Effects of ageing and Alzheimer disease on haemodynamic response function: A challenge for event-related fMRI

被引:9
作者
Asemani D. [1 ,2 ]
Morsheddost H. [2 ]
Shalchy M.A. [2 ]
机构
[1] Division of Radiology, Medical University of South Carolina, Charleston, 29407, SC
[2] Biomedical Engineering Department, K. N. Toosi University of Technology, Tehran
关键词
58;
D O I
10.1049/htl.2017.0005
中图分类号
学科分类号
摘要
Functional magnetic resonance imaging (fMRI) can generate brain images that show neuronal activity due to sensory, cognitive or motor tasks. Haemodynamic response function (HRF) may be considered as a biomarker to discriminate the Alzheimer disease (AD) from healthy ageing. As blood-oxygenation-level-dependent fMRI signal is much weak and noisy, particularly for the elderly subjects, a robust method is necessary for HRF estimation to efficiently differentiate the AD. After applying minimum description length wavelet as an extra denoising step, deconvolution algorithm is here employed for HRF estimation, substituting the averaging method used in the previous works. The HRF amplitude peaks are compared for three groups HRF of young, non-demented and demented elderly groups for both vision and motor regions. Prior works often reported significant differences in the HRF peak amplitude between the young and the elderly. The authors' experimentations show that the HRF peaks are not significantly different comparing the young adults with the elderly (either demented or non-demented). It is here demonstrated that the contradictory findings of the previous studies on the HRF peaks for the elderly compared with the young are originated from the noise contribution in fMRI data.
引用
收藏
页码:109 / 114
页数:5
相关论文
共 58 条
  • [51] Grunwald P.D., Myung I.J., Pitt M.A., Advances in Minimum Description Length: Theory and Applications, (2005)
  • [52] Rissanen J., Fisher information and stochastic complexity, IEEE Trans. Inf. Theory, 42, pp. 40-47, (1996)
  • [53] Rissanen J., Modeling by shortest data description, Automatica, 14, pp. 445-471, (1978)
  • [54] Hirai S., Yamanishi K., Efficient computation of normalized maximum likelihood coding for Gaussian mixtures with its applications to optimal clustering, Proc. IEEE Int. Symp. on Information Theory Proc. (ISIT), pp. 1031-1035, (2011)
  • [55] Meena S., Annadurai S., Improved spatially adaptive MDL denoising of images using normalized maximum likelihood density, Image Vis. Comput., 26, pp. 1524-1529, (2008)
  • [56] Rissanen J., Strong optimality of the normalized ML models as universal codes and information in data, IEEE Trans. Inf. Theory, 47, pp. 1712-1717, (2001)
  • [57] Muthukumaraswamy S.D., Evans C.J., Edden R.A., Et al., Individual variability in the shape and amplitude of the BOLD-HRF correlates with endogenous GABAergic inhibition, Hum. Brain Mapp., 33, pp. 455-465, (2012)
  • [58] Payne S.J., A model of the interaction between autoregulation and neural activation in the brain, Math. Biosci., 204, pp. 260-281, (2006)