Extrinsic and Intrinsic Brain Network Connectivity Maintains Cognition across the Lifespan Despite Accelerated Decay of Regional Brain Activation

Abstract

The maintenance of wellbeing across the lifespan depends on the preservation of cognitive function. We propose that successful cognitive aging is determined by interactions both within and between large-scale functional brain networks. Such connectivity can be estimated from task-free functional magnetic resonance imaging (fMRI), also known as resting-state fMRI (rs-fMRI). However, common correlational methods are confounded by age-related changes in the neurovascular signaling. To estimate network interactions at the neuronal rather than vascular level, we used generative models that specified both the neural interactions and a flexible neurovascular forward model. The networks' parameters were optimized to explain the spectral dynamics of rs-fMRI data in 602 healthy human adults from population-based cohorts who were approximately uniformly distributed between 18 and 88 years ( www.cam-can.com ). We assessed directed connectivity within and between three key large-scale networks: the salience network, dorsal attention network, and default mode network. We found that age influences connectivity both within and between these networks, over and above the effects on neurovascular coupling. Canonical correlation analysis revealed that the relationship between network connectivity and cognitive function was age-dependent: cognitive performance relied on neural dynamics more strongly in older adults. These effects were driven partly by reduced stability of neural activity within all networks, as expressed by an accelerated decay of neural information. Our findings suggest that the balance of excitatory connectivity between networks, and the stability of intrinsic neural representations within networks, changes with age. The cognitive function of older adults becomes increasingly dependent on these factors. SIGNIFICANCE STATEMENT Maintaining cognitive function is critical to successful aging. To study the neural basis of cognitive function across the lifespan, we studied a large population-based cohort ( n = 602, 18–88 years), separating neural connectivity from vascular components of fMRI signals. Cognitive ability was influenced by the strength of connection within and between functional brain networks, and this positive relationship increased with age. In older adults, there was more rapid decay of intrinsic neuronal activity in multiple regions of the brain networks, which related to cognitive performance. Our data demonstrate increased reliance on network flexibility to maintain cognitive function, in the presence of more rapid decay of neural activity. These insights will facilitate the development of new strategies to maintain cognitive ability.

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References 74

1. 1.

   Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38:95-113.

   Not in our library10.1016/j.neuroimage.2007.07.007

2. 2.

   Ashburner J, Friston KJ. Unified segmentation. Neuroimage. 2005;26:839-851.

   Not in our library10.1016/j.neuroimage.2005.02.018

3. 3.

   Baddeley A, Emslie H, Nimmo-Smith I. The spot-the-word test: a robust estimate of verbal intelligence based on lexical decision. Br J Clin Psychol. 1993;32:55-65.

   Not in our library10.1111/j.2044-8260.1993.tb01027.x

4. 4.

   Baldassarre A, Corbetta M. Resting state network changes in aging and cognitive decline. Hear Balanc Commun. 2015;13:1-7.

   Not in our library10.3109/21695717.2014.997096

5. 5.

   Beddington J, Cooper CL, Field J, Goswami U, Huppert FA, Jenkins R, Jones HS, Kirkwood TB, Sahakian BJ, Thomas SM. The mental wealth of nations. Nature. 2008;455:1057-1060.

   Not in our library10.1038/4551057a

6. 6.

   Benton AL, Hamsher KD, Varney NR, Spreen O. Contributions to neuropsychological assessment: a clinical manual. 1983.

   Not in our library

7. 7.

   Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, Beckmann CF, Adelstein JS, Buckner RL, Colcombe S, Dogonowski AM, Ernst M, Fair D, Hampson M, Hoptman MJ, Hyde JS, Kiviniemi VJ, Kötter R, Li SJ, Lin CP, et al. Toward discovery science of human brain function. Proc Natl Acad Sci U S A. 2010;107:4734-4739.

   Not in our library10.1073/pnas.0911855107

8. 8.

   Brown HR, Friston KJ. The functional anatomy of attention: a DCM study. Front Hum Neurosci. 2013;7:784.

   Not in our library10.3389/fnhum.2013.00784

9. 9.

   Buxton RB, Uludağ K, Dubowitz DJ, Liu TT. Modeling the hemodynamic response to brain activation. Neuroimage. 2004;23:S220-S233.

   Not in our library10.1016/j.neuroimage.2004.07.013

10. 10.

    Buzsáki G, Draguhn A. Neuronal oscillations in cortical networks. Science. 2004;304:1926-1929.

    Not in our library10.1126/science.1099745

11. 11.

    Calhoun VD, Adali T, Pearlson GD, Pekar JJ. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp. 2001;14:140-151.

    Not in our library10.1002/hbm.1048

12. 12.

    Cattell RB, Cattell AKS. Handbook for the individual or group culture fair intelligence test. 1960.

    Not in our library

13. 13.

    Chan MY, Park DC, Savalia NK, Petersen SE, Wig GS. Decreased segregation of brain systems across the healthy adult lifespan. Proc Natl Acad Sci U S A. 2014;111:4997-5006.

    Not in our library10.1073/pnas.1415122111

14. 14.

    Cole MW, Bassett DS, Power JD, Braver TS, Petersen SE. Intrinsic and task-evoked network architectures of the human brain. Neuron. 2014;83:238-251.

    Not in our library10.1016/j.neuron.2014.05.014

15. 15.

    Cusack R, Vicente-Grabovetsky A, Mitchell DJ, Wild CJ, Auer T, Linke AC, Peelle JE. Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using MATLAB and XML. Front Neuroinform. 2014;8:90.

    Not in our library10.3389/fninf.2014.00090

16. 16.

    Di X, Biswal BB. Identifying the default mode network structure using dynamic causal modeling on resting-state functional magnetic resonance imaging. Neuroimage. 2014;86:53-59.

