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Deep learning-based brain age prediction in normal aging and dementia

Nature Aging volume 2, pages 412–424 (2022)Cite this article

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Abstract

Brain aging is accompanied by patterns of functional and structural change. Alzheimer’s disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging. Here, we developed a deep learning-based brain age prediction model using a large collection of fluorodeoxyglucose positron emission tomography and structural magnetic resonance imaging and tested how the brain age gap relates to degenerative syndromes including mild cognitive impairment, AD, frontotemporal dementia and Lewy body dementia. Occlusion analysis, performed to facilitate the interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap was highly correlated with cognitive impairment and the AD biomarker. The higher gap also showed a longitudinal predictive nature across clinical categories, including cognitively unimpaired individuals who converted to a clinical stage. However, regions generating brain age gaps were different for each diagnostic group of which the AD continuum showed similar patterns to normal aging.

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Fig. 1: 3D-DenseNet architecture for age prediction and layout of occlusion analysis.
Fig. 2: Brain age predictions on cognitively unimpaired participants.
Fig. 3: Visualization of saliency maps shown on coronal slices.
Fig. 4: Regression plots of a corrected brain age gap as a function of chronological age for clinical diagnostic groups.
Fig. 5: Association of brain age gap with meta-ROI PiB and Tau PET SUVR.
Fig. 6: Longitudinal nature of the brain age gap.
Fig. 7: Voxel-wise linear regression analysis of the brain age gap.

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Data availability

The Mayo dataset that supports the findings of this study is not publicly available. Anonymized data are available from the corresponding author upon reasonable request. The MRI and PET data from ADNI are available to researchers via the data access procedure described at http://adni.loni.usc.edu/data-samples/access-data/. Source data are provided with this paper.

Code availability

The code developed for this work is available at https://github.com/Neurology-AI-Program/Brain_age_prediction.git.

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Acknowledgements

We acknowledge the support of the NVIDIA Corporation with the donation of the Tesla P100 GPU used for this research. This work was funded in part by National Institutes of Health grant nos. P30 AG62677 (D.J.), R01 AG011378 (C.J.), R01 AG041851 (C.J.), P50 AG016574 (R.P.), U01 AG06786 (R.P.) and RO1 AG073282 (V.L.), and by the Elsie and Marvin Dekelboum Family Foundation, Edson Family Foundation, Liston Family Foundation, GHR Foundation, Foundation Dr. Corinne Schuler (Geneva, Switzerland), Race Against Dementia and the Mayo Foundation.

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Authors and Affiliations

  1. Department of Radiology, Mayo Clinic, Rochester, MN, USA

    Jeyeon Lee, Brian J. Burkett, Hoon-Ki Min, Jeffrey L. Gunter, Christopher G. Schwarz, Kejal Kantarci, Val J. Lowe, Clifford R. Jack Jr. & David T. Jones

  2. Department of Information Technology, Mayo Clinic, Rochester, MN, USA

    Matthew L. Senjem

  3. Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA

    Emily S. Lundt

  4. Department of Neurology, Mayo Clinic, Rochester, MN, USA

    Hugo Botha, Jonathan Graff-Radford, Leland R. Barnard, David S. Knopman, Bradley F. Boeve, Ronald C. Petersen & David T. Jones

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Contributions

J.L., H.K.M. and D.T.J. conceptualized the study. J.L. and L.R.B. were responsible for the software. M.L.S. was responsible for preprocessing the data. J.L., H.K.M., M.L.S., E.S.L., H.B., J.G., J.L.G. and C.G.S. were responsible for the study methodology. J.L., B.J.B., H.K.M., V.J.L. and D.T.J. wrote the original manuscript. All authors revised the draft. K.K., D.S.K., B.F.B., V.J.L., R.C.P., C.R.J. and D.T.J. supervised the study. All authors gave final approval for this version of the article.

Corresponding author

Correspondence to David T. Jones.

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Nature Aging thanks Ahmad Hariri, Brian Gordon and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Brain age predictions on the ADNI dataset.

