Automated segmentation of epilepsy surgical resection cavities: Comparison of four methods to manual segmentation.

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.

Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy.

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.

Machine learning approaches for imaging-based prognostication of the outcome of surgery for mesial temporal lobe epilepsy.

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.

The effect of injection time on rates of epileptogenic zone localization using SISCOM and STATISCOM.

BACKGROUND: The identification of hyperperfusion on ictal single-photon emission computed tomography (SPECT) scan is a technique for the localization of the epileptogenic zone (EZ) in patients with focal epilepsy undergoing presurgical evaluation. The accuracy of this technique has been improved by subtraction from an interictal image and coregistration with magnetic resonance imaging (MRI) (subtraction ictal SPECT coregistered to MRI (SISCOM)), and subsequently by the development of Statistical Ictal SPECT Co-registered to MRI (STATISCOM) which is reported to further improve localization accuracy by statistically accounting for random variation between images. However, the use of ictal SPECT is limited by the necessity for rapid injection of the radiotracer. The purpose of this study was to investigate the effect of tracer injection time on EZ localization rates using both STATISCOM and SISCOM. METHODS: Consecutive patients with drug-resistant focal epilepsy who had an ictal SPECT scan while admitted to the video-electroencephalography (EEG) monitoring unit at the Royal Melbourne Hospital, Victoria, Australia, and a subsequent interictal scan, between 2009 and 2017 were included. The information collected included age, sex, seizure type, epilepsy diagnosis, and injection time. Statistical Ictal SPECT Co-registered to MRI and SISCOM images were generated and reviewed by two blinded reviewers. The rates of potential localization of the EZ, and the agreement with the EEG, were determined for each scan. Localization rates were compared between ictal scans with different radiotracer injection time windows (<30 s, 30-45 s, 45-60 s, 60-90 s, 90-120 s, >120 s). RESULTS: Seventy patients (male = 32, 16-67 years) were included in the study. Overall agreement between the primary raters was moderate for STATISCOM (k = 0.44) and SISCOM (k = 0.57). The ability of SPECT to localize the potential EZ was 69% (48/70) for STATISCOM and 59% (41/70) for SISCOM. Injection time was not associated with the rate of localizing the potential EZ for STATISCOM (p = 0.64), whereas for SISCOM there was a trend that shorter injection times were associated with better ability to localize the potential EZ (p = 0.06). Agreement between SPECT and video-EEG data was 54% (38/70) for STATISCOM and 39% (27/70) for SISCOM. Statistical Ictal SPECT Co-registered to MRI did not show any difference of agreement across injection time groups (p = 0.42) whereas SISCOM showed better agreement with video-EEG data in the earlier injection time groups (p = 0.02). No differences in agreement between SPECT and video-EEG data were seen between patients with and without MRI lesions for either STATISCOM or SISCOM. Statistical Ictal SPECT Co-registered to MRI showed significantly better agreement for temporal than extratemporal seizures, with no difference of agreement between early (<45 s) and late (>45 s) injections. CONCLUSION: Statistical Ictal SPECT Co-registered to MRI showed overall higher agreement rates with EZ localization by video-EEG than SISCOM, which was not affected by the injection times. Statistical Ictal SPECT Co-registered to MRI may provide localizing information for "late" injections where visual reads and SISCOM are inconclusive.

Substantially thinner internal granular layer and reduced molecular layer surface in the cerebellar cortex of the Tc1 mouse model of down syndrome - a comprehensive morphometric analysis with active staining contrast-enhanced MRI.

Down Syndrome is a chromosomal disorder that affects the development of cerebellar cortical lobules. Impaired neurogenesis in the cerebellum varies among different types of neuronal cells and neuronal layers. In this study, we developed an imaging analysis framework that utilizes gadolinium-enhanced ex vivo mouse brain MRI. We extracted the middle Purkinje layer of the mouse cerebellar cortex, enabling the estimation of the volume, thickness, and surface area of the entire cerebellar cortex, the internal granular layer, and the molecular layer in the Tc1 mouse model of Down Syndrome. The morphometric analysis of our method revealed that a larger proportion of the cerebellar thinning in this model of Down Syndrome resided in the inner granule cell layer, while a larger proportion of the surface area shrinkage was in the molecular layer.

