Share：
Share this content in WeChat
X
[Chinese][PDF]173
• Clinical Article •
Altered brain morphometry and structural covariant networks based on cortical thickness in Alzheimer's disease
WANG Yan  ZHAO Kui  ZHU Zilin  LI Yilin  QIU Shijun 

Cite this article as: WANG Y, ZHAO K, ZHU Z L, et al. Altered brain morphometry and structural covariant networks based on cortical thickness in Alzheimer's disease[J]. Chin J Magn Reson Imaging, 2024, 15(8): 52-58. DOI:10.12015/issn.1674-8034.2024.08.008.


[Abstract] Objective To investigate the alteration of cerebral grey matter volume and cortical thickness and structural covariance network (SCN) based on cortical thickness in patients with Alzheimer's disease (AD).Materials and Methods In this study, a total of 100 patients with AD and 150 healthy controls (HCs) were included. Firstly, we conducted voxel-based morphometry (VBM) and surface-based morphometry (SBM) analysis in Computational Anatomy Toolbox 12 (CAT12) to acquire grey matter volume and cortical thickness. Subsequently, partial correlation analysis was applied to explore the correlation between brain regions with statistical differences and cognitive scales. Lastly, we constructed the SCN based on cortical thickness and analyzed its alternation of topology properties by graph theory analysis.Results Firstly, we observed the decreased grey matter volume and cortical thickness in patients with AD [P-values after family-wise error (FWE) correction, PFWE-corr<0.001]. The volumetrically decreased brain regions included bilateral hippocampus, bilateral orbitofrontal cortex, left insula, right inferior occipital gyrus,left precuneus, left precentral gyrus, left middle cingulate gyrus. The cerebral regions with thinner cortical thickness in AD group included bilateral temporal lobe, frontal lobe, parietal lobe, cingulate gyrus, fusiform gyrus, insula, precuneus, et al. Secondly, partial correlation analysis in AD group showed that Mini-Mental State Examination (MMSE) scores were respectively positively correlated to the volumes of right hippocampus [rs=0.35, P-values after false discovery rate (FDR) correction, PFDR-corr<0.001], left hippocampus (rs=0.38, PFDR-corr<0.001), the thickness of right fusiform gyrus (rs=0.38, PFDR-corr<0.001), and the clinical dementia rating sum of boxes (CDR-SB) scores was negatively correlated to the thickness of left fusiform gyrus (rs=-0.39, PFDR-corr<0.001). Lastly,in SCN analysis, we found the global efficiency (P<0.001), local efficiency (P=0.03), sigma (P<0.001) were higher in AD patients compared to HCs, while the shortest path length (P<0.001) was lower in AD patients.Conclusions The combination of morphological analysis by VBM and SBM and SCN analysis by graph theory was helpful to comprehensively understand the reconfiguration of brain networks and its significance, and thus provided new insights and evidence for neuroimaging changes in AD patients.
[Keywords] Alzheimer's disease；morphological analysis；magnetic resonance imaging；brain atrophy；structural covariance networks；graph theory；network reorganization

WANG Yan1, 2   ZHAO Kui1, 2   ZHU Zilin1   LI Yilin1   QIU Shijun1, 2*  

1 Department of Medical Imaging, the First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510000, China

2 State Key Laboratory of Traditional Chinese Medicine Syndrome, Guangzhou 510000, China

Corresponding author: QIU S J, E-mail: qiu-sj@163.com

Conflicts of interest   None.

Received  2024-01-26
Accepted  2024-07-03
DOI: 10.12015/issn.1674-8034.2024.08.008
Cite this article as: WANG Y, ZHAO K, ZHU Z L, et al. Altered brain morphometry and structural covariant networks based on cortical thickness in Alzheimer's disease[J]. Chin J Magn Reson Imaging, 2024, 15(8): 52-58. DOI:10.12015/issn.1674-8034.2024.08.008.

