Characterizing coma in large vessel occlusion stroke

BACKGROUND

Coma is an unresponsive state of disordered consciousness characterized by impaired arousal and awareness. The epidemiology and pathophysiology of coma in ischemic stroke has been underexplored. We sought to characterize the incidence and clinical features of coma as a presentation of large vessel occlusion (LVO) stroke.

METHODS

Individuals who presented with LVO were retrospectively identified from July 2018 to December 2020. Coma was defined as an unresponsive state of impaired arousal and awareness, operationalized as a score of 3 on NIHSS item 1a.

RESULTS

28/637 (4.4%) patients with LVO stroke were identified as presenting with coma. The median NIHSS was 32 (IQR 29-34) for those with coma versus 11 (5-18) for those without (p < 0.0001). In coma, occlusion locations included basilar (13), vertebral (2), internal carotid (5), and middle cerebral (9) arteries. 8/28 were treated with endovascular thrombectomy (EVT), and 20/28 died during the admission. 65% of patients not treated with EVT had delayed presentations or large established infarcts. In models accounting for pre-stroke mRS, basilar occlusion location, intravenous thrombolysis, and EVT, coma independently increased the odds of transitioning to comfort care during admission (aOR 6.75; 95% CI 2.87,15.84; p < 0.001) and decreased the odds of 90-day mRS 0-2 (aOR 0.12; 95% CI 0.03,0.55; p = 0.007).

CONCLUSIONS

It is not uncommon for patients with LVO to present with coma, and delayed recognition of LVO can lead to poor outcomes, emphasizing the need for maintaining a high index of suspicion. While more commonly thought to result from posterior LVO, coma in our cohort was similarly likely to result from anterior LVO. Efforts to improve early diagnosis and care of patients with LVO presenting with coma are crucial.

Brain volume is a better biomarker of outcomes in ischemic stroke compared to brain atrophy

Brain parenchymal fraction (BPF) has been used as a surrogate measure of global brain atrophy, and as a biomarker of brain reserve in studies evaluating clinical outcomes after brain injury. Total brain volume at the time of injury has recently been shown to influence functional outcomes, where larger brain volumes are associated with better outcomes. Here, we assess if brain volume at the time of ischemic stroke injury is a better biomarker of functional outcome than BPF. Acute ischemic stroke cases at a single center between 2003 and 2011, with MR neuroimaging obtained within 48 hours from presentation were eligible. Functional outcomes represented by the modified Rankin Score (mRS) at 90 days post admission (mRS<3 deemed a favorable outcome) were obtained via patient interview or per chart review. Deep learning enabled automated segmentation pipelines were used to calculate brain volume, intracranial volume (ICV), and BPF on the acute neuroimaging data. Patient outcomes were modeled through logistic regressions, and model comparison was conducted using the Bayes Information Criterion (BIC). 467 patients with arterial ischemic stroke were included in the analysis. Median age was 65.8 years, and 65.3% were male. In both models, age and a larger stroke lesion volume were associated with worse functional outcomes. Higher BPF and a larger brain volume were both associated with favorable functional outcomes, however, comparison of both models suggested that the brain volume model (BIC=501) explains the data better compared to the BPF model (BIC=511). The extent of global brain atrophy has been regarded as an important biomarker of post-stroke functional outcomes and resilience to acute injury. Here, we demonstrate that a higher global brain volume at the time of injury better explains favorable functional outcomes, which can be directly clinically assessed.

Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity

Deep learning has allowed for remarkable progress in many medical scenarios. Deep learning prediction models often require 105-107 examples. It is currently unknown whether deep learning can also enhance predictions of symptoms post-stroke in real-world samples of stroke patients that are often several magnitudes smaller. Such stroke outcome predictions however could be particularly instrumental in guiding acute clinical and rehabilitation care decisions. We here compared the capacities of classically used linear and novel deep learning algorithms in their prediction of stroke severity. Our analyses relied on a total of 1430 patients assembled from the MRI-Genetics Interface Exploration collaboration and a Massachusetts General Hospital-based study. The outcome of interest was National Institutes of Health Stroke Scale-based stroke severity in the acute phase after ischaemic stroke onset, which we predict by means of MRI-derived lesion location. We automatically derived lesion segmentations from diffusion-weighted clinical MRI scans, performed spatial normalization and included a principal component analysis step, retaining 95% of the variance of the original data. We then repeatedly separated a train, validation and test set to investigate the effects of sample size; we subsampled the train set to 100, 300 and 900 and trained the algorithms to predict the stroke severity score for each sample size with regularized linear regression and an eight-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R2) in the test set. While linear regression performed significantly better for a sample size of 100 patients, deep learning started to significantly outperform linear regression when trained on 900 patients. Average prediction performance improved by ∼20% when increasing the sample size 9× [maximum for 100 patients: 0.279 ± 0.005 (R2, 95% confidence interval), 900 patients: 0.337 ± 0.006]. In summary, for sample sizes of 900 patients, deep learning showed a higher prediction performance than typically employed linear methods. These findings suggest the existence of non-linear relationships between lesion location and stroke severity that can be utilized for an improved prediction performance for larger sample sizes.

Neuroimaging markers of patient-reported outcome measures in acute ischemic stroke

Objectives

To determine the relationship between patient-reported outcome measures (PROMs) and volumetric imaging markers in acute ischemic stroke (AIS).

Patients and Methods

Patients presenting at Massachusetts General Hospital between February 14, 2017 and February 5, 2020 with a confirmed AIS by MRI were eligible and underwent a telephone interview including PROM-10 questionnaires 3-15 months after stroke. White matter hyperintensity (VWMH) and brain volumes (VBrain) were automatically determined using admission clinical MRI. Stroke lesions were manually segmented and volumes calculated (VLesion). Multivariable and ordinal regression analyses were performed to identify associations between global and PROM-10 subscores with brain volumetrics and clinical variables.

Results

Utilizing data from 167 patients (mean age: 64.7; 41.9% female), higher VWMH was associated with worse global physical (β=-0.6), global mental (β=-0.65), physical health (OR=0.68), social satisfaction (OR=0.66), fatigue (OR=0.69) and social activities (OR=0.59) scores. Higher VLesion was associated with poorer global mental (β=-0.79), mental health (OR=0.68), physical (OR=0.66) and social activities (OR=0.55), and emotional distress (OR=0.68) scores. Higher VBrain was linked to better global mental (β=0.93), global physical (β=0.79), mental health (OR=1.54) and physical activities (OR=1.72) scores.

Conclusions

Neuroimaging biomarkers were significantly associated with PROMs, where higher VWMH and VLesion led to worse outcome, while higher VBrain was protective. The inclusion of neuroimaging analyses and PROMs in routine assessment provides enhanced understanding of post-stroke outcomes.

Neuroimaging standards for research into small vessel disease-advances since 2013

Cerebral small vessel disease (SVD) is common during ageing and can present as stroke, cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive and other symptoms and affect activities of daily living. Standards for Reporting Vascular Changes on Neuroimaging 1 (STRIVE-1) categorised and standardised the diverse features of SVD that are visible on structural MRI. Since then, new information on these established SVD markers and novel MRI sequences and imaging features have emerged. As the effect of combined SVD imaging features becomes clearer, a key role for quantitative imaging biomarkers to determine sub-visible tissue damage, subtle abnormalities visible at high-field strength MRI, and lesion-symptom patterns, is also apparent. Together with rapidly emerging machine learning methods, these metrics can more comprehensively capture the effect of SVD on the brain than the structural MRI features alone and serve as intermediary outcomes in clinical trials and future routine practice. Using a similar approach to that adopted in STRIVE-1, we updated the guidance on neuroimaging of vascular changes in studies of ageing and neurodegeneration to create STRIVE-2.

Spatial patterns of white matter hyperintensities: a systematic review

Background

White matter hyperintensities are an important marker of cerebral small vessel disease. This disease burden is commonly described as hyperintense areas in the cerebral white matter, as seen on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging data. Studies have demonstrated associations with various cognitive impairments, neurological diseases, and neuropathologies, as well as clinical and risk factors, such as age, sex, and hypertension. Due to their heterogeneous appearance in location and size, studies have started to investigate spatial distributions and patterns, beyond summarizing this cerebrovascular disease burden in a single metric-its volume. Here, we review the evidence of association of white matter hyperintensity spatial patterns with its risk factors and clinical diagnoses.

Design/methods

We performed a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Statement. We used the standards for reporting vascular changes on neuroimaging criteria to construct a search string for literature search on PubMed. Studies written in English from the earliest records available until January 31st, 2023, were eligible for inclusion if they reported on spatial patterns of white matter hyperintensities of presumed vascular origin.

Results

A total of 380 studies were identified by the initial literature search, of which 41 studies satisfied the inclusion criteria. These studies included cohorts based on mild cognitive impairment (15/41), Alzheimer's disease (14/41), Dementia (5/41), Parkinson's disease (3/41), and subjective cognitive decline (2/41). Additionally, 6 of 41 studies investigated cognitively normal, older cohorts, two of which were population-based, or other clinical findings such as acute ischemic stroke or reduced cardiac output. Cohorts ranged from 32 to 882 patients/participants [median cohort size 191.5 and 51.6% female (range: 17.9-81.3%)]. The studies included in this review have identified spatial heterogeneity of WMHs with various impairments, diseases, and pathologies as well as with sex and (cerebro)vascular risk factors.

Conclusion

The results show that studying white matter hyperintensities on a more granular level might give a deeper understanding of the underlying neuropathology and their effects. This motivates further studies examining the spatial patterns of white matter hyperintensities.

The relevance of rich club regions for functional outcome post-stroke is enhanced in women

This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich-club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI-GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non-rich club regions. In spatial specificity analyses, we randomized the split into "rich club" and "non-rich club" regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex-specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female-specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club-combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long-term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.

Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke

BACKGROUND AND OBJECTIVES

While chronological age is one of the most influential determinants of poststroke outcomes, little is known of the impact of neuroimaging-derived biological "brain age." We hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of patients with stroke will be associated with cardiovascular risk factors and worse functional outcomes.

METHODS

We extracted radiomics from T2-FLAIR images acquired during acute stroke clinical evaluation. Brain age was determined from brain parenchyma radiomics using an ElasticNet linear regression model. Subsequently, relative brain age (RBA), which expresses brain age in comparison with chronological age-matched peers, was estimated. Finally, we built a linear regression model of RBA using clinical cardiovascular characteristics as inputs and a logistic regression model of favorable functional outcomes taking RBA as input.

RESULTS

We reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age = 62.8 years, 42.0% female patients). T2-FLAIR radiomics predicted chronological ages (mean absolute error = 6.9 years, r = 0.81). After adjustment for covariates, RBA was higher and therefore described older-appearing brains in patients with hypertension, diabetes mellitus, a history of smoking, and a history of a prior stroke. In multivariate analyses, age, RBA, NIHSS, and a history of prior stroke were all significantly associated with functional outcome (respective adjusted odds ratios: 0.58, 0.76, 0.48, 0.55; all p-values < 0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes.

DISCUSSION

T2-FLAIR radiomics can be used to predict brain age and derive RBA. Older-appearing brains, characterized by a higher RBA, reflect cardiovascular risk factor accumulation and are linked to worse outcomes after stroke.

Abstract TMP72: Multimodal Prediction Of Stroke Severity

Introduction: Predicting individual outcomes post-stroke with the highest possible accuracy is a crucial steppingstone in the realization of precision medicine. We here evaluated various types of lesion information in their capacity to predict stroke severity in a large cohort of patients with acute ischemic stroke.

Methods: A total of 1,075 patients of the MRI-GENIE study [age: 64.2(14.7), 38% women] contributed to analyses (N=792 as train and N=283 as test sample). We employed ridge regression with hyperparameter optimization and nested five-fold cross-validation to predict acute NIHSS-based stroke severity. Our baseline model considered DWI lesion volume. Further models tested structural lesion location, indirect structural and functional lesion connectivity, individually and combined with lesion volume. Data was preprocessed by principal component analysis (PCA), retaining 95% of the variance of the original data. Model performance was compared in terms of explained variance (R-squared) and 95% confidence intervals in the outer loop of the training set and the test set.

Results: Structural lesion connectivity enabled the highest prediction performance in the test set ( Figure 1 ). Prediction performance did not change notably with inclusion of lesion volume information. This contrasted with the combination of functional lesion connectivity and lesion volume that achieved a substantially higher prediction performance compared to functional lesion connectivity or lesion volume in isolation. Lesion location resulted in a prediction performance that was in the range of lesion volume alone and significantly lower than the ones of structural or functional connectivity with lesion volume.

Conclusions: Structural connectivity facilitated the most convincing prediction of stroke severity. The capacity of functional lesion connectivity was substantially improved by the inclusion of lesion volume, indicating both represent complementary information.

Abstract WP140: Coma As A Presentation For Large Vessel Occlusion Stroke

Introduction: Coma is an unresponsive state of disordered consciousness characterized by impaired arousal and awareness. While coma has been studied in the context of traumatic brain injury, the epidemiology and pathophysiology of coma in ischemic stroke has been underexplored. We sought to characterize the incidence and clinical features of coma as a presentation of large vessel occlusion (LVO) stroke.

Methods: Individuals who presented with LVO were identified from July 2018 to December 2020. Coma was defined as an unresponsive state of impaired arousal and awareness, operationalized as a score of 3 on NIHSS item 1a: responds only with reflex motor or autonomic effects or totally unresponsive, flaccid, and areflexic.

Results: A total of 28/638 (4.4%) patients with large vessel occlusion stroke were identified as presenting with coma. The median age was 65 (IQR 48-78), and 57% were female. The median NIHSS was 32 (IQR 29-34). Occlusion locations included basilar (10), vertebral (2), P1 posterior cerebral (2), internal carotid (4), M1 middle cerebral (6), and M2 middle cerebral arteries (4). In all 14 patients with anterior LVO, the acute LVO was unilateral. Of these 14, 6 had evidence of acute or chronic stroke involving the contralateral hemisphere; 1 experienced seizure; 1 experienced cardiac arrest; and 1 had chronic occlusion of the contralateral ICA. Overall, 20/28 died during the admission. Of those who died, 9 presented with anterior LVO and 11 with posterior LVO. Eight patients were treated with EVT. For those not treated, reasons included established infarct with ASPECTS <6 (11), delayed presentation (2), pre-stroke disability and goals of care (3), and absence of intracranial proximal occlusion on repeat imaging (2).

Conclusions: It is not uncommon for patients with LVO stroke to present with coma, and 65% of patients not treated with EVT had delayed presentations or large established infarcts, emphasizing the need for maintaining a high index of suspicion. While more commonly thought to result from posterior LVO, coma in our cohort was similarly likely to result from anterior LVO. Further study of these patients may shed light on the pathophysiology of coma. Efforts to improve early diagnosis and care are crucial given their poor outcomes.

Abstract WP179: Menopause Is Related To Accelerated Accumulation Of White Matter Hyperintensity Burden In Small Artery Occlusion Strokes

Introduction: Sex differences in white matter hyperintensity (WMH) burden exist and are affected by menopausal status. Here, we investigate the difference in age-related WMH burden with respect to stroke subtype (TOAST) and menopausal status.

Methods: We reviewed 4443 patients across 18 sites with information on age, TOAST, prior stroke, hypertensive (HTN) and diabetic (DM2) status, smoking history, and WMH and brain volume. WMH load was defined as the ratio between WMH and brain volume and Box Cox transformed. All variables underwent z-score standardization. Pre- (PRM), peri- (PEM), and post-menopausal (POM) categories were defined using age (PEM: 45-59 years). WMH load was modeled using age, prior stroke, HTN, DM2, smoking status, and interaction terms between age, TOAST, and sex/menopausal status. A second model included TOAST as an independent variable. Models accounted for acquisition site and were compared using Bayes Information Criteria (BIC). Significance was set to p<0.05.

Results: Of all patients (median age: 64), 42% were female (6%, 10%, and 26% PRM, PEM, and POM, respectively). The model without TOAST as an independent variable was selected (BIC 9237 compared to 9779). Our results (Figure 1) show (beta [95% confidence interval]) prior stroke (0.22 [0.15-0.29]), smoking (0.09 [0.04-0.14]), HTN (0.19 [0.14-0.25]), DM2 (0.08 [0.01-0.15]), and age (0.54 [0.43-0.65]) as independent factors. TOAST did not affect age-related WMH load in men and PRE. Effects of age were modified for small artery occlusion strokes in POM (0.40 [0.03-0.76]), increasing the age-related disease burden.

Conclusions: Menopausal status affects age-related WMH burden for different TOAST.

Abstract TP128: Hemorrhagic Transformation After Endovascular Thrombectomy Is Associated With Basal Ganglia Infarct Volume

Introduction: With continued expansion in indications for endovascular thrombectomy (EVT), understanding the pathophysiology of reperfusion injury and the risks of hemorrhagic transformation (HT) becomes increasingly important. Pre-EVT infarct topography may have implications for treatment decisions acutely (e.g. stenting), and with post EVT care (e.g. antithrombotics). We sought to quantify region-specific volumes of infarcted tissue on pre-EVT MRI, understand their importance for HT, and identify associations with clinical and imaging characteristics.

Methods: Patients with pre-EVT MRI were identified from a prospectively maintained database. Each patient’s diffusion weighted sequence underwent manual infarct delineation and was registered to a standard space for overlay with cortical, subcortical, and white matter atlases. HT was defined as ECASS PH1 or PH2. Variables with p<0.10 in univariate analyses were included in multivariable models.

Results: A total of 165 participants [median age 69 (IQR 56-79), 56% women] were identified. Intravenous alteplase was administered to 52%; 70% achieved TICI 2b-3 reperfusion. HT occurred in 8%. The distribution of pre-EVT infarcts was 48% (38-60%) white matter, 23% (6-47%) cortex, and 15% (4-28%) basal ganglia. Pre-EVT infarct volumes [median (IQR)] were 22 cc (12-43 cc) for total, 11 cc (6-19 cc) for white matter, 5 cc (1-19 cc) for cortex, and 3 cc (1-6 cc) for basal ganglia infarct. Paramagnetic sequences showed 3% had petechial hemorrhage and 40% had susceptibility vessel sign. Basal ganglia infarct volume was independently associated with HT (OR=1.342, 95%CI=1.002,1.797) in a model including white matter infarct volume, cortex infarct volume, smoking, and puncture-to-recanalization time. Basal ganglia infarct volume was linked to susceptibility vessel sign (Beta=0.233, p=0.006) and NIHSS (Beta=0.220, p=0.012), when controlling for total infarct volume.

