Advertisement

AXONAL DENSITY IS ASSOCIATED WITH SUBJECTIVE COGNITIVE DECLINE (SCD) IN OLDER ADULTS ASSESSED USING THE COGNITIVE CHANGE INDEX

      Background

      Converging evidence suggests that the pathophysiologic processes in the brains of Alzheimer’s disease (AD) patients begin decades before symptoms occur1-3. Individuals in the preclinical stages of AD often report self-perceived decline in cognitive functions. The self-perception of cognitive decline can be assessed via the cognitive change index (CCI)4,5, a recently proposed measure of perceived neuropsychological function focusing on the memory, executive, and language domains from both self and informant perspectives. In this study, we examine the association of CCI with a novel diffusion metric for axonal density. The axonal density metric is derived via a three-compartmental model, Neurite Orientation Distribution and Density Imaging (NODDI)6.

      Methods

      Sixty-one participants with cognitively normal (CN, N=21), subjective cognitive decline (SCD, N=23) and mild cognitive impairment (MCI, N=17) underwent Hybrid Diffusion Imaging (HYDI)7,8 that consists of five b-value shells. Diffusion images were corrected for motion, eddy and geometric distortion9 prior to nonlinear transformation to standard MNI space using ANTS registration10. Median values of diffusion metrics including DTI, NODDI, and q-space analysis were extracted from skeletonized 48 white matter (WM) ROIs provided in the JHU atlas11. General linear regression analyses were performed to test associations between diffusion metrics and CCI scores adjusted for age, sex, and education. For comparisons of demographic and cognitive variables, ANOVAs were employed with Tukey's tests followed by post-hoc t-tests.

      Results

      The three groups did not differ significantly in age, sex, and education distribution (Table 1). Strong associations (p<0.001) between CCI and diffusion metrics were found in ROIs belonging to the limbic system. Specifically, fractional anisotropy (FA) of DTI in the right stria terminalis had negative correlation with CCI-12 (i.e., episodic memory) (p<0.001, Figure 1). Axonal density in the right uncinate fasciculus negatively correlated with both CCI-12 and CCI-TOT (20 item) (p<0.001, Figure 2). Other ROIs had no significant correlations.

      Conclusions

      These results suggest that lower axonal density and fiber coherence are risk factors for self-perceived memory decline. The two most vulnerable white matter tracts - the right stria terminalis and uncinate fasciculus, connect between the amygdala and hippocampus - two of the areas that show the earliest disease-associated changes.
      Figure thumbnail fx1
      Figure 1ROI map and scatter plot of CCI score and fractional anisotropy (FA). A. The ROI in red denotes the right stria terminalis. B. FA of diffusion tensor imaging (DTI) negatively correlated with CCI-12 (episodic memory) with p=0.0007. Age, sex and education were adjusted in the linear regression analysis. Green dots denote cognitively normal (CN); blue dots denote subjective cognitive decline (SCD); red dots denote mild cognitive impairment (MCI); and the black solid line denotes the regression line.
      Figure thumbnail fx2
      Figure 2ROI map and scatter plot of CCI score and axonal density. A. The ROI in red denotes the right uncinate fasciculus. B. Axonal density negatively correlated with both CCI-12 (episodic memory) with p=0.00009 and CCI-TOT (20 items) with p=0.0002. Age, sex and education were adjusted in the linear regression analysis. Green dots denote cognitively normal (CN); blue dots denote subjective cognitive decline (SCD); red dots denote mild cognitive impairment (MCI); and the black solid line denotes the regression line.
      Table 1Demographic and cognitive comparisons of the three cohorts' characteristics
      CNMCISCDF/χ2p valueSCD-CNMCI-CNMCI-SCD
      Age (yrs)68.1 (5.8)67.9 (10)69.7 (10.3)0.2360.7910.840.8
      Sex (M:F)6:159:147:100.3840.680.760.710.99
      Education (yrs)17 (2.2)16.5 (2.6)16.1 (2.9)0.5050.610.820.580.9
      CCI_1215.3 (1.9)27.6 (6.8)33.6 (10.3)33.471p<1×10-5p<1×10-5p<1×10-5p<0.05
      CCl_TOT24.3 (2.6)41 (11)51 (17.6)24.682p<1×10-5p<1×10-5p<1×10-5p<0.05
      CCI_INF1215.6 (6)16.1 (5.7)32.7 (11.3)22.029p<1×10-50.97p<1×10-5p<1×10-5
      CCl_INFTOT25.7 (10.1)25.7 (8.8)53.3 (19.4)21.3p<1×10-51p<1×10-5p<1×10-5
      References: 1. Villemagne, V.L., S. Burnham, P. Bourgeat, B. Brown, K.A. Ellis, O. Salvado, C. Szoeke, S.L. Macaulay, R. Martins, P. Maruff, D. Ames, C.C. Rowe, C.L. Masters, B. Australian Imaging, and G. Lifestyle Research, Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study. Lancet Neurol, 2013. 12(4): p. 357-67. 2. Jack, C.R., Jr., V.J. Lowe, S.D. Weigand, HJ. Wiste, M.L. Senjem, D.S. Knopman, M.M. Shiung, J.L. Gunter, B.F. Boeve, B.J. Kemp, M. Weiner, R.C. Petersen, and I. Alzheimer's Disease Neuroimaging, Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer's disease: implications for sequence of pathological events in Alzheimer's disease. Brain, 2009. 132(Pt 5): p. 1355-65. 3. Sperling, R.A., P.S. Aisen, L.A. Beckett, D.A. Bennett, S. Craft, A.M. Fagan, T. Iwatsubo, C.R. Jack, Jr., J. Kaye, T.J. Montine, D.C. Park, E.M. Reiman, C.C. Rowe, E. Siemers, Y. Stern, K. Yaffe, M.C. Carrillo, B. Thies, M. Morrison-Bogorad, M.V. Wagster, and C.H. Phelps, Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement, 2011. 7(3): p. 280-92. 4. Saykin, A.J., H.A. Wishart, L.A. Rabin, R.B. Santulli, L.A. Flashman, J.D. West, T.L. McHugh, and A.C. Mamourian, Older adults with cognitive complaints show brain atrophy similar to that of amnestic MCI. Neurology, 2006. 67(5): p. 834-42. 5. Rattanabannakit, C., S.L. Risacher, S. Gao, K.A. Lane, S.A. Brown, B.C. McDonald, F.W. Unverzagt, L.G. Apostolova, A.J. Saykin, and M.R. Farlow, The Cognitive Change Index as a Measure of Self and Informant Perception of Cognitive Decline: Relation to Neuropsychological Tests. J Alzheimers Dis, 2016. 51(4): p. 1145-55. 6. Zhang, H., T. Schneider, C.A. Wheeler-Kingshott, and D.C. Alexander, NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 2012. 61(4): p. 1000-16. 7. Wu, Y.C. and A.L. Alexander, Hybrid diffusion imaging. Neuroimage, 2007. 36(3): p. 617-29. 8. Kodiweera, C., A.L. Alexander, J. Harezlak, T.W. McAllister, and Y.C. Wu, Age effects and sex differences in human brain white matter of young to middle-aged adults: A DTI, NODDI, and q-space study. Neuroimage, 2016. 128: p. 180-92. 9. Yamada, H., O. Abe, T. Shizukuishi, J. Kikuta, T. Shinozaki, K. Dezawa, A. Nagano, M. Matsuda, H. Haradome, and Y. Imamura, Efficacy of distortion correction on diffusion imaging: comparison of FSL eddy and eddy_correct using 30 and 60 directions diffusion encoding. PLoS One, 2014. 9(11): p. e112411. 10. Avants, B.B., N.J. Tustison, G. Song, P.A. Cook, A. Klein, and J.C. Gee, A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage, 2011. 54(3): p. 2033-44. 11. Oishi, K., K. Zilles, K. Amunts, A. Faria, H. Jiang, X. Li, K. Akhter, K. Hua, R. Woods, A.W. Toga, G.B. Pike, P. Rosa-Neto, A. Evans, J. Zhang, H. Huang, M.I. Miller, P.C. van Zijl, J. Mazziotta, and S. Mori, Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter. Neuroimage, 2008. 43(3): p. 447-57.