Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study

Tønnesen, Siren; Kaufmann, Tobias; Lange, Ann-Marie G. de; Richard, Geneviève; Doan, Nhat Trung; Alnæs, Dag; Meer, Dennis van der; Rokicki, Jaroslav; Moberget, Torgeir; Maximov, Ivan I.; Agartz, Ingrid; Aminoff, Sofie Ragnhild; Beck, Dani; Deanna, M; Bereśniewicz, Justyna; Červenka, Simon; Fatouros‐Bergman, Helena; Craven, Alexander R.; Flyckt, Lena; Gurholt, Tiril P.; Haukvik, Unn K.; Hugdahl, Kenneth; Johnsen, Erik; Jönsson, Erik G.; Schizophrenia, Karolinska; Kolskår, Knut K.; Kroken, Rune A.; Lagerberg, Trine Vik; Løberg, Else‐Marie; Nordvik, Jan Egil; Sanders, Anne-Marthe; Ulrichsen, Kristine M.; Andreassen, Ole A.; Westlye, Lars T.

doi:10.1016/j.bpsc.2020.06.014

Cited by 35 publications

(58 citation statements)

References 64 publications

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“…Our results demonstrating that elevated systolic blood pressure and smoking were associated with faster brain ageing over time is in line with previous cross-sectional studies, with systolic blood pressure reportedly being associated with white matter BAG (de Lange et al, 2020) and reduced cerebral vascular density (Williamson et al, 2018). Similarly, smoking has also been associated with decreased total brain volume (Reiman et al, 2008) and reduced cerebral vascular density (Williamson et al, 2018).…”

Section: Associations Between Cmr and Brain Ageing And Interactions supporting

confidence: 92%

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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Section: Associations Between Cmr and Brain Ageing And Interactions supporting

confidence: 92%

Cardiometabolic risk factors associated with brain age and accelerate brain ageing

Beck

Lange

Pedersen

et al. 2021

Preprint

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“…One concern is that of averaging over regions of interests and the entire white matter skeleton, which is complicated by the direction and magnitude of age associations varying regionally. Recent findings (Tønnesen et al, 2020) found that the global mean skeleton model outperformed region of interest-based single-metric models, providing evidence for relevant information required for brain age prediction is captured at a global level. Indeed, previous studies have suggested that regional DTI-based indices of brain aging reflect relatively global processes (Penke et al, 2010;Westlye et al, 2010), which is also supported by a genetically informed approach demonstrating that a substantial proportion of the tract-wise heritability is accounted for by a general genetic factor (Gustavson et al, 2019).…”

Section: Discussionmentioning

confidence: 96%

“…Predicting the age of a brain, and subsequently looking at the disparity between predicted and chronological age, can identify important individualised markers of brain integrity that may reveal risk of neurological and/or neuropsychiatric disorders (Kaufmann et al, 2019). While brain age prediction has grown more popular in recent years, most studies have used grey matter features for brain age prediction, while only few have exclusively (Tønnesen et al, 2020), or partly (James H Cole, 2019;Maximov et al, 2020;Richard et al, 2018;S. M. Smith, Elliott, et al, 2019;S.…”

Section: Introductionmentioning

confidence: 99%

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

Beck

Lange

Maximov

et al. 2020

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The macro-and microstructural architecture of human brain white matter undergo substantial alterations throughout development and ageing. Most of our understanding of the spatial and temporal characteristics of these lifespan adaptations come from magnetic resonance imaging (MRI), in particular diffusion MRI (dMRI), which enables visualization and quantification of brain white matter with unprecedented sensitivity and detail. However, with some notable exceptions, previous studies have relied on cross-sectional designs, limited age ranges, and diffusion tensor imaging (DTI) based on conventional single-shell dMRI. In this mixed crosssectional and longitudinal study (mean interval: 15.2 months) including 702 multi-shell dMRI datasets, we combined complementary dMRI models to investigate age trajectories in healthy individuals aged 18 to 94 years (56.98% women). Using linear mixed effect models and machine learning based brain age prediction, we assessed the age-dependence of diffusion metrics, and compared the prediction accuracy of six different diffusion models, including diffusion tensor (DTI) and kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), restriction spectrum imaging (RSI), spherical mean technique multicompartment (SMT-mc), and white matter tract integrity (WMTI). The results showed that the age slopes for conventional DTI metrics (fractional anisotropy [FA], medial diffusivity [MD], radial diffusivity [RD]) were largely consistent with previous research, and that all diffusion models indicated lower white matter integrity with older age. Linear mixed effects models and brain age prediction showed that the 'FA fine' metric of the RSI model and 'orientation dispersion' (OD) metric of the NODDI model showed highest sensitivity to age. The results indicate that advanced diffusion models (DKI, NODDI, RSI, SMT mc, WMTI) yield the capability of detecting sensitive measures of age-related microstructural changes of white matter in the brain that complement and extend the contribution of conventional DTI.

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“…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). Further, DTI in combination with other modalities has been shown to be beneficial to brain age prediction accuracy (Cherubini et al, 2016;Niu et al, 2020) and is one of the possible future directions towards better prediction and characterization of brain age.…”

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

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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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Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study

Cited by 35 publications

References 64 publications

Cardiometabolic risk factors associated with brain age and accelerate brain ageing

Cardiometabolic risk factors associated with brain age and accelerate brain ageing

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

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

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