Grey and white matter atrophy one year after stroke aphasia

Magnetic Resonance Imaging and Anatomical Correlation of Human Temporal Lobe Landmarks in 3D Euclidean Space: A Study of Control and Epilepsy Disease Subjects

Epilepsy is a common neurological disorder of great importance to patients and society. Sclerosis is associated with neuronal loss and neurodegeneration in specific regions of the hippocampal formation. The hippocampal formation and temporal lobe are not the only regions affected; the chronicity of the disease extends the involvement to other brain regions. Our aim is to investigate the spatial relationship of anatomical structures in both control (CO) and epileptic (EP) subjects using magnetic resonance imaging (MRI) in order to determine changes in epileptic patients compared to healthy anatomical structures. Anatomical landmarks are identified and registered in 3D space to provide a reference for the brain structures; the 3D network is described quantitatively using planar distances, as well as measuring rostrocaudal and Euclidean distances. The planar and rostrocaudal distances are the most remarkable discriminators between CO and EP groups, especially between structures located in and outside the temporal lobe. The study achieves a 100% discrimination between the control group and the epileptic group with the discriminant use of two distances: D_PL, Hpe/Cde and D_RC, As/cae. Finally, discriminates 100% between the three study groups, control group CO, extratemporal lobe epilepsy ETLE and temporal lobe epilepsy TLE, with a total of 12 distances distributed in the three axes of space. This study allows us to hope for a future application, its clinical utility may allow us not only to identify processes (in our case, epilepsy), but also to obtain parameters of the evolution of the disease.

Validation of the abbreviated version of the Token Test in Latin American Spanish stroke patients

BACKGROUND

The abbreviated version of the Token Test (aTT) is widely used to assess language comprehension deficits in stroke patients (SPs). However, aTT has not been validated for Latin American Spanish speakers, so clinicians tend to use cut-off scores for aTT validated in developed countries.

AIMS

To provide normative data for the Spanish aTT (Sp-aTT) in healthy Chilean Spanish-speaking and SP, determining the influence of sociodemographic variables such as gender, age and education on Sp-aTT performance.

METHODS & PROCEDURES

A total of 210 healthy volunteers (age range = 18-88 years) and 197 SPs (age range = 23-94 years), all native speakers of Chilean Spanish, were recruited. The association of age, gender and years of education on the Sp-aTT performance was analysed. Specificity and sensibility analyses of the Sp-aTT to diagnose language comprehension deficits were completed.

OUTCOMES & RESULTS

Only age (p < 0.001) and years of education (p < 0.001) impacted the total score of Sp-aTT. Gender did not show an association with Sp-aTT performance (p = 0.181). For SPs, the Sp-aTT score showed a significant positive correlation (rho = 0.4, p < 0.001) with the aphasia severity rating scale (ASRS) score. For Sp-aTT, the area under the curve was 0.97, and the optimal cut-off score for the Sp-aTT was 30 (0.73 of sensitivity, 0.92 of specificity and a Youden index of 0.644).

CONCLUSIONS & IMPLICATIONS

Age and years of education are two key factors to be controlled for when determining the optimal cut-off points for the Sp-aTT. Our results also highlight the need for language-specific norms in stroke and aphasia research.

WHAT THIS PAPER ADDS

What is already known on the subject The aTT has been validated and adapted in several countries. Its properties in screening and detecting comprehensive deficits in SPs highlight its potential as a screening tool in clinical practice. Moreover, considering that stroke is the third largest cause of death worldwide, research and clinical practice have focused on how to improve early detection of deficits in these people, especially those related to cognition, language and functionality in SPs. Therefore, counting with validated and adapted tools is essential for clinicians because it could contribute to accurate intervention and classification of language disorders. What this paper adds to the existing knowledge The main contribution of this study is to provide normative data for the aTT in Latin American Spanish speakers. No previous studies have focused on validating this test and analysing the influence of three critical variables (age, gender and years of education) on its performance in SPs from Latin America. In addition, we propose a classification of the severity of comprehension deficits in SPs. Finally, we found comprehension deficits in patients with right and left hemisphere stroke, which would imply that these deficits would not be exclusive to left hemisphere stroke. What are the potential or actual clinical implications of this work? Contribute with validation of language comprehension tools, such as the aTT, could improve early diagnosis of patients with language disorders. This validation provides a test based on the sociodemographic characteristics of Latin American Speakers, which has yet to be established. Due to this, normative data considering the sociodemographic characteristics of the target population is crucial for accurately classifying comprehension deficits after brain damage.

Distinct brain morphometry patterns revealed by deep learning improve prediction of post-stroke aphasia severity

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.

Distinct brain morphometry patterns revealed by deep learning improve prediction of aphasia severity

Emerging evidence suggests that post-stroke aphasia severity depends on the integrity of the brain beyond the stroke lesion. While measures of lesion anatomy and brain integrity combine synergistically to explain aphasic symptoms, significant interindividual variability remains unaccounted for. A possible explanatory factor may be the spatial distribution of brain atrophy beyond the lesion. This includes not just the specific brain areas showing atrophy, but also distinct three-dimensional patterns of atrophy. Here, we tested whether deep learning with Convolutional Neural Networks (CNN) on whole brain morphometry (i.e., segmented tissue volumes) and lesion anatomy can better predict which individuals with chronic stroke (N=231) have severe aphasia, and whether encoding spatial dependencies in the data might be capable of improving predictions by identifying unique individualized spatial patterns. We observed that CNN achieves significantly higher accuracy and F1 scores than Support Vector Machine (SVM), even when the SVM is nonlinear or integrates linear and nonlinear dimensionality reduction techniques. Performance parity was only achieved when the SVM was directly trained on the latent features learned by the CNN. Saliency maps demonstrated that the CNN leveraged widely distributed patterns of brain atrophy predictive of aphasia severity, whereas the SVM focused almost exclusively on the area around the lesion. Ensemble clustering of CNN saliency maps revealed distinct morphometry patterns that were unrelated to lesion size, highly consistent across individuals, and implicated unique brain networks associated with different cognitive processes as measured by the wider neuroimaging literature. Individualized predictions of severity depended on both ipsilateral and contralateral features outside of the location of stroke. Our findings illustrate that three-dimensional network distributions of atrophy in individuals with aphasia are directly associated with aphasia severity, underscoring the potential for deep learning to improve prognostication of behavioral outcomes from neuroimaging data, and highlighting the prospective benefits of interrogating spatial dependence at different scales in multivariate feature space.

Changes in White Matter Microstructure Over 3 Years in People With and Without Stroke

BACKGROUND AND OBJECTIVES

Cerebral white matter health can be estimated by MRI-derived indices of microstructure. White matter dysfunction is increasingly recognized as a contributor to neurodegenerative disorders affecting cognition and to functional outcomes after stroke. Reduced indices of white matter microstructure have been demonstrated cross-sectionally in stroke survivors compared with stroke-free participants, but longitudinal changes in the structure of white matter after stroke remain largely unexplored. We aimed to characterize white matter micro- and macrostructure over 3 years after stroke and study associations with white matter metrics and cognitive functions.

METHODS

Patients with first-ever or recurrent ischemic stroke of any etiology in any vascular territory were compared with stroke-free age- and sex-matched controls. Those diagnosed with hemorrhagic stroke, TIA, venous infarction, or significant medical comorbidities, psychiatric and neurodegenerative disorders, substance abuse, or history of dementia were excluded. Diffusion-weighted MRI data at 3, 12, and 36 months were analyzed using a longitudinal fixel-based analysis, sensitive to fiber tract-specific differences within a voxel. It was used to examine whole-brain white matter degeneration in stroke compared with control participants. We studied microstructural differences in fiber density and macrostructural changes in fiber-bundle cross-section, in relation to cognitive performance. Analyses were performed controlling for age, intracranial volume, and education (family-wise error-corrected p < 0.05, nonparametric testing over 5,000 permutations).

RESULTS

We included 71 participants with stroke (age 66 ± 12 years, 22 women) and 36 controls (age 69 ± 5 years, 13 women). We observed extensive white matter structural degeneration across the whole brain, particularly affecting the thalamic, cerebellar, striatal, and superior longitudinal tracts and corpus callosum. Importantly, follow-up regression analyses in 72 predefined tracts showed that the decline in fiber density and cross-section from 3 months to 3 years was associated with worse cognitive performance at 3 years after stroke, especially affecting visuospatial processing, processing speed, language, and recognition memory.

DISCUSSION

We conclude that white matter neurodegeneration in ipsi- and contralesional thalamic, striatal, and cerebellar tracts continues to be greater in stroke survivors compared with stroke-free controls. White matter degeneration persists even years after stroke and is associated with poststroke cognitive impairment.

TRIAL REGISTRATION INFORMATION

ClinicalTrails.gov NCT02205424.

The elusive metric of lesion load

One of the widely used metrics in lesion-symptom mapping is lesion load that codes the amount of damage to a given brain region of interest. Lesion load aims to reduce the complex 3D lesion information into a feature that can reflect both site of damage, defined by the location of the region of interest, and size of damage within that region of interest. Basically, the process of estimation of lesion load converts a voxel-based lesion map into a region-based lesion map, with regions defined as atlas-based or data-driven spatial patterns. Here, after examining current definitions of lesion load, four methodological issues are discussed: (1) lesion load is agnostic to the location of damage within the region of interest, and it disregards damage outside the region of interest, (2) lesion load estimates are prone to errors introduced by the uncertainty in lesion delineation, spatial warping of the lesion/region, and binarization of the lesion/region, (3) lesion load calculation depends on brain parcellation selection, and (4) lesion load does not necessarily reflect a white matter disconnection. Overall, lesion load, when calculated in a robust way, can serve as a clinically-useful feature for explaining and predicting post-stroke outcome and recovery.

Advanced Diffusion MRI of Stroke Recovery

There is an urgent need for ways to improve our understanding of poststroke recovery to inform the development of novel rehabilitative interventions, and improve the clinical management of stroke patients. Supported by the notion that predictive information on poststroke recovery is embedded not only in the individual brain regions, but also the connections throughout the brain, majority of previous investigations have focused on the relationship between brain functional connections and post-stroke deficit and recovery. However, considering the fact that it is the static anatomical brain connections that constrain and facilitate the dynamic functional brain connections, the microstructures and structural connections of the brain may potentially be better alternatives to the functional MRI-based biomarkers of stroke recovery. This review, therefore, seeks to provide an overview of the basic concept and applications of two recently proposed advanced diffusion MRI techniques, namely lesion network mapping and fixel-based morphometry, that may be useful for the investigation of stroke recovery at the local and global levels of the brain. This review will also highlight the application of some of other emerging advanced diffusion MRI techniques that warrant further investigation in the context of stroke recovery research.

Inter-hemispheric synchronicity and symmetry: The functional connectivity consequences of stroke and neurodegenerative disease

Stroke and neurodegenerative diseases differ along several dimensions, including their temporal trajectories -abrupt onset versus slow disease progression. Despite these differences, they can give rise to very similar cognitive impairments, such as specific forms of aphasia. What has been scarcely investigated, however, is the extent to which the underlying functional neuroplastic consequences are similar or different for these diseases. Here, for the first time, we directly compare changes in the brain's functional network connectivity, measured with resting-state fMRI, in stroke and progressive neurological disease. Specifically, we examined two groups of individuals with chronic post-stroke aphasia or non-fluent primary progressive aphasia, matched for their behavioral profiles and distribution of left-hemisphere damage. Using previous proposals regarding the neural functional connectivity (FC) phenotype of stroke as a starting point, we compared the two diseases in terms of homotopic FC, intra-hemispheric FC changes and also the symmetry of the FC patterns between the two hemispheres. We found, first, that progressive disease showed significantly higher levels of homotopic connectivity than neurotypical controls and, further, that stroke showed the reverse pattern. For both groups these effects were found to be behaviorally relevant. In addition, within the directly impacted left hemisphere, FC changes for the two diseases were significantly correlated. In contrast, in the right hemisphere, the FC changes differed markedly between the two groups, with the progressive disease group exhibiting rather symmetrical FC changes across the hemispheres whereas the post-stroke group showed asymmetrical FC changes across the hemispheres. These findings constitute novel evidence that the functional connectivity consequences of stroke and neurodegenerative disease can be very different despite similar behavioral outcomes and damage foci. Specifically, stroke may lead to greater independence of hemispheric responses, while neurodegenerative disease may produce more symmetrical changes across the hemispheres and more synchronized activity between the two hemispheres.