Abstract
The purpose of this study was to explore the differences in interhemispheric functional connectivity in patients with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI) based on a triple network model consisting of the default mode network (DMN), salience network (SN), and executive control network (ECN). The technique of voxel-mirrored homotopic connectivity (VMHC) analysis was applied to explore the aberrant connectivity of all patients. The results showed that: (1) the statistically significant connections of interhemispheric brain regions included DMN-related brain regions (i.e. precuneus, calcarine, fusiform, cuneus, lingual gyrus, temporal inferior gyrus, and hippocampus), SN-related brain regions (i.e. frontoinsular cortex), and ECN-related brain regions (i.e. frontal middle gyrus and frontal inferior); (2) the precuneus and frontal middle gyrus in the AD group exhibited lower VMHC values than those in the aMCI and healthy control (HC) groups, but no significant difference was observed between the aMCI and HC groups; and (3) significant correlations were found between peak VMHC results from the precuneus and Mini Mental State Examination (MMSE) and Montreal Cognitive Scale (MOCA) scores and their factor scores in the AD, aMCI, and AD plus aMCI groups, and between the results from the frontal middle gyrus and MOCA factor scores in the aMCI group. These findings indicated that impaired interhemispheric functional connectivity was observed in AD and could be a sensitive neuroimaging biomarker for AD. More specifically, the DMN was inhibited, while the SN and ECN were excited. VMHC results were correlated with MMSE and MOCA scores, highlighting that VMHC could be a sensitive neuroimaging biomarker for AD and the progression from aMCI to AD.
摘要
目的
探讨阿尔茨海默病(AD)和遗忘型轻度认知功能障碍(aMCI)在默认脑网络(DMN)、突显网络(SN)和执行控制网络(ECN)这三个脑网络中的半球间脑功能连接的差异性。
创新点
利用体素镜像同伦功能连接(VMHC)来观察AD 和aMCI 在多个脑网络基础上的半球间功能连接特点。
方法
该研究纳入了浙江省人民医院就诊的30 例AD 患者、14 例aMCI 患者和18 例老年健康对照者,均给予静息态功能磁共振扫描,利用VMHC 进行数据分析,联合简易智力状态检查量表(MMSE) 和蒙特利尔认知评估量表(MOCA)进行相关分 析。
结论
(1)位于三个脑网络的异常半球功能连接主要存在于AD 组,可以作为AD 诊断的一个敏感性 指标;(2)VMHC 值可以作为预测AD 进展包括aMCI发展为AD 的一个敏感性指标。
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References
Anderson JS, Druzgal TJ, Froehlich A, et al., 2011. Decreased interhemispheric functional connectivity in autism. Cereb Cortex, 21(5):1134–1146. https://doi.org/10.1093/cercor/bhq190
Blautzik J, Keeser D, Paolini M, et al., 2016. Functional connectivity increase in the default-mode network of patients with Alzheimer’s disease after long-term treatment with galantamine. Eur Neuropsychopharmacol, 26(3):602–613. https://doi.org/10.1016/j.euroneuro.2015.12.006
Botvinick MM, Cohen JD, Carter CS, 2004. Conflict monitoring and anterior cingulate cortex: an update. Trends Cogn Sci, 8(12):539–546. https://doi.org/10.1016/j.tics.2004.10.003
Buckner RL, Snyder AZ, Shannon BJ, et al., 2005. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci, 25(34):7709–7717. https://doi.org/10.1523/JNEUROSCI.2177-05.2005
Burns JM, Church JA, Johnson DK, et al., 2005. White matter lesions are prevalent but differentially related with cognition in aging and early Alzheimer disease. Arch Neurol, 62(12):1870–1876. https://doi.org/10.1001/archneur.62.12.1870
Cai SP, Peng YL, Chong T, et al., 2017. Differentiated effective connectivity patterns of the executive control network in progressive MCI: a potential biomarker for predicting AD. Curr Alzheimer Res, 14(9):937–950. https://doi.org/10.2174/1567205014666170309120200
Chang YT, Huang CW, Chang YH, et al., 2015. Amyloid burden in the hippocampus and default mode network: relationships with gray matter volume and cognitive performance in mild stage Alzheimer disease. Medicine, 94(16):e763. https://doi.org/10.1097/MD.0000000000000763
Donchin O, Gribova A, Steinberg O, et al., 1998. Primary motor cortex is involved in bimanual coordination. Nature, 395(6699):274–278. https://doi.org/10.1038/26220
Francx W, Oldehinkel M, Oosterlaan J, et al., 2015. The executive control network and symptomatic improvement in attention-deficit/hyperactivity disorder. Cortex, 73:62–72. https://doi.org/10.1016/j.cortex.2015.08.012
Ho CSH, Zhang MWB, Ho RCM, 2016. Optical topography in psychiatry: a chip off the old block or a new look beyond the mind-brain frontiers? Front Psychiatry, 7:74. https://doi.org/10.3389/fpsyt.2016.00074
Hoptman MJ, Zuo XN, D'Angelo D, et al., 2012. Decreased interhemispheric coordination in schizophrenia: a resting state fMRI study. Schizophr Res, 141(1):1–7. https://doi.org/10.1016/j.schres.2012.07.027
Jiang ZY, 2005. Abnormal cortical functional connections in Alzheimer’s disease: analysis of inter-and intra-hemispheric EEG coherence. J Zhejiang Univ SCI, 6B(4):259–264. https://doi.org/10.1631/jzus.2005.B0259
Joo SH, Lim HK, Lee CU, 2016. Three large-scale functional brain networks from resting-state functional MRI in subjects with different levels of cognitive impairment. Psychiatry Investig, 13(1):1–7. https://doi.org/10.4306/pi.2016.13.1.1
Kelly C, Zuo XN, Gotimer K, et al., 2011. Reduced interhemispheric resting state functional connectivity in cocaine addiction. Biol Psychiatry, 69(7):684–692. https://doi.org/10.1016/j.biopsych.2010.11.022
Lai CYY, Ho CSH, Lim CR, et al., 2017. Functional nearinfrared spectroscopy in psychiatry. BJPsych Adv, 23(5): 324–330. https://doi.org/10.1192/apt.bp.115.015610
Lakmache Y, Lassonde M, Gauthier S, et al., 1998. Interhemispheric disconnection syndrome in Alzheimer’s disease. Proc Natl Acad Sci USA, 95(15):9042–9046. https://doi.org/10.1073/pnas.95.15.9042
Lehmann M, Madison C, Ghosh PM, et al., 2015. Loss of functional connectivity is greater outside the default mode network in nonfamilial early-onset Alzheimer’s disease variants. Neurobiol Aging, 36(10):2678–2686. https://doi.org/10.1016/j.neurobiolaging.2015.06.029
Li Q, Liu JR, Wang W, et al., 2018. Disrupted coupling of large-scale networks is associated with relapse behaviour in heroin-dependent men. J Psychiatry Neurosci, 43(1): 48–57. https://doi.org/10.1503/jpn.170011
Liu AK, Belliveau JW, Dale AM, 1998. Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations. Proc Natl Acad Sci USA, 95(15):8945–8950. https://doi.org/10.1073/pnas.95.15.8945
Liu JX, Zhu MY, Feng CY, et al., 2015. Bamboo leaf extract improves spatial learning ability in a rat model with senile dementia. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 16(7):593–601. https://doi.org/10.1631/jzus.B1400249
Lu HN, Ma SL, Wong SWH, et al., 2017. Aberrant interhemispheric functional connectivity within default mode network and its relationships with neurocognitive features in cognitively normal APOE ε 4 elderly carriers. Int Psychogeriatr, 29(5):805–814. https://doi.org/10.1017/S1041610216002477
Lustig C, Snyder AZ, Bhakta M, et al., 2003. Functional deactivations: change with age and dementia of the Alzheimer type. Proc Natl Acad Sci USA, 100(24):14504–14509. https://doi.org/10.1073/pnas.2235925100
McKhann GM, Knopman DS, Chertkow 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(3):263–269. https://doi.org/10.1016/j.jalz.2011.03.005
Menon V, 2011. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci, 15(10): 483–506. https://doi.org/10.1016/j.tics.2011.08.003
Menon V, Uddin LQ, 2010. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct, 214(5-6):655–667. https://doi.org/10.1007/s00429-010-0262-0
Mueller-Bierl BM, Uludag K, Pereira PL, et al., 2007. Magnetic field distribution and signal decay in functional MRI in very high fields (up to 9.4 T) using Monte Carlo diffusion modeling. Int J Biomed Imaging, 2007:70309. https://doi.org/10.1155/2007/70309
