Mutual Information Guided Diffusion for Zero-shot Cross-modality Medical Image Translation.

Cross-modality data translation has attracted great interest in medical image computing. Deep generative models show performance improvement in addressing related challenges. Nevertheless, as a fundamental challenge in image translation, the problem of zero-shot learning cross-modality image translation with fidelity remains unanswered. To bridge this gap, we propose a novel unsupervised zero-shot learning method called Mutual Information guided Diffusion Model, which learns to translate an unseen source image to the target modality by leveraging the inherent statistical consistency of Mutual Information between different modalities. To overcome the prohibitive high dimensional Mutual Information calculation, we propose a differentiable local-wise mutual information layer for conditioning the iterative denoising process. The Local-wise-Mutual-Information-Layer captures identical cross-modality features in the statistical domain, offering diffusion guidance without relying on direct mappings between the source and target domains. This advantage allows our method to adapt to changing source domains without the need for retraining, making it highly practical when sufficient labeled source domain data is not available. We demonstrate the superior performance of MIDiffusion in zero-shot cross-modality translation tasks through empirical comparisons with other generative models, including adversarial-based and diffusion-based models. Finally, we showcase the real-world application of MIDiffusion in 3D zero-shot learning-based cross-modality image segmentation tasks.

Thrombolysis for Wake-Up Stroke Versus Non-Wake-Up Unwitnessed Stroke: EOS Individual Patient Data Meta-Analysis.

BACKGROUND: Stroke with unknown time of onset can be categorized into 2 groups; wake-up stroke (WUS) and unwitnessed stroke with an onset time unavailable for reasons other than wake-up (non-wake-up unwitnessed stroke, non-WUS). We aimed to assess potential differences in the efficacy and safety of intravenous thrombolysis (IVT) between these subgroups. METHODS: Patients with an unknown-onset stroke were evaluated using individual patient-level data of 2 randomized controlled trials (WAKE-UP [Efficacy and Safety of MRI-Based Thrombolysis in Wake-Up Stroke], THAWS [Thrombolysis for Acute Wake-Up and Unclear-Onset Strokes With Alteplase at 0.6 mg/kg]) comparing IVT with placebo or standard treatment from the EOS (Evaluation of Unknown-Onset Stroke Thrombolysis trial) data set. A favorable outcome was prespecified as a modified Rankin Scale score of 0 to 1 at 90 days. Safety outcomes included symptomatic intracranial hemorrhage at 22 to 36 hours and 90-day mortality. The IVT effect was compared between the treatment groups in the WUS and non-WUS with multivariable logistic regression analysis. RESULTS: Six hundred thirty-four patients from 2 trials were analyzed; 542 had WUS (191 women, 272 receiving alteplase), and 92 had non-WUS (42 women, 43 receiving alteplase). Overall, no significant interaction was noted between the mode of onset and treatment effect (P value for interaction=0.796). In patients with WUS, the frequencies of favorable outcomes were 54.8% and 45.5% in the IVT and control groups, respectively (adjusted odds ratio, 1.47 [95% CI, 1.01-2.16]). Death occurred in 4.0% and 1.9%, respectively (P=0.162), and symptomatic intracranial hemorrhage in 1.8% and 0.3%, respectively (P=0.194). In patients with non-WUS, no significant difference was observed in favorable outcomes relative to the control (37.2% versus 29.2%; adjusted odds ratio, 1.76 [0.58-5.37]). One death and one symptomatic intracranial hemorrhage were reported in the IVT group, but none in the control. CONCLUSIONS: There was no difference in the effect of IVT between patients with WUS and non-WUS. IVT showed a significant benefit in patients with WUS, while there was insufficient statistical power to detect a substantial benefit in the non-WUS subgroup. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: CRD42020166903.

Subtle white matter intensity changes on fluid-attenuated inversion recovery imaging in patients with ischaemic stroke.

