Publicly available datasets developed in 2021. Datasets developed as part of the Row Fogo Centre for Research into Ageing and the Brain in 2021. Expand the following shortcuts to read more about the datasets and access related links. Dataset: Systematic Review and Meta-Analysis on incidence and risk of stroke in individuals with long-term exposure to air pollutants Authors: Ruairidh Morrison; Dr Maria Valdés Hernández Publication date: June 2021 Description Exposure to air pollution is now recognized globally by governments, leading research scientists, and civil society as one of the greatest public health hazards of the 21st century. Stroke is a major cause of disability and death worldwide, and its incidence and mortality rate have been found associated with air pollution. Since 2013 up to 2019, 11 journal review articles have summarised and analysed, separately, the effect of different air pollutants in stroke incidence and/or mortality, some as part of all-cause mortality analyses, highlighting the elevated incidence and mortality rates in highly polluted countries vs. others considered with low air pollution. These analyses mainly have considered the effect of particulate matter (PM) and nitrogen oxide (NOx). But the moderating role of vascular risk factors and the differential role of these pollutants compared to other sources of air pollution have not been analysed. This dataset contains the results of the systematic search, data extraction and meta-analyses from systematically reviewing the existent literature on stroke-related factors (incidence, vascular risk factors and characteristics of the populations) in relation to several air pollutants (i.e. particulate matter (PM), nitrogen dioxide (NO2)/oxide (NO), ozone (O3), carbon monoxide (CO), lead, sulphur dioxide (SO2), polycyclic aromatic hydrocarbons (PAH), black carbon). The data is contained in one excel spreadsheet. Worksheets not containing meta-analyses are also provided in csv (comma delimited) format. This dataset aims to inform studies of stroke on the differential effect of air pollution in stroke up to December 2020. Related links Dataset link -Datashare (external link) Dr Maria Valdes-Hernandez research profile Dataset: Metrics for quality control of results from super-resolution machine-learning algorithms – Data extracted from publications in the period 2017- May 2021 Authors: Castorina, Leonardo V.; Li, Bryan M.; Storkey, Amos; Valdés Hernández, Maria. Publication date: June 2021 Description The quality enhancement and restoration of poor-quality low-resolution magnetic resonance (MR) data are paramount for improving patient care, accuracy in diagnosis and quality in clinical research. Since 2017, with the popularity of deep-learning machine-learning algorithms, there have been an increase in interest, and consequently funding, in applying these algorithms to enhance the spatial resolution of MR data. Deep-learning schemes have demonstrated superiority over the more conventional machine-learning algorithms, as they have produced very accurate results in different medical image processing tasks. The increase in the attempts of applying these algorithms to increase the spatial resolution of MRI data parallels an increase in the number of metrics considered for evaluating their performance. This dataset summarises the metrics and strategies to evaluate the performance of super-resolution machine-learning algorithms applied to MRI, from the articles published up to May 2021 in this field. The aims are two-fold: 1) to inform on the metrics used to evaluate results of super-resolution algorithms 2) to inform on publications that have applied the state-of-the-art deep-learning algorithms to increase the spatial resolution of magnetic resonance images Related links Dataset link -Datashare (external link) Dr Maria Valdes-Hernandez research profile Dataset: White Matter Hyperintensities Evolution Patterns 1 Year Post-lacunar Stroke and their association with post-stroke cognition Authors: Valdés Hernández, Maria; Grimsley-Moore, Tara; Sakka, Eleni; Thrippleton, Michael J.; Chappell, Francesca M.; Armitage, Paul A.; Makin, Stephen; Wardlaw, Joanna M. Publication date: June 2021 Description Lacunar strokes are a common type of ischemic stroke. They are associated with long-term disability, but the factors affecting the dynamic of the infarcted lesion and the brain imaging features associated with them, reflective of small vessel disease (SVD) severity, are still largely unknown. We investigated whether the distribution, volume and 1-year evolution of white matter hyperintensities (WMH), one of these SVD features, relate to the extent and location of these infarcts, accounting for vascular risk factors and to cognition 1 and 3 years after the stroke. This dataset contains the scripts and the voxel-wise results from our investigations in patients [n = 118, mean age 64.9 (SD 11.75) years old] who presented to a regional hospital with a lacunar stroke syndrome within the years 2010 and 2013 and consented to participate in a study of stroke mechanisms. Related links Dataset link -Datashare (external link) Dr Maria Valdes-Hernandez research profile Eleni Sakka research profile Michael J. Thrippleton research profile Francesca M. Chappell research profile Joanna M. Wardlaw research profile Dataset: Systematic Review and Meta-Analysis on the Impact of Polycyclic Aromatic Hydrocarbons Exposure in Cognitive Function and Neurodegeneration in Humans Authors: Jessica Humphreys, Dr Maria del C. Valdés Hernández Publication date: May 2021 Description Exposure to air pollution is now recognized globally by governments, leading research scientists, and civil society as one of the greatest public health hazards of the 21st century. Polycyclic aromatic hydrocarbons (PAH) are a group of air pollutants discharged mainly from incomplete combustion and pyrolysis of hydrocarbons, predominantly found in: coal, oil, wood and petrol. PAH exist in the atmosphere in a gaseous state or adsorbed to particulate matter, mainly of 2.5mm diameter or less (PM2.5). Given the anthropogenic nature of PAH sources and their small size, they are difficult to regulate and account for, but their exposure has been associated with worsening asthma, coronary heart disease, various types of cancers, decreased immune function and organ damage. However, their implications on cognitive functions and neuronal health in humans has not been systematically recorded/analysed up to date as only sporadic studies have been conducted in few countries. This submission contains the results of the systematic search, data extraction and meta-analyses from systematically reviewing the existent literature on the impact of PAH exposure in cognitive function and neurodegeneration in humans. The data is contained in two excel spreadsheets. One spreadsheet contains details of the search (i.e., search strategy, keywords, databases, search results, included references, excluded references with reasons for exclusion, and duplicates). The other spreadsheet contains the data extracted from analysing the papers reviewed (i.e., included), and the meta-analyses (multi-variable graphs and forest plots with their sources). Worksheets not containing meta-analyses are also provided in csv (comma delimited) format. Related links Dataset link -Datashare (external link) Dr Maria Valdes-Hernandez research profile Dataset: Systematic review of signal post-processing methods in blood-brain barrier dysfunction assessments via dynamic-contrast enhanced magnetic resonance imaging Authors: Jose Bernal Publication date: May 2021 Description DCE-MRI enables quantification of vascular permeability, but current acquisition and processing protocols may be sub-optimal. While the literature has started to recognise this is indeed a problem, decisions concerning the application of post-processing or the lack thereof is still being driven by intuition or experience in high permeability scenarios and not necessarily by facts, potentially compromising the quality of subsequent analyses. In this systematic revision of the literature, we seek to identify MRI issues affecting BBB assessments via DCE-MRI, identify post-processing techniques typically considered to cope with them, and determine whether researchers thoroughly evaluate their effect prior to their application. This dataset contains the results of such a systematic search and data extraction. ## Funders ## This work was supported by the following: - the MRC Doctoral Training Programme in Precision Medicine (JB - Award Reference No. 2096671); - the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK MRC, Alzheimer's Society and Alzheimer's Research UK; - the Fondation Leducq Network for the Study of Perivascular Spaces in Small Vessel Disease (16 CVD 05); - the Stroke Association 'Small Vessel Disease-Spotlight on Symptoms (SVD-SOS)' (SAPG 19\100068); - the Row Fogo Charitable Trust Centre for Research into Aging and the Brain (MVH) (BRO-D.FID3668413); - a British Heart Foundation Chair award (RMT) (CH/12/4/29762); - the European Union Horizon 2020, PHC03-15, project No666881, 'SVDs@Target'. Related links Dataset link - Datashare Jose Bernal research profile Dataset: Human Brain Atlases across the lifespan – Anatomical Segmentations Authors: Dr Maria del C. Valdés Hernández Publication date: May 2021 Description Dickie and colleagues generated seven age-specific brain tissue sets/atlases of T1-weighted brain MRI illustrative of the adult lifespan from 25 to 92 years, which are representative of the atrophy and neurovascular health characteristics of normal individuals in seven age-bands: 1) 25-34 years, 2) 35-44 years, 3) 45-54 years, 4) 55-64 years, 5) 71-74 years, 6) 75-78 years, and 7) 91-93 years. These atlases can be downloaded from Edinburgh Datashare at https://doi.org/10.7488/ds/1369. The present dataset contains the anatomical segmentations of the average T1-weighted brain template from each of these sets, with the exception of the cortical segments, obtained using freesurfer 6.0 (https://surfer.nmr.mgh.harvard.edu/). It also contains the scripts used to generate the nifti-1-formatted files of this dataset, written as Linux shell and Matlab scripts. Related links Dataset link - Datashare Dr Maria Valdes-Hernandez research profile Expand all Collapse all This article was published on 2024-08-27