Conclusions: Greater basal ganglia infarct volume was associated with a higher risk of HT when accounting for infarct volumes in other regions. Susceptibility vessel sign was associated with basal ganglia infarct volume, which may be related to acute middle cerebral artery perforator occlusion.

Abstract WMP58: Scaling Behaviors Of Deep Learning And Linear Algorithms For The Prediction Of Stroke Severity

Introduction: Deep learning (DL) has allowed for substantial progress in many, often machine vision-focused medical scenarios. Since DL models typically require 10 5 -10 7 examples, it is currently unknown whether DL can also enhance predictions of outcomes post-stroke in real-world samples of stroke patients that are several magnitudes smaller. We here compared the capacities of linear and DL algorithms regarding their prediction of stroke severity.

Methods: Our analyses relied on a total of 1435 patients assembled from the MRI-GENIE study and a Massachusetts General Hospital-based study. The primary outcome was acute NIHSS-based stroke severity, which we predicted by means of lesion location. We automatically derived lesion segmentations from DWI scans, performed spatial normalization and principal component analysis (PCA), retaining 95% of the variance of the original data. We then separated a train, validation, and test set, of sizes 918, 230, and 287 respectively. We downsampled the train set to 100, 300, and 900 and learned to predict the NIHSS score for each sample size with regularized linear regression and an 8-layered neural network. We selected hyperparameters on the validation set and conducted 500 dataset splits. We here report explained variance (R-squared) in the test set.

Results: While linear regression performed favorably for a sample size of 100 patients (R-squared=0.28), DL started to significantly outperform linear regression when trained on 900 patients (R-squared=0.34, Figure 1 ). Average prediction performance improved by ~20% when increasing the sample size 9x.

Conclusions: For sample sizes of ~1000 patients, DL showed a more convincing prediction performance for stroke severity than typically employed linear methods. Our findings suggest the existence of non-linear relationships between lesion location and outcome post-stroke that can be utilized for an improved prediction performance once samples are reasonably large.

Association of Stroke Lesion Pattern and White Matter Hyperintensity Burden With Stroke Severity and Outcome

BACKGROUND AND OBJECTIVES

To examine whether high white matter hyperintensity (WMH) burden is associated with greater stroke severity and worse functional outcomes in lesion pattern-specific ways.

METHODS

MR neuroimaging and NIH Stroke Scale data at index stroke and the modified Rankin Scale (mRS) score at 3-6 months after stroke were obtained from the MRI-Genetics Interface Exploration study of patients with acute ischemic stroke (AIS). Individual WMH volume was automatically derived from fluid-attenuated inversion recovery images. Stroke lesions were automatically segmented from diffusion-weighted imaging (DWI) images, parcellated into atlas-defined brain regions and further condensed to 10 lesion patterns via machine learning-based dimensionality reduction. Stroke lesion effects on AIS severity and unfavorable outcomes (mRS score >2) were modeled within purpose-built Bayesian linear and logistic regression frameworks. Interaction effects between stroke lesions and a high vs low WMH burden were integrated via hierarchical model structures. Models were adjusted for age, age2, sex, total DWI lesion and WMH volumes, and comorbidities. Data were split into derivation and validation cohorts.

RESULTS

A total of 928 patients with AIS contributed to acute stroke severity analyses (age: 64.8 [14.5] years, 40% women) and 698 patients to long-term functional outcome analyses (age: 65.9 [14.7] years, 41% women). Stroke severity was mainly explained by lesions focused on bilateral subcortical and left hemispherically pronounced cortical regions across patients with both a high and low WMH burden. Lesions centered on left-hemispheric insular, opercular, and inferior frontal regions and lesions affecting right-hemispheric temporoparietal regions had more pronounced effects on stroke severity in case of high compared with low WMH burden. Unfavorable outcomes were predominantly explained by lesions in bilateral subcortical regions. In difference to the lesion location-specific WMH effects on stroke severity, higher WMH burden increased the odds of unfavorable outcomes independent of lesion location.

DISCUSSION

Higher WMH burden may be associated with an increased stroke severity in case of stroke lesions involving left-hemispheric insular, opercular, and inferior frontal regions (potentially linked to language functions) and right-hemispheric temporoparietal regions (potentially linked to attention). Our findings suggest that patients with specific constellations of WMH burden and lesion locations may have greater benefits from acute recanalization treatments. Future clinical studies are warranted to systematically assess this assumption and guide more tailored treatment decisions.

Deep profiling of multiple ischemic lesions in a large, multi-center cohort: Frequency, spatial distribution, and associations to clinical characteristics

Background purpose

A substantial number of patients with acute ischemic stroke (AIS) experience multiple acute lesions (MAL). We here aimed to scrutinize MAL in a large radiologically deep-phenotyped cohort.

Materials and methods

Analyses relied upon imaging and clinical data from the international MRI-GENIE study. Imaging data comprised both Fluid-attenuated inversion recovery (FLAIR) for white matter hyperintensity (WMH) burden estimation and diffusion-weighted imaging (DWI) sequences for the assessment of acute stroke lesions. The initial step featured the systematic evaluation of occurrences of MAL within one and several vascular supply territories. Associations between MAL and important imaging and clinical characteristics were subsequently determined. The interaction effect between single and multiple lesion status and lesion volume was estimated by means of Bayesian hierarchical regression modeling for both stroke severity and functional outcome.

Results

We analyzed 2,466 patients (age = 63.4 ± 14.8, 39% women), 49.7% of which presented with a single lesion. Another 37.4% experienced MAL in a single vascular territory, while 12.9% featured lesions in multiple vascular territories. Within most territories, MAL occurred as frequently as single lesions (ratio ∼1:1). Only the brainstem region comprised fewer patients with MAL (ratio 1:4). Patients with MAL presented with a significantly higher lesion volume and acute NIHSS (7.7 vs. 1.7 ml and 4 vs. 3, p FDR < 0.001). In contrast, patients with a single lesion were characterized by a significantly higher WMH burden (6.1 vs. 5.3 ml, p FDR = 0.048). Functional outcome did not differ significantly between patients with single versus multiple lesions. Bayesian analyses suggested that the association between lesion volume and stroke severity between single and multiple lesions was the same in case of anterior circulation stroke. In case of posterior circulation stroke, lesion volume was linked to a higher NIHSS only among those with MAL.

Conclusion

Multiple lesions, especially those within one vascular territory, occurred more frequently than previously reported. Overall, multiple lesions were distinctly linked to a higher acute stroke severity, a higher total DWI lesion volume and a lower WMH lesion volume. In posterior circulation stroke, lesion volume was linked to a higher stroke severity in multiple lesions only.

The Relation Between Sex, Menopause, and White Matter Hyperintensities: The Rhineland Study

BACKGROUND AND OBJECTIVE

Mounting evidence implies that there are sex differences in white matter hyperintensity (WMH) burden in the elderly. Questions remain regarding possible differences in WMH burden between men and women of younger age, sex-specific age trajectories and effects of (un)controlled hypertension, and the effect of menopause on WMH. Therefore, our aim is to investigate these sex differences and age-dependencies in WMH load across the adult life span, and to examine the effect of menopause.

METHODS

This cross-sectional analysis was based on participants of the population-based Rhineland Study (30 - 95 years) who underwent brain MRI. We automatically quantified WMH using T1-weighted, T2-weighted and FLAIR images. Menopausal status was self-reported. We examined associations of sex and menopause with WMH load (logit-transformed and z-standardised) using linear regression models, while adjusting for age, age-squared, and vascular risk factors. We checked for an age*sex and (un)controlled hypertension*sex interaction and stratified for menopausal status comparing men with premenopausal women (persons aged ≤ 59 years), men with postmenopausal women (persons aged ≥ 45 years), and pre- with postmenopausal women (age range 45 - 59 years).

RESULTS

Of 3410 participants with a mean age of 54.3 years (SD = 13.7), 1973 (57.9%) were women, of which 1167 (59.1%) were postmenopausal. We found that the increase in WMH load accelerates with age and in a sex-dependent way. Premenopausal women and men of similar age did not differ in WMH burden. WMH burden was higher and accelerated faster in postmenopausal women compared to men of similar age. Additionally, we observed changes related to menopause, in that postmenopausal women had more WMH than premenopausal women of similar age.. Women with uncontrolled hypertension had a higher WMH burden compared to men, which was unrelated to menopausal status.

DISCUSSION

After menopause, women displayed a higher burden of WMH than contemporary premenopausal women and men, and an accelerated increase in WMH. Sex-specific effects of uncontrolled hypertension on WMH were not related to menopause. Further studies are warranted to investigate menopause-related physiological changes, that may inform on causal mechanisms involved in cerebral small vessel disease progression.

Abstract WMP64: Multimodal Bayesian Modeling Of Stroke Severity

Introduction: Understanding the relations between lesions and outcomes is a particularly promising avenue to support tailored stroke care. We here employed a novel Bayesian framework integrating lesion location and functional lesion connectivity, i.e., lesion network mapping data, aiming to augment the prediction of stroke severity and the interpretability of lesion-symptom associations in a multi-center cohort of acute ischemic stroke (AIS) patients.

