Functional alignment with anatomical networks is associated with cognitive flexibility

Abstract

Cognitive flexibility describes the human ability to switch between modes of mental function to achieve goals. Mental switching is accompanied by transient changes in brain activity, which must occur atop an anatomical architecture that bridges disparate cortical and subcortical regions via underlying white matter tracts. However, an integrated understanding of how white matter networks might constrain brain dynamics during cognitive processes requiring flexibility has remained elusive. Here, to address this challenge, we applied emerging tools from graph signal processing to examine whether blood oxygen level-dependent signals measured at each point in time correspond to complex underlying anatomical networks in 28 individuals performing a perceptual task that probed cognitive flexibility. We found that the alignment between functional signals and the architecture of the underlying white matter network was associated with greater cognitive flexibility across subjects. By computing a concise measure using multi-modal neuroimaging data, we uncovered an integrated structure–function relation of human behaviour.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Cognitive task requiring perceptual switching.
Fig. 2: Multimodal approach to the study of cognitive switching using emerging graph signal processing tools.
Fig. 3: Signal decomposition into anatomy.
Fig. 4: Signal frequency in the time domain versus alignment in the graph domain.
Fig. 5: Non-parametric permutation test for signal concentration within cognitive systems.
Fig. 6: Lower independence is associated with lower switch costs.

References

  1. 1.

    Rogers, R. D. & Monsell, S. Costs of a predictible switch between simple cognitive tasks. J. Exp. Psychol. Gen. 124, 207–231 (1995).

    Article  Google Scholar 

  2. 2.

    Szczepanski, S. M. & Knight, R. T. Insights into human behavior from lesions to the prefrontal cortex. Neuron 83, 1002–1018 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Clark, L. R. et al. Specific measures of executive function predict cognitive decline in older adults. J. Int. Neuropsychol. Soc. 18, 118–127 (2012).

    Article  PubMed  Google Scholar 

  4. 4.

    Richland, L. E. & Burchinal, M. R. Early executive function predicts reasoning development. Psychol. Sci. 24, 87–92 (2013).

    Article  PubMed  Google Scholar 

  5. 5.

    Davis, J. C., Marra, C. A., Najafzadeh, M. & Liu-Ambrose, T. The independent contribution of executive functions to health related quality of life in older women. BMC Geriatr. 10, 16 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Gunaydin, L. A. & Kreitzer, A. C. Cortico-basal ganglia circuit function in psychiatric disease. Annu. Rev. Physiol. 78, 327–350 (2016).

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Casey, B. et al. Early development of subcortical regions involved in non-cued attention switching. Dev. Sci. 7, 534–542 (2004).

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Cole, M. W. et al. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat. Neurosci. 16, 1348–1355 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Heyder, K., Suchan, B. & Daum, I. Cortico-subcortical contributions to executive control. Acta Psychol. 115, 271–289 (2004).

    Article  Google Scholar 

  10. 10.

    Luk, G., Green, D. W., Abutalebi, J. & Grady, C. Cognitive control for language switching in bilinguals: a quantitative meta-analysis of functional neuroimaging studies. Lang. Cogn. Process. 27, 1479–1488 (2012).

    Article  Google Scholar 

  11. 11.

    Quilodran, R., Rothe, M. & Procyk, E. Behavioral shifts and action valuation in the anterior cingulate cortex. Neuron 57, 314–325 (2008).

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Ridderinkhof, K. R., Van Den Wildenberg, W. P., Segalowitz, S. J. & Carter, C. S. Neurocognitive mechanisms of cognitive control: the role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning. Brain Cogn. 56, 129–140 (2004).

    Article  PubMed  Google Scholar 

  13. 13.

    Esterman, M., Chiu, Y.-C., Tamber-Rosenau, B. J. & Yantis, S. Decoding cognitive control in human parietal cortex. Proc. Natl Acad. Sci. USA 106, 17974–17979 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Hikosaka, O. & Isoda, M. Switching from automatic to controlled behavior: cortico-basal ganglia mechanisms. Trends Cogn. Sci. 14, 154–161 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Hosoda, C., Hanakawa, T., Nariai, T., Ohno, K. & Honda, M. Neural mechanisms of language switch. J. Neurolinguist. 25, 44–61 (2012).

    Article  Google Scholar 

  16. 16.

    Leunissen, I. et al. Subcortical volume analysis in traumatic brain injury: the importance of the fronto-striato-thalamic circuit in task switching. Cortex 51, 67–81 (2014).

    Article  PubMed  Google Scholar 

  17. 17.

    Yehene, E., Meiran, N. & Soroker, N. Basal ganglia play a unique role in task switching within the frontal-subcortical circuits: evidence from patients with focal lesions. J. Cogn. Neurosci. 20, 1079–1093 (2008).

    Article  PubMed  Google Scholar 

  18. 18.

    Sporns, O., Tononi, G. & Kötter, R. The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1, e42 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Alstott, J., Breakspear, M., Hagmann, P., Cammoun, L. & Sporns, O. Modeling the impact of lesions in the human brain. PLoS Comput. Biol. 5, e1000408 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Hermundstad, A. M. et al. Structural foundations of resting-state and task-based functional connectivity in the human brain. Proc. Natl Acad. Sci. USA 110, 6169–6174 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Honey, C. J., Kötter, R., Breakspear, M. & Sporns, O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl Acad. Sci. USA 104, 10240–10245 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Medaglia, J. D., Lynall, M.-E. & Bassett, D. S. Cognitive network neuroscience. J. Cogn. Neurosci. 27, 1471–1491 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Sporns, O. Contributions and challenges for network models in cognitive neuroscience. Nat. Neurosci. 17, 652–660 (2014).

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Power, J. D. et al. Functional network organization of the human brain. Neuron 72, 665–678 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Navon, D. Forest before trees: the precedence of global features in visual perception. Cognit. Psychol. 9, 353–383 (1977).

    Article  Google Scholar 

  26. 26.

    Cammoun, L. et al. Mapping the human connectome at multiple scales with diffusion spectrum MRI. J. Neurosci. Methods 203, 386–397 (2012).

    Article  PubMed  Google Scholar 

  27. 27.

    Diedrichsen, J., Balsters, J. H., Flavell, J., Cussans, E. & Ramnani, N. A probabilistic MR atlas of the human cerebellum. Neuroimage 46, 39–46 (2009).

    Article  PubMed  Google Scholar 

  28. 28.

    Sandryhaila, A. & Moura, J. M. Discrete signal processing on graphs. IEEE Trans. Signal Process. 61, 1644–1656 (2013).

    Article  Google Scholar 

  29. 29.

    Braver, T. S. The variable nature of cognitive control: a dual mechanisms framework. Trends Cogn. Sci. 16, 106–113 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Botvinick, M. & Braver, T. Motivation and cognitive control: from behavior to neural mechanism. Annu. Rev. Psychol. 66, 83–113 (2015).

    Article  PubMed  Google Scholar 

  31. 31.

    Gu, S. et al. Controllability of structural brain networks. Nat. Commun. 6, 8414 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Cohen, J. D., Dunbar, K. & McClelland, J. L. On the control of automatic processes: a parallel distributed processing account of the Stroop effect. Psychol. Rev. 97, 332–361 (1990).

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Zatorre, R. J., Fields, R. D. & Johansen-Berg, H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat. Neurosci. 15, 528–536 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Li, P., Legault, J. & Litcofsky, K. A. Neuroplasticity as a function of second language learning: anatomical changes in the human brain. Cortex 58, 301–324 (2014).

    Article  PubMed  Google Scholar 

  35. 35.

    Wang, X., Casadio, M., Weber, K. A., Mussa-Ivaldi, F. A. & Parrish, T. B. White matter microstructure changes induced by motor skill learning utilizing a body machine interface. Neuroimage 88, 32–40 (2014).

    Article  PubMed  Google Scholar 

  36. 36.

    Reid, L. B., Sale, M. V., Cunnington, R., Mattingley, J. B. & Rose, S. E. Brain changes following four weeks of unimanual motor training: evidence from fMRI-guided diffusion MRI tractography. Hum. Brain Mapp. 38, 4302–4312 (2017).

    Article  PubMed  Google Scholar 

  37. 37.

    Braun, U. et al. Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc. Natl Acad. Sci. USA 112, 11678–11683 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Mayhew, S. D. et al. Global signal modulation of single-trial fMRI response variability: effect on positive vs negative bold response relationship. Neuroimage 133, 62–74 (2016).

    CAS  Article  PubMed  Google Scholar 

  39. 39.

    Marrelec, G., Messé, A., Giron, A. & Rudrauf, D. Functional connectivity’s degenerate view of brain computation. PLoS Comput.l Biol. 12, e1005031 (2016).

    Article  Google Scholar 

  40. 40.

    Sekutowicz, M. et al. Striatal activation as a neural link between cognitive and perceptual flexibility. Neuroimage 141, 393–398 (2016).

