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Cognitive decline is associated with frequency-specific resting state functional changes in normal aging

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Abstract

Resting state low-frequency brain activity may aid in our understanding of the mechanisms of aging-related cognitive decline. Our purpose was to explore the characteristics of the amplitude of low-frequency fluctuations (ALFF) in different frequency bands of fMRI to better understand cognitive aging. Thirty-seven cognitively normal older individuals underwent a battery of neuropsychological tests and MRI scans at baseline and four years later. ALFF from five different frequency bands (typical band, slow-5, slow-4, slow-3, and slow-2) were calculated and analyzed. A two-way ANOVA was used to explore the interaction effects in voxel-wise whole brain ALFF of the time and frequency bands. Paired-sample t-test was used to explore within-group changes over four years. Partial correlation analysis was performed to assess associations between the altered ALFF and cognitive function. Significant interaction effects of time × frequency were distributed over inferior frontal gyrus, superior frontal gyrus, right rolandic operculum, left thalamus, and right putamen. Significant ALFF reductions in all five frequency bands were mainly found in the right hemisphere and the posterior cerebellum; whereas localization of the significantly increased ALFF were mainly found in the cerebellum at typical band, slow-5 and slow-4 bands, and left hemisphere and the cerebellum at slow-3, slow-2 bands. In addition, ALFF changes showed frequency-specific correlations with changes in cognition. These results suggest that changes of local brain activity in cognitively normal aging should be investigated in multiple frequency bands. The association between ALFF changes and cognitive function can potentially aid better understanding of the mechanisms underlying normal cognitive aging.

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References

  • Anderson, T. M., Sachdev, P. S., Brodaty, H., Trollor, J. N., & Andrews, G. (2007). Effects of sociodemographic and health variables on Mini-Mental State Exam scores in older Australians. The American Journal of Geriatric Psychiatry, 15(6), 467–476. https://doi.org/10.1097/JGP.0b013e3180547053

    Article  PubMed  Google Scholar 

  • Bai, F., Xie, C., Watson, D. R., Shi, Y., Yuan, Y., Wang, Y., . . . Zhang, Z. (2011). Aberrant hippocampal subregion networks associated with the classifications of aMCI subjects: a longitudinal resting-state study. PLoS One, 6(12), e29288. https://doi.org/10.1371/journal.pone.0029288

  • Beason-Held, L. L., Kraut, M. A., & Resnick, S. M. (2008). I. Longitudinal changes in aging brain function. Neurobiology of Aging, 29(4), 483–496. https://doi.org/10.1016/j.neurobiolaging.2006.10.031

    Article  CAS  PubMed  Google Scholar 

  • Benton, A. L., Sivan, A. B., & Spreen, O. (1996). Der Benton Test (7th ed.). Huber.

    Google Scholar 

  • Bernard, J. A., & Seidler, R. D. (2014). Moving forward: Age effects on the cerebellum underlie cognitive and motor declines. Neuroscience & Biobehavioral Reviews, 42, 193–207.

    Article  Google Scholar 

  • Bernard, J. A., Seidler, R. D., Hassevoort, K. M., Benson, B. L., Welsh, R. C., Wiggins, J. L., … Peltier, S. J. (2012). Resting state cortico-cerebellar functional connectivity networks: a comparison of anatomical and self-organizing map approaches. Front Neuroanat, 6, 31. https://doi.org/10.3389/fnana.2012.00031

  • Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34(4), 537–541.

    Article  CAS  Google Scholar 

  • Buzsáki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science, 304(5679), 1926–1929.

    Article  Google Scholar 

  • Cao, W., Cao, X., Hou, C., Li, T., Cheng, Y., Jiang, L., … Yao, D. (2016). Effects of Cognitive Training on Resting-State Functional Connectivity of Default Mode, Salience, and Central Executive Networks. Frontiers in Aging Neuroscience, 8(70). https://doi.org/10.3389/fnagi.2016.00070

  • Cohen, A. D., Chang, C., & Wang, Y. (2021a). Using multiband multi-echo imaging to improve the robustness and repeatability of co-activation pattern analysis for dynamic functional connectivity. NeuroImage, 243, 118555.

    Article  Google Scholar 

  • Cohen, A. D., Yang, B., Fernandez, B., Banerjee, S., & Wang, Y. (2021b). Improved resting state functional connectivity sensitivity and reproducibility using a multiband multi-echo acquisition. NeuroImage, 225, 117461.

    Article  Google Scholar 

  • de Jong, L. W., van der Hiele, K., Veer, I. M., Houwing, J. J., Westendorp, R. G. J., Bollen, E. L. E. M., ... & van der Grond, J. (2008). Strongly reduced volumes of putamen and thalamus in Alzheimer's disease: an MRI study. Brain, 131(12), 3277–3285.

  • Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135–168. https://doi.org/10.1146/annurev-psych-113011-143750

    Article  PubMed  Google Scholar 

  • Durrani, R., Friedrich, M. G., Schulze, K. M., Awadalla, P., Balasubramanian, K., Black, S. E., … Smith, E. E. (2021). Effect of Cognitive Reserve on the Association of Vascular Brain Injury With Cognition: Analysis of the PURE and CAHHM Studies. Neurology, 97(17), e1707–e1716. https://doi.org/10.1212/WNL.0000000000012765

  • Elliott, M. L., Knodt, A. R., & Hariri, A. R. (2021). Striving toward translation: Strategies for reliable fMRI measurement. Trends in Cognitive Sciences, 25(9), 776–787. https://doi.org/10.1016/j.tics.2021.05.008

    Article  PubMed  PubMed Central  Google Scholar 

  • Fama, R., & Sullivan, E. V. (2015). Thalamic structures and associated cognitive functions: Relations with age and aging. Neuroscience and Biobehavioral Reviews, 54, 29–37. https://doi.org/10.1016/j.neubiorev.2015.03.008

    Article  PubMed  PubMed Central  Google Scholar 

  • Farfel, J. M., Nitrini, R., Suemoto, C. K., Grinberg, L. T., Ferretti, R. E. L., Leite, R. E. P., ... & Brazilian Aging Brain Study Group. (2013). Very low levels of education and cognitive reserve: a clinicopathologic study. Neurology, 81(7), 650–657.

  • 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. Journal of Psychiatric Research, 12(3), 189–198.

    Article  CAS  Google Scholar 

  • Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S., & Turner, R. (1996). Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine, 35(3), 346–355. https://doi.org/10.1002/mrm.1910350312

    Article  CAS  PubMed  Google Scholar 

  • Giménez, M., Guinea-Izquierdo, A., Villalta-Gil, V., Martínez-Zalacaín, I., Segalàs, C., Subirà, M., … behavior. (2017). Brain alterations in low-frequency fluctuations across multiple bands in obsessive compulsive disorder. Brain imaging behavior, 11(6), 1690–1706.

  • Gohel, S., Gallego, J. A., Robinson, D. G., DeRosse, P., Biswal, B., & Szeszko, P. R. (2018). Frequency specific resting state functional abnormalities in psychosis. Human Brain Mapping, 39(11), 4509–4518. https://doi.org/10.1002/hbm.24302

    Article  PubMed  PubMed Central  Google Scholar 

  • Gohel, S. R., & Biswal, B. B. (2015). Functional integration between brain regions at rest occurs in multiple-frequency bands. Brain Connectivity, 5(1), 23–34.

    Article  Google Scholar 

  • Habas, C., Kamdar, N., Nguyen, D., Prater, K., Beckmann, C. F., Menon, V., & Greicius, M. D. (2009). Distinct cerebellar contributions to intrinsic connectivity networks. Journal of Neuroscience, 29(26), 8586–8594.

    Article  CAS  Google Scholar 

  • Hamilton, O. K., Backhouse, E. V., Janssen, E., Jochems, A. C., Maher, C., Ritakari, T. E., ... & Wardlaw, J. M. (2021). Cognitive impairment in sporadic cerebral small vessel disease: a systematic review and meta‐analysis. Alzheimer's & Dementia, 17(4), 665–685.