Metabolic patterns and seizure outcomes following anterior temporal lobectomy.

OBJECTIVE: We investigated the relationship between the interictal metabolic patterns, the extent of resection of 18 F-fluorodeoxyglucose positron emission tomography (18 FDG-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 18 FDG-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 18 FDG-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.

Magnetic resonance spin-spin relaxation time estimation in a rat model of fatty liver disease.

PURPOSE: To compare mono- and bi-exponential relaxation model equations to discriminate between normal and fatty liver disease. MATERIALS AND METHODS: Six rats on a choline deficient amino acid modified (CDAA) diet and six on normal chow were studied. Multiple spin echo images with increasing echo times (TEs) were collected at 9.4T. Pixel-wise T2 maps were generated using mono-exponential decay function to calculate T2M , and a bi-exponential to calculate, short T2 component (T2S ), long T2 component (T2L ), and fractions of these components (ρS , ρL ), respectively. Statistical F-tests and Akaike's information criterion (AIC) were used to assess the relative performance of the two models. RESULTS: F-test and AIC showed that in the CDAA group, T2 bi-exponential model described the signal of T2 weighted imaging of the liver better than the mono-exponential model. Controls were best described by the mono-exponential model. Mean values for T2M , T2L , T2S , ρS , ρL were 31.2 ± 0.7 ms, 72.8 ± 3.3 ms, 8.2 ± 0.6 ms,71.2 ± 2.1%, 30.4 ± 1.3%, respectively, in CDAA rats, compared with 18.8 ± 0.5 ms, 32.3 ± 0.7 ms, 9.2 ± 1.8 ms, 79 ± 2%, 21.0 ± 1.1% in controls. CONCLUSION: In the fatty liver of CDAA rats we have shown that T2 weighted images fit the bi-exponential model better than mono-exponential decays thus providing a better description of the data. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:468-476.

Heritability of the network architecture of intrinsic brain functional connectivity.

The brain's functional network exhibits many features facilitating functional specialization, integration, and robustness to attack. Using graph theory to characterize brain networks, studies demonstrate their small-world, modular, and "rich-club" properties, with deviations reported in many common neuropathological conditions. Here we estimate the heritability of five widely used graph theoretical metrics (mean clustering coefficient (γ), modularity (Q), rich-club coefficient (ϕnorm), global efficiency (λ), small-worldness (σ)) over a range of connection densities (k=5-25%) in a large cohort of twins (N=592, 84 MZ and 89 DZ twin pairs, 246 single twins, age 23 ± 2.5). We also considered the effects of global signal regression (GSR). We found that the graph metrics were moderately influenced by genetic factors h(2) (γ=47-59%, Q=38-59%, ϕnorm=0-29%, λ=52-64%, σ=51-59%) at lower connection densities (≤ 15%), and when global signal regression was implemented, heritability estimates decreased substantially h(2) (γ=0-26%, Q=0-28%, ϕnorm=0%, λ=23-30%, σ=0-27%). Distinct network features were phenotypically correlated (|r|=0.15-0.81), and γ, Q, and λ were found to be influenced by overlapping genetic factors. Our findings suggest that these metrics may be potential endophenotypes for psychiatric disease and suitable for genetic association studies, but that genetic effects must be interpreted with respect to methodological choices.

Role of the glymphatic system and perivascular spaces as a potential biomarker for post-stroke epilepsy.

Stroke is one of the most common causes of acquired epilepsy, which can also result in disability and increased mortality rates particularly in elderly patients. No preventive treatment for post-stroke epilepsy is currently available. Development of such treatments has been greatly limited by the lack of biomarkers to reliably identify high-risk patients. The glymphatic system, including perivascular spaces (PVS), is the brain's waste clearance system, and enlargement or asymmetry of PVS (ePVS) is hypothesized to play a significant role in the pathogenesis of several neurological conditions. In this article, we discuss potential mechanisms for the role of perivascular spaces in the development of post-stroke epilepsy. Using advanced MR-imaging techniques, it has been shown that there is asymmetry and impairment of glymphatic function in the setting of ischemic stroke. Furthermore, studies have described a dysfunction of PVS in patients with different focal and generalized epilepsy syndromes. It is thought that inflammatory processes involving PVS and the blood-brain barrier, impairment of waste clearance, and sustained hypertension affecting the glymphatic system during a seizure may play a crucial role in epileptogenesis post-stroke. We hypothesize that impairment of the glymphatic system and asymmetry and dynamics of ePVS in the course of a stroke contribute to the development of PSE. Automated ePVS detection in stroke patients might thus assist in the identification of high-risk patients for post-stroke epilepsy trials. PLAIN LANGUAGE SUMMARY: Stroke often leads to epilepsy and is one of the main causes of epilepsy in elderly patients, with no preventative treatment available. The brain's waste removal system, called the glymphatic system which consists of perivascular spaces, may be involved. Enlargement or asymmetry of perivascular spaces could play a role in this and can be visualised with advanced brain imaging after a stroke. Detecting enlarged perivascular spaces in stroke patients could help identify those at risk for post-stroke epilepsy.

Association of Localizing 18F-FDG-PET Hypometabolism and Outcome Following Epilepsy Surgery: Systematic Review and Meta-Analysis.

BACKGROUND AND OBJECTIVES: Although commonly used in the evaluation of patients for epilepsy surgery, the association between the detection of localizing 18fluorine fluorodeoxyglucose PET (18F-FDG-PET) hypometabolism and epilepsy surgery outcome is uncertain. We conducted a systematic review and meta-analysis to determine whether localizing 18F-FDG-PET hypometabolism is associated with favorable outcome after epilepsy surgery. METHODS: A systematic literature search was undertaken. Eligible publications included evaluation with 18F-FDG-PET before epilepsy surgery, with ≥10 participants, and those that reported surgical outcome at ≥12 months. Random-effects meta-analysis was used to calculate the odds of achieving a favorable outcome, defined as Engel class I, International League Against Epilepsy class 1-2, or seizure-free, with localizing 18F-FDG-PET hypometabolism, defined as concordant with the epilepsy surgery resection zone. Meta-regression was used to characterize sources of heterogeneity. RESULTS: The database search identified 8,916 studies, of which 98 were included (total patients n = 4,104). Localizing 18F-FDG-PET hypometabolism was associated with favorable outcome after epilepsy surgery for all patients with odds ratio (OR) 2.68 (95% CI 2.08-3.45). Subgroup analysis yielded similar findings for those with (OR 2.64, 95% CI 1.54-4.52) and without epileptogenic lesion detected on MRI (OR 2.49, 95% CI 1.80-3.44). Concordance with EEG (OR 2.34, 95% CI 1.43-3.83), MRI (OR 1.69, 95% CI 1.19-2.40), and triple concordance with both (OR 2.20, 95% CI 1.32-3.64) was associated with higher odds of favorable outcome. By contrast, diffuse 18F-FDG-PET hypometabolism was associated with worse outcomes compared with focal hypometabolism (OR 0.34, 95% CI 0.22-0.54). DISCUSSION: Localizing 18F-FDG-PET hypometabolism is associated with favorable outcome after epilepsy surgery, irrespective of the presence of an epileptogenic lesion on MRI. The extent of 18F-FDG-PET hypometabolism provides additional information, with diffuse hypometabolism associated with worse surgical outcome than focal 18F-FDG-PET hypometabolism. These findings support the incorporation of 18F-FDG-PET into routine noninvasive investigations for patients being evaluated for epilepsy surgery to improve epileptogenic zone localization and to aid patient selection for surgery.

A WORLDWIDE ENIGMA STUDY ON EPILEPSY-RELATED GRAY AND WHITE MATTER COMPROMISE ACROSS THE ADULT LIFESPAN.

Objectives: Temporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of grey and white matter structures. Evidence supports a progressive condition although the temporal evolution of TLE is poorly defined. This ENIGMA-Epilepsy study utilized multimodal magnetic resonance imaging (MRI) data to investigate structural alterations in TLE patients across the adult lifespan. We charted both grey and white matter changes and explored the covariance of age-related alterations in both compartments. Methods: We studied 769 TLE patients and 885 healthy controls across an age range of 17-73 years, from multiple international sites. To assess potentially non-linear lifespan changes in TLE, we harmonized data and combined median split assessments with cross-sectional sliding window analyses of grey and white matter age-related changes. Covariance analyses examined the coupling of grey and white matter lifespan curves. Results: In TLE, age was associated with a robust grey matter thickness/volume decline across a broad cortico-subcortical territory, extending beyond the mesiotemporal disease epicentre. White matter changes were also widespread across multiple tracts with peak effects in temporo-limbic fibers. While changes spanned the adult time window, changes accelerated in cortical thickness, subcortical volume, and fractional anisotropy (all decreased), and mean diffusivity (increased) after age 55 years. Covariance analyses revealed strong limbic associations between white matter tracts and subcortical structures with cortical regions. Conclusions: This study highlights the profound impact of TLE on lifespan changes in grey and white matter structures, with an acceleration of aging-related processes in later decades of life. Our findings motivate future longitudinal studies across the lifespan and emphasize the importance of prompt diagnosis as well as intervention in patients.

Reduced total number of enlarged perivascular spaces in post-traumatic epilepsy patients with unilateral lesions - a feasibility study.

BACKGROUND: We investigated the value of automated enlarged perivascular spaces (ePVS) quantification to distinguish chronic traumatic brain injury (TBI) patients with post-traumatic epilepsy (PTE+) from chronic TBI patients without PTE (PTE-) in a feasibility study. METHODS: Patients with and without PTE were recruited and underwent an MRI post-TBI. Multimodal auto identification of ePVS algorithm was applied to T1-weighted MRIs to segment ePVS. The total number of ePVS was calculated and corrected for white matter volume, and an asymmetry index (AI) derived. RESULTS: PTE was diagnosed in 7 out of the 99 participants (male=69) after a median time of less than one year since injury (range 10-22). Brain lesions were observed in all 7 PTE+ cases (unilateral=4, 57%; bilateral=3, 43%) as compared to 40 PTE- cases (total 44%; unilateral=17, 42%; bilateral=23, 58%). There was a significant difference between PTE+ (M=1.21e-4, IQR [8.89e-5]) and PTE- cases (M=2.79e-4, IQR [6.25e-5]) in total corrected numbers of ePVS in patients with unilateral lesions (p=0.024). No differences in AI, trauma severity and lesion volume were seen between groups. CONCLUSION: This study has shown that automated quantification of ePVS is feasible and provided initial evidence that individuals with PTE with unilateral lesions may have fewer ePVS compared to TBI patients without epilepsy. Further studies with larger sample sizes should be conducted to determine the value of ePVS quantification as a PTE-biomarker.

Associations of Enlarged Perivascular Spaces With Brain Lesions, Brain Age, and Clinical Outcomes in Chronic Traumatic Brain Injury.

BACKGROUND AND OBJECTIVES: Enlarged perivascular spaces (ePVS) have been identified as a key signature of glymphatic system dysfunction in neurologic conditions. The incidence and clinical implications of ePVS after traumatic brain injury (TBI) are not yet understood. We investigated whether individuals with chronic moderate-to-severe TBI had an increased burden of ePVS and whether ePVS burden is modulated by the presence of focal lesions, older brain age, and poorer sleep quality. We examined whether an increased burden of ePVS was associated with poorer cognitive and emotional outcomes. METHODS: Using a cross-sectional design, participants with a single moderate-to-severe chronic TBI (sustained ≥10 years ago) were recruited from an inpatient rehabilitation program. Control participants were recruited from the community. Participants underwent 3T brain MRI, neuropsychological assessment, and clinical evaluations. ePVS burden in white matter was quantified using automated segmentation. The relationship between the number of ePVS, group membership, focal lesions, brain age, current sleep quality, and outcome was modeled using negative binomial and linear regressions. RESULTS: This study included 100 participants with TBI (70% male; mean age = 56.8 years) and 75 control participants (54.3% male; mean age = 59.8 years). The TBI group had a significantly greater burden of ePVS (prevalence ratio rate [PRR] = 1.29, p = 0.013, 95% CI 1.05-1.57). The presence of bilateral lesions was associated with greater ePVS burden (PRR = 1.41, p = 0.021, 95% CI 1.05-1.90). There was no association between ePVS burden, sleep quality (PRR = 1.01, p = 0.491, 95% CI 0.98-1.048), and sleep duration (PRR = 1.03, p = 0.556, 95% CI 0.92-1.16). ePVS was associated with verbal memory (ß = -0.42, p = 0.006, 95% CI -0.72 to -0.12), but not with other cognitive domains. The burden of ePVS was not associated with emotional distress (ß = -0.70, p = 0.461, 95% CI -2.57 to 1.17) or brain age (PRR = 1.00, p = 0.665, 95% CI 0.99-1.02). DISCUSSION: TBI is associated with a greater burden of ePVS, especially when there have been bilateral brain lesions. ePVS was associated with reduced verbal memory performance. ePVS may indicate ongoing impairments in glymphatic system function in the chronic postinjury period.

A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging.

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.

Perivascular spaces as a potential biomarker of Alzheimer's disease.

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-ß 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-ß 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-ß. 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.

Asymmetric distribution of enlarged perivascular spaces in centrum semiovale may be associated with epilepsy after acute ischemic stroke.

OBJECTIVE: To investigate the factors influencing enlarged perivascular space (EPVS) characteristics at the onset of acute ischemic stroke (AIS), and whether the PVS characteristics can predict later post-stroke epilepsy (PSE). METHODS: A total of 312 patients with AIS were identified, of whom 58/312 (18.6%) developed PSE. Twenty healthy participants were included as the control group. The number of PVS in the basal ganglia (BG), centrum semiovale (CS), and midbrain (MB) was manually calculated on T2 -weighted MRI. The scores and asymmetry index (AI) of EPVS in each region were compared among the enrolled participants. Other potential risk factors for PSE were also analyzed, including NIHSS at admission and stroke etiologies. RESULTS: The EPVS scores were significantly higher in the bilateral BG and CS of AIS patients compared to those of the control group (both p < 0.01). No statistical differences in EPVS scores in BG, CS, and MB were obtained between the PSE group and the nonepilepsy AIS group (all p > 0.01). However, markedly different AI scores in CS were found between the PSE group and the nonepilepsy AIS group (p = 0.004). Multivariable analysis showed that high asymmetry index of EPVS (AI≥0.2) in CS was an independent predictor for PSE (OR = 3.7, 95% confidence interval 1.5-9.1, p = 0.004). CONCLUSIONS: Asymmetric distribution of EPVS in CS may be an independent risk factor and a novel imaging biomarker for the development of PSE. Further studies to understand the mechanisms of this association and confirmation with larger patient populations are warranted.

Perivascular spaces as a marker of disease severity and neurodegeneration in patients with behavioral variant frontotemporal dementia.

