Dataset Information

Brain structural differences between 73- and 92-year olds matched for childhood intelligence, social background, and intracranial volume.

ABSTRACT: Fully characterizing age differences in the brain is a key task for combating aging-related cognitive decline. Using propensity score matching on 2 independent, narrow-age cohorts, we used data on childhood cognitive ability, socioeconomic background, and intracranial volume to match participants at mean age of 92 years (n = 42) to very similar participants at mean age of 73 years (n = 126). Examining a variety of global and regional structural neuroimaging variables, there were large differences in gray and white matter volumes, cortical surface area, cortical thickness, and white matter hyperintensity volume and spatial extent. In a mediation analysis, the total volume of white matter hyperintensities and total cortical surface area jointly mediated 24.9% of the relation between age and general cognitive ability (tissue volumes and cortical thickness were not significant mediators in this analysis). These findings provide an unusual and valuable perspective on neurostructural aging, in which brains from the 8th and 10th decades of life differ widely despite the same cognitive, socioeconomic, and brain-volumetric starting points.

SUBMITTER: Ritchie SJ

PROVIDER: S-EPMC5759896 | biostudies-other | 2018 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Brain structural differences between 73- and 92-year olds matched for childhood intelligence, social background, and intracranial volume.

Ritchie Stuart J SJ Dickie David Alexander DA Cox Simon R SR Valdés Hernández Maria Del C MDC Sibbett Ruth R Pattie Alison A Anblagan Devasuda D Redmond Paul P Royle Natalie A NA Corley Janie J Maniega Susana Muñoz SM Taylor Adele M AM Karama Sherif S Booth Tom T Gow Alan J AJ Starr John M JM Bastin Mark E ME Wardlaw Joanna M JM Deary Ian J IJ

Neurobiology of aging 20171016

Fully characterizing age differences in the brain is a key task for combating aging-related cognitive decline. Using propensity score matching on 2 independent, narrow-age cohorts, we used data on childhood cognitive ability, socioeconomic background, and intracranial volume to match participants at mean age of 92 years (n = 42) to very similar participants at mean age of 73 years (n = 126). Examining a variety of global and regional structural neuroimaging variables, there were large difference ...[more]

PMID: 29149632

Similar Datasets

Project description:PurposeMost studies evaluating artificial intelligence (AI) models that detect abnormalities in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently well-validated, leading to poor generalisability to real-world tasks. The aim was to determine the diagnostic test accuracy and summarise the evidence supporting the use of AI models performing first-line, high-volume neuroimaging tasks.MethodsMedline, Embase, Cochrane library and Web of Science were searched until September 2021 for studies that temporally or externally validated AI capable of detecting abnormalities in first-line computed tomography (CT) or magnetic resonance (MR) neuroimaging. A bivariate random effects model was used for meta-analysis where appropriate. This study was registered on PROSPERO as CRD42021269563.ResultsOut of 42,870 records screened, and 5734 potentially eligible full texts, only 16 studies were eligible for inclusion. Included studies were not compromised by unrepresentative datasets or inadequate validation methodology. Direct comparison with radiologists was available in 4/16 studies and 15/16 had a high risk of bias. Meta-analysis was only suitable for intracranial hemorrhage detection in CT imaging (10/16 studies), where AI systems had a pooled sensitivity and specificity 0.90 (95% confidence interval [CI] 0.85-0.94) and 0.90 (95% CI 0.83-0.95), respectively. Other AI studies using CT and MRI detected target conditions other than hemorrhage (2/16), or multiple target conditions (4/16). Only 3/16 studies implemented AI in clinical pathways, either for pre-read triage or as post-read discrepancy identifiers.ConclusionThe paucity of eligible studies reflects that most abnormality detection AI studies were not adequately validated in representative clinical cohorts. The few studies describing how abnormality detection AI could impact patients and clinicians did not explore the full ramifications of clinical implementation.

Dataset Information

Brain structural differences between 73- and 92-year olds matched for childhood intelligence, social background, and intracranial volume.

Publications

Brain structural differences between 73- and 92-year olds matched for childhood intelligence, social background, and intracranial volume.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets