This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
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.
References
-
-
1)
-
6. Scheltens, P.: ‘Early diagnosis of dementia: neuroimaging’, J. Neurol., 1999, 246, pp. 16–20 (doi: 10.1007/s004150050300).
-
2)
-
5. Tripoliti, E.E., Fotiadis, D.I., Argyropoulou, M.: ‘A supervised method to assist the diagnosis of Alzheimer's disease based on functional magnetic resonance imaging’. Engineering in Medicine and Biology Society (EMBS), 29th Annual Int. Conf. of the IEEE, 2007, pp. 3426–3429.
-
3)
-
28. Donoho, D.L., Johnstone, I.M.: ‘Ideal spatial adaptation via wavelet shrinkage’, Biometrika, 1994, 81, pp. 425–455 (doi: 10.1093/biomet/81.3.425).
-
4)
-
29. Monir, S., Siyal, M.: ‘Denoising functional magnetic resonance imaging time-series using anisotropic spatial averaging’, Biomed. Signal Process. Control, 2009, 4, pp. 16–25 (doi: 10.1016/j.bspc.2008.07.004).
-
5)
-
47. Mallat, S.: ‘A wavelet tour of signal processing’ (Academic Press, New York, USA, 1998).
-
6)
-
22. Sreenivasan, K.R., Havlicek, M., Deshpande, G.: ‘Non-parametric hemodynamic deconvolution of fMRI using homomorphic filtering’, IEEE Trans. Med. Imaging, 2015, 34, pp. 1155–1163 (doi: 10.1109/TMI.2014.2379914).
-
7)
-
24. Hernandez-Garcia, L., Ulfarsson, M.: ‘Neuronal event detection in fMRI time series using iterative deconvolution techniques’, Magn. Reson. Imaging, 2011, 29, pp. 353–364 (doi: 10.1016/j.mri.2010.10.012).
-
8)
-
17. Buckner, R.L., Snyder, A.Z., Sanders, A.L., et al: ‘Functional brain imaging of young, nondemented, and demented older adults’, J. Cogn. Neurosci., 2000, 12, pp. 24–34 (doi: 10.1162/089892900564046).
-
9)
-
56. Rissanen, J.: ‘Strong optimality of the normalized ML models as universal codes and information in data’, IEEE Trans. Inf. Theory, 2001, 47, pp. 1712–1717 (doi: 10.1109/18.930912).
-
10)
-
12. Boynton, G.M., Engel, S.A., Glover, G.H., et al: ‘Linear systems analysis of functional magnetic resonance imaging in human V1’, J. Neurosci., 1996, 16, pp. 4207–4221.
-
11)
-
9. Brown, W.R., Thore, C.R.: ‘Review: cerebral microvascular pathology in ageing and neurodegeneration’, Neuropathol. Appl. Neurobiol., 2011, 37, pp. 56–74 (doi: 10.1111/j.1365-2990.2010.01139.x).
-
12)
-
44. Jenkinson, M., Smith, S.M.: ‘A global optimisation method for robust affine registration of brain images’, Med. Image Anal., 2001, 5, pp. 143–156 (doi: 10.1016/S1361-8415(01)00036-6).
-
13)
-
45. Smith, S.: ‘Fast robust automated brain extraction’, Hum. Brain Mapp., 2002, 17, pp. 143–155 (doi: 10.1002/hbm.10062).
-
14)
-
28. Duarte, J.V., Pereira, J.M., Quendera, B., et al: ‘Early disrupted neurovascular coupling and changed event level hemodynamic response function in type 2 diabetes: an fMRI study’, J. Cereb. Blood Flow Metab., 2015, 35, pp. 1671–1680 (doi: 10.1038/jcbfm.2015.106).
-
15)
-
10. Chen, J.J., Rosas, H.D., Salat, D.H.: ‘Age-associate reductions in cerebral blood flow are independent from regional atrophy’, Neuroimage, 2011, 55, pp. 468–478 (doi: 10.1016/j.neuroimage.2010.12.032).
-
16)
-
16. Huettel, S.A., Singerman, J.D., McCarthy, G.: ‘The effects of aging upon the hemodynamic response measured by functional MRI’, Neuroimage, 2001, 13, pp. 161–175 (doi: 10.1006/nimg.2000.0675).
-
17)
-
18. D'Esposito, M., Zarahn, E., Aguirre, G.K., et al: ‘The effect of normal aging on the coupling of neural activity to the bold hemodynamic response’, Neuroimage, 1999, 10, pp. 6–14 (doi: 10.1006/nimg.1999.0444).
-
18)
-
52. Rissanen, J.: ‘Fisher information and stochastic complexity’, IEEE Trans. Inf. Theory, 1996, 42, pp. 40–47 (doi: 10.1109/18.481776).
-
19)
-
38. Berg, L., McKeel, D.W., Miller, J.P., et al: ‘Clinicopathological studies in cognitively healthy aging and Alzheimer's disease: relation of histologic markers to dementia severity, age, sex, and APOE genotype’, Arch. Neurol., 1998, 55, pp. 326–335 (doi: 10.1001/archneur.55.3.326).
-
20)
-
20. Hong, S.L., Rebec, G.V.: ‘A new perspective on behavioral inconsistency and neural noise in aging: compensatory speeding of neural communication’, Front. Aging Neurosci., 2012, 4, pp. 1–6 (doi: 10.3389/fnagi.2012.00027).
-
21)
-
53. Rissanen, J.: ‘Modeling by shortest data description’, Automatica, 1978, 14, pp. 445–471 (doi: 10.1016/0005-1098(78)90005-5).
-
22)
-
13. Gauthier, C.J., Madjar, C., Desjardins-Crépeau, L., et al: ‘Age dependence of hemodynamic response characteristics in human functional magnetic resonance imaging’, Neurobiol. Aging, 2013, 34, pp. 1469–1485 (doi: 10.1016/j.neurobiolaging.2012.11.002).
-
23)
-
1. Hao, J., Li, K., Li, K., et al: ‘Visual attention deficits in Alzheimer's disease: an fMRI study’, Neurosci. Lett., 2005, 385, pp. 18–23 (doi: 10.1016/j.neulet.2005.05.028).
-
24)
-
25)
-
58. Payne, S.J.: ‘A model of the interaction between autoregulation and neural activation in the brain’, Math. Biosci., 2006, 204, pp. 260–281 (doi: 10.1016/j.mbs.2006.08.006).
-
26)
-
7. Burggren, A.C., Bookheimer, S.Y.: ‘Structural and functional neuroimaging in Alzheimer's disease: an update’, Curr. Top. Med. Chem., 2002, 2, pp. 385–393 (doi: 10.2174/1568026024607544).
-
27)
-
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., 2012, 33, pp. 455–465 (doi: 10.1002/hbm.21223).
-
28)
-
25. Serences, J.T.: ‘A comparison of methods for characterizing the event-related BOLD timeseries in rapid fMRI’, Neuroimage, 2004, 21, pp. 1690–1700 (doi: 10.1016/j.neuroimage.2003.12.021).
-
29)
-
15. Aizenstein, H.J., Clark, K.A., Butters, M.A., et al: ‘The BOLD hemodynamic response in healthy aging’, J. Cogn. Neurosci., 2004, 16, pp. 786–793 (doi: 10.1162/089892904970681).
-
30)
-
43. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., et al: ‘Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain’, Neuroimage, 2002, 15, pp. 273–289 (doi: 10.1006/nimg.2001.0978).
-
31)
-
34. Roos, T., Myllymaki, P., Rissanen, J.: ‘MDL denoising revisited’, IEEE Trans. Signal Process., 2009, 57, pp. 839–849 (doi: 10.1109/TSP.2009.2021633).
-
32)
-
2. Carr, D.B., Goate, A., Phil, D., et al: ‘Current concepts in the pathogenesis of Alzheimer's disease’, Am. J. Med., 1997, 103, pp. 3S–10S (doi: 10.1016/S0002-9343(97)00262-3).
-
33)
-
14. Mohtasib, R.S., Lumley, G., Goodwin, J.A., et al: ‘Calibrated fMRI during a cognitive Stroop task reveals reduced metabolic response with increasing age’, Neuroimage, 2012, 59, pp. 1143–1151 (doi: 10.1016/j.neuroimage.2011.07.092).
-
34)
-
35. Bazargani, N., Nosratinia, A.: ‘MDL-based estimation of the hemodynamic response function for fMRI data’ (International Society of Magnetic Resonance in Medicine, 2008), p. 2479.
-
35)
-
26. Gitelman, D.R., Penny, W.D., Ashburner, J., et al: ‘Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution’, Neuroimage, 2003, 19, pp. 200–207 (doi: 10.1016/S1053-8119(03)00058-2).
-
36)
-
8. Lu, H., Xu, F., Rodrigue, K.M., et al: ‘Alterations in cerebral metabolic rate and blood supply across the adult lifespan’, Cereb. Cortex, 2011, 21, pp. 1426–1434 (doi: 10.1093/cercor/bhq224).
-
37)
-
37. Morris, J.C.: ‘Clinical dementia rating’, Neurology, 1993, 43, pp. 2412–2414 (doi: 10.1212/WNL.43.11.2412-a).
-
38)
-
50. Grünwald, P.D.: ‘The minimum description length principle’ (MIT Press, Cambridge, USA, 2007).
-
39)
-
27. Glover, G.H.: ‘Deconvolution of impulse response in event related BOLD fMRI’, Neuroimage, 1999, 9, pp. 416–429 (doi: 10.1006/nimg.1998.0419).
-
40)
-
51. Grünwald, P.D., Myung, I.J., Pitt, M.A.: ‘Advances in minimum description length: theory and applications’ (MIT Press, Cambridge, USA, 2005).
-
41)
-
33. Morsheddost, H., Asemani, D., Mirahadi, N.: ‘Optimization of MDL-based wavelet denoising for fMRI data analysis’. Proc. IEEE 11th Int. Symp. on Biomedical Imaging (ISBI), Beijing, China, 2014, pp. 33–36.
-
42)
-
14. Donoho, D.L., Johnstone, I.: ‘Adapting to unknown smoothness via wavelet shrinkage’, J. Am. Stat. Assoc., 1995, 90, pp. 1200–1224 (doi: 10.1080/01621459.1995.10476626).
-
43)
-
21. D'Esposito, M., Deouell, L.Y., Gazzale, A.: ‘Alterations in the bold fMRI signal with ageing and disease: a challenge for neuroimaging’, Nat. Rev. Neurosci., 2003, 4, pp. 863–872 (doi: 10.1038/nrn1246).
-
44)
-
39. Cohen, J.D., MacWhinney, B., Flatt, M., et al: ‘PsyScope: an interactive graphic system for designing and controlling experiments in the psychology laboratory using Macintosh computers’, Behav. Res. Methods Instrum. C, 1993, 25, pp. 257–271 (doi: 10.3758/BF03204507).
-
45)
-
3. Petrella, J.R., Coleman, R.E., Doraiswamy, P.M.: ‘Neuroimaging and early diagnosis of Alzheimer disease: a look to the future’, Radiology, 2003, 226, pp. 315–336 (doi: 10.1148/radiol.2262011600).
-
46)
-
23. Bush, K., Cisler, J.: ‘Decoding neural events from fMRI BOLD signal: a comparison of existing approaches and development of a new algorithm’, Magn. Reson. Imaging, 2013, 31, pp. 976–989 (doi: 10.1016/j.mri.2013.03.015).
-
47)
-
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), St. Petersburg, 2011, pp. 1031–1035.
-
48)
-
4. Tripoliti, E.E., Fotiadis, D.I., Argyropoulou, M.: ‘A supervised method to assist the diagnosis and monitor progression of Alzheimer's disease using data from an fMRI experiment’, Artif. Intell. Med., 2011, 53, pp. 35–45 (doi: 10.1016/j.artmed.2011.05.005).
-
49)
-
40. Miezin, F., Maccotta, L., Ollinger, J., et al: ‘Characterizing the hemodynamic response: effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing’, Neuroimage, 2000, 11, pp. 735–759 (doi: 10.1006/nimg.2000.0568).
-
50)
-
32. Morsheddost, H., Asemani, D., Shalchy, M.A.: ‘Effects of aging on BOLD hemodynamic response: healthy aging versus Alzheimer disease’. Proc. IEEE 22nd Iranian Conf. on Electrical Engineering (ICEE), Tehran, Iran, 2014, pp. 1907–1911.
-
51)
-
19. Ross, M.H., Yurgelun-Todd, D.A., Renshaw, P.F., et al: ‘Age-related reduction in functional MRI response to photic stimulation’, Neurology, 1997, 48, pp. 173–176 (doi: 10.1212/WNL.48.1.173).
-
52)
-
31. Buckner, R.L., Bandettini, P.A., O'Craven, K.M., et al: ‘Detection of cortical activation during averaged single trials of a cognitive task using functional magnetic resonance imaging’, Proc. Natl. Acad. Sci., 1996, 93, pp. 14878–14883 (doi: 10.1073/pnas.93.25.14878).
-
53)
-
11. Powers, R.: ‘Neurobiology of aging’ (American Psychiatric Press, Washington, DC, USA, 2000), pp. 33–80.
-
54)
-
36. Rissanen, J.: ‘MDL denoising’, IEEE Trans. Inf. Theory, 2000, 46, pp. 2537–2543 (doi: 10.1109/18.887861).
-
55)
-
55. Meena, S., Annadurai, S.: ‘Improved spatially adaptive MDL denoising of images using normalized maximum likelihood density’, Image Vis. Comput., 2008, 26, pp. 1524–1529 (doi: 10.1016/j.imavis.2008.04.011).
-
56)
-
42. Poldrack, R.A., Mumford, J.A., Nichols, T.E.: ‘Handbook of functional MRI data analysis’ (Cambridge University Press, New York, USA, 2011).
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