title: Sample entropy reveals high discriminative power between young and elderly adults in short fMRI data sets.
abstract: Some studies have placed Sample entropy on the same data length constraint of 10 (m) -20 (m) (m: pattern length) as approximate entropy, even though Sample entropy is largely independent of data length and displays relative consistency over a broader range of possible parameters (r, tolerance value; m, pattern length; N, data length) under circumstances where approximate entropy does not. This is particularly erroneous for some fMRI experiments where the working data length is less than 100 volumes (when m = 2). We therefore investigated whether Sample entropy is able to effectively discriminate fMRI data with data length, N less than 10 (m) (where m = 2) and r = 0.30, from a small group of 10 younger and 10 elderly adults, and the whole cohort of 43 younger and 43 elderly adults, that are significantly (p < 0.001) different in age. Ageing has been defined as a loss of entropy; where signal complexity decreases with age. For the small group analysis, the results of the whole brain analyses show that Sample entropy portrayed a good discriminatory ability for data lengths, 85 ≤ N ≤ 128, with an accuracy of 85% at N = 85 and 80% at N = 128, at q < 0.05. The regional analyses show that Sample entropy discriminated more brain regions at N = 128 than N = 85 and some regions common to both data lengths. As data length, N increased from 85 to 128, the noise level decreased. This was reflected in the accuracy of the whole brain analyses and the number of brain regions discriminated in the regional analyses. The whole brain analyses suggest that Sample entropy is relatively independent of data length, while the regional analyses show that fMRI data with length of 85 volumes is consistent with our hypothesis of a loss of entropy with ageing. In the whole cohort analysis, Sample entropy discriminated regionally between the younger and elderly adults only at N = 128. The whole cohort analysis at N = 85 was indicative of the ageing process but this indication was not significant (p > 0.05).
authors.affiliation: MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University Cardiff, UK ; Imaging Science, Cardiff University Brain Research Imaging Centre, Cardiff University Cardiff, UK.
authors.forename: Moses O
authors.initials: MO
authors.lastname: Sokunbi
keywords: ageing; blood oxygen level dependent (BOLD); data length; functional magnetic resonance imaging (fMRI); noise level; sample entropy
references.citation: Psychiatry Res. 2013 Dec 30;214(3):341-8
references.pmid: 24183857
references.citation: IEEE Trans Biomed Eng. 2011 Nov;58(11):3206-14
references.pmid: 21859598
references.citation: Electroencephalogr Clin Neurophysiol. 1994 Aug;91(2):118-30
references.pmid: 7519141
references.citation: Am J Physiol Heart Circ Physiol. 2000 Jun;278(6):H2039-49
references.pmid: 10843903
references.citation: Science. 1989 Feb 3;243(4891):604-7
references.pmid: 2916117
references.citation: Neuroimage. 2006 May 1;30(4):1433-40
references.pmid: 16410052
references.citation: Neurobiol Aging. 2002 Jan-Feb;23(1):23-6
references.pmid: 11755014
references.citation: Phys Rev Lett. 2002 Aug 5;89(6):068102
references.pmid: 12190613
references.citation: Neurosci Lett. 2010 Jul 19;479(1):79-84
references.pmid: 20560166
references.citation: Anesthesiology. 2007 Apr;106(4):696-706
references.pmid: 17413907
references.citation: Ann Biomed Eng. 2013 Feb;41(2):349-65
references.pmid: 23064819
references.citation: IEEE Trans Biomed Eng. 2008 Aug;55(8):1966-72
references.pmid: 18632359
references.citation: Chaos. 1995 Mar;5(1):110-117
references.pmid: 12780163
references.citation: Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:1765-8
references.pmid: 22254669
references.citation: IEEE Trans Biomed Eng. 2001 Mar;48(3):312-23
references.pmid: 11327499
references.citation: Am J Physiol. 1994 Apr;266(4 Pt 2):H1643-56
references.pmid: 8184944
references.citation: Ann Biomed Eng. 2010 Apr;38(4):1483-96
references.pmid: 20099031
references.citation: Ann N Y Acad Sci. 2001 Dec;954:245-67
references.pmid: 11797860
references.citation: Br Med Bull. 2009;92:135-52
references.pmid: 19776035
references.citation: AJNR Am J Neuroradiol. 2007 Feb;28(2):226-35
references.pmid: 17296985
references.citation: Neurobiol Aging. 2002 Jan-Feb;23(1):1-11
references.pmid: 11755010
references.citation: Clin Chem. 1993 Apr;39(4):561-77
references.pmid: 8472349
references.citation: Sci Aging Knowledge Environ. 2004 Apr 21;2004(16):pe16
references.pmid: 15103055
references.citation: Physiol Meas. 2006 Mar;27(3):241-53
references.pmid: 16462011
references.citation: Proc Natl Acad Sci U S A. 1991 Mar 15;88(6):2297-301
references.pmid: 11607165
references.citation: Sci Rep. 2013 Oct 09;3:2853
references.pmid: 24103922
references.citation: ScientificWorldJournal. 2014;2014:196513
references.pmid: 24977184
references.citation: PLoS One. 2014 Mar 21;9(3):e89948
references.pmid: 24657999
references.citation: Med Eng Phys. 2009 Jan;31(1):61-8
references.pmid: 18538625
references.citation: PLoS One. 2014 May 13;9(5):e95146
references.pmid: 24824731
references.citation: Neurobiol Aging. 2013 Feb;34(2):428-38
references.pmid: 22683008