectomy and were classified as obtaining a seizure-free (Engel class I, n = 35) vs not seizure-free (Engel classes II-IV, n = 21) outcome at 1 year after surgery, and 28 matched healthy controls were enrolled. On the basis of their presurgical resting-state functional connectivity, network properties, including nodal hubness (importance of a node to the network; degree, betweenness, and eigenvector centralities) and integration (global efficiency), were estimated and compared across our experimental groups. Cross-validations with support vector machine (SVM) were used to examine whether selective nodal hubness exceeded standard clinical characteristics in outcome prediction.

RESULTS

Compared to the seizure-free patients and healthy controls, the not seizure-free patients displayed a specific increase in nodal hubness (degree and eigenvector centralities) involving both the ipsilateral and contralateral thalami, contributed by an increase in the number of connections to regions distributed mostly in the contralateral hemisphere. Simulating removal of thalamus reduced network integration more dramatically in not seizure-free patients. Lastly, SVM models built on these thalamic hubness measures produced 76% prediction accuracy, while models built with standard clinical variables yielded only 58% accuracy (both were cross-validated).

CONCLUSIONS

A thalamic network associated with seizure recurrence may already be established presurgically. Thalamic hubness can serve as a potential biomarker of surgical outcome, outperforming the clinical characteristics commonly used in epilepsy surgery centers.

Neuroimaging-Based Phenotyping of the Autism Spectrum

Recent advances in neuroimaging have offered a rich array of structural and functional markers to probe the organization of regional and large-scale brain networks. The current chapter provides a brief introduction into these techniques and overviews their contribution to the understanding of autism spectrum disorder (ASD), a neurodevelopmental condition associated with atypical social cognition, language function, and repetitive behaviors/interests. While it is generally recognized that ASD relates to structural and functional network anomalies, the extent and overall pattern of reported findings have been rather heterogeneous. Indeed, while several attempts have been made to label the main neuroimaging phenotype of ASD (e.g., 'early brain overgrowth hypothesis', 'amygdala theory', 'disconnectivity hypothesis'), none of these frameworks has been without controversy. Methodological sources of inconsistent results may include differences in subject inclusion criteria, variability in image processing, and analysis methodology. However, inconsistencies may also relate to high heterogeneity across the autism spectrum itself. It, therefore, remains to be investigated whether a consistent imaging phenotype that adequately describes the entire autism spectrum can, in fact, be established. On the other hand, as previous findings clearly emphasize the value of neuroimaging in identifying atypical brain morphology, function, and connectivity, they ultimately support its high potential to identify biologically and clinically relevant endophenotypes.

Preoperative prediction of temporal lobe epilepsy surgery outcome

PURPOSE

There is controversy about relative contributions of ictal scalp video EEG recording (vEEG), routine scalp outpatient interictal EEG (rEEG), intracranial EEG (iEEG) and MRI for predicting seizure-free outcomes after temporal lobectomy. We reviewed NIH experience to determine contributions at specific time points as well as long-term predictive value of standard pre-surgical investigations.

METHODS

Raw data was obtained via retrospective chart review of 151 patients. After exclusions, 118 remained (median 5 years follow-up). MRI-proven mesial temporal sclerosis (MTSr) was considered a separate category for analysis. Logistic regression estimated odds ratios at 6-months, 1-year, and 2 years; proportional hazard models estimated long-term comparisons. Subset analysis of the proportional hazard model was performed including only patients with commonly encountered situations in each of the modalities, to maximize statistical inference.

RESULTS

Any MRI finding, MRI proven MTS, rEEG, vEEG and iEEG did not predict two-year seizure-free outcome. MTSr was predictive at six months (OR=2.894, p=0. 0466), as were MRI and MTSr at one year (OR=10.4231, p=0. 0144 and OR=3.576, p=0. 0091). Correcting for rEEG and MRI, vEEG failed to predict outcome at 6 months, 1year and 2 years. Proportional hazard analysis including all available follow-up failed to achieve significance for any modality. In the subset analysis of 83 patients with commonly encountered results, vEEG modestly predicted long-term seizure-free outcomes with a proportional hazard ratio of 1.936 (p=0.0304).

CONCLUSIONS

In this study, presurgical tools did not provide unambiguous long-term outcome predictions. Multicenter prospective studies are needed to determine optimal presurgical epilepsy evaluation.

Impaired cerebral blood flow networks in temporal lobe epilepsy with hippocampal sclerosis: A graph theoretical approach

Graph theory is an emerging method to investigate brain networks. Altered cerebral blood flow (CBF) has frequently been reported in temporal lobe epilepsy (TLE), but graph theoretical findings of CBF are poorly understood. Here, we explored graph theoretical networks of CBF in TLE using arterial spin labeling imaging. We recruited patients with TLE and unilateral hippocampal sclerosis (HS) (19 patients with left TLE, and 21 with right TLE) and 20 gender- and age-matched healthy control subjects. We obtained all participants' CBF maps using pseudo-continuous arterial spin labeling and analyzed them using the Graph Analysis Toolbox (GAT) software program. As a result, compared to the controls, the patients with left TLE showed a significantly low clustering coefficient (p=0.024), local efficiency (p=0.001), global efficiency (p=0.010), and high transitivity (p=0.015), whereas the patients with right TLE showed significantly high assortativity (p=0.046) and transitivity (p=0.011). The group with right TLE also had high characteristic path length values (p=0.085), low global efficiency (p=0.078), and low resilience to targeted attack (p=0.101) at a trend level. Lower normalized clustering coefficient (p=0.081) in the left TLE and higher normalized characteristic path length (p=0.089) in the right TLE were found also at a trend level. Both the patients with left and right TLE showed significantly decreased clustering in similar areas, i.e., the cingulate gyri, precuneus, and occipital lobe. Our findings revealed differing left-right network metrics in which an inefficient CBF network in left TLE and vulnerability to irritation in right TLE are suggested. The left-right common finding of regional decreased clustering might reflect impaired default-mode networks in TLE.

Subregional Mesiotemporal Network Topology Is Altered in Temporal Lobe Epilepsy

Temporal lobe epilepsy (TLE) is the most frequent drug-resistant epilepsy in adults and commonly associated with variable degrees of mesiotemporal atrophy on magnetic resonance imaging (MRI). Analyses of inter-regional connectivity have unveiled disruptions in large-scale cortico-cortical networks; little is known about the topological organization of the mesiotemporal lobe, the limbic subnetwork central to the disorder. We generated covariance networks based on high-resolution MRI surface-shape descriptors of the hippocampus, entorhinal cortex, and amygdala in 134 TLE patients and 45 age- and sex-matched controls. Graph-theoretical analysis revealed increased path length and clustering in patients, suggesting a shift toward a more regularized arrangement; findings were reproducible after split-half assessment and across 2 parcellation schemes. Analysis of inter-regional correlations and module participation showed increased within-structure covariance, but decreases between structures, particularly with regards to the hippocampus and amygdala. While higher clustering possibly reflects topological consequences of axonal sprouting, decreases in interstructure covariance may be a consequence of disconnection within limbic circuitry. Preoperative network parameters, specifically the segregation of the ipsilateral hippocampus, predicted long-term seizure freedom after surgery.

The superficial white matter in temporal lobe epilepsy: a key link between structural and functional network disruptions

Drug-resistant temporal lobe epilepsy is increasingly recognized as a system-level disorder affecting the structure and function of large-scale grey matter networks. While diffusion magnetic resonance imaging studies have demonstrated deep fibre tract alterations, the superficial white matter immediately below the cortex has so far been neglected despite its proximity to neocortical regions and key role in maintaining cortico-cortical connectivity. Using multi-modal 3 T magnetic resonance imaging, we mapped the topography of superficial white matter diffusion alterations in 61 consecutive temporal lobe epilepsy patients relative to 38 healthy controls and studied the relationship to large-scale structural as well as functional networks. Our approach continuously sampled mean diffusivity and fractional anisotropy along surfaces running 2 mm below the cortex. Multivariate statistics mapped superficial white matter diffusion anomalies in patients relative to controls, while correlation and mediation analyses evaluated their relationship to structural (cortical thickness, mesiotemporal volumetry) and functional parameters (resting state functional magnetic resonance imaging amplitude) and clinical variables. Patients presented with overlapping anomalies in mean diffusivity and anisotropy, particularly in ipsilateral temporo-limbic regions. Diffusion anomalies did not relate to cortical thinning; conversely, they mediated large-scale functional amplitude decreases in patients relative to controls in default mode hub regions (i.e. anterior and posterior midline regions, lateral temporo-parietal cortices), and were themselves mediated by hippocampal atrophy. With respect to clinical variables, we observed more marked diffusion anomalies in patients with a history of febrile convulsions and those with longer disease duration. Similarly, more marked diffusion alterations were associated with seizure-free outcome. Bootstrap analyses indicated high reproducibility of our findings, suggesting generalizability. The temporo-limbic distribution of superficial white matter anomalies, together with the mediation-level findings, suggests that this so far neglected region serves a key link between the hippocampal atrophy and large-scale default mode network alterations in temporal lobe epilepsy.

The spectrum of structural and functional imaging abnormalities in temporal lobe epilepsy

OBJECTIVE

Although most temporal lobe epilepsy (TLE) patients show marked hippocampal sclerosis (HS) upon pathological examination, 40% present with no significant cell loss but gliotic changes only. To evaluate effects of hippocampal pathology on brain structure and functional networks, we aimed at dissociating multimodal magnetic resonance imaging (MRI) characteristics in patients with HS (TLE-HS) and those with gliosis only (TLE-G).

METHODS

In 20 TLE-HS, 19 TLE-G, and 25 healthy controls, we carried out a novel MRI-based hippocampal subfield surface analysis that integrated volume, T2 signal intensity, and diffusion markers with seed-based hippocampal functional connectivity.

RESULTS

Compared to controls, TLE-HS presented with marked ipsilateral atrophy, T2 hyperintensity, and mean diffusivity increases across all subfields, whereas TLE-G presented with dentate gyrus hypertrophy, focal increases in T2 intensity and mean diffusivity. Multivariate assessment confirmed a more marked ipsilateral load of anomalies across all subfields in TLE-HS, whereas anomalies in TLE-G were restricted to the subiculum. A between-cohort dissociation was independently suggested by resting-state functional connectivity analysis, revealing marked hippocampal decoupling from anterior and posterior default mode hubs in TLE-HS, whereas TLE-G did not differ from controls. Back-projection connectivity analysis from cortical targets revealed consistently decreased network embedding across all subfields in TLE-HS, while changes in TLE-G were limited to the subiculum. Hippocampal disconnectivity strongly correlated to T2 hyperintensity and marginally to atrophy.

INTERPRETATION

Multimodal MRI reveals diverging structural and functional connectivity profiles across the TLE spectrum. Pathology-specific modulations of large-scale functional brain networks lend novel evidence for a close interplay of structural and functional disruptions in focal epilepsy. Ann Neurol 2016;80:142-153.

Substrates of metacognition on perception and metacognition on higher-order cognition relate to different subsystems of the mentalizing network

Humans have the ability to reflect upon their perception, thoughts, and actions, known as metacognition (MC). The brain basis of MC is incompletely understood, and it is debated whether MC on different processes is subserved by common or divergent networks. We combined behavioral phenotyping with multi-modal neuroimaging to investigate whether structural substrates of individual differences in MC on higher-order cognition (MC-C) are dissociable from those underlying MC on perceptual accuracy (MC-P). Motivated by conceptual work suggesting a link between MC and cognitive perspective taking, we furthermore tested for overlaps between MC substrates and mentalizing networks. In a large sample of healthy adults, individual differences in MC-C and MC-P did not correlate. MRI-based cortical thickness mapping revealed a structural basis of this independence, by showing that individual differences in MC-P related to right prefrontal cortical thickness, while MC-C scores correlated with measures in lateral prefrontal, temporo-parietal, and posterior midline regions. Surface-based superficial white matter diffusivity analysis revealed substrates resembling those seen for cortical thickness, confirming the divergence of both MC faculties using an independent imaging marker. Despite their specificity, substrates of MC-C and MC-P fell clearly within networks known to participate in mentalizing, confirmed by task-based fMRI in the same subjects, previous meta-analytical findings, and ad-hoc Neurosynth-based meta-analyses. Our integrative multi-method approach indicates domain-specific substrates of MC; despite their divergence, these nevertheless likely rely on component processes mediated by circuits also involved in mentalizing. Hum Brain Mapp 37:3388-3399, 2016. © 2016 Wiley Periodicals, Inc.

