publication . Preprint . 2020

Functional brain age prediction suggests accelerated aging in preclinical familial Alzheimer’s disease, irrespective of fibrillar amyloid-beta pathology

Gonneaud, Julie; Baria, Alex T.; Binette, Alexa Pichet; Gordon, Brian A.; Chhatwal, Jasmeer P.; Cruchaga, Carlos; Jucker, Mathias; Levin, Johannes; Salloway, Stephen; Farlow, Martin; ...
Open Access English
  • Published: 08 May 2020
  • Publisher: Cold Spring Harbor Laboratory
Abstract
Abstract We aimed at developing a model able to predict brain aging from resting state functional connectivity (rs-fMRI) and assessing whether genetic risk/determinants of Alzheimer’s disease (AD) and amyloid (Aβ) pathology contributes to accelerated brain aging. Using data collected in 1340 cognitively unimpaired participants from 18 to 94 years old selected across multi-site cohorts, we showed that chronological age can be predicted across the whole lifespan from topological properties of graphs constructed from rs-fMRI. We subsequently used the difference between the model-predicted age and the chronological age in pre-symptomatic autosomal dominant AD (ADAD)...
Funded by
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
NIH| DIAN Genetics Core
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5U19AG032438-03
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
Download from
85 references, page 1 of 6

1. Jagust, W. Vulnerable neural systems and the borderland of brain aging and neurodegeneration. Neuron 77, 219-234 (2013). [OpenAIRE]

2. Cole, J. H. & Franke, K. Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers. Trends Neurosci. 40, 681-690 (2017).

3. Franke, K., Ziegler, G., Klöppel, S., Gaser, C. & Alzheimer's Disease Neuroimaging Initiative. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage 50, 883-892 (2010).

4. Dosenbach, N. U. F. et al. Prediction of individual brain maturity using fMRI. Science 329, 1358-1361 (2010). [OpenAIRE]

5. Mwangi, B., Hasan, K. M. & Soares, J. C. Prediction of individual subject's age across the human lifespan using diffusion tensor imaging: a machine learning approach. Neuroimage 75, 58-67 (2013).

6. Zhai, J. & Li, K. Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks. Front Hum Neurosci 13, (2019).

7. Khan, S. et al. Maturation trajectories of cortical resting-state networks depend on the mediating frequency band. Neuroimage 174, 57-68 (2018).

8. Liem, F. et al. Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage 148, 179-188 (2017).

9. Steffener, J. et al. Differences between chronological and brain age are related to education and self-reported physical activity. Neurobiol Aging 40, 138-144 (2016). [OpenAIRE]

10. Ronan, L. et al. Obesity associated with increased brain age from midlife. Neurobiol Aging 47, 63-70 (2016). [OpenAIRE]

11. Luders, E., Cherbuin, N. & Gaser, C. Estimating brain age using high-resolution pattern recognition: Younger brains in long-term meditation practitioners. NeuroImage 134, 508-513 (2016). [OpenAIRE]

12. Rogenmoser, L., Kernbach, J., Schlaug, G. & Gaser, C. Keeping brains young with making music. Brain Struct Funct 223, 297-305 (2018).

13. Guggenmos, M. et al. Quantitative neurobiological evidence for accelerated brain aging in alcohol dependence. Transl Psychiatry 7, (2017). [OpenAIRE]

14. Löwe, L. C., Gaser, C. & Franke, K. The Effect of the APOE Genotype on Individual BrainAGE in Normal Aging, Mild Cognitive Impairment, and Alzheimer's Disease. PLoS One 11, (2016).

15. Gaser, C. et al. BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease. PLoS ONE 8, e67346 (2013). [OpenAIRE]

85 references, page 1 of 6
Abstract
Abstract We aimed at developing a model able to predict brain aging from resting state functional connectivity (rs-fMRI) and assessing whether genetic risk/determinants of Alzheimer’s disease (AD) and amyloid (Aβ) pathology contributes to accelerated brain aging. Using data collected in 1340 cognitively unimpaired participants from 18 to 94 years old selected across multi-site cohorts, we showed that chronological age can be predicted across the whole lifespan from topological properties of graphs constructed from rs-fMRI. We subsequently used the difference between the model-predicted age and the chronological age in pre-symptomatic autosomal dominant AD (ADAD)...
Funded by
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
NIH| DIAN Genetics Core
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5U19AG032438-03
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
Download from
85 references, page 1 of 6

1. Jagust, W. Vulnerable neural systems and the borderland of brain aging and neurodegeneration. Neuron 77, 219-234 (2013). [OpenAIRE]

2. Cole, J. H. & Franke, K. Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers. Trends Neurosci. 40, 681-690 (2017).

3. Franke, K., Ziegler, G., Klöppel, S., Gaser, C. & Alzheimer's Disease Neuroimaging Initiative. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage 50, 883-892 (2010).

4. Dosenbach, N. U. F. et al. Prediction of individual brain maturity using fMRI. Science 329, 1358-1361 (2010). [OpenAIRE]

5. Mwangi, B., Hasan, K. M. & Soares, J. C. Prediction of individual subject's age across the human lifespan using diffusion tensor imaging: a machine learning approach. Neuroimage 75, 58-67 (2013).

6. Zhai, J. & Li, K. Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks. Front Hum Neurosci 13, (2019).

7. Khan, S. et al. Maturation trajectories of cortical resting-state networks depend on the mediating frequency band. Neuroimage 174, 57-68 (2018).

8. Liem, F. et al. Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage 148, 179-188 (2017).

9. Steffener, J. et al. Differences between chronological and brain age are related to education and self-reported physical activity. Neurobiol Aging 40, 138-144 (2016). [OpenAIRE]

10. Ronan, L. et al. Obesity associated with increased brain age from midlife. Neurobiol Aging 47, 63-70 (2016). [OpenAIRE]

11. Luders, E., Cherbuin, N. & Gaser, C. Estimating brain age using high-resolution pattern recognition: Younger brains in long-term meditation practitioners. NeuroImage 134, 508-513 (2016). [OpenAIRE]

12. Rogenmoser, L., Kernbach, J., Schlaug, G. & Gaser, C. Keeping brains young with making music. Brain Struct Funct 223, 297-305 (2018).

13. Guggenmos, M. et al. Quantitative neurobiological evidence for accelerated brain aging in alcohol dependence. Transl Psychiatry 7, (2017). [OpenAIRE]

14. Löwe, L. C., Gaser, C. & Franke, K. The Effect of the APOE Genotype on Individual BrainAGE in Normal Aging, Mild Cognitive Impairment, and Alzheimer's Disease. PLoS One 11, (2016).

15. Gaser, C. et al. BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease. PLoS ONE 8, e67346 (2013). [OpenAIRE]

85 references, page 1 of 6
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