Skip to main content Accessibility help

A Computational Network Control Theory Analysis of Depression Symptoms

  • Yoed N. Kenett (a1), Roger E. Beaty (a2) and John D. Medaglia (a3) (a4)

Rumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the or variations. ‘’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      A Computational Network Control Theory Analysis of Depression Symptoms
      Available formats
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      A Computational Network Control Theory Analysis of Depression Symptoms
      Available formats
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      A Computational Network Control Theory Analysis of Depression Symptoms
      Available formats
This is an Open Access article, distributed under the terms of the Creative Commons Attribution- NonCommercial-NoDerivatives licence (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited.
Corresponding author
*Author for correspondence: Yoed N. Kenett, E-mail:
Hide All
Abdelnour, F., Voss, H. U. Raj, A. (2014). Network diffusion accurately models the relationship between structural and functional brain connectivity networks. Neuroimage, 90, 335347.
Andrews-Hanna, J. R., Smallwood, J. Spreng, R. N. (2014). The default network and self-generated thought: Component processes, dynamic control, and clinical relevance. Annals of the New York Academy of Sciences, 1316, 2952.
Bai, F., Shu, N., Yuan, Y., Shi, Y., Yu, H., Wu, D. , … Zhang, Z. (2012). Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. Journal of Neuroscience, 32, 43074318.
Bassett, D. S., Brown, J. A., Deshpande, V., Carlson, J. M. Grafton, S. T. (2011). Conserved and variable architecture of human white matter connectivity. Neuroimage, 54, 12621279.
Beck, A. T. (1976). Cognitive therapy and the emotional disorders. New York, NY: International University Press.
Beck, A. T., Steer, R. A. Carbin, M. G. (1988). Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation. Clinical Psychology Review, 8, 77100.
Beck, A. T., Ward, C. H., Mendelson, M. M., Mock, J. J. Erbaugh, J. J. (1961). An inventory for measuring depression. Archives of General Psychiatry, 4, 561571.
Benjamini, Y. Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B, 57, 289300.
Benjamini, Y. Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29, 11651188.
Bettinardi, R. G., Deco, G., Karlaftis, V. M., Van Hartevelt, T. J., Fernandes, H. M., Kourtzi, Z. , … Zamora-López, G. (2017). How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain’s spontaneous correlation structure. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27, 047409.
Betzel, R. F., Gu, S., Medaglia, J. D., Pasqualetti, F. Bassett, D. S. (2016). Optimally controlling the human connectome: The role of network topology. Scientific Reports, 6, 30770.
Burrows, C. A., Timpano, K. R. Uddin, L. Q. (2017). Putative brain networks underlying repetitive negative thinking and comorbid internalizing problems in autism. Clinical Psychological Science, 5, 522536.
Calhoon, G. G. Tye, K. M. (2015). Resolving the neural circuits of anxiety. Nature Neuroscience, 18, 13941404.
Cammoun, L., Gigandet, X., Meskaldji, D., Thiran, J. P., Sporns, O., Do, K. Q. , … Hagmann, P. (2012). Mapping the human connectome at multiple scales with diffusion spectrum MRI. Journal of Neuroscience Methods, 203, 386397.
Canli, T., Sivers, H., Thomason, M. E., Whitfield-Gabrieli, S., Gabrieli, J. D. E. Gotlib, I. H. (2004). Brain activation to emotional words in depressed vs healthy subjects. NeuroReport, 15, 25852588.
Choi, K. S., Holtzheimer, P. E., Franco, A. R., Kelley, M. E., Dunlop, B. W., Hu, X. P., & Mayberg, H. S. (2014). Reconciling variable findings of white matter integrity in major depressive disorder. Neuropsychopharmacology, 39, 13321339.
Cieslak, M. Grafton, S. T. (2014). Local termination pattern analysis: A tool for comparing white matter morphology. Brain Imaging and Behavior, 8, 292299.
Cole, M. W., Ito, T., Bassett, D. S. Schultz, D. H. (2016). Activity flow over resting-state networks shapes cognitive task activations. Nature Neuroscience, 19, 17181726.
Cole, M. W., Repovš, G. Anticevic, A. (2014). The frontoparietal control system: A central role in mental health. The Neuroscientist, 20, 652664.
Cooney, R. E., Joormann, J., Eugène, F., Dennis, E. L. Gotlib, I. H. (2010). Neural correlates of rumination in depression. Cognitive, Affective, & Behavioral Neuroscience, 10, 470478.
Daducci, A., Gerhard, S., Griffa, A., Lemkaddem, A., Cammoun, L., Gigandet, X. , … Thiran, J.-P. (2012). The connectome mapper: An open-source processing pipeline to map connectomes with MRI. PLoS One, 7, e48121.
De Witte, N. A. J. Mueller, S. C. (2017). White matter integrity in brain networks relevant to anxiety and depression: Evidence from the human connectome project dataset. Brain Imaging and Behavior, 11, 16041615.
Diener, C., Kuehner, C., Brusniak, W., Ubl, B., Wessa, M. Flor, H. (2012). A meta-analysis of neurofunctional imaging studies of emotion and cognition in major depression. Neuroimage, 61, 677685.
Dosenbach, N. U. F., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A. , … Lessov-Schlaggar, C. N. (2010). Prediction of individual brain maturity using fMRI. Science, 329, 13581361.
Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L. , … Jiang, T. (2016). The human brainnetome atlas: A new brain atlas based on connectional architecture. Cerebral Cortex, 26, 35083526.
Fischl, B. (2012). FreeSurfer. Neuroimage, 62, 774781.
Galán, R. F. (2008). On how network architecture determines the dominant patterns of spontaneous neural activity. PloS One, 3, e2148.
Gong, Q. He, Y. (2015). Depression, neuroimaging and connectomics: A selective overview. Biological Psychiatry, 77, 223235.
Griffa, A., Baumann, P. S., Thiran, J.-P. Hagmann, P. (2013). Structural connectomics in brain diseases. NeuroImage, 80, 515526.
Gu, S., Betzel, R. F., Mattar, M. G., Cieslak, M., Delio, P. R., Grafton, S. T. , … Bassett, D. S. (2017). Optimal trajectories of brain state transitions. NeuroImage, 148, 305317.
Gu, S., Pasqualetti, F., Cieslak, M., Telesford, Q. K., Yu, A. B., Kahn, A. E., … Bassett, D. S. (2015). Controllability of structural brain networks. Nature Communications, 6, 110.
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology, 6, e159.
Hamilton, J. P., Chen, G., Thomason, M. E., Schwartz, M. E. Gotlib, I. H. (2011). Investigating neural primacy in major depressive disorder: Multivariate Granger causality analysis of resting-state fMRI time-series data. Molecular Psychiatry, 16, 763772.
Hamilton, J. P., Chen, M. C. Gotlib, I. H. (2013). Neural systems approaches to understanding major depressive disorder: An intrinsic functional organization perspective. Neurobiology of Disease, 52, 411.
Hamilton, J. P., Furman, D. J., Chang, C., Thomason, M. E., Dennis, E. Gotlib, I. H. (2011). Default-mode and task-positive network activity in major depressive disorder: Implications for adaptive and maladaptive rumination. Biological Psychiatry, 70, 327333.
Hamilton, J. P., Glover, G. H., Bagarinao, E., Chang, C., Mackey, S., Sacchet, M. D., & Gotlib, I. H. (2016). Effects of salience-network-node neurofeedback training on affective biases in major depressive disorder. Psychiatry Research: Neuroimaging, 249, 9196.
Hao, L., Yang, J., Wang, Y., Zhang, S., Xie, P., Luo, Q. , … Qiu, J. (2015). Neural correlates of causal attribution in negative events of depressed patients: Evidence from an fMRI study. Clinical Neurophysiology, 126, 13311337.
Hermundstad, A. M., Bassett, D. S., Brown, K. S., Aminoff, E. M., Clewett, D., Freeman, S. , … Miller, M. B. (2013). Structural foundations of resting-state and task-based functional connectivity in the human brain. Proceedings of the National Academy of Sciences, 110, 61696174.