In vivo MRI signatures of hippocampal subfield pathology in intractable epilepsy

OBJECTIVES

Our aim is to assess the subfield-specific histopathological correlates of hippocampal volume and intensity changes (T1, T2) as well as diff!usion MRI markers in TLE, and investigate the efficacy of quantitative MRI measures in predicting histopathology in vivo.

EXPERIMENTAL DESIGN

We correlated in vivo volumetry, T2 signal, quantitative T1 mapping, as well as diffusion MRI parameters with histological features of hippocampal sclerosis in a subfield-specific manner. We made use of on an advanced co-registration pipeline that provided a seamless integration of preoperative 3 T MRI with postoperative histopathological data, on which metrics of cell loss and gliosis were quantitatively assessed in CA1, CA2/3, and CA4/DG.

PRINCIPAL OBSERVATIONS

MRI volumes across all subfields were positively correlated with neuronal density and size. Higher T2 intensity related to increased GFAP fraction in CA1, while quantitative T1 and diffusion MRI parameters showed negative correlations with neuronal density in CA4 and DG. Multiple linear regression analysis revealed that in vivo multiparametric MRI can predict neuronal loss in all the analyzed subfields with up to 90% accuracy.

CONCLUSION

Our results, based on an accurate co-registration pipeline and a subfield-specific analysis of MRI and histology, demonstrate the potential of MRI volumetry, diffusion, and quantitative T1 as accurate in vivo biomarkers of hippocampal pathology.

Computational analysis in epilepsy neuroimaging: A survey of features and methods

Epilepsy affects 65 million people worldwide, a third of whom have seizures that are resistant to anti-epileptic medications. Some of these patients may be amenable to surgical therapy or treatment with implantable devices, but this usually requires delineation of discrete structural or functional lesion(s), which is challenging in a large percentage of these patients.

Advances in neuroimaging and machine learning allow semi-automated detection of malformations of cortical development (MCDs), a common cause of drug resistant epilepsy. A frequently asked question in the field is what techniques currently exist to assist radiologists in identifying these lesions, especially subtle forms of MCDs such as focal cortical dysplasia (FCD) Type I and low grade glial tumors. Below we introduce some of the common lesions encountered in patients with epilepsy and the common imaging findings that radiologists look for in these patients. We then review and discuss the computational techniques introduced over the past 10 years for quantifying and automatically detecting these imaging findings. Due to large variations in the accuracy and implementation of these studies, specific techniques are traditionally used at individual centers, often guided by local expertise, as well as selection bias introduced by the varying prevalence of specific patient populations in different epilepsy centers. We discuss the need for a multi-institutional study that combines features from different imaging modalities as well as computational techniques to definitively assess the utility of specific automated approaches to epilepsy imaging. We conclude that sharing and comparing these different computational techniques through a common data platform provides an opportunity to rigorously test and compare the accuracy of these tools across different patient populations and geographical locations. We propose that these kinds of tools, quantitative imaging analysis methods and open data platforms for aggregating and sharing data and algorithms, can play a vital role in reducing the cost of care, the risks of invasive treatments, and improve overall outcomes for patients with epilepsy.

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We introduce common epileptogenic lesions encountered in patients with drug resistant epilepsy.

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We discuss state of the art computational techniques used to detect lesions.

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There is a need for multi-institutional studies that combine these techniques.

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Clinically validated pipelines alongside the advances in imaging and electrophysiology will improve outcomes.

Whole-brain MRI phenotyping in dysplasia-related frontal lobe epilepsy

OBJECTIVE

To perform whole-brain morphometry in patients with frontal lobe epilepsy and evaluate the utility of group-level patterns for individualized diagnosis and prognosis.

METHODS

We compared MRI-based cortical thickness and folding complexity between 2 frontal lobe epilepsy cohorts with histologically verified focal cortical dysplasia (FCD) (13 type I; 28 type II) and 41 closely matched controls. Pattern learning algorithms evaluated the utility of group-level findings to predict histologic FCD subtype, the side of the seizure focus, and postsurgical seizure outcome in single individuals.

RESULTS

Relative to controls, FCD type I displayed multilobar cortical thinning that was most marked in ipsilateral frontal cortices. Conversely, type II showed thickening in temporal and postcentral cortices. Cortical folding also diverged, with increased complexity in prefrontal cortices in type I and decreases in type II. Group-level findings successfully guided automated FCD subtype classification (type I: 100%; type II: 96%), seizure focus lateralization (type I: 92%; type II: 86%), and outcome prediction (type I: 92%; type II: 82%).

CONCLUSION

FCD subtypes relate to diverse whole-brain structural phenotypes. While cortical thickening in type II may indicate delayed pruning, a thin cortex in type I likely results from combined effects of seizure excitotoxicity and the primary malformation. Group-level patterns have a high translational value in guiding individualized diagnostics.

Gray matter structural compromise is equally distributed in left and right temporal lobe epilepsy

In drug-resistant temporal lobe epilepsy (TLE), MRI studies have shown consistent mesiotemporal and neocortical structural alterations when comparing patients to healthy controls. It remains, however, relatively unclear whether the side of seizure focus differentially impacts the degree of structural damage. This work performed a comprehensive surface-based analysis of mesiotemporal and neocortical morphology on preoperative 1.5 T MRI in 25/35 LTLE/RTLE patients that achieved seizure freedom after surgery (i.e., Engel-I outcome; 7 ± 2 years follow-up), an imaging-independent confirmation of focus lateralization. Compared to 46 age- and sex-matched controls, both TLE groups displayed marked ipsilateral atrophy in mesiotemporal regions, while cortical thinning was bilateral. Direct contrasts between LTLE and RTLE did not reveal significant differences. Bootstrap simulations indicated low reproducibility of observing a between-cohort difference; power analysis revealed that more than 110 patients would be necessary to detect subtle differences. No difference between LTLE and RTLE was confirmed when using voxel-based morphometry, an independent proxy of gray matter volume. Similar results were obtained analyzing a separate 3 T dataset (15/15 LTLE/RTLE patients; Engel-I after 4 ± 2 years follow-up; 42 controls). Our results strongly support equivalent gray matter compromise in left and right TLE. The morphological profile of seizure-free patients, presenting with ipsilateral mesiotemporal and bilateral cortical atrophy, motivates the development of neuromarkers of outcome that consider both mesiotemporal and neocortical structures. Hum Brain Mapp 37:515-524, 2016. © 2015 Wiley Periodicals, Inc.

Neuroimaging evaluation in refractory epilepsy

PURPOSE

To describe the application of neuroimaging analysis, compared to neuropsychological tests and video-electroencephalogram, for the evaluation of refractory epilepsy in a reference centre in Cali, Colombia.

METHODS

Between March 2013 and November 2014, 29 patients, 19 men and 10 women, aged 9-65 years and with refractory epilepsy, were assessed by structural and functional magnetic resonance imaging while performing tasks related to language, verbal and non-verbal memory. Also, volumetric evaluation was performed. A 1.5 Tesla magnetic resonance imaging scanner was used in all cases.

RESULTS

Neuroimaging evaluation identified 13 patients with mesial temporal sclerosis. The remaining patients were classified as: 10 patients with neoplastic masses, two patients with cortical atrophy, two patients with scarring lesions and two patients with non-structural aetiology. Among patients with mesial temporal sclerosis, comparison between techniques for lateralising the epileptogenic foci was made; the κ index between functional magnetic resonance imaging and hippocampi volumetry was κ=1.00, agreement between neuroimaging and video-electroencephalogram was good (κ=0.78) and comparison with a neuropsychological test was mild (κ=0.24).

CONCLUSIONS

Neuroimaging studies allow the assessment of functional and structural damage related to epileptogenic lesions and foci, and are helpful to select surgical treatment, conduct intraoperative neuronavigation techniques, predict surgical deficits and evaluate patient recovery.

Frontal gray matter abnormalities predict seizure outcome in refractory temporal lobe epilepsy patients

Developing more reliable predictors of seizure outcome following temporal lobe surgery for intractable epilepsy is an important clinical goal. In this context, we investigated patients with refractory temporal lobe epilepsy (TLE) before and after temporal resection. In detail, we explored gray matter (GM) volume change in relation with seizure outcome, using a voxel-based morphometry (VBM) approach. To do so, this study was divided into two parts. The first one involved group analysis of differences in regional GM volume between the groups (good outcome (GO), e.g., no seizures after surgery; poor outcome (PO), e.g., persistent postoperative seizures; and controls, N = 24 in each group), pre- and post-surgery. The second part of the study focused on pre-surgical data only (N = 61), determining whether the degree of GM abnormalities can predict surgical outcomes. For this second step, GM abnormalities were identified, within each lobe, in each patient when compared with an ad hoc sample of age-matched controls. For the first analysis, the results showed larger GM atrophy, mostly in the frontal lobe, in PO patients, relative to both GO patients and controls, pre-surgery. When comparing pre-to-post changes, we found relative GM gains in the GO but not in the PO patients, mostly in the non-resected hemisphere. For the second analysis, only the frontal lobe displayed reliable prediction of seizure outcome. 81% of the patients showing pre-surgical increased GM volume in the frontal lobe became seizure free, post-surgery; while 77% of the patients with pre-surgical reduced frontal GM volume had refractory seizures, post-surgery. A regression analysis revealed that the proportion of voxels with reduced frontal GM volume was a significant predictor of seizure outcome (p = 0.014). Importantly, having less than 1% of the frontal voxels with GM atrophy increased the likelihood of being seizure-free, post-surgery, by seven times. Overall, our results suggest that using pre-surgical GM abnormalities within the frontal lobe is a reliable predictor of seizure outcome post-surgery in TLE. We believe that this frontal GM atrophy captures seizure burden outside the pre-existing ictal temporal lobe, reflecting either the development of epileptogenesis or the loss of a protective, adaptive force helping to control or limit seizures. This study provides evidence of the potential of VBM-based approaches to predict surgical outcomes in refractory TLE candidates.

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Gray matter abnormalities within the frontal lobe predicts seizure outcome in TLE.

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Poor outcome patients suffer from GM atrophy in the frontal lobe, pre-surgery.

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Good outcome patients show gain of GM in the non-resected hemisphere, post-surgery.

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Frontal GM atrophy captures seizure burden outside the ictal temporal lobe.

Machine learning classification of mesial temporal sclerosis in epilepsy patients

BACKGROUND AND PURPOSE

Novel approaches applying machine-learning methods to neuroimaging data seek to develop individualized measures that will aid in the diagnosis and treatment of brain-based disorders such as temporal lobe epilepsy (TLE). Using a large cohort of epilepsy patients with and without mesial temporal sclerosis (MTS), we sought to automatically classify MTS using measures of cortical morphology, and to further relate classification probabilities to measures of disease burden.

MATERIALS AND METHODS

Our sample consisted of high-resolution T1 structural scans of 169 adults with epilepsy collected across five different 1.5T and four different 3T scanners at UCLA. We applied a multiple support vector machine recursive feature elimination algorithm to morphological measures generated from FreeSurfer's automated segmentation and parcellation in order to classify Epilepsy patients with MTS (n=85) from those without MTS (N=84).

RESULTS

In addition to hippocampal volume, we found that alterations in cortical thickness, surface area, volume and curvature in inferior frontal and anterior and inferior temporal regions contributed to a classification accuracy of up to 81% (p=1.3×10(-17)) in identifying MTS. We also found that MTS classification probabilities were associated with a longer duration of disease for epilepsy patients both with and without MTS.

CONCLUSIONS

In addition to implicating extra-hippocampal involvement of MTS, these findings shed further light on the pathogenesis of TLE and may ultimately assist in the development of automated tools that incorporate multiple neuroimaging measures to assist clinicians in detecting more subtle cases of TLE and MTS.

Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy

Recent years have witnessed a paradigm shift in the study and conceptualization of epilepsy, which is increasingly understood as a network-level disorder. An emblematic case is temporal lobe epilepsy (TLE), the most common drug-resistant epilepsy that is electroclinically defined as a focal epilepsy and pathologically associated with hippocampal sclerosis. In this review, we will summarize histopathological, electrophysiological, and neuroimaging evidence supporting the concept that the substrate of TLE is not limited to the hippocampus alone, but rather is broadly distributed across multiple brain regions and interconnecting white matter pathways. We will introduce basic concepts of graph theory, a formalism to quantify topological properties of complex systems that has recently been widely applied to study networks derived from brain imaging and electrophysiology. We will discuss converging graph theoretical evidence indicating that networks in TLE show marked shifts in their overall topology, providing insight into the neurobiology of TLE as a network-level disorder. Our review will conclude by discussing methodological challenges and future clinical applications of this powerful analytical approach.