Hippocampal volume assessment in temporal lobe epilepsy: How good is automated segmentation?

Advances in MRI-based diagnosis of temporal lobe epilepsy: Correlating hippocampal subfield volumes with histopathology

Epilepsy, affecting 0.5%-1% of the global population, presents a significant challenge with 30% of patients resistant to medical treatment. Temporal lobe epilepsy, a common cause of medically refractory epilepsy, is often caused by hippocampal sclerosis (HS). HS can be divided further by subtype, as defined by the International League Against Epilepsy (ILAE). Type 1 HS, the most prevalent form (60%-80% of all cases), is characterized by cell loss and gliosis predominantly in the subfields cornu ammonis (CA1) and CA4. Type 2 HS features cell loss and gliosis primarily in the CA1 sector, and type 3 HS features cell loss and gliosis predominantly in the CA4 subfield. This literature review evaluates studies on hippocampal subfields, exploring whether observable atrophy patterns from in vivo and ex vivo magnetic resonance imaging (MRI) scans correlate with histopathological examinations with manual or automated segmentation techniques. Our findings suggest only ex vivo 1.5 Tesla (T) or 3T MRI with manual segmentation or in vivo 7T MRI with manual or automated segmentations can consistently correlate subfield patterns with histopathologically derived ILAE-HS subtypes. In conclusion, manual and automated segmentation methods offer advantages and limitations in diagnosing ILAE-HS subtypes, with ongoing research crucial for refining hippocampal subfield segmentation techniques and enhancing clinical applicability.

Comparing Automated Subcortical Volume Estimation Methods; Amygdala Volumes Estimated by FSL and FreeSurfer Have Poor Consistency

Subcortical volumes are a promising source of biomarkers and features in biosignatures, and automated methods facilitate extracting them in large, phenotypically rich datasets. However, while extensive research has verified that the automated methods produce volumes that are similar to those generated by expert annotation, the consistency of methods with each other is understudied. Using data from the UK Biobank, we compare the estimates of subcortical volumes produced by two popular software suites: FSL and FreeSurfer. Although most subcortical volumes exhibit good to excellent consistency across the methods, the tools produce diverging estimates of amygdalar volume. Through simulation, we show that this poor consistency can lead to conflicting results, where one but not the other tool suggests statistical significance, or where both tools suggest a significant relationship but in opposite directions. Considering these issues, we discuss several ways in which care should be taken when reporting on relationships involving amygdalar volume.

Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach

BACKGROUND

MRI magnetization-prepared rapid acquisition (MPRAGE) is an easily available imaging modality for dementia diagnosis. Previous studies suggested that volumetric analysis plays a crucial role in various stages of dementia classification. In this study, volumetry, radiomics and demographics were integrated as inputs to develop an artificial intelligence model for various stages, including Alzheimer's disease (AD), mild cognitive decline (MCI) and cognitive normal (CN) dementia classifications.

METHOD

The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset was separated into training and testing groups, and the Open Access Series of Imaging Studies (OASIS) dataset was used as the second testing group. The MRI MPRAGE image was reoriented via statistical parametric mapping (SPM12). Freesurfer was employed for brain segmentation, and 45 regional brain volumes were retrieved. The 3D Slicer software was employed for 107 radiomics feature extractions from within the whole brain. Data on patient demographics were collected from the datasets. The feed-forward neural network (FFNN) and the other most common artificial intelligence algorithms, including support vector machine (SVM), ensemble classifier (EC) and decision tree (DT), were used to build the models using various features.

RESULTS

The integration of brain regional volumes, radiomics and patient demographics attained the highest overall accuracy at 76.57% and 73.14% in ADNI and OASIS testing, respectively. The subclass accuracies in MCI, AD and CN were 78.29%, 89.71% and 85.14%, respectively, in ADNI testing, as well as 74.86%, 88% and 83.43% in OASIS testing. Balanced sensitivity and specificity were obtained for all subclass classifications in MCI, AD and CN.

CONCLUSION

The FFNN yielded good overall accuracy for MCI, AD and CN categorization, with balanced subclass accuracy, sensitivity and specificity. The proposed FFNN model is simple, and it may support the triage of patients for further confirmation of the diagnosis.

Correlation studies of Hippocampal Morphometry and Plasma NFL Levels in Cognitively Unimpaired Subjects

Alzheimer's disease(AD) is being the burden of society and family. Applying computing-aided strategies to reveal its pathology is one of the research highlights. Plasma neurofilament light (NFL) is an emerging noninvasive and economic biomarker for AD molecular pathology. It is valuable to reveal the correlations between the plasma NFL levels and neurodegeneration, especially hippcampal deformations at the preclinical stage. The negative correlation between plasma NFL levels and hippocampal volumes has been documented. However, the relationship between the plasma NFL levels and the hippocampal morphometry details at the preclinical stage is still elusive. This study seeks to demonstrate the capacity of our proposed surface-based hippocampal morphometry system to discern the plasma NFL positive (NFL+>41.9 pg/L) level and plasma NFL negative (NFL-<41.9pg/L) level and illustrate its superiority to the hippocampal volume measurement by drawing the cohort of 154 CU middle aged and elderly adults. We also apply this morphometry measure and a proposed sparse coding based classification algorithm to classify CU individuals with NFL+ and NFL- levels. Experimental results show that the proposed hippocampal morphometry system offers stronger statistical power to discriminate CU subjects with NFL+ and NFL- levels, comparing with the hippocampal volume measure. Furthermore, this system can discriminate plasma NFL levels in CU individuals (Accuracy=0.86). Both the group level and individual level analysis results indicate that the association between plasma NFL levels and the hippocampal shapes can be mapped at the preclinical stage.

Omega-3 Fatty Acids, Cognition, and Brain Volume in Older Adults

The elderly population is growing at increased rates and is expected to double in size by 2050 in the United States and worldwide. The consumption of healthy foods and enriched diets have been associated with improved cognition and brain health. The key nutrients common to many healthy foods and diets are the omega-3 polyunsaturated fatty acids (omega-3 FAs), such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). We explored whether omega-3 FA levels are associated with brain volume and cognition. Forty healthy, cognitively normal, Seventh-day Adventist older adults (mean age 76.3 years at MRI scan, 22 females) completed neurocognitive testing, a blood draw, and structural neuroimaging from 2016 to 2018. EPA and an overall omega-3 index were associated with individual measures of delayed recall (RAVLT-DR) and processing speed (Stroop Color) as well as entorhinal cortex thickness. EPA, DHA, and the omega-3 index were significantly correlated with the total white matter volume. The entorhinal cortex, frontal pole, and total white matter were associated with higher scores on delayed memory recall. This exploratory study found that among healthy, cognitively older adults, increased levels of omega-3 FAs are associated with better memory, processing speed, and structural brain measures.

Robustness of radiomics to variations in segmentation methods in multimodal brain MRI

Radiomics in neuroimaging uses fully automatic segmentation to delineate the anatomical areas for which radiomic features are computed. However, differences among these segmentation methods affect radiomic features to an unknown extent. A scan-rescan dataset (n = 46) of T1-weighted and diffusion tensor images was used. Subjects were split into a sleep-deprivation and a control group. Scans were segmented using four segmentation methods from which radiomic features were computed. First, we measured segmentation agreement using the Dice-coefficient. Second, robustness and reproducibility of radiomic features were measured using the intraclass correlation coefficient (ICC). Last, difference in predictive power was assessed using the Friedman-test on performance in a radiomics-based sleep deprivation classification application. Segmentation agreement was generally high (interquartile range = 0.77–0.90) and median feature robustness to segmentation method variation was higher (ICC > 0.7) than scan-rescan reproducibility (ICC 0.3–0.8). However, classification performance differed significantly among segmentation methods (p < 0.001) ranging from 77 to 84%. Accuracy was higher for more recent deep learning-based segmentation methods. Despite high agreement among segmentation methods, subtle differences significantly affected radiomic features and their predictive power. Consequently, the effect of differences in segmentation methods should be taken into account when designing and evaluating radiomics-based research methods.

Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach

Magnetic resonance imaging (MRI)-based brain segmentation has recently been revolutionized by deep learning methods. These methods use large numbers of annotated segmentations to train algorithms that have the potential to perform brain segmentations reliably and quickly. However, training data for these algorithms are frequently obtained from automated brain segmentation systems, which may contain inaccurate neuroanatomy. Thus, the neuroimaging community would benefit from an open source database of high quality, neuroanatomically curated and manually edited MRI brain images, as well as the publicly available tools and detailed procedures for generating these curated data. Manual segmentation approaches are regarded as the gold standard for brain segmentation and parcellation. These approaches underpin the construction of neuroanatomically accurate human brain atlases. In addition, neuroanatomically precise definitions of MRI-based regions of interest (ROIs) derived from manual brain segmentation are essential for accuracy in structural connectivity studies and in surgical planning for procedures such as deep brain stimulation. However, manual segmentation procedures are time and labor intensive, and not practical in studies utilizing very large datasets, large cohorts, or multimodal imaging. Automated segmentation methods were developed to overcome these issues, and provide high data throughput, increased reliability, and multimodal imaging capability. These methods utilize manually labeled brain atlases to automatically parcellate the brain into different ROIs, but do not have the anatomical accuracy of skilled manual segmentation approaches. In the present study, we developed a custom software module for manual editing of brain structures in the freely available 3D Slicer software platform that employs principles and tools based on pioneering work from the Center for Morphometric Analysis (CMA) at Massachusetts General Hospital. We used these novel 3D Slicer segmentation tools and techniques in conjunction with well-established neuroanatomical definitions of subcortical brain structures to manually segment 50 high resolution T1w MRI brains from the Human Connectome Project (HCP) Young Adult database. The structural definitions used herein are associated with specific neuroanatomical ontologies to systematically interrelate histological and MRI-based morphometric definitions. The resulting brain datasets are publicly available and will provide the basis for a larger database of anatomically curated brains as an open science resource.

Statistical asymmetry analysis of volumetric MRI and FDG PET in temporal lobe epilepsy

PURPOSE

To analyze statistically derived threshold values of volumetric MRI and 18F fluorodeoxyglucose (FDG) PET asymmetry, independent of normative data, for non-invasive detection/exclusion of temporal lobe epilepsy (TLE).

METHODS

We retrospectively analyzed amygdalohippocampal volumetry and temporal FDG PET metabolism in 33 patients (age: 29.27 ± 8.56 years) who underwent surgery following Stereo-EEG implantation and had postsurgical seizure freedom lasting >12 months. The temporal lobe epilepsy group and the extratemporal lobe epilepsy (ETLE) group were compared. Percentage volume loss (PVL) was calculated from manually traced amygdalohippocampal volumetry whereas percentage metabolic loss (PML) was calculated from PET using amygdalohippocampal trace and temporal neocortical Brodmann areas (BA) template.

RESULTS

Receiver operating characteristic (ROC) curve analysis identified a cutoff hippocampal PVL of 4.21% as the minimum indicating probable hippocampal involvement in seizure onset, with sensitivity of 88.89% and the specificity of 100% (p < 0.001). Region of interest (ROI)-based PML values in PET imaging showed a significant correlation with the presence of TLE in the TLE group of patients and its absence in the ETLE group of patients. Region of interest curve analysis yielded PML cutoffs of 5.77% and 8.36%, respectively, for the hippocampus and BA 38 (temporopolar neocortex) to detect TLE with the sensitivity of 72.7% and specificity of 77.8%.

CONCLUSION

We describe statistical thresholds for asymmetry analysis of hippocampal volumetry and FDG PET to improve detection of TLE. These threshold parameters warrant further validation in prospective studies.

Nomograms of human hippocampal volume shifted by polygenic scores

Nomograms are important clinical tools applied widely in both developing and aging populations. They are generally constructed as normative models identifying cases as outliers to a distribution of healthy controls. Currently used normative models do not account for genetic heterogeneity. Hippocampal volume (HV) is a key endophenotype for many brain disorders. Here, we examine the impact of genetic adjustment on HV nomograms and the translational ability to detect dementia patients. Using imaging data from 35,686 healthy subjects aged 44-82 from the UK Biobank (UKB), we built HV nomograms using Gaussian process regression (GPR), which - compared to a previous method - extended the application age by 20 years, including dementia critical age ranges. Using HV polygenic scores (HV-PGS), we built genetically adjusted nomograms from participants stratified into the top and bottom 30% of HV-PGS. This shifted the nomograms in the expected directions by ~100 mm3 (2.3% of the average HV), which equates to 3 years of normal aging for a person aged ~65. Clinical impact of genetically adjusted nomograms was investigated by comparing 818 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database diagnosed as either cognitively normal (CN), having mild cognitive impairment (MCI) or Alzheimer's disease (AD) patients. While no significant change in the survival analysis was found for MCI-to-AD conversion, an average of 68% relative decrease was found in intra-diagnostic-group variance, highlighting the importance of genetic adjustment in untangling phenotypic heterogeneity.

Subcortical and hippocampal brain segmentation in 5-year-old children: validation of FSL-FIRST and FreeSurfer against manual segmentation

Developing accurate subcortical volumetric quantification tools is crucial for neurodevelopmental studies, as they could reduce the need for challenging and time‐consuming manual segmentation. In this study, the accuracy of two automated segmentation tools, FSL‐FIRST (with three different boundary correction settings) and FreeSurfer, were compared against manual segmentation of the hippocampus and subcortical nuclei, including the amygdala, thalamus, putamen, globus pallidus, caudate and nucleus accumbens, using volumetric and correlation analyses in 80 5‐year‐olds.

Both FSL‐FIRST and FreeSurfer overestimated the volume on all structures except the caudate, and the accuracy varied depending on the structure. Small structures such as the amygdala and nucleus accumbens, which are visually difficult to distinguish, produced significant overestimations and weaker correlations with all automated methods. Larger and more readily distinguishable structures such as the caudate and putamen produced notably lower overestimations and stronger correlations. Overall, the segmentations performed by FSL‐FIRST's default pipeline were the most accurate, whereas FreeSurfer's results were weaker across the structures.

In line with prior studies, the accuracy of automated segmentation tools was imperfect with respect to manually defined structures. However, apart from amygdala and nucleus accumbens, FSL‐FIRST's agreement could be considered satisfactory (Pearson correlation > 0.74, intraclass correlation coefficient (ICC) > 0.68 and Dice score coefficient (DSC) > 0.87) with highest values for the striatal structures (putamen, globus pallidus, caudate) (Pearson correlation > 0.77, ICC > 0.87 and DSC > 0.88, respectively). Overall, automated segmentation tools do not always provide satisfactory results, and careful visual inspection of the automated segmentations is strongly advised.

Radiomics-Based Artificial Intelligence Differentiation of Neurodegenerative Diseases with Reference to the Volumetry

This study aimed to build automated detection models—one by brain regional volume (V-model), and the other by radiomics features of the whole brain (R-model)—to differentiate mild cognitive impairment (MCI) from cognitive normal (CN), and Alzheimer’s Disease (AD) from mild cognitive impairment (MCI). The objectives are to compare the models and identify whether radiomics or volumetry can provide a better prediction for differentiating different types of dementia. Method: 582 MRI T1-weighted images were retrieved from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, which is a multicenter operating open source database for AD. In total, 97 images of AD, 293 images of MCI patient and 192 images of cognitive normal were divided into a training, a validation and a test group at a ratio of 70:15:15. For each T1-weighted image, volumetric segmentation was performed with the image analysis software FreeSurfer, and radiomics features were retrieved by imaging research software 3D slicers. Brain regional volume and radiomics features were used to build the V-model and R-model, respectively, using the random forest algorithm by R. The receiver operating characteristics (ROC) curve of both models were used to evaluate their diagnostic accuracy and reliability to differentiate AD, MCI and CN. Results: To differentiate MCI and CN, both V-model and R-model achieved excellent performance, with an AUC of 0.9992 ± 0.0022 and 0.9850 ± 0.0032, respectively. No significant difference was found between the two AUCs, indicating both models attained similar good performance. In MCI and AD differentiation, the V-model and R-model yielded AUC of 0.9986 ± 0.0013 and 0.9714 ± 0.0175, respectively. The best performance was to differentiate AD from CN, where the V-model and R-model yielded AUC of 0.9994 ± 0.0019 and 0.9830 ± 0.009, respectively. The results suggested that both volumetry and radiomics approaches could be used in differentiating AD, MCI and CN, based on T1 weighted MR images using random forest algorithm successfully. Conclusion: This study showed that the radiomics features from T1-weighted MR images achieved excellence performance in differentiating AD, MCI and CN. Compared to the volumetry method, the accuracy, sensitivity and specificity are slightly lower in using radiomics, but still attained very good and reliable classification of the three stages of neurodegenerations. In view of the convenience and operator independence in feature extraction, radiomics can be a quantitative biomarker to differentiate the disease groups.

Lateralizing Characteristics of Morphometric Changes to Hippocampus and Amygdala in Unilateral Temporal Lobe Epilepsy with Hippocampal Sclerosis

Background andObjective: In the present study, a detailed investigation of substructural volume change in the hippocampus (HC) and amygdala (AMG) was performed and the association with clinical features in patients with mesial temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) determined. Methods: The present study included 22 patients with left-sided TLE-HS (LTLE-HS) and 26 patients with right-sided TLE-HS (RTLE-HS). In addition, 28 healthy controls underwent high-resolution T2-weighted image (T2WI) and T1-weighted image (T1WI) MRI scanning. Subfield analysis of HC and AMG was performed using FreeSurfer version 6.0. Results: Patients with TLE-HS showed a decrease in the volume of substructures in both HC and AMG, and this change was observed on the contralateral side and the ipsilateral side with HS. The volume reduction pattern of substructures showed laterality-dependent characteristics. Patients with LTLE-HS had smaller volumes of the ipsilateral subiculum (SUB), contralateral SUB, and ipsilateral cortical nucleus of AMG than patients with RTLE-HS. Patients with RTLE-HS had reduced ipsilateral cornu ammonis (CA) 2/3 and contralateral cortico-amygdaloid transition area (CAT) volumes. The relationship between clinical variables and subregions was different based on the lateralization of the seizure focus. Focal to bilateral tonic-clonic seizures (FTBTCS) was associated with contralateral and ipsilateral side subregions only in LTLE-HS. The abdominal FAS was associated with the volume reduction of AMG subregions only in LTLE-HS, but the volume reduction was less than in patients without FAS. Conclusions: The results indicate that unilateral TLE-HS is a bilateral disease that shows different laterality-dependent characteristics based on the subfield analysis of HC and AMG. Subfield volumes of HC and AMG were associated with clinical variables, and the more damaged substructures depended on laterality in TLE-HS. These findings support the evidence that LTLE-HS and RTLE-HS are disparate epilepsy entities rather than simply identical syndromes harboring a mesial temporal lesion. In addition, the presence of FAS supports good localization value, and abdominal FAS has a high localization value, especially in patients with LTLE-HS.

A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI

Purpose

Hippocampal volumetry is an important biomarker to quantify atrophy in patients with mesial temporal lobe epilepsy. We investigate the sensitivity of automated segmentation methods to support radiological assessments of hippocampal sclerosis (HS). Results from FreeSurfer and FSL-FIRST are contrasted to a deep learning (DL)-based segmentation method.

Materials and Methods

We used T1-weighted MRI scans from 105 patients with epilepsy and 354 healthy controls. FreeSurfer, FSL, and a DL-based method were applied for brain anatomy segmentation. We calculated effect sizes (Cohen's d) between left/right HS and healthy controls based on the asymmetry of hippocampal volumes. Additionally, we derived 14 shape features from the segmentations and determined the most discriminating feature to identify patients with hippocampal sclerosis by a support vector machine (SVM).

Results

Deep learning-based segmentation of the hippocampus was the most sensitive to detecting HS. The effect sizes of the volume asymmetries were larger with the DL-based segmentations (HS left d= −4.2, right = 4.2) than with FreeSurfer (left= −3.1, right = 3.7) and FSL (left= −2.3, right = 2.5). For the classification based on the shape features, the surface-to-volume ratio was identified as the most important feature. Its absolute asymmetry yielded a higher area under the curve (AUC) for the deep learning-based segmentation (AUC = 0.87) than for FreeSurfer (0.85) and FSL (0.78) to dichotomize HS from other epilepsy cases. The robustness estimated from repeated scans was statistically significantly higher with DL than all other methods.

Conclusion

Our findings suggest that deep learning-based segmentation methods yield a higher sensitivity to quantify hippocampal sclerosis than atlas-based methods and derived shape features are more robust. We propose an increased asymmetry in the surface-to-volume ratio of the hippocampus as an easy-to-interpret quantitative imaging biomarker for HS.

Individual differences in the experience of body ownership are related to cortical thickness

The widely used rubber hand illusion (RHI) paradigm provides insight into how the brain manages conflicting multisensory information regarding bodily self-consciousness. Previous functional neuroimaging studies have revealed that the feeling of body ownership is linked to activity in the premotor cortex, the intraparietal areas, the occipitotemporal cortex, and the insula. The current study investigated whether the individual differences in the sensation of body ownership over a rubber hand, as measured by subjective report and the proprioceptive drift, are associated with structural brain differences in terms of cortical thickness in 67 healthy young adults. We found that individual differences measured by the subjective report of body ownership are associated with the cortical thickness in the somatosensory regions, the temporo-parietal junction, the intraparietal areas, and the occipitotemporal cortex, while the proprioceptive drift is linked to the premotor area and the anterior cingulate cortex. These results are in line with functional neuroimaging studies indicating that these areas are indeed involved in processes such as cognitive-affective perspective taking, visual processing of the body, and the experience of body ownership and bodily awareness. Consequently, these individual differences in the sensation of body ownership are pronounced in both functional and structural differences.

Hippocampal subregional alterations and verbal fluency in the early stage of type 2 diabetes mellitus

Growing evidence indicates that type 2 diabetes mellitus (T2DM)-related cognitive dysfunction may develop in the early stage of the disease and is often accompanied by hippocampal structural alterations. In the current study, we investigated volume and shape alterations of the hippocampus at a subregional level in patients with T2DM. With the use of high-resolution brain structural images that were obtained from 30 T2DM patients with less than 5 years of disease duration and 30 healthy individuals, volumetric and shape analyses were performed. We also assessed the relationship between T2DM-related hippocampal structural alterations and performance on verbal fluency. In volumetric analysis, total hippocampal volume was smaller in the T2DM group, relative to the control group. At a subregional level, T2DM patients showed significant inward deformation and volume reduction of the right dentate gyrus and cornu ammonis 2/3 subregions as compared with healthy individuals. In particular, T2DM patients with lower performance on verbal fluency had smaller right dentate gyrus volumes relative to those with higher performance. These findings suggest that the hippocampus may undergo atrophy at a subregional level even in the early stage of T2DM, and this subregion-specific atrophy may be associated with reduced performance on verbal fluency.

Automated subcortical volume estimation from 2D MRI in epilepsy and implications for clinical trials

Purpose

Most techniques used for automatic segmentation of subcortical brain regions are developed for three-dimensional (3D) MR images. MRIs obtained in non-specialist hospitals may be non-isotropic and two-dimensional (2D). Automatic segmentation of 2D images may be challenging and represents a lost opportunity to perform quantitative image analysis. We determine the performance of a modified subcortical segmentation technique applied to 2D images in patients with idiopathic generalised epilepsy (IGE).

Methods

Volume estimates were derived from 2D (0.4 × 0.4 × 3 mm) and 3D (1 × 1x1mm) T1-weighted acquisitions in 31 patients with IGE and 39 healthy controls. 2D image segmentation was performed using a modified FSL FIRST (FMRIB Integrated Registration and Segmentation Tool) pipeline requiring additional image reorientation, cropping, interpolation and brain extraction prior to conventional FIRST segmentation. Consistency between segmentations was assessed using Dice coefficients and volumes across both approaches were compared between patients and controls. The influence of slice thickness on consistency was further assessed using 2D images with slice thickness increased to 6 mm.

Results

All average Dice coefficients showed excellent agreement between 2 and 3D images across subcortical structures (0.86–0.96). Most 2D volumes were consistently slightly lower compared to 3D volumes. 2D images with increased slice thickness showed lower agreement with 3D images with lower Dice coefficients (0.55–0.83). Significant volume reduction of the left and right thalamus and putamen was observed in patients relative to controls across 2D and 3D images.

Conclusion

Automated subcortical volume estimation of 2D images with a resolution of 0.4 × 0.4x3mm using a modified FIRST pipeline is consistent with volumes derived from 3D images, although this consistency decreases with an increased slice thickness. Thalamic and putamen atrophy has previously been reported in patients with IGE. Automated subcortical volume estimation from 2D images is feasible and most reliable at using in-plane acquisitions greater than 1 mm x 1 mm and provides an opportunity to perform quantitative image analysis studies in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00234-021-02811-x.

Seizure control does not predict hippocampal subfield volume change in children with focal drug-resistant epilepsy

Background and purpose

Recurrent seizures have been reported to induce neuronal loss in the hippocampus. It is unclear whether seizure control influences hippocampal volume. The aims of this study were to determine if there was a change in total or subfield hippocampal volume over time in children with focal drug-resistant epilepsy, and whether seizure control influenced total or subfield hippocampal volumes.

Methods

Using FreeSurfer’s automated segmentation of brain magnetic resonance imaging scans, we calculated the total and subfield (including CA1, CA3, CA4, subiculum, presubiculum, parasubiculum, molecular layer and dentate gyrus) hippocampal volumes of children with non-lesional focal epilepsy. Seizure frequency and hippocampal volumes were assessed at baseline and follow-up. Patients were classified into those who were seizure free or have improvement in seizures (group 1) and those with no improvement in seizures (group 2) at follow-up.

Results

Thirty-seven patients were included, with mean age 10.31 ± 3.68 years at baseline. The interval between the two magnetic resonance imaging scans was 2.59 ± 1.25 years. There was no significant difference in the total and subfield hippocampal volumes for the whole cohort at follow-up compared to baseline (all P > 0.002). Seizure control of the two groups did not predict total or subfield hippocampal volume, after controlling for baseline volume, age, severity of seizure frequency at baseline and time interval between the magnetic resonance imaging scans (all P > 0.002).

Conclusion

We have found that total and subfield hippocampal volumes did not change, and seizure control did not predict hippocampal volumes at follow-up in children with drug-resistant epilepsy.

Analysis of structural brain asymmetries in attention-deficit/hyperactivity disorder in 39 datasets

OBJECTIVE

Some studies have suggested alterations of structural brain asymmetry in attention-deficit/hyperactivity disorder (ADHD), but findings have been contradictory and based on small samples. Here, we performed the largest ever analysis of brain left-right asymmetry in ADHD, using 39 datasets of the ENIGMA consortium.

METHODS

We analyzed asymmetry of subcortical and cerebral cortical structures in up to 1,933 people with ADHD and 1,829 unaffected controls. Asymmetry Indexes (AIs) were calculated per participant for each bilaterally paired measure, and linear mixed effects modeling was applied separately in children, adolescents, adults, and the total sample, to test exhaustively for potential associations of ADHD with structural brain asymmetries.

RESULTS

There was no evidence for altered caudate nucleus asymmetry in ADHD, in contrast to prior literature. In children, there was less rightward asymmetry of the total hemispheric surface area compared to controls (t = 2.1, p = .04). Lower rightward asymmetry of medial orbitofrontal cortex surface area in ADHD (t = 2.7, p = .01) was similar to a recent finding for autism spectrum disorder. There were also some differences in cortical thickness asymmetry across age groups. In adults with ADHD, globus pallidus asymmetry was altered compared to those without ADHD. However, all effects were small (Cohen's d from -0.18 to 0.18) and would not survive study-wide correction for multiple testing.

CONCLUSION

Prior studies of altered structural brain asymmetry in ADHD were likely underpowered to detect the small effects reported here. Altered structural asymmetry is unlikely to provide a useful biomarker for ADHD, but may provide neurobiological insights into the trait.

Diagnostic Performance of MRI Volumetry in Epilepsy Patients With Hippocampal Sclerosis Supported Through a Random Forest Automatic Classification Algorithm

Introduction: Several methods offer free volumetry services for MR data that adequately quantify volume differences in the hippocampus and its subregions. These methods are frequently used to assist in clinical diagnosis of suspected hippocampal sclerosis in temporal lobe epilepsy. A strong association between severity of histopathological anomalies and hippocampal volumes was reported using MR volumetry with a higher diagnostic yield than visual examination alone. Interpretation of volumetry results is challenging due to inherent methodological differences and to the reported variability of hippocampal volume. Furthermore, normal morphometric differences are recognized in diverse populations that may need consideration. To address this concern, we highlighted procedural discrepancies including atlas definition and computation of total intracranial volume that may impact volumetry results. We aimed to quantify diagnostic performance and to propose reference values for hippocampal volume from two well-established techniques: FreeSurfer v.06 and volBrain-HIPS.

Methods: Volumetry measures were calculated using clinical T1 MRI from a local population of 61 healthy controls and 57 epilepsy patients with confirmed unilateral hippocampal sclerosis. We further validated the results by a state-of-the-art machine learning classification algorithm (Random Forest) computing accuracy and feature relevance to distinguish between patients and controls. This validation process was performed using the FreeSurfer dataset alone, considering morphometric values not only from the hippocampus but also from additional non-hippocampal brain regions that could be potentially relevant for group classification. Mean reference values and 95% confidence intervals were calculated for left and right hippocampi along with hippocampal asymmetry degree to test diagnostic accuracy.

Results: Both methods showed excellent classification performance (AUC:> 0.914) with noticeable differences in absolute (cm³) and normalized volumes. Hippocampal asymmetry was the most accurate discriminator from all estimates (AUC:1~0.97). Similar results were achieved in the validation test with an automatic classifier (AUC:>0.960), disclosing hippocampal structures as the most relevant features for group differentiation among other brain regions.

Conclusion: We calculated reference volumetry values from two commonly used methods to accurately identify patients with temporal epilepsy and hippocampal sclerosis. Validation with an automatic classifier confirmed the principal role of the hippocampus and its subregions for diagnosis.

Exploring the performance of outcome measures in MS for predicting cognitive and clinical progression in the following years

BACKGROUND

The demand for better outcome measures in multiple sclerosis (MS) management has been increasingly recognized. Nevertheless, the prognostic impacts of available outcome measures for long-term clinical and especially cognitive disability have not been thoroughly investigated. We, therefore, aimed to explore the sustainability and long-term predictive value of outcome measures in MS.

METHODS

We studied a cohort of 42 relapsing-remitting MS patients and 30 healthy subjects. Evaluations were performed at baseline and after two (Y2) and six years (Y6), and included neurological and neuropsychological evaluation (BRBN), MRI (3T), and quality of life assessment. Combined clinical and cognitive measures were evaluated, such as minimal and no evidence of disease activity (MEDA and NEDA, respectively). We performed logistic regression with bootstrapping and calculated the diagnostic properties to identify patients who reached six-year clinical and/or cognitive worsening.

RESULTS

NEDA status was observed in up to 30.8% of patients at Y2, but only in 5% at Y6, and did not preclude cognitive decline (SDMT and BRBN). The absence of MRI activity and MEDA status at Y2 were associated with less EDSS worsening in the following years but without impact on cognition. The absence of deterioration on combined clinical/cognitive measures at Y2 (e.g., T25W+ 9HPT + BRBN) was associated with better outcomes in the following years (clinical and cognitive), with moderate to large effect sizes. For the identification of clinical worsening at Y6, best accuracies were found for MEDA (70.6%), and clinical worsening (71.4%), but only MEDA remained in the final model after multivariable logistic regression analysis (OR = 6.81, p = 0.017). For combined clinical and cognitive worsening at Y6, only T25W+ 9HPT + BRBN remained in the final model (OR = 8.5, p = 0.017).

CONCLUSIONS

Early MS inflammatory disease activity is associated with future clinical disability. Nevertheless, NEDA was difficult to sustain in the long-term and did not preclude cognitive deterioration. Clinical and cognitive measures combined predicted outcomes better than each one isolated. Our data suggest that the evaluation of more than one cognitive domain yields a better predictive outcome measure.

Diagnosis of Hippocampal Sclerosis in Children: Comparison of Automated Brain MRI Volumetry and Readers of Varying Experience

BACKGROUND. Hippocampal sclerosis (HS) is a leading cause of medically refractory temporal lobe epilepsy in children. The diagnosis is clinically important because most patients with HS have good postsurgical outcomes. OBJECTIVE. This study aimed to compare the performance of a fully automated brain MRI volumetric tool and readers of varying experience in the diagnosis of pediatric HS. METHODS. This retrospective study included 22 children with HS diagnosed between January 2009 and January 2020 who underwent surgery and an age- and sex-matched control group of 44 patients with normal MRI findings and extratemporal epilepsy diagnosed between January 2009 and January 2020. Regional brain MRI volumes were calculated from a high-resolution 3D T1-weighted sequence using an automated volumetric tool. Four readers (two pediatric radiologists [experienced] and two radiology residents [inexperienced]) visually assessed each MRI examination to score the likelihood of HS. One inexperienced reader repeated the evaluations using the volumetric tool. The area under the ROC curve (AUROC), sensitivity, and specificity for HS were computed for the volumetric tool and the readers. Diagnostic performances were compared using McNemar tests. RESULTS. In the HS group, the hippocampal volume (affected vs unaffected, 3.54 vs 4.59 cm³) and temporal lobe volume (affected vs unaffected, 5.66 vs 6.89 cm³) on the affected side were significantly lower than on the unaffected side (p < .001) using the volu-metric tool. AUROCs of the volumetric tool were 0.813-0.842 in patients with left HS and 0.857-0.980 in patients with right HS (sensitivity, 81.8-90.9%; specificity, 70.5-95.5%). No significant difference (p = .63 to > .99) was observed between the performance of the volumetric tool and the performance of the two experienced readers as well as one inexperienced reader (AUROCs for these three readers, 0.968-0.999; sensitivity, 86.4-90.9%; specificity, 100.0%). The volumetric tool had better performance (p < .001) than the other inexperienced reader (AUROC, 0.806; sensitivity, 81.8%; specificity, 47.7%). With subsequent use of the tool, this inexperienced reader showed a nonsignificant increase (p = .10) in AUROC (0.912) as well as in sensitivity (86.4%) and specificity (84.1%). CONCLUSION. A fully automated volumetric brain MRI tool outperformed one of two inexperienced readers and performed as well as two experienced readers in identifying and lateralizing HS in pediatric patients. The tool improved the performance of an inexperienced reader. CLINICAL IMPACT. A fully automated volumetric tool facilitates diagnosis of HS in pediatric patients, especially for an inexperienced reader.

Validity of automated FreeSurfer segmentation compared to manual tracing in detecting prenatal alcohol exposure-related subcortical and corpus callosal alterations in 9- to 11-year-old children

In recent years a number of semi-automated and automated segmentation tools and brain atlases have been developed to facilitate morphometric analyses of large MRI datasets. These tools are much faster than manual tracing and demonstrate excellent test-retest reliabilities. Reliabilities of automated segmentations relative to "gold standard" manual tracings have, however, been shown to vary by brain region and in different cohorts. It remains uncertain to what extent smaller brain volumes and potential changes in grey/white matter contrasts in paediatric brains impact on the performance of automated methods, and how pathology may influence performance. This study examined whether using data from automated FreeSurfer segmentation would alter our ability, compared to manual segmentation, to detect prenatal alcohol exposure (PAE)-related volume changes in subcortical regions and the corpus callosum (CC) in pre-adolescent children. High-resolution T1-weighted images were acquired, using a sequence optimized for morphometric neuroanatomical analysis, on a Siemens 3T Allegra MRI scanner in 71 right-handed, 9- to 11-year-old children (27 fetal alcohol syndrome (FAS) and partial FAS (PFAS), 25 non-syndromal heavily exposed (HE) and 19 non-exposed controls) from a high-risk community in Cape Town, South Africa. Data from timeline follow-back interviews administered to the mothers prospectively during pregnancy were used to quantify the amount of alcohol (in ounces absolute alcohol per day, AA/day) that the children had been exposed to prenatally. Volumes of corpus callosum (CC) and bilateral caudate nuclei, hippocampi and nucleus accumbens (NA) were obtained by manual tracing and automated segmentation using both FreeSurfer versions 5.1 and 6.0. Reliability across methods was assessed using intraclass correlation (ICC) estimates for consistency and absolute agreement, and Cronbach's α. Ability to detect regions showing PAE effects was assessed separately for each segmentation method using ANOVA and linear regression of regional volumes with AA/day. Our results support findings from other studies showing excellent reliability across methods for easy-to-segment structures, such as the CC and caudate nucleus. Volumes from FreeSurfer 6.0 were smaller than those from version 5.1 in all regions except the right caudate, for which they were similar, and right hippocampus and CC, for which they were larger. Despite poor absolute agreement between methods in the NA and hippocampus, all three segmentation methods detected dose-dependent volume reductions in regions for which reliabilities on ICC consistency across methods reached at least 0.70, namely the CC, and bilateral caudate nuclei and hippocampi. PAE-related changes in the NA for which ICC consistency did not reach this minimum were inconsistent across methods and should be interpreted with caution. This is the first study to demonstrate in a pre-adolescent cohort the ability of automated segmentation with FreeSurfer to detect regional volume changes associated with pathology similar to those found using manual tracing.

Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline

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A completely automated surface-based ventricular morphometry system.

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Generate a whole connected 3D ventricular shape model.

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Test-retest the system in two independent CU subject cohorts.

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Subregional ventricular abnormalities prior to clinically memory decline.

Ventricular volume (VV) is a widely used structural magnetic resonance imaging (MRI) biomarker in Alzheimer’s disease (AD) research. Abnormal enlargements of VV can be detected before clinically significant memory decline. However, VV does not pinpoint the details of subregional ventricular expansions. Here we introduce a ventricular morphometry analysis system (VMAS) that generates a whole connected 3D ventricular shape model and encodes a great deal of ventricular surface deformation information that is inaccessible by VV. VMAS contains an automated segmentation approach and surface-based multivariate morphometry statistics. We applied VMAS to two independent datasets of cognitively unimpaired (CU) groups. To our knowledge, it is the first work to detect ventricular abnormalities that distinguish normal aging subjects from those who imminently progress to clinically significant memory decline. Significant bilateral ventricular morphometric differences were first shown in 38 members of the Arizona APOE cohort, which included 18 CU participants subsequently progressing to the clinically significant memory decline within 2 years after baseline visits (progressors), and 20 matched CU participants with at least 4 years of post-baseline cognitive stability (non-progressors). VMAS also detected significant differences in bilateral ventricular morphometry in 44 Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects (18 CU progressors vs. 26 CU non-progressors) with the same inclusion criterion. Experimental results demonstrated that the ventricular anterior horn regions were affected bilaterally in CU progressors, and more so on the left. VMAS may track disease progression at subregional levels and measure the effects of pharmacological intervention at a preclinical stage.

The morphological characteristics of hippocampus and thalamus in mesial temporal lobe epilepsy

Background

Mesial temporal lobe epilepsy (MTLE) is the most common form of focal epilepsy, which is frequently characterized by hippocampal sclerosis (HS). Accumulating studies have suggested widespread cortico-cortical connections related to MTLE. The role of subcortical structures involved in general epilepsy has been extensively investigated, but it is still limited in MTLE. Our purpose was to determine the specific morphological correlation between sclerotic hippocampal and thalamic sub-regions, using quantitative analysis, in MTLE.

Methods

In this study, 23 MTLE patients with unilateral hippocampal sclerosis and 24 healthy controls were examined with three-dimensional T1 MRI. Volume quantitative analysis in the hippocampus and thalamus was conducted and group-related volumetric difference was assessed. Moreover, vertex analysis was further performed using automated software to delineate detailed morphological patterns of the hippocampus and thalamus. The correlation was used to examine whether there is a relationship between volume changes of two subcortical structures and clinical characteristics.

Results

The patients had a significant volume decrease in the sclerotic hippocampus (p < 0.001). Compared to controls, obvious atrophic patterns were observed in the bilateral hippocampus in MTLE (p < 0.05). Only small patches of shrinkage were noted in the bilateral thalamus (p < 0.05). Moreover, the volume change of the hippocampus had a significant positive correlation with that of the thalamus (P < 0.001). Intriguingly, volume changes of the hippocampus and thalamus were correlated with the duration of epilepsy (hippocampus: P = 0.024; thalamus: P = 0.022). However, only volume changes of thalamus possibly differentiated between two prognostic groups in patients (P = 0.026).

Conclusions

We demonstrated the morphological characteristics of the hippocampus and thalamus in MTLE, providing new insights into the interrelated mechanisms between the hippocampus and thalamus, which have potential clinical significance for refining neuromodulated targets.

Accuracy of automated amygdala MRI segmentation approaches in Huntington's disease in the IMAGE-HD cohort

Smaller manually-segmented amygdala volumes have been associated with poorer motor and cognitive function in Huntington's disease (HD). Manual segmentation is the gold standard in terms of accuracy; however, automated methods may be necessary in large samples. Automated segmentation accuracy has not been determined for the amygdala in HD. We aimed to determine which of three automated approaches would most accurately segment amygdalae in HD: FreeSurfer, FIRST, and ANTS nonlinear registration followed by FIRST segmentation. T1-weighted images for the IMAGE-HD cohort including 35 presymptomatic HD (pre-HD), 36 symptomatic HD (symp-HD), and 34 healthy controls were segmented using FreeSurfer and FIRST. For the third approach, images were nonlinearly registered to an MNI template using ANTS, then segmented using FIRST. All automated methods overestimated amygdala volumes compared with manual segmentation. Dice overlap scores, indicating segmentation accuracy, were not significantly different between automated approaches. Manually segmented volumes were most statistically differentiable between groups, followed by those segmented by FreeSurfer, then ANTS/FIRST. FIRST-segmented volumes did not differ between groups. All automated methods produced a bias where volume overestimation was more severe for smaller amygdalae. This bias was subtle for FreeSurfer, but marked for FIRST, and moderate for ANTS/FIRST. Further, FreeSurfer introduced a hemispheric bias not evident with manual segmentation, producing larger right amygdalae by 8%. To assist choice of segmentation approach, we provide sample size estimation graphs based on sample size and other factors. If automated segmentation is employed in samples of the current size, FreeSurfer may effectively distinguish amygdala volume between controls and HD.

A quantitative MRI index for assessing the severity of hippocampal sclerosis in temporal lobe epilepsy

Background

Hippocampal sclerosis (HS) is associated with post-surgery outcome in patients with temporal lobe epilepsy (TLE), and an automated method that quantifies HS severity is still lacking. Here, we aim to propose an MRI-based HS index (HSI) that integrates hippocampal volume and FLAIR signal to measure the severity of HS.

Methods

Forty-two pre-surgery TLE patients were included retrospectively, with T1-weighted (T1W) and FLAIR images acquired from each subject. Two experienced neurosurgeons (W.D. and C.S.) and one neurologist (Q.L.) rated HS severity with a four-class grading scale (normal, mild, moderate and severe) based on both hippocampal volume loss and increased FLAIR signal. A consensus of HS severity for each subject was made by voting among the three visual rating results. Regarding the automatic quantification, the hippocampal volume was quantified by AccuBrain on T1W image, and the FLAIR signal of hippocampus was calculated as the mean intensity of hippocampal region on the FLAIR image (normalized by the mean intensity of gray matter). To fit the HSI from visual rating, we applied ordinal regression with the voted visual rating as the dependent variable, and hippocampal volume and FLAIR signal as the independent variables. The HSI was calculated by weighting the predicted probabilities of the four-class grading scales from ordinal regression.

Results

The intra-class correlation coefficient (single measure) of the three raters was 0.806. The generated HSI was significantly correlated with the visual rating scales of the three raters (W.D.: 0.823, Q.L.: 0.817, C.S.: 0.717). HSI scores well differentiated the different HS categories as defined by the agreed HS visual rating (normal vs. mild: p < 0.001, mild vs. moderate: p < 0.001, moderate vs. severe: p = 0.001).

Conclusions

The proposed HSI was consistent with visual rating scales from epileptologists and sensitive to HS severity. This MRI-based index may help to evaluate HS severity in clinical practice. Further validations are needed to associate HSI with post-surgery outcomes.

Using Virtual Reality to Improve Performance and User Experience in Manual Correction of MRI Segmentation Errors by Non-experts

Segmentation of MRI scans is a critical part of the workflow process before we can further analyze neuroimaging data. Although there are several automatic tools for segmentation, no segmentation software is perfectly accurate, and manual correction by visually inspecting the segmentation errors is required. The process of correcting these errors is tedious and time-consuming, so we present a novel method of performing this task in a head-mounted virtual reality interactive system with a new software, Virtual Brain Segmenter (VBS). We provide the results of user testing on 30 volunteers to show the benefits of our tool as a more efficient, intuitive, and engaging alternative compared with the current method of correcting segmentation errors.

Whole Brain and Cranial Size Adjustments in Volumetric Brain Analyses of Sex- and Age-Related Trends

Our goal was to determine the influence of sex, age and the head/brain size on the compartmental brain volumes in the radiologically verified healthy population (96 subjects; 54 women and 42 men) from the Upper Silesia region in Poland. The MRI examinations were done using 3T Philips Achieva with the same T1-weighted and T2-weighted protocols. The image segmentation procedures were performed with SPM (Statistical Parameter Mapping) and FSL-FIRST software. The volumes of 14 subcortical structures for the left and right hemispheres and 4 overall volumes were calculated. The General Linear Models (GLM) analysis was used with and without the Total Brain Volume (TBV) and Intracranial Volume (ICV) parameters as the covariates to study the regional vs. global brain atrophy. After the ICV/TBV adjustments, the majority of sex differences in the specific volumes of interest (VOIs) revealed to be linked to the difference in the head/brain size parameters. The analysis also confirmed the significant effect of the aging process on the brain loss. After the TBV adjustment, the age- and sex-related volumetric trends for the gray and white matter volumes were observed: the negative age dependence of the gray matter volume is more pronounced in the males, while in case of the white matter the positive age-related trend in the female group is weaker. The local losses of the left caudate nucleus and the right thalamus are more advanced than the global brain atrophy. Different head-size correction strategies are not interchangeable and may yield various volumetric results, but when used together, facilitate studies on the regional dependencies inherent to a healthy, but aging, brain.

Shared hippocampal abnormalities in sporadic temporal lobe epilepsy patients and their siblings

OBJECTIVE

To examine the shared familial contribution to hippocampal and extrahippocampal morphological abnormalities in patients with sporadic temporal lobe epilepsy (TLE) and their unaffected siblings.

METHODS

We collected clinical, electrophysiological, and T1-weighted magnetic resonance imaging (MRI) data of 18 sporadic patients with TLE without lesions other than hippocampal sclerosis (12 right, 6 left), their 18 unaffected full siblings, and 18 matched healthy volunteers. We compared between-group differences in cortical thickness and volumes of five subcortical areas (hippocampus, amygdala, thalamus, putamen, and pallidum). We determined the subregional extent of hippocampal abnormalities using surface shape analysis. All our imaging results were corrected for multiple comparisons using random field theory.

RESULTS

We detected smaller hippocampal volumes in patients (right TLE: median right hippocampus 1.92 mL, interquartile range [IQR] 1.39-2.62, P < .001; left TLE: left hippocampus 2.05 mL, IQR 1.99-2.33, P = .01) and their unaffected siblings (right hippocampus 2.65 mL, IQR 2.32-2.80, P < .001; left hippocampus 2.39 mL, IQR 2.18-2.53, P < .001) compared to healthy controls (right hippocampus 2.94 mL, IQR 2.77-3.24; left hippocampus 2.71 mL, IQR 2.37-2.89). Surface shape analysis showed that patients with TLE had bilateral subregional atrophy in both hippocampi (right > left). Similar but less-pronounced subregional atrophy was detected in the right hippocampus of unaffected siblings. Patients with TLE had reduced cortical thickness in bilateral premotor/prefrontal cortices and the right precentral gyrus. Siblings did not show abnormalities in cortical or subcortical areas other than the hippocampus.

SIGNIFICANCE

Our results demonstrate a shared vulnerability of the hippocampus in both patients with TLE and their unaffected siblings, pointing to a contribution of familial factors to hippocampal atrophy. This neuroimaging trait could represent an endophenotype of TLE, which might precede the onset of epilepsy in some individuals.

Regional brain volumetric changes despite 2 years of treatment initiated during acute HIV infection

OBJECTIVE

To assess changes in regional brain volumes after 24 months among individuals who initiated combination antiretroviral therapy (cART) within weeks of HIV exposure.

DESIGN

Prospective cohort study of Thai participants in the earliest stages of HIV-1infection.

METHODS

Thirty-four acutely HIV-infected individuals (AHI; Fiebig I-V) underwent brain magnetic resonance (MR) imaging and MR spectroscopy at 1.5 T and immediately initiated cART. Imaging was repeated at 24 months. Regional brain volumes were quantified using FreeSurfer's longitudinal pipeline. Voxel-wise analyses using tensor-based morphometry (TBM) were conducted to verify regional assessments. Baseline brain metabolite levels, blood and cerebrospinal fluid biomarkers assessed by ELISA, and peripheral blood monocyte phenotypes measured by flow cytometry were examined as predictors of significant volumetric change.

RESULTS

Participants were 31 ± 8 years old. The estimated mean duration of infection at cART initiation was 15 days. Longitudinal analyses revealed reductions in volumes of putamen (P < 0.001) and caudate (P = 0.006). TBM confirmed significant atrophy in the putamen and caudate, and also in thalamic and hippocampal regions. In exploratory post-hoc analyses, higher baseline frequency of P-selectin glycoprotein ligand-1 (PSGL-1)-expressing total monocytes correlated with greater caudate volumetric decrease (ρ = 0.67, P = 0.017), whereas the baseline density of PSGL-1-expressing inflammatory (CD14CD16) monocytes correlated with putamen atrophy (ρ = 0.65, P = 0.022).

CONCLUSION

Suppressive cART initiated during AHI may not prevent brain atrophy. Volumetric decrease appears greater than expected age-related decline, although examination of longitudinal change in demographically similar HIV-uninfected Thai individuals is needed. Mechanisms underlying progressive HIV-related atrophy may include early activation and enhanced adhesive and migratory capacity of circulating monocyte populations.

Comparison of Manual Cross-Sectional Measurements and Automatic Volumetry of the Corpus Callosum, and Their Clinical Impact: A Study on Type 1 Diabetes and Healthy Controls

Background and purpose: Degenerative change of the corpus callosum might serve as a clinically useful surrogate marker for net pathological cerebral impact of diabetes type 1. We compared manual and automatic measurements of the corpus callosum, as well as differences in callosal cross-sectional area between subjects with type 1 diabetes and healthy controls. Materials and methods: This is a cross-sectional study on 188 neurologically asymptomatic participants with type 1 diabetes and 30 healthy age- and sex-matched control subjects, recruited as part of the Finnish Diabetic Nephropathy Study. All participants underwent clinical work-up and brain MRI. Callosal area was manually measured and callosal volume quantified with FreeSurfer. The measures were normalized using manually measured mid-sagittal intracranial area and volumetric intracranial volume, respectively. Results: Manual and automatic measurements correlated well (callosal area vs. volume: ρ = 0.83, p < 0.001 and mid-sagittal area vs. intracranial volume: ρ = 0.82, p < 0.001). We found no significant differences in the callosal measures between cases and controls. In type 1 diabetes, the lowest quartile of normalized callosal area was associated with higher insulin doses (p = 0.029) and reduced insulin sensitivity (p = 0.033). In addition, participants with more than two cerebral microbleeds had smaller callosal area (p = 0.002). Conclusion: Manually measured callosal area and automatically segmented are interchangeable. The association seen between callosal size with cerebral microbleeds and insulin resistance is indicative of small vessel disease pathology in diabetes type 1.

Mesial temporal lobe epilepsy: Revisiting the relation of hippocampal volumetry with memory deficits

OBJECTIVE

Neuropsychological tests can infer the lateralization of the epileptogenic focus, associating verbal memory to mesial structures in the left temporal lobe and visual or nonverbal memory to the right side. High-field magnetic resonance imaging (MRI) with high-resolution protocols allows acquisitions suitable for advanced postprocessing with precise volumetry of brain structures, and functional MRI demonstrates evidence that epilepsy should be seen as a network pathology, involving several structures in the brain. Since the literature showing associations between the volumetry of brain structures in left and right mesial temporal lobe epilepsy (MTLE) and verbal and visual memory performance on neuropsychological tests is conflicting, we revisited these relationships, considering the hippocampal volumetry of patients with unilateral MTLE.

METHODS

Automatized hippocampal volumes were obtained using FreeSurfer software from MRI exams of 35 patients with unilateral MTLE and hippocampal atrophy and homolateral ictal onset zone defined by video electroencephalography concordant to the side of hippocampal volume reduction (15 on the left side). Verbal memory was assessed using the Rey Auditory-Verbal Learning Test (RAVLT), and visual memory tests employed the Rey-Osterrieth Complex Figure Test (ROCFT). The statistical analysis explored relationships between hippocampal volumetry, lateralization, and performance on memory tests.

RESULTS

In general, we observed deficits in both verbal and visual memory for patients with left and right hippocampal volume reduction. Patients with left hippocampal volume reduction had poorer performance on verbal memory tests compared with those with right hippocampal atrophy (t = -3.813, p < 0.001). Visual memory deficits were seen on both left and right MTLE without a statistically significant difference (t = 0.074, p = 0.942). The correlation between the Hippocampal Asymmetry Index (HAI) and visual and verbal Z-scores was significant only for visual Z-score in right MTLE (R = -0.45, p = 0.048).

CONCLUSIONS

Verbal memory deficit seems to be more consistent in patients with left hippocampal volume reduction. Although it had only a moderate correlation to HAI, visual memory deficit is suggested as a poorer indicator for right MTLE. Considering that verbal and visual memory deficits are seen on both right and left MTLE, MTLE should not be regarded as a unilateral, focal, or local insult but as a multifactorial and network pathology, possibly involving several brain structures.

Standardized Brain MRI Acquisition Protocols Improve Statistical Power in Multicenter Quantitative Morphometry Studies

BACKGROUND AND PURPOSE

In this study, we used power analysis to calculate required sample sizes to detect group-level changes in quantitative neuroanatomical estimates derived from MRI scans obtained from multiple imaging centers. Sample size estimates were derived from (i) standardized 3T image acquisition protocols and (ii) nonstandardized clinically acquired images obtained at both 1.5 and 3T as part of the multicenter Human Epilepsy Project. Sample size estimates were compared to assess the benefit of standardizing acquisition protocols.

METHODS

Cortical thickness, hippocampal volume, and whole brain volume were estimated from whole brain T1-weighted MRI scans processed using Freesurfer v6.0. Sample sizes required to detect a range of effect sizes were calculated using (i) standard t-test based power analysis methods and (ii) a nonparametric bootstrap approach.

RESULTS

A total of 32 participants were included in our analyses, aged 29.9 ± 12.62 years. Standard deviation estimates were lower for all quantitative neuroanatomical metrics when assessed using standardized protocols. Required sample sizes per group to detect a given effect size were markedly reduced when using standardized protocols, particularly for cortical thickness changes <.2 mm and hippocampal volume changes <10%.

CONCLUSIONS

The use of standardized protocols yielded up to a five-fold reduction in required sample sizes to detect disease-related neuroanatomical changes, and is particularly beneficial for detecting subtle effects. Standardizing image acquisition protocols across scanners prior to commencing a study is a valuable approach to increase the statistical power of multicenter MRI studies.

Segmentation errors and intertest reliability in automated and manually traced hippocampal volumes

Objective

To rigorously compare automated atlas‐based and manual tracing hippocampal segmentation for accuracy, repeatability, and clinical acceptability given a relevant range of imaging abnormalities in clinical epilepsy.

Methods

Forty‐nine patients with hippocampal asymmetry were identified from our institutional radiology database, including two patients with significant anatomic deformations. Manual hippocampal tracing was performed by experienced technologists on 3T MPRAGE images, measuring hippocampal volume up to the tectal plate, excluding the hippocampal tail. The same images were processed using NeuroQuant and FreeSurfer software. Ten subjects underwent repeated manual hippocampal tracings by two additional technologists blinded to previous results to evaluate consistency. Ten patients with two clinical MRI studies had volume measurements repeated using NeuroQuant and FreeSurfer.

Results

FreeSurfer raw volumes were significantly lower than NeuroQuant (P < 0.001, right and left), and hippocampal asymmetry estimates were lower for both automatic methods than manual tracing (P < 0.0001). Differences remained significant after scaling volumes to age, gender, and scanner matched normative percentiles. Volume reproducibility was fair (0.4–0.59) for manual tracing, and excellent (>0.75) for both automated methods. Asymmetry index reproducibility was excellent (>0.75) for manual tracing and FreeSurfer segmentation and fair (0.4–0.59) for NeuroQuant segmentation. Both automatic segmentation methods failed on the two cases with anatomic deformations. Segmentation errors were visually identified in 25 NeuroQuant and 27 FreeSurfer segmentations, and nine (18%) NeuroQuant and six (12%) FreeSurfer errors were judged clinically significant.

Interpretation

Automated hippocampal volumes are more reproducible than hand‐traced hippocampal volumes. However, these methods fail in some cases, and significant segmentation errors can occur.

Preservation of Memory Despite Unresected Contralateral Hippocampal Volume Loss After Resection of Hippocampal Sclerosis in Seizure-Free Patients

OBJECTIVE

To determine postoperative long-term changes of hippocampal volume (HV) correlating with cognitive functions in patients who underwent surgery for hippocampal sclerosis with postoperative freedom from seizures.

METHODS

We studied 1.5T magnetic resonance imaging before and after surgery in 24 patients (mean ± SD age, 36.9 ± 11.0 years) with hippocampal sclerosis. We performed serial magnetic resonance imaging at 6 months to 1 year, 1-2 years, 2-3 years, and 3-5 years postoperatively. We compared HVs of 24 patients with HVs of 14 age-matched control subjects. We analyzed correlations between consecutive HVs and seizure duration and age at surgery. We compared consecutive changes in HVs between dominant and nondominant hemispheres with concurrent cognitive functions.

RESULTS

Preoperative HVs of unresected contralateral hippocampus were significantly smaller than HVs of control subjects (P < 0.01). Unresected contralateral HV changes compared with preoperative HVs were -3.6% ± 6.9%, -2.3% ± 8.5%, -3.6% ± 10.2% (P < 0.05), and -5.0% ± 9.5% (P < 0.05) at consecutive postoperative periods. Largest change in HVs at 3-5 years was significantly correlated with older age at surgery (P < 0.05). Unresected contralateral dominant 14 HVs remained consistently smaller than nondominant 10 HVs up to 5 years with statistical significance (P < 0.05). Verbal memory was preserved in 14 patients with unresected contralateral smaller dominant hippocampus.

CONCLUSIONS

In seizure-free patients after hippocampal sclerosis resection , unresected contralateral HV significantly declined with older age at surgery. Visual memory was preserved regardless of side and volume loss. Despite significantly reduced HVs, verbal memory was preserved with the unresected contralateral dominant hippocampus. Earlier surgical intervention may have lower potential risk for memory decline secondary to postoperative HV loss.

Hippocampal volumetric integrity in mesial temporal lobe epilepsy: A fast novel method for analysis of structural MRI

OBJECTIVE

We investigate whether a rapid and novel automated MRI processing technique for assessing hippocampal volumetric integrity (HVI) can be used to identify hippocampal sclerosis (HS) in patients with mesial temporal lobe epilepsy (mTLE) and determine its performance relative to hippocampal volumetry (HV) and visual inspection.

METHODS

We applied the HVI technique to T1-weighted brain images from healthy control (n = 35), mTLE (n = 29), non-HS temporal lobe epilepsy (TLE, n = 44), and extratemporal focal epilepsy (EXTLE, n = 25) subjects imaged using a standardized epilepsy research imaging protocol and on non-standardized clinically acquired images from mTLE subjects (n = 40) to investigate if the technique is translatable to clinical practice. Performance of HVI, HV, and visual inspection was assessed using receiver operating characteristic (ROC) analysis.

RESULTS

mTLE patients from both research and clinical groups had significantly reduced ipsilateral HVI relative to controls (effect size: -0.053, 5.62%, p =  0.002 using a standardized research imaging protocol). For lateralizing mTLE, HVI had a sensitivity of 88% compared with a HV sensitivity of 92% when using specificity equal to 70%.

CONCLUSIONS

The novel HVI approach can effectively detect HS in clinical populations, with an average image processing time of less than a minute. The fast processing speed suggests this technique could have utility as a quantitative tool to assist with imaging-based diagnosis and lateralization of HS in a clinical setting.

BDNF and COMT, but not APOE, alleles are associated with psychiatric symptoms in refractory epilepsy

OBJECTIVE

The objective of this study was to determine whether three common genetic polymorphisms [apolipoprotein (APOE) ε4 (rs42938 and rs7412), brain derived neurotrophic factor (BDNF) Met (rs6265), and catechol-O-methyltransferase (COMT) Val (rs4680)] are associated with increased psychiatric symptomatology in individuals with pharmacoresistant epilepsy.

METHODS

One hundred forty-eight adults (M_age = 38 years; 53% female) with refractory epilepsy completed self-report measures of mood, anxiety, and/or personality/psychopathology. Mann-Whitney U, t-tests, and Fisher's exact tests were used to determine if APOE4, BDNF Val66Met, or COMT Val158Met are associated with increased psychiatric symptomatology in people with epilepsy.

RESULTS

As a group, BDNF Met carriers reported greater symptoms of depression on the Personality Assessment Inventory (PAI) than those without a Met allele (p = 0.004); COMT Val carriers reported greater symptoms on the PAI Schizophrenia (p = 0.007), Antisocial Features (p = 0.04), and Alcohol Problems (p = 0.03) scales than noncarriers. On the individual level, a significantly greater proportion of BDNF Met carriers demonstrated elevated PAI Depression scores compared to those without a Met allele (p = 0.046). There was also a larger proportion of COMT Val carriers with elevated PAI Anxiety scores as compared to those without a Val allele (p = 0.036).

SIGNIFICANCE

This retrospective cross-sectional study provides preliminary evidence for a genetic basis of psychiatric comorbidities in epilepsy and suggests that BDNF and COMT may play an important role in the pathophysiology of mental health problems in this vulnerable population.

Applying surface-based hippocampal morphometry to study APOE-E4 allele dose effects in cognitively unimpaired subjects

Apolipoprotein E (APOE) e4 is the major genetic risk factor for late-onset Alzheimer's disease (AD). The dose-dependent impact of this allele on hippocampal volumes has been documented, but its influence on general hippocampal morphology in cognitively unimpaired individuals is still elusive. Capitalizing on the study of a large number of cognitively unimpaired late middle aged and older adults with two, one and no APOE-e4 alleles, the current study aims to characterize the ability of our automated surface-based hippocampal morphometry algorithm to distinguish between these three levels of genetic risk for AD and demonstrate its superiority to a commonly used hippocampal volume measurement. We examined the APOE-e4 dose effect on cross-sectional hippocampal morphology analysis in a magnetic resonance imaging (MRI) database of 117 cognitively unimpaired subjects aged between 50 and 85 years (mean = 57.4, SD = 6.3), including 36 heterozygotes (e3/e4), 37 homozygotes (e4/e4) and 44 non-carriers (e3/e3). The proposed automated framework includes hippocampal surface segmentation and reconstruction, higher-order hippocampal surface correspondence computation, and hippocampal surface deformation analysis with multivariate statistics. In our experiments, the surface-based method identified APOE-e4 dose effects on the left hippocampal morphology. Compared to the widely-used hippocampal volume measure, our hippocampal morphometry statistics showed greater statistical power by distinguishing cognitively unimpaired subjects with two, one, and no APOE-e4 alleles. Our findings mirrored previous studies showing that APOE-e4 has a dose effect on the acceleration of brain structure deformities. The results indicated that the proposed surface-based hippocampal morphometry measure is a potential preclinical AD imaging biomarker for cognitively unimpaired individuals.

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Applied surface-based hippocampal morphometry on cognitively unimpaired subjects.

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Our study identified APOE-e4 dose effects on cognitively unimpaired subjects.

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Surface-based hippocampal morphometry outperformed the hippocampal volume measure.

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Surface-based hippocampal morphometry may be a potential preclinical AD biomarker.

Differentiation between young adult Internet addicts, smokers, and healthy controls by the interaction between impulsivity and temporal lobe thickness

BACKGROUND AND AIMS

Internet addiction is a non-substance-related addiction disorder with progressively growing prevalence. Internet addiction, like substance-related addictions, has been linked with high impulsivity, low inhibitory control, and poor decision-making abilities. Cortical thickness measurements and trait impulsivity have been shown to have a distinct relationship in addicts compared to healthy controls. Thus, we test whether the cortical correlates of trait impulsivity are different in Internet addicts and healthy controls, using an impulsive control group (smokers).

METHODS

Thirty Internet addicts (15 females) and 60 age- and gender-matched controls (30 smokers, all young adults aged 19-28 years) were scanned using a 3T MRI scanner and completed the Barratt Impulsiveness Scale.

RESULTS

Internet addicts had a thinner left superior temporal cortex than controls. Impulsivity had a significant main effect on the left pars orbitalis and bilateral insula, regardless of group membership. We identified divergent relationships between trait impulsivity and thicknesses of the bilateral middle temporal, right superior temporal, left inferior temporal, and left transverse temporal cortices between Internet addicts and healthy controls. Further analysis with smokers revealed that the left middle temporal and left transverse temporal cortical thickness change might be exclusive to Internet addiction.

DISCUSSION

The effects of impulsivity, combined with a long-term exposure to some specific substance or stimuli, might result in different natures of relationships between impulsivity and brain structure when compared to healthy controls.

CONCLUSION

These results may indicate that Internet addiction is similar to substance-related addictions, such that inefficient self-control could result in maladaptive behavior and inability to resist Internet use.

Performance of semi-automated hippocampal subfield segmentation methods across ages in a pediatric sample

Episodic memory function has been shown to depend critically on the hippocampus. This region is made up of a number of subfields, which differ in both cytoarchitectural features and functional roles in the mature brain. Recent neuroimaging work in children and adolescents has suggested that these regions may undergo different developmental trajectories-a fact that has important implications for how we think about learning and memory processes in these populations. Despite the growing research interest in hippocampal structure and function at the subfield level in healthy young adults, comparatively fewer studies have been carried out looking at subfield development. One barrier to studying these questions has been that manual segmentation of hippocampal subfields-considered by many to be the best available approach for defining these regions-is laborious and can be infeasible for large cross-sectional or longitudinal studies of cognitive development. Moreover, manual segmentation requires some subjectivity and is not impervious to bias or error. In a developmental sample of individuals spanning 6-30 years, we assessed the degree to which two semi-automated segmentation approaches-one approach based on Automated Segmentation of Hippocampal Subfields (ASHS) and another utilizing Advanced Normalization Tools (ANTs)-approximated manual subfield delineation on each individual by a single expert rater. Our main question was whether performance varied as a function of age group. Across several quantitative metrics, we found negligible differences in subfield validity across the child, adolescent, and adult age groups, suggesting that these methods can be reliably applied to developmental studies. We conclude that ASHS outperforms ANTs overall and is thus preferable for analyses carried out in individual subject space. However, we underscore that ANTs is also acceptable and may be well-suited for analyses requiring normalization to a single group template (e.g., voxelwise analyses across a wide age range). Previous work has supported the use of such methods in healthy young adults, as well as several special populations such as older adults and those suffering from mild cognitive impairment. Our results extend these previous findings to show that ASHS and ANTs can also be used in pediatric populations as young as six.

FAst Segmentation Through SURface Fairing (FASTSURF): A novel semi-automatic hippocampus segmentation method

Objective

The objective is to present a proof-of-concept of a semi-automatic method to reduce hippocampus segmentation time on magnetic resonance images (MRI).

Materials and methods

FAst Segmentation Through SURface Fairing (FASTSURF) is based on a surface fairing technique which reconstructs the hippocampus from sparse delineations. To validate FASTSURF, simulations were performed in which sparse delineations extracted from full manual segmentations served as input. On three different datasets with different diagnostic groups, FASTSURF hippocampi were compared to the original segmentations using Jaccard overlap indices and percentage volume differences (PVD). In one data set for which back-to-back scans were available, unbiased estimates of overlap and PVD were obtained. Using longitudinal scans, we compared hippocampal atrophy rates measured by manual, FASTSURF and two automatic segmentations (FreeSurfer and FSL-FIRST).

Results

With only seven input contours, FASTSURF yielded mean Jaccard indices ranging from 72(±4.3)% to 83(±2.6)% and PVDs ranging from 0.02(±2.40)% to 3.2(±3.40)% across the three datasets. Slightly poorer results were obtained for the unbiased analysis, but the performance was still considerably better than both tested automatic methods with only five contours.

Conclusions

FASTSURF segmentations have high accuracy and require only a fraction of the delineation effort of fully manual segmentation. Atrophy rate quantification based on completely manual segmentation is well reproduced by FASTSURF. Therefore, FASTSURF is a promising tool to be implemented in clinical workflow, provided a future prospective validation confirms our findings.

Hippocampal Dentate Gyrus Atrophy Predicts Pattern Separation Impairment in Patients with LGI1 Encephalitis

Day-to-day life involves the perception of events that resemble one another. For the sufficient encoding and correct retrieval of similar information, the hippocampus provides two essential cognitive processes. Pattern separation refers to the differentiation of similar input information, whereas pattern completion reactivates memory representations based on noisy or degraded stimuli. It has been shown that pattern separation specifically relies on the hippocampal dentate gyrus (DG), whereas pattern completion is performed within CA3 networks. Lesions to these hippocampal networks emerging in the course of neurological disorders may thus affect both processes. In anti-leucine-rich, glioma-inactivated 1 (LGI1) encephalitis it has been shown in animal models and human imaging studies that hippocampal DG and CA3 are preferentially involved in the pathophysiology process. Thus, in order to elucidate the structure-function relationship and contribution of hippocampal subfields to pattern separation, we examined patients (n = 15, age range: 36-77 years) with the rare LGI1 encephalitis showing lesions to hippocampal subfields. Patients were tested 3.53 ± 0.65 years after the acute phase of the disease. Structural sequelae were determined by hippocampal subfield volumetry for the DG, CA1, and CA2/3. Patients showed an overall memory deficit including a significant reduction in pattern separation performance (p = 0.016). In volumetry, we found a global hippocampal volume reduction. The deficits in pattern separation performance were best predicted by the DG (p = 0.029), whereas CA1 was highly predictive of recognition memory deficits (p < 0.001). These results corroborate the framework of a regional specialization of hippocampal functions involved in cognitive processing.

Normal cerebral cortical thickness in first-degree relatives of temporal lobe epilepsy patients

Objective

To examine cerebral cortex thickness in asymptomatic first-degree relatives of patients with mesial temporal lobe epilepsy (MTLE).

Methods

We investigated 127 asymptomatic first-degree relatives of patients with MTLE due to hippocampal sclerosis (HS) (mean age ± SD = 39.4 ± 13 years) and 203 healthy control individuals (mean age ± SD = 36.0 ± 11 years). Participants underwent a comprehensive clinical evaluation and structural brain MRI at 3 study sites. Images were processed simultaneously at each site using a surface-based morphometry method to quantify global brain measures, hippocampal volumes, and cerebral cortical thickness. Differences in brain measures between relatives of patients and controls were examined using generalized models, while controlling for relevant covariates, including age and sex.

Results

None of the asymptomatic first-degree relatives of MTLE + HS patients showed evidence of HS on qualitative image assessments. Compared to the healthy controls, the asymptomatic relatives of patients displayed no significant differences in intracranial volume, average hemispheric surface area, or hippocampal volume. Similarly, no significant cerebral cortical thinning was identified in the relatives of patients. This was consistent across the 3 cohorts.

Conclusion

Lack of cortical thickness changes in the asymptomatic relatives of patients indicates that the previously characterized MTLE + HS-related cortical thinning is not heritable, and is likely driven by disease-related factors. This finding therefore argues for early and aggressive intervention in patients with medically intractable epilepsy.

Manual Hippocampal Subfield Segmentation Using High-Field MRI: Impact of Different Subfields in Hippocampal Volume Loss of Temporal Lobe Epilepsy Patients

In patients with temporal lobe epilepsy (TLE), presurgical magnetic resonance imaging (MRI) often reveals hippocampal atrophy, while neuropathological assessment indicates the different types of hippocampal sclerosis (HS). Different HS types are not discriminated in MRI so far. We aimed to define the volume of each hippocampal subfield on MRI manually and to compare automatic and manual segmentations for the discrimination of HS types. The T2-weighted images from 14 formalin-fixed age-matched control hippocampi were obtained with 4.7T MRI to evaluate the volume of each subfield at the anatomical level of the hippocampal head, body, and tail. Formalin-fixed coronal sections at the level of the body of 14 control cases, as well as tissue samples from 24 TLE patients, were imaged with a similar high-resolution sequence at 3T. Presurgical three-dimensional (3D) T1-weighted images from TLE went through a FreeSurfer 6.0 hippocampal subfield automatic assessment. The manual delineation with the 4.7T MRI was identified using Luxol Fast Blue stained 10-μm-thin microscopy slides, collected at every millimeter. An additional section at the level of the body from controls and TLE cases was submitted to NeuN immunohistochemistry for neuronal density estimation. All TLE cases were classified according to the International League Against Epilepsy's (ILAE's) HS classification. Manual volumetry in controls revealed that the dentate gyrus (DG)+CA4 region, CA1, and subiculum accounted for almost 90% of the hippocampal volume. The manual 3T volumetry showed that all TLE patients with type 1 HS (TLE-HS1) had lower volumes for DG+CA4, CA2, and CA1, whereas those TLE patients with HS type 2 (TLE-HS2) had lower volumes only in CA1 (p ≤ 0.038). Neuronal cell densities always decreased in CA4, CA3, CA2, and CA1 of TLE-HS1 but only in CA1 of TLE-HS2 (p ≤ 0.003). In addition, TLE-HS2 had a higher volume (p = 0.016) and higher neuronal density (p < 0.001) than the TLE-HS1 in DG + CA4. Automatic segmentation failed to match the manual or histological findings and was unable to differentiate TLE-HS1 from TLE-HS2. Total hippocampal volume correlated with DG+CA4 and CA1 volumes and neuronal density. For the first time, we also identified subfield-specific pathology patterns in the manual evaluation of volumetric MRI scans, showing the importance of manual segmentation to assess subfield-specific pathology patterns.

A comparison of automated segmentation and manual tracing in estimating hippocampal volume in ischemic stroke and healthy control participants

Manual quantification of the hippocampal atrophy state and rate is time consuming and prone to poor reproducibility, even when performed by neuroanatomical experts. The automation of hippocampal segmentation has been investigated in normal aging, epilepsy, and in Alzheimer's disease. Our first goal was to compare manual and automated hippocampal segmentation in ischemic stroke and to, secondly, study the impact of stroke lesion presence on hippocampal volume estimation. We used eight automated methods to segment T1-weighted MR images from 105 ischemic stroke patients and 39 age-matched controls sampled from the Cognition And Neocortical Volume After Stroke (CANVAS) study. The methods were: AdaBoost, Atlas-based Hippocampal Segmentation (ABHS) from the IDeALab, Computational Anatomy Toolbox (CAT) using 3 atlas variants (Hammers, LPBA40 and Neuromorphometics), FIRST, FreeSurfer v5.3, and FreeSurfer v6.0-Subfields. A number of these methods were employed to re-segment the T1 images for the stroke group after the stroke lesions were masked (i.e., removed). The automated methods were assessed on eight measures: process yield (i.e. segmentation success rate), correlation (Pearson's R and Shrout's ICC), concordance (Lin's RC and Kandall's W), slope 'a' of best-fit line from correlation plots, percentage of outliers from Bland-Altman plots, and significance of control-stroke difference. We eliminated the redundant measures after analysing between-measure correlations using Spearman's rank correlation. We ranked the automated methods based on the sum of the remaining non-redundant measures where each measure ranged between 0 and 1. Subfields attained an overall score of 96.3%, followed by AdaBoost (95.0%) and FIRST (94.7%). CAT using the LPBA40 atlas inflated hippocampal volumes the most, while the Hammers atlas returned the smallest volumes overall. FIRST (p = 0.014), FreeSurfer v5.3 (p = 0.007), manual tracing (p = 0.049), and CAT using the Neuromorphometics atlas (p = 0.017) all showed a significantly reduced hippocampal volume mean for the stroke group compared to control at three months. Moreover, masking of the stroke lesions prior to segmentation resulted in hippocampal volumes which agreed less with manual tracing. These findings recommend an automated segmentation without lesion masking as a more reliable procedure for the estimation of hippocampal volume in ischemic stroke.

Clinical validation of automated hippocampal segmentation in temporal lobe epilepsy

OBJECTIVE

To provide a multi-atlas framework for automated hippocampus segmentation in temporal lobe epilepsy (TLE) and clinically validate the results with respect to surgical lateralization and post-surgical outcome.

METHODS

We retrospectively identified 47 TLE patients who underwent surgical resection and 12 healthy controls. T1-weighted 3 T MRI scans were acquired for all subjects, and patients were identified by a neuroradiologist with regards to lateralization and degree of hippocampal sclerosis (HS). Automated segmentation was implemented through the Joint Label Fusion/Corrective Learning (JLF/CL) method. Gold standard lateralization was determined from the surgically resected side in Engel I (seizure-free) patients at the two-year timepoint. ROC curves were used to identify appropriate thresholds for hippocampal asymmetry ratios, which were then used to analyze JLF/CL lateralization.

RESULTS

The optimal template atlas based on subject images with varying appearances, from normal-appearing to severe HS, was demonstrated to be composed entirely of normal-appearing subjects, with good agreement between automated and manual segmentations. In applying this atlas to 26 surgically resected seizure-free patients at a two-year timepoint, JLF/CL lateralized seizure onset 92% of the time. In comparison, neuroradiology reads lateralized 65% of patients, but correctly lateralized seizure onset in these patients 100% of the time. When compared to lateralized neuroradiology reads, JLF/CL was in agreement and correctly lateralized all 17 patients. When compared to nonlateralized radiology reads, JLF/CL correctly lateralized 78% of the nine patients.

SIGNIFICANCE

While a neuroradiologist's interpretation of MR imaging is a key, albeit imperfect, diagnostic tool for seizure localization in medically-refractory TLE patients, automated hippocampal segmentation may provide more efficient and accurate epileptic foci localization. These promising findings demonstrate the clinical utility of automated segmentation in the TLE MR imaging pipeline prior to surgical resection, and suggest that further investigation into JLF/CL-assisted MRI reading could improve clinical outcomes. Our JLF/CL software is publicly available at https://www.nitrc.org/projects/ashs/.

Use of an Automated Quantitative Analysis of Hippocampal Volume, Signal, and Glucose Metabolism to Detect Hippocampal Sclerosis

Purpose: Magnetic resonance imaging (MRI) and positron emission tomography (PET) with ¹⁸F-fluorodeoxyglucose (¹⁸FDG) are valuable tools for evaluating hippocampal sclerosis (HS); however, bias may arise during visual analyses. The aim of this study was to evaluate and compare MRI and PET post-processing techniques, automated quantitative hippocampal volume (Q-volume), and fluid-attenuated inversion-recovery (FLAIR) signal (Q-FLAIR) and glucose metabolism (Q-PET) analyses in patients with HS.

Methods: We collected MRI and ¹⁸FDG-PET images from 54 patients with HS and 22 healthy controls and independently performed conventional visual analyses (CVA) of PET (CVA-PET) and MRI (CVA-MRI) images. During the subsequent quantitative analyses, the hippocampus was segmented from the 3D T1 image, and the mean volumetric, FLAIR intensity and standardized uptake value ratio (SUVR) values of the left and right hippocampus were assessed in each subject. Threshold confidence levels calculated from the mean volumetric, FLAIR intensity and SUVR values of the controls were used to identify healthy subjects or subjects with HS. The performance of the three methods was assessed using receiver operating characteristic (ROC) curves, and the detection rates of CVA-MRI, CVA-PET, Q-volume, Q-FLAIR, and Q-PET were statistically compared.

Results: The areas under the curves (AUCs) for the Q-volume, Q-FLAIR, and Q-PET ROC analyses were 0.88, 0.41, and 0.98, which suggested a diagnostic method with moderate, poor, and high accuracy, respectively. Although Q-PET had the highest detection rate among the two CVA methods and three quantitative methods, the difference between Q-volume and Q-PET did not reach statistical significance. Regarding the HS subtypes, CVA-MRI, CVA-PET, Q-volume, and Q-PET had similar detection rates for type 1 HS, and Q-PET was the most sensitive method for detecting types 2 and 3 HS.

Conclusions: In MRI or ¹⁸FDG-PET images that have been visually assessed by experts, the quantification of hippocampal volume or glucose uptake can increase the detection of HS and appear to be additional valuable diagnostic tools for evaluating patients with epilepsy who are suspected of having HS.

Inter-observer variation of hippocampus delineation in hippocampal avoidance prophylactic cranial irradiation

BACKGROUND

Hippocampal avoidance prophylactic cranial irradiation (HA-PCI) techniques have been developed to reduce radiation damage to the hippocampus. An inter-observer hippocampus delineation analysis was performed and the influence of the delineation variability on dose to the hippocampus was studied.

MATERIALS AND METHODS

For five patients, seven observers delineated both hippocampi on brain MRI. The intra-class correlation (ICC) with absolute agreement and the generalized conformity index (CI_gen) were computed. Median surfaces over all observers' delineations were created for each patient and regional outlining differences were analysed. HA-PCI dose plans were made from the median surfaces and we investigated whether dose constraints in the hippocampus could be met for all delineations.

RESULTS

The ICC for the left and right hippocampus was 0.56 and 0.69, respectively, while the CI_gen ranged from 0.55 to 0.70. The posterior and anterior-medial hippocampal regions had most variation with SDs ranging from approximately 1 to 2.5 mm. The mean dose (D_mean) constraint was met for all delineations, but for the dose received by 1% of the hippocampal volume (D_1%) violations were observed.

CONCLUSION

The relatively low ICC and CI_gen indicate that delineation variability among observers for both left and right hippocampus was large. The posterior and anterior-medial border have the largest delineation inaccuracy. The hippocampus D_mean constraint was not violated.