Imaging of adeno-associated viral capsids for purposes of gene editing using CEST NMR/MRI.

PURPOSE: Gene therapy using adeno-associated virus (AAV) vector-mediated gene delivery has undergone substantial growth in recent years with promising results in both preclinical and clinical studies, as well as emerging regulatory approval. However, the inability to quantify the efficacy of gene therapy from cellular delivery of gene-editing technology to specific functional outcomes is an obstacle for efficient development of gene therapy treatments. Building on prior works that used the CEST reporter gene lysine rich protein, we hypothesized that AAV viral capsids may generate endogenous CEST contrast from an abundance of surface lysine residues. METHODS: NMR experiments were performed on isolated solutions of AAV serotypes 1-9 on a Bruker 800-MHz vertical scanner. In vitro experiments were performed for testing of CEST-NMR contrast of AAV2 capsids under varying pH, density, biological transduction stage, and across multiple serotypes and mixed biological media. Reverse transcriptase-polymerase chain reaction was used to quantify virus concentration. Subsequent experiments at 7 T optimized CEST saturation schemes for AAV contrast detection and detected AAV2 particles encapsulated in a biocompatible hydrogel administered in the hind limb of mice. RESULTS: CEST-NMR experiments revealed CEST contrast up to 52% for AAV2 viral capsids between 0.6 and 0.8 ppm. CEST contrast generated by AAV2 demonstrated high levels of CEST contrast across a variety of chemical environments, concentrations, and saturation schemes. AAV2 CEST contrast displayed significant positive correlations with capsid density (R2 > 0.99, p < 0.001), pH (R2 = 0.97, p = 0.01), and viral titer per cell count (R2 = 0.92, p < 0.001). Transition to a preclinical field strength yielded up to 11.8% CEST contrast following optimization of saturation parameters. In vivo detection revealed statistically significant molecular contrast between viral and empty hydrogels using both mean values (4.67 ± 0.75% AAV2 vs. 3.47 ± 0.87% empty hydrogel, p = 0.02) and quantile analysis. CONCLUSION: AAV2 viral capsids exhibit strong capacity as an endogenous CEST contrast agent and can potentially be used for monitoring and evaluation of AAV vector-mediated gene therapy protocols.

Effects of Acupuncture upon cerebral hemodynamics in cerebral small vessel disease: A pilot study.

Background and aims: Recent preclinical studies and meta-analysis of clinical trials suggested that acupuncture may improve cognition in cerebral small vessel disease (CSVD). We investigated the cerebral hemodynamics of acupuncture in subjects with CSVD and compared its impact upon the cerebral hemodynamics in normal elderly subjects. Methods: 10 subjects with CSVD (CSVD group) and 10 aged-matched control subjects who had no or insignificant CSVD (control group) were recruited. A single session of acupuncture was applied for 30 min in both groups. We assessed the effect of our acupuncture intervention on cerebral hemodynamics by transcranial Doppler ultrasound (TCD). Peak systolic velocity (PSV) and pulsatility index (PI) of the middle cerebral artery (MCA) were assessed. Results: We observed that PSV increased by a maximum of 39% at 20 min (p<0.05), while there was no significant change in PI in the CSVD group during the acupuncture session. In the control group, although we observed no significant change in PSV during the acupuncture session, there was a significant decrease in PI by a maximum of 22% at 20 min (p<0.05). No adverse events were reported during or after the procedure. Conclusion: This study suggested that our acupuncture prescription was associated with an increase in cerebral blood flow in subjects with established moderate to severe CSVD yet without apparent impact on distal vascular resistance. While, in subjects with no or insignificant CSVD, it may reduce cerebral small vessel distal vascular resistance. A larger study is needed to confirm our findings.

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.

Prevalence, Incidence, and Predictors of Self-reported Swallowing Difficulties in Community-Dwelling Adults: A Population-Based Study from the Canadian Longitudinal Study on Aging (CLSA).

There is a paucity of evidence from population-based studies identifying prevalence and incidence of dysphagia, as well as health and sociodemographic risk factors that may contribute to its development. As such, the current study aimed to determine prevalence, incidence, and associated predictors of dysphagia in adults. The Canadian Longitudinal Study on Aging is a nationally representative population study that follows 51,338 Canadians over 45 years of age. Biological, medical, psychological, social, lifestyle and economic data are collected. A secondary analysis of the data was conducted to determine prevalence, incidence, and the predictors of self-reported swallowing difficulty in adults between 45 and 85 years of age. Rates of swallowing difficulty by demographic risk factor, as well as lifestyle and health factors were analyzed using descriptive statistics. Associations between lifestyle and health variables with dysphagia were tested using Chi-square tests or t tests, as appropriate. Logistic regression was used to determine the predictors of self-reported swallowing difficulties. Overall prevalence of self-reported swallowing difficulties in adults over the age of 45 was 10.6% and increased to 13.7% after 3 years. Significant differences (p < 0.001) in self-reported swallowing difficulty at baseline were apparent across smoking status, requiring help to prepare meals, life satisfaction, social participation, all disease categories except dementia, number of medications, cognition, oral health status, and frailty. Incidence of dysphagia was 8.6%. Regression analyses suggested the following independent predictors of reports of swallowing difficulty: older age; non-white ethnicity; female sex; poor oral health; malnutrition; and frailty. These predictors should be carefully considered to ensure we are screening at-risk populations. Social determinants of health, such as ethnicity, must also be considered to ensure equitable care across the population.

Comparing machine learning-derived MRI-based and blood-based neurodegeneration biomarkers in predicting syndromal conversion in early AD.

INTRODUCTION: We compared the machine learning-derived, MRI-based Alzheimer's disease (AD) resemblance atrophy index (AD-RAI) with plasma neurofilament light chain (NfL) level in predicting conversion of early AD among cognitively unimpaired (CU) and mild cognitive impairment (MCI) subjects. METHODS: We recruited participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had the following data: clinical features (age, gender, education, Montreal Cognitive Assessment [MoCA]), structural MRI, plasma biomarkers (p-tau181 , NfL), cerebrospinal fluid biomarkers (CSF) (Aß42, p-tau181 ), and apolipoprotein E (APOE) ε4 genotype. We defined AD using CSF Aß42 (A+) and p-tau181 (T+). We defined conversion (C+) if a subject progressed to the next syndromal stage within 4 years. RESULTS: Of 589 participants, 96 (16.3%) were A+T+C+. AD-RAI performed better than plasma NfL when added on top of clinical features, plasma p-tau181 , and APOE ε4 genotype (area under the curve [AUC] = 0.832 vs. AUC = 0.650 among CU, AUC = 0.853 vs. AUC = 0.805 among MCI) in predicting A+T+C+. DISCUSSION: AD-RAI outperformed plasma NfL in predicting syndromal conversion of early AD. HIGHLIGHTS: AD-RAI outperformed plasma NfL in predicting syndromal conversion among early AD. AD-RAI showed better metrics than volumetric hippocampal measures in predicting syndromal conversion. Combining clinical features, plasma p-tau181 and apolipoprotein E (APOE) with AD-RAI is the best model for predicting syndromal conversion.

Impact of Cerebral Microbleeds in Stroke Patients with Atrial Fibrillation.

OBJECTIVES: Cerebral microbleeds are associated with the risks of ischemic stroke and intracranial hemorrhage, causing clinical dilemmas for antithrombotic treatment decisions. We aimed to evaluate the risks of intracranial hemorrhage and ischemic stroke associated with microbleeds in patients with atrial fibrillation treated with vitamin K antagonists, direct oral anticoagulants, antiplatelets, and combination therapy (i.e. concurrent oral anticoagulant and antiplatelet). METHODS: We included patients with documented atrial fibrillation from the pooled individual patient data analysis by the Microbleeds International Collaborative Network. Risks of subsequent intracranial hemorrhage and ischemic stroke were compared between patients with and without microbleeds, stratified by antithrombotic use. RESULTS: A total of 7,839 patients were included. The presence of microbleeds was associated with an increased relative risk of intracranial hemorrhage (adjusted hazard ratio [aHR] = 2.74, 95% confidence interval = 1.76-4.26) and ischemic stroke (aHR = 1.29, 95% confidence interval = 1.04-1.59). For the entire cohort, the absolute incidence of ischemic stroke was higher than intracranial hemorrhage regardless of microbleed burden. However, for the subgroup of patients taking combination of anticoagulant and antiplatelet therapy, the absolute risk of intracranial hemorrhage exceeded that of ischemic stroke in those with 2 to 4 microbleeds (25 vs 12 per 1,000 patient-years) and ≥ 11 microbleeds (94 vs 48 per 1,000 patient-years). INTERPRETATION: Patients with atrial fibrillation and high burden of microbleeds receiving combination therapy have a tendency of higher rate of intracranial hemorrhage than ischemic stroke, with potential for net harm. Further studies are needed to help optimize stroke preventive strategies in this high-risk group. ANN NEUROL 2023;94:61-74.

The global burden of cerebral small vessel disease in low- and middle-income countries: A systematic review and meta-analysis.

BACKGROUND: Cerebral small vessel disease (cSVD) is a major cause of stroke and dementia. Previous studies on the prevalence of cSVD are mostly based on single geographically defined cohorts in high-income countries. Studies investigating the prevalence of cSVD in low- and middle-income countries (LMICs) are expanding but have not been systematically assessed. AIM: This study aims to systematically review the prevalence of cSVD in LMICs. RESULTS: Articles were searched from the Ovid MEDLINE and EMBASE databases from 1 January 2000 to 31 March 2022, without language restrictions. Title/abstract screening, full-text review, and data extraction were performed by two to seven independent reviewers. The prevalence of cSVD and study sample size were extracted by pre-defined world regions and health status. The Risk of Bias for Non-randomized Studies tool was used. The protocol was registered on PROSPERO (CRD42022311133). A meta-analysis of proportion was performed to assess the prevalence of different magnetic resonance imaging markers of cSVD, and a meta-regression was performed to investigate associations between cSVD prevalence and type of study, age, and male: female ratio. Of 2743 studies identified, 42 studies spanning 12 global regions were included in the systematic review. Most of the identified studies were from China (n = 23). The median prevalence of moderate-to-severe white matter hyperintensities (WMHs) was 20.5%, 40.5%, and 58.4% in the community, stroke, and dementia groups, respectively. The median prevalence of lacunes was 0.8% and 33.5% in the community and stroke groups. The median prevalence of cerebral microbleeds (CMBs) was 10.7% and 22.4% in the community and stroke groups. The median prevalence of moderate-to-severe perivascular spaces was 25.0% in the community. Meta-regression analyses showed that the weighted median age (51.4 ± 0.0 years old; range: 36.3-80.2) was a significant predictor of the prevalence of moderate-to-severe WMH and lacunes, while the type of study was a significant predictor of the prevalence of CMB. The heterogeneity of studies was high (>95%). Male participants were overrepresented. CONCLUSIONS: This systematic review and meta-analysis provide data on cSVD prevalence in LMICs and demonstrated the high prevalence of the condition. cSVD research in LMICs is being published at an increasing rate, especially between 2010 and 2022. More data are particularly needed from Sub-Saharan Africa and Central Europe, Eastern Europe, and Central Asia.

Efficacy and safety of cilostazol in decreasing progression of cerebral white matter hyperintensities-A randomized controlled trial.

Introduction: Cerebral small vessel disease (SVD) is an important cause of dementia that lacks effective treatment. We evaluated the efficacy and safety of cilostazol, an antiplatelet agent with potential neurovascular protective effects, in slowing the progression of white matter hyperintensities (WMHs) in stroke- and dementia-free subjects harboring confluent WMH on magnetic resonance imaging (MRI). Methods: In this single-center, randomized, double-blind, placebo-controlled study, we randomized stroke- and dementia-free subjects with confluent WMHs to receive cilostazol or placebo for 2 years in a 1:1 ratio. The primary outcome was change in WMH volume over 2 years. Secondary outcomes were changes in brain volumes, lacunes, cerebral microbleeds, perivascular space, and alterations in white matter microstructural integrity, cognition, motor function, and mood. Results: We recruited 120 subjects from October 27, 2014, to January 21, 2019. A total of 55 subjects in the cilostazol group and 54 subjects in the control group were included for intention-to-treat analysis. At 2-year follow-up, the changes in WMH volume were not statistically different between cilostazol treatment and placebo (0.3±1.0 mL vs -0.1±0.8 mL, p = 0.167). Secondary outcomes, bleeding and vascular events, were also not statistically different between the two groups. Discussion: In this trial with stroke- and dementia-free subjects with confluent WMHs, cilostazol did not impact WMH progression but demonstrated an acceptable safety profile. Future studies should address the treatment effects of cilostazol on subjects at different clinical stages of SVD.

Improved sensitivity and precision in multicentre diffusion MRI network analysis using thresholding and harmonization.

PURPOSE: To investigate if network thresholding and raw data harmonization improve consistency of diffusion MRI (dMRI)-based brain networks while also increasing precision and sensitivity to detect disease effects in multicentre datasets. METHODS: Brain networks were reconstructed from dMRI of five samples with cerebral small vessel disease (SVD; 629 patients, 166 controls), as a clinically relevant exemplar condition for studies on network integrity. We evaluated consistency of network architecture in age-matched controls, by calculating cross-site differences in connection probability and fractional anisotropy (FA). Subsequently we evaluated precision and sensitivity to disease effects by identifying connections with low FA in sporadic SVD patients relative to controls, using more severely affected patients with a pure form of genetically defined SVD as reference. RESULTS: In controls, thresholding and harmonization improved consistency of network architecture, minimizing cross-site differences in connection probability and FA. In patients relative to controls, thresholding improved precision to detect disrupted connections by removing false positive connections (precision, before: 0.09-0.19; after: 0.38-0.70). Before harmonization, sensitivity was low within individual sites, with few connections surviving multiple testing correction (k = 0-25 connections). Harmonization and pooling improved sensitivity (k = 38), while also achieving higher precision when combined with thresholding (0.97). CONCLUSION: We demonstrated that network consistency, precision and sensitivity to detect disease effects in SVD are improved by thresholding and harmonization. We recommend introducing these techniques to leverage large existing multicentre datasets to better understand the impact of disease on brain networks.

Network impact score is an independent predictor of post-stroke cognitive impairment: A multicenter cohort study in 2341 patients with acute ischemic stroke.

BACKGROUND: Post-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction. AIMS: To determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline. METHODS: We pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI < 3 months post-stroke to no PSCI at follow-up, and cognitive decline as conversion from no PSCI to PSCI. The network impact score was related to serial measures of PSCI using Generalized Estimating Equations (GEE) models, and to PSCI stratified according to post-stroke interval (<3, 3-12, 12-24, >24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site. RESULTS: We included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) <3 months, 709/1640 (43%) at 3-12 months, 243/853 (28%) at 12-24 months, and 208/522 (40%) >24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34-1.68) and multivariable (OR 1.27, 95%CI 1.10-1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline. CONCLUSIONS: The network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/.

Risk factors for delayed-onset dementia after stroke or transient ischemic attack-A five-year longitudinal cohort study.

BACKGROUND: Stroke not only substantially increases the risk of incident dementia early after stroke but also the risk remains elevated years after. AIM: We aimed to determine the risk factors of dementia onset more than three to six months after stroke or transient ischemic attack. METHODS: This is a single-center prospective cohort study. We recruited consecutive subjects with stroke/transient ischemic attack without early-onset dementia. We conducted an annual neuropsychological assessment for five years. We investigated the association between baseline demographic, clinical, genetic (APOEɛ4 allele), and radiological factors as well as incident recurrent stroke with delayed-onset dementia using Cox proportional hazards models. RESULTS: In total, 1007 patients were recruited, of which 88 with early-onset dementia and 162 who lost to follow-ups were excluded. Forty-nine (6.5%) out of 757 patients have incident delayed-onset dementia. The presence of ≥3 lacunes, history of ischemic heart disease, history of ischemic stroke, and a lower baseline Hong Kong version of the Montreal Cognitive Assessment (MoCA) score were significantly associated with delayed-onset dementia. APOEɛ4 allele, medial temporal lobe atrophy, and recurrent stroke were not predictive. CONCLUSION: The presence of ≥3 lacunes, history of ischemic heart disease, history of ischemic stroke, and a lower baseline MoCA score are associated with delayed-onset dementia after stroke/transient ischemic attack.

Diffusion MRI harmonization enables joint-analysis of multicentre data of patients with cerebral small vessel disease.

OBJECTIVES: Acquisition-related differences in diffusion magnetic resonance imaging (dMRI) hamper pooling of multicentre data to achieve large sample sizes. A promising solution is to harmonize the raw diffusion signal using rotation invariant spherical harmonic (RISH) features, but this has not been tested in elderly subjects. Here we aimed to establish if RISH harmonization effectively removes acquisition-related differences in multicentre dMRI of elderly subjects with cerebral small vessel disease (SVD), while preserving sensitivity to disease effects. METHODS: Five cohorts of patients with SVD (N = 397) and elderly controls (N = 175) with 3 Tesla MRI on different systems were included. First, to establish effectiveness of harmonization, the RISH method was trained with data of 13 to 15 age and sex-matched controls from each site. Fractional anisotropy (FA) and mean diffusivity (MD) were compared in matched controls between sites using tract-based spatial statistics (TBSS) and voxel-wise analysis, before and after harmonization. Second, to assess sensitivity to disease effects, we examined whether the contrast (effect sizes of FA, MD and peak width of skeletonized MD - PSMD) between patients and controls within each site remained unaffected by harmonization. Finally, we evaluated the association between white matter hyperintensity (WMH) burden, FA, MD and PSMD using linear regression analyses both within individual cohorts as well as with pooled scans from multiple sites, before and after harmonization. RESULTS: Before harmonization, significant differences in FA and MD were observed between matched controls of different sites (p < 0.05). After harmonization these site-differences were removed. Within each site, RISH harmonization did not alter the effect sizes of FA, MD and PSMD between patients and controls (relative change in Cohen's d = 4 %) nor the strength of association with WMH volume (relative change in R2 = 2.8 %). After harmonization, patient data of all sites could be aggregated in a single analysis to infer the association between WMH volume and FA (R2 = 0.62), MD (R2 = 0.64), and PSMD (R2 = 0.60). CONCLUSIONS: We showed that RISH harmonization effectively removes acquisition-related differences in dMRI of elderly subjects while preserving sensitivity to SVD-related effects. This study provides proof of concept for future multicentre SVD studies with pooled datasets.

Machine-learning method for localization of cerebral white matter hyperintensities in healthy adults based on retinal images.

Retinal vessels are known to be associated with various cardiovascular and cerebrovascular disease outcomes. Recent research has shown significant correlations between retinal characteristics and the presence of cerebral small vessel disease as measured by white matter hyperintensities from cerebral magnetic resonance imaging. Early detection of age-related white matter changes using retinal images is potentially helpful for population screening and allow early behavioural and lifestyle intervention. This study investigates the ability of the machine-learning method for the localization of brain white matter hyperintensities. All subjects were age 65 or above without any history of stroke and dementia and recruited from local community centres and community networks. Subjects with known retinal disease or disease influencing vessel structure in colour retina images were excluded. All subjects received MRI on the brain, and age-related white matter changes grading was determined from MRI as the primary endpoint. The presence of age-related white matter changes on each of the six brain regions was also studied. Retinal images were captured using a fundus camera, and the analysis was done based on a machine-learning approach. A total of 240 subjects are included in the study. The analysis of various brain regions included the left and right sides of frontal lobes, parietal-occipital lobes and basal ganglia. Our results suggested that data from both eyes are essential for detecting age-related white matter changes in the brain regions, but the retinal parameters useful for estimation of the probability of age-related white matter changes in each of the brain regions may differ for different locations. Using a classification and regression tree approach, we also found that at least three significant heterogeneous subgroups of subjects were identified to be essential for the localization of age-related white matter changes. Namely those with age-related white matter changes in the right frontal lobe, those without age-related white matter changes in the right frontal lobe but with age-related white matter changes in the left parietal-occipital lobe, and the rest of the subjects. Outcomes such as risks of severe grading of age-related white matter changes and the proportion of hypertension were significantly related to these subgroups. Our study showed that automatic retinal image analysis is a convenient and non-invasive screening tool for detecting age-related white matter changes and cerebral small vessel disease with good overall performance. The localization analysis for various brain regions shows that the classification models on each of the six brain regions can be done, and it opens up potential future clinical application.

MRI-based Alzheimer's disease-resemblance atrophy index in the detection of preclinical and prodromal Alzheimer's disease.

Alzheimer's Disease-resemblance atrophy index (AD-RAI) is an MRI-based machine learning derived biomarker that was developed to reflect the characteristic brain atrophy associated with AD. Recent study showed that AD-RAI (≥0.5) had the best performance in predicting conversion from mild cognitive impairment (MCI) to dementia and from cognitively unimpaired (CU) to MCI. We aimed to validate the performance of AD-RAI in detecting preclinical and prodromal AD. We recruited 128 subjects (MCI=50, CU=78) from two cohorts: CU-SEEDS and ADNI. Amyloid (A+) and tau (T+) status were confirmed by PET (11C-PIB, 18F-T807) or CSF analysis. We investigated the performance of AD-RAI in detecting preclinical and prodromal AD (i.e. A+T+) among MCI and CU subjects and compared its performance with that of hippocampal measures. AD-RAI achieved the best metrics among all subjects (sensitivity 0.74, specificity 0.91, accuracy 85.94%) and among MCI subjects (sensitivity 0.92, specificity 0.81, accuracy 86.00%) in detecting A+T+ subjects over other measures. Among CU subjects, AD-RAI yielded the best specificity (0.95) and accuracy (85.90%) over other measures, while hippocampal volume achieved a higher sensitivity (0.73) than AD-RAI (0.47) in detecting preclinical AD. These results showed the potential of AD-RAI in the detection of early AD, in particular at the prodromal stage.

Accelerated multi-target chemical exchange saturation transfer magnetic resonance imaging of the mouse heart.

Cardiac chemical exchange saturation transfer-magnetic resonance imaging (CEST-MRI) has been used to probe levels of various metabolites that provide insight into myocardial structure and function. However, imaging of the heart using CEST-MRI is prolonged by the need to repeatedly acquire multiple images for a full Z-spectrum and to perform saturation and acquisition around cardiac and respiratory cycles. Compressed sensing (CS) reconstruction of sparse data enables accelerated acquisition, but reconstruction artifacts may bias subsequently derived measures of CEST contrast. In this study, we examine the impact of CS reconstruction of increasingly under-sampled cardiac CEST-MRI data on subsequent CEST contrasts of amine-containing metabolites and amide-containing proteins. Cardiac CEST-MRI data sets were acquired in six mice using low and high RF saturation for single and dual contrast generation, respectively. CEST-weighted images were reconstructed using CS methods at 2-5× levels of under-sampling. CEST contrasts were derived from corresponding Z-spectra and the impact of accelerated imaging on accuracy was assessed via analysis of variance. CS reconstruction preserved myocardial signal to noise ratio as compared to conventional reconstruction. However, greater absolute error and distribution of derived contrasts was observed with increasing acceleration factors. The results from this study indicate that acquisition of radial cardiac CEST-MRI data can be modestly, but meaningfully, accelerated via CS reconstructions with little error in CEST contrast quantification.

High burden of cerebral white matter lesion in 9 Asian cities.

Age-related white matter lesion (WML) is considered a manifestation of sporadic cerebral small vessel disease and an important pathological substrate for dementia. Asia is notable for its large population with a looming dementia epidemic. Yet, the burden of WML and its associated risk factors across different Asian societies are unknown. Subjects from 9 Asian cities (Bangkok, Bandung, Beijing, Bengaluru, Hong Kong, Kaohsiung, Manila, Seoul, and Singapore) were recruited (n = 5701) and classified into (i) stroke/transient ischemic attack (TIA), (ii) Alzheimer's disease (AD)/mild cognitive impairment (MCI), or (iii) control groups. Data on vascular risk factors and cognitive performance were collected. The severity of WML was visually rated on MRI or CT. The prevalence of moderate-to-severe WML was the highest in subjects with stroke/TIA (43.3%). Bandung Indonesia showed the highest prevalence of WML, adjusted for age, sex, education, disease groups, and imaging modality. Hypertension and hyperlipidemia were significant risk factors for WML, and WML was negatively associated with MMSE in all groups. WML is highly prevalent in Asia and is associated with increasing age, hypertension, hyperlipidemia, and worse cognitive performance. Concerted efforts to prevent WML will alleviate the huge dementia burden in the rapidly aging Asian societies.

Strategic infarct locations for post-stroke cognitive impairment: a pooled analysis of individual patient data from 12 acute ischaemic stroke cohorts.

BACKGROUND: Post-stroke cognitive impairment (PSCI) occurs in approximately half of people in the first year after stroke. Infarct location is a potential determinant of PSCI, but a comprehensive map of strategic infarct locations predictive of PSCI is unavailable. We aimed to identify infarct locations most strongly predictive of PSCI after acute ischaemic stroke and use this information to develop a prediction model. METHODS: In this large-scale multicohort lesion-symptom mapping study, we pooled and harmonised individual patient data from 12 cohorts through the Meta-analyses on Strategic Lesion Locations for Vascular Cognitive Impairment using Lesion-Symptom Mapping (Meta VCI Map) consortium. The identified cohorts (as of Jan 1, 2019) comprised patients with acute symptomatic infarcts on CT or MRI (with available infarct segmentations) and a cognitive assessment up to 15 months after acute ischaemic stroke onset. PSCI was defined as performance lower than the fifth percentile of local normative data, on at least one cognitive domain on a multidomain neuropsychological assessment or on the Montreal Cognitive Assessment. Voxel-based lesion-symptom mapping (VLSM) was used to calculate voxel-wise odds ratios (ORs) for PSCI that were mapped onto a three-dimensional brain template to visualise PSCI risk per location. For the prediction model of PSCI risk, a location impact score on a 5-point scale was derived from the VLSM results on the basis of the mean voxel-wise coefficient (ln[OR]) within each patient's infarct. We did combined internal-external validation by leave-one-cohort-out cross-validation for all 12 cohorts using logistic regression. Predictive performance of a univariable model with only the location impact score was compared with a multivariable model with addition of other clinical PSCI predictors (age, sex, education, time interval between stroke onset and cognitive assessment, history of stroke, and total infarct volume). Testing of visual ratings was done by three clinicians, and accuracy, inter-rater reliability, and intra-rater reliability were assessed with Cohen's weighted kappa. FINDINGS: In our sample of 2950 patients (mean age 66·8 years [SD 11·6]; 1157 [39·2%] women), 1286 (43·6%) had PSCI. We achieved high lesion coverage of the brain in our analyses (86·9%). Infarcts in the left frontotemporal lobes, left thalamus, and right parietal lobe were strongly associated with PSCI (after false discovery rate correction, q<0·01; voxel-wise ORs >20). On cross-validation, the location impact score showed good correspondence, based on visual assessment of goodness of fit, between predicted and observed risk of PSCI across cohorts after adjusting for cohort-specific PSCI occurrence. Cross-validations showed that the location impact score by itself had similar performance to the combined model with other PSCI predictors, while allowing for easy visual assessment. Therefore the univariable model with only the location impact score was selected as the final model. Correspondence between visual ratings and actual location impact score (Cohen's weighted kappa: range 0·88-0·92), inter-rater agreement (0·85-0·87), and intra-rater agreement (for a single rater, 0·95) were all high. INTERPRETATION: To the best of our knowledge, this study provides the first comprehensive map of strategic infarct locations associated with risk of PSCI. A location impact score was derived from this map that robustly predicted PSCI across cohorts. Furthermore, we developed a quick and reliable visual rating scale that might in the future be applied by clinicians to identify individual patients at risk of PSCI. FUNDING: The Netherlands Organisation for Health Research and Development.