•The SPM-based automated epilepsy surgery segmentation tools performed better than the deep learning-based tools on our mixed cohort of subjects who had either temporal or extratemporal epilepsy ...surgery.•All four tools performed similarly well on the temporal epilepsy subgroup.•The accuracy of each model improved as the size of the resection cavity increased.•Quality control is an important step when implementing the tools, as no algorithm was able to segment every epilepsy surgery resection cavity.
Accurate resection cavity segmentation on MRI is important for neuroimaging research involving epilepsy surgical outcomes. Manual segmentation, the gold standard, is highly labour intensive. Automated pipelines are an efficient potential solution; however, most have been developed for use following temporal epilepsy surgery. Our aim was to compare the accuracy of four automated segmentation pipelines following surgical resection in a mixed cohort of subjects following temporal or extra temporal epilepsy surgery. We identified 4 open-source automated segmentation pipelines. Epic-CHOP and ResectVol utilise SPM-12 within MATLAB, while Resseg and Deep Resection utilise 3D U-net convolutional neural networks. We manually segmented the resection cavity of 50 consecutive subjects who underwent epilepsy surgery (30 temporal, 20 extratemporal). We calculated Dice similarity coefficient (DSC) for each algorithm compared to the manual segmentation. No algorithm identified all resection cavities. ResectVol (n = 44, 88 %) and Epic-CHOP (n = 42, 84 %) were able to detect more resection cavities than Resseg (n = 22, 44 %, P < 0.001) and Deep Resection (n = 23, 46 %, P < 0.001). The SPM-based pipelines (Epic-CHOP and ResectVol) performed better than the deep learning-based pipelines in the overall and extratemporal surgery cohorts. In the temporal cohort, the SPM-based pipelines had higher detection rates, however there was no difference in the accuracy between methods. These pipelines could be applied to machine learning studies of outcome prediction to improve efficiency in pre-processing data, however human quality control is still required.
Alzheimer’s disease (AD) is a highly damaging disease that affects one’s cognition and memory and presents an increasing societal and economic burden globally. Considerable research has gone into ...understanding AD; however, there is still a lack of effective biomarkers that aid in early diagnosis and intervention. The recent discovery of the glymphatic system and associated Perivascular Spaces (PVS) has led to the theory that enlarged PVS (ePVS) may be an indicator of AD progression and act as an early diagnostic marker. Visible on Magnetic Resonance Imaging (MRI), PVS appear to enlarge when known biomarkers of AD, amyloid-β and tau, accumulate. The central goal of ePVS and AD research is to determine when ePVS occurs in AD progression and if ePVS are causal or epiphenomena. Furthermore, if ePVS are indeed causative, interventions promoting glymphatic clearance are an attractive target for research. However, it is necessary first to ascertain where on the pathological progression of AD ePVS occurs. This review aims to examine the knowledge gap that exists in understanding the contribution of ePVS to AD. It is essential to understand whether ePVS in the brain correlate with increased regional tau distribution and global or regional Amyloid-β distribution and to determine if these spaces increase proportionally over time as individuals experience neurodegeneration. This review demonstrates that ePVS are associated with reduced glymphatic clearance and that this reduced clearance is associated with an increase in amyloid-β. However, it is not yet understood if ePVS are the outcome or driver of protein accumulation. Further, it is not yet clear if ePVS volume and number change longitudinally. Ultimately, it is vital to determine early diagnostic criteria and early interventions for AD to ease the burden it presents to the world; ePVS may be able to fulfill this role and therefore merit further research.
Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This ...multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes.
The glymphatic system is responsible for waste clearance in the brain. It is comprised of perivascular spaces (PVS) that surround penetrating blood vessels. These spaces are filled with cerebrospinal ...fluid and interstitial fluid, and can be seen with magnetic resonance imaging. Various algorithms have been developed to automatically label these spaces in MRI. This has enabled volumetric and morphological analyses of PVS in healthy and disease cohorts. However, there remain inconsistencies between PVS measures reported by different methods of automated segmentation. The present review emphasizes that importance of voxel-wise evaluation of model performance, mainly with the Sørensen Dice similarity coefficient. Conventional count correlations for model validation are inadequate if the goal is to assess volumetric or morphological measures of PVS. The downside of voxel-wise evaluation is that it requires manual segmentations that require large amounts of time to produce. One possible solution is to derive these semi-automatically. Additionally, recommendations are made to facilitate rigorous development and validation of automated PVS segmentation models. In the application of automated PVS segmentation tools, publication of image quality metrics, such as the contrast-to-noise ratio, alongside descriptive statistics of PVS volumes and counts will facilitate comparability between studies. Lastly, a head-to-head comparison between two algorithms, applied to two cohorts of astronauts reveals how results can differ substantially between techniques.
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.