(a-f) 3D Densenet model trained on the Mayo dataset was applied to the ADNI data. (a-c) FDG based brain age prediction. (d-f) MRI based brain age prediction. (g-l) Prediction performance of 3D Densenet model trained on the Mayo and ADNI dataset together. (g-i) FDG based brain age prediction. (j-l) MRI based brain age prediction. The black solid line and dotted lines in each figure represent a regression line and its 95% confidence bands, respectively. m, The corrected MAE evaluated on the test data (n = 5) was compared between the datasets using a two−sided two-sample Student's t-test. The data is shown as mean ± SD. * p < 0.05, ** p < 0.005.

Source data

Extended Data Fig. 2 Regional mean saliency.

After calculating the saliency map from occlusion analysis, mean saliency value was calculated for each ROI. Box plots represent the minimum and maximum values (whiskers), the first and third quartile (box boundaries), and the median (internal line) for the 5-fold cross-validations (n = 5). Yellow-colored boxes indicate the left hemisphere and blue-colored boxes indicate the right hemisphere.

Source data

Extended Data Fig. 3 Comparison of correlation between FDG- and MRI-based brain age gap.

Error bars indicate 95% confidence intervals of Pearson’s correlation coefficient. A statistical comparison was performed with the CU group. Pearson’s r (95% confidence interval) = 0.5873 (0.5628 to 0.6108), 0.6396 (0.5945 to 0.6847), 0.6735 (0.6138 to 0.7255), 0.7824 (0.6697 to 0.8598), and 0.6548 (0.5489 to 0.7400), for CU, MCI, AD, FTD, and DLB, respectively. Exact p values: CU versus MCI, p = 1.5 × 10−9; CU versus AD, p < 1 × 10−15; CU versus FTD, p < 1 × 10−15; CU versus DLB, p < 1 × 10−15, *** p < 0.001. *** p < 0.001, two-sided z test after Fisher’s r to z transformation.

Source data

Extended Data Fig. 4 Regression plots of a corrected brain age gap as a function of chronological age for clinical diagnostic groups in ADNI cohort.

a, Violin plots of corrected brain age gap for each diagnostic group. The corrected brain age gap of disease groups was compared with CU using one-way ANOVA with Holm-Sidak’s multiple comparisons test. *** p < 0.001. b, FDG-based brain age gap estimation for MCI and AD, respectively. c, Violin plots of corrected brain age gap for each clinical diagnosis group. The corrected brain age gap of disease groups was compared with CU using one-way ANOVA with Holm-Sidak’s multiple comparisons test. *** p < 0.001. d, MRI-based brain age gap estimation for MCI and AD, respectively. e, Relationship between FDG- and MRI-based brain age gap. The black solid line and dotted lines in each figure represent a regression line and its 95% confidence bands, respectively. r indicates Pearson’s correlation coefficient.

Source data

Extended Data Fig. 5 Association of a brain age gap with cognitive scores.

(a-c) Scatter plots of FDG model-based brain age gap with Mini-Mental State Examinations (MMSE), Short Test of Mental Status (STMS) and Clinical Dementia Rating Sum of boxes (CDR-SB), respectively. (d-f) Scatter plots of MRI model-based brain age gap with MMSE, STMS and CDR-SB, respectively. r, Pearson correlation coefficient; p, correlation test p value.

Source data

Extended Data Fig. 6 Association of brain age gap with meta-ROI Amyloid- and Tau PET SUVr in ADNI cohort.

a, Scatter plots between FDG-based brain age gap with meta-ROI amyloid PET SUVr for MCI and AD, respectively. b, Scatter plots between FDG-based brain age gap with meta-ROI tau PET SUVr. c, Scatter plots between MRI-based brain age gap with meta-ROI PiB PET SUVr. d, Scatter plots between MRI-based brain age gap with meta-ROI Tau PET SUVr. The black solid line and dotted lines in each figure represent a regression line and its 95% confidence bands, respectively. r, Pearson correlation coefficient; p, correlation test p value.

Source data

Extended Data Fig. 7 Association of sex to the age gap estimation.

The blue-colored dot indicates female and red indicates male individuals. Comparisons were calculated by two-sided Student's t-test. Exact p values: for FDG, CU, t(2877) = 4.088, p = 4.5 × 10−5; MCI, t(664) = 0.3193, p = 0.7496; AD, t(370) = 2.625, p = 0.009; FTD, t(67) = 0.3496, p = 0.7277; DLB, t(139) = 0.7241, p = 0.4702; for MRI, CU, t(2877) = 3.290, p = 0.001; MCI, t(664) = 0.6509, p = 0.5154; AD, t(370) = 2.809, p = 0.0052; FTD, t(67) = 0.3811, p = 0.7043; DLB, t(139) = 2.886, p = 0.0045; * p < 0.05, **p < 0.005, *** p < 0.001.

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Extended Data Fig. 8 Interscan interval bias test.

a, Interscan interval for total subjects. A statistical test was performed within the same baseline groups (one-way ANOVA with Holm-Sidak post hoc test). Exact p values: CU to CU versus CU to MCI/AD, p = 0.004; MCI to MCI versus MCI to AD, p = 0.79; MCI to MCI versus MCI to FTD, p = 0.95; MCI to MCI versus MCI to DLB, p = 0.95. n = 1054, 104, 169, 49, 6, and 11 for CU to CU, CU to MCI/AD, MCI to MCI, MCI to AD, MCI to FTD, and MCI to DLB group, respectively. b, Interscan interval after excluding participants with interscan interval of >2 years (one-way ANOVA with Holm-Sidak post hoc test). Exact p values: CU to CU versus CU to MCI/AD, p = 0.77; MCI to MCI versus MCI to AD, p = 0.29; MCI to MCI versus MCI to FTD, p = 0.77; MCI to MCI versus MCI to DLB, p = 0.77. n = 258, 52, 127, 41, 4, and 8 for CU to CU, CU to MCI/AD, MCI to MCI, MCI to AD, MCI to FTD, and MCI to DLB group, respectively. (c,d) Baseline brain age gap comparison between groups after excluding participants with interscan interval of >2 years for FDG and MRI, respectively. The comparison was performed within the same baseline groups (one-way ANOVA with Holm-Sidak post hoc test). For c panel, Exact p values: CU to CU versus CU to MCI/AD, p = 0.001; MCI to MCI versus MCI to AD, p = 0.07; MCI to MCI versus MCI to FTD, p = 3.2 × 10−5; MCI to MCI versus MCI to DLB, p = 0.38. For d panel, Exact p values: CU to CU versus CU to MCI/AD, p = 9.1 × 10−4; MCI to MCI versus MCI to AD, p = 0.02; MCI to MCI versus MCI to FTD, p = 0.12; MCI to MCI versus MCI to DLB, p = 0.94. * p < 0.05, **p < 0.005, *** p < 0.001. n = 258, 52, 127, 41, 4, and 8 for CU to CU, CU to MCI/AD, MCI to MCI, MCI to AD, MCI to FTD, and MCI to DLB group, respectively. Box plots represent the minimum and maximum values (whiskers), the first and third quartile (box boundaries), and the median (internal line).

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Extended Data Fig. 9 Longitudinal nature of the brain age gap in ADNI cohort.

a, Baseline brain age gap comparison between groups for FDG model. A statistical test was performed within the same baseline groups using a two-sided two-sample Student's t-test. b, For FDG model, the annual Δ brain age gap of each group was compared with the CU to CU group using one-way ANOVA with Holm-Sidak post hoc. c, Baseline brain age gap comparison between groups for MRI model. A statistical test was performed within the same baseline groups using a two-sided two-sample Student's t-test. d, For MRI model, the annual Δ brain age gap of each group was compared with the CU to CU group using one-way ANOVA with Holm-Sidak post hoc test). * p < 0.05, **p < 0.005, *** p < 0.001. n = 124, 20, 237, 46, and 28 for CU to CU, CU to MCI/AD, MCI to MCI, MCI to AD, and AD to AD group, respectively. Box plots represent the minimum and maximum values (whiskers), the first and third quartile (box boundaries), and the median (internal line).

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Extended Data Fig. 10 Voxel-wise linear regression analysis of brain age gap shown on coronal slices.

Clinical diagnosis group (MCI, AD, FTD and DLB)-specific results from voxel-wise whole-brain linear regression examining the brain age gap-related change (FDR corrected, q < 0.01). The chronological age was specified as nuisance covariance. For CU (bottom row), voxel-wise linear regression analysis was performed using the chronological age as a regressor to show the age-related change. A left panel shows the results for the FDG-based model and a right panel shows the results for the MRI-based model.

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Lee, J., Burkett, B.J., Min, HK. et al. Deep learning-based brain age prediction in normal aging and dementia. Nat Aging 2, 412–424 (2022). https://doi.org/10.1038/s43587-022-00219-7

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