Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression

Palma, Marco; Tavakoli, Shahin; Brettschneider, Julia; Nichols, Thomas E.; Initiative, Alzheimer’s Disease Neuroimaging

doi:10.1016/j.neuroimage.2020.116938

Cited by 23 publications

(14 citation statements)

References 58 publications

(89 reference statements)

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“…The quantile regression introduced by Koenker and Bassett, 1978 ) and developed Koenker and Hallock ( 2001 ) does not require economic variables sequence to conform to a normal distribution. Quantile regression determines the model for the selected quantities in the conditional distribution of the dependent variable (Palma et al 2020 ; Sirin and Yilmaz 2020 ; Xu and Lin 2020 ). Hence traditionally, the linear regression model is expressed as the linear regression model equation as follows: …”

Section: Methodsmentioning

confidence: 99%

The roles of economic growth and health expenditure on CO2 emissions in selected Asian countries: a quantile regression model approach

Bilgili

Kuşkaya

Khan

et al. 2021

Environ Sci Pollut Res

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Continuous economic growth and the rise in energy consumption are linked with environmental pollution. Demand for health care expenditure increased after the COVID-19 pandemic. This study is interesting in modeling the nexus between public and private health expenditure, carbon dioxide emissions, and economic growth. To this end, the present study analyzed the nexus between public and private health care expenditure, economic growth, and environmental pollution for 36 Asian countries for the period 1991–2017. FMOLS, GMM, and quantile regression analysis confirm the EKC hypothesis in Asia. Besides, FMOLS and quantile regressions reached the reducing effects of government and private health expenditures on CO 2 emissions. While quantile regression results show that public and private health expenditures can mitigate CO 2 emissions; however, these results differ for various levels of CO 2 . Findings of quantile regression show a significant impact of both public and private health expenditures in reducing CO 2 at the 50 th and 75 th quantiles but results are insignificant for the 25 th quantile. Overall, the paper concludes that both government and private health sectors’ expenditures caused CO 2 emissions to decrease in Asia and that the negative impact of the private health sector on CO 2 emissions is greater than that of the government health sector. The concluding remark is that the higher the health spending, the higher the environmental quality will be in Asia. Hence, the health administrators need to increase public and private health expenditures with an effective cost-service and energy-efficient management approach to reach sustainable health services and a sustainable environment in Asia. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-13639-6.

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Section: Methodsmentioning

confidence: 99%

The roles of economic growth and health expenditure on CO2 emissions in selected Asian countries: a quantile regression model approach

Bilgili

Kuşkaya

Khan

et al. 2021

Environ Sci Pollut Res

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“…MCCQR outperforms RVR in the MACS and IXI datasets. Note that incorporating epistemic or aleatory uncertainty alone -as has recently been suggested [20] for brain-age models -systematically underestimates uncertainty (see Supplementary Figure S1).…”

Section: Uncertainty Quantificationmentioning

confidence: 70%

“…For high-dimensional inputs, however, uncertainty estimation becomes exceedingly difficult using these methods. This has sparked a plethora of research into alternative approaches, especially for neural networks [18,19,20]. While interesting, most of these approaches do not consider aleatory and epistemic uncertainty together and none have been applied to brain-age research.…”

Section: Introductionmentioning

confidence: 99%

An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling

Hahn¹,

Ernsting²,

Winter³

et al. 2021

Preprint

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The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a cornerstone of biological age-research. However, Machine Learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared due to data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte-Carlo Dropout Composite-Quantile-Regression (MCCQR) Neural Network trained on N=10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared to existing models across ten recruitment centers and in three independent validation samples (N=4,004). In two examples, we demonstrate that it prevents spurious associations and increases power to detect accelerated brain-aging. We make the pre-trained model publicly available.

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“…This estimate provides a useful summary of the large voxelwise spaces generated by standard high-resolution T 1 -w scans into a single interpretable value. The difference between the predicted brain-age and the true chronological age is a versatile measure that has been associated with cognitive ability (Cole et al, 2019b;Cole, 2020) as well as clinical status and severity, with significantly "older" brain-ages associated with traumatic brain injury (TBI) (Cole et al, 2015), mild cognitive impairment (MCI) and Alzheimer's disease (AD) (Palma et al, 2020). Furthermore, predicted brain-age could be a better predictor of disease risk than chronological age (Franke and Gaser, 2019;Cole, 2020).…”

Section: Quantitative Comparison Of Epimix and Standard T 1 Contrastsmentioning

confidence: 99%

Tissue volume estimation and age prediction using rapid structural brain scans

Hobday

Cole

Stanyard

et al. 2022

Preprint

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The multicontrast EPImix sequence generates 6 contrasts, including a T1-weighted scan, in ~1 minute. EPImix shows comparable diagnostic performance to conventional scans under qualitative clinical evaluation, and similarities in simple quantitative measures including contrast intensity. However, EPImix scans have not yet been compared to standard MRI scans using established quantitative measures. In this study, we compared conventional and EPImix-derived T1-weighted scans of 64 healthy participants using tissue volume estimates and predicted brain-age. All scans were pre-processed using the SPM12 DARTEL pipeline, generating measures of grey matter, white matter and cerebrospinal fluid volume. Brain-age was predicted using brainageR, a Gaussian process regression model previously trained on a large sample of standard T1-weighted scans. Estimates of both global and voxel-wise tissue volume showed significantly similar results between standard and EPImix-derived T1-weighted scans. Brain-age estimates from both sequences were significantly correlated, although EPImix T1-weighted scans showed a systematic offset in predictions of chronological age. Supplementary analyses suggest that this is likely caused by the reduced field-of-view of EPImix scans, and the use of a brain-age model trained using conventional T1-weighted scans. However, this systematic error can be corrected using additional regression of T1-predicted brain-age onto EPImix-predicted brain-age. Finally, retest EPImix scans acquired for 10 participants demonstrated high test-retest reliability in all evaluated quantitative measurements. Quantitative analysis of EPImix scans holds potential to reduce scanning time, increasing participant comfort and reducing cost, as well as to support automation of scanning, utilising active learning for faster and individually-tailored (neuro)imaging.

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Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression

Cited by 23 publications

References 58 publications

The roles of economic growth and health expenditure on CO2 emissions in selected Asian countries: a quantile regression model approach

The roles of economic growth and health expenditure on CO2 emissions in selected Asian countries: a quantile regression model approach

An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling

Tissue volume estimation and age prediction using rapid structural brain scans

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