Leukoaraiosis is a neuroimaging marker of small-vessel disease that is characterized by high signal intensity on fluid-attenuated inversion recovery MRI. There is increasing evidence from pathology and neuroimaging suggesting that the structural abnormalities that characterize leukoaraiosis are actually present within regions of normal-appearing white matter, and that the underlying pathophysiology of white matter damage related to small-vessel disease involves blood-brain barrier damage. In this study, we aim to verify whether leukoaraiosis is associated with elevated signal intensity on fluid-attenuated inversion recovery imaging, a marker of brain tissue free-water accumulation, in normal-appearing white matter. We performed a cross-sectional study of adult patients admitted to our hospital with a diagnosis of acute ischaemic stroke or transient ischaemic attack. Leukoaraiosis was segmented using a semi-automated method involving manual outlining and signal thresholding. White matter regions were segmented based on the probabilistic tissue maps from the International Consortium for Brain Mapping 152 atlas. Also, normal-appearing white matter was further segmented based on voxel distance from leukoaraiosis borders, resulting in five normal-appearing white matter strata at increasing voxel distances from leukoaraiosis. The relationship between mean normalized fluid-attenuated inversion recovery signal intensity on normal-appearing white matter and leukoaraiosis volume was studied in a multivariable statistical analysis using linear mixed modelling, having normal-appearing white matter strata as a clustering variable. One hundred consecutive patients meeting inclusion and exclusion criteria were selected for analysis (53% female, mean age 68 years). Mean normalized fluid-attenuated inversion recovery signal intensity on normal-appearing white matter was higher in the vicinity of leukoaraiosis and progressively lower at increasing distances from leukoaraiosis. In a multivariable analysis, the mean normalized fluid-attenuated inversion recovery signal intensity on normal-appearing white matter was positively associated with leukoaraiosis volume and age (B = 0.025 for each leukoaraiosis quartile increase; 95% confidence interval 0.019-0.030). This association was found similarly across normal-appearing white matter strata. Voxel maps of the mean normalized fluid-attenuated inversion recovery signal intensity on normal-appearing white matter showed an increase in signal intensity that was not adjacent to leukoaraiosis regions. Our results show that normal-appearing white matter exhibits subtle signal intensity changes on fluid-attenuated inversion recovery imaging that are related to leukoaraiosis burden. These results suggest that diffuse free-water accumulation is likely related to the aetiopathogenic processes underlying the development of white matter damage related to small-vessel disease.

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.

Hyperperfusion profiles after recanalization differentially associate with outcomes in a rat ischemic stroke model.

Futile recanalization hampers prognoses of ischemic stroke after successful mechanical thrombectomy, hypothetically through post-recanalization perfusion deficits, onset-to-groin delays and sex effects. Clinically, acute multiparametric imaging studies remain challenging. We assessed possible relationships between these factors and disease outcome after experimental cerebral ischemia-reperfusion, using translational MRI, behavioral testing and multi-model inference analyses. Male and female rats (N = 60) were subjected to 45-/90-min filament-induced transient middle cerebral artery occlusion. Diffusion, T2- and perfusion-weighted MRI at occlusion, 0.5 h and four days after recanalization, enabled tracking of tissue fate, and relative regional cerebral blood flow (rrCBF) and -volume (rrCBV). Lesion areas were parcellated into core, salvageable tissue and delayed injury, verified by histology. Recanalization resulted in acute-to-subacute lesion volume reductions, most apparently in females (n = 19). Hyperacute normo-to-hyperperfusion in the post-ischemic lesion augmented towards day four, particularly in males (n = 23). Tissue suffering delayed injury contained higher ratios of hypoperfused voxels early after recanalization. Regressed against acute-to-subacute lesion volume change, increased rrCBF associated with lesion growth, but increased rrCBV with lesion reduction. Similar relationships were detected for behavioral outcome. Post-ischemic hyperperfusion may develop differentially in males and females, and can be beneficial or detrimental to disease outcome, depending on which perfusion parameter is used as explanatory variable.

Neuroimmune activation and increased brain aging in chronic pain patients after the COVID-19 pandemic onset.

The COVID-19 pandemic has exerted a global impact on both physical and mental health, and clinical populations have been disproportionally affected. To date, however, the mechanisms underlying the deleterious effects of the pandemic on pre-existing clinical conditions remain unclear. Here we investigated whether the onset of the pandemic was associated with an increase in brain/blood levels of inflammatory markers and MRI-estimated brain age in patients with chronic low back pain (cLBP), irrespective of their infection history. A retrospective cohort study was conducted on 56 adult participants with cLBP (28 'Pre-Pandemic', 28 'Pandemic') using integrated Positron Emission Tomography/ Magnetic Resonance Imaging (PET/MRI) and the radioligand [11C]PBR28, which binds to the neuroinflammatory marker 18 kDa Translocator Protein (TSPO). Image data were collected between November 2017 and January 2020 ('Pre-Pandemic' cLBP) or between August 2020 and May 2022 ('Pandemic' cLBP). Compared to the Pre-Pandemic group, the Pandemic patients demonstrated widespread and statistically significant elevations in brain TSPO levels (P =.05, cluster corrected). PET signal elevations in the Pandemic group were also observed when 1) excluding 3 Pandemic subjects with a known history of COVID infection, or 2) using secondary outcome measures (volume of distribution -VT- and VT ratio - DVR) in a smaller subset of participants. Pandemic subjects also exhibited elevated serum levels of inflammatory markers (IL-16; P <.05) and estimated BA (P <.0001), which were positively correlated with [11C]PBR28 SUVR (r's ≥ 0.35; P's < 0.05). The pain interference scores, which were elevated in the Pandemic group (P <.05), were negatively correlated with [11C]PBR28 SUVR in the amygdala (r = -0.46; P<.05). This work suggests that the pandemic outbreak may have been accompanied by neuroinflammation and increased brain age in cLBP patients, as measured by multimodal imaging and serum testing. This study underscores the broad impact of the pandemic on human health, which extends beyond the morbidity solely mediated by the virus itself.

Understanding Bias in Artificial Intelligence: A Practice Perspective.

In the fall of 2021, several experts in this space delivered a Webinar hosted by the American Society of Neuroradiology (ASNR) Diversity and Inclusion Committee, focused on expanding the understanding of bias in artificial intelligence, with a health equity lens, and provided key concepts for neuroradiologists to approach the evaluation of these tools. In this perspective, we distill key parts of this discussion, including understanding why this topic is important to neuroradiologists and lending insight on how neuroradiologists can develop a framework to assess health equity-related bias in artificial intelligence tools. In addition, we provide examples of clinical workflow implementation of these tools so that we can begin to see how artificial intelligence tools will impact discourse on equitable radiologic care. As continuous learners, we must be engaged in new and rapidly evolving technologies that emerge in our field. The Diversity and Inclusion Committee of the ASNR has addressed this subject matter through its programming content revolving around health equity in neuroradiologic advances.

Increased task-relevant fMRI responsiveness in comatose cardiac arrest patients is associated with improved neurologic outcomes.

Early prediction of the recovery of consciousness in comatose cardiac arrest patients remains challenging. We prospectively studied task-relevant fMRI responses in 19 comatose cardiac arrest patients and five healthy controls to assess the fMRI's utility for neuroprognostication. Tasks involved instrumental music listening, forward and backward language listening, and motor imagery. Task-specific reference images were created from group-level fMRI responses from the healthy controls. Dice scores measured the overlap of individual subject-level fMRI responses with the reference images. Task-relevant responsiveness index (Rindex) was calculated as the maximum Dice score across the four tasks. Correlation analyses showed that increased Dice scores were significantly associated with arousal recovery (P < 0.05) and emergence from the minimally conscious state (EMCS) by one year (P < 0.001) for all tasks except motor imagery. Greater Rindex was significantly correlated with improved arousal recovery (P = 0.002) and consciousness (P = 0.001). For patients who survived to discharge (n = 6), the Rindex's sensitivity was 75% for predicting EMCS (n = 4). Task-based fMRI holds promise for detecting covert consciousness in comatose cardiac arrest patients, but further studies are needed to confirm these findings. Caution is necessary when interpreting the absence of task-relevant fMRI responses as a surrogate for inevitable poor neurological prognosis.

A cross-sectional study to test equivalence of low- versus intermediate-flip angle dynamic susceptibility contrast MRI measures of relative cerebral blood volume in patients with high-grade gliomas at 1.5 Tesla field strength.

Introduction: 1.5 Tesla (1.5T) remain a significant field strength for brain imaging worldwide. Recent computer simulations and clinical studies at 3T MRI have suggested that dynamic susceptibility contrast (DSC) MRI using a 30° flip angle ("low-FA") with model-based leakage correction and no gadolinium-based contrast agent (GBCA) preload provides equivalent relative cerebral blood volume (rCBV) measurements to the reference-standard acquisition using a single-dose GBCA preload with a 60° flip angle ("intermediate-FA") and model-based leakage correction. However, it remains unclear whether this holds true at 1.5T. The purpose of this study was to test this at 1.5T in human high-grade glioma (HGG) patients. Methods: This was a single-institution cross-sectional study of patients who had undergone 1.5T MRI for HGG. DSC-MRI consisted of gradient-echo echo-planar imaging (GRE-EPI) with a low-FA without preload (30°/P-); this then subsequently served as a preload for the standard intermediate-FA acquisition (60°/P+). Both normalized (nrCBV) and standardized relative cerebral blood volumes (srCBV) were calculated using model-based leakage correction (C+) with IBNeuro™ software. Whole-enhancing lesion mean and median nrCBV and srCBV from the low- and intermediate-FA methods were compared using the Pearson's, Spearman's and intraclass correlation coefficients (ICC). Results: Twenty-three HGG patients composing a total of 31 scans were analyzed. The Pearson and Spearman correlations and ICCs between the 30°/P-/C+ and 60°/P+/C+ acquisitions demonstrated high correlations for both mean and median nrCBV and srCBV. Conclusion: Our study provides preliminary evidence that for HGG patients at 1.5T MRI, a low FA, no preload DSC-MRI acquisition can be an appealing alternative to the reference standard higher FA acquisition that utilizes a preload.

Mapping Lesion-Related Epilepsy to a Human Brain Network.

Importance: It remains unclear why lesions in some locations cause epilepsy while others do not. Identifying the brain regions or networks associated with epilepsy by mapping these lesions could inform prognosis and guide interventions. Objective: To assess whether lesion locations associated with epilepsy map to specific brain regions and networks. Design, Setting, and Participants: This case-control study used lesion location and lesion network mapping to identify the brain regions and networks associated with epilepsy in a discovery data set of patients with poststroke epilepsy and control patients with stroke. Patients with stroke lesions and epilepsy (n = 76) or no epilepsy (n = 625) were included. Generalizability to other lesion types was assessed using 4 independent cohorts as validation data sets. The total numbers of patients across all datasets (both discovery and validation datasets) were 347 with epilepsy and 1126 without. Therapeutic relevance was assessed using deep brain stimulation sites that improve seizure control. Data were analyzed from September 2018 through December 2022. All shared patient data were analyzed and included; no patients were excluded. Main Outcomes and Measures: Epilepsy or no epilepsy. Results: Lesion locations from 76 patients with poststroke epilepsy (39 [51%] male; mean [SD] age, 61.0 [14.6] years; mean [SD] follow-up, 6.7 [2.0] years) and 625 control patients with stroke (366 [59%] male; mean [SD] age, 62.0 [14.1] years; follow-up range, 3-12 months) were included in the discovery data set. Lesions associated with epilepsy occurred in multiple heterogenous locations spanning different lobes and vascular territories. However, these same lesion locations were part of a specific brain network defined by functional connectivity to the basal ganglia and cerebellum. Findings were validated in 4 independent cohorts including 772 patients with brain lesions (271 [35%] with epilepsy; 515 [67%] male; median [IQR] age, 60 [50-70] years; follow-up range, 3-35 years). Lesion connectivity to this brain network was associated with increased risk of epilepsy after stroke (odds ratio [OR], 2.82; 95% CI, 2.02-4.10; P < .001) and across different lesion types (OR, 2.85; 95% CI, 2.23-3.69; P < .001). Deep brain stimulation site connectivity to this same network was associated with improved seizure control (r, 0.63; P < .001) in 30 patients with drug-resistant epilepsy (21 [70%] male; median [IQR] age, 39 [32-46] years; median [IQR] follow-up, 24 [16-30] months). Conclusions and Relevance: The findings in this study indicate that lesion-related epilepsy mapped to a human brain network, which could help identify patients at risk of epilepsy after a brain lesion and guide brain stimulation therapies.

Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest.

BACKGROUND AND OBJECTIVES: Epileptiform activity and burst suppression are neurophysiology signatures reflective of severe brain injury after cardiac arrest. We aimed to delineate the evolution of coma neurophysiology feature ensembles associated with recovery from coma after cardiac arrest. METHODS: Adults in acute coma after cardiac arrest were included in a retrospective database involving 7 hospitals. The combination of 3 quantitative EEG features (burst suppression ratio [BSup], spike frequency [SpF], and Shannon entropy [En]) was used to define 5 distinct neurophysiology states: epileptiform high entropy (EHE: SpF ≥4 per minute and En ≥5); epileptiform low entropy (ELE: SpF ≥4 per minute and <5 En); nonepileptiform high entropy (NEHE: SpF <4 per minute and ≥5 En); nonepileptiform low entropy (NELE: SpF <4 per minute and <5 En), and burst suppression (BSup ≥50% and SpF <4 per minute). State transitions were measured at consecutive 6-hour blocks between 6 and 84 hours after return of spontaneous circulation. Good neurologic outcome was defined as best cerebral performance category 1-2 at 3-6 months. RESULTS: One thousand thirty-eight individuals were included (50,224 hours of EEG), and 373 (36%) had good outcome. Individuals with EHE state had a 29% rate of good outcome, while those with ELE had 11%. Transitions out of an EHE or BSup state to an NEHE state were associated with good outcome (45% and 20%, respectively). No individuals with ELE state lasting >15 hours had good recovery. DISCUSSION: Transition to high entropy states is associated with an increased likelihood of good outcome despite preceding epileptiform or burst suppression states. High entropy may reflect mechanisms of resilience to hypoxic-ischemic brain injury.

Tau Positron Emission Tomography and Neurocognitive Function Among Former Professional American-Style Football Players.

American-style football (ASF) players experience repetitive head impacts that may result in chronic traumatic encephalopathy neuropathological change (CTE-NC). At present, a definitive diagnosis of CTE-NC requires the identification of localized hyperphosphorylated Tau (p-Tau) after death via immunohistochemistry. Some studies suggest that positron emission tomography (PET) with the radiotracer [18F]-Flortaucipir (FTP) may be capable of detecting p-Tau and thus establishing a diagnosis of CTE-NC among living former ASF players. To assess associations between FTP, football exposure, and objective neuropsychological measures among former professional ASF players, we conducted a study that compared former professional ASF players with age-matched male control participants without repetitive head impact exposure. Former ASF players and male controls underwent structural magnetic resonance imaging and PET using FTP for p-Tau and [11C]-PiB for amyloid-ß. Former players underwent neuropsychological testing. The ASF exposure was quantified as age at first exposure, professional career duration, concussion signs and symptoms burden, and total years of any football play. Neuropsychological testing included measures of memory, executive functioning, and depression symptom severity. P-Tau was quantified as FTP standardized uptake value ratios (SUVR) and [11C]-PiB by distribution volume ratios (DVR) using cerebellar grey matter as the reference region. There were no significant differences in [18F]-FTP uptake among former ASF players (n = 27, age = 50 ± 7 years) compared with control participants (n = 11, age = 55 ± 4 years), nor did any participant have significant amyloid-ß burden. Among ASF participants, there were no associations between objective measures of neurocognitive functioning and [18F]-FTP uptake. There was a marginally significant difference, however, between [18F]-FTP uptake isolated to the entorhinal cortex among players in age-, position-, and race-adjusted models (p = 0.05) that may represent an area of future investigation. The absence of increased [18F]-FTP uptake in brain regions previously implicated in CTE among former professional ASF players compared with controls questions the utility of [18F]-FTP PET for clinical evaluation in this population.

The relationship between mindfulness and enduring somatic threat severity in long-term cardiac arrest survivors.

BACKGROUND: Cardiac arrest (CA) survivors experience continuous exposures to potential traumas though chronic cognitive, physical and emotional sequelae and enduring somatic threats (ESTs) (i.e., recurring somatic traumatic reminders of the event). Sources of ESTs can include the daily sensation of an implantable cardioverter defibrillator (ICD), ICD-delivered shocks, pain from rescue compressions, fatigue, weakness, and changes in physical function. Mindfulness, defined as non-judgmental present-moment awareness, is a teachable skill that might help CA survivors cope with ESTs. Here we describe the severity of ESTs in a sample of long-term CA survivors and explore the cross-sectional relationship between mindfulness and severity of ESTs. METHODS: We analyzed survey data of long-term CA survivors who were members of the Sudden Cardiac Arrest Foundation (collected 10-11/2020). We assessed ESTs using 4 cardiac threat items from the Anxiety Sensitivity Index-revised (items range from 0 "very little" to 4 "very much") which we summed to create a score reflecting total EST burden (range 0-16). We assessed mindfulness using the Cognitive and Affective Mindfulness Scale-Revised. First, we summarized the distribution of EST scores. Second, we used linear regression to describe the relationship between mindfulness and EST severity adjusting for age, gender, time since arrest, COVID-19-related stress, and loss of income due to COVID. RESULTS: We included 145 CA survivors (mean age: 51 years, 52% male, 93.8% white, mean time since arrest: 6 years, 24.1% scored in the upper quarter of EST severity). Greater mindfulness (ß: -30, p = 0.002), older age (ß: -0.30, p = 0.01) and longer time since CA (ß: -0.23, p = 0.005) were associated with lower EST severity. Male sex was also associated with greater EST severity (ß: 0.21, p = 0.009). CONCLUSION: ESTs are common among CA survivors. Mindfulness may be a protective skill that CA survivors use to cope with ESTs. Future psychosocial interventions for the CA population should consider using mindfulness as a core skill to reduce ESTs.

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.

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.

Alteplase for Stroke With Unknown Onset Time in Chronic Kidney Disease: A Pooled Analysis of Individual Participant Data.

BACKGROUND: Although chronic kidney disease (CKD) is associated with worse stroke outcomes, data regarding the influence of CKD on intravenous thrombolysis outcomes are scarce. We sought to assess the efficacy and safety of intravenous thrombolysis for acute ischemic stroke with unknown onset time in patients with CKD. METHODS: Patients with an acute stroke of unknown onset time from the EOS trials (Evaluation of Unknown Onset Stroke Thrombolysis) collaboration were evaluated using an individual patient-level database of randomized controlled trials comparing intravenous thrombolysis with placebo/standard treatment. CKD was defined as baseline estimated glomerular filtration rate of <60 ml/min/1.73m2 Mixed-effect logistic-regression analysis was performed to evaluate treatment effects. A favorable outcome was defined as a modified Rankin Scale score of 0 to 1 at 90 days. Safety outcomes were symptomatic intracranial hemorrhage at 22 to 36 hours and 90-day mortality. RESULTS: Baseline data on renal function were available for 688 of 843 patients. Of these, CKD was present in 146 (21%), including 69 of 351 patients receiving alteplase and 77 of 337 patients receiving placebo/standard treatment. Overall, treatment with alteplase was associated with higher odds of favorable outcome, and CKD did not modify the treatment effect (Pinteraction=0.834). A favorable outcome was observed in 31 of 69 (46%) patients with CKD in the alteplase group and in 28 of 77 (36%) patients with CKD in the control group (adjusted odds ratio, 1.19 [95% CI, 0.55-2.58]). Among patients with CKD, symptomatic intracranial hemorrhage occurred in 2 patients (3%) in the alteplase group but in none of the controls (P=0.133). At 90 days, death was reported in 3 patients (4%) in the alteplase group compared with 2 patients (3%) in the controls (P=0.539). CONCLUSIONS: The present analysis indicates that the benefit of alteplase does not differ between stroke patients with unknown onset time with and without CKD, although the statistical power was lacking to confirm the efficacy in subgroups. This study only applies to mild-to-moderate or predialysis CKD.

Severe cerebral edema in substance-related cardiac arrest patients.

BACKGROUND: Studies of neurologic outcomes have found conflicting results regarding differences between patients with substance-related cardiac arrests (SRCA) and non-SRCA. We investigate the effects of SRCA on severe cerebral edema development, a neuroimaging intermediate endpoint for neurologic injury. METHODS: 327 out-of-hospital comatose cardiac arrest patients were retrospectively analyzed. Demographics and baseline clinical characteristics were examined. SRCA categorization was based on admission toxicology screens. Severe cerebral edema classification was based on radiology reports. Poor clinical outcomes were defined as discharge Cerebral Performance Category scores > 3. RESULTS: SRCA patients (N = 86) were younger (P < 0.001), and more likely to have non-shockable rhythms (P < 0.001), be unwitnessed (P < 0.001), lower Glasgow Coma Scale scores (P < 0.001), absent brainstem reflexes (P < 0.05) and develop severe cerebral edema (P < 0.001) than non-SRCA patients (N = 241). Multivariable analyses found younger age (P < 0.001), female sex (P = 0.008), non-shockable rhythm (P = 0.01) and SRCA (P = 0.05) to be predictors of severe cerebral edema development. Older age (P < 0.001), non-shockable rhythm (P = 0.02), severe cerebral edema (P < 0.001), and absent pupillary light reflexes (P = 0.004) were predictors of poor outcomes. SRCA patients had higher proportion of brain deaths (P < 0.001) compared to non-SRCA patients. CONCLUSIONS: SRCA results in higher rates of severe cerebral edema development and brain death. The absence of statistically significant differences in discharge outcomes or survival between SRCA and non-SRCA patients may be related to the higher rate of withdrawal of life-sustaining treatment (WLST) in the non-SRCA group. Future neuroprognostic studies may opt to include neuroimaging markers as intermediate measures of neurologic injury which are not influenced by WLST decisions.