[1]
ROSTAGNO A A. Pathogenesis of Alzheimer's disease[J/OL]. Int J Mol Sci, 2022, 24(1): 107 [2024-01-26]. https://pubmed.ncbi.nlm.nih.gov/36613544/. DOI: 10.3390/ijms24010107.
[2]
2023 Alzheimer's disease facts and figures[J]. Alzheimers Dement, 2023, 19(4): 1598-1695. DOI: 10.1002/alz.13016.
[3]
LI W, SUN L, YUE L, et al. Alzheimer's disease and COVID-19: Interactions, intrinsic linkages, and the role of immunoinflammatory responses in this process[J/OL]. Front Immunol, 2023, 14: 1120495 [2024-01-26]. https://pubmed.ncbi.nlm.nih.gov/36845144/. DOI: 10.3389/fimmu.2023.1120495.
[4]
CHEN Z, MO X, CHEN R, et al. A Reparametrized CNN Model to Distinguish Alzheimer's Disease Applying Multiple Morphological Metrics and Deep Semantic Features From Structural MRI[J/OL]. Front Aging Neurosci, 2022, 14: 856391 [2024-01-26]. https://pubmed.ncbi.nlm.nih.gov/35721011/. DOI: 10.3389/fnagi.2022.856391.
[5]
PINI L, PIEVANI M, BOCCHETTA M, et al. Brain atrophy in Alzheimer's Disease and aging[J]. Ageing Res Rev, 2016, 30: 25-48. DOI: 10.1016/j.arr.2016.01.002.
[6]
SAKURAI K, KANEDA D, MORIMOTO S, et al. Voxel-based and surface-based morphometry analysis in patients with pathologically confirmed argyrophilic grain disease and Alzheimer's disease[J]. J Alzheimers Dis, 2023, 93(1): 379-387. DOI: 10.3233/JAD-230068.
[7]
MAGALHÃES T, CASSEB R F, GERBELLI C, et al. Whole-brain DTI parameters associated with tau protein and hippocampal volume in Alzheimer's disease[J/OL]. Brain Behav, 2023, 13(2): e2863 [2024-01-26]. https://pubmed.ncbi.nlm.nih.gov/36601694/. DOI: 10.1002/brb3.2863.
[8]
LI Y, CHU T, CHE K, et al. Altered gray matter structural covariance networks in postpartum depression: a graph theoretical analysis[J]. J Affect Disord, 2021, 293: 159-167. DOI: 10.1016/j.jad.2021.05.018.
[9]
VEITCH D P, WEINER M W, MILLER M, et al. The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022[J]. Alzheimers Dement, 2024, 20(1): 652-694. DOI: 10.1002/alz.13449.
[10]
FORTIN J P, CULLEN N, SHELINE Y I, et al. Harmonization of cortical thickness measurements across scanners and sites[J]. Neuroimage, 2018, 167: 104-120. DOI: 10.1016/j.neuroimage.2017.11.024.
[11]
WANG Y W, CHEN X, YAN C G. Comprehensive evaluation of harmonization on functional brain imaging for multisite data-fusion[J/OL]. Neuroimage, 2023, 274: 120089 [2024-01-26]. https://pubmed.ncbi.nlm.nih.gov/37086875/. DOI: 10.1016/j.neuroimage.2023.120089.
[12]
HOSSEINI S M, HOEFT F, KESLER S R. GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks[J/OL]. PLoS One, 2012, 7(7): e40709 [2024-01-26]. https://pubmed.ncbi.nlm.nih.gov/22808240/. DOI: 10.1371/journal.pone.0040709.
[13]
BAO B, SUN Y, LIN J, et al. Altered cortical thickness and structural covariance networks in upper limb amputees: A graph theoretical analysis[J]. CNS Neurosci Ther, 2023, 29(10): 2901-2911. DOI: 10.1111/cns.14226.
[14]
ASHBURNER J. A fast diffeomorphic image registration algorithm[J]. Neuroimage, 2007, 38(1): 95-113. DOI: 10.1016/j.neuroimage.2007.07.007.
[15]
LEUNG K K, BARTLETT J W, BARNES J, et al. Cerebral atrophy in mild cognitive impairment and Alzheimer disease: rates and acceleration[J]. Neurology, 2013, 80(7): 648-654. DOI: 10.1212/WNL.0b013e318281ccd3.
[16]
CONTADOR J, PÉREZ-MILLÁN A, TORT-MERINO A, et al. Longitudinal brain atrophy and CSF biomarkers in early-onset Alzheimer's disease[J/OL]. Neuroimage Clin, 2021, 32: 102804 [2024-01-26]. https://pubmed.ncbi.nlm.nih.gov/34474317/. DOI: 10.1016/j.nicl.2021.102804.
[17]
BACHMANN T, SCHROETER M L, CHEN K, et al. Longitudinal changes in surface based brain morphometry measures in amnestic mild cognitive impairment and Alzheimer's Disease[J/OL]. Neuroimage Clin, 2023, 38: 103371 [2024-01-26]. https://pubmed.ncbi.nlm.nih.gov/36924681/. DOI: 10.1016/j.nicl.2023.103371.
[18]
CHEN Y, WANG J, CUI C, et al. Evaluating the association between brain atrophy, hypometabolism, and cognitive decline in Alzheimer's disease: a PET/MRI study[J]. Aging (Albany NY), 2021, 13(5): 7228-7246. DOI: 10.18632/aging.202580.
[19]
QING Z, CHEN F, LU J, et al. Causal structural covariance network revealing atrophy progression in Alzheimer's disease continuum[J]. Hum Brain Mapp, 2021, 42(12): 3950-3962. DOI: 10.1002/hbm.25531.
[20]
BRAAK H, DEL T K. The preclinical phase of the pathological process underlying sporadic Alzheimer's disease[J]. Brain, 2015, 138(Pt 10): 2814-2833. DOI: 10.1093/brain/awv236.
[21]
HYMAN B T, PHELPS C H, BEACH T G, et al. National Institute on Aging-Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease[J]. Alzheimers Dement, 2012, 8(1): 1-13. DOI: 10.1016/j.jalz.2011.10.007.
[22]
PENG L, BESTARD-LORIGADOS I, SONG W. The synapse as a treatment avenue for Alzheimer's Disease[J]. Mol Psychiatry, 2022, 27(7): 2940-2949. DOI: 10.1038/s41380-022-01565-z.
[23]
MINKOVA L, HABICH A, PETER J, et al. Gray matter asymmetries in aging and neurodegeneration: A review and meta-analysis[J]. Hum Brain Mapp, 2017, 38(12): 5890-5904. DOI: 10.1002/hbm.23772.
[24]
HU X, MEIER M, PRUESSNER J. Challenges and opportunities of diagnostic markers of Alzheimer's disease based on structural magnetic resonance imaging[J/OL]. Brain Behav, 2023, 13(3): e2925 [2024-01-26]. https://pubmed.ncbi.nlm.nih.gov/36795041/. DOI: 10.1002/brb3.2925.
[25]
ZHANG J, XIE L, CHENG C, et al. Hippocampal subfield volumes in mild cognitive impairment and alzheimer's disease: a systematic review and meta-analysis[J]. Brain Imaging Behav, 2023, 17(6): 778-793. DOI: 10.1007/s11682-023-00804-3.
[26]
RAO Y L, GANARAJA B, MURLIMANJU B V, et al. Hippocampus and its involvement in Alzheimer's disease: a review[J/OL]. 3 Biotech, 2022, 12(2): 55 [2024-01-26]. https://pubmed.ncbi.nlm.nih.gov/35116217/. DOI: 10.1007/s13205-022-03123-4.
[27]
RIBEIRO-DOS-SANTOS A, de BRITO L M, de ARAÚJO G S. The fusiform gyrus exhibits differential gene-gene co-expression in Alzheimer's disease[J/OL]. Front Aging Neurosci, 2023, 15: 1138336 [2024-01-26]. https://pubmed.ncbi.nlm.nih.gov/37255536/. DOI: 10.3389/fnagi.2023.1138336.
[28]
FENG M, ZHANG Y, LIU Y, et al. White matter structural network analysis to differentiate Alzheimer's disease and subcortical ischemic vascular dementia[J/OL]. Front Aging Neurosci, 2021, 13: 650377 [2024-01-26]. https://pubmed.ncbi.nlm.nih.gov/33867969/. DOI: 10.3389/fnagi.2021.650377.
[29]
MA X, LIU Y, CLARIANA R, et al. From eye movements to scanpath networks: A method for studying individual differences in expository text reading[J]. Behav Res Methods, 2023, 55(2): 730-750. DOI: 10.3758/s13428-022-01842-3.
[30]
ADAMS J N, CHAPPEL-FARLEY M G, YAROS J L, et al. Functional network structure supports resilience to memory deficits in cognitively normal older adults with amyloid-β pathology[J/OL]. Sci Rep, 2023, 13(1): 13953 [2024-01-26]. https://pubmed.ncbi.nlm.nih.gov/37626094/. DOI: 10.1038/s41598-023-40092-x.
[31]
DUAN H, JIANG J, XU J, et al. Differences in Aβ brain networks in Alzheimer's disease and healthy controls[J]. Brain Res, 2017, 1655: 77-89. DOI: 10.1016/j.brainres.2016.11.019.
[32]
ZHANG H Q, CHAU A, SHEA Y F, et al. Disrupted structural white matter network in Alzheimer's disease continuum, vascular dementia, and mixed dementia: A diffusion tensor imaging study[J]. J Alzheimers Dis, 2023, 94(4): 1487-1502. DOI: 10.3233/JAD-230341.

PREV Intra- and peritumoral sagittal T2WI radiomics nomogram for preoperative prediction of patients with stage ⅠB and stage ⅡA cervical cancer
NEXT A resting-state functional magnetic resonance imaging study of abnormal brain function in patients with Internet gaming disorder
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn