Medicine (RMH) - Research Publications
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ItemTopographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy(OXFORD UNIV PRESS, 2022-03-25)Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of temporal lobe epilepsy and may inform future discovery and validation of complementary MRI biomarkers in temporal lobe epilepsy.
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ItemMachine learning approaches for imaging-based prognostication of the outcome of surgery for mesial temporal lobe epilepsy(WILEY, 2022-05)OBJECTIVES: Around 30% of patients undergoing surgical resection for drug-resistant mesial temporal lobe epilepsy (MTLE) do not obtain seizure freedom. Success of anterior temporal lobe resection (ATLR) critically depends on the careful selection of surgical candidates, aiming at optimizing seizure freedom while minimizing postoperative morbidity. Structural MRI and FDG-PET neuroimaging are routinely used in presurgical assessment and guide the decision to proceed to surgery. In this study, we evaluate the potential of machine learning techniques applied to standard presurgical MRI and PET imaging features to provide enhanced prognostic value relative to current practice. METHODS: Eighty two patients with drug resistant MTLE were scanned with FDG-PET pre-surgery and T1-weighted MRI pre- and postsurgery. From these images the following features of interest were derived: volume of temporal lobe (TL) hypometabolism, % of extratemporal hypometabolism, presence of contralateral TL hypometabolism, presence of hippocampal sclerosis, laterality of seizure onset volume of tissue resected and % of temporal lobe hypometabolism resected. These measures were used as predictor variables in logistic regression, support vector machines, random forests and artificial neural networks. RESULTS: In the study cohort, 24 of 82 (28.3%) who underwent an ATLR for drug-resistant MTLE did not achieve Engel Class I (i.e., free of disabling seizures) outcome at a minimum of 2 years of postoperative follow-up. We found that machine learning approaches were able to predict up to 73% of the 24 ATLR surgical patients who did not achieve a Class I outcome, at the expense of incorrect prediction for up to 31% of patients who did achieve a Class I outcome. Overall accuracies ranged from 70% to 80%, with an area under the receiver operating characteristic curve (AUC) of .75-.81. We additionally found that information regarding overall extent of both total and significantly hypometabolic tissue resected was crucial to predictive performance, with AUC dropping to .59-.62 using presurgical information alone. Incorporating the laterality of seizure onset and the choice of machine learning algorithm did not significantly change predictive performance. SIGNIFICANCE: Collectively, these results indicate that "acceptable" to "good" patient-specific prognostication for drug-resistant MTLE surgery is feasible with machine learning approaches utilizing commonly collected imaging modalities, but that information on the surgical resection region is critical for optimal prognostication.
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Item7T Magnetic Resonance Imaging Quantification of Brain Glutamate in Acute Ischaemic Stroke(Korean Stroke Society, 2021-05-01)
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ItemSeven-tesla quantitative magnetic resonance spectroscopy of glutamate, γ-aminobutyric acid, and glutathione in the posterior cingulate cortex/precuneus in patients with epilepsy(WILEY, 2020-12)OBJECTIVE: The posterior cingulate cortex (PCC)/precuneus is a key hub of the default mode network, whose function is known to be altered in epilepsy. Glutamate and γ-aminobutyric acid (GABA) are the main excitatory and inhibitory neurotransmitters in the central nervous system, respectively. Glutathione (GSH) is the most important free radical scavenging compound in the brain. Quantification of these molecules by magnetic resonance spectroscopy (MRS) up to 4 T is limited by overlapping resonances from other molecules. In this study, we used ultra-high-field (7 T) MRS to quantify their concentrations in patients with different epilepsy syndromes. METHODS: Nineteen patients with temporal lobe epilepsy (TLE) and 16 with idiopathic generalized epilepsy (IGE) underwent magnetic resonance imaging scans using a 7-T research scanner. Single-voxel (8 cm3 ) MRS, located in the PCC/precuneus, was acquired via stimulated echo acquisition mode. Their results were compared to 10 healthy volunteers. RESULTS: Mean concentrations of glutamate, GABA, and the glutamate/GABA ratio did not differ between the IGE, TLE, and healthy volunteer groups. The mean ± SD concentration of GSH was 1.9 ± 0.3 mmol·L-1 in healthy controls, 2.0 ± 0.2 mmol·L-1 in patients with TLE, and 2.2 ± 0.4 mmol·L-1 in patients with IGE. One-way analysis of variance with post hoc Tukey-Kramer test revealed a significant difference in the concentration of GSH between patients with IGE and controls (P = .03). Short-term seizure freedom in patients with epilepsy was predicted by an elevated concentration of glutamate in the PCC/precuneus (P = .01). In patients with TLE, the concentration of GABA declined with age (P = .03). SIGNIFICANCE: Patients with IGE have higher concentrations of GSH in the PCC/precuneus than healthy controls. There is no difference in the concentrations of glutamate and GABA, or their ratio, in the PCC/precuneus between patients with IGE, patients with TLE, and healthy controls. Measuring the concentration of glutamate in the PCC/precuneus may assist with predicting drug response.
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ItemResting-state functional connectivity and quantitation of glutamate and GABA of the PCC/precuneus by magnetic resonance spectroscopy at 7T in healthy individuals(PUBLIC LIBRARY SCIENCE, 2020-12-29)The default mode network (DMN) is the main large-scale network of the resting brain and the PCC/precuneus is a major hub of this network. Glutamate and GABA (γ-amino butyric acid) are the main excitatory and inhibitory neurotransmitters in the CNS, respectively. We studied glutamate and GABA concentrations in the PCC/precuneus via magnetic resonance spectroscopy (MRS) at 7T in relation to age and correlated them with functional connectivity between this region and other DMN nodes in ten healthy right-handed volunteers ranging in age between 23-68 years. Mean functional connectivity of the PCC/precuneus to the other DMN nodes and the glutamate/GABA ratio significantly correlated with age (r = 0.802, p = 0.005 and r = 0.793, p = 0.006, respectively) but not with each other. Glutamate and GABA alone did not significantly correlate with age nor with functional connectivity within the DMN. The glutamate/GABA ratio and functional connectivity of the PCC/precuneus are, therefore, independent age-related biomarkers of the DMN and may be combined in a multimodal pipeline to study DMN alterations in various disease states.
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ItemReproducibility of Glutamate, Glutathione, and GABA Measurementsin vivoby Single-Voxel STEAM Magnetic Resonance Spectroscopy at 7-Tesla in Healthy Individuals(FRONTIERS MEDIA SA, 2020-09-15)BACKGROUND AND PURPOSE: Derangements in brain glutamate, glutathione, and γ-amino butyric acid (GABA) are implicated in a range of neurological disorders. Reliable methods to measure these compounds non-invasively in vivo are needed. We evaluated the reproducibility of their measurements in brain regions involved in the default mode network using quantitative MRS at 7-Tesla in healthy individuals. METHODS: Ten right-handed healthy volunteers underwent 7-Tesla MRI scans on 2 separate days, not more than 2 weeks apart. On each day two scanning sessions took place, with a re-positioning break in between. High-resolution isotropic anatomical scans were acquired prior to each scan, followed by single-voxel 1H-MRS using the STEAM pulse sequence on an 8 mL midline cubic voxel, positioned over the posterior cingulate and precuneus regions. Concentrations were corrected for partial-volume effects. RESULTS: Maximal Cramér-Rao lower bounds for glutamate, glutathione, and GABA were 2.0, 8.0, and 14.0%, respectively. Mean coefficients of variation within sessions were 5.9 ± 4.8%, 9.3 ± 7.6%, and 11.5 ± 8.8%, and between sessions were 4.6 ± 4.5%, 8.3 ± 5.7%, and 9.2 ± 8.7%, respectively. The mean (±SD) Dice's coefficient for voxel overlap was 90 ± 4% within sessions and 86 ± 7% between sessions. CONCLUSION: Glutamate, glutathione, and GABA can be reliably quantified using STEAM MRS at 7-Tesla from the posterior cingulate and precuneus cortices of healthy human subjects. STEAM MRS at 7-Tesla may be used to study the metabolic behavior of this important resting-state hub in various disease states.
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ItemThe ENIGMA-Epilepsy working group: Mapping disease from large data sets(WILEY, 2022-01)Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy.