Translational neuroscience: the state of the nation (a PhD student perspective)

Many brain disorders are currently untreatable. It has been suggested that taking a ‘translational’ approach to neuroscientific research might change this. We discuss what ‘translational neuroscience’ is and argue for the need to expand the traditional translational model if we are to make further advances in treating brain disorders

Relationship Between Venules and Perivascular Spaces in Sporadic Small Vessel Diseases

Background and Purpose— Perivascular spaces (PVS) around venules may help drain interstitial fluid from the brain. We examined relationships between suspected venules and PVS visible on brain magnetic resonance imaging. Methods— We developed a visual venular quantification method to examine the spatial relationship between venules and PVS. We recruited patients with lacunar stroke or minor nondisabling ischemic stroke and performed brain magnetic resonance imaging and retinal imaging. We quantified venules on gradient echo or susceptibility-weighted imaging and PVS on T2-weighted magnetic resonance imaging in the centrum semiovale and then determined overlap between venules and PVS. We assessed associations between venular count and patient demographic characteristics, vascular risk factors, small vessel disease features, retinal vessels, and venous sinus pulsatility. Results— Among 67 patients (69% men, 69.0±9.8 years), only 4.6% (range, 0%–18%) of venules overlapped with PVS. Total venular count increased with total centrum semiovale PVS count in 55 patients after accounting for venule-PVS overlap (β=0.468 [95% CI, 0.187–0.750]) and transverse sinus pulsatility (β=0.547 [95% CI, 0.309–0.786]) and adjusting for age, sex, and systolic blood pressure. Conclusions— Despite increases in both visible PVS and suspected venules, we found minimal spatial overlap between them in patients with sporadic small vessel disease, suggesting that most magnetic resonance imaging-visible centrum semiovale PVS are periarteriolar rather than perivenular

Sex Differences in Poststroke Cognitive Impairment : A Multicenter Study in 2343 Patients With Acute Ischemic Stroke

Funding Information: Dr Exalto is supported by Alzheimer Nederland WE.03-2019-15 and Netherlands CardioVascular Research Initiative: the Dutch Heart Foundation (CVON 2018-28 & 2012-06). The Meta-VCI Map consortium is supported by Vici Grant 918.16.616 from The Netherlands Organisation for Health Research and Development (ZonMw) to Dr Biessels. Harmonization analyses were supported by a Rudolf Magnus Young Talent Fellowship from the University Medical Center Utrecht Brain Center to Dr Biesbroek. The CASPER cohort was supported by Maastricht University, Health Foundation Limburg, and Stichting Adriana van Rinsum-Ponsen. The CROMIS-2 cohort was funded by the UK Stroke Association and the British Heart Foundation (grant number TSA BHF 2009/01). The CU-STRIDE cohort was supported by the Health and Health Services Research Fund of the Food and Health Bureau of the Government of Hong Kong (grant number 0708041), the Lui Che Woo Institute of Innovative Medicine, and Therese Pei Fong Chow Research Center for Prevention of Dementia. The GRECogVASC cohort was funded by Amiens University Hospital and by a grant from the French Ministry of Health (grant number DGOS R1/2013/144). The MSS-2 cohort is funded by the Wellcome Trust (grant number WT088134/Z/09/A to Dr Wardlaw) and the Row Fogo Charitable Trust. The PROCRAS cohort was funded via ZonMW as part of the TopZorg project in 2015 (grant number 842003011). The CODECS cohort (ongoing) is supported by a grant from Stichting Coolsingel (grant number 514). The Bundang VCI and Hallym VCI cohort groups do not wish to report any relevant funding sources. At the time of contribution, Dr Hamilton was funded by the College of Medicine and Veterinary Medicine at the University of Edinburgh and was supported by the Wellcome Trust through the Translational Neuroscience PhD program at the University of Edinburgh. Publisher Copyright: © 2023 Lippincott Williams and Wilkins. All rights reserved.Peer reviewedPublisher PD

Association between home working and mental health by key worker status during the Covid-19 pandemic. Evidence from four British longitudinal studies

Little is known about the relationship between homeworking and mental health during the Covid-19 pandemic and how it might differ by keyworker status. To understand this relationship, we use longitudinal data collected over three time points during the pandemic from three British cohort studies born in 1958 (National Child Development Study), 1970 (British Cohort Study) and 1989–90 (Next Step) as well as from a population-based study stratified by four age groups (Understanding Society). We estimate the association between life satisfaction, anxiety, depression, and psychological distress and homeworking by key worker status using mixed effects models with maximum likelihood estimation to account for repeated measurements across the pandemic, allowing intercepts to vary across individuals after controlling for a set of covariates including pre-pandemic home working propensities and loneliness. Results show that key workers working from home showed the greatest decline in mental health outcomes relative to other groups. Pre-pandemic homeworking did not significantly change the nature of such a relationship and loneliness slightly attenuated some of the effects. Finally, mental health outcomes varied across age-groups and time points. The discussion emphasises the need to pay attention to key workers when assessing the relationship between mental health and homeworking

White matter hyperintensity volume and post-stroke cognition: an individual patient data pooled analysis of nine ischemic stroke cohort studies

Background and aims: White matter hyperintensities (WMH) are associated with cognitive dysfunction after ischemic stroke. Yet uncertainty remains about affected domains, the role of other pre-existing brain injury, and infarct-types in the relation between WMH burden and post-stroke cognition. We aimed to disentangle these factors in a large sample of patients with ischemic stroke from different cohorts. Methods We pooled and harmonized individual patient data (n=1568) from 9 cohorts, through the Meta VCI Map consortium (www.metavcimap.org). Included cohorts comprised patients with available MRI and multi-domain cognitive assessment <15 months post73 stroke. Linear mixed models were used to determine the association between WMH volume and domain-specific cognitive functioning (z-scores; attention & executive functioning, processing speed, language and verbal memory) for the total sample and stratified by infarct-type. Pre-existing brain injury was accounted for in the multivariable models and all analyses were corrected for study site as a random effect.. Results In the total sample (67 years (SD 11.5), 40% female), we found a dose-dependent inverse relationship between WMH volume and post-stroke cognitive functioning across all four cognitive domains (coefficients ranging from -0.09 (SE 0.04, p=0.01) for verbal memory to -0.19 (SE 0.03, p<0.001) for attention & executive functioning). This relation was independent of acute infarct volume and presence of lacunes and old infarcts. In stratified analyses, the relation between WMH volume and domain86 specific functioning was also largely independent of infarct-type. Conclusion: In patients with ischemic stroke, increasing WMH volume is independently associated with worse cognitive functioning across all major domains, regardless of old ischemic lesions and infarct-type

Sex differences in post-stroke cognitive impairment: a multicenter study in 2343 patients with acute ischemic stroke

No abstract available

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

© 2022 The Author(s)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/.N