Measures of Morphological Complexity of Gray Matter on Magnetic Resonance Imaging for Control Age Grouping

Article, Other literature type English OPEN
Pham, Tuan ; Abe, Taishi ; Oka, Ryuichi ; Chen, Yung-Fu (2015)
  • Publisher: MDPI AG
  • Journal: (issn: 1099-4300)
  • Related identifiers: doi: 10.3390/e17127868
  • Subject: magnetic resonance imaging | chaos | brain-age grouping | Medical Engineering | measures of complexity | neurodegeneration | Astrophysics | Medicinteknik | phylogenetic tree reconstruction | brain-age prediction | QB460-466 | Q | Science | Physics | QC1-999 | nonlinear dynamics | brain-age grouping; brain-age prediction; magnetic resonance imaging; phylogenetic tree reconstruction; measures of complexity; chaos; nonlinear dynamics; neurodegeneration

Current brain-age prediction methods using magnetic resonance imaging (MRI) attempt to estimate the physiological brain age via some kind of machine learning of chronological brain age data to perform the classification task. Such a predictive approach imposes greater r... View more
  • References (68)
    68 references, page 1 of 7

    1. Teverovskiy, L.A.; Becker, J.T.; Lopez, O.L.; Liu, Y. Quantified brain asymmetry for age estimation of normal and AD/MCI subjects. In Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2008), Paris, France, 14-17 May 2008; pp. 1509-1512.

    2. Franke, K.; Ziegler, G.; Kloppel, S.; Gaser, C. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters. NeuroImage 2010, 50, 883-892.

    3. Wang, B.; Pham, T.D. MRI-based age prediction using hidden Markov models. J. Neurosci. Methods 2011, 199, 140-145.

    4. Dukart, J.; Schroeter, M.L.; Mueller, K. Age correction in dementia-matching to a healthy brain. PLoS ONE 2011, 6, e22193; doi:10.1371/journal.pone.0022193.

    5. Kandel, B.M.; Wolk, D.A.; Gee, J.C.; Avants, B. Predicting cognitive data from medical images using sparse linear regression. In Information Processing in Medical Imaging; Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., ZÃ u˝llei, L., Eds.; Springer: Berlin/Heidelberg, Germany, 2013; Volume 7917, pp. 86-97.

    6. Irimia, A.; Torgerson, C.M.; Goh, S.Y.; van Horn, J.D. Statistical estimation of physiological brain age as a descriptor of senescence rate during adulthood. Brain Imaging Behav. 2015, 9, 678-689, doi:10.1007/s11682-014-9321-0.

    7. Cole, J.H.; Leech, R.; Sharp, D.J. Prediction of brain age suggests accelerated atrophy after traumatic brain injury. Ann. Neurol. 2015, 77, 571-581.

    8. Spulber, G.; Niskanen, E.; MacDonald, S.; Smilovici, O.; Chen, K.; Reimanet E.M.; Jauhiainen, A.M.; Hallikainen, M.; Tervo, S.; Wahlund, L.-O.; et al. Whole brain atrophy rate predicts progression from MCI to Alzheimer's disease. Neurobiol. Aging 2010, 31, 1601-1605.

    9. Pham, T.D.; Salvetti, F.; Wang, B.; Diani, M.; Heindel, W.; Knecht, S.; Wersching, H.; Baune, B.T.; Berger, K. The hidden-Markov brain: Comparison and inference of white matter hyperintensities on magnetic resonance imaging (MRI). J. Neural Eng. 2011, 8, 016004; doi:10.1088/1741-2560/8/1/016004.

    10. Su, L.; Wang, L.; Hu, D. Predicting the age of healthy adults from structural MRI by sparse representation. In Intelligent Science and Intelligent Data Engineering; Yang, J., Fang, F., Sun, C., Eds,; Springer: Berlin/Heidelberg, Germany, 2013, Volume 7751, pp. 271-279.

  • Related Research Results (2)
  • Metrics
    No metrics available
Share - Bookmark