Post-stroke Cognition at 1 and 3 Years Is Influenced by the Location of White Matter Hyperintensities in Patients With Lacunar Stroke

Prediction of white matter hyperintensities evolution one-year post-stroke from a single-point brain MRI and stroke lesions information

Predicting the evolution of white matter hyperintensities (WMH), a common feature in brain magnetic resonance imaging (MRI) scans of older adults (i.e., whether WMH will grow, remain stable, or shrink with time) is important for personalised therapeutic interventions. However, this task is difficult mainly due to the myriad of vascular risk factors and comorbidities that influence it, and the low specificity and sensitivity of the image intensities and textures alone for predicting WMH evolution. Given the predominantly vascular nature of WMH, in this study, we evaluate the impact of incorporating stroke lesion information to a probabilistic deep learning model to predict the evolution of WMH 1-year after the baseline image acquisition, taken soon after a mild stroke event, using T2-FLAIR brain MRI. The Probabilistic U-Net was chosen for this study due to its capability of simulating and quantifying the uncertainties involved in the prediction of WMH evolution. We propose to use an additional loss called volume loss to train our model, and incorporate stroke lesions information, an influential factor in WMH evolution. Our experiments showed that jointly segmenting the disease evolution map (DEM) of WMH and stroke lesions, improved the accuracy of the DEM representing WMH evolution. The combination of introducing the volume loss and joint segmentation of DEM of WMH and stroke lesions outperformed other model configurations with mean volumetric absolute error of 0.0092 ml (down from 1.7739 ml) and 0.47% improvement on average Dice similarity coefficient in shrinking, growing and stable WMH.

Strategic white matter hyperintensity locations associated with post-stroke cognitive impairment: A multicenter study in 1568 stroke patients

BACKGROUND

Post-stroke cognitive impairment (PSCI) occurs in up to 50% of stroke survivors. Presence of pre-existing vascular brain injury, in particular the extent of white matter hyperintensities (WMH), is associated with worse cognitive outcome after stroke, but the role of WMH location in this association is unclear.

AIMS

We determined if WMH in strategic white matter tracts explain cognitive performance after stroke.

METHODS

Individual patient data from nine ischemic stroke cohorts with magnetic resonance imaging (MRI) were harmonized through the Meta VCI Map consortium. The association between WMH volumes in strategic tracts and domain-specific cognitive functioning (attention and executive functioning, information processing speed, language and verbal memory) was assessed using linear mixed models and lasso regression. We used a hypothesis-driven design, primarily addressing four white matter tracts known to be strategic in memory clinic patients: the left and right anterior thalamic radiation, forceps major, and left inferior fronto-occipital fasciculus.

RESULTS

The total study sample consisted of 1568 patients (39.9% female, mean age = 67.3 years). Total WMH volume was strongly related to cognitive performance on all four cognitive domains. WMH volume in the left anterior thalamic radiation was significantly associated with cognitive performance on attention and executive functioning and information processing speed and WMH volume in the forceps major with information processing speed. The multivariable lasso regression showed that these associations were independent of age, sex, education, and total infarct volume and had larger coefficients than total WMH volume.

CONCLUSION

These results show tract-specific relations between WMH volume and cognitive performance after ischemic stroke, independent of total WMH volume. This implies that the concept of strategic lesions in PSCI extends beyond acute infarcts and also involves pre-existing WMH.

DATA ACCESS STATEMENT

The Meta VCI Map consortium is dedicated to data sharing, following our guidelines.

Differential risk factor profile and neuroimaging markers of small vessel disease between lacunar ischemic stroke and deep intracerebral hemorrhage

Background

Lacunar ischemic stroke (LIS) and deep intracerebral hemorrhage (dICH) are two stroke phenotypes of deep perforator arteriopathy. It is unclear what factors predispose individuals with deep perforator arteriopathy to either ischemic or hemorrhagic events.

Objectives

We aimed to investigate risk factors and neuroimaging features of small vessel disease (SVD) associated with LIS versus dICH in a cross-sectional study.

Methods

We included patients with clinically presenting, magnetic resonance imaging-confirmed LIS or dICH from two tertiary hospitals between 2010 and 2021. We recorded vascular risk factors and SVD markers, including lacunes, white matter hyperintensities (WMH), perivascular spaces (PVS), and cerebral microbleeds (CMB). Logistic regression modeling was used to determine the association between vascular risk factors, SVD markers, and stroke phenotype. We further created WMH probability maps to compare WMH distribution between LIS and dICH.

Results

A total of 834 patients with LIS (mean age 61.7 ± 12.1 years) and 405 with dICH (57.7 ± 13.2 years) were included. Hypertension was equally frequent between LIS and dICH (72.3% versus 74.8%, p = 0.349). Diabetes mellitus, hyperlipidemia, smoking, and prior ischemic stroke were more associated with LIS [odds ratio (OR) (95% confidence interval (CI)), 0.35 (0.25-0.48), 0.32 (0.22-0.44), 0.31 (0.22-0.44), and 0.38 (0.18-0.75)]. Alcohol intake and prior ICH were more associated with dICH [OR (95% CI), 2.34 (1.68-3.28), 2.53 (1.31-4.92)]. Lacunes were more prevalent in LIS [OR (95% CI) 0.23 (0.11-0.43)], while moderate-to-severe basal-ganglia PVS and CMB were more prevalent in dICH [OR (95% CI) 2.63 (1.35-5.27), 4.95 (2.71-9.42)]. WMH burden and spatial distribution did not differ between groups.

Conclusion

The microangiopathy underlying LIS and dICH reflects distinct risk profiles and SVD features, hence possibly SVD subtype susceptibility. Prospective studies with careful phenotyping and genetics are needed to clarify the mechanisms underlying this difference.

Interpreting and validating complexity and causality in lesion-symptom prognoses

This paper considers the steps needed to generate pragmatic and interpretable lesion-symptom mappings that can be used for clinically reliable prognoses. The novel contributions are 3-fold. We first define and inter-relate five neurobiological and five methodological constraints that need to be accounted for when interpreting lesion-symptom associations and generating synthetic lesion data. The first implication is that, because of these constraints, lesion-symptom mapping needs to focus on probabilistic relationships between Lesion and Symptom, with Lesion as a multivariate spatial pattern, Symptom as a time-dependent behavioural profile and evidence that Lesion raises the probability of Symptom. The second implication is that in order to assess the strength of probabilistic causality, we need to distinguish between causal lesion sites, incidental lesion sites, spared but dysfunctional sites and intact sites, all of which might affect the accuracy of the predictions and prognoses generated. We then formulate lesion-symptom mappings in logical notations, including combinatorial rules, that are then used to evaluate and better understand complex brain-behaviour relationships. The logical and theoretical framework presented applies to any type of neurological disorder but is primarily discussed in relationship to stroke damage. Accommodating the identified constraints, we discuss how the 1965 Bradford Hill criteria for inferring probabilistic causality, post hoc, from observed correlations in epidemiology-can be applied to lesion-symptom mapping in stroke survivors. Finally, we propose that rather than rely on post hoc evaluation of how well the causality criteria have been met, the neurobiological and methodological constraints should be addressed, a priori, by changing the experimental design of lesion-symptom mappings and setting up an open platform to share and validate the discovery of reliable and accurate lesion rules that are clinically useful.

Analysis of Risk Factors for White Matter Hyperintensity in Older Adults without Stroke

BACKGROUND

White matter hyperintensity (WMH) is prevalent in older adults aged 60 and above. A large proportion of people with WMH have not experienced stroke and little has been reported in the literature.

METHODS

The case data of patients aged ≥60 years without stroke in Wuhan Tongji Hospital from January 2015 to December 2019 were retrospectively analyzed. It was a cross-sectional study. Univariate analysis and logistic regression were used to analyze independent risk factors for WMH. The severity of WMH was assessed using the Fazekas scores. The participants with WMH were divided into periventricular white matter hyperintensity (PWMH) group and deep white matter hyperintensity (DWMH) group, then the risk factors of WMH severity were explored separately.

RESULTS

Eventually, 655 patients were included; among the patients, 574 (87.6%) were diagnosed with WMH. Binary logistic regression showed that age and hypertension were associated with the prevalence of WMH. Ordinal logistic regression showed that age, homocysteine, and proteinuria were associated with the severity of WMH. Age and proteinuria were associated with the severity of PWMH. Age and proteinuria were associated with the severity of DWMH.

CONCLUSIONS

The present study showed that in patients aged ≥60 years without stroke, age and hypertension were independent risk factors for the prevalence of WMH; while the increasing of age, homocysteine, and proteinuria were associated with greater WMH burden.

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.

Precision Capsular Infarct Modeling to Produce Hand Motor Deficits in Cynomolgus Macaques

Stroke research in non-human primates (NHPs) with gyrencephalic brains is a critical step in overcoming the translational barrier that limits the development of new pharmaceutical and rehabilitative strategies for stroke. White-matter stroke (WMS) has a unique pathophysiology from gray-matter stroke and is not well understood because of a lack of pertinent animal models. To create a precise capsular infarct model in the cynomolgus macaque, we first used electrical stimulation to map hand movements, followed by viral tracing of the hand motor fibers (hMFs). This enabled us to identify stereotactic targets in the posterior limb of the internal capsule (PLIC). Neural tracing showed that hMFs occupy the full width of the PLIC, owing to overlap with the motor fibers for the leg. Furthermore, the hMFs were distributed in an oblique shape, requiring coronal tilting of the target probe. We used the photothrombotic infarct lesioning technique to precisely destroy the hMFs within the internal capsule. Double-point infarct lesioning that fully compromised the hMFs resulted in persistent hand motor and walking deficits whereas single-point lesioning did not. Minor deviations in targeting failed to produce persistent motor deficits. Accurate stereotactic targeting with thorough involvement of motor fibers is critical for the production of a capsular infarct model with persistent motor deficits. In conclusion, the precision capsular infarct model can be translated to the NHP system to show persistent motor deficits and may be useful to investigate the mechanism of post-stroke recovery as well as to develop new therapeutic strategies for the WMS.