Login / Signup

Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Yuetong YuHao-Qi CuiShalaila S HaasFaye NewNicole SanfordKevin YuDenghuang ZhanGuoyuan YangJia-Hong GaoDongtao WeiJiang QiuNerisa BanajDorret I BoomsmaAlan BreierHenry BrodatyRandy L BucknerJan K BuitelaarDara M CannonXavier CaserasVincent P ClarkPatricia J ConrodFabrice CrivelloEveline A CroneUdo DannlowskiChristopher G DaveyLieuwe de HaanGreig I de ZubicarayAnnabella Di GiorgioLukas FischSimon E FisherBarbara FrankeDavid C GlahnDominik GrotegerdOliver GruberRaquel E GurRuben C GurTim HahnBen J HarrisonSean HattonIan B HickieHilleke E Hulshoff PolAlec J JamiesonTerry L JerniganJiyang JiangAndrew J KalninSim KangNicole A KochanAnna KrausJim LagopoulosLuisa LazaroBrenna C McDonaldColm McDonaldKatie L McMahonBenson MwangiGianfranco SpallettaRaul Rodriguez-CrucesJessica RoyerPerminder Singh SachdevTheodore Daniel SatterthwaiteAndrew J SaykinGunter SchumannPierluigi SevaggiJordan W SmollerJair C SoaresGianfranco SpallettaChristian K TamnesJulian N TrollorDennis Van't EntDaniela VecchioHenrik WalterYang WangBernd WeberWei WenLara M WierengaSteven C R WilliamsMon-Ju WuGiovana B Zunta-SoaresBoris C BernhardtPaul ThompsonSophia FrangouRuiyang Genull null
Published in: Human brain mapping (2024)
Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.
Keyphrases
  • white matter
  • resting state
  • public health
  • cerebral ischemia
  • high throughput
  • deep learning
  • cross sectional
  • big data
  • resistance training
  • induced pluripotent stem cells