Abnormal Changes of Brain Cortical Anatomy and the Association with Plasma MicroRNA107 Level in Amnestic Mild Cognitive Impairment

Tao Wang, Feng Shi, Yan Jin, Weixiong Jiang, Dinggang Shen, Shifu Xiao, Tao Wang, Feng Shi, Yan Jin, Weixiong Jiang, Dinggang Shen, Shifu Xiao

Abstract

MicroRNA107 (Mir107) has been thought to relate to the brain structure phenotype of Alzheimer's disease. In this study, we evaluated the cortical anatomy in amnestic mild cognitive impairment (aMCI) and the relation between cortical anatomy and plasma levels of Mir107 and beta-site amyloid precursor protein (APP) cleaving enzyme 1 (BACE1). Twenty aMCI (20 aMCI) and 24 cognitively normal control (NC) subjects were recruited, and T1-weighted MR images were acquired. Cortical anatomical measurements, including cortical thickness (CT), surface area (SA), and local gyrification index (LGI), were assessed. Quantitative RT-PCR was used to examine plasma expression of Mir107, BACE1 mRNA. Thinner cortex was found in aMCI in areas associated with episodic memory and language, but with thicker cortex in other areas. SA decreased in aMCI in the areas associated with working memory and emotion. LGI showed a significant reduction in aMCI in the areas involved in language function. Changes in Mir107 and BACE1 messenger RNA plasma expression were correlated with changes in CT and SA. We found alterations in key left brain regions associated with memory, language, and emotion in aMCI that were significantly correlated with plasma expression of Mir107 and BACE1 mRNA. This combination study of brain anatomical alterations and gene information may shed lights on our understanding of the pathology of AD.

Clinical trial registration: http://www.ClinicalTrials.gov, identifier NCT01819545.

Keywords: Alzheimer’s disease; amnestic mild cognitive impairment; biological markers; genetics; surface-based morphometry.

Figures

Figure 1
Figure 1
(A) The cortex thicknesses are thinner in the superior parietal gyrus, postcentral gyrus, lingual gyrus and paracentral gyrus than other brain regions both in amnestic mild cognitive impairment (aMCI) and normal control (NC) in general. (B) aMCI shows less cortical thickness (CT) than NC when age increases but no significant difference is found between groups (p = 0.122). There is significant difference within group of aMCI when age increases (p = 0.014), while there is no significant difference within group of NC when age increases (p = 0.747). (C) The ROI-based analysis of CT revealed that the left postcentral gyrus (PoCG.L), the left inferior parietal gyrus (IPG.L), the left precuneus (PCUN.L) and the upside right supramarginal gyrus (SMG.RU) were significant group differences (p < 0.05) between the aMCI and the NC groups with the aMCI having thinner cortex than the NC. In addition, the left superior temporal gyrus (STG.L), the left insula (INS.L), the low side right supramarginal gyrus (SMG.RL) and the right fusiform gyrus (FFG.R) exhibited significantly (p < 0.05) larger thickness in the aMCI compared with the NC.
Figure 2
Figure 2
(A) There is no significant difference between two groups on the total surface area (SA). But aMCI shows less SA than NC when age increases. The surface areas are less in the superior parietal gyrus, postcentral gyrus, middle temporal gyrus and anterior cingulate gyrus than other brain regions both in aMCI and NC in general. (B) aMCI shows less SA than NC when age increases but no significant difference is found between groups (p = 0.567). There are no significant difference within group of aMCI (p = 0.847) and NC (p = 0.524) when age increases, respectively. (C) ROI based group analysis found significantly (p < 0.05) smaller SA in the left superior frontal gyrus (SFG.L), the right supramarginal gyrus (SMG.R), the right caudal middle frontal gyrus (CMFG.R) and the right posterior cingulate gyrus (PCG.R) in the aMCI, compared with the NC. In addition, the left postcentral gyrus (PoCG.L; p < 0.01) exhibited significantly larger SA in the aMCI compared with the NC.
Figure 3
Figure 3
(A) Compared with controls, aMCI has significant difference on the general local gyrification index (LGI) and one cluster where aMCI has significant LGI reduction. The LGI are higher in the superior temporal gyrus, and middle temporal gyrus than other brain regions both in aMCI and NC in general. (B) aMCI shows less CT than NC when age increases and significant difference is found between groups (p = 0.038). There is significant difference within group of aMCI when age increases (p = 0.046), while there is no significant difference within group of NC when age increases (p = 0.349). (C) The ROI based LGI group analysis shows a significant effect of the right superior temporal gyrus (STG.R; p < 0.05) in aMCI.
Figure 4
Figure 4
The relationship between brain regions with abnormal surface anatomy in aMCI and neuropsychological test scores and the plasma target gene expressions. Changes in CT, SA and LGI in aMCI were correlated with neuropsychological scores and/or plasma MiRNA 107 expression. (A) The relationship of CT with neuropsycological test score and the plasma expression of MiRNA 107. (B) The relationship of SA with neuropsycological test score and the plasma expression of MiRNA 107 and BACE1 mRNA. (C) The relationship of local gyrification and neuropsycological test score.

References

    1. Albert M. S., DeKosky S. T., Dickson D., Dubois B., Feldman H. H., Fox N. C., et al. . (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 270–279. 10.1016/j.jalz.2011.03.008
    1. Apostolova L. G., Steiner C. A., Akopyan G. G., Dutton R. A., Hayashi K. M., Toga A. W., et al. . (2007). Three-dimensional gray matter atrophy mapping in mild cognitive impairment and mild Alzheimer disease. Arch. Neurol. 64, 1489–1495. 10.1001/archneur.64.10.1489
    1. Bettens K., Brouwers N., Engelborghs S., Van Miegroet H., De Deyn P. P., Theuns J., et al. . (2009). APP and BACE1 miRNA genetic variability has no major role in risk for Alzheimer disease. Hum. Mutat. 30, 1207–1213. 10.1002/humu.21027
    1. Casanova M. F., El-Baz A., Vanbogaert E., Narahari P., Switala A. (2010). A topographic study of minicolumnar core width by lamina comparison between autistic subjects and controls: possible minicolumnar disruption due to an anatomical element in-common to multiple laminae. Brain Pathol. 20, 451–458. 10.1111/j.1750-3639.2009.00319.x
    1. Davatzikos C. (2004). Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. Neuroimage 23, 17–20. 10.1016/j.neuroimage.2004.05.010
    1. De Strooper B. (2010). Proteases and proteolysis in Alzheimer disease: a multifactorial view on the disease process. Physiol. Rev. 90, 465–494. 10.1152/physrev.00023.2009
    1. Dickerson B. C., Bakkour A., Salat D. H., Feczko E., Pacheco J., Greve D. N., et al. . (2009). The cortical signature of Alzheimer’s disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cereb. Cortex 19, 497–510. 10.1093/cercor/bhn113
    1. Dislich B., Lichtenthaler S. F. (2012). The membrane-bound aspartyl protease BACE1: molecular and functional properties in Alzheimer’s disease and beyond. Front. Physiol. 3:8. 10.3389/fphys.2012.00008
    1. Eickhoff S., Walters N. B., Schleicher A., Kril J., Egan G. F., Zilles K., et al. . (2005). High-resolution MRI reflects myeloarchitecture and cytoarchitecture of human cerebral cortex. Hum. Brain Mapp. 24, 206–215. 10.1002/hbm.20082
    1. Fischl B., Dale A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U S A. 97, 11050–11055. 10.1073/pnas.200033797
    1. Fleisher A. S., Chen K., Quiroz Y. T., Jakimovich L. J., Gomez M. G., Langois C. M., et al. . (2012). Florbetapir PET analysis of amyloid-β deposition in the presenilin 1 E280A autosomal dominant Alzheimer’s disease kindred: a cross-sectional study. Lancet Neurol. 11, 1057–1065. 10.1016/S1474-4422(12)70227-2
    1. Folstein M. F., Folstein S. E., McHugh P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–198. 10.1007/springerreference_61498
    1. He W., Liu D., Radua J., Li G., Han B., Sun Z. (2015). Meta-analytic comparison between PIB-PET and FDG-PET results in Alzheimer’s disease and MCI. Cell Biochem. Biophys. 71, 17–26. 10.1007/s12013-014-0138-7
    1. Hébert S. S., Papadopoulou A. S., Smith P., Galas M. C., Planel E., Silahtaroglu A. N., et al. . (2010). Genetic ablation of Dicer in adult forebrain neurons results in abnormal tau hyperphosphorylation and neurodegeneration. Hum. Mol. Genet. 19, 3959–3969. 10.1093/hmg/ddq311
    1. Honea R. A., Swerdlow R. H., Vidoni E. D., Goodwin J., Burns J. M. (2010). Reduced gray matter volume in normal adults with a maternal family history of Alzheimer Disease. Neurology 74, 113–120. 10.1212/wnl.0b013e3181c918cb
    1. Im K., Lee J. M., Seo S. W., Hyung Kim S., Kim S. I., Na D. L. (2008). Sulcal morphology changes and their relationship with cortical thickness and gyral white matter volume in mild cognitive impairment and Alzheimer’s disease. Neuroimage 43, 103–113. 10.1016/j.neuroimage.2008.07.016
    1. Jin Y., Shi Y., Zhan L., Thompson P. M. (2015). “Automated Multi-atlas labeling of the fornix and its integrity in Alzheimer’s disease”, in Proceedings of IEEE International Symposium Biomedical Imaging, (New York: ), 140–143.
    1. Kandimalla R. J., Prabhakar S., Wani W. Y., Kaushal A., Gupta N., Sharma D. R., et al. . (2013b). CSF p-Tau levels in the prediction of Alzheimer’s disease. Biol. Open., 2:1119–1124. 10.1242/bio.20135447
    1. Kandimalla R. J., Wani W. Y., Anand R., Kaushal A., Prabhakar S., Grover V. K., et al. . (2013a). Apolipoprotein E levels in the cerebrospinal fluid of north Indian patients with Alzheimer’s disease. Am. J. Alzheimers Dis. Other Demen. 28, 258–262. 10.1177/1533317513481097
    1. Katzman R., Zhang M. Y., Ouang-Ya-Qu, Wang Z. Y., Liu W. T., Yu E., et al. (1988). A Chinese version of the Mini-Mental State Examination; impact of illiteracy in a Shanghai dementia survey. J. Clin. Epidemiol. 41, 971–978. 10.1007/springerreference_61498
    1. King R. D., Brown B., Hwang M., Jeon T., George A. T. (2010). Fractal dimension analysis of the cortical ribbon in mild Alzheimer’s disease. Neuroimage 53, 471–479. 10.1016/j.neuroimage.2010.06.050
    1. Kochunov P., Mangin J. F., Coyle T., Lancaster J., Thompson P., Rivière D., et al. . (2005). Age-related morphology trends of cortical sulci. Hum. Brain Mapp. 26, 210–220. 10.1002/hbm.20198
    1. Lebed E., Jacova C., Wang L., Beg M. F. (2013). Novel surface-smoothing based local gyrification index. IEEE Trans. Med. Imaging 32, 660–669. 10.1109/tmi.2012.2230640
    1. Lee G. J., Lu P. H., Medina L. D., Rodriguez-Agudelo Y., Melchor S., Coppola G., et al. . (2013). Regional brain volume differences in symptomatic and presymptomatic carriers of familial Alzheimer’s disease mutations. J. Neurol. Neurosurg. Psychiatry 84, 154–162. 10.1136/jnnp-2011-302087
    1. Li J., Jin Y., Shi Y., Dinov I. D., Wang D. J., Toga A. W., et al. . (2013). Voxelwise spectral diffusional connectivity and its application to Alzheimer’s disease and intelligence prediction. Med. Image Comput. Comput. Assist. Interv. 8149, 655–662. 10.1007/978-3-642-40811-3_82
    1. Li G., Wang L., Shi F., Lyall A. E., Lin W., Gilmore J. H., et al. . (2014a). Mapping longitudinal development of local cortical gyrification in infants from birth to 2 years of age. J. Neurosci. 34, 4228–4238. 10.1523/jneurosci.3976-13.2014
    1. Li S., Yuan X., Pu F., Li D., Fan Y., Wu L., et al. . (2014b). Abnormal changes of multidimensional surface features using multivariate pattern classification in amnestic mild cognitive impairment patients. J. Neurosci. 34, 10541–10553. 10.1523/jneurosci.4356-13.2014
    1. Liao W., Long X., Jiang C., Diao Y., Liu X., Zheng H., et al. . (2014). Discerning mild cognitive impairment and Alzheimer disease from normal aging: morphologic characterization based on univariate and multivariate models. Acad. Radiol. 21, 597–604. 10.1016/j.acra.2013.12.001
    1. Libero L. E., DeRamus T. P., Deshpande H. D., Kana R. K. (2014). Surface-based morphometry of the cortical architecture of autism spectrum disorders: volume, thickness, area and gyrification. Neuropsychologia 62, 1–10. 10.1016/j.neuropsychologia.2014.07.001
    1. Liu T., Lipnicki D. M., Zhu W., Tao D., Zhang C., Cui Y., et al. . (2012). Cortical gyrification and sulcal spans in early stage Alzheimer’s disease. PLoS One 7: e31083. 10.1371/journal.pone.0031083
    1. Liu T., Wen W., Zhu W., Kochan N. A., Trollor J. N., Reppermund S., et al. . (2011). The relationship between cortical sulcal variability and cognitive performance in the elderly. Neuroimage 56, 865–873. 10.1016/j.neuroimage.2011.03.015
    1. Madsen S. K., Rajagopalan P., Joshi S. H., Toga A. W., Thompson P. M., the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (2015). Higher homocysteie associated with thinner cortical gray matter in 803 participants from the Alzheimer’s Disease Neuroimaging Initiative. Neurobiol. Aging 36, S203–S210. 10.1016/j.neurobiolaging.2014.01.154
    1. McKhann G. M., Knopman D. S., Chertkow H., Hyman B. T., Jack C. R., Kawas C. H., et al. (2011). The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 263–269. 10.1016/j.jalz.2011.03.005
    1. Meda S. A., Koran M. E., Pryweller J. R., Vega J. N., Thornton-Wells T. A., the Alzheimer’s Disease Neuroimaging Initiative (2013). Genetic interactions associated with 12-month atrophy in hippocampus and entorhinal cortex in Alzheimer’s Disease Neuroimaging Initiative. Neurobiol. Aging 34, 1518.e9–1518.e18. 10.1016/j.neurobiolaging.2012.09.020
    1. Nasreddine Z. S., Phillips N. A., Bédirian V., Charbonneau S., Whitehead V., Collin I., et al. . (2005). The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc 53, 695–699. 10.1111/j.1532-5415.2005.53221.x
    1. Petersen R. C., Parisi J. E., Dickson D. W., Johnson K. A., Knopman D. S., Boeve B. F., et al. . (2006). Neuropathology of amnestic mild cognitive impairment. Arch. Neurol. 63, 645–646. 10.1001/archneur.63.5.645
    1. Sabuncu M. R., Desikan R. S., Sepulcre J., Yeo B. T., Liu H., Schmansky N. J., et al. . (2011). The dynamics of cortical and hippocampal atrophy in Alzheimer disease. Arch. Neurol. 68, 1040–1048. 10.1001/archneurol.2011.167
    1. Sadigh-Eteghad S., Majdi A., Farhoudi M., Talebi M., Mahmoudi J. (2014). Different patterns of brain activation in normal aging and Alzheimer’s disease from cognitional sight: meta analysis using activation likelihood estimation. J. Neurol. Sci. 343, 159–166. 10.1016/j.jns.2014.05.066
    1. Sathya M., Premkumar P., Karthick C., Moorthi P., Jayachandran K. S., Anusuyadevi M. (2012). BACE1 in Alzheimer’s disease. Clin. Chim. Acta. 414, 171–178. 10.1016/j.cca.2012.08.013
    1. Schaer M., Cuadra M. B., Tamarit L., Lazeyras F., Eliez S., Thiran J. P. (2008). A surface-based approach to quantify local cortical gyrification. IEEE Trans. Med. Imaging. 27, 161–170. 10.1109/tmi.2007.903576
    1. Sowell E. R., Thompson P. M., Leonard C. M., Welcome S. E., Kan E., Toga A. W. (2004). Longitudinal mapping of cortical thickness and brain growth in normal children. J. Neurosci. 24, 8223–8231. 10.1523/jneurosci.1798-04.2004
    1. Van Essen D. C. (1997). A tension-based theory of morphogenesis and compact wiring in the central nervous system. Nature 385, 313–318. 10.1038/385313a0
    1. Wang T., Shi F., Jin Y., Yap P.-T., Wee C. Y., Zhang J., et al. . (2016). Multilevel deficiency of white matter connectivity networks in Alzheimer’s disease: a diffusion MRI study with DTI and HARDI models. Neural Plast. 2016:2947136. 10.1155/2016/2947136
    1. Wang W. X., Huang Q., Hu Y., Stromberg A. J., Nelson P. T. (2011). Patterns of microRNA expression in normal and early Alzheimer’s disease human temporal cortex: white matter versus gray matter. Acta. Neuropathol. 121, 193–205. 10.1007/s00401-010-0756-0
    1. Watson R., Colloby S. J., Blamire A. M., O’Brien J. T. (2015). Assessment of regional gray matter loss in dementia with Lewy bodies: a surface-based MRI analysis. Am. J. Geriatr. Psychiatry 23, 38–46. 10.1016/j.jagp.2014.07.005
    1. Ye B. S., Seo S. W., Yang J. J., Kim H. J., Kim Y. J., Yoon C. W., et al. . (2014). Comparison of cortical thickness in patients with early-stage versus late-stage amnestic mild cognitive impairment. Eur. J. Neurol. 21, 86–92. 10.1111/ene.12251
    1. Zhan L., Nie Z., Ye J., Wang J., Jin Y., Jahanshad N., et al. (2014). “Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximation (GLRAM)”, in Computational Diffusion MRI. Mathematics and Visualization, (Switzerland: Springer; ), 35–44.
    1. Zhan L., Zhou J., Wang Y., Jin Y., Jahanshad N., Prasad G., et al. . (2015). Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease. Front. Aging Neurosci. 7:48. 10.3389/fnagi.2015.00048

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