Methods: Analyses relied on 1,077 AIS patients of the MRI-GENIE study [age: 64.2(14.7), 38%women]. NIHSS-based stroke severity was modeled via Bayesian linear regression. Structural location and functional connectivity information was represented by ten unique patterns each. We compared the out-of-sample predictive performances of structural vs. structural-functional models in Bayesian model comparisons. In ancillary analyses, we extracted functional connectivity pattern relevant for stroke severity and repeated analyses after stratifying for lesion size (50% of patients with larger lesions). Models were generally adjusted for age, sex, comorbidities and total DWI lesion volume.

Results: Model comparison indicated a benefit of the combined structural-functional stroke severity prediction model compared to the structural one (model weights: 82% vs. 18%). This benefit of the combined model became even more apparent in case of patients with large lesions (97% vs. 3%). Positive lesion connectivity to the bilateral ventral attention (VA) and somatomotor networks, as well as temporal default mode network (DMN) predicted higher severity, while positive connectivity to more medial parts of the DMN and the visual network predicted lower severity. Additionally, negative lesion connectivity to the bilateral DMN, somatomotor, dorsal attention, and control networks predicted higher, and negative connectivity to the right control and bilateral VA networks predicted lower severity.

Conclusions: Model comparisons indicated a benefit of integrating structural and functional information for the prediction of stroke severity, especially in case of larger lesions. Lesion connectivity data can enhance our understanding of why specific lesion locations can lead to symptoms post-stroke.

Abstract WP102: Risk Score Estimation For The Prognostication Of Post-stroke Functional Independence Using Data-driven Score Assignment With Clinical And Imaging Biomarkers

Objective: Accurate prognostication of functional outcomes after an acute ischemic stroke (AIS) is essential for the planning of post-stroke treatments. We sought to provide an improved risk score for the prognostication by leveraging an interpretable machine learning and neuroradiologic biomarker.

Data: We evaluated 913 patients with available 90-day mRS and admission NIHSS from the MRI-GENIE study. Age, sex, hypertension, atrial fibrillation, diabetes, coronary artery disease, smoking and prior stroke were available. We quantified WMH volume (WMHv) on FLAIR images. We divided patients into development and validation sets from multiple sites, and an external validation set from an individual cohort (Figure (a)).

Methods: We measured the unaccounted WMHv (uWMH) by the residual of the log-linear regression model of WMHv regarding the clinical characteristics. Excessive uWMH (eWMH) was defined as present vs. absent based on a uWMH higher than +1 standard deviation. The clinical characteristics, admission NIHSS, and presence of eWMH were used as the inputs of a constrained logistic regression model with variable selection (RiskSLIM) that estimated a risk score for the prediction of post-stroke functional independence (mRS 0-2).

Results: Admission NIHSS, age, sex, diabetes, prior stroke, and eWMH were selected with assigned risk scores (Figure (b)). The estimated risk score outperformed the conventional THRIVE and SPAN100 in the development, validation, and external validation sets regarding the area under curve (AUC) and the brier score (Figure (c)).

Conclusion: This proposed risk score leveraging the interpretable machine learning and imaging biomarkers improved the prognostication of the post-stroke functional independence. Since the proposed risk score system does not require high computational resources, it is expected to be widely used in daily clinical practices for centers with MR imaging capability.

OUP accepted manuscript

Stroke represents a considerable burden of disease for both men and women. However, a growing body of literature suggests clinically relevant sex differences in the underlying causes, presentations and outcomes of acute ischaemic stroke. In a recent study, we reported sex divergences in lesion topographies: specific to women, acute stroke severity was linked to lesions in the left-hemispheric posterior circulation. We here determined whether these sex-specific brain manifestations also affect long-term outcomes. We relied on 822 acute ischaemic patients [age: 64.7 (15.0) years, 39% women] originating from the multi-centre MRI-GENIE study to model unfavourable outcomes (modified Rankin Scale >2) based on acute neuroimaging data in a Bayesian hierarchical framework. Lesions encompassing bilateral subcortical nuclei and left-lateralized regions in proximity to the insula explained outcomes across men and women (area under the curve = 0.81). A pattern of left-hemispheric posterior circulation brain regions, combining left hippocampus, precuneus, fusiform and lingual gyrus, occipital pole and latero-occipital cortex, showed a substantially higher relevance in explaining functional outcomes in women compared to men [mean difference of Bayesian posterior distributions (men – women) = −0.295 (90% highest posterior density interval = −0.556 to −0.068)]. Once validated in prospective studies, our findings may motivate a sex-specific approach to clinical stroke management and hold the promise of enhancing outcomes on a population level.

Global white matter structural integrity mediates the effect of age on ischemic stroke outcomes

BACKGROUND

The relationship of global white matter microstructural integrity and ischemic stroke outcomes is not well understood.

AIMS

To investigate the relationship of global white matter microstructural integrity with clinical variables and functional outcomes after acute ischemic stroke.

METHODS

A retrospective analysis of neuroimaging data from 300 acute ischemic stroke patients with magnetic resonance imaging brain obtained within 48 hours of stroke onset and long-term functional outcomes (modified Rankin, mRS) was performed. Peak width of skeletonized mean diffusivity (PSMD), as a measure of global white matter microstructural injury, was calculated in the hemisphere contralateral to the acute infarct. Multivariable linear and logistic regression analyses were performed to identify variables associated with PSMD and excellent functional outcome (mRS < 2) at 90 days, respectively. Mediation analysis was then pursued to characterize how PSMD mediates the effect of age on acute ischemic stroke functional outcomes.

RESULTS

White matter hyperintensity volume, age, pre-stroke disability, and normal-appearing white matter mean diffusivity were independently associated with increased PSMD. In logistic regression analysis, increased infarct volume and PSMD were independent predictors of excellent functional outcome. Additionally, the effect of age on functional outcomes was indirectly mediated by PSMD (P < 0.001).

CONCLUSIONS

As a marker of global white matter microstructural injury, increased PSMD mediates the effect of increased age to contribute to poor acute ischemic stroke functional outcomes. PSMD could serve as a putative radiographic marker of brain age for stroke outcomes prognostication.

Excessive White Matter Hyperintensity Increases Susceptibility to Poor Functional Outcomes After Acute Ischemic Stroke

Objective: To personalize the prognostication of post-stroke outcome using MRI-detected cerebrovascular pathology, we sought to investigate the association between the excessive white matter hyperintensity (WMH) burden unaccounted for by the traditional stroke risk profile of individual patients and their long-term functional outcomes after a stroke. Methods: We included 890 patients who survived after an acute ischemic stroke from the MRI-Genetics Interface Exploration (MRI-GENIE) study, for whom data on vascular risk factors (VRFs), including age, sex, atrial fibrillation, diabetes mellitus, hypertension, coronary artery disease, smoking, prior stroke history, as well as acute stroke severity, 3- to-6-month modified Rankin Scale score (mRS), WMH, and brain volumes, were available. We defined the unaccounted WMH (uWMH) burden via modeling of expected WMH burden based on the VRF profile of each individual patient. The association of uWMH and mRS score was analyzed by linear regression analysis. The odds ratios of patients who achieved full functional independence (mRS < 2) in between trichotomized uWMH burden groups were calculated by pair-wise comparisons. Results: The expected WMH volume was estimated with respect to known VRFs. The uWMH burden was associated with a long-term functional outcome (β = 0.104, p < 0.01). Excessive uWMH burden significantly reduced the odds of achieving full functional independence after a stroke compared to the low and average uWMH burden [OR = 0.4, 95% CI: (0.25, 0.63), p < 0.01 and OR = 0.61, 95% CI: (0.42, 0.87), p < 0.01, respectively]. Conclusion: The excessive amount of uWMH burden unaccounted for by the traditional VRF profile was associated with worse post-stroke functional outcomes. Further studies are needed to evaluate a lifetime brain injury reflected in WMH unrelated to the VRF profile of a patient as an important factor for stroke recovery and a plausible indicator of brain health.

Endovascular Treatment for Acute Stroke in Cerebral Amyloid Angiopathy

Background and Purpose

We aimed to compare outcome of endovascular thrombectomy in acute ischemic stroke in patients with and without cerebral amyloid angiopathy (CAA).

Methods

We included patients with and without possible or probable CAA based on the modified Boston criteria from an observational multicenter cohort of patients with acute ischemic stroke and endovascular thrombectomy, the German Stroke Registry Endovascular Treatment trial. We analyzed baseline characteristics, procedural parameters, and functional outcome after 90 days.

Results

Twenty-eight (17.3%) of 162 acute ischemic stroke patients were diagnosed with CAA based on iron-sensitive magnetic resonance imaging performed before endovascular thrombectomy. CAA patients were less likely to have a good 90-day outcome (14.3 versus 37.8%). National Institutes of Health Stroke Scale score (adjusted odds ratio, 0.88; P<0.001), successful recanalization (adjusted odds ratio 6.82; P=0.005), and CAA (adjusted odds ratio 0.28; P=0.049) were independent outcome predictors. Intravenous thrombolysis was associated with an increased rate of good outcome (36.3% versus 0%, P=0.031) in CAA.

Conclusions

Endovascular thrombectomy with or without thrombolysis appears beneficial in acute ischemic stroke patients with possible or probable CAA, but is associated with a worse functional outcome.

Registration

URL: https://www.clinicaltrials.gov; Unique identifier: NCT03356392.

MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes

OBJECTIVE

Neuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes.

METHODS

We analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. Radiomic features were extracted from normal-appearing brain tissue (brain mask-WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA).

RESULTS

Radiomic features were predictive of WMH burden (R 2 = 0.855 ± 0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected p-values CV 1 - 6 < 0.001, p-value CV 7 = 0.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD), and CV7 by CAD and diabetes.

CONCLUSION

Radiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients' brain health.

Outcome after acute ischemic stroke is linked to sex-specific lesion patterns

Acute ischemic stroke affects men and women differently. In particular, women are often reported to experience higher acute stroke severity than men. We derived a low-dimensional representation of anatomical stroke lesions and designed a Bayesian hierarchical modeling framework tailored to estimate possible sex differences in lesion patterns linked to acute stroke severity (National Institute of Health Stroke Scale). This framework was developed in 555 patients (38% female). Findings were validated in an independent cohort (n = 503, 41% female). Here, we show brain lesions in regions subserving motor and language functions help explain stroke severity in both men and women, however more widespread lesion patterns are relevant in female patients. Higher stroke severity in women, but not men, is associated with left hemisphere lesions in the vicinity of the posterior circulation. Our results suggest there are sex-specific functional cerebral asymmetries that may be important for future investigations of sex-stratified approaches to management of acute ischemic stroke.

Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke

The aim of the current study was to explore the whole-brain dynamic functional connectivity patterns in acute ischemic stroke (AIS) patients and their relation to short and long-term stroke severity. We investigated resting-state functional MRI-based dynamic functional connectivity of 41 AIS patients two to five days after symptom onset. Re-occurring dynamic connectivity configurations were obtained using a sliding window approach and k-means clustering. We evaluated differences in dynamic patterns between three NIHSS-stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we built Bayesian hierarchical models to evaluate the predictive capacity of dynamic connectivity and examine the interrelation with clinical measures, such as white matter hyperintensity lesions. Finally, we established correlation analyses between dynamic connectivity and AIS severity as well as 90-day neurological recovery (ΔNIHSS). We identified three distinct dynamic connectivity configurations acutely post-stroke. More severely affected patients spent significantly more time in a configuration that was characterized by particularly strong connectivity and isolated processing of functional brain domains (three-level ANOVA: p < .05, post hoc t tests: p < .05, FDR-corrected). Configuration-specific time estimates possessed predictive capacity of stroke severity in addition to the one of clinical measures. Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson's r = -.68, p = .003, FDR-corrected). Our findings demonstrate transiently increased isolated information processing in multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first 3 months poststroke.

Peak Width of Skeletonized Mean Diffusivity as Neuroimaging Biomarker in Cerebral Amyloid Angiopathy

BACKGROUND AND PURPOSE

Whole-brain network connectivity has been shown to be a useful biomarker of cerebral amyloid angiopathy and related cognitive impairment. We evaluated an automated DTI-based method, peak width of skeletonized mean diffusivity, in cerebral amyloid angiopathy, together with its association with conventional MRI markers and cognitive functions.

MATERIALS AND METHODS

We included 24 subjects (mean age, 74.7 [SD, 6.0] years) with probable cerebral amyloid angiopathy and mild cognitive impairment and 62 patients with MCI not attributable to cerebral amyloid angiopathy (non-cerebral amyloid angiopathy-mild cognitive impairment). We compared peak width of skeletonized mean diffusivity between subjects with cerebral amyloid angiopathy-mild cognitive impairment and non-cerebral amyloid angiopathy-mild cognitive impairment and explored its associations with cognitive functions and conventional markers of cerebral small-vessel disease, using linear regression models.

RESULTS

Subjects with Cerebral amyloid angiopathy-mild cognitive impairment showed increased peak width of skeletonized mean diffusivity in comparison to those with non-cerebral amyloid angiopathy-mild cognitive impairment (P < .001). Peak width of skeletonized mean diffusivity values were correlated with the volume of white matter hyperintensities in both groups. Higher peak width of skeletonized mean diffusivity was associated with worse performance in processing speed among patients with cerebral amyloid angiopathy, after adjusting for other MRI markers of cerebral small vessel disease. The peak width of skeletonized mean diffusivity did not correlate with cognitive functions among those with non-cerebral amyloid angiopathy-mild cognitive impairment.

CONCLUSIONS

Peak width of skeletonized mean diffusivity is altered in cerebral amyloid angiopathy and is associated with performance in processing speed. This DTI-based method may reflect the degree of white matter structural disruption in cerebral amyloid angiopathy and could be a useful biomarker for cognition in this population.

Abstract MP16: Excessive White Matter Hyperintensity Burden and Functional Outcomes After Acute Ischemic Stroke

Objective: Ability of the brain to recover after an acute ischemic stroke (AIS) is linked to the pre-stroke burden of white matter hyperintensity (WMH), a radiographic marker of brain health. We sought to determine the excessive WMH burden in an AIS population and investigate its association with 3-month stroke outcomes.

Data: We used 2,435 subjects from the MRI-GENIE study. Three-month functional outcomes of 872 subjects among those subjects were measured by 90-day modified Ranking Scale (mRS).

Methods: We automatically quantified WMH volume (WMHv) on FLAIR images and adjusted for a brain volume. We modeled a trend using the factor analysis (FA) log-linear regression using age, sex, atrial fibrillation, diabetes, hypertension, coronary artery disease and smoking as input variables. We categorized three WMH burden groups based on the conditional probability given by the model (LOW: lower 33%, MED: middle 34%, and HIGH: upper 33%). The subgroups were compared with respect to mRS (median and dichotomized odds ratio (OR) (good/poor: mRS 0-2/3-6)).

Results: Five FA components out of seven with significant relationship to WMHv (p<0.001) were used for the regression modeling (R 2 =0.359). The HIGH group showed higher median (median=2, IQR=2) mRS score than LOW (median=1, IQR=1) and MED (median=1, IQR=1). The odds (OR) of good AIS outcome for LOW and MED were 1.8 (p=0.0001) and 1.6 (p=0.006) times higher than HIGH, respectively.

Conclusion: Once accounted for clinical covariates, the excessive WMHv was associated with worse 3-month stroke outcomes. These data suggest that a life-time of injury to the white matter reflected in WMH is an important factor for stroke recovery and an indicator of the brain health.

Abstract P323: Stroke Lesion Pattern-Specific Influence of White Matter Hyperintensities on Stroke Severity in Acute Ischemic Stroke

Introduction: MRI-detected white matter hyperintensity (WMH) burden is linked to the overall brain health and incident stroke outcomes. However, data are limited as to whether specific acute stroke lesion patterns purport greater stroke severity in patients with extensive pre-existing WMH. In this analysis, we sought to investigate lesion pattern-specific WMH effects on AIS severity.

Methods: We analyzed clinical and lesion data from 621 AIS patients enrolled in the multi-site MRI-GENIE study. The acute NIHSS was modelled via Bayesian hierarchical regression. Using ten stroke lesion patterns, that served as input variables of main interest, we introduced a hierarchical level differentiating between patients with higher and lower than median WMH volume (WMHv). Lesion pattern posterior distributions for higher and lower WMHv patients were subtracted to infer substantial differences.

Results: In this AIS cohort [age: 65.3±14.6, 60% male], a higher WMHv was associated with greater stroke severity only when specific left-hemispheric brain regions were infarcted. This “lesion pattern 3” was mainly characterized by left middle and inferior frontal gyrus, insular and opercular cortex, pre- and post-central gyrus, and subcortical basal ganglia regions ( Fig ).

Conclusions: Higher WMH burden appears to enhance the detrimental effect of acute stroke lesions involving left-hemispheric brain regions underlying language and motor functions. This effect might be due to an exacerbated disruption of functional network integrity by the combination of WMH and stroke lesions and could be explored further in functional imaging studies that simultaneously considered information from both lesion types.

Abstract P613: Genetic Risk of Vascular Risk Factors and Severity of Leukoaraiosis in Patients With Ischemic Stroke: The MRI-GENIE Study

Introduction: Severity of leukoaraiosis detected on T2 MRI scans as white matter hyperintensities (WMH) is associated with infarct growth and poor poststroke outcomes in patients with acute ischemic stroke (AIS). Traditional vascular risk factors (VRF) such as age, hypertension (HTN), type 2 diabetes mellitus (T2D), and cigarette smoking are linked to WMH in large population-based studies, yet casual inferences for WMH in AIS patients are limited. We sought to examine the VRFs for evidence of causal relationships with WMH burden in AIS patients using mendelian randomization principles and polygenic risk score (PRS) methods.

Method: We examined FLAIR MRIs obtained within 48 hours of AIS onset in 4,362 European Caucasian patients from the MRI-GENetics Interface Exploration (MRI-GENIE) study. WMH volume (WMHv) was measured using a fully automated deep-learning trained algorithm. We considered 13 VRFs: blood pressure (HTN, SBP, DBP, Pulse Pressure), lipid (total cholesterol, HDL, LDL, TG), BMI, T2D, atrial fibrillation, alcohol use and smoking. For each factor, we calculated a weighted PRS for each individual based on the most recent GWAS with various GWAS p-value cutoff. We then used linear regression to estimate associations between each PRS and log transformed WMHv, controlling for age, gender and principal components of genetic ancestries. Strata-specific estimates were combined using inverse-variance weighting based meta-analysis.