  • Han, Y., Wang, J., Zhao, Z., Min, B., Lu, J., Li, K., … Jia, J. (2011). Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: a resting-state fMRI study. Neuroimage, 55(1), 287–295. https://doi.org/10.1016/j.neuroimage.2010.11.059

  • Hazem, S. R., Awan, M., Lavrador, J. P., Patel, S., Wren, H. M., Lucena, O., … Vergani, F. (2021). Middle Frontal Gyrus and Area 55b: Perioperative Mapping and Language Outcomes. Front Neurol, 12(194), 646075. https://doi.org/10.3389/fneur.2021.646075

  • Hu, S., Chao, H. H., Zhang, S., Ide, J. S., & Li, C. S. (2014). Changes in cerebral morphometry and amplitude of low-frequency fluctuations of BOLD signals during healthy aging: Correlation with inhibitory control. Brain Structure & Function, 219(3), 983–994. https://doi.org/10.1007/s00429-013-0548-0

    Article  Google Scholar 

  • Jacobs, H. I., Hopkins, D. A., Mayrhofer, H. C., Bruner, E., van Leeuwen, F. W., Raaijmakers, W., & Schmahmann, J. D. (2018). The cerebellum in Alzheimer’s disease: Evaluating its role in cognitive decline. Brain, 141(1), 37–47.

    Article  Google Scholar 

  • Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825–841.

    Article  Google Scholar 

  • Jiao, F., Gao, Z., Shi, K., Jia, X., Wu, P., Jiang, C., . . . Shi, S. (2019). Frequency-dependent relationship between resting-state fMRI and glucose metabolism in the elderly. Frontiers in Neurology, 10, 566. https://doi.org/10.3389/fneur.2019.00566

  • La, C., Mossahebi, P., Nair, V. A., Young, B. M., Stamm, J., Birn, R., … Prabhakaran, V. (2016a). Differing Patterns of Altered Slow-5 Oscillations in Healthy Aging and Ischemic Stroke. Front Hum Neurosci, 10, 156. https://doi.org/10.3389/fnhum.2016.00156

  • La, C., Nair, V. A., Mossahebi, P., Young, B. M., Chacon, M., Jensen, M., … Prabhakaran, V. (2016b). Implication of the Slow-5 Oscillations in the Disruption of the Default-Mode Network in Healthy Aging and Stroke. Brain Connect, 6(6), 482–495. https://doi.org/10.1089/brain.2015.0375

  • Lee, H. L., Li, Z. M., Coulson, E. J., & Chuang, K. H. (2019). Ultrafast fMRI of the rodent brain using simultaneous multi-slice EPI. NeuroImage, 195, 48–58. https://doi.org/10.1016/j.neuroimage.2019.03.045

    Article  PubMed  Google Scholar 

  • Li, Q., Dong, C., Liu, T., Chen, X., Perry, A., Jiang, J., … Wen, W. (2020). Longitudinal Changes in Whole-Brain Functional Connectivity Strength Patterns and the Relationship With the Global Cognitive Decline in Older Adults. Front Aging Neurosci, 12, 71. https://doi.org/10.3389/fnagi.2020.00071

  • Lin, F., Ren, P., Lo, R. Y., Chapman, B. P., Jacobs, A., Baran, T. M., … Alzheimer's Disease Neuroimaging, I. (2017). Insula and Inferior Frontal Gyrus' Activities Protect Memory Performance Against Alzheimer's Disease Pathology in Old Age. J Alzheimers Dis, 55(2), 669–678. https://doi.org/10.3233/JAD-160715

  • Lin, X., Jia, X., Zang, Y. F., & Dong, G. (2015). Frequency-dependent changes in the amplitude of low-frequency fluctuations in internet gaming disorder. Frontiers in Psychology, 6, 1471. https://doi.org/10.3389/fpsyg.2015.01471

    Article  PubMed  PubMed Central  Google Scholar 

  • Lipnicki, D. M., Crawford, J. D., Dutta, R., Thalamuthu, A., Kochan, N. A., Andrews, G., . . . Matthews, F. E.  (2017). Age-related cognitive decline and associations with sex, education and apolipoprotein E genotype across ethnocultural groups and geographic regions: a collaborative cohort study. PLoS Medicine, 14(3), e1002261. https://doi.org/10.1371/journal.pmed.1002261

  • Liu, X., Chen, J., Shen, B., Wang, G., Li, J., Hou, H., . . . Mao, C. (2018). Altered Intrinsic Coupling between Functional Connectivity Density and Amplitude of Low-Frequency Fluctuation in Mild Cognitive Impairment with Depressive Symptoms. Neural plasticity, 2018. https://doi.org/10.1155/2018/1672708

  • Lv, H., Wang, Z., Tong, E., Williams, L. M., Zaharchuk, G., Zeineh, M., … Wintermark, M. (2018). Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know. American Journal of Neuroradiology, 39(8), 1390–1399. https://doi.org/10.3174/ajnr.A5527

  • Lynch, C. J., Power, J. D., Scult, M. A., Dubin, M., Gunning, F. M., & Liston, C. (2020). Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI. Cell Reports, 33(12), 108540. ARTN 108540.

    Article  CAS  Google Scholar 

  • Nugent, A. C., Martinez, A., D’alfonso, A., Zarate, C. A., & Theodore, W. H. (2015). The relationship between glucose metabolism, resting-state fMRI BOLD signal, and GABAA-binding potential: A preliminary study in healthy subjects and those with temporal lobe epilepsy. Journal of Cerebral Blood Flow & Metabolism, 35(4), 583–591.

    Article  CAS  Google Scholar 

  • Park, D. C., Lautenschlager, G., Hedden, T., Davidson, N. S., Smith, A. D., & Smith, P. K. (2002). Models of visuospatial and verbal memory across the adult life span. Psychology and Aging, 17(2), 299–320.

    Article  Google Scholar 

  • 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

    Article  PubMed  PubMed Central  Google Scholar 

  • Penny, W. D., Friston, K. J., Ashburner, J. T., Kiebel, S. J., & Nichols, T. E. (Eds.). (2011). Statistical parametric mapping: the analysis of functional brain images. Elsevier.

    Google Scholar 

  • Pessoa, L., Gutierrez, E., Bandettini, P., & Ungerleider, L. (2002). Neural correlates of visual working memory: FMRI amplitude predicts task performance. Neuron, 35(5), 975–987. https://doi.org/10.1016/s0896-6273(02)00817-6

    Article  CAS  PubMed  Google Scholar 

  • Ren, P., Lo, R. Y., Chapman, B. P., Mapstone, M., Porsteinsson, A., & Lin, F. (2016). Longitudinal alteration of intrinsic brain activity in the striatum in mild cognitive impairment. Journal of Alzheimer’s Disease, 54(1), 69–78.

    Article  CAS  Google Scholar 

  • Sachdev, P. S., Brodaty, H., Reppermund, S., Kochan, N. A., Trollor, J. N., Draper, B., … Broe, G. A. (2010). The Sydney Memory and Ageing Study (MAS): methodology and baseline medical and neuropsychiatric characteristics of an elderly epidemiological non-demented cohort of Australians aged 70–90 years. J International psychogeriatrics, 22(8), 1248–1264.

  • Schmahmann, J. D. (2004). Disorders of the cerebellum: Ataxia, dysmetria of thought, and the cerebellar cognitive affective syndrome. The Journal of Neuropsychiatry and Clinical Neurosciences, 16(3), 367–378.

    Article  Google Scholar 

  • Schmahmann, J. D., & Sherman, J. C. (1998). The cerebellar cognitive affective syndrome. Brain: A Journal of Neurology, 121(4), 561–579.

    Article  Google Scholar 

  • Shea, T. B., & Remington, R. (2018). Apparent cognitive decline as revealed by an executive function test within a cohort of elderly individuals self-reporting normal cognitive performance. Journal of Alzheimer’s Disease, 61(3), 913–915.

    Article  Google Scholar 

  • Singh-Manoux, A., Marmot, M. G., Glymour, M., Sabia, S., Kivimaki, M., & Dugravot, A. (2011). Does cognitive reserve shape cognitive decline? Annals of Neurology, 70(2), 296–304. https://doi.org/10.1002/ana.22391

    Article  PubMed  PubMed Central  Google Scholar 

  • Stoodley, C. J., & Schmahmann, J. D. (2009). The cerebellum and language: Evidence from patients with cerebellar degeneration. Brain and Language, 110(3), 149–153. https://doi.org/10.1016/j.bandl.2009.07.006

    Article  PubMed  Google Scholar 

  • Stoodley, C. J., Valera, E. M., & Schmahmann, J. D. (2012). Functional topography of the cerebellum for motor and cognitive tasks: An fMRI study. NeuroImage, 59(2), 1560–1570.

    Article  Google Scholar 

  • Strauss, E., Sherman, E. M., & Spreen, O. (2006). A compendium of neuropsychological tests: Administration, norms, and commentary. American Chemical Society.

    Google Scholar 

  • Sun, Y., Dai, Z., Li, Y., Sheng, C., Li, H., Wang, X., … Han, Y. (2016). Subjective cognitive decline: mapping functional and structural brain changes—a combined resting-state functional and structural MR imaging study. Radiology, 281(1), 185–192.

  • Tombaugh, T. N. (2004). Trail Making Test A and B: Normative data stratified by age and education. Archives of Clinical Neuropsychology, 19(2), 203–214. https://doi.org/10.1016/S0887-6177(03)00039-8

    Article  PubMed  Google Scholar 

  • Veldsman, M., Egorova, N., Singh, B., Mungas, D., DeCarli, C., & Brodtmann, A. (2017). Low-frequency oscillations in default mode subnetworks are associated with episodic memory impairments in Alzheimer’s disease. Neurobiology of Aging, 59, 98–106.

    Article  Google Scholar 

  • Wang, L., Kong, Q., Li, K., Su, Y., Zeng, Y., Zhang, Q., … Si, T. (2016). Frequency-dependent changes in amplitude of low-frequency oscillations in depression: A resting-state fMRI study. Neurosci Lett, 614, 105–111. https://doi.org/10.1016/j.neulet.2016.01.012

  • Wang, S., Rao, J., Yue, Y., Xue, C., Hu, G., Qi, W., … Chen, J. (2021). Altered Frequency-Dependent Brain Activation and White Matter Integrity Associated With Cognition in Characterizing Preclinical Alzheimer's Disease Stages. Front Hum Neurosci, 15(11), 625232. https://doi.org/10.3389/fnhum.2021.625232

  • Wang, X., Ren, P., Baran, T. M., Raizada, R. D., Mapstone, M., Lin, F., Alzheimer’s Disease Neuroimaging Initiative. (2019). Longitudinal functional brain mapping in Supernormals. Cerebral Cortex, 29(1), 242–252.

  • Wang, Z., Yan, C., Zhao, C., Qi, Z., Zhou, W., Lu, J., ... & Li, K. (2011). Spatial patterns of intrinsic brain activity in mild cognitive impairment and Alzheimer's disease: A resting‐state functional MRI study. Human brain mapping, 32(10), 1720–1740.

  • Wechsler, D. (1981). WAIS-R manual. The Psychological Corporation.

    Google Scholar 

  • Wechsler, D. (1997a). Wechsler Adult Intelligence Scale-III. The Psychological Corporation.

    Google Scholar 

  • Wechsler, D. (1997b). Wechsler Memory Scale. Third edition manual. The Psychological Corporation.

    Google Scholar 

  • Wong, C. H., Liu, J., Lee, T. M., Tao, J., Wong, A. W., Chau, B. K., … Chan, C. C. (2020). Fronto-cerebellar connectivity mediating cognitive processing speed. Neuroimage, 226, 117556.

