Abstract
Subjective cognitive decline (SCD) is the preclinical stage of Alzheimer’s disease (AD), the most common neurodegenerative disease in the elderly. We collected resting-state functional MRI data and applied novel graph-theoretical analyses to investigate the dynamic spatiotemporal cerebral connectivities in 63 individuals with SCD and 67 normal controls (NC). Temporal flexibility and spatiotemporal diversity were mapped to reflect dynamic time-varying functional interactions among the brain regions within and outside communities. Temporal flexibility indicates how frequently a brain region interacts with regions of other communities across time; spatiotemporal diversity describes how evenly a brain region interacts with regions belonging to other communities. SCD and NC differed in large-scale brain dynamics characterized by the two measures, which, with support vector machine, demonstrated higher classification accuracies than conventional static parameters and structural metrics. The findings characterize dynamic network dysfunction that may serve as a biomarker of the preclinical stage of AD.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- AD:
-
Alzheimer’s disease
- SCD:
-
Subjective cognitive decline
- rs-fMRI:
-
Resting-state functional magnetic resonance imaging
- NC:
-
Normal controls
- aMCI:
-
Amnestic mild cognitive impairment
- BOLD:
-
Blood oxygenation level-dependent
- SVM:
-
Support vector machines
- HAMD:
-
Hamilton depression rating scale
- AVLT-H:
-
Auditory Verbal Learning Test – HuaShan version
- AFT:
-
Animal Fluency Test
- BNT:
-
Boston Naming Test
- STT-A:
-
Shape Trails Test Parts A
- STT-B:
-
Shape Trails Test Parts B
- MMSE:
-
Mini–Mental State Examination
- MoCA-B:
-
Montreal Cognitive Assessment-basic
- MES:
-
Memory and executive function screening instrument
- FAQ:
-
Functional Activities Questionnaire
- GDS:
-
Geriatric Depression Scale
- HAMA:
-
Hamilton Anxiety Scale
- NPI:
-
Neuropsychiatric Inventory
- GMV:
-
Grey matter volume
- TIV:
-
Total intracranial volume
- ROC:
-
Receiver operating characteristic
- AUC:
-
The area under the receiver operating characteristic curve
- ANCOVA:
-
Analysis of covariance
- FDR:
-
False discovery rate
- AVLT-I:
-
AVLT-immediate recall
- AVLT-D:
-
AVLT-delayed recall
- AVLT-R:
-
AVLT-recognition recall
References
Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2014). Tracking whole-brain connectivity dynamics in the resting state. Cerebral Cortex, 24(3), 663–676. https://doi.org/10.1093/cercor/bhs352.
Ashburner, J., & Friston, K. J. (1999). Nonlinear spatial normalization using basis functions. Human Brain Mapping, 7(4), 254–266. https://doi.org/10.1002/(SICI)1097-0193(1999)7:43.3.CO;2-7.
Barulli, D., & Stern, Y. (2013). Efficiency, capacity, compensation, maintenance, plasticity: Emerging concepts in cognitive reserve. Trends in Cognitive Sciences, 17(10), 502–509. https://doi.org/10.1016/j.tics.2013.08.012.
Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011). Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences of the United States of America, 108(18), 7641–7646. https://doi.org/10.1073/pnas.1018985108.
Bassett, D. S., Wymbs, N. F., Rombach, M. P., Porter, M. A., Mucha, P. J., & Grafton, S. T. (2013). Task-based core-periphery organization of human brain dynamics. PLoS Computational Biology, 9(9), e1003171. https://doi.org/10.1371/journal.pcbi.1003171.
Bassett, D. S., Yang, M., Wymbs, N. F., & Grafton, S. T. (2015). Learning-induced autonomy of sensorimotor systems. Nature Neuroscience, 18(5), 744–751. https://doi.org/10.1038/nn.3993.
Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics, 2008(10), 155–168. https://doi.org/10.1088/1742-5468/2008/10/p10008.
Braun, U., Schäfer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., et al. (2015). Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proceedings of the National Academy of Sciences, 112(37), 11678–11683. https://doi.org/10.1073/pnas.1422487112.
Brenner, E. K., Hampstead, B. M., Grossner, E. C., Bernier, R. A., Gilbert, N., Sathian, K., et al. (2018). Diminished neural network dynamics in amnestic mild cognitive impairment. International Journal of Psychophysiology, 130, 63–72. https://doi.org/10.1016/j.ijpsycho.2018.05.001.
Bressler, S. L., & Menon, V. (2010). Large-scale brain networks in cognition: Emerging methods and principles. Trends in Cognitive Sciences, 14(6), 277–290. https://doi.org/10.1016/j.tics.2010.04.004.
Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., et al. (2009). Cortical hubs revealed by intrinsic functional connectivity: Mapping, assessment of stability, and relation to Alzheimer's disease. Journal of Neuroscience, 29(6), 1860–1873. https://doi.org/10.1523/jneurosci.5062-08.2009.
Calhoun, V. D., Miller, R., Pearlson, G., & Adali, T. (2014). The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron, 84(2), 262–274. https://doi.org/10.1016/j.neuron.2014.10.015.
Chang, C., & Glover, G. H. (2010). Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage, 50(1), 81–98. https://doi.org/10.1016/j.neuroimage.2009.12.011.
Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. [article]. ACM Transactions on Intelligent Systems and Technology, 2(3), 27. https://doi.org/10.1145/1961189.1961199.
Chen, Cai, W., Ryali, S., Supekar, K., & Menon, V. (2016a). Distinct global brain dynamics and spatiotemporal Organization of the Salience Network. PLoS Biology, 14(6), e1002469. https://doi.org/10.1371/journal.pbio.1002469.
Chen, Xu, Y., Chu, A. Q., Ding, D., Liang, X. N., Nasreddine, Z. S., et al. (2016b). Validation of the Chinese version of Montreal cognitive assessment basic for screening mild cognitive impairment. Journal of the American Geriatrics Society, 64(12), e285–e290. https://doi.org/10.1111/jgs.14530.
Cohen, J. R. (2018). The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity. NeuroImage, 180(Pt B), 515–525. https://doi.org/10.1016/j.neuroimage.2017.09.036.
Cohen, J. R., & D'Esposito, M. (2016). The segregation and integration of distinct brain networks and their relationship to cognition. The Journal of Neuroscience, 36(48), 12083–12094. https://doi.org/10.1523/jneurosci.2965-15.2016.
Cummings, J. L. (1997). The neuropsychiatric inventory: Assessing psychopathology in dementia patients. Neurology, 48(5 Suppl 6), S10–S16. https://doi.org/10.1212/wnl.48.5_suppl_6.10s.
Damaraju, E., Allen, E. A., Belger, A., Ford, J. M., McEwen, S., Mathalon, D. H., et al. (2014). Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage Clin, 5, 298–308. https://doi.org/10.1016/j.nicl.2014.07.003.
de Lacy, N., Doherty, D., King, B. H., Rachakonda, S., & Calhoun, V. D. (2017). Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum. Neuroimage Clin, 15, 513–524. https://doi.org/10.1016/j.nicl.2017.05.024.
Dillen, K. N. H., Jacobs, H. I. L., Kukolja, J., Richter, N., von Reutern, B., Onur, O. A., et al. (2017). Functional disintegration of the default mode network in prodromal Alzheimer's disease. Journal of Alzheimer's Disease, 59(1), 169–187. https://doi.org/10.3233/JAD-161120.
Dosenbach, N. U., Visscher, K. M., Palmer, E. D., Miezin, F. M., Wenger, K. K., Kang, H. C., et al. (2006). A core system for the implementation of task sets. Neuron, 50(5), 799–812. https://doi.org/10.1016/j.neuron.2006.04.031.
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., et al. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 1664–1671. https://doi.org/10.1038/nn.4135.
Fornito, A., Harrison, B. J., Zalesky, A., & Simons, J. S. (2012). Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection. Proceedings of the National Academy of Sciences, 109(31), 12788–12793. https://doi.org/10.1073/pnas.1204185109.
Friston, K. J., Holmes, A. P., Poline, J. B., Grasby, P. J., Williams, S. C. R., Frackowiak, R. S. J., et al. (1995). Analysis of fMRI time-series revisited. NeuroImage, 2(1), 45–53. https://doi.org/10.1006/nimg.1995.1023.
Genin, E., Hannequin, D., Wallon, D., Sleegers, K., Hiltunen, M., Combarros, O., et al. (2011). APOE and Alzheimer disease: A major gene with semi-dominant inheritance. [article]. Molecular Psychiatry, 16(9), 903–907. https://doi.org/10.1038/mp.2011.52.
Guo, Q. H., Zhou, B., Zhao, Q. H., Wang, B., & Hong, Z. (2012). Memory and executive screening (MES): A brief cognitive test for detecting mild cognitive impairment. BMC Neurology, 12, 119. https://doi.org/10.1186/1471-2377-12-119.
Gusnard, D. A., Raichle, M. E., & Raichle, M. E. (2001). Searching for a baseline: Functional imaging and the resting human brain. Nature Reviews. Neuroscience, 2(10), 685–694. https://doi.org/10.1038/35094500.
Hamilton, M. (1959). The assessment of anxiety states by rating. The British Journal of Medical Psychology, 32(1), 50–55. https://doi.org/10.1111/j.2044-8341.1959.tb00467.x.
Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23, 56–62. https://doi.org/10.1136/jnnp.23.1.56.
Harms, M. P., Wang, L., Csernansky, J. G., & Barch, D. M. (2013). Structure-function relationship of working memory activity with hippocampal and prefrontal cortex volumes. Brain Structure & Function, 218(1), 173–186. https://doi.org/10.1007/s00429-012-0391-8.
Hillary, F. G., Genova, H. M., Chiaravalloti, N. D., Rypma, B., & DeLuca, J. (2006). Prefrontal modulation of working memory performance in brain injury and disease. Human Brain Mapping, 27(11), 837–847. https://doi.org/10.1002/hbm.20226.
Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., et al. (2013). Dynamic functional connectivity: Promise, issues, and interpretations. NeuroImage, 80, 360–378. https://doi.org/10.1016/j.neuroimage.2013.05.079.
Hyett, M. P., Breakspear, M. J., Friston, K. J., Guo, C. C., & Parker, G. B. (2015). Disrupted effective connectivity of cortical systems supporting attention and interoception in melancholia. JAMA Psychiatry, 72(4), 350–358. https://doi.org/10.1001/jamapsychiatry.2014.2490.
James, C. E., Oechslin, M. S., Van De Ville, D., Hauert, C. A., Descloux, C., & Lazeyras, F. (2014). Musical training intensity yields opposite effects on grey matter density in cognitive versus sensorimotor networks. Brain Structure & Function, 219(1), 353–366. https://doi.org/10.1007/s00429-013-0504-z.
Jessen, F., Amariglio, R. E., van Boxtel, M., Breteler, M., Ceccaldi, M., Chetelat, G., et al. (2014). A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease. Alzheimers Dement, 10(6), 844–852. https://doi.org/10.1016/j.jalz.2014.01.001.
Kaiser, R. H., Whitfield-Gabrieli, S., Dillon, D. G., Goer, F., Beltzer, M., Minkel, J., et al. (2016). Dynamic resting-state functional connectivity in major depression. Neuropsychopharmacology, 41(7), 1822–1830. https://doi.org/10.1038/npp.2015.352.
Kang, J., Wang, L., Yan, C., Wang, J., Liang, X., & He, Y. (2011). Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches. NeuroImage, 56(3), 1222–1234. https://doi.org/10.1016/j.neuroimage.2011.03.033.
Kim, J., Criaud, M., Cho, S. S., Diez-Cirarda, M., Mihaescu, A., Coakeley, S., et al. (2017). Abnormal intrinsic brain functional network dynamics in Parkinson's disease. Brain, 140(11), 2955–2967. https://doi.org/10.1093/brain/awx233.
Liegeois, R., & Li, J. (2019). Resting brain dynamics at different timescales capture distinct aspects of human behavior. 10(1), 2317, https://doi.org/10.1038/s41467-019-10317-7.
Liegeois, R., Laumann, T. O., Snyder, A. Z., Zhou, J., & Yeo, B. T. T. (2017). Interpreting temporal fluctuations in resting-state functional connectivity MRI. NeuroImage, 163, 437–455. https://doi.org/10.1016/j.neuroimage.2017.09.012.
Liu, J., Liao, X., Xia, M., & He, Y. (2018). Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns. Human Brain Mapping, 39(2), 902–915. https://doi.org/10.1002/hbm.23890.
Mattar, M. G., Cole, M. W., Thompson-Schill, S. L., & Bassett, D. S. (2015). A functional cartography of cognitive systems. PLoS Computational Biology, 11(12), e1004533. https://doi.org/10.1371/journal.pcbi.1004533.
McDade, E., & Bateman, R. J. (2017). Stop Alzheimer's before it starts. Nature, 547(7662), 153–155. https://doi.org/10.1038/547153a.
Melrose, R. J., Campa, O. M., Harwood, D. G., Osato, S., Mandelkern, M. A., & Sultzer, D. L. (2009). The neural correlates of naming and fluency deficits in Alzheimer's disease: An FDG-PET study. International Journal of Geriatric Psychiatry, 24(8), 885–893. https://doi.org/10.1002/gps.2229.
Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: A network model of insula function. Brain Structure & Function, 214(5–6), 655–667. https://doi.org/10.1007/s00429-010-0262-0.
Morgan, V. L., Abou-Khalil, B., & Rogers, B. P. (2015). Evolution of functional connectivity of brain networks and their dynamic interaction in temporal lobe epilepsy. Brain Connectivity, 5(1), 35–44. https://doi.org/10.1089/brain.2014.0251.
Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878. https://doi.org/10.1126/science.1184819.
Newman, M. E. (2004). Fast algorithm for detecting community structure in networks. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 69(6 Pt 2), 066133. https://doi.org/10.1103/PhysRevE.69.066133.
Park, D. C., & Reuter-Lorenz, P. (2009). The adaptive brain: Aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173–196. https://doi.org/10.1146/annurev.psych.59.103006.093656.
Patterson, C. (2018). The World Alzheimer report 2018-the state of the art of dementia research: new frontiers. Alzheimer’s Disease International.
Pfeffer, R. I., Kurosaki, T. T., Harrah Jr., C. H., Chance, J. M., & Filos, S. (1982). Measurement of functional activities in older adults in the community. Journal of Gerontology, 37(3), 323–329. https://doi.org/10.1093/geronj/37.3.323.
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., et al. (2011). Functional network organization of the human brain. Neuron, 72(4), 665–678. https://doi.org/10.1016/j.neuron.2011.09.006.
Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N., & Petersen, S. E. (2013). Evidence for hubs in human functional brain networks. Neuron, 79(4), 798–813. https://doi.org/10.1016/j.neuron.2013.07.035.
Rabin, L. A., Smart, C. M., & Amariglio, R. E. (2017). Subjective cognitive decline in preclinical Alzheimer's disease. Annual Review of Clinical Psychology, 13, 369–396. https://doi.org/10.1146/annurev-clinpsy-032816-045136.
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003.
Scheltens, P., Blennow, K., Breteler, M. M. B., de Strooper, B., Frisoni, G. B., Salloway, S., et al. (2016). Alzheimer's disease. The Lancet, 388(10043), 505–517. https://doi.org/10.1016/s0140-6736(15)01124-1.
Shu, N., Liang, Y., Li, H., Zhang, J., Li, X., Wang, L., et al. (2012). Disrupted topological organization in white matter structural networks in amnestic mild cognitive impairment: Relationship to subtype. Radiology, 265(2), 518. https://doi.org/10.1148/radiol.12112361.
Shu, N., Wang, X., Bi, Q., Zhao, T., & Han, Y. (2018). Disrupted topologic efficiency of white matter structural connectome in individuals with subjective cognitive decline. Radiology, 286(1), 229–238. https://doi.org/10.1148/radiol.2017162696.
Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., et al. (2011). 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, 7(3), 280–292. https://doi.org/10.1016/j.jalz.2011.03.003.
Taghia, J., Cai, W., Ryali, S., Kochalka, J., Nicholas, J., Chen, T., et al. (2018). Uncovering hidden brain state dynamics that regulate performance and decision-making during cognition. Nature Communications, 9(1), 2505. https://doi.org/10.1038/s41467-018-04723-6.
Thompson, G. J., Magnuson, M. E., Merritt, M. D., Schwarb, H., Pan, W. J., McKinley, A., et al. (2013). Short-time windows of correlation between large-scale functional brain networks predict vigilance intraindividually and interindividually. Human Brain Mapping, 34(12), 3280–3298. https://doi.org/10.1002/hbm.22140.
van den Heuvel, M. P., Bullmore, E. T., & Sporns, O. (2016). Comparative Connectomics. Trends in Cognitive Sciences, 20(5), 345–361. https://doi.org/10.1016/j.tics.2016.03.001.
Verfaillie, S. C. J., Pichet Binette, A., Vachon-Presseau, E., Tabrizi, S., Savard, M., Bellec, P., et al. (2018). Subjective cognitive decline is associated with altered default mode network connectivity in individuals with a family history of Alzheimer's disease. Biol Psychiatry Cogn Neurosci Neuroimaging, 3(5), 463–472. https://doi.org/10.1016/j.bpsc.2017.11.012.
Viviano, R. P., Hayes, J. M., Pruitt, P. J., Fernandez, Z. J., van Rooden, S., van der Grond, J., et al. (2018). Aberrant memory system connectivity and working memory performance in subjective cognitive decline. NeuroImage, 2019: 556–564. https://doi.org/10.1016/j.neuroimage.2018.10.015.
Wang, J., Zuo, X., Dai, Z., Xia, M., Zhao, Z., Zhao, X., et al. (2013). Disrupted functional brain connectome in individuals at risk for Alzheimer's disease. Biological Psychiatry, 73(5), 472–481. https://doi.org/10.1016/j.biopsych.2012.03.026.
Wang, J., Wang, X., Xia, M., Liao, X., Evans, A., & He, Y. (2015). GRETNA: A graph theoretical network analysis toolbox for imaging connectomics. Frontiers in Human Neuroscience, 9, 386. https://doi.org/10.3389/fnhum.2015.00386.
Yaesoubi, M., Miller, R. L., Bustillo, J., Lim, K. O., Vaidya, J., & Calhoun, V. D. (2017). A joint time-frequency analysis of resting-state functional connectivity reveals novel patterns of connectivity shared between or unique to schizophrenia patients and healthy controls. Neuroimage Clin, 15, 761–768. https://doi.org/10.1016/j.nicl.2017.06.023.
Yan, C.-G., Wang, X.-D., Zuo, X.-N., & Zang, Y.-F. (2016). DPABI: Data Processing & Analysis for (resting-state) brain imaging. [journal article]. Neuroinformatics, 14(3), 339–351. https://doi.org/10.1007/s12021-016-9299-4.
Yan, T., Wang, W., Yang, L., Chen, K., Chen, R., & Han, Y. (2018). Rich club disturbances of the human connectome from subjective cognitive decline to Alzheimer's disease. Theranostics, 8(12), 3237–3255. https://doi.org/10.7150/thno.23772.
Yang, L., Yan, Y., Wang, Y., Hu, X., Lu, J., Chan, P., et al. (2018). Gradual disturbances of the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF in Alzheimer Spectrum. Frontiers in Neuroscience, 12, 975. https://doi.org/10.3389/fnins.2018.00975.
Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., et al. (1982). Development and validation of a geriatric depression screening scale: A preliminary report. Journal of Psychiatric Research, 17(1), 37–49. https://doi.org/10.1016/0022-3956(82)90033-4.
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., & Breakspear, M. (2014). Time-resolved resting-state brain networks. Proceedings of the National Academy of Sciences, 111(28), 10341–10346. https://doi.org/10.1073/pnas.1400181111.
Zhang, D., & Raichle, M. E. (2010). Disease and the brain's dark energy. Nature Reviews. Neurology, 6(1), 15–28. https://doi.org/10.1038/nrneurol.2009.198.
Zhang, S., Ide, J. S., Hu, S., Zhornitsky, S., Wang, W., et al. (2018). Dynamic network dysfunction in cocaine dependence: Graph theoretical metrics and stop signal reaction time. Neuroimage Clin, 18, 793–801. https://doi.org/10.1016/j.nicl.2018.03.016.
Zhao, Q., Guo, Q., Li, F., Zhou, Y., Wang, B., & Hong, Z. (2013). The Shape Trail test: Application of a new variant of the trail making test. PLoS One, 8(2), e57333. https://doi.org/10.1371/journal.pone.0057333.
Zhao, Q., Guo, Q., Liang, X., Chen, M., Zhou, Y., Ding, D., et al. (2015). Auditory verbal learning test is superior to Rey-Osterrieth complex figure memory for predicting mild cognitive impairment to Alzheimer's disease. Current Alzheimer Research, 12(6), 520–526. https://doi.org/10.1016/j.jalz.2015.06.422.
Acknowledgements
This study was supported by The National Key Research and Development Program of China (2016YFC1306300), National Natural Science Foundation of China (Grant 81471743, 61633018, 81801052, 81430037, 81471731), Beijing Nature Science Foundation (7161009), Beijing Municipal Commission of Health and Family Planning (PXM2019_026283_000002), and the U.S. NIH grants R21DA044749-02S1. We thank Yu Sun, Xuanyu Li, Guanqun Chen, Jiachen Li, Xiaoqi Wang, Weina Zhao, Ying Chen, Ziqi Wang, Li Lin, and Qin Yang for assistance in data collection.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Ethics statement
All procedures performed in the study were in accordance with the 1964 Helsinki declaration and its later amendment and with a protocol approved by the Medical Research Ethics Committee and Institutional Review Board of Xuanwu Hospital, Beijing, China. Written informed consent was obtained from all participants prior to the study.
Declarations of financial interest
None.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Dong, G., Yang, L., Li, Cs.R. et al. Dynamic network connectivity predicts subjective cognitive decline: the Sino-Longitudinal Cognitive impairment and dementia study. Brain Imaging and Behavior 14, 2692–2707 (2020). https://doi.org/10.1007/s11682-019-00220-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11682-019-00220-6