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Current Alzheimer Research

Editor-in-Chief

ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

Research Article

Disrupted Balance of Gray Matter Volume and Directed Functional Connectivity in Mild Cognitive Impairment and Alzheimer’s Disease

Author(s): Yu Xiong, Chenghui Ye, Ruxin Sun, Ying Chen, Xiaochun Zhong, Jiaqi Zhang, Zhanhua Zhong, Hongda Chen* and Min Huang*

Volume 20, Issue 3, 2023

Published on: 05 July, 2023

Page: [161 - 174] Pages: 14

DOI: 10.2174/1567205020666230602144659

Price: $65

Abstract

Background: Alterations in functional connectivity have been demonstrated in Alzheimer’s disease (AD), an age-progressive neurodegenerative disorder that affects cognitive function; however, directional information flow has never been analyzed.

Objective: This study aimed to determine changes in resting-state directional functional connectivity measured using a novel approach, granger causality density (GCD), in patients with AD, and mild cognitive impairment (MCI) and explore novel neuroimaging biomarkers for cognitive decline detection.

Methods: In this study, structural MRI, resting-state functional magnetic resonance imaging, and neuropsychological data of 48 Alzheimer’s Disease Neuroimaging Initiative participants were analyzed, comprising 16 patients with AD, 16 with MCI, and 16 normal controls. Volume-based morphometry (VBM) and GCD were used to calculate the voxel-based gray matter (GM) volumes and directed functional connectivity of the brain. We made full use of voxel-based between-group comparisons of VBM and GCD values to identify specific regions with significant alterations. In addition, Pearson’s correlation analysis was conducted between directed functional connectivity and several clinical variables. Furthermore, receiver operating characteristic (ROC) analysis related to classification was performed in combination with VBM and GCD.

Results: In patients with cognitive decline, abnormal VBM and GCD (involving inflow and outflow of GCD) were noted in default mode network (DMN)-related areas and the cerebellum. GCD in the DMN midline core system, hippocampus, and cerebellum was closely correlated with the Mini- Mental State Examination and Functional Activities Questionnaire scores. In the ROC analysis combining VBM with GCD, the neuroimaging biomarker in the cerebellum was optimal for the early detection of MCI, whereas the precuneus was the best in predicting cognitive decline progression and AD diagnosis.

Conclusion: Changes in GM volume and directed functional connectivity may reflect the mechanism of cognitive decline. This discovery could improve our understanding of the pathology of AD and MCI and provide available neuroimaging markers for the early detection, progression, and diagnosis of AD and MCI.

[1]
World Alzheimer Report 2021. Journey through the diagnosis of dementia Alzheimer’s Disease International. 2021.
[2]
Querfurth HW, LaFerla FM. Alzheimer’s Disease. N Engl J Med 2010; 362(4): 329-44.
[http://dx.doi.org/10.1056/NEJMra0909142] [PMID: 20107219]
[3]
Masters CL, Bateman R, Blennow K, Rowe CC, Sperling RA, Cummings JL. Alzheimer’s disease. Nat Rev Dis Primers 2015; 1(1): 15056.
[http://dx.doi.org/10.1038/nrdp.2015.56] [PMID: 27188934]
[4]
Soria Lopez JA, González HM, Léger GC. Alzheimer’s disease. Handb Clin Neurol 2019; 167: 231-55.
[http://dx.doi.org/10.1016/B978-0-12-804766-8.00013-3] [PMID: 31753135]
[5]
Chetelat G, Baron JC. Early diagnosis of alzheimer’s disease: Contribution of structural neuroimaging. Neuroimage 2003; 18(2): 525-41.
[http://dx.doi.org/10.1016/S1053-8119(02)00026-5] [PMID: 12595205]
[6]
Jack CR Jr, Barkhof F, Bernstein MA, et al. Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer’s disease. Alzheimers Dement 2011; 7(4): 474-485.e4.
[http://dx.doi.org/10.1016/j.jalz.2011.04.007] [PMID: 21784356]
[7]
Matsuda H. MRI morphometry in Alzheimer’s disease. Ageing Res Rev 2016; 30: 17-24.
[http://dx.doi.org/10.1016/j.arr.2016.01.003] [PMID: 26812213]
[8]
Steen RG, Mull C, Mcclure R, Hamer RM, Lieberman JA. Brain volume in first-episode schizophrenia. Br J Psychiatry 2006; 188(6): 510-8.
[http://dx.doi.org/10.1192/bjp.188.6.510] [PMID: 16738340]
[9]
Arnone D, McIntosh AM, Ebmeier KP, Munafò MR, Anderson IM. Magnetic resonance imaging studies in unipolar depression: Systematic review and meta-regression analyses. Eur Neuropsychopharmacol 2012; 22(1): 1-16.
[http://dx.doi.org/10.1016/j.euroneuro.2011.05.003] [PMID: 21723712]
[10]
Han KM, De Berardis D, Fornaro M, Kim YK. Differentiating between bipolar and unipolar depression in functional and structural MRI studies. Prog Neuropsychopharmacol Biol Psychiatry 2019; 91: 20-7.
[http://dx.doi.org/10.1016/j.pnpbp.2018.03.022] [PMID: 29601896]
[11]
Li SJ, Li Z, Wu G, Zhang MJ, Franczak M, Antuono PG. Alzheimer Disease: Evaluation of a functional MR imaging index as a marker. Radiology 2002; 225(1): 253-9.
[http://dx.doi.org/10.1148/radiol.2251011301] [PMID: 12355013]
[12]
Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL. Functional-anatomic fractionation of the brain’s default network. Neuron 2010; 65(4): 550-62.
[http://dx.doi.org/10.1016/j.neuron.2010.02.005] [PMID: 20188659]
[13]
Anna BS, Benjamin C, Fleur G, et al. Sleep: The tip of the iceberg in the bidirectional link between alzheimer’s disease and epilepsy. Front Neurol 2022; 13: 836292.
[http://dx.doi.org/10.3389/fneur.2022.836292]
[14]
Sheline YI, Raichle ME. Resting state functional connectivity in preclinical Alzheimer’s disease. Biol Psychiatry 2013; 74(5): 340-7.
[http://dx.doi.org/10.1016/j.biopsych.2012.11.028] [PMID: 23290495]
[15]
Berron D, van Westen D, Ossenkoppele R, Strandberg O, Hansson O. Medial temporal lobe connectivity and its associations with cognition in early Alzheimer’s disease. Brain 2020; 143(4): 1233-48.
[http://dx.doi.org/10.1093/brain/awaa068] [PMID: 32252068]
[16]
Mao Y, Liao Z, Liu X, et al. Disrupted balance of long and short-range functional connectivity density in Alzheimer’s disease (AD) and mild cognitive impairment (MCI) patients: A resting-state fMRI study. Ann Transl Med 2021; 9(1): 65.
[http://dx.doi.org/10.21037/atm-20-7019] [PMID: 33553358]
[17]
Dai XJ, Xu Q, Hu J, et al. BECTS substate classification by granger causality density based support vector machine model. Front Neurol 2019; 10: 1201.
[http://dx.doi.org/10.3389/fneur.2019.01201] [PMID: 31798523]
[18]
Dai XJ, Yang Y, Wang N, Tao W, Fan J, Wang Y. Reliability and availability of granger causality density in localization of Rolandic focus in BECTS. Brain Imaging Behav 2021; 15(3): 1542-52.
[http://dx.doi.org/10.1007/s11682-020-00352-0] [PMID: 32737823]
[19]
Dai X, Yang Y, Wang Y. Interictal epileptiform discharges changed epilepsy-related brain network architecture in BECTS. Brain Imaging Behav 2022; 16(2): 909-20.
[http://dx.doi.org/10.1007/s11682-021-00566-w] [PMID: 34677785]
[20]
Gaubert S, Raimondo F, Houot M, et al. EEG evidence of compensatory mechanisms in preclinical Alzheimer’s disease. Brain 2019; 142(7): 2096-112.
[http://dx.doi.org/10.1093/brain/awz150] [PMID: 31211359]
[21]
Ashburner J, Friston KJ. Voxel-based morphometry-the methods. Neuroimage 2000; 11(6): 805-21.
[http://dx.doi.org/10.1006/nimg.2000.0582] [PMID: 10860804]
[22]
Nicastro N, Rodriguez PV, Malpetti M, et al. 18F-AV1451 PET imaging and multimodal MRI changes in progressive supranuclear palsy. J Neurol 2020; 267(2): 341-9.
[http://dx.doi.org/10.1007/s00415-019-09566-9] [PMID: 31641878]
[23]
Farokhian F, Beheshti I, Sone D, Matsuda H. Comparing CAT12 and VBM8 for detecting brain morphological abnormalities in temporal lobe epilepsy. Front Neurol 2017; 8: 428.
[http://dx.doi.org/10.3389/fneur.2017.00428] [PMID: 28883807]
[24]
Zuo XN, Di Martino A, Kelly C, et al. The oscillating brain: Complex and reliable. Neuroimage 2010; 49(2): 1432-45.
[http://dx.doi.org/10.1016/j.neuroimage.2009.09.037] [PMID: 19782143]
[25]
Sperling RA, Aisen PS, Beckett LA, et al. 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): 280-92.
[http://dx.doi.org/10.1016/j.jalz.2011.03.003] [PMID: 21514248]
[26]
Gauthier CJ, Fan AP. BOLD signal physiology: Models and applications. Neuroimage 2019; 187: 116-27.
[http://dx.doi.org/10.1016/j.neuroimage.2018.03.018] [PMID: 29544818]
[27]
Halliday G. Pathology and hippocampal atrophy in Alzheimer’s disease. Lancet Neurol 2017; 16(11): 862-4.
[http://dx.doi.org/10.1016/S1474-4422(17)30343-5] [PMID: 29029840]
[28]
Li B, Zhang M, Jang I, et al. Amyloid-beta influences memory via functional connectivity during memory retrieval in alzheimer’s disease. Front Aging Neurosci 2021; 13: 721171.
[http://dx.doi.org/10.3389/fnagi.2021.721171] [PMID: 34539382]
[29]
Ward AM, Schultz AP, Huijbers W, Van Dijk KRA, Hedden T, Sperling RA. The parahippocampal gyrus links the default-mode cortical network with the medial temporal lobe memory system. Hum Brain Mapp 2014; 35(3): 1061-73.
[http://dx.doi.org/10.1002/hbm.22234] [PMID: 23404748]
[30]
Ossenkoppele R, Schonhaut DR, Schöll M, et al. Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer’s disease. Brain 2016; 139(5): 1551-67.
[http://dx.doi.org/10.1093/brain/aww027] [PMID: 26962052]
[31]
Barrett DGT, Denève S, Machens CK. Optimal compensation for neuron loss. eLife 2016; 5e12454.
[32]
Ammassari-Teule M. Neural compensation in presymptomatic hAPP mouse models of Alzheimer’s disease. Learn Mem 2020; 27(9): 390-4.
[http://dx.doi.org/10.1101/lm.050401.119] [PMID: 32817305]
[33]
Salami A, Wåhlin A, Kaboodvand N, Lundquist A, Nyberg L. Longitudinal evidence for dissociation of anterior and posterior mtl resting-state connectivity in aging: Links to perfusion and memory. Cereb Cortex 2016; 26(10): 3953-63.
[http://dx.doi.org/10.1093/cercor/bhw233] [PMID: 27522073]
[34]
Sheng X, Chen H, Shao P, et al. Brain structural network compensation is associated with cognitive impairment and alzheimer’s disease pathology. Front Neurosci 2021; 15: 630278.
[http://dx.doi.org/10.3389/fnins.2021.630278] [PMID: 33716654]
[35]
Busche MA, Eichhoff G, Adelsberger H, et al. Clusters of hyperactive neurons near amyloid plaques in a mouse model of Alzheimer’s disease. Science 2008; 321(5896): 1686-9.
[http://dx.doi.org/10.1126/science.1162844] [PMID: 18802001]
[36]
Müller L, Kirschstein T, Köhling R, Kuhla A, Teipel S. Neuronal hyperexcitability in APPSWE/PS1dE9 mouse models of alzheimer’s disease. J Alzheimers Dis 2021; 81(3): 855-69.
[http://dx.doi.org/10.3233/JAD-201540] [PMID: 33843674]
[37]
Sun JL, Stokoe SA, Roberts JP, et al. Co-activation of selective nicotinic acetylcholine receptors is required to reverse beta amyloid–induced Ca2+ hyperexcitation. Neurobiol Aging 2019; 84: 166-77.
[http://dx.doi.org/10.1016/j.neurobiolaging.2019.09.005] [PMID: 31629115]
[38]
Mattson MP. Involvement of GABAergic interneuron dysfunction and neuronal network hyperexcitability in Alzheimer’s disease: Amelioration by metabolic switching. Int Rev Neurobiol 2020; 154: 191-205.
[http://dx.doi.org/10.1016/bs.irn.2020.01.006] [PMID: 32739004]
[39]
Busche MA, Chen X, Henning HA, et al. Critical role of soluble amyloid-β for early hippocampal hyperactivity in a mouse model of Alzheimer’s disease. Proc Natl Acad Sci USA 2012; 109(22): 8740-5.
[http://dx.doi.org/10.1073/pnas.1206171109] [PMID: 22592800]
[40]
Salami A, Pudas S, Nyberg L. Elevated hippocampal resting-state connectivity underlies deficient neurocognitive function in aging. Proc Natl Acad Sci 2014; 111(49): 17654-9.
[http://dx.doi.org/10.1073/pnas.1410233111] [PMID: 25422457]
[41]
Pasquini L, Scherr M, Tahmasian M, et al. Link between hippocampus’ raised local and eased global intrinsic connectivity in AD. Alzheimers Dement 2015; 11(5): 475-84.
[http://dx.doi.org/10.1016/j.jalz.2014.02.007] [PMID: 25043909]
[42]
Tahmasian M, Pasquini L, Scherr M, et al. The lower hippocampus global connectivity, the higher its local metabolism in Alzheimer disease. Neurology 2015; 84(19): 1956-63.
[http://dx.doi.org/10.1212/WNL.0000000000001575] [PMID: 25878180]
[43]
Stoodley CJ. Distinct regions of the cerebellum show gray matter decreases in autism, ADHD, and developmental dyslexia. Front Syst Neurosci 2014; 8: 92.
[http://dx.doi.org/10.3389/fnsys.2014.00092] [PMID: 24904314]
[44]
Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BTT. The organization of the human cerebellum estimated by intrinsic functional connectivity. J Neurophysiol 2011; 106(5): 2322-45.
[http://dx.doi.org/10.1152/jn.00339.2011] [PMID: 21795627]
[45]
Ashida R, Cerminara NL, Edwards RJ, Apps R, Brooks JCW. Sensorimotor, language, and working memory representation within the human cerebellum. Hum Brain Mapp 2019; 40(16): 4732-47.
[http://dx.doi.org/10.1002/hbm.24733] [PMID: 31361075]
[46]
Singh-Bains MK, Linke V, Austria MDR, et al. Altered microglia and neurovasculature in the Alzheimer’s disease cerebellum. Neurobiol Dis 2019; 13: 2104589.
[http://dx.doi.org/10.1016/j.nbd.2019.104589] [PMID: 31454549]
[47]
Toniolo S, Serra L, Olivito G, et al. Cerebellar white matter disruption in alzheimer’s disease patients: A diffusion tensor imaging study. J Alzheimers Dis 2020; 74(2): 615-24.
[http://dx.doi.org/10.3233/JAD-191125] [PMID: 32065792]
[48]
Toniolo S, Serra L, Olivito G, Marra C, Bozzali M, Cercignani M. Patterns of cerebellar gray matter atrophy across alzheimer’s disease progression. Front Cell Neurosci 2018; 12: 430.
[http://dx.doi.org/10.3389/fncel.2018.00430] [PMID: 30515080]
[49]
Lin WY. Crossed cerebellar diaschisis: Related to lesion location or disease duration? J Nucl Med 1997; 38(12): 2006.
[PMID: 9430487]
[50]
Dennis EL, Thompson PM. Functional brain connectivity using fMRI in aging and Alzheimer’s disease. Neuropsychol Rev 2014; 24(1): 49-62.
[http://dx.doi.org/10.1007/s11065-014-9249-6] [PMID: 24562737]
[51]
Zheng W, Cui B, Han Y, et al. Disrupted regional cerebral blood flow, functional activity and connectivity in alzheimer’s disease: A combined ASL perfusion and resting state fMRI study. Front Neurosci 2019; 13: 738.
[http://dx.doi.org/10.3389/fnins.2019.00738] [PMID: 31396033]
[52]
Zhang M, Sun W, Guan Z, et al. Simultaneous PET/fMRI detects distinctive alterations in functional connectivity and glucose metabolism of precuneus subregions in alzheimer’s disease. Front Aging Neurosci 2021; 13: 737002.
[http://dx.doi.org/10.3389/fnagi.2021.737002] [PMID: 34630070]

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