Reduced olfactory bulb volume accompanies olfactory dysfunction after mild SARS-CoV-2 infection.

Despite its high prevalence, the determinants of smelling impairment in COVID-19 remain not fully understood. In this work, we aimed to examine the association between olfactory bulb volume and the clinical trajectory of COVID-19-related smelling impairment in a large-scale magnetic resonance imaging (MRI) analysis. Data of non-vaccinated COVID-19 convalescents recruited within the framework of the prospective Hamburg City Health Study COVID Program between March and December 2020 were analyzed. At baseline, 233 participants underwent MRI and neuropsychological testing as well as a structured questionnaire for olfactory function. Between March and April 2022, olfactory function was assessed at follow-up including quantitative olfactometric testing with Sniffin' Sticks. This study included 233 individuals recovered from mainly mild to moderate SARS-CoV-2 infections. Longitudinal assessment demonstrated a declining prevalence of self-reported olfactory dysfunction from 67.1% at acute infection, 21.0% at baseline examination and 17.5% at follow-up. Participants with post-acute self-reported olfactory dysfunction had a significantly lower olfactory bulb volume at baseline than normally smelling individuals. Olfactory bulb volume at baseline predicted olfactometric scores at follow-up. Performance in neuropsychological testing was not significantly associated with the olfactory bulb volume. Our work demonstrates an association of long-term self-reported smelling dysfunction and olfactory bulb integrity in a sample of individuals recovered from mainly mild to moderate COVID-19. Collectively, our results highlight olfactory bulb volume as a surrogate marker that may inform diagnosis and guide rehabilitation strategies in COVID-19.

Self-supervised learning for classifying paranasal anomalies in the maxillary sinus.

PURPOSE: Paranasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the diversity of anomalies, supervised learning methods require large labelled dataset exhibiting diverse anomaly morphology. Self-supervised learning (SSL) can be used to learn representations from unlabelled data. However, there are no SSL methods designed for the downstream task of classifying paranasal anomalies in the maxillary sinus (MS). METHODS: Our approach uses a 3D convolutional autoencoder (CAE) trained in an unsupervised anomaly detection (UAD) framework. Initially, we train the 3D CAE to reduce reconstruction errors when reconstructing normal maxillary sinus (MS) image. Then, this CAE is applied to an unlabelled dataset to generate coarse anomaly locations by creating residual MS images. Following this, a 3D convolutional neural network (CNN) reconstructs these residual images, which forms our SSL task. Lastly, we fine-tune the encoder part of the 3D CNN on a labelled dataset of normal and anomalous MS images. RESULTS: The proposed SSL technique exhibits superior performance compared to existing generic self-supervised methods, especially in scenarios with limited annotated data. When trained on just 10% of the annotated dataset, our method achieves an area under the precision-recall curve (AUPRC) of 0.79 for the downstream classification task. This performance surpasses other methods, with BYOL attaining an AUPRC of 0.75, SimSiam at 0.74, SimCLR at 0.73 and masked autoencoding using SparK at 0.75. CONCLUSION: A self-supervised learning approach that inherently focuses on localizing paranasal anomalies proves to be advantageous, particularly when the subsequent task involves differentiating normal from anomalous maxillary sinuses. Access our code at https://github.com/mtec-tuhh/self-supervised-paranasal-anomaly .

Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes.

In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a machine learning framework based on rsfMRI features in a dataset of 20,000 healthy individuals from the UK Biobank, focusing on temporal complexity and functional connectivity measures. Our analysis across four behavioral phenotypes revealed that both temporal complexity and functional connectivity measures provide comparable predictive performance. However, individual characteristics consistently outperformed rsfMRI features in predictive accuracy, particularly in analyses involving smaller sample sizes. Integrating rsfMRI features with demographic data sometimes enhanced predictive outcomes. The efficacy of different predictive modeling techniques and the choice of brain parcellation atlas were also examined, showing no significant influence on the results. To summarize, while individual characteristics are superior to rsfMRI in predicting behavioral phenotypes, rsfMRI still conveys additional predictive value in the context of machine learning, such as investigating the role of specific brain regions in behavioral phenotypes.

Mapping the interplay of atrial fibrillation, brain structure, and cognitive dysfunction.

INTRODUCTION: Atrial fibrillation (AF) is associated with an elevated risk of cognitive impairment and dementia. Understanding the cognitive sequelae and brain structural changes associated with AF is vital for addressing ensuing health care needs. METHODS AND RESULTS: We examined 1335 stroke-free individuals with AF and 2683 matched controls using neuropsychological assessments and multimodal neuroimaging. The analysis revealed that individuals with AF exhibited deficits in executive function, processing speed, and reasoning, accompanied by reduced cortical thickness, elevated extracellular free-water content, and widespread white matter abnormalities, indicative of small vessel pathology. Notably, brain structural differences statistically mediated the relationship between AF and cognitive performance. DISCUSSION: Integrating a comprehensive analysis approach with extensive clinical and magnetic resonance imaging data, our study highlights small vessel pathology as a possible unifying link among AF, cognitive decline, and abnormal brain structure. These insights can inform diagnostic approaches and motivate the ongoing implementation of effective therapeutic strategies. Highlights We investigated neuropsychological and multimodal neuroimaging data of 1335 individuals with atrial fibrillation (AF) and 2683 matched controls. Our analysis revealed AF-associated deficits in cognitive domains of attention, executive function, processing speed, and reasoning. Cognitive deficits in the AF group were accompanied by structural brain alterations including reduced cortical thickness and gray matter volume, alongside increased extracellular free-water content as well as widespread differences of white matter integrity. Structural brain changes statistically mediated the link between AF and cognitive performance, emphasizing the potential of structural imaging markers as a diagnostic tool in AF-related cognitive decline.

Dopaminergic mesolimbic structural reserve is positively linked to better outcome after severe stroke.

The concept of brain reserve capacity has emerged in stroke recovery research in recent years. Imaging-based biomarkers of brain health have helped to better understand outcome variability in clinical cohorts. Still, outcome inferences are far from being satisfactory, particularly in patients with severe initial deficits. Neurorehabilitation after stroke is a complex process, comprising adaption and learning processes, which, on their part, are critically influenced by motivational and reward-related cognitive processes. Amongst others, dopaminergic neurotransmission is a key contributor to these mechanisms. The question arises, whether the amount of structural reserve capacity in the dopaminergic system might inform about outcome variability after severe stroke. For this purpose, this study analysed imaging and clinical data of 42 severely impaired acute stroke patients. Brain volumetry was performed within the first 2 weeks after the event using the Computational Anatomy Toolbox CAT12, grey matter volume estimates were collected for seven key areas of the human dopaminergic system along the mesocortical, mesolimbic and nigrostriatal pathways. Ordinal logistic regression models related regional volumes to the functional outcome, operationalized by the modified Rankin Scale, obtained 3-6 months after stroke. Models were adjusted for age, lesion volume and initial impairment. The main finding was that larger volumes of the amygdala and the nucleus accumbens at baseline were positively associated with a more favourable outcome. These data suggest a link between the structural state of mesolimbic key areas contributing to motor learning, motivational and reward-related brain networks and potentially the success of neurorehabilitation. They might also provide novel evidence to reconsider dopaminergic interventions particularly in severely impaired stroke patients to enhance recovery after stroke.

Longitudinal microstructural alterations surrounding subcortical ischemic stroke lesions detected by free-water imaging.

In this study we explore the spatio-temporal trajectory and clinical relevance of microstructural white matter changes within and beyond subcortical stroke lesions detected by free-water imaging. Twenty-seven patients with subcortical infarct with mean age of 66.73 (SD 11.57) and median initial NIHSS score of 4 (IQR 3-7) received diffusion MRI 3-5 days, 1 month, 3 months, and 12 months after symptom-onset. Extracellular free-water and fractional anisotropy of the tissue (FAT) were averaged within stroke lesions and the surrounding tissue. Linear models showed increased free-water and decreased FAT in the white matter of patients with subcortical stroke (lesion [free-water/FAT, mean relative difference in %, ipsilesional vs. contralesional hemisphere at 3-5 days, 1 month, 3 months, and 12 months after symptom-onset]: +41/-34, +111/-37, +208/-26, +251/-18; perilesional tissue [range in %]: +[5-24]/-[0.2-7], +[2-20]/-[3-16], +[5-43]/-[2-16], +[10-110]/-[2-12]). Microstructural changes were most prominent within the lesion and gradually became less pronounced with increasing distance from the lesion. While free-water elevations continuously increased over time and peaked after 12 months, FAT decreases were most evident 1 month post-stroke, gradually returning to baseline values thereafter. Higher perilesional free-water and higher lesional FAT at baseline were correlated with greater reductions in lesion size (rho = -0.51, p = .03) in unadjusted analyses only, while there were no associations with clinical measures. In summary, we find a characteristic spatio-temporal pattern of extracellular and cellular alterations beyond subcortical stroke lesions, indicating a dynamic parenchymal response to ischemia characterized by vasogenic edema, cellular damage, and white matter atrophy.

Early REperfusion therapy with intravenous alteplase for recovery of VISION in acute central retinal artery occlusion (REVISION): Study protocol of a phase III trial.

RATIONALE: Meta-analyses of case series of non-arteritic central retinal artery occlusion (CRAO) indicate beneficial effects of intravenous thrombolysis when initiated early after symptom onset. Randomized data are lacking to address this question. AIMS: The REperfusion therapy with intravenous alteplase for recovery of VISION in acute central retinal artery occlusion (REVISION) investigates intravenous alteplase within 4.5 h of monocular vision loss due to acute CRAO. METHODS: This study is the randomized (1:1), double-blind, placebo-controlled, multicenter adaptive phase III trial. STUDY OUTCOMES: Primary outcome is functional recovery to normal or mildly impaired vision in the affected eye defined as best-corrected visual acuity of the Logarithm of the Minimum Angle of Resolution of 0.5 or less at 30 days (intention-to-treat analysis). Secondary efficacy outcomes include modified Rankin Score at 90 days and quality of life. Safety outcomes include symptomatic intracranial hemorrhage, major bleeding (International Society on Thrombosis and Haemostasis definition) and mortality. Exploratory analyses of optical coherence tomography/angiography, ultrasound and magnetic resonance imaging (MRI) biomarkers will be conducted. SAMPLE SIZE: Using an adaptive design with interim analysis at 120 patients, up to 422 participants (211 per arm) would be needed for 80% power (one-sided alpha = 0.025) to detect a difference of 15%, assuming functional recovery rates of 10% in the placebo arm and 25% in the alteplase arm. DISCUSSION: By enrolling patients within 4.5 h of CRAO onset, REVISION uses insights from meta-analyses of CRAO case series and randomized thrombolysis trials in acute ischemic stroke. Increased rates of early reperfusion and good neurological outcomes in stroke may translate to CRAO with its similar pathophysiology. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04965038; EU Trial Number: 2023-507388-21-00.

Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients.

Introduction: White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating cognitive health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. We propose that lesion network mapping (LNM), enables to infer if brain networks are connected to lesions, and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed this approach to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. Methods & results: We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity across 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. The capacity of total and regional WMH volumes and LNM scores in predicting cognitive function was compared using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention and executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater disruptive effects of WMH on regional connectivity, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Conclusion: Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network effects, particularly in attentionrelated brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

Computer-Aided Diagnosis of Maxillary Sinus Anomalies: Validation and Clinical Correlation.

OBJECTIVE: Computer aided diagnostics (CAD) systems can automate the differentiation of maxillary sinus (MS) with and without opacification, simplifying the typically laborious process and aiding in clinical insight discovery within large cohorts. METHODS: This study uses Hamburg City Health Study (HCHS) a large, prospective, long-term, population-based cohort study of participants between 45 and 74 years of age. We develop a CAD system using an ensemble of 3D Convolutional Neural Network (CNN) to analyze cranial MRIs, distinguishing MS with opacifications (polyps, cysts, mucosal thickening) from MS without opacifications. The system is used to find correlations of participants with and without MS opacifications with clinical data (smoking, alcohol, BMI, asthma, bronchitis, sex, age, leukocyte count, C-reactive protein, allergies). RESULTS: The evaluation metrics of CAD system (Area Under Receiver Operator Characteristic: 0.95, sensitivity: 0.85, specificity: 0.90) demonstrated the effectiveness of our approach. MS with opacification group exhibited higher alcohol consumption, higher BMI, higher incidence of intrinsic asthma and extrinsic asthma. Male sex had higher prevalence of MS opacifications. Participants with MS opacifications had higher incidence of hay fever and house dust allergy but lower incidence of bee/wasp venom allergy. CONCLUSION: The study demonstrates a 3D CNN's ability to distinguish MS with and without opacifications, improving automated diagnosis and aiding in correlating clinical data in population studies. LEVEL OF EVIDENCE: 3 Laryngoscope, 2024.

A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition.

The link between metabolic syndrome (MetS) and neurodegenerative as well as cerebrovascular conditions holds substantial implications for brain health in at-risk populations. This study elucidates the complex relationship between MetS and brain health by conducting a comprehensive examination of cardiometabolic risk factors, brain morphology, and cognitive function in 40,087 individuals. Multivariate, data-driven statistics identified a latent dimension linking more severe MetS to widespread brain morphological abnormalities, accounting for up to 71% of shared variance in the data. This dimension was replicable across sub-samples. In a mediation analysis, we could demonstrate that MetS-related brain morphological abnormalities mediated the link between MetS severity and cognitive performance in multiple domains. Employing imaging transcriptomics and connectomics, our results also suggest that MetS-related morphological abnormalities are linked to the regional cellular composition and macroscopic brain network organization. By leveraging extensive, multi-domain data combined with a dimensional stratification approach, our analysis provides profound insights into the association of MetS and brain health. These findings can inform effective therapeutic and risk mitigation strategies aimed at maintaining brain integrity.

Subjective evaluation of home environment and levels of self-reported depression in middle to old age: Results from the HCHS study.

OBJECTIVES: The immediate living environment might, like other lifestyle factors, be significantly related to mental well-being. The current study addresses the question whether five relevant subjective home environment variables (i.e., protection from disturbing nightlight, daylight entering the home, safety at home, quality of window views, and noise disturbance) are associated with levels of self-reported depression over and above well-known sociodemographic and common lifestyle variables. METHODS: Data from the Hamburg City Health Study (HCHS) were analyzed. In N = 8757 with available PHQ-9 depression data, multiple linear regression models were computed, with demographic data, lifestyle variables, and variables describing the subjective evaluation of the home environment. RESULTS: The model explained 15% of variance in depression levels, with ratings for the subjective evaluation of home environment accounting for 6%. Better protection from disturbing light at night, more daylight entering the home, feeling safer, and perceived quality of the window views, were all significantly associated with lower, while more annoyance by noise was associated with higher levels of self-reported depression. Results did not differ if examining a sample of the youngest (middle-aged participants: 46-50 years) versus oldest (70-78 years) participants within HCHS. CONCLUSION: Beyond studying the role of lifestyle factors related to self-reported depression, people's homes may be important for subclinical levels of depression in middle and older age, albeit the direction of effects or causality cannot be inferred from the present study. The development of a consensus and tools for a standardized home environment assessment is needed.

Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches.

Background: Voxel-based lesion symptom mapping (VLSM) assesses the relation of lesion location at a voxel level with a specific clinical or functional outcome measure at a population level. Spatial normalization, that is, mapping the patient images into an atlas coordinate system, is an essential pre-processing step of VLSM. However, no consensus exists on the optimal registration approach to compute the transformation nor are downstream effects on VLSM statistics explored. In this work, we evaluate four registration approaches commonly used in VLSM pipelines: affine (AR), nonlinear (NLR), nonlinear with cost function masking (CFM), and enantiomorphic registration (ENR). The evaluation is based on a standard VLSM scenario: the analysis of statistical relations of brain voxels and regions in imaging data acquired early after stroke onset with follow-up modified Rankin Scale (mRS) values. Materials and methods: Fluid-attenuated inversion recovery (FLAIR) MRI data from 122 acute ischemic stroke patients acquired between 2 and 3 days after stroke onset and corresponding lesion segmentations, and 30 days mRS values from a European multicenter stroke imaging study (I-KNOW) were available and used in this study. The relation of the voxel location with follow-up mRS was assessed by uni- as well as multi-variate statistical testing based on the lesion segmentations registered using the four different methods (AR, NLR, CFM, ENR; implementation based on the ANTs toolkit). Results: The brain areas evaluated as important for follow-up mRS were largely consistent across the registration approaches. However, NLR, CFM, and ENR led to distortions in the patient images after the corresponding nonlinear transformations were applied. In addition, local structures (for instance the lateral ventricles) and adjacent brain areas remained insufficiently aligned with corresponding atlas structures even after nonlinear registration. Conclusions: For VLSM study designs and imaging data similar to the present work, an additional benefit of nonlinear registration variants for spatial normalization seems questionable. Related distortions in the normalized images lead to uncertainties in the VLSM analyses and may offset the theoretical benefits of nonlinear registration.

Association of structural connectivity with functional brain network segregation in a middle-aged to elderly population.

Introduction: The deterioration of white matter pathways is one of the hallmarks of the ageing brain. In theory, this decrease in structural integrity leads to disconnection between regions of brain networks and thus to altered functional connectivity and a decrease in cognitive abilities. However, in many studies, associations between structural and functional connectivity are rather weak or not observed at all. System segregation, defined as the extent of partitioning between different resting state networks has increasingly gained attention in recent years as a new metric for functional changes in the aging brain. Yet there is a shortage of previous reports describing the association of structural integrity and functional segregation. Methods: Therefore, we used a large a large sample of 2,657 participants from the Hamburg City Health Study, a prospective population-based study including participants aged 46-78 years from the metropolitan region Hamburg, Germany. We reconstructed structural and functional connectomes to analyze whether there is an association between age-related differences in structural connectivity and functional segregation, and whether this association is stronger than between structural connectivity and functional connectivity. In a second step, we investigated the relationship between functional segregation and executive cognitive function and tested whether this association is stronger than that between functional connectivity and executive cognitive function. Results: We found a significant age-independent association between decreasing structural connectivity and decreasing functional segregation across the brain. In addition, decreasing functional segregation showed an association with decreasing executive cognitive function. On the contrary, no such association was observed between functional connectivity and structural connectivity or executive function. Discussion: These results indicate that the segregation metric is a more sensitive biomarker of cognitive ageing than functional connectivity at the global level and offers a unique and more complementary network-based explanation.

Kidney dysfunction predicts 90 days mortality after stroke thrombectomy independent of cardiovascular risk factors and chronic kidney disease.

INTRODUCTION: Kidney dysfunction (KD) is a risk factor for cerebrovascular events and has been shown to have a detrimental effect on outcome after stroke. We evaluated the influence of KD at admission and pre-existing diagnosis of chronic kidney disease (CKD) before thrombectomy for anterior circulation stroke on functional independence and mortality 90 days after stroke in this cross-sectional study. PATIENTS AND METHODS: We included patients with acute ischemic stroke in the anterior circulation treated with thrombectomy at our hospital between June 2015 and May 2022. We analyzed clinical characteristics, laboratory values and pre-existing diagnosis of CKD. KD at admission was defined as glomerular filtration rate (GFR) <60 ml/min/1.73 m2. Outcomes were defined as a modified Rankin Scale Score of 0-2 for functional independence and mortality at 90 days. We fitted multivariate regression analysis to examine the influence of pre-treatment KD and pre-diagnosed CKD on outcome. RESULTS: Nine hundred fifty-three patients were included in this analysis (mean age 73.8 years, 54.2% female). KD was present in 31.8%, and patients with KD were older and more often female, presented more often with comorbidities such as arterial hypertension, diabetes, and atrial fibrillation, and were less often independent before the index stroke. In multivariate analysis adjusted for age, independence before the index stroke, diabetes, hypertension, atrial fibrillation, initial NIHSS, thrombolysis treatment, and recanalization outcome, KD on admission had no significant influence on functional independence 90 days after stroke, but predicted mortality with an odds ratio of 1.80 (95% CI 1.23-2.63, p = 0.003). This influence also persisted when controlling for pre-diagnosed CKD (OR 1.60, 95% CI 1.05-2.43, p = 0.027). DISCUSSION: KD might function as a surrogate parameter for comorbidity burden and thus increased risk of mortality in this cohort. CONCLUSIONS: KD on admission is associated with an 80% higher risk of mortality at 90 days after stroke thrombectomy independent of cardiovascular risk factors and CKD awareness. KD on admission should not exclude patients from thrombectomy but might support prognostic evaluation.

Multiple instance ensembling for paranasal anomaly classification in the maxillary sinus.

PURPOSE: Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionally, current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. These challenges necessitate the need for further research and development in this area. METHODS: We investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary sinuses (MS) and MS with polyps or cysts. The task of accurately localizing the relevant MS volume within larger head and neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a strategy which includes the use of a novel sampling technique that not only effectively localizes the relevant MS volume, but also increases the size of the training dataset and improves classification results. Additionally, we employ a Multiple Instance Ensembling (MIE) prediction method to further boost classification performance. RESULTS: With sampling and MIE, we observe that there is consistent improvement in classification performance of all 3D ResNet and 3D DenseNet architecture with an average AUPRC percentage increase of 21.86 ± 11.92% and 4.27 ± 5.04% by sampling and 28.86 ± 12.80% and 9.85 ± 4.02% by sampling and MIE, respectively. CONCLUSION: Sampling and MIE can be effective techniques to improve the generalizability of CNNs for paranasal anomaly classification. We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy through sampling alongside a novel MIE strategy that proves to be beneficial for paranasal anomaly classification in the MS.

Independent external validation of a stroke recurrence score in patients with embolic stroke of undetermined source.

BACKGROUND: Embolic stroke of undetermined source (ESUS) accounts for a substantial proportion of ischaemic strokes. A stroke recurrence score has been shown to predict the risk of recurrent stroke in patients with ESUS based on a combination of clinical and imaging features. This study aimed to externally validate the performance of the ESUS recurrence score using data from a randomized controlled trial. METHODS: The validation dataset consisted of eligible stroke patients with available magnetic resonance imaging (MRI) data enrolled in the PreDAFIS sub-study of the MonDAFIS study. The score was calculated using three variables: age (1 point per decade after 35 years), presence of white matter hyperintensities (2 points), and multiterritorial ischaemic stroke (3 points). Patients were assigned to risk groups as described in the original publication. The model was evaluated using standard discrimination and calibration methods. RESULTS: Of the 1054 patients, 241 (22.9%) were classified as ESUS. Owing to insufficient MRI quality, three patients were excluded, leaving 238 patients (median age 65.5 years [IQR 20.75], 39% female) for analysis. Of these, 30 (13%) patients experienced recurrent ischaemic stroke or transient ischemic attack (TIA) during a follow-up period of 383 patient-years, corresponding to an incidence rate of 7.8 per 100 patient-years (95% CI 5.3-11.2). Patients with an ESUS recurrence score value of ≥ 7 had a 2.46 (hazard ratio (HR), 95% CI 1.02-5.93) times higher risk of stroke recurrence than patients with a score of 0-4. The cumulative probability of stroke recurrence in the low-(0-4), intermediate-(5-6), and high-risk group (≥ 7) was 9%, 13%, and 23%, respectively (log-rank test, χ2 = 4.2, p = 0.1). CONCLUSIONS: This external validation of a published scoring system supports a threshold of ≥ 7 for identifying ESUS patients at high-risk of stroke recurrence. However, further adjustments may be required to improve the model's performance in independent cohorts. The use of risk scores may be helpful in guiding extended diagnostics and further trials on secondary prevention in patients with ESUS. TRIAL REGISTRATION: Clinical Trials, NCT02204267. Registered 30 July 2014, https://clinicaltrials.gov/ct2/show/NCT02204267 .

Endovascular thrombectomy for acute ischaemic stroke with established large infarct: multicentre, open-label, randomised trial.

BACKGROUND: Recent evidence suggests a beneficial effect of endovascular thrombectomy in acute ischaemic stroke with large infarct; however, previous trials have relied on multimodal brain imaging, whereas non-contrast CT is mostly used in clinical practice. METHODS: In a prospective multicentre, open-label, randomised trial, patients with acute ischaemic stroke due to large vessel occlusion in the anterior circulation and a large established infarct indicated by an Alberta Stroke Program Early Computed Tomographic Score (ASPECTS) of 3-5 were randomly assigned using a central, web-based system (using a 1:1 ratio) to receive either endovascular thrombectomy with medical treatment or medical treatment (ie, standard of care) alone up to 12 h from stroke onset. The study was conducted in 40 hospitals in Europe and one site in Canada. The primary outcome was functional outcome across the entire range of the modified Rankin Scale at 90 days, assessed by investigators masked to treatment assignment. The primary analysis was done in the intention-to-treat population. Safety endpoints included mortality and rates of symptomatic intracranial haemorrhage and were analysed in the safety population, which included all patients based on the treatment they received. This trial is registered with ClinicalTrials.gov, NCT03094715. FINDINGS: From July 17, 2018, to Feb 21, 2023, 253 patients were randomly assigned, with 125 patients assigned to endovascular thrombectomy and 128 to medical treatment alone. The trial was stopped early for efficacy after the first pre-planned interim analysis. At 90 days, endovascular thrombectomy was associated with a shift in the distribution of scores on the modified Rankin Scale towards better outcome (adjusted common OR 2·58 [95% CI 1·60-4·15]; p=0·0001) and with lower mortality (hazard ratio 0·67 [95% CI 0·46-0·98]; p=0·038). Symptomatic intracranial haemorrhage occurred in seven (6%) patients with thrombectomy and in six (5%) with medical treatment alone. INTERPRETATION: Endovascular thrombectomy was associated with improved functional outcome and lower mortality in patients with acute ischaemic stroke from large vessel occlusion with established large infarct in a setting using non-contrast CT as the predominant imaging modality for patient selection. FUNDING: EU Horizon 2020.

Functional Lesion Network Mapping of Sensory Deficits After Ischemic Stroke.

BACKGROUND: Sensory deficits are common after stroke, leading to disability and poor quality of life. Although lesion locations and patterns of structural brain network disruption have been associated with sensory disturbances, the relation with functional lesion connectivity has not yet been established. METHODS: Retrospective analysis of a prospective cohort study of patients with acute ischemic stroke. Indirect functional lesion network mapping to identify brain regions remote from the primary lesion associated with deficits on the Rivermead Assessment of Somatosensory Performance test. Associations between Rivermead Assessment of Somatosensory Performance scores and functional connectivity of the lesion site with prespecified components of the somatosensory system. RESULTS: One hundred one patients (mean age, 62 years; 32% women) from the TOPOS study (Topological and Clinical Prospective Study About Somatosensation in Stroke). Lesion network mapping identified a bilateral fronto-parietal network associated with sensory deficits in the acute phase after stroke. There were graded associations between deficits and functional lesion connectivity to sensory cortices, but not the thalamus. CONCLUSIONS: Infarcts in brain regions remote from, but functionally connected, to the somatosensory network are associated with somatosensory deficits measured by the Rivermead Assessment of Somatosensory Performance test, reflecting the hierarchical functional anatomy of sensory processing. Further research is needed to translate these findings into improved prognosis and personalized rehabilitation strategies.

Clinical Judgment vs Triage Scales for Detecting Large Vessel Occlusions in Suspected Acute Stroke.

This cohort study examines clinical judgment of large vessel occlusions compared with triage scales in a sample of patients admitted to the emergency department with suspicion of acute stroke.

Disability and persistent motor deficits are linked to structural crossed cerebellar diaschisis in chronic stroke.

Brain imaging has significantly contributed to our understanding of the cerebellum being involved in recovery after non-cerebellar stroke. Due to its connections with supratentorial brain networks, acute stroke can alter the function and structure of the contralesional cerebellum, known as crossed cerebellar diaschisis (CCD). Data on the spatially precise distribution of structural CCD and their implications for persistent deficits after stroke are notably limited. In this cross-sectional study, structural MRI and clinical data were analyzed from 32 chronic stroke patients, at least 6 months after the event. We quantified lobule-specific contralesional atrophy, as a surrogate of structural CCD, in patients and healthy controls. Volumetric data were integrated with clinical scores of disability and motor deficits. Diaschisis-outcome models were adjusted for the covariables age, lesion volume, and damage to the corticospinal tract. We found that structural CCD was evident for the whole cerebellum, and particularly for lobules V and VI. Lobule VI diaschisis was significantly correlated with clinical scores, that is, volume reductions in contralesional lobule VI were associated with higher levels of disability and motor deficits. Lobule V and the whole cerebellum did not show similar diaschisis-outcome relationships across the spectrum of the clinical scores. These results provide novel insights into stroke-related cerebellar plasticity and might thereby promote lobule VI as a key area prone to structural CCD and potentially involved in recovery and residual motor functioning.