  • Wu, D., Zhao, H., Gu, H., Han, B., Wang, Q., Man, X., … Sun, J. (2021). The Effects of rs405509 on APOEepsilon4 Non-carriers in Non-demented Aging. Frontiers in Neuroscience, 15, 677823. https://doi.org/10.3389/fnins.2021.677823

  • Yan, C. G., Wang, X. D., Zuo, X. N., & Zang, Y. F. (2016). DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics, 14(3), 339–351. https://doi.org/10.1007/s12021-016-9299-4

    Article  PubMed  Google Scholar 

  • Yan, L., Zhuo, Y., Wang, B., & Wang, D. J. (2011). Loss of Coherence of Low Frequency Fluctuations of BOLD FMRI in Visual Cortex of Healthy Aged Subjects. Open Neuroimag J, 5, 105–111. https://doi.org/10.2174/1874440001105010105

    Article  PubMed  PubMed Central  Google Scholar 

  • Yang, H., Long, X. Y., Yang, Y. H., Yan, H., Zhu, C. Z., Zhou, X. P., … Gong, Q. Y. (2007). Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. Neuroimage, 36(1), 144–152. https://doi.org/10.1016/j.neuroimage.2007.01.054

  • Yu, R., Chien, Y. L., Wang, H. L. S., Liu, C. M., Liu, C. C., Hwang, T. J., … Tseng, W. Y. I. (2014). Frequency‐specific alternations in the amplitude of low‐frequency fluctuations in schizophrenia. Human Brain Mapping, 35(2), 627–637.

  • Zang, Y. F., He, Y., Zhu, C. Z., Cao, Q. J., Sui, M. Q., Liang, M., … Wang, Y. F. (2007). Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain and Development, 29(2), 83–91.

  • Zhang, H., Bai, X., & Diaz, M. T. (2021). The intensity and connectivity of spontaneous brain activity in a language network relate to aging and language. Neuropsychologia, 154, 107784. https://doi.org/10.1016/j.neuropsychologia.2021.107784

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhang, Y., Zhu, C., Chen, H., Duan, X., Lu, F., Li, M., … Chen, H. (2015). Frequency-dependent alterations in the amplitude of low-frequency fluctuations in social anxiety disorder. J Affect Disord, 174, 329–335. https://doi.org/10.1016/j.jad.2014.12.001

  • Zuo, X. N., Di Martino, A., Kelly, C., Shehzad, Z. E., Gee, D. G., Klein, D. F., … Milham, M. P. (2010). The oscillating brain: complex and reliable. Neuroimage, 49(2), 1432–1445.

  • Zuo, X. N., & Xing, X. X. (2014). Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective. Neuroscience and Biobehavioral Reviews, 45, 100–118. https://doi.org/10.1016/j.neubiorev.2014.05.009

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank the participants and their informants for their time and generosity in contributing to this research. We also acknowledge the MAS research team.

Funding

This research received support from the National Natural Science Foundation of China (Grant No. 81871434 and 61971017) and Beijing Natural Science Foundation (Grant No. Z200016). MAS (The Sydney Memory and Ageing Study) cohort was supported by National Health and Medical Research Council (NHMRC) Australia Project Program Grants ID350833, ID568969 and ID1093083.

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Authors and Affiliations

  1. Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China

    Dongqiong Fan & Tao Liu

  2. Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW, Sydney, New South Wales, Australia

    Jiyang Jiang, Nicole A. Kochan, Henry Brodaty, Perminder S. Sachdev & Wei Wen

  3. Department of Neurology, Beijing TianTan Hospital, Capital Medical University, Beijing, China

    Yilong Wang

  4. Dementia Centre for Research Collaboration, School of Psychiatry, UNSW, Sydney, New South Wales, Australia

    Henry Brodaty

  5. Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, New South Wales, Australia

    Perminder S. Sachdev & Wei Wen

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Contributions

DF contributed to data analysis and the writing of the manuscript. NK, HB, PS, and WW contributed to the acquisition of data. TL and WL contributed to the study concept and design; WW and PS contributed to the critical revision of the report. JJ assisted with interpretation of findings. TL and PS assisted with funding and administration. All authors critically reviewed the first and final draft and approved final version for publication.

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Correspondence to Tao Liu or Yilong Wang.

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Fan, D., Liu, T., Jiang, J. et al. Cognitive decline is associated with frequency-specific resting state functional changes in normal aging. Brain Imaging and Behavior 16, 2120–2132 (2022). https://doi.org/10.1007/s11682-022-00682-1

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