Background: Behavioural Variant Frontotemporal Dementia (bvFTD) is a rapidly progressing neurodegenerative proteinopathy. Perivascular spaces (PVS) form a part of the brain's glymphatic clearance system. When enlarged due to poor glymphatic clearance of toxic proteins, PVS become larger and more conspicuous on MRI. Therefore, enlarged PVS may be a useful biomarker of disease severity and progression in neurodegenerative proteinopathies such as bvFTD. This study aimed to determine the utility of PVS as a biomarker of disease progression in patients with bvFTD. Materials and methods: Serial baseline and week 52 MRIs acquired from ten patients with bvFTD prospectively recruited and followed in a Phase 1b open label trial of sodium selenate for bvFTD were used in this study. An automated algorithm quantified PVS on MRI, which was visually inspected and validated by a member of the study team. The number and volume of PVS were extracted and mixed models used to assess the relationship between PVS burden and other measures of disease (cognition, carer burden scale, protein biomarkers). Additional exploratory analysis investigated PVS burden in patients who appeared to not progress over the 12 months of selenate treatment (i.e., "non-progressors"). Results: Overall, PVS cluster number (ß = -3.27, CI [-7.80 - 1.27], p = 0.267) and PVS volume (ß = -36.8, CI [-84.9 - 11.3], p = 0.171) did not change over the paired MRI scans 12 months apart. There was association between cognition total composite scores and the PVS burden (PVS cluster ß = -0.802e-3, CI [9.45e - 3 - -6.60e - 3, p ≤ 0.001; PVS volume ß = -1.30e - 3, CI [-1.55e - 3 - -1.05e - 3], p ≤ 0.001), as well as between the change in the cognition total composite score and the change in PVS volume (ß = 4.36e - 3, CI [1.33e - 3 - 7.40e - 3], p = 0.046) over the trial period. There was a significant association between CSF t-tau and the number of PVS clusters (ß = 2.845, CI [0.630 - 5.06], p = 0.036). Additionally, there was a significant relationship between the change in CSF t-tau and the change in the number of PVS (ß = 1.54, CI [0.918 - 2.16], p < 0.001) and PVS volume (ß = 13.8, CI [6.37 - 21.1], p = 0.003) over the trial period. An association was found between the change in NfL and the change in PVS volume (ß = 1.40, CI [0.272 - 2.52], p = 0.045) over time. Within the "non-progressor" group (n = 7), there was a significant relationship between the change in the CSF total-tau (t-tau) levels and the change in the PVS burden (PVS cluster (ß = 1.46, CI [0.577 - 2.34], p = 0.014; PVS volume ß = 14.6, CI [3.86 - 25.4], p = 0.032) over the trial period. Additionally, there was evidence of a significant relationship between the change in NfL levels and the change in the PVS burden over time (PVS cluster ß = 0.296, CI [0.229 - 0.361], p ≤ 0.001; PVS volume ß = 3.67, CI [2.42 - 4.92], p = 0.002). Conclusion: Analysis of serial MRI scans 12 months apart in patients with bvFTD demonstrated a relationship between PVS burden and disease severity as measured by the total cognitive composite score and CSF t-tau. Further studies are needed to confirm PVS as a robust marker of neurodegeneration in proteinopathies.

18F-FDG-PET hypometabolism as a predictor of favourable outcome in epilepsy surgery: protocol for a systematic review and meta-analysis.

INTRODUCTION: A substantial proportion of patients who undergo surgery for drug resistant focal epilepsy do not become seizure free. While some factors, such as the detection of hippocampal sclerosis or a resectable lesion on MRI and electroencephalogram-MRI concordance, can predict favourable outcomes in epilepsy surgery, the prognostic value of the detection of focal hypometabolism with 18F-fluorodeoxyglucose positive emission tomography (18F-FDG-PET) hypometabolism is uncertain. We propose a protocol for a systematic review and meta-analysis to examine whether localisation with 18F-FDG-PET hypometabolism predicts favourable outcomes in epilepsy surgery. METHODS AND ANALYSIS: A systematic literature search of Medline, Embase and Web of Science will be undertaken. Publications which include evaluation with 18F-FDG-PET prior to surgery for drug resistant focal epilepsy, and which report ≥12 months of postoperative surgical outcome data will be included. Non-human, non-English language publications, publications with fewer than 10 participants and unpublished data will be excluded. Screening and full-text review of publications for inclusion will be undertaken by two independent investigators, with discrepancies resolved by consensus or a third investigator. Data will be extracted and pooled using random effects meta-analysis, with heterogeneity quantified using the I2 analysis. ETHICS AND DISSEMINATION: Ethics approval is not required. Once complete, the systematic review will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42022324823.

Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression.

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.

Longitudinal changes of focal cortical glucose hypometabolism in adults with chronic drug resistant temporal lobe epilepsy.

A high proportion of patients with drug-resistant temporal lobe epilepsy (TLE) show focal relative hypometabolism in the region of the epileptogenic zone on [18F]-Fluorodeoxyglucose positron emission tomography (FDG PET). However, whether focal (hypo)metabolism changes over time has not been well studied. We analysed repeated [18F]-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 [18F]-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 [18F]-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 [18F]-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. [18F]-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.