Orgeta V, Qazi A, Spector A, et al., 2015. Psychological treatments for depression and anxiety in dementia and mild cognitive impairment: systematic review and metaanalysis. Br J Psychiatry, 207(4):293–298. https://doi.org/10.1192/bjp.bp.114.148130
Perri R, Monaco M, Fadda L, et al., 2014. Neuropsychological correlates of behavioral symptoms in Alzheimer’s disease, frontal variant of frontotemporal, subcortical vascular, and Lewy body dementias: a comparative study. J Alzheimers Dis, 39(3):669–677. https://doi.org/10.3233/JAD-131337
Petersen RC, Doody R, Kurz A, et al., 2001. Current concepts in mild cognitive impairment. Arch Neurol, 58(12): 1985–1992. https://doi.org/10.1001/archneur.58.12.1985
Russo MJ, Campos J, Vázquez S, et al., 2017. Adding recognition discriminability index to the delayed recall is useful to predict conversion from mild cognitive impairment to Alzheimer’s disease in the Alzheimer’s disease neuroimaging initiative. Front Aging Neurosci, 9:46. https://doi.org/10.3389/fnagi.2017.00046
Stein MB, Simmons AN, Feinstein JS, et al., 2007. Increased amygdala and insula activation during emotion processing in anxiety-prone subjects. Am J Psychiatry, 164(2):318–327. https://doi.org/10.1176/ajp.2007.164.2.318
Wang L, Li K, Zhang QE, et al., 2013. Interhemispheric functional connectivity and its relationships with clinical characteristics in major depressive disorder: a resting state fMRI study. PLoS ONE, 8(3):e60191. https://doi.org/10.1371/journal.pone.0060191
Wang ZQ, Wang JL, Zhang H, et al., 2015. Interhemispheric functional and structural disconnection in Alzheimer’s disease: a combined resting-state fMRI and DTI study. PLoS ONE, 10(5):e0126310. https://doi.org/10.1371/journal.pone.0126310
Xu P, Xu SP, Wang KZ, et al., 2016. Cognitive-enhancing effects of hydrolysate of polygalasaponin in SAMP8 mice. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 17(7): 503–514. https://doi.org/10.1631/jzus.B1500321
Yu EY, Liao ZL, Tan YF, et al., 2017. High-sensitivity neuroimaging biomarkers for the identification of amnestic mild cognitive impairment based on resting-state fMRI and a triple network model. Brain Imaging Behav, online first. https://doi.org/10.1007/s11682-017-9727-6
Zhan YF, Ma JH, Xu KB, et al., 2016. Impaired episodic memory network in subjects at high risk for Alzheimer’s disease. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, Orlando, FL, USA, p.4017–4020. https://doi.org/10.1109/EMBC.2016.7591608
Zhong YF, Huang LY, Cai SP, et al., 2014. Altered effective connectivity patterns of the default mode network in Alzheimer’s disease: an fMRI study. Neurosci Lett, 578: 171–175. https://doi.org/10.1016/j.neulet.2014.06.043
Zhou Y, Milham M, Zuo XN, et al., 2013. Functional homotopic changes in multiple sclerosis with resting-state functional MR imaging. AJNR Am J Neuroradiol, 34(6): 1180–1187. https://doi.org/10.3174/ajnr.A3386
Zhu D, Liu GY, Lv Z, et al., 2014. Inverse associations of outdoor activity and vitamin D intake with the risk of Parkinson’s disease. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 15(10):923–927. https://doi.org/10.1631/jzus.B1400005
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Project supported by the National Natural Science Foundation of China (No. 81771158) and the Science Foundation of the Health Commission of Zhejiang Province (Nos. 2016147373 and 2019321345), China
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Liao, Zl., Tan, Yf., Qiu, Yj. et al. Interhemispheric functional connectivity for Alzheimer’s disease and amnestic mild cognitive impairment based on the triple network model. J. Zhejiang Univ. Sci. B 19, 924–934 (2018). https://doi.org/10.1631/jzus.B1800381
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DOI: https://doi.org/10.1631/jzus.B1800381
Key words
- Voxel-mirrored homotopic connectivity
- Alzheimer’s disease
- Amnestic mild cognitive impairment
- Default mode network
- Salience network
- Executive control network