Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study

Lange, Ann-Marie G. de; Anatürk, Melis; Suri, Sana; Kaufmann, Tobias; Cole, James H.; Griffanti, Ludovica; Zsoldos, Enikő; Jensen, Daria E. A.; Filippini, Nicola; Singh‐Manoux, Archana; Kivimäki, Mika; Westlye, Lars T.; Ebmeier, Klaus P.

doi:10.1016/j.neuroimage.2020.117292

Cited by 108 publications

(89 citation statements)

References 67 publications

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“…Physical activity also has beneficial effects for the neurovascular system, as measured using cerebral blood flow and perfusion (Klenk et al, 2013). Supporting the link between cardiovascular and brain health, higher brain age in middle-aged and elderly people with cardiovascular risk factors such as high blood pressure, alcohol intake, and stroke risk score were recently reported, with blood pressure showing a stronger association with white matter compared to gray matter (de Lange et al, 2020a).…”

Section: Discussionmentioning

confidence: 90%

Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults

Sanders

Richard

Kolskår

et al. 2021

Preprint

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Maintaining high levels of daily activity and physical capability have been proposed as important constituents to promote healthy brain and cognitive aging. Studies investigating the associations between brain health and physical activity in late life have, however, mainly been based on self-reported data or measures designed for clinical populations. In the current study, we examined cross-sectional associations between physical activity, recorded by an ankle-positioned accelerometer for seven days, physical capability (grip strength, postural control, and walking speed), and neuroimaging based surrogate markers of brain health in 122 healthy older adults aged 65-88 years. We used a multimodal brain imaging approach offering two complementary structural MRI based indicators of brain health: white matter diffusivity and coherence based on diffusion tensor imaging and subcortical and global brain age based on brain morphology inferred from T1-weighted MRI data. The analyses revealed a significant association between global white matter fractional anisotropy (FA) and walking speed, indicating higher white matter coherence in people with higher pace. We also found a significant interaction between sex and brain age on number of daily steps, indicating younger-appearing brains in more physically active women, with no significant associations among men. These results provide insight into the intricate associations between different measures of brain and physical health in old age, and corroborate established public health advice promoting physical activity.

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Section: Discussionmentioning

confidence: 90%

Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults

Sanders

Richard

Kolskår

et al. 2021

Preprint

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“…Model fit discrepancies between the current and prior T1w based BAG studies (Bashyam et al, 2020;Beheshti, Maikusa, & Matsuda, 2018;Beheshti, Mishra, Sone, Khanna, & Matsuda, 2020) could presumably be partly explained by the application of voxelwise features versus summary statistics based on Freesurfer atlases and parcellation schemes, as well as differences in analysis pipelines and algorithms used. Furthermore, age prediction accuracy has been shown to depend on sample characteristics including sample size and age range (de Lange et al, 2020).…”

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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“…An individual's estimated brain age can be compared to their chronological age to calculate deviation from normative age trajectories, often referred to as the brain-age gap (BAG). Such deviations have been associated with a range of clinical risk factors [28][29][30] as well as neurological and neuropsychiatric diseases [31][32][33][34]. Estimated brain age in TBI patients has been shown to be higher relative to their chronological age [35].…”

Section: Introductionmentioning

confidence: 99%

Apolipoprotein ɛ4 Status and Brain Structure 12 Months after Mild Traumatic Injury: Brain Age Prediction Using Brain Morphometry and Diffusion Tensor Imaging

Hellstrøm

Anđelić

Lange

et al. 2021

JCM

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Background: Apolipoprotein E (APOE) ɛ4 is associated with poor outcome following moderate to severe traumatic brain injury (TBI). There is a lack of studies investigating the influence of APOE ɛ4 on intracranial pathology following mild traumatic brain injury (MTBI). This study explores the association between APOE ɛ4 and MRI measures of brain age prediction, brain morphometry, and diffusion tensor imaging (DTI). Methods: Patients aged 16 to 65 with acute MTBI admitted to the trauma center were included. Multimodal MRI was performed 12 months after injury and associated with APOE ɛ4 status. Corrections for multiple comparisons were done using false discovery rate (FDR). Results: Of included patients, 123 patients had available APOE, volumetric, and DTI data of sufficient quality. There were no differences between APOE ɛ4 carriers (39%) and non-carriers in demographic and clinical data. Age prediction revealed high accuracy both for the DTI-based and the brain morphometry based model. Group comparisons revealed no significant differences in brain-age gap between ɛ4 carriers and non-carriers, and no significant differences in conventional measures of brain morphometry and volumes. Compared to non-carriers, APOE ɛ4 carriers showed lower fractional anisotropy (FA) in the hippocampal part of the cingulum bundle, which did not remain significant after FDR adjustment. Conclusion: APOE ɛ4 carriers might be vulnerable to reduced neuronal integrity in the cingulum. Larger cohort studies are warranted to replicate this finding.

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Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study

Cited by 108 publications

References 67 publications

Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults

Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults

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

Apolipoprotein ɛ4 Status and Brain Structure 12 Months after Mild Traumatic Injury: Brain Age Prediction Using Brain Morphometry and Diffusion Tensor Imaging

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