Amygdala enlargement in temporal lobe epilepsy: Histopathology and surgical outcomes.

OBJECTIVES: Amygdala enlargement is detected on magnetic resonance imaging (MRI) in some patients with drug-resistant temporal lobe epilepsy (TLE), but its clinical significance remains uncertain We aimed to assess if the presence of amygdala enlargement (1) predicted seizure outcome following anterior temporal lobectomy with amygdalohippocampectomy (ATL-AH) and (2) was associated with specific histopathological changes. METHODS: This was a case-control study. We included patients with drug-resistant TLE who underwent ATL-AH with and without amygdala enlargement detected on pre-operative MRI. Amygdala volumetry was done using FreeSurfer for patients who had high-resolution T1-weighted images. Mann-Whitney U test was used to compare pre-operative clinical characteristics between the two groups. The amygdala volume on the epileptogenic side was compared to the amygdala volume on the contralateral side among cases and controls. Then, we used a two-sample, independent t test to compare the means of amygdala volume differences between cases and controls. The chi-square test was used to assess the correlation of amygdala enlargement with (1) post-surgical seizure outcomes and (2) histopathological changes. RESULTS: Nineteen patients with and 19 patients without amygdala enlargement were studied. Their median age at surgery was 38 years for cases and 39 years for controls, and 52.6% were male. There were no statistically significant differences between the two groups in their pre-operative clinical characteristics. There were significant differences in the means of volume difference between cases and controls (Diff = 457.2 mm3, 95% confidence interval [CI] 289.6-624.8; p < .001) and in the means of percentage difference (p < .001). However, there was no significant association between amygdala enlargement and surgical outcome (p = .72) or histopathological changes (p = .63). SIGNIFICANCE: The presence of amygdala enlargement on the pre-operative brain MRI in patients with TLE does not affect the surgical outcome following ATL-AH, and it does not necessarily suggest abnormal histopathology. These findings suggest that amygdala enlargement might reflect a secondary reactive process to seizures in the epileptogenic temporal lobe.

Automated Methods for Detecting and Quantitation of Enlarged Perivascular spaces on MRI.

The brain's glymphatic system is a network of intracerebral vessels that function to remove "waste products" such as degraded proteins from the brain. It comprises of the vasculature, perivascular spaces (PVS), and astrocytes. Poor glymphatic function has been implicated in numerous diseases; however, its contribution is still unknown. Efforts have been made to image the glymphatic system to further assess its role in the pathogenesis of different diseases. Numerous imaging modalities have been utilized including two-photon microscopy and contrast-enhanced magnetic resonance imaging (MRI). However, these are associated with limitations for clinical use. PVS form a part of the glymphatic system and can be visualized on standard MRI sequences when enlarged. It is thought that PVS become enlarged secondary to poor glymphatic drainage of metabolites. Thus, quantitating PVS could be a good surrogate marker for glymphatic function. Numerous manual rating scales have been developed to measure the PVS number and size on MRI scans; however, these are associated with many limitations. Instead, automated methods have been created to measure PVS more accurately in different diseases. In this review, we discuss the imaging techniques currently available to visualize the glymphatic system as well as the automated methods currently available to measure PVS, and the strengths and limitations associated with each technique. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.

Atlas of lesion locations and postsurgical seizure freedom in focal cortical dysplasia: A MELD study.

OBJECTIVE: Drug-resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs). The distribution of these lesions across the cerebral cortex and the impact of lesion location on clinical presentation and surgical outcome are largely unknown. We created a neuroimaging cohort of patients with individually mapped FCDs to determine factors associated with lesion location and predictors of postsurgical outcome. METHODS: The MELD (Multi-centre Epilepsy Lesion Detection) project collated a retrospective cohort of 580 patients with epilepsy attributed to FCD from 20 epilepsy centers worldwide. Magnetic resonance imaging-based maps of individual FCDs with accompanying demographic, clinical, and surgical information were collected. We mapped the distribution of FCDs, examined for associations between clinical factors and lesion location, and developed a predictive model of postsurgical seizure freedom. RESULTS: FCDs were nonuniformly distributed, concentrating in the superior frontal sulcus, frontal pole, and temporal pole. Epilepsy onset was typically before the age of 10 years. Earlier epilepsy onset was associated with lesions in primary sensory areas, whereas later epilepsy onset was associated with lesions in association cortices. Lesions in temporal and occipital lobes tended to be larger than frontal lobe lesions. Seizure freedom rates varied with FCD location, from around 30% in visual, motor, and premotor areas to 75% in superior temporal and frontal gyri. The predictive model of postsurgical seizure freedom had a positive predictive value of 70% and negative predictive value of 61%. SIGNIFICANCE: FCD location is an important determinant of its size, the age at epilepsy onset, and the likelihood of seizure freedom postsurgery. Our atlas of lesion locations can be used to guide the radiological search for subtle lesions in individual patients. Our atlas of regional seizure freedom rates and associated predictive model can be used to estimate individual likelihoods of postsurgical seizure freedom. Data-driven atlases and predictive models are essential for evidence-based, precision medicine and risk counseling in epilepsy.

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study.

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

White and Gray Matter Abnormalities in Australian Footballers With a History of Sports-Related Concussion: An MRI Study.

Sports-related concussion (SRC) is a form of mild traumatic brain injury that has been linked to long-term neurological abnormalities. Australian rules football is a collision sport with wide national participation and is growing in popularity worldwide. However, the chronic neurological consequences of SRC in Australian footballers remain poorly understood. This study investigated the presence of brain abnormalities in Australian footballers with a history of sports-related concussion (HoC) using multimodal MRI. Male Australian footballers with HoC (n = 26), as well as noncollision sport athletes with no HoC (n = 27), were recruited to the study. None of the footballers had sustained a concussion in the preceding 6 months, and all players were asymptomatic. Data were acquired using a 3T MRI scanner. White matter integrity was assessed using diffusion tensor imaging. Cortical thickness, subcortical volumes, and cavum septum pellucidum (CSP) were analyzed using structural MRI. Australian footballers had evidence of widespread microstructural white matter damage and cortical thinning. No significant differences were found regarding subcortical volumes or CSP. These novel findings provide evidence of persisting white and gray matter abnormalities in Australian footballers with HoC, and raise concerns related to the long-term neurological health of these athletes.

Fully-Automated µMRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome.

We describe a fully automated pipeline for the morphometric phenotyping of mouse brains from µMRI data, and show its application to the Tc1 mouse model of Down syndrome, to identify new morphological phenotypes in the brain of this first transchromosomic animal carrying human chromosome 21. We incorporate an accessible approach for simultaneously scanning multiple ex vivo brains, requiring only a 3D-printed brain holder, and novel image processing steps for their separation and orientation. We employ clinically established multi-atlas techniques-superior to single-atlas methods-together with publicly-available atlas databases for automatic skull-stripping and tissue segmentation, providing high-quality, subject-specific tissue maps. We follow these steps with group-wise registration, structural parcellation and both Voxel- and Tensor-Based Morphometry-advantageous for their ability to highlight morphological differences without the laborious delineation of regions of interest. We show the application of freely available open-source software developed for clinical MRI analysis to mouse brain data: NiftySeg for segmentation and NiftyReg for registration, and discuss atlases and parameters suitable for the preclinical paradigm. We used this pipeline to compare 29 Tc1 brains with 26 wild-type littermate controls, imaged ex vivo at 9.4T. We show an unexpected increase in Tc1 total intracranial volume and, controlling for this, local volume and grey matter density reductions in the Tc1 brain compared to the wild-types, most prominently in the cerebellum, in agreement with human DS and previous histological findings.

Identification of dehydration-responsive cysteine proteases during post-harvest senescence of broccoli florets.

Harvest-induced senescence of broccoli results in tissue wilting and sepal chlorosis. As senescence progresses, chlorophyll and protein levels in floret tissues decline and endo-protease activity (measured with azo-casein) increases. Protease activity increased from 24 h after harvest for tissues held in air at 20 degrees C. Activity was lower in floret tissues from branchlets that had been held in solutions of sucrose (2% w/v) or under high carbon dioxide, low oxygen (10% CO(2), 5% O(2)) conditions. Four protease-active protein bands were identified in senescing floret tissue by zymography, and the use of chemical inhibitors of protease action suggests that some 44% of protease activity in senescing floret tissue 72 h after harvest is due to the action of cysteine and serine proteases. Four putative cysteine protease cDNAs have been isolated from broccoli floret tissue (BoCP1, BoCP2, BoCP3, BoCP4). The cDNAs are most similar (73-89% at the amino acid level) to dehydration-responsive cysteine proteases previously isolated from Arabidopsis thaliana (RD19, RD21). The mRNAs encoded by the broccoli cDNAs are expressed in floret tissue during harvest-induced senescence with mRNA accumulating within 6 h of harvest for BoCP1, 12 h of harvest for BoCP4 and within 24 h of harvest for BoCP2 and BoCP3. Induction of the cDNAs is differentially delayed when broccoli branchlets are held in solutions of water or sucrose. In addition, the expression of BoCP1 and BoCP3 is inhibited in tissue held in atmospheres of high carbon dioxide/low oxygen (10% CO(2), 5% O(2)). The putative cysteine protease mRNAs are expressed before measurable increases in endo-protease activity, loss of protein, chlorophyll or tissue chlorosis.

Controlled atmospheres and sugar can delay malate synthase gene expression during asparagus senescence.

A cDNA clone encoding malate synthase (MS; EC 4.1.3.2) was isolated from a 48-h postharvest asparagus (Asparagus officinalis L.) spear cDNA library using a MS clone from Brassica napus. The asparagus MS (AoMS1) cDNA hybridized to a 1.9-kb transcript that increased in abundance preferentially in spear-tip tissue during postharvest storage. The AoMS1 transcript also accumulated during natural foliar senescence of asparagus fern. The cDNA consists of 1960 nucleotides with an open reading frame of 1665 nucleotides or 555 amino acids, and encodes a deduced protein with a predicted Mr of 63 kDa and a pI of 8.1. The deduced amino acid sequence of AoMS1 showed high identity with the B. napus MS clone (77.2%) used to isolate it, and with MS from cucumber (77%). Genomic Southern analysis suggests that a single gene in asparagus encodes AoMS1. Controlled- atmosphere treatments aimed at reducing deterioration of harvested asparagus spears reduced the expression of AoMS1. The reduction was correlated with the reduced oxygen level, and reduced MS enzyme activity was also observed. Asparagus cell cultures were used to test the role of sugar status in regulating AoMS1 gene expression. In cultures without sucrose there was an accumulation of AoMS1 transcript that was absent in cultures containing sucrose.