Results: PRS of both SBP and DBP were positively and robustly associated with WMHv in the meta-analysis (p value of the association ranging from <0.001 to 0.046 for various SNP selection strategies (GWAS p-value cutoff ranging from p<1E-5 to p<1E-8)), unlike the PRS of other risk factors, although stratum-specific significance was achieved for some factors.

Conclusion: Using mendelian randomization, our results lend further evidence that high blood pressure is a causal risk factor for WMH in AIS patients. This result is consistent with previous epidemiological studies of leukoaraiosis in stroke-free populations, and it supports universal control of HTN as common contributor to WMH burden and the overall brain health.

Abstract 10: Radiomic Signature of the White Matter Hyperintensity Burden Correlates With Clinical Phenotypes

Introduction: Structural integrity of cerebral parenchyma is an essential radiographic equivalent of brain health; but its assessment usually requires dedicated advanced image acquisitions. Radiomics analyses bear the potential to describe radiophenotypes beyond what meets the naked eye. We sought to: 1) evaluate this novel approach to predict white matter hyperintensity (WMH) burden and 2) uncover latent clinico-radiological associations.

Methods: An international, multi-site cohort of 4,164 acute ischemic stroke (AIS) patients with FLAIR MRI (MRI-GENIE study) underwent total brain and WMH lesion segmentation using convolutional neural networks. Radiomic features (n=1905) were extracted from clinical FLAIR images outside of the WMH (brain mask - WMH mask). Prediction of the WMH burden using radiomics was done using LASSO regression. Radiomic signature of WMH was built with the most stable selected features, then compared to the clinical variables using canonical correlation analysis.

Results: In this cohort, (mean age=62.8±15.0, median WMH volume=4.2cc IQR 1.4-11.2), radiomic features were highly predictive of WMH burden (R2=0.8±0.012). Radiomic signature of WMH included 68 features. All 7 pairs of extracted canonical variates (CV) were statistically significant with respective canonical correlations of 0.79, 0.64, 0.44, 0.21, 0.16, 0.15 (Bonferroni corrected p-values CV1-6 <.001, p-value CV7 =.003). Upon examination, CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes mellitus (DM), CV4 by hypertension, CV5 by atrial fibrillation (AF) and DM, CV6 by coronary artery disease (CAD) and CV7 by CAD and DM.

Conclusion: Radiomics extracted from clinical grade FLAIR images of AIS patients seem able to capture structural integrity of the cerebral parenchyma and to correlate with clinical phenotypes. Further research could evaluate radiomics to predict the progression of cerebral small vessel disease on longitudinal data.

Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge

Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI (rsfMRI) time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided Challenge participants with demographic information of age, sex, IQ, and handedness. The second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Classification methodologies were submitted in a standardized format as containerized Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 metrics: accuracy, area under the curve, F1-score, false discovery rate, false negative rate, false omission rate, false positive rate, geometric mean, informedness, markedness, Matthew's correlation coefficient, negative predictive value, optimized precision, precision, sensitivity, and specificity. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each methodology. Five participants submitted their method for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are still needed to reach the clinical translation of functional connectomics. We have kept the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of connectomics.

Infarct Growth despite Endovascular Thrombectomy Recanalization in Large Vessel Occlusive Stroke

BACKGROUND AND PURPOSE

Endovascular thrombectomy (EVT) has revolutionized large vessel occlusion stroke care. However, not all patients with good endovascular results achieve good outcomes. We sought to understand the clinical significance of magnetic resonance imaging defined infarct growth despite adequate reperfusion and identify associated clinical and radiographic variables.

METHODS

History, presentation, treatments, and outcomes for consecutive EVT patients at a referral center were collected. Adequate reperfusion was defined as thrombolysis in cerebral infarction (TICI) score 2b-3. Region-specific infarct volumes in white matter, cortex, and basal ganglia were determined on diffusion-weighted imaging. Infarct growth was defined as post-EVT minus pre-EVT volume. Good outcome was defined as 90-day modified Rankin Scale ≤2.

RESULTS

Forty-four patients with adequate reperfusion were identified with median age 72 years; 64% were women. Each region showed infarct growth: white matter (median pre-EVT 7 cubic centimeters [cc], post-EVT 16 cc), cortex (4 cc, 15 cc), basal ganglia (2 cc, 4 cc), total (20 cc, 39 cc). In multivariable regression, total infarct growth independently decreased the odds of good outcome (odds ratio = .946, 95% CI = .897, .998). Further multivariable analyses for determinants of infarct growth identified female sex was associated with less total growth (β = -.294, P = .042), TICI 3 was associated with less white matter growth (β = -.277, P = .048) and cortical growth (β = -.335, P = .017), and both female sex (β = -.332, P = .015) and coronary disease (β = -.337, P = .015) were associated with less cortical growth.

CONCLUSIONS

Infarct growth occurred despite adequate reperfusion, disproportionately in the cortex, and independently decreased the odds of good outcome. Infarct growth occurred while patients were hospitalized and may represent a therapeutic target. Potential determinants of region-specific infarct growth were identified that require confirmation in larger studies.

White Matter Acute Infarct Volume After Thrombectomy for Anterior Circulation Large Vessel Occlusion Stroke is Associated with Long Term Outcomes

OBJECTIVES

Despite the proven efficacy of endovascular thrombectomy (EVT) for large vessel occlusion stroke, over half treated remain functionally disabled or die. Infarct topography may have implications for prognostication, patient selection, and the development of tissue-specific neuroprotective agents. We sought to quantify white matter injury in anterior circulation acute infarcts post-EVT to understand its significance and identify its determinants.

MATERIALS AND METHODS

Demographics, history, presentations, and outcomes for consecutive patients treated with EVT were recorded in a prospectively maintained database at a single center. Acute infarct masks were coregistered to standard space. Standard atlases of white matter, cortex, and basal ganglia were used to determine region-specific infarct volumes.

RESULTS

167 individuals were identified with median age 69 years and 53% women. 85% achieved adequate reperfusion (TICI 2b-3) after EVT; 43% achieved 90-day functional independence (mRS 0-2). Median infarct volumes were 45cc (IQR 18-122) for total, 17cc (6-49) for white matter, 21cc (4-53) for cortex, and 5cc (1-8) for basal ganglia. The odds of 90-day mRS 0-2 were reduced in patients with larger white matter infarct volume (cc, OR=0.89, 95%CI=0.81-0.96), independent of cortex infarct volume, basal ganglia infarct volume, age, NIHSS, and TICI 2b-3 reperfusion. Reperfusion-to-MRI time was associated with white matter infarct volume (hr, β=0.119, p=0.017), but not cortical or basal ganglia infarct volume.

CONCLUSIONS

These data quantitatively describe region-specific infarct volumes after EVT and suggest the clinical relevance of white matter infarct volume as a predictor of long-term outcomes. Further study is warranted to examine delayed white matter infarction and the significance of specific white matter tracts.

Predictors for Late Post-Intracerebral Hemorrhage Dementia in Patients with Probable Cerebral Amyloid Angiopathy

BACKGROUND AND OBJECTIVE

Cerebral amyloid angiopathy (CAA) accounts for the majority of lobar intracerebral hemorrhage (ICH); however, the risk factors for dementia conversion after ICH occurrence in CAA patients are unknown, especially in the long-term period after ICH. Therefore, we aimed to unravel the predictors for late post-ICH dementia (6 months after ICH event) in probable CAA patients.

METHODS

From a large consecutive MRI prospective cohort of spontaneous ICH (2006-2017), we identified probable CAA patients (modified Boston criteria) without dementia 6 months post-ICH. Cognitive outcome during follow-up was determined based on the information from standardized clinical visit notes. We used Cox regression analysis to investigate the association between baseline demographic characteristics, past medical history, MRI biomarkers, and late post-ICH dementia conversion (dementia occurred after 6 months).

RESULTS

Among 97 non-demented lobar ICH patients with probable CAA, 25 patients (25.8%) developed dementia during a median follow-up time of 2.5 years (IQR 1.5-3.8 years). Pre-existing mild cognitive impairment, increased white matter hyperintensities (WMH) burden, the presence of disseminated cortical superficial siderosis (cSS), and higher total small vessel disease score for CAA were all independent predictors for late dementia conversion.

CONCLUSION

In probable CAA patients presenting with lobar ICH, high WMH burden and presence of disseminated cSS are useful neuroimaging biomarkers for dementia risk stratification. These findings have implications for clinical practice and future trial design.

Effects of White Matter Hyperintensities on 90-Day Functional Outcome after Large Vessel and Non-Large Vessel Stroke

INTRODUCTION

White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype related to the diagnosis and prognosis of acute ischemic stroke. The effect of WMH burden on functional outcome in large vessel occlusion (LVO) stroke has only been sparsely assessed, and direct LVO and non-LVO comparisons are currently lacking.

MATERIAL AND METHODS

We reviewed acute ischemic stroke patients admitted between 2009 and 2017 at a large healthcare system in the USA. Patients with LVO were identified and clinical characteristics, including 90-day functional outcomes, were assessed. Clinical brain MRIs obtained at the time of the stroke underwent quantification of WMH using a fully automated algorithm. The pipeline incorporated automated brain extraction, intensity normalization, and WMH segmentation.

RESULTS

A total of 1,601 acute ischemic strokes with documented 90-day mRS were identified, including 353 (22%) with LVO. Among those strokes, WMH volume was available in 1,285 (80.3%) who had a brain MRI suitable for WMH quantification. Increasing WMH volume from 0 to 4 mL, age, female gender, a number of stroke risk factors, presence of LVO, and higher NIHSS at presentation all decreased the odds for a favorable outcome. Increasing WMH above 4 mL, however, was not associated with decreasing odds of favorable outcome. While WMH volume was associated with functional outcome in non-LVO stroke (p = 0.0009), this association between WMH and functional status was not statistically significant in the complete case multivariable model of LVO stroke (p = 0.0637).

CONCLUSION

The burden of WMH has effects on 90-day functional outcome after LVO and non-LVO strokes. Particularly, increases from no measurable WMH to 4 mL of WMH correlate strongly with the outcome. Whether this relationship of increasing WMH to worse outcome is more pronounced in non-LVO than LVO strokes deserves additional investigation.

Diffusion-Weighted Imaging, MR Angiography, and Baseline Data in a Systematic Multicenter Analysis of 3,301 MRI Scans of Ischemic Stroke Patients-Neuroradiological Review Within the MRI-GENIE Study

Background: Magnetic resonance imaging (MRI) serves as a cornerstone in defining stroke phenotype and etiological subtype through examination of ischemic stroke lesion appearance and is therefore an essential tool in linking genetic traits and stroke. Building on baseline MRI examinations from the centralized and structured radiological assessments of ischemic stroke patients in the Stroke Genetics Network, the results of the MRI-Genetics Interface Exploration (MRI-GENIE) study are described in this work.

Methods: The MRI-GENIE study included patients with symptoms caused by ischemic stroke (N = 3,301) from 12 international centers. We established and used a structured reporting protocol for all assessments. Two neuroradiologists, using a blinded evaluation protocol, independently reviewed the baseline diffusion-weighted images (DWIs) and magnetic resonance angiography images to determine acute lesion and vascular occlusion characteristics.

Results: In this systematic multicenter radiological analysis of clinical MRI from 3,301 acute ischemic stroke patients according to a structured prespecified protocol, we identified that anterior circulation infarcts were most prevalent (67.4%), that infarcts in the middle cerebral artery (MCA) territory were the most common, and that the majority of large artery occlusions 0 to 48 h from ictus were in the MCA territory. Multiple acute lesions in one or several vascular territories were common (11%). Of 2,238 patients with unilateral DWI lesions, 52.6% had left-sided infarct lateralization (P = 0.013 for χ² test).

Conclusions: This large-scale analysis of a multicenter MRI-based cohort of AIS patients presents a unique imaging framework facilitating the relationship between imaging and genetics for advancing the knowledge of genetic traits linked to ischemic stroke.

Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation

Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, registration can produce poor results. Visual assessment of registration quality in large clinical datasets is inefficient. In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain. The method consists of automatically segmenting the ventricles of a given scan using a neural network, and comparing the segmentation to the atlas ventricles propagated to image space. We used the proposed method to improve clinical image registration to a general atlas by computing multiple registrations - one directly to the general atlas and others via different age-specific atlases - and then selecting the registration that yielded the highest ventricle overlap. Finally, as an example application of the complete pipeline, a voxelwise map of white matter hyperintensity burden was computed using only the scans with registration quality above a predefined threshold. Methods were evaluated in a single-site dataset of more than 1000 scans, as well as a multi-center dataset comprising 142 clinical scans from 12 sites. The automated ventricle segmentation reached a Dice coefficient with manual annotations of 0.89 in the single-site dataset, and 0.83 in the multi-center dataset. Registration via age-specific atlases could improve ventricle overlap compared to a direct registration to the general atlas (Dice similarity coefficient increase up to 0.15). Experiments also showed that selecting scans with the registration quality assessment method could improve the quality of average maps of white matter hyperintensity burden, instead of using all scans for the computation of the white matter hyperintensity map. In this work, we demonstrated the utility of an automated tool for assessing image registration quality in clinical scans. This image quality assessment step could ultimately assist in the translation of automated neuroimaging pipelines to the clinic.

White matter hyperintensity burden in acute stroke patients differs by ischemic stroke subtype

OBJECTIVE

To examine etiologic stroke subtypes and vascular risk factor profiles and their association with white matter hyperintensity (WMH) burden in patients hospitalized for acute ischemic stroke (AIS).

METHODS

For the MRI Genetics Interface Exploration (MRI-GENIE) study, we systematically assembled brain imaging and phenotypic data for 3,301 patients with AIS. All cases underwent standardized web tool-based stroke subtyping with the Causative Classification of Ischemic Stroke (CCS). WMH volume (WMHv) was measured on T2 brain MRI scans of 2,529 patients with a fully automated deep-learning trained algorithm. Univariable and multivariable linear mixed-effects modeling was carried out to investigate the relationship of vascular risk factors with WMHv and CCS subtypes.

RESULTS

Patients with AIS with large artery atherosclerosis, major cardioembolic stroke, small artery occlusion (SAO), other, and undetermined causes of AIS differed significantly in their vascular risk factor profile (all p < 0.001). Median WMHv in all patients with AIS was 5.86 cm³ (interquartile range 2.18-14.61 cm³) and differed significantly across CCS subtypes (p < 0.0001). In multivariable analysis, age, hypertension, prior stroke, smoking (all p < 0.001), and diabetes mellitus (p = 0.041) were independent predictors of WMHv. When adjusted for confounders, patients with SAO had significantly higher WMHv compared to those with all other stroke subtypes (p < 0.001).

CONCLUSION

In this international multicenter, hospital-based cohort of patients with AIS, we demonstrate that vascular risk factor profiles and extent of WMH burden differ by CCS subtype, with the highest lesion burden detected in patients with SAO. These findings further support the small vessel hypothesis of WMH lesions detected on brain MRI of patients with ischemic stroke.

Brain Volume: An Important Determinant of Functional Outcome After Acute Ischemic Stroke

OBJECTIVE

To determine whether brain volume is associated with functional outcome after acute ischemic stroke (AIS).

PATIENTS AND METHODS

This study was conducted between July 1, 2014, and March 16, 2019. We analyzed cross-sectional data of the multisite, international hospital-based MRI-Genetics Interface Exploration study with clinical brain magnetic resonance imaging obtained on admission for index stroke and functional outcome assessment. Poststroke outcome was determined using the modified Rankin Scale score (0-6; 0 = asymptomatic; 6 = death) recorded between 60 and 190 days after stroke. Demographic characteristics and other clinical variables including acute stroke severity (measured as National Institutes of Health Stroke Scale score), vascular risk factors, and etiologic stroke subtypes (Causative Classification of Stroke system) were recorded during index admission.

RESULTS

Utilizing the data from 912 patients with AIS (mean ± SD age, 65.3±14.5 years; male, 532 [58.3%]; history of smoking, 519 [56.9%]; hypertension, 595 [65.2%]) in a generalized linear model, brain volume (per 155.1 cm³) was associated with age (β -0.3 [per 14.4 years]), male sex (β 1.0), and prior stroke (β -0.2). In the multivariable outcome model, brain volume was an independent predictor of modified Rankin Scale score (β -0.233), with reduced odds of worse long-term functional outcomes (odds ratio, 0.8; 95% CI, 0.7-0.9) in those with larger brain volumes.

CONCLUSION

Larger brain volume quantified on clinical magnetic resonance imaging of patients with AIS at the time of stroke purports a protective mechanism. The role of brain volume as a prognostic, protective biomarker has the potential to forge new areas of research and advance current knowledge of the mechanisms of poststroke recovery.

Detailed phenotyping of posterior vs. anterior circulation ischemic stroke: a multi-center MRI study

OBJECTIVE

Posterior circulation ischemic stroke (PCiS) constitutes 20-30% of ischemic stroke cases. Detailed information about differences between PCiS and anterior circulation ischemic stroke (ACiS) remains scarce. Such information might guide clinical decision making and prevention strategies. We studied risk factors and ischemic stroke subtypes in PCiS vs. ACiS and lesion location on magnetic resonance imaging (MRI) in PCiS.

METHODS

Out of 3,301 MRIs from 12 sites in the National Institute of Neurological Disorders and Stroke (NINDS) Stroke Genetics Network (SiGN), we included 2,381 cases with acute DWI lesions. The definition of ACiS or PCiS was based on lesion location. We compared the groups using Chi-squared and logistic regression.

RESULTS

PCiS occurred in 718 (30%) patients and ACiS in 1663 (70%). Diabetes and male sex were more common in PCiS vs. ACiS (diabetes 27% vs. 23%, p < 0.05; male sex 68% vs. 58%, p < 0.001). Both were independently associated with PCiS (diabetes, OR = 1.29; 95% CI 1.04-1.61; male sex, OR = 1.46; 95% CI 1.21-1.78). ACiS more commonly had large artery atherosclerosis (25% vs. 20%, p < 0.01) and cardioembolic mechanisms (17% vs. 11%, p < 0.001) compared to PCiS. Small artery occlusion was more common in PCiS vs. ACiS (20% vs. 14%, p < 0.001). Small artery occlusion accounted for 47% of solitary brainstem infarctions.

CONCLUSION

Ischemic stroke subtypes differ between the two phenotypes. Diabetes and male sex have a stronger association with PCiS than ACiS. Definitive MRI-based PCiS diagnosis aids etiological investigation and contributes additional insights into specific risk factors and mechanisms of injury in PCiS.

Abstract WP73: Automatic Classification of Clinical MRI Stroke Datasets With a Recurrent Convolutional Neural Network

Background: Fundamental advances in stroke care will require pooling imaging phenotype data from multiple centers, to complement the current aggregation of genomic, environmental, and clinical information. Sharing clinically acquired MRI data from multiple hospitals is challenging due to inherent heterogeneity of clinical data, where the same MRI series may be labeled differently depending on vendor and hospital. Furthermore, the de-identification process may remove data describing the MRI series, requiring human review. However, manually annotating the MRI series is not only laborious and slow but prone to human error. In this work, we present a recurrent convolutional neural network (RCNN) for automated classification of the MRI series.

Methods: We randomly selected 1000 subjects from the MRI-GENetics Interface Exploration study and partitioned them into 800 training, 100 validation and 100 testing subjects. We categorized the MRI series into 24 groups (see Table). The RCNN used a modified AlexNet to extract features from 2D slices. AlexNet was pretrained on ImageNet photographs. Since clinical MRI are 3D and 4D, a gated recurrent unit neural network was used to aggregate information from multiple 2D slices to make the final prediction.

Results: We achieved a classification accuracy (correct/total cases) of 99.8%, 98.5% and 97.5% on the training, validation and testing set, respectively. The averaged F1-score (percent overlap between predicted cases and actual cases) over all categories were 99.8% 98.2% and 94.4% on the training, validation and testing set.

Conclusion: We showed that automated annotation of MRI series by repurposing deep-learning techniques used for photographic image recognition tasks is feasible. Such methods can be used to facilitate high throughput curation of MRI data acquired across multiple centers and enable scientifically productive collaboration by researchers and, ultimately enhancing big data stroke research.

Brain Connectivity Measures Improve Modeling of Functional Outcome After Acute Ischemic Stroke

Background and Purpose- The ability to model long-term functional outcomes after acute ischemic stroke represents a major clinical challenge. One approach to potentially improve prediction modeling involves the analysis of connectomics. The field of connectomics represents the brain's connectivity as a graph, whose topological properties have helped uncover underlying mechanisms of brain function in health and disease. Specifically, we assessed the impact of stroke lesions on rich club organization, a high capacity backbone system of brain function. Methods- In a hospital-based cohort of 41 acute ischemic stroke patients, we investigated the effect of acute infarcts on the brain's prestroke rich club backbone and poststroke functional connectomes with respect to poststroke outcome. Functional connectomes were created using 3 anatomic atlases, and characteristic path-length (L) was calculated for each connectome. The number of rich club regions affected were manually determined using each patient's diffusion weighted image. We investigated differences in L with respect to outcome (modified Rankin Scale score; 90 days) and the National Institutes of Health Stroke Scale (NIHSS; early: 2-5 days; late: 90-day follow-up). Furthermore, we assessed the effect of including number of rich club regions and L in outcome models, using linear regression and assessing the explained variance (R2). Results- Of 41 patients (mean age [range]: 70 [45-89] years), 61% were male. Lower L was generally associated with better outcome. Including number of rich club regions in the backward selection models of outcome, R2 increased between 1.3- and 2.6-fold beyond that of traditional markers (age and acute lesion volume) for NIHSS and modified Rankin Scale score. Conclusions- In this proof-of-concept study, we showed that information on network topology can be leveraged to improve modeling of poststroke functional outcome. Future studies are warranted to validate this approach in larger prospective studies of outcome prediction in stroke.

Rich-Club Organization: An Important Determinant of Functional Outcome After Acute Ischemic Stroke

Objective: To determine whether the rich-club organization, essential for information transport in the human connectome, is an important biomarker of functional outcome after acute ischemic stroke (AIS). Methods: Consecutive AIS patients (N = 344) with acute brain magnetic resonance imaging (MRI) (<48 h) were eligible for this study. Each patient underwent a clinical MRI protocol, which included diffusion weighted imaging (DWI). All DWIs were registered to a template on which rich-club regions have been defined. Using manual outlines of stroke lesions, we automatically counted the number of affected rich-club regions and assessed its effect on the National Institute of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS; obtained at 90 days post-stroke) scores through ordinal regression. Results: Of 344 patients (median age 65, inter-quartile range 54-76 years) with a median DWI lesion volume (DWIv) of 3cc, 64% were male. We established that an increase in number of rich-club regions affected by a stroke increases the odds of poor stroke outcome, measured by NIHSS (OR: 1.77, 95%CI 1.41-2.21) and mRS (OR: 1.38, 95%CI 1.11-1.73). Additionally, we demonstrated that the OR exceeds traditional markers, such as DWIv (OR_NIHSS 1.08, 95%CI 1.06-1.11; OR_mRS 1.05, 95%CI 1.03-1.07) and age (OR_NIHSS 1.03, 95%CI 1.01-1.05; OR_mRS 1.05, 95%CI 1.03-1.07). Conclusion: In this proof-of-concept study, the number of rich-club nodes affected by a stroke lesion presents a translational biomarker of stroke outcome, which can be readily assessed using standard clinical AIS imaging protocols and considered in functional outcome prediction models beyond traditional factors.

Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data

Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm3 (0.9-16.6 cm3). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.

Network structural dependency in the human connectome across the life-span

Principles of network topology have been widely studied in the human connectome. Of particular interest is the modularity of the human brain, where the connectome is divided into subnetworks from which changes with development, aging or disease can be investigated. We present a weighted network measure, the Network Dependency Index (NDI), to identify an individual region’s importance to the global functioning of the network. Importantly, we utilize NDI to differentiate four subnetworks (Tiers) in the human connectome following Gaussian mixture model fitting. We analyze the topological aspects of each subnetwork with respect to age and compare it to rich club-based subnetworks (rich club, feeder, and seeder). Our results first demonstrate the efficacy of NDI to identify more consistent, central nodes of the connectome across age groups, when compared with the rich club framework. Stratifying the connectome by NDI led to consistent subnetworks across the life-span, revealing distinct patterns associated with age where, for example, the key relay nuclei and cortical regions are contained in a subnetwork with highest NDI. The divisions of the human connectome derived from our data-driven NDI framework have the potential to reveal topological alterations described by network measures through the life-span.

White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study

White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery.

In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94–0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of −2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing.

Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.

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Fully automated high-throughput white matter hyperintensity segmentation pipeline.

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Methodology designed for and applied to international clinical multi-site data.

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Calculation of disease burden in 2533 acute ischemic stroke patients.

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Total brain volume change with age (−2.4 cc/year) used in automated quality control.

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Increase of white matter hyperintensity burden of 0.95 cc/year.

68Ga-NODAGA-Indole: An Allysine-Reactive Positron Emission Tomography Probe for Molecular Imaging of Pulmonary Fibrogenesis

Oxidized collagen, wherein lysine residues are converted to the aldehyde allysine, is a universal feature of fibrogenesis, i.e. actively progressive fibrosis. Here we report the small molecule, allysine-binding positron emission tomography probe, ⁶⁸Ga-NODAGA-indole, that can noninvasively detect and quantify pulmonary fibrogenesis. We demonstrate that the uptake of ⁶⁸Ga-NODAGA-indole in actively fibrotic lungs is 7-fold higher than in control groups and that uptake is linearly correlated ( R² = 0.98) with the concentration of lung allysine.

Spatial Signature of White Matter Hyperintensities in Stroke Patients

Purpose: White matter hyperintensity (WMH) is a common phenotype across a variety of neurological diseases, particularly prevalent in stroke patients; however, vascular territory dependent variation in WMH burden has not yet been identified. Here, we sought to investigate the spatial specificity of WMH burden in patients with acute ischemic stroke (AIS). Materials and Methods: We created a novel age-appropriate high-resolution brain template and anatomically delineated the cerebral vascular territories. We used WMH masks derived from the clinical T2 Fluid Attenuated Inverse Recovery (FLAIR) MRI scans and spatial normalization of the template to discriminate between WMH volume within each subject's anterior cerebral artery (ACA), middle cerebral artery (MCA), and posterior cerebral artery (PCA) territories. Linear regression modeling including age, sex, common vascular risk factors, and TOAST stroke subtypes was used to assess for spatial specificity of WMH volume (WMHv) in a cohort of 882 AIS patients. Results: Mean age of this cohort was 65.23 ± 14.79 years, 61.7% were male, 63.6% were hypertensive, 35.8% never smoked. Mean WMHv was 11.58c ± 13.49 cc. There were significant differences in territory-specific, relative to global, WMH burden. In contrast to PCA territory, age (0.018 ± 0.002, p < 0.001) and small-vessel stroke subtype (0.212 ± 0.098, p < 0.001) were associated with relative increase of WMH burden within the anterior (ACA and MCA) territories, whereas male sex (-0.275 ± 0.067, p < 0.001) was associated with a relative decrease in WMHv. Conclusions: Our data establish the spatial specificity of WMH distribution in relation to vascular territory and risk factor exposure in AIS patients and offer new insights into the underlying pathology.