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Functional alignment with anatomical networks is associated with cognitive flexibility

Nature Human Behaviourvolume 2pages156164 (2018) | Download Citation


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.

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  1. 1.

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

  2. 2.

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

  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).

  4. 4.

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

  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).

  6. 6.

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

  7. 7.

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

  8. 8.

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

  9. 9.

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

  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).

  11. 11.

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

  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).

  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).

  14. 14.

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

  15. 15.

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

  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).

  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).

  18. 18.

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

  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).

  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).

  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).

  22. 22.

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

  23. 23.

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

  24. 24.

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

  25. 25.

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

  26. 26.

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

  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).

  28. 28.

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

  29. 29.

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

  30. 30.

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

  31. 31.

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

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  40. 40.

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

  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).

  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).

  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).

  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).

  45. 45.

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

  46. 46.

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

  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).

  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).

  49. 49.

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

  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).

  51. 51.

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

  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).

  53. 53.

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

  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).

  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).

  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).

  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).

  58. 58.

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

  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).

  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).

  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).

  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).

  63. 63.

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

  64. 64.

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

  65. 65.

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

  66. 66.

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

  67. 67.

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

  68. 68.

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

  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).

  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).

  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).

  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).

  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).

  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).

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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


  1. Department of Psychology, Drexel University, Philadelphia, PA, USA

    • John D. Medaglia
    •  & Apoorva Kelkar
  2. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    • John D. Medaglia
  3. Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA

    • Weiyu Huang
    • , Alejandro Ribeiro
    •  & Danielle S. Bassett
  4. Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA

    • Elisabeth A. Karuza
    •  & Sharon L. Thompson-Schill
  5. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

    • Danielle S. Bassett


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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.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Danielle S. Bassett.

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