(1) Background: 18FFlumazenil 1 (18FFMZ) is an established positron emission tomography (PET) radiotracer for the imaging of the gamma-aminobutyric acid (GABA) receptor subtype, GABAA in the brain. ...The production of 18FFMZ 1 for its clinical use has proven to be challenging, requiring harsh radiochemical conditions, while affording low radiochemical yields. Fully characterized, new methods for the improved production of 18FFMZ 1 are needed. (2) Methods: We investigate the use of late-stage copper-mediated radiofluorination of aryl stannanes to improve the production of 18FFMZ 1 that is suitable for clinical use. Mass spectrometry was used to identify the chemical by-products that were produced under the reaction conditions. (3) Results: The radiosynthesis of 18FFMZ 1 was fully automated using the iPhase FlexLab radiochemistry module, affording a 22.2 ± 2.7% (n = 5) decay-corrected yield after 80 min. 18FFMZ 1 was obtained with a high radiochemical purity (>98%) and molar activity (247.9 ± 25.9 GBq/µmol). (4) Conclusions: The copper-mediated radiofluorination of the stannyl precursor is an effective strategy for the production of clinically suitable 18FFMZ 1.
Objective
We investigated the relationship between the interictal metabolic patterns, the extent of resection of 18F‐fluorodeoxyglucose positron emission tomography (18FDG‐PET) hypometabolism, and ...seizure outcomes in patients with unilateral drug‐resistant mesial temporal lobe epilepsy (MTLE) following anterior temporal lobe (TL) resection.
Methods
Eighty‐two patients with hippocampal sclerosis or normal magnetic resonance imaging (MRI) findings, concordant 18FDG‐PET hypometabolism, and at least 2 years of postoperative follow‐up were included in this 2‐center study. The hypometabolic regions in each patient were identified with reference to 20 healthy controls (p < 0.005). The resected TL volume and the volume of resected TL PET hypometabolism (TLH) were calculated from the pre‐ and postoperative MRI scans coregistered with interictal 18FDG‐PET.
Results
Striking differences in metabolic patterns were observed depending on the lateralization of the epileptogenic TL. The extent of the ipsilateral TLH was significantly greater in left MTLE patients (p < 0.001), whereas right MTLE patients had significantly higher rates of contralateral (CTL) TLH (p = 0.016). In right MTLE patients, CTL hypometabolism was the strongest predictor of an unfavorable seizure outcome, associated with a 5‐fold increase in the likelihood of seizure recurrence (odds ratio OR = 4.90, 95% confidence interval CI = 1.07–22.39, p = 0.04). In left MTLE patients, greater extent of resection of ipsilateral TLH was associated with lower rates of seizure recurrence (p = 0.004) in univariate analysis; however, its predictive value did not reach statistical significance (OR = 0.96, 95% CI = 0.90–1.02, p = 0.19).
Interpretation
The difference in metabolic patterns depending on the lateralization of MTLE may represent distinct epileptic networks in patients with right versus left MTLE, and can guide preoperative counseling and surgical planning. Ann Neurol 2019; 1–10 ANN NEUROL 2019;85:241–250.
A high proportion of patients with drug-resistant temporal lobe epilepsy (TLE) show focal relative hypometabolism in the region of the epileptogenic zone on
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F-Fluorodeoxyglucose positron emission ...tomography (FDG PET). However, whether focal (hypo)metabolism changes over time has not been well studied. We analysed repeated
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F-FDG PET scans of patients with TLE to determine longitudinal changes in glucose metabolism. Adults (
n
= 16; 9 female, 7 male) diagnosed with drug resistant chronic TLE were assessed. Each patient had two
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F-FDG PET scans that were 2–95 months apart. Region-of-interest analysis was performed on MR images onto which PET scans were coregistered to determine the relative
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F-FDG uptake (normalised to pons) in the bilateral hippocampi and temporal lobes. Statistical Parametric Mapping analysis investigated global voxel-wise changes in relative metabolism between timepoints. Normalised
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F-FDG uptake did not change with time in the ipsilateral (baseline 1.14 ± 0.03, follow-up 1.19 ± -0.04) or contralateral hippocampus (baseline 1.18 ± 0.03, follow-up 1.19 ± 0.03). Uptake in the temporal neocortex also remained stable (ipsilateral baseline 1.35 ± 0.03, follow-up 1.30 ± 0.04; contralateral baseline 1.38 ± 0.04, follow-up 1.33 ± 0.03). The was no relationship between change in uptake on the repeated scans and the time between the scans. SPM analysis showed increases in metabolism in the ipsilateral temporal lobe in 2/16 patients. No areas of decreased metabolism concordant to the epileptogenic zone were identified.
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F-FDG uptake showed no significant changes over time in patients with drug-resistant TLE. This suggests that repeating FDG-PET scans in patients with subtle or no hypometabolism is of low clinical yield.
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