Hermundstad, A. M., Brown, K. S., Bassett, D. S., Aminoff, E. M., Frithsen, A., Johnson, A. , … Carlson, J. M. (2014). Structurally-constrained relationships between cognitive states in the human brain. PLoS Computational Biology, 10, e1003591.
Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J.-P., Meuli, R. Hagmann, P. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences, 106, 20352040.
Honey, C. J., Thivierge, J.-P. Sporns, O. (2010). Can structure predict function in the human brain? Neuroimage, 52, 766776.
Iwabuchi, S. J., Peng, D., Fang, Y., Jiang, K., Liddle, E. B., Liddle, P. F., & Palaniyappan, L. (2014). Alterations in effective connectivity anchored on the insula in major depressive disorder. European Neuropsychopharmacology, 24, 17841792.
Jeganathan, J., Perry, A., Bassett, D. S., Roberts, G., Mitchell, P. B. Breakspear, M. (2018). Fronto-limbic dysconnectivity leads to impaired brain network controllability in young people with bipolar disorder and those at high genetic risk. NeuroImage: Clinical, 19, 71–81.
Jung, J. Y., Kang, J., Won, E., Nam, K., Lee, M.-S., Tae, W. S., & Ham, B.-J. (2014). Impact of lingual gyrus volume on antidepressant response and neurocognitive functions in major depressive disorder: A voxel-based morphometry study. Journal of Affective Disorders, 169, 179187.
Jung, R. E., Mead, B. S., Carrasco, J. Flores, R. A. (2013). The structure of creative cognition in the human brain. Frontiers in Human Neuroscience, 7, 330.
Kaiser, R. H., Whitfield-Gabrieli, S., Dillon, D. G., Goer, F., Beltzer, M., Minkel, J. , … Pizzagalli, D. A. (2016). Dynamic resting-state functional connectivity in major depression. Neuropsychopharmacology, 41, 18221830.
Keedwell, P., Drapier, D., Surguladze, B., Giampietro, V., Brammer, M. Phillips, M. (2009). Neural markers of symptomatic improvement during antidepressant therapy in severe depression: Subgenual cingulate and visual cortical responses to sad, but not happy, facial stimuli are correlated with changes in symptom score. Journal of Psychopharmacology, 23, 775788.
Kenett, Y. N., Medaglia, J. D., Beaty, R. E., Chen, Q., Betzel, R. F., Thompson-Schill, S. L., & Qiu, J. (2018). Driving the brain towards creativity and intelligence: A network control theory analysis. Neuropsychologia.
Korgaonkar, M. S., Fornito, A., Williams, L. M. Grieve, S. M. (2014). Abnormal structural networks characterize major depressive disorder: A connectome analysis. Biological Psychiatry, 76, 567574.
Lebel, C., Treit, S. Beaulieu, C. (2017). A review of diffusion MRI of typical white matter development from early childhood to young adulthood. NMR in Biomedicine, e3778.
Li, H., Wei, D., Sun, J., Chen, Q., Zhang, Q. Qiu, J. (2015). Brain structural alterations associated with young women with subthreshold depression. Scientific Reports, 5, 9707.
Liu, W., Wei, D., Chen, Q., Yang, W., Meng, J., Wu, G. , … Qiu, J. (2017). Longitudinal test-retest neuroimaging data from healthy young adults in southwest China. Scientific Data, 4, 170017.
Liu, Y.-Y., Slotine, J.-J. Barabási, A.-L. (2011). Controllability of complex networks. Nature, 473, 167173.
Liu, Z., Xu, C., Xu, Y., Wang, Y., Zhao, B., Lv, Y. , … Du, C. (2010). Decreased regional homogeneity in insula and cerebellum: A resting-state fMRI study in patients with major depression and subjects at high risk for major depression. Psychiatry Research: Neuroimaging, 182, 211215.
Manoliu, A., Meng, C., Brandl, F., Doll, A., Tahmasian, M., Scherr, M. , … Sorg, C. (2014). Insular dysfunction within the salience network is associated with severity of symptoms and aberrant inter-network connectivity in major depressive disorder. Frontiers in Human Neuroscience, 7, 930.
Medaglia, J. D., Gu, S., Pasqualetti, F., Ashare, R. L., Lerman, C., Kable, J., & Bassett, D. S. (2016). Cognitive control in the controllable connectome. arXiv preprint.
Medaglia, J. D., Harvey, D. Y., White, N., Kelkar, A., Zimmerman, J., Bassett, D. S., & Hamilton, R. H. (2018a). Network controllability in the inferior frontal gyrus relates to controlled language variability and susceptibility to TMS. The Journal of Neuroscience, 38, 6399–6410.
Medaglia, J. D., Huang, W., Karuza, E. A., Kelkar, A., Thompson-Schill, S. L., Ribeiro, A., & Bassett, D. S. (2018b). Functional alignment with anatomical networks is associated with cognitive flexibility. Nature Human Behaviour, 2, 156–164.
Menara, T., Gu, S., Bassett, D. S. Pasqualetti, F. (2017). On structural controllability of symmetric (brain) networks. arXiv preprint.
Menon, V. (2011). Large-scale brain networks and psychopathology: A unifying triple network model. Trends in Cognitive Sciences, 15, 483506.
Menon, V. Uddin, L. Q. (2010). Saliency, switching, attention and control: A network model of insula function. Brain Structure and Function, 214, 655667.
Mišić, B., Betzel, R. F., Nematzadeh, A., Goñi, J., Griffa, A., Hagmann, P. , … Sporns, O. (2015). Cooperative and competitive spreading dynamics on the human connectome. Neuron, 86, 15181529.
Muldoon, S. F., Pasqualetti, F., Gu, S., Cieslak, M., Grafton, S. T., Vettel, J. M., & Bassett, D. S. (2016). Stimulation-based control of dynamic brain networks. PLoS Computational Biology, 12, e1005076.
Murphy, M. L. Frodl, T. (2011). Meta-analysis of diffusion tensor imaging studies shows altered fractional anisotropy occurring in distinct brain areas in association with depression. Biology of Mood & Anxiety Disorders, 1, 3.
Nolen-Hoeksema, S. Morrow, J. (1993). Effects of rumination and distraction on naturally occurring depressed mood. Cognition & Emotion, 7, 561570.
Pasqualetti, F., Zampieri, S. Bullo, F. (2014). Controllability metrics, limitations and algorithms for complex networks. IEEE Transactions on Control of Network Systems, 1, 4052.
Qin, J., Wei, M., Liu, H., Yan, R., Luo, G., Yao, Z., & Lu, Q. (2014). Abnormal brain anatomical topological organization of the cognitive‐emotional and the frontoparietal circuitry in major depressive disorder. Magnetic Resonance in Medicine, 72, 13971407.
Rizk, M. M., Rubin-Falcone, H., Keilp, J., Miller, J. M., Sublette, M. E., Burke, A. , … Mann, J. J. (2017). White matter correlates of impaired attention control in major depressive disorder and healthy volunteers. Journal of Affective Disorders, 222, 103111.
Roalf, D. R., Quarmley, M., Elliott, M. A., Satterthwaite, T. D., Vandekar, S. N., Ruparel, K. , … Gur, R. E. (2016). The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort. NeuroImage, 125, 903919.
Schultz, D. H., Ito, T., Solomyak, L. I., Chen, R. H., Mill, R. D., Anticevic, A., & Cole, M. W. (2018). Global connectivity of the frontoparietal cognitive control network is related to depression symptoms in the general population. Network Neuroscience, posted online April 12, 2018.
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H. , … Greicius, M. D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. Journal of Neuroscience, 27, 23492356.
Sexton, C. E., Mackay, C. E. Ebmeier, K. P. (2009). A systematic review of diffusion tensor imaging studies in affective disorders. Biological Psychiatry, 66, 814823.
Sheline, Y. I., Barch, D. M., Price, J. L., Rundle, M. M., Vaishnavi, S. N., Snyder, A. Z. , … Raichle, M. E. (2009). The default mode network and self-referential processes in depression. Proceedings of the National Academy of Sciences, 106, 19421947.
Sotiropoulos, S. N. Zalesky, A. (2017). Building connectomes using diffusion MRI: Why, how and but. NMR in Biomedicine, e3752, 123.
Sridharan, D., Levitin, D. J. Menon, V. (2008). A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences, 105, 1256912574.
Steer, R. A., Ball, R., Ranieri, W. F. Beck, A. T. (1999). Dimensions of the Beck depression inventory‐II in clinically depressed outpatients. Journal of Clinical Psychology, 55, 117128.<117::AID-JCLP12>3.0.CO;2-A
Storch, E. A., Robert, J. W. Roth, D. A. (2004). Factor structure, concurrent validity, and internal consistency of the beck depression inventory—second edition in a sample of college students. Depression and Anxiety, 19, 187189.
Strigo, I. A., Matthews, S. C. Simmons, A. N. (2010). Right anterior insula hypoactivity during anticipation of homeostatic shifts in major depressive disorder. Psychosomatic Medicine, 72, 316323.
Summers, T. H., Cortesi, F. L. Lygeros, J. (2016). On submodularity and controllability in complex dynamical networks. IEEE Transactions on Control of Network Systems, 3, 91101.
Tang, E. Bassett, D. S. (2018). Colloquium: Control of dynamics in brain networks. Reviews of Modern Physics, 90, 031003.
Tang, E., Giusti, C., Baum, G. L., Gu, S., Pollock, E., Kahn, A. E. , … Bassett, D. S. (2017). Developmental increases in white matter network controllability support a growing diversity of brain dynamics. Nature Communications, 8, 1252.
Tu, C., Rocha, R. P., Corbetta, M., Zampieri, S., Zorzi, M., & Suweis, S. (2018). Warnings and caveats in brain controllability. NeuroImage, 176, 83–91.
Uddin, L. Q. (2015). Salience processing and insular cortical function and dysfunction. Nature Reviews Neuroscience, 16, 5561.
Veer, I. M., Beckmann, C. F., Van Tol, M.-J., Ferrarini, L., Milles, J., Veltman, D. J. , … Rombouts, S. A. (2010). Whole brain resting-state analysis reveals decreased functional connectivity in major depression. Frontiers in Systems Neuroscience, 4, 41.
Wedeen, V. J., Wang, R. P., Schmahmann, J. D., Benner, T., Tseng, W. Y. I., Dai, G. , … de Crespigny, A. J. (2008). Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. Neuroimage, 41, 12671277.
White, T., Nelson, M. Lim, K. O. (2008). Diffusion tensor imaging in psychiatric disorders. Topics in Magnetic Resonance Imaging, 19, 97109.
Wiebking, C., de Greck, M., Duncan, N. W., Tempelmann, C., Bajbouj, M. Northoff, G. (2015). Interoception in insula subregions as a possible state marker for depression—an exploratory fMRI study investigating healthy, depressed and remitted participants. Frontiers in Behavioral Neuroscience, 9, 82.
Wu-Yan, E., Betzel, R. F., Tang, E., Gu, S., Pasqualetti, F. Bassett, D. S. (2018). Benchmarking measures of network controllability on canonical graph models. Journal of Nonlinear Science.
Xia, M., Wang, J. He, Y. (2013). BrainNet viewer: A network visualization tool for human brain connectomics. PLoS One, 8, e68910.
Yan, G., Vértes, P. E., Towlson, E. K., Chew, Y. L., Walker, D. S., Schafer, W. R., & Barabási, A.-L. (2017). Network control principles predict neuron function in the Caenorhabditis elegans connectome. Nature, 550, 519523.
Yeh, F.-C., Wedeen, V. J. Tseng, W.-Y. I. (2011). Estimation of fiber orientation and spin density distribution by diffusion deconvolution. Neuroimage, 55, 10541062.
Zabelina, D. L. Andrews-Hanna, J. R. (2016). Dynamic network interactions supporting internally-oriented cognition. Current opinion in Neurobiology, 40, 8693.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Personality Neuroscience
  • ISSN: -
  • EISSN: 2513-9886
  • URL: /core/journals/personality-neuroscience
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed