Skip to main content
Log in

RETRACTED ARTICLE: Gray Matter-Based Age Prediction Characterizes Different Regional Patterns

  • Letter to the Editor
  • Published:
Neuroscience Bulletin Aims and scope Submit manuscript

This article was retracted on 13 September 2022

This article has been updated

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Change history

References

  1. Zuo N, Salami A, Liu H, Yang Z, Jiang T. Functional maintenance in the multiple demand network characterizes superior fluid intelligence in aging. Neurobiol Aging 2020, 85: 145–153.

    Article  Google Scholar 

  2. Cole JH, Ritchie SJ, Bastin ME, Valdes Hernandez MC, Munoz Maniega S, Royle N, et al. Brain age predicts mortality. Mol Psychiatry 2018, 23: 1385–1392.

    Article  CAS  Google Scholar 

  3. Liem F, Varoquaux G, Kynast J, Beyer F, Kharabian Masouleh S, Huntenburg JM, et al. Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage 2017, 148: 179–188.

    Article  Google Scholar 

  4. Valizadeh SA, Hanggi J, Merillat S, Jancke L. Age prediction on the basis of brain anatomical measures. Hum Brain Mapp 2017, 38: 997–1008.

    Article  CAS  Google Scholar 

  5. Goyal MS, Blazey TM, Su Y, Couture LE, Durbin TJ, Bateman RJ, et al. Persistent metabolic youth in the aging female brain. Proc Natl Acad Sci U S A 2019, 116: 3251–3255.

    Article  CAS  Google Scholar 

  6. Fan L, Li H, Zhuo J, Zhang Y, Wang J, Chen L, et al. The human brainnetome atlas: A new brain atlas based on connectional architecture. Cereb Cortex 2016, 26: 3508–3526.

    Article  Google Scholar 

  7. Krishnan A, Williams LJ, McIntosh AR, Abdi H. Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage 2011, 56: 455–475.

    Article  Google Scholar 

  8. Taylor JR, Williams N, Cusack R, Auer T, Shafto MA, Dixon M, et al. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. Neuroimage 2017, 144: 262–269.

    Article  Google Scholar 

  9. Tran TN, Afanador NL, Buydens LMC, Blanchet L. Interpretation of variable importance in Partial Least Squares with Significance Multivariate Correlation (sMC). Chemom Intell Lab Syst 2014, 138: 153–160.

    Article  CAS  Google Scholar 

  10. Behrens TE, Johansen-Berg H, Woolrich MW, Smith SM, Wheeler-Kingshott CA, Boulby PA, et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci 2003, 6: 750–757.

    Article  CAS  Google Scholar 

  11. West KL, Zuppichini MD, Turner MP, Sivakolundu DK, Zhao Y, Abdelkarim D, et al. BOLD hemodynamic response function changes significantly with healthy aging. Neuroimage 2019, 188: 198–207.

    Article  Google Scholar 

  12. Lin Y, Li M, Zhou Y, Deng W, Ma X, Wang Q, et al. Age-related reduction in cortical thickness in first-episode treatment-naive patients with schizophrenia. Neurosci Bull 2019, 35: 688–696.

    Article  Google Scholar 

  13. Sala-Llonch R, Bartres-Faz D, Junque C. Reorganization of brain networks in aging: a review of functional connectivity studies. Front Psychol 2015, 6: 663.

    Article  Google Scholar 

  14. Esteves M, Magalhaes R, Marques P, Castanho TC, Portugal-Nunes C, Soares JM, et al. Functional Hemispheric (A)symmetries in the aged brain-relevance for working memory. Front Aging Neurosci 2018, 10: 58.

    Article  Google Scholar 

  15. Nie Y, Lau S, Liau AK. Role of academic self-efficacy in moderating the relation between task importance and test anxiety. Learn Individ Differ 2011, 21: 736–741.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61971420), the Beijing Brain Initiative of the Beijing Municipal Science and Technology Commission (Z181100001518003), Special Projects of Brain Science of the Beijing Municipal Science and Technology Commission (Z161100000216139 and Z171100000117002), and the International Cooperation and Exchange of the National Natural Science Foundation of China (31620103905). Data were provided by the Cambridge Centre for Ageing and Neuroscience (http://www.mrc-cbu.cam.ac.uk/datasets/camcan).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nianming Zuo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest in this work.

Additional information

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12264-022-00945-3

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 2471 kb)

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zuo, N., Hu, T., Liu, H. et al. RETRACTED ARTICLE: Gray Matter-Based Age Prediction Characterizes Different Regional Patterns. Neurosci. Bull. 37, 94–98 (2021). https://doi.org/10.1007/s12264-020-00558-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12264-020-00558-8

Navigation