    Not in our library10.1016/j.neuroimage.2013.07.071

17. 17.

    Duarte JM, Do KQ, Gruetter R. Longitudinal neurochemical modifications in the aging mouse brain measured in vivo by 1H magnetic resonance spectroscopy. Neurobiol Aging. 2014;35:1660-1668.

    Not in our library10.1016/j.neurobiolaging.2014.01.135

18. 18.

    Ferreira LK, Busatto GF. Resting-state functional connectivity in normal brain aging. Neurosci Biobehav Rev. 2013;37:384-400.

    Not in our library10.1016/j.neubiorev.2013.01.017

19. 19.

    Flück D, Beaudin AE, Steinback CD, Kumarpillai G, Shobha N, McCreary CR, Peca S, Smith EE, Poulin MJ. Effects of aging on the association between cerebrovascular responses to visual stimulation, hypercapnia and arterial stiffness. Front Physiol. 2014;5:49.

    Not in our library10.3389/fphys.2014.00049

20. 20.

    Folstein MF, Folstein SE, McHugh PR. “Mini mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189-198.

    Not in our library10.1016/0022-3956(75)90026-6

21. 21.

    Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A. 2005;102:9673-9678.

    Not in our library10.1073/pnas.0504136102

22. 22.

    Friston K. The free-energy principle: a unified brain theory? Nat Rev Neurosci. 2010;11:127-138.

    Not in our library10.1038/nrn2787

23. 23.

    Friston KJ, Mechelli A, Turner R, Price CJ. Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. Neuroimage. 2000;12:466-477.

    Not in our library10.1006/nimg.2000.0630

24. 24.

    Friston KJ, Harrison L, Penny W. Dynamic causal modelling. Neuroimage. 2003;19:1273-1302.

    Not in our library10.1016/s1053-8119(03)00202-7

25. 25.

    Friston KJ, Li B, Daunizeau J, Stephan KE. Network discovery with DCM. Neuroimage. 2011;56:1202-1221.

    Not in our library10.1016/j.neuroimage.2010.12.039

26. 26.

    Friston KJ, Kahan J, Biswal B, Razi A. A DCM for resting state fMRI. Neuroimage. 2014;94:396-407.

    Not in our library10.1016/j.neuroimage.2013.12.009

27. 27.

    Geerligs L, Renken RJ, Saliasi E, Maurits NM, Lorist MM. A brain-wide study of age-related changes in functional connectivity. Cereb Cortex. 2015;25:1987-1999.

    Not in our library10.1093/cercor/bhu012

28. 28.

    Goulden N, Khusnulina A, Davis NJ, Bracewell RM, Bokde AL, McNulty JP, Mullins PG. The salience network is responsible for switching between the default mode network and the central executive network: replication from DCM. Neuroimage. 2014;99:180-190.

    Not in our library10.1016/j.neuroimage.2014.05.052

29. 29.

    Grady C. The cognitive neuroscience of ageing. Nat Rev Neurosci. 2012;13:491-505.

    Not in our library10.1038/nrn3256

30. 30.

    Hasher L, Zacks RT, Bower GH. Working memory, comprehension, and aging: a review and a new view. The psychology of learning and motivation. 1988;193-225.

    Not in our library

31. 31.

    Heise KF, Zimerman M, Hoppe J, Gerloff C, Wegscheider K, Hummel FC. The aging motor system as a model for plastic changes of GABA-mediated intracortical inhibition and their behavioral relevance. J Neurosci. 2013;33:9039-9049.

    Not in our library10.1523/jneurosci.4094-12.2013

32. 32.

    Hoeting JA, Madigan D, Raftery AE, Volinsky CT. Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors. Stat Sci. 1999;14:382-417.

    Not in our library10.1214/ss/1009212519

33. 33.
    A BOLD perspective on age-related flow-metabolism coupling and neural efficiency changes in human visual cortex · 2013

    Not checked
34. 34.

    Hyett MP, Breakspear MJ, Friston KJ, Guo CC, Parker GB. Disrupted effective connectivity of cortical systems supporting attention and interoception in melancholia. JAMA psychiatry. 2015;72:350-358.

    Not in our library10.1001/jamapsychiatry.2014.2490

35. 35.

    Imbrosci B, Mittmann T. Functional consequences of the disturbances in the GABA-mediated inhibition induced by injuries in the cerebral cortex. Neural Plast. 2011;2011:614329.

    Not in our library10.1155/2011/614329

36. 36.

    Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002;17:825-841.

    Not in our library10.1006/nimg.2002.1132

37. 37.

    Keller JB, Hedden T, Thompson TW, Anteraper SA, Gabrieli JD, Whitfield-Gabrieli S. Resting-state anticorrelations between medial and lateral prefrontal cortex: association with working memory, aging, and individual differences. Cortex. 2015;64:271-280.

    Not in our library10.1016/j.cortex.2014.12.001

38. 38.

    Kelly AM, Uddin LQ, Biswal BB, Castellanos FX, Milham MP. Competition between functional brain networks mediates behavioral variability. Neuroimage. 2008;39:527-537.

    Not in our library10.1016/j.neuroimage.2007.08.008

39. 39.

    Legon W, Punzell S, Dowlati E, Adams SE, Stiles AB, Moran RJ. Altered prefrontal excitation/inhibition balance and prefrontal output: markers of aging in human memory networks. Cereb Cortex. 2015.

    Not in our library10.1093/cercor/bhv200

40. 40.

    Li B, Daunizeau J, Stephan KE, Penny W, Hu D, Friston K. Generalised filtering and stochastic DCM for fMRI. Neuroimage. 2011;58:442-457.

    Not in our library10.1016/j.neuroimage.2011.01.085

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