Summary
Surgical resection of the hippocampus is the most successful treatment for medication‐refractory medial temporal lobe epilepsy (MTLE) due to hippocampal sclerosis. Unfortunately, at least one ...of four operated patients continue to have disabling seizures after surgery, and there is no existing method to predict individual surgical outcome. Prior to surgery, patients who become seizure free appear identical to those who continue to have seizures after surgery. Interestingly, newly converging presurgical data from magnetic resonance imaging (MRI) and intracranial electroencephalography (EEG) suggest that the entorhinal and perirhinal cortices may play an important role in seizure generation. These areas are not consistently resected with surgery and it is possible that they continue to generate seizures after surgery in some patients. Therefore, subtypes of MTLE patients can be considered according to the degree of extrahippocampal damage and epileptogenicity of the medial temporal cortex. The identification of these subtypes has the potential to drastically improve surgical results via optimized presurgical planning. In this review, we discuss the current data that suggests neural network damage in MTLE, focusing on the medial temporal cortex. We explore how this evidence may be applied to presurgical planning and suggest approaches for future investigation.
•Anodal tDCS to DLPFC improved sustained attention in persons with aphasia.•Persons with aphasia learned artificial grammar after 10 training sessions.•Attention improvements after tDCS to DLPFC may ...facilitate language learning.
Abstract Background Emerging evidence suggests that post-stroke aphasia severity depends on the integrity of the brain beyond the lesion. While measures of lesion anatomy and brain integrity combine ...synergistically to explain aphasic symptoms, substantial interindividual variability remains unaccounted. One explanatory factor may be the spatial distribution of morphometry beyond the lesion (e.g., atrophy), including not just specific brain areas, but distinct three-dimensional patterns. Methods Here, we test whether deep learning with Convolutional Neural Networks (CNNs) on whole brain morphometry (i.e., segmented tissue volumes) and lesion anatomy better predicts chronic stroke individuals with severe aphasia ( N = 231) than classical machine learning (Support Vector Machines; SVMs), evaluating whether encoding spatial dependencies identifies uniquely predictive patterns. Results CNNs achieve higher balanced accuracy and F1 scores, even when SVMs are nonlinear or integrate linear or nonlinear dimensionality reduction. Parity only occurs when SVMs access features learned by CNNs. Saliency maps demonstrate that CNNs leverage distributed morphometry patterns, whereas SVMs focus on the area around the lesion. Ensemble clustering of CNN saliencies reveals distinct morphometry patterns unrelated to lesion size, consistent across individuals, and which implicate unique networks associated with different cognitive processes as measured by the wider neuroimaging literature. Individualized predictions depend on both ipsilateral and contralateral features outside the lesion. Conclusions Three-dimensional network distributions of morphometry are directly associated with aphasia severity, underscoring the potential for CNNs to improve outcome prognostication from neuroimaging data, and highlighting the prospective benefits of interrogating spatial dependence at different scales in multivariate feature space.
•Do white matter hyperintensities predict acute poststroke outcome?•Cross-validated support vector regression was applied to predict spatial neglect.•Voxel-based maps (not ordinal scales) ...significantly improved prediction accuracy.
White matter hyperintensities (WMH) are frequently observed in brain scans of elderly people. They are associated with an increased risk of stroke, cognitive decline, and dementia. However, it is unknown yet if measures of WMH provide information that improve the understanding of poststroke outcome compared to only state-of-the-art stereotaxic structural lesion data.
We implemented high-dimensional machine learning models, based on support vector regression, to predict the severity of spatial neglect in 103 acute right hemispheric stroke patients.
We found that (1) the additional information of right hemispheric or bilateral voxel-based topographic WMH extent indeed yielded a significant improvement in predicting acute neglect severity (compared to the voxel-based stroke lesion map alone). (2) Periventricular WMH appeared more relevant for prediction than deep subcortical WMH. (3) Among different measures of WMH, voxel-based maps as measures of topographic extent allowed more accurate predictions compared to the use of traditional ordinally assessed visual rating scales (Fazekas-scale, Cardiovascular Health Study-scale).
In summary, topographic WMH appear to be a valuable clinical imaging biomarker for predicting the severity of cognitive deficits and bears great potential for rehabilitation guidance of acute stroke patients.
Population-wide reductions in cardiovascular disease (CVD) have not been equally shared in the African American community due to a higher burden of CVD risk factors such as metabolic disorders and ...obesity. Differential concentrations of sphingolipids such as ceramide, sphingosine, and sphingosine 1-phosphate (S1P) has been associated with the development of CVD, metabolic disorders (MetD), and obesity. Whether African Americans have disparate expression levels of sphingolipids that explain higher burdens of CVD remains unknown.
A cross sectional analysis of plasma concentrations of ceramides, sphingosine, and S1P were measured from 8 whites and 7 African Americans without metabolic disorders and 7 whites and 8 African Americans with metabolic disorders using high performance liquid chromatography/tandem mass spectrometry methodology (HPLC/MS-MS). Subjects were stratified by both race and metabolic status. Subjects with one or more of the following physician confirmed diagnosis: diabetes, hypertension, hypercholesterolemia, or dyslipidemia were classified as having metabolic disease (MetD). Data was analyzed using a Two-Way ANOVA and Tukey's post hoc test.
Total ceramide levels were increased in African Americans compared to African Americans with MetD. Ceramide C16 levels were higher in whites with MetD compared to African Americans with MetD (p<0.05). Ceramide C20 levels were higher in whites with MetD compared to whites. Ceramide C20 levels were higher in African Americans compared to African Americans with MetD. Furthermore, whites with MetD had higher levels of C20 compared to African Americans with MetD (p<0.0001). Ceramide C24:0 and C24:1 in African Americans was higher compared to African Americans with MetD (p<0.05). The plasma concentration of Sph-1P ceramide was higher in African Americans vs whites (p = 0.01). Lastly, ceramide C20 negatively correlated with hemoglobin A1c (HbA1c) levels in our study cohort.
Plasma ceramide concentration patterns are distinct in African Americans with MetD. Further research with larger samples sizes are needed to confirm these findings and to understand whether racial disparities in sphingolipid concentrations have potential therapeutic implications for CVD-related health outcomes.
•White matter hyperintensities are the most studied marker of brain health.•Brain health plays a role in the extent of language recovery in chronic aphasia.•Current stroke literature emphasizes ...cognition rather than measures of aphasia.•Many current cognitive tests present a barrier for people with language disorders.•There is a need for a uniform grading system for small vessel disease.
For the past decade, brain health has been an emerging line of scientific inquiry assessing the impact of age-related neurostructural changes on cognitive decline and recovery from brain injury. Typically, compromised brain health is attributed to the presence of small vessel disease (SVD) and brain tissue atrophy, which are represented by various neuroimaging features. However, to date, the relationship between brain health markers and chronic aphasia severity remains unclear. Thus, the goal of this scoping review was to assess the current body of evidence regarding the relationship between SVD-related brain health biomarkers and post-stroke aphasia and cognition. In all, 187 articles were identified from 3 databases, of which 16 articles met the criteria for inclusion. Among these studies, 11 focused on cognition rather than aphasia, while 2 investigated both. Of the 10 studies that used white matter hyperintensities (WMHs) as an indicator of SVD severity, 8 studies (80%) demonstrated a relationship between WMH load and worse cognition in stroke patients. Interestingly, among the studies that specifically investigated aphasia, all 5 studies (100%) demonstrated a relationship between SVD and worse language performance. They also indicated that factors other than brain health (e.g., lesion, age, time post onset) played an important role in determining aphasia severity at a single timepoint. These findings suggest that brain health is likely a crucial factor in the context of aphasia recovery, possibly indicating the necessity of cognitive reserve thresholds for the multimodal cognitive demands associated with language recovery. While SVD and structural brain health are not commonly considered as predictors of aphasia severity, more comprehensive models incorporating brain health have the potential to improve prognosis of post-stroke cognitive and language deficits. Given the variability in the existing literature, a uniform grading system for overall SVD would be beneficial for future research on the mechanisms related to brain networks and neuroplasticity, and their translational impact.
Elevated levels of FGF23 in individuals with chronic kidney disease (CKD) are associated with adverse health outcomes, such as increased mortality, large vessel disease, and reduced white matter ...volume, cardiovascular and cerebrovascular events. Apart from the well-known link between cardiovascular (CV) risk factors, especially diabetes and hypertension, and cerebrovascular damage, elevated FGF23 is also postulated to be associated with cerebrovascular damage independently of CKD. Elevated FGF23 predisposes to vascular calcification and is associated with vascular stiffness and endothelial dysfunction in the general population with normal renal function. These factors may lead to microangiopathic changes in the brain, cumulative ischemia, and eventually to the loss of white matter fibers. The relationship between FGF23 and brain integrity in individuals without CKD has hitherto not been investigated. In this study, we aimed to determine the association between FGF23, and white matter integrity in a cohort of 50 participants with varying degrees of CV risk burden, using high resolution structural human brain connectomes constructed from MRI diffusion images. We observed that increased FGF23 was associated with axonal loss in the frontal lobe, leading to a fragmentation of white matter network organization. This study provides the first description of the relationship between elevated levels of FGF23, white matter integrity, and brain health. We suggest a synergistic interaction of CV risk factors and FGF23 as a potentially novel determinant of brain health.
Background
It is common for patients diagnosed with medial temporal lobe epilepsy (TLE) to have extrahippocampal damage. However, it is unclear whether microstructural extrahippocampal abnormalities ...are consistent enough to enable classification using diffusion MRI imaging. Therefore, we implemented a support vector machine (SVM)‐based method to predict TLE from three different imaging modalities: mean kurtosis (MK), mean diffusivity (MD), and fractional anisotropy (FA). While MD and FA can be calculated from traditional diffusion tensor imaging (DTI), MK requires diffusion kurtosis imaging (DKI).
Methods
Thirty‐two TLE patients and 36 healthy controls underwent DKI imaging. To measure predictive capability, a fivefold cross‐validation (CV) was repeated for 1000 iterations. An ensemble of SVM models, each with a different regularization value, was trained with the subject images in the training set, and had performance assessed on the test set. The different regularization values were determined using a Bayesian‐based method.
Results
Mean kurtosis achieved higher accuracy than both FA and MD on every iteration, and had far superior average accuracy: 0.82 (MK), 0.68 (FA), and 0.51 (MD). Finally, the MK voxels with the highest coefficients in the predictive models were distributed within the inferior medial aspect of the temporal lobes.
Conclusion
These results corroborate our earlier publications which indicated that DKI shows more promise in identifying TLE‐associated pathological features than DTI. Also, the locations of the contributory MK voxels were in areas with high fiber crossing and complex fiber anatomy. These traits result in non‐Gaussian water diffusion, and hence render DTI less likely to detect abnormalities. If the location of consistent microstructural abnormalities can be better understood, then it may be possible in the future to identify the various phenotypes of TLE. This is important since treatment outcome varies dependent on type of TLE.
This study applies machine learning to diffusion‐derived measures (mean diffusivity, MD; fractional anisotropy, FA; and mean kurtosis, MK) to predict medial temporal lobe epilepsy (TLE) with linear classifiers that operate on a voxel‐wise level. The best classification was achieved with MK, corroborating our previous work that kurtosis‐based imaging is more sensitive to microstructural abnormalities associated with TLE than traditional diffusion metrics. The strong prediction performance of the models trained with the MK‐images indicates that there may be a common pathological pattern across individuals with TLE.
Temporal lobe epilepsy (TLE) is one of the most common forms of epilepsy. Unfortunately, the clinical outcomes of TLE cannot be determined based only on current diagnostic modalities. A better ...understanding of white matter (WM) connectivity changes in TLE may aid the identification of network abnormalities associated with TLE and the phenotypic characterisation of the disease.
We implemented a novel approach for characterising microstructural changes along WM pathways using diffusional kurtosis imaging (DKI). Along-the-tract measures were compared for 32 subjects with left TLE and 36 age-matched and gender-matched controls along the left and right fimbria-fornix (FF), parahippocampal WM bundle (PWMB), arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF) and cingulum bundle (CB). Limbic pathways were investigated in relation to seizure burden and control with antiepileptic drugs.
By evaluating measures along each tract, it was possible to identify abnormalities localised to specific tract subregions. Compared with healthy controls, subjects with TLE demonstrated pathological changes in circumscribed regions of the FF, PWMB, UF, AF and ILF. Several of these abnormalities were detected only by kurtosis-based and not by diffusivity-based measures. Structural WM changes correlated with seizure burden in the bilateral PWMB and cingulum.
DKI improves the characterisation of network abnormalities associated with TLE by revealing connectivity abnormalities that are not disclosed by other modalities. Since TLE is a neuronal network disorder, DKI may be well suited to fully assess structural network abnormalities related to epilepsy and thus serve as a tool for phenotypic characterisation of epilepsy.
•Left and right temporal lobe epilepsy patients with systemic language impairment were identified.•The white matter structural network model provided superior prediction of impairment.•Lateral ...temporal connections contributed the most to the network model.•Additional highlighted connections were distributed and bilateral.
The distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.
T1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.
The SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.
The SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance.
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