    Article  PubMed  Google Scholar 

  41. 41.

    Liston, C., Matalon, S., Hare, T. A., Davidson, M. C. & Casey, B. Anterior cingulate and posterior parietal cortices are sensitive to dissociable forms of conflict in a task-switching paradigm. Neuron 50, 643–653 (2006).

    CAS  Article  PubMed  Google Scholar 

  42. 42.

    Pelvig, D. P., Pakkenberg, H., Stark, A. K. & Pakkenberg, B. Neocortical glial cell numbers in human brains. Neurobiol. Aging 29, 1754–1762 (2008).

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Middleton, F. A. & Strick, P. L. Anatomical evidence for cerebellar and basal ganglia involvement in higher cognitive function. Science 266, 458–461 (1994).

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Greicius, M. D., Supekar, K., Menon, V. & Dougherty, R. F. Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb. Cortex 19, 72–78 (2009).

    Article  PubMed  Google Scholar 

  45. 45.

    Hermundstad, A. M. et al. Structurally-constrained relationships between cognitive states in the human brain. PLoS Comput. Biol. 10, e1003591 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Honey, C. et al. Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl Acad. Sci. USA 106, 2035–2040 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Morgan, V. L., Mishra, A., Newton, A. T., Gore, J. C. & Ding, Z. Integrating functional and diffusion magnetic resonance imaging for analysis of structure–function relationship in the human language network. PLoS ONE 4, e6660 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Uddin, L. Q., Supekar, K. S., Ryali, S. & Menon, V. Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. J. Neurosci. 31, 18578–18589 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Mattar, M. G., Betzel, R. F. & Bassett, D. S. The flexible brain. Brain 139, 2110–2112 (2016).

    Article  PubMed  Google Scholar 

  50. 50.

    Miyake, A. et al. The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: a latent variable analysis. Cognit. Psychol. 41, 49–100 (2000).

    CAS  Article  PubMed  Google Scholar 

  51. 51.

    Fedorenko, E. The role of domain-general cognitive control in language comprehension. Front. Psychol. 5, 335 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Gajewski, P. D. et al. Effects of aging and job demands on cognitive flexibility assessed by task switching. Biol. Psychol. 85, 187–199 (2010).

    Article  PubMed  Google Scholar 

  53. 53.

    Eddy, C. M., Rizzo, R. & Cavanna, A. E. Neuropsychological aspects of Tourette syndrome: a review. J. Psychosom. Res. 67, 503–513 (2009).

    Article  PubMed  Google Scholar 

  54. 54.

    Cools, R., Barker, R. A., Sahakian, B. J. & Robbins, T. W. Enhanced or impaired cognitive function in Parkinson’s disease as a function of dopaminergic medication and task demands. Cereb. Cortex 11, 1136–1143 (2001).

    CAS  Article  PubMed  Google Scholar 

  55. 55.

    Stephan, K. E., Tittgemeyer, M., Knösche, T. R., Moran, R. J. & Friston, K. J. Tractography-based priors for dynamic causal models. Neuroimage 47, 1628–1638 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Belleville, S., Bherer, L., Lepage, É., Chertkow, H. & Gauthier, S. Task switching capacities in persons with Alzheimer’s disease and mild cognitive impairment. Neuropsychologia 46, 2225–2233 (2008).

    Article  PubMed  Google Scholar 

  57. 57.

    Kehagia, A. A., Barker, R. A. & Robbins, T. W. Neuropsychological and clinical heterogeneity of cognitive impairment and dementia in patients with Parkinson’s disease. Lancet Neurol. 9, 1200–1213 (2010).

    Article  PubMed  Google Scholar 

  58. 58.

    Kinnunen, K. M. et al. White matter damage and cognitive impairment after traumatic brain injury. Brain 134, 449–463 (2011).

    Article  PubMed  Google Scholar 

  59. 59.

    Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).

    Article  PubMed  Google Scholar 

  60. 60.

    Kennedy, D. et al. Gyri of the human neocortex: an MRI-based analysis of volume and variance. Cereb. Cortex 8, 372–384 (1998).

    CAS  Article  PubMed  Google Scholar 

  61. 61.

    Betzel, R. F., Gu, S., Medaglia, J. D., Pasqualetti, F. & Bassett, D. S. Optimally controlling the human connectome: the role of network topology. Sci. Rep. 6, 30770 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Yeh, F.-C., Wedeen, V. J. & Tseng, W.-Y. I. Estimation of fiber orientation and spin density distribution by diffusion deconvolution. Neuroimage 55, 1054–1062 (2011).

