Editorial: Predicting Chronological Age From Structural Neuroimaging: The Predictive Analytics Competition 2019

Fisch, L.; Leenings, Ramona; Winter, Nils R.; Dannlowski, Udo; Gaser, Christian; Cole, James H.; Hahn, Tim

doi:10.3389/fpsyt.2021.710932

Cited by 14 publications

(13 citation statements)

References 14 publications

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“…We found that a GPR model trained on volumetric MRI features predicted chronological age in a CN adult sample with an MAE of about 6 years. This level of performance is comparable to other structural models, which have reported MAEs as low as 3 to 5 years (3,13,18,25,72–74). As previously reported (29), the FC-trained model predicted age with an MAE of about 8 years, again consistent with previous FC models, which have achieved MAEs from 5 to 11 years (25–28).…”

Section: Discussionsupporting

confidence: 84%

Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study

Millar

Gordon²,

Luckett

et al. 2022

Preprint

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Background: Estimates of “brain-predicted age” quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD), but has not been well explored in preclinical AD. Prior studies have typically modeled BAG with structural magnetic resonance imaging (MRI), but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods: We trained three models to predict age from FC, volumetric (Vol), or multimodal MRI (Vol+FC) in 390 control participants (18-89 years old). In independent samples of 144 older adult controls, 154 preclinical AD participants, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid, tau, and neurodegeneration, as well as a global cognitive composite. Results: All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG and Vol+FC-BAG were marginally reduced in preclinical AD participants compared to controls. In CI participants only, elevated Vol-BAG and Vol+FC-BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions: Both FC-BAG and Vol-BAG are elevated in CI participants. However, FC and volumetric MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to preclinical AD pathology, while Vol-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model captures these modality-specific patterns, and further, improves sensitivity to healthy age differences. Funding: This work was supported by the National Institutes of Health (P01-AG026276, P01-AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer’s Association (SG-20-690363-DIAN).

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Section: Discussionsupporting

confidence: 84%

Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study

Millar

Gordon²,

Luckett

et al. 2022

Preprint

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“…Based on this, we evaluated seven grand challenges: PAC (Fisch et al, 2021) Estimation of age from MRI.…”

Section: Grand Challenges In Dementiamentioning

confidence: 99%

“…• Predictive Analytics Competition (PAC, 2019) 7 : the challenge task was to estimate brain age from healthy individuals (age range: 17-90 years) based on T1-weighted MRI data (Fisch et al, 2021). A second task was to estimate brain age with a minimal bias towards chronological age.…”

Section: Grand Challenges In Dementiamentioning

confidence: 99%

“…PAC (Fisch et al, 2021) What is the best current strategy for accurate prediction of age from brain MRI in an healthy population and what is the best strategy while avoiding bias of age estimation towards the mean age of the training dataset?…”

Section: Mci-nimentioning

confidence: 99%

“…Over the past years, the number and the impact of such grand challenges has increased (Maier-Hein et al, 2018). Also in the field of machine learning algorithms for dementia, such grand challenges have been organized to gain insight into successful approaches and their potential for use in clinical practice and clinical trials (Cash et al, 2015;Allen et al, 2016;Bron et al, 2015;Sabuncu and Konukoglu, 2015;Sarica et al, 2018;Marinescu et al, 2018;Fisch et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Ten years of image analysis and machine learning competitions in dementia

Bron¹,

Klein²,

Reinke³

et al. 2021

Preprint

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Machine learning methods exploiting multi-parametric biomarkers, especially based on neuroimaging, have huge potential to improve early diagnosis of dementia and to predict which individuals are at-risk of developing dementia. To benchmark algorithms in the field of machine learning and neuroimaging in dementia and assess their potential for use in clinical practice and clinical trials, seven grand challenges have been organized in the last decade:

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