White matter microstructure across the adult lifespan: A mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction

Beck, Dani; Lange, Ann-Marie G. de; Maximov, Ivan I.; Richard, Geneviève; Andreassen, Ole A.; Nordvik, Jan Egil; Westlye, Lars T.

doi:10.1016/j.neuroimage.2020.117441

Cited by 148 publications

(255 citation statements)

References 84 publications

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“…Brain aging is a highly heterogenous process and the current study could be improved by inclusion of white matter diffusion MRI (dMRI) measures, which comprises distinct tissue class with largely differential biological and environmental modifiers and age trajectories (Beck et al, 2021;Westlye et al, 2010b). Brain age of white matter can be assessed using modalities derived from diffusion tensor imaging (DTI) and serves to represent a partly independent process of brain aging compared to brain age based on gray matter measures (Richard et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Brain age of white matter can be assessed using modalities derived from diffusion tensor imaging (DTI) and serves to represent a partly independent process of brain aging compared to brain age based on gray matter measures (Richard et al, 2018). Diffusion weighted imaging-based metrics were shown to have high sensitivity to age, with conventional DTI modalities being among the best in age prediction (Beck et al, 2021). In addition, DTI-based brain age prediction revealed group differences between patients with SZ and HC in a recent multi-site study (Tønnesen et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

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Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders

Rokicki

Wolfers

Nordhøy

et al. 2020

Human Brain Mapping

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The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age‐matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two‐group case–control classifications revealed highest accuracy for AD using global T1‐weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF‐based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain‐based mapping of overlapping and distinct pathophysiology in common disorders.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders

Rokicki

Wolfers

Nordhøy

et al. 2020

Human Brain Mapping

Self Cite

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“…Their results further supported the association between age and white matter decline, in addition to indicating a sex difference in the overall number of white matter tracts that exhibited age-related declines in anisotropy (Tseng et al, 2020). Such prior dMRI work robustly demonstrates that multiple aspects of white matter microstructure are significantly associated with participant age and sex (Beck et al, 2020; Cox et al, 2016; Damoiseaux, 2017; Jahanshad & Thompson, 2017; Ritchie et al, 2018; Salminen et al, 2020; Toschi et al, 2020; Tseng et al, 2020; Zavaliangos-Petropulu et al, 2019). However, it remains an open question how age, sex, and their interaction may be related to additional measures of white matter microstructure obtained from other advanced dMRI models such as TDF and MAPMRI, as well as how age effects on microstructure may manifest in middle to late adulthood when using more complex, data-driven statistical approaches for modeling age.…”

Section: Introductionmentioning

confidence: 96%

Age and Sex Effects on Advanced White Matter Microstructure Measures in 15,628 Older Adults: A UK Biobank Study

Lawrence

Nabulsi

Santhalingam

et al. 2020

Preprint

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The brain’s white matter microstructure, as assessed using diffusion-weighted MRI (DWI), changes significantly with age and also exhibits significant sex differences. Here we examined the ability of a traditional diffusivity metric (fractional anisotropy derived from diffusion tensor imaging, DTI-FA) and advanced diffusivity metrics (fractional anisotropy derived from the tensor distribution function, TDF-FA; neurite orientation dispersion and density imaging measures of intra-cellular volume fraction, NODDI-ICVF; orientation dispersion index, NODDI-ODI; and isotropic volume fraction, NODDI-ISOVF) to detect sex differences in white matter aging. We also created normative aging reference curves based on sex. Diffusion tensor imaging (DTI) applies a single-tensor diffusion model to single-shell DWI data, while the tensor distribution function (TDF) fits a continuous distribution of tensors to single-shell DWI data. Neurite orientation dispersion and density imaging (NODDI) fits a multi-compartment model to multi-shell DWI data to distinguish intra- and extra-cellular contributions to diffusion. We analyzed these traditional and advanced diffusion measures in a large population sample available through the UK Biobank (15,394 participants; age-range: 45-80 years) by using linear regression and fractional polynomials. Advanced diffusivity metrics (NODDI-ODI, NODDI-ISOVF, TDF-FA) detected significant sex differences in aging, whereas a traditional metric (DTI-FA) did not. These findings suggest that future studies examining sex differences in white matter aging may benefit from including advanced diffusion measures.

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“…The model input included 276 features for the DTI-based age prediction and 269 features for the age prediction based on T1-weighted data, as described in sections 2.3 and 2.4. Age prediction was performed using XGBoost regression (https://xgboost.readthedocs.io/en/latest/python), which is based on a decision-tree ensemble algorithm used in several recent brain age prediction studies (Beck et al, 2021;de Lange et al, 2019;Richard et al, 2020). Parameters were tuned in nested cross-validations using 5 inner folds for grid search, and 10 outer folds for validating model performance within the training sample.…”

Section: Brain Age Predictionmentioning

confidence: 99%

Cardiometabolic risk factors associated with brain age and accelerate brain ageing

Beck

Lange

Pedersen

et al. 2021

Preprint

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The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs) are associated with dementia and other brain disorders. In this mixed cross-sectional and longitudinal study (interval mean [standard deviation] = 19.7 [0.5] months), including 1062 datasets from 790 healthy individuals (mean (range) age = 46.7 (18-94) years, 54% women), we investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). We performed tissue specific brain age prediction using machine learning and performed Bayesian multilevel modelling to assess changes in each CMR over time, their respective association with brain age gap (BAG), and their interaction effects with time and age on the tissue-specific BAGs. The results showed credible associations between DTI-based BAG and blood levels of phosphate and mean cell volume (MCV), and between T1-based BAG and systolic blood pressure, smoking, pulse, and C-reactive protein (CRP), indicating older-appearing brains in people with higher cardiometabolic risk (smoking, higher blood pressure and pulse, low-grade inflammation). Longitudinal evidence supported interactions between both BAGs and waist-to-hip ratio (WHR), and between DTI-based BAG and systolic blood pressure and smoking, indicating accelerated ageing in people with higher cardiometabolic risk (smoking, higher blood pressure, and WHR). The results demonstrate that cardiometabolic risk factors are associated with brain ageing. While randomised controlled trials are needed to establish causality, our results indicate that public health initiatives and treatment strategies targeting modifiable cardiometabolic risk factors may also improve risk trajectories and delay brain ageing. Key words: T1 MRI, DTI, Brain age, Cardiometabolic risk

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White matter microstructure across the adult lifespan: A mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction

Cited by 148 publications

References 84 publications

Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders

Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders

Age and Sex Effects on Advanced White Matter Microstructure Measures in 15,628 Older Adults: A UK Biobank Study

Cardiometabolic risk factors associated with brain age and accelerate brain ageing

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