    Article  PubMed  Google Scholar 

  63. 63.

    Fischl, B. Freesurfer. Neuroimage 62, 774–781 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Cieslak, M. & Grafton, S. Local termination pattern analysis: a tool for comparing white matter morphology. Brain Imaging Behav. 8, 292–299 (2014).

    CAS  Article  PubMed  Google Scholar 

  65. 65.

    Hagmann, P. et al. Mapping the structural core of human cerebral cortex. PloS. Biol. 6, e159 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Voogd, J. & Glickstein, M. The anatomy of the cerebellum. Trends Cogn. Sci. 2, 307–313 (1998).

    CAS  Article  PubMed  Google Scholar 

  67. 67.

    Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W. & Smith, S. M. Fsl. Neuroimage 62, 782–790 (2012).

    Article  PubMed  Google Scholar 

  68. 68.

    Greve, D. N. & Fischl, B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48, 63–72 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Zhang, Y., Brady, M. & Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation–maximization algorithm. IEEE Trans. Med. Imaging 20, 45–57 (2001).

    CAS  Article  PubMed  Google Scholar 

  70. 70.

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

    Article  PubMed  Google Scholar 

  71. 71.

    Chung, F. R. K. Spectral Graph Theory Vol. 92 (American Mathematical Soc., 1997).

  72. 72.

    Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A. & Vandergheynst, P. The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag. 30, 83–98 (2013).

    Article  Google Scholar 

  73. 73.

    Ma, J., Huang, W., Segarra, S. & Ribeiro, A. Diffusion filtering for graph signals and its use in recommendation systems. In IEEE Int. Conf. on Acoustics, Speech and Signal Processing 4563–4567 (Shanghai, 2016).

  74. 74.

    Segarra, S., Huang, W. & Ribeiro, A. Diffusion and superposition distances for signals supported on networks. IEEE Trans. Signal Inform. Process. Network 1, 20–32 (2015).

    Article  Google Scholar 

  75. 75.

    Huang, W., Segarra, S. & Ribeiro, A. Diffusion distance for signals supported on networks. In Proc. Asilomar Conf. Signals Syst. Comput. 1219–1223 (Asilomar, CA, 2015).

  76. 76.

    Huang, W. et al. Graph frequency analysis of brain signals. IEEE J. Sel. Top. Signal Process. 10, 1189–1203 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Spielman, D. Spectral graph theory and its applications. In 48th Annual IEEE Symposium on  Foundations of Computer Science, 2007. FOCS'07 29–38 (2007).

Download references

Acknowledgements

J.D.M. acknowledges support from the Office of the Director at the National Institutes of Health through grant number 1-DP5-OD-021352-01 and the Perelman School of Medicine. D.S.B. acknowledges support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the Army Research Laboratory and the Army Research Office through contract numbers W911NF-10-2-0022 and W911NF-14-1-0679, the National Institute of Health (R01-DC-009209-11, R01-HD-086888-01, R01-MH-107235, R01-MH107703, R01-MH-109520, R01-NS-099348 and R21-MH-106799), the Office of Naval Research and the National Science Foundation (BCS-1441502, CAREER PHY-1554488, BCS-1631550, and CNS-1626008). The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Affiliations

Authors

Contributions

J.D.M. conceptualized the overall project, created the behavioural tasks, collected the data, wrote the manuscript and conducted behavioural and network data processing and analyses. W.H. performed primary analyses using GFT to integrate BOLD fMRI data with anatomical networks and to correlate them with cognitive measures. E.A.K. preprocessed BOLD fMRI data. A.K. adapted processing procedures to analyse the Human Connectome Project data. S.L.T.-S. assisted with the behavioural task design. A.R. supervised applications of the GFT analysis to the imaging data. D.S.B. funded the data acquisition, assisted with the interpretation of the primary findings and edited the manuscript.

Corresponding author

Correspondence to Danielle S. Bassett.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Results, Supplementary Tables 1–39, Supplementary Figures 1–8, Supplementary References.

Life Sciences Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Medaglia, J.D., Huang, W., Karuza, E.A. et al. Functional alignment with anatomical networks is associated with cognitive flexibility. Nat Hum Behav 2, 156–164 (2018). https://doi.org/10.1038/s41562-017-0260-9

Download citation

Further reading

Search

Sign up for the Nature Briefing newsletter for a daily update on COVID-19 science.
Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing