Abstract
Understanding how few distributed areas can steer large-scale brain activity is a fundamental question that has practical implications, which range from inducing specific patterns of behavior to counteracting disease. Recent endeavors based on network controllability provided fresh insights into the potential ability of single regions to influence whole brain dynamics through the underlying structural connectome. However, controlling the entire brain activity is often unfeasible and might not always be necessary. The question whether single areas can control specific target subsystems remains crucial, albeit still poorly explored. Furthermore, the structure of the brain network exhibits progressive changes across the lifespan, but little is known about the possible consequences in the controllability properties. To address these questions, we adopted a novel target controllability approach that quantifies the centrality of brain nodes in controlling specific target anatomo-functional systems. We then studied such target control centrality in human connectomes obtained from healthy individuals aged from 5 to 85. Main results showed that the sensorimotor system has a high influencing capacity, but it is difficult for other areas to influence it. Furthermore, we reported that target control centrality varies with age and that temporal-parietal regions, whose cortical thinning is crucial in dementia-related diseases, exhibit lower values in older people. By simulating targeted attacks, such as those occurring in focal stroke, we showed that the ipsilesional hemisphere is the most affected one regardless of the damaged area. Notably, such degradation in target control centrality was more evident in younger people, thus supporting early-vulnerability hypotheses after stroke.
Similar content being viewed by others
Data availability statement
All the experimental data used in this work are fully accessible from the NKI-Rockland database (Nooner et al. 2012).
References
Abdelnour F, Dayan M, Devinsky O, Thesen T, Raj A (2018) Functional brain connectivity is predictable from anatomic network’s Laplacian eigen-structure. Neuroimage 172:728–739. https://doi.org/10.1016/j.neuroimage.2018.02.016
Adolphs R, Damasio H, Tranel D, Cooper G, Damasio AR (2000) A Role for somatosensory cortices in the visual recognition of emotion as revealed by three-dimensional lesion mapping. J Neurosci 20(7):2683–2690. https://doi.org/10.1523/JNEUROSCI.20-07-02683.2000
Allen JS, Bruss J, Brown CK, Damasio H (2005) Normal neuroanatomical variation due to age: the major lobes and a parcellation of the temporal region. Neurobiol Aging 26(9):1245–1260. https://doi.org/10.1016/j.neurobiolaging.2005.05.023
Anderson V, Spencer-Smith M, Wood A (2011) Do children really recover better? Neurobehavioural plasticity after early brain insult. Brain 134(8):2197–2221. https://doi.org/10.1093/brain/awr103
Avena-Koenigsberger A, Misic B, Sporns O (2018) Communication dynamics in complex brain networks. Nat Rev Neurosci 19(1):17–33. https://doi.org/10.1038/nrn.2017.149
Bagarinao E, Watanabe H, Maesawa S, Mori D, Hara K, Kawabata K et al (2019) Reorganization of brain networks and its association with general cognitive performance over the adult lifespan. Sci Rep 9(1):11352. https://doi.org/10.1038/s41598-019-47922-x
Bagarinao E, Watanabe H, Maesawa S, Mori D, Hara K, Kawabata K et al (2020) Aging impacts the overall connectivity strength of regions critical for information transfer among brain networks. Front Aging Neurosci. https://doi.org/10.3389/fnagi.2020.592469
Bakkour A, Morris JC, Wolk DA, Dickerson BC (2013) The effects of aging and Alzheimer’s disease on cerebral cortical anatomy: specificity and differential relationships with cognition. Neuroimage 76:332–344. https://doi.org/10.1016/j.neuroimage.2013.02.059
Bartolomeo P (2021) From competition to cooperation: visual neglect across the hemispheres. Revue Neurologique 177(9):1104–1111. https://doi.org/10.1016/j.neurol.2021.07.015
Bartolomeo P, de Schotten MT (2016) Let thy left brain know what thy right brain doeth: inter-hemispheric compensation of functional deficits after brain damage. Neuropsychologia 93:407–412. https://doi.org/10.1016/j.neuropsychologia.2016.06.016
Bartolomeo P, Seidel Malkinson T (2019) Hemispheric lateralization of attention processes in the human brain. Curr Opin Psychol 29:90–96. https://doi.org/10.1016/j.copsyc.2018.12.023
Bassett DS, Yang M, Wymbs NF, Grafton ST (2015) Learning-induced autonomy of sensorimotor systems. Nat Neurosci 18(5):744–751. https://doi.org/10.1038/nn.3993
Bassignana G, Fransson J, Henry V, Colliot O, Zujovic V, De Vico Fallani F (2021) Stepwise target controllability identifies dysregulations of macrophage networks in multiple sclerosis. Netw Neurosci 5(2):337–357. https://doi.org/10.1162/netn_a_00180
Behfar Q, Behfar SK, von Reutern B, Richter N, Dronse J, Fassbender R et al (2020) Graph theory analysis reveals resting-state compensatory mechanisms in healthy aging and prodromal Alzheimer’s disease. Front Aging Neurosci 12:576627
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol) 57(1):289–300
Bernard JA, Seidler RD (2012) Evidence for motor cortex dedifferentiation in older adults. Neurobiol Aging 33(9):1890–1899. https://doi.org/10.1016/j.neurobiolaging.2011.06.021
Betzel RF, Byrge L, He Y, Goñi J, Zuo XN, Sporns O (2014) Changes in structural and functional connectivity among resting-state networks across the human lifespan. Neuroimage 102:345–357. https://doi.org/10.1016/j.neuroimage.2014.07.067
Betzel RF, Gu S, Medaglia JD, Pasqualetti F, Bassett DS (2016) Optimally controlling the human connectome: the role of network topology. Sci Rep 6(1):30770. https://doi.org/10.1038/srep30770
Bikson M, Grossman P, Thomas C, Zannou AL, Jiang J, Adnan T et al (2016) Safety of transcranial direct current stimulation: evidence based update 2016. Brain Stimul 9(5):641–661. https://doi.org/10.1016/j.brs.2016.06.004
Botvinick M, Braver T (2015) Motivation and cognitive control: from behavior to neural mechanism. Annu Rev Psychol 66(1):83–113. https://doi.org/10.1146/annurev-psych-010814-015044
Brown JA, Van Horn JD (2016) Connected brains and minds—the UMCD repository for brain connectivity matrices. Neuroimage 124:1238–1241. https://doi.org/10.1016/j.neuroimage.2015.08.043
Brown J, Rudie J, Bandrowski A, Van Horn J, Bookheimer S (2012) The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis. Front Neuroinform 6:28
Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain’s default network. Ann N Y Acad Sci 1124(1):1–38. https://doi.org/10.1196/annals.1440.011
Buetefisch CM (2015) Role of the contralesional hemisphere in post-stroke recovery of upper extremity motor function. Front Neurol 6:214. https://doi.org/10.3389/fneur.2015.00214
Cabeza R (2002) Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychol Aging 17(1):85–100. https://doi.org/10.1037//0882-7974.17.1.85
Cabeza R, Daselaar SM, Dolcos F, Prince SE, Budde M, Nyberg L (2004) Task-independent and task-specific age effects on brain activity during working memory, visual attention and episodic retrieval. Cereb Cortex 14(4):364–375. https://doi.org/10.1093/cercor/bhg133
Catani M, Dell’acqua F, Thiebaut de Schotten M (2013) A revised limbic system model for memory, emotion and behaviour. Neurosci Biobehav Rev 37(8):1724–1737. https://doi.org/10.1016/j.neubiorev.2013.07.001
Chan MY, Park DC, Savalia NK, Petersen SE, Wig GS (2014) Decreased segregation of brain systems across the healthy adult lifespan. Proc Natl Acad Sci 111(46):E4997–E5006. https://doi.org/10.1073/pnas.1415122111
Charras P, Herbet G, Deverdun J, de Champfleur NM, Duffau H, Bartolomeo P et al (2015) Functional reorganization of the attentional networks in low-grade glioma patients: a longitudinal study. Cortex 63:27–41. https://doi.org/10.1016/j.cortex.2014.08.010
Chen H, Yong EH (2020) Optimizing target nodes selection for the control energy of directed complex networks. Sci Rep 10(1):18112. https://doi.org/10.1038/s41598-020-75101-w
Cheng L, Wu Z, Fu Y, Miao F, Sun J, Tong S (2012) Reorganization of functional brain networks during the recovery of stroke: a functional MRI study. In: 2012 annual international conference of the IEEE Engineering in Medicine and Biology Society, pp 4132–4135
Cocchi L, Zalesky A, Fornito A, Mattingley JB (2013) Dynamic cooperation and competition between brain systems during cognitive control. Trends Cogn Sci 17(10):493–501. https://doi.org/10.1016/j.tics.2013.08.006
Corbetta M, Shulman GL (2002) Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 3(3):201–215. https://doi.org/10.1038/nrn755
Corbetta M, Patel G, Shulman GL (2008) The reorienting system of the human brain: from environment to theory of mind. Neuron 58(3):306–324. https://doi.org/10.1016/j.neuron.2008.04.017
Craddock RC, James GA, Holtzheimer PE, Hu XP, Mayberg HS (2012) A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum Brain Mapp 33(8):1914–1928. https://doi.org/10.1002/hbm.21333
Cui Z, Stiso J, Baum GL, Kim JZ, Roalf DR, Betzel RF et al (2020) Optimization of energy state transition trajectory supports the development of executive function during youth. eLife 9:e53060. https://doi.org/10.7554/eLife.53060
Damoiseaux JS, Smith SM, Witter MP, Sanz-Arigita EJ, Barkhof F, Scheltens P et al (2009) White matter tract integrity in aging and Alzheimer’s disease. Hum Brain Mapp 30(4):1051–1059. https://doi.org/10.1002/hbm.20563
Davey J, Thompson HE, Hallam G, Karapanagiotidis T, Murphy C, De Caso I et al (2016) Exploring the role of the posterior middle temporal gyrus in semantic cognition: integration of anterior temporal lobe with executive processes. Neuroimage 137:165–177. https://doi.org/10.1016/j.neuroimage.2016.05.051
de Schotten MT, Urbanski M, Duffau H, Volle E, Lévy R, Dubois B et al (2005) Direct evidence for a parietal-frontal pathway subserving spatial awareness in humans. Science 309(5744):2226–2228. https://doi.org/10.1126/science.1116251
De Vico Fallani F, Richiardi J, Chavez M, Achard S (2014) Graph analysis of functional brain networks: practical issues in translational neuroscience. Philos Trans R Soc B 369(1653):20130521. https://doi.org/10.1098/rstb.2013.0521
Demirtaş M, Burt JB, Helmer M, Ji JL, Adkinson BD, Glasser MF et al (2019) Hierarchical heterogeneity across human cortex shapes large-scale neural dynamics. Neuron 101(6):1181-1194.e13. https://doi.org/10.1016/j.neuron.2019.01.017
Dennis M, Spiegler BJ, Juranek JJ, Bigler ED, Snead OC, Fletcher JM (2013) Age, plasticity, and homeostasis in childhood brain disorders. Neurosci Biobehav Rev 37(10, Part 2):2760–2773. https://doi.org/10.1016/j.neubiorev.2013.09.010
Dosenbach NUF, Nardos B, Cohen AL, Fair DA, Power JD, Church JA et al (2010) Prediction of individual brain maturity using fMRI. Science 329(5997):1358–1361. https://doi.org/10.1126/science.1194144
Dubois J, de Berker AO, Tsao DY (2015) Single-unit recordings in the macaque face patch system reveal limitations of fMRI MVPA. J Neurosci 35(6):2791–2802. https://doi.org/10.1523/JNEUROSCI.4037-14.2015
Duda BM, Owens MM, Hallowell ES, Sweet LH (2019) Neurocompensatory effects of the default network in older adults. Front Aging Neurosci 11:111. https://doi.org/10.3389/fnagi.2019.00111
Fair DA, Cohen AL, Power JD, Dosenbach NUF, Church JA, Miezin FM et al (2009) Functional brain networks develop from a “local to distributed’’ organization. PLoS Comput Biol 5(5):e1000381. https://doi.org/10.1371/journal.pcbi.1000381
Gao J, Liu YY, D’Souza RM, Barabási AL (2014) Target control of complex networks. Nat Commun 5:5415. https://doi.org/10.1038/ncomms6415
Geerligs L, Renken RJ, Saliasi E, Maurits NM, Lorist MM (2015) A brain-wide study of age-related changes in functional connectivity. Cereb Cortex 25(7):1987–1999. https://doi.org/10.1093/cercor/bhu012
Giza CC, Prins ML (2006) Is being plastic fantastic? Mechanisms of altered plasticity after developmental traumatic brain injury. Dev Neurosci 28(4–5):364–379. https://doi.org/10.1159/000094163
Gómez-Gardeñes J, Latora V (2008) Entropy rate of diffusion processes on complex networks. Phys Rev E 78(6):065102. https://doi.org/10.1103/PhysRevE.78.065102
Gong G, Rosa-Neto P, Carbonell F, Chen ZJ, He Y, Evans AC (2009) Age- and gender-related differences in the cortical anatomical network. J Neurosci 29(50):15684–15693. https://doi.org/10.1523/JNEUROSCI.2308-09.2009
Goñi J, Avena-Koenigsberger A, Velez de Mendizabal N, van den Heuvel MP, Betzel RF, Sporns O (2013) Exploring the morphospace of communication efficiency in complex networks. PLoS One 8(3):e58070. https://doi.org/10.1371/journal.pone.0058070
Gotts SJ, Jo HJ, Wallace GL, Saad ZS, Cox RW, Martin A (2013) Two distinct forms of functional lateralization in the human brain. Proc Natl Acad Sci. https://doi.org/10.1073/pnas.1302581110
Grady C (2012) The cognitive neuroscience of ageing. Nat Rev Neurosci 13(7):491–505. https://doi.org/10.1038/nrn3256
Grady C, Sarraf S, Saverino C, Campbell K (2016) Age differences in the functional interactions among the default, frontoparietal control, and dorsal attention networks. Neurobiol Aging 41:159–172. https://doi.org/10.1016/j.neurobiolaging.2016.02.020
Grefkes C, Fink GR (2014) Connectivity-based approaches in stroke and recovery of function. Lancet Neurol 13(2):206–216. https://doi.org/10.1016/S1474-4422(13)70264-3
Griffis JC, Metcalf NV, Corbetta M, Shulman GL (2019) Structural disconnections explain brain network dysfunction after stroke. Cell Rep 28(10):2527-2540.e9. https://doi.org/10.1016/j.celrep.2019.07.100
Gu S, Pasqualetti F, Cieslak M, Telesford QK, Yu AB, Kahn AE et al (2015) Controllability of structural brain networks. Nat Commun 6:8414. https://doi.org/10.1038/ncomms9414
Guillon J, Chavez M, Battiston F, Attal Y, La Corte V, Thiebaut de Schotten M et al (2019) Disrupted core-periphery structure of multimodal brain networks in Alzheimer’s disease. Netw Neurosci 3(2):635–652
Hampson M, Tokoglu F, Shen X, Scheinost D, Papademetris X, Constable RT (2012) Intrinsic brain connectivity related to age in young and middle aged adults. PLoS One 7(9):e44067. https://doi.org/10.1371/journal.pone.0044067
Hoffman P, Morcom AM (2018) Age-related changes in the neural networks supporting semantic cognition: a meta-analysis of 47 functional neuroimaging studies. Neurosci Biobehav Rev 84:134–150. https://doi.org/10.1016/j.neubiorev.2017.11.010
Honey CJ, Thivierge JP, Sporns O (2010) Can structure predict function in the human brain? Neuroimage 52(3):766–776. https://doi.org/10.1016/j.neuroimage.2010.01.071
Huntenburg JM, Bazin PL, Margulies DS (2018) Large-scale gradients in human cortical organization. Trends Cogn Sci 22(1):21–31. https://doi.org/10.1016/j.tics.2017.11.002
Husain M, Nachev P (2007) Space and the parietal cortex. Trends Cogn Sci 11(1):30–36. https://doi.org/10.1016/j.tics.2006.10.011
Jiang J, Lai YC (2019) Irrelevance of linear controllability to nonlinear dynamical networks. Nat Commun 10(1):3961. https://doi.org/10.1038/s41467-019-11822-5
Kaas JH (1995) The evolution of isocortex. Brain Behav Evol 46(4–5):187–196. https://doi.org/10.1159/000113273
Kalman RE (1963) Mathematical description of linear dynamical systems. J Soc Ind Appl Math Ser A Control 1(2):152–192. https://doi.org/10.1137/0301010
Kerepesi C, Szalkai B, Varga B, Grolmusz V (2016) How to direct the edges of the connectomes: dynamics of the consensus connectomes and the development of the connections in the human brain. PLoS One 11(6):e0158680. https://doi.org/10.1371/journal.pone.0158680
Koch G, Cercignani M, Bonnì S, Giacobbe V, Bucchi G, Versace V et al (2011) Asymmetry of parietal interhemispheric connections in humans. J Neurosci 31(24):8967–8975. https://doi.org/10.1523/JNEUROSCI.6567-10.2011
Kong X, Kong R, Orban C, Wang P, Zhang S, Anderson K et al (2021) Sensory-motor cortices shape functional connectivity dynamics in the human brain. Nat Commun 12(1):6373. https://doi.org/10.1038/s41467-021-26704-y
Kornfeld S, Delgado Rodríguez JA, Everts R, Kaelin-Lang A, Wiest R, Weisstanner C et al (2015) Cortical reorganisation of cerebral networks after childhood stroke: impact on outcome. BMC Neurol 15(1):90. https://doi.org/10.1186/s12883-015-0309-1
Kubicki A, Fautrelle L, Bourrelier J, Rouaud O, Mourey F (2016) The early indicators of functional decrease in mild cognitive impairment. Front Aging Neurosci 8:193
Kullmann DM (2019) Editorial. Brain 142(4):833. https://doi.org/10.1093/brain/awz077
Li SC, Rieckmann A (2014) Neuromodulation and aging: implications of aging neuronal gain control on cognition. Curr Opin Neurobiol 29:148–158. https://doi.org/10.1016/j.conb.2014.07.009
Liu YY, Barabási AL (2016) Control principles of complex networks. Rev Mod Phys. https://doi.org/10.1103/RevModPhys.88.035006
Liu YY, Slotine JJ, Barabási AL (2011) Controllability of complex networks. Nature 473(7346):167–173. https://doi.org/10.1038/nature10011
Logan JM, Sanders AL, Snyder AZ, Morris JC, Buckner RL (2002) Under-recruitment and nonselective recruitment: dissociable neural mechanisms associated with aging. Neuron 33(5):827–840. https://doi.org/10.1016/S0896-6273(02)00612-8
Malkinson TS, Bayle DJ, Bourgeois A, Lehongre K, Fernandez-Vidal S, Navarro V et al (2021) From perception to action: intracortical recordings reveal cortical gradients of human exogenous attention. biorxiv. https://doi.org/10.1101/2021.01.02.425103
Mancuso L, Costa T, Nani A, Manuello J, Liloia D, Gelmini G et al (2019) The homotopic connectivity of the functional brain: a meta-analytic approach. Sci Rep 9(1):3346. https://doi.org/10.1038/s41598-019-40188-3
Max JE, Bruce M, Keatley E, Delis D (2010) Pediatric stroke: plasticity, vulnerability, and age of lesion onset. J Neuropsychiatry Clin Neurosci 22(1):30–39. https://doi.org/10.1176/jnp.2010.22.1.30
Medaglia JD, Pasqualetti F, Hamilton RH, Thompson-Schill SL, Bassett DS (2017) Brain and cognitive reserve: translation via network control theory. Neurosci Biobehav Rev 75:53–64. https://doi.org/10.1016/j.neubiorev.2017.01.016
Medaglia JD, Harvey DY, White N, Kelkar A, Zimmerman J, Bassett DS et al (2018) Network controllability in the inferior frontal gyrus relates to controlled language variability and susceptibility to TMS. J Neurosci 38(28):6399–6410
Morcom AM, Johnson W (2015) Neural reorganization and compensation in aging. J Cogn Neurosci 27(7):1275–1285
Muldoon SF, Pasqualetti F, Gu S, Cieslak M, Grafton ST, Vettel JM et al (2016) Stimulation-based control of dynamic brain networks. PLOS Comput Biol 12(9):e1005076. https://doi.org/10.1371/journal.pcbi.1005076
Murray SO, Wojciulik E (2004) Attention increases neural selectivity in the human lateral occipital complex. Nat Neurosci 7(1):70–74. https://doi.org/10.1038/nn1161
Narayanan NS, Kimchi EY, Laubach M (2005) Redundancy and synergy of neuronal ensembles in motor cortex. J Neurosci 25(17):4207–4216. https://doi.org/10.1523/JNEUROSCI.4697-04.2005
Nooner KB, Colcombe S, Tobe R, Mennes M, Benedict M, Moreno A et al (2012) The NKI-Rockland sample: a model for accelerating the pace of discovery science in psychiatry. Front Neurosci. https://doi.org/10.3389/fnins.2012.00152
Paquola C, Wael RVD, Wagstyl K, Bethlehem RAI, Hong SJ, Seidlitz J et al (2019) Microstructural and functional gradients are increasingly dissociated in transmodal cortices. PLoS Biol 17(5):e3000284. https://doi.org/10.1371/journal.pbio.3000284
Park DC, Reuter-Lorenz P (2009) The adaptive brain: aging and neurocognitive scaffolding. Annu Rev Psychol 60(1):173–196. https://doi.org/10.1146/annurev.psych.59.103006.093656
Pasqualetti F, Zampieri S, Bullo F (2014) Controllability metrics, limitations and algorithms for complex networks. IEEE Trans Control Netw Syst 1(1):40–52. https://doi.org/10.1109/TCNS.2014.2310254
Raj A, Kuceyeski A, Weiner M (2012) A network diffusion model of disease progression in dementia. Neuron 73(6):1204–1215. https://doi.org/10.1016/j.neuron.2011.12.040
Rasia-Filho AA, Guerra KTK, Vásquez CE, Dall’Oglio A, Reberger R, Jung CR et al (2021) The subcortical–allocortical–neocortical continuum for the emergence and morphological heterogeneity of pyramidal neurons in the human brain. Front Synaptic Neurosci 13:7. https://doi.org/10.3389/fnsyn.2021.616607
Ravindran V, Sunitha V, Bagler G (2017) Identification of critical regulatory genes in cancer signaling network using controllability analysis. Phys A Stat Mech Appl 474:134–143. https://doi.org/10.1016/j.physa.2017.01.059
Ravindran V, Nacher JC, Akutsu T, Ishitsuka M, Osadcenco A, Sunitha V et al (2019) Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems. Sci Rep 9(1):2066. https://doi.org/10.1038/s41598-018-38224-9
Reich DS, Mechler F, Victor JD (2001) Independent and Redundant information in nearby cortical neurons. Science 294(5551):2566–2568. https://doi.org/10.1126/science.1065839
Rolls ET (2015) Limbic systems for emotion and for memory, but no single limbic system. Cortex 62:119–157. https://doi.org/10.1016/j.cortex.2013.12.005
Rugh WJ, Kailath T (1995) Linear system theory, 2nd edn. Pearson, Upper Saddle River
Salvalaggio A, De Filippo De Grazia M, Zorzi M, Thiebaut de Schotten M, Corbetta M (2020) Post-stroke deficit prediction from lesion and indirect structural and functional disconnection. Brain 143(7):2173–2188. https://doi.org/10.1093/brain/awaa156
Scheltens P, Leys D, Barkhof F, Huglo D, Weinstein HC, Vermersch P et al (1992) Atrophy of medial temporal lobes on MRI in “probable’’ Alzheimer’s disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry 55(10):967–972. https://doi.org/10.1136/jnnp.55.10.967
Schilling KG, Janve V, Gao Y, Stepniewska I, Landman BA, Anderson AW (2018) Histological validation of diffusion MRI fiber orientation distributions and dispersion. Neuroimage 165:200–221. https://doi.org/10.1016/j.neuroimage.2017.10.046
Seguin C, Razi A, Zalesky A (2019) Inferring neural signalling directionality from undirected structural connectomes. Nat Commun 10(1):1–13. https://doi.org/10.1038/s41467-019-12201-w
Shafiei G, Markello RD, Vos de Wael R, Bernhardt BC, Fulcher BD, Misic B (2020) topographic gradients of intrinsic dynamics across neocortex. Life. 9:e62116. https://doi.org/10.7554/eLife.62116
Shulman GL, Pope DLW, Astafiev SV, McAvoy MP, Snyder AZ, Corbetta M (2010) Right hemisphere dominance during spatial selective attention and target detection occurs outside the dorsal frontoparietal network. J Neurosci 30(10):3640–3651. https://doi.org/10.1523/JNEUROSCI.4085-09.2010
Slotine JJ, Li W (1991) Applied nonlinear control. Pearson, Englewood Cliffs
So K, Ganguly K, Jimenez J, Gastpar MC, Carmena JM (2012) Redundant information encoding in primary motor cortex during natural and prosthetic motor control. J Comput Neurosci 32(3):555–561. https://doi.org/10.1007/s10827-011-0369-1
Sohn H, Meirhaeghe N, Rajalingham R, Jazayeri M (2021) A network perspective on sensorimotor learning. Trends Neurosci 44(3):170–181. https://doi.org/10.1016/j.tins.2020.11.007
Song J, Birn RM, Boly M, Meier TB, Nair VA, Meyerand ME et al (2014) Age-related reorganizational changes in modularity and functional connectivity of human brain networks. Brain Connect 4(9):662–676. https://doi.org/10.1089/brain.2014.0286
Spreng RN, Turner GR (2019) The shifting architecture of cognition and brain function in older adulthood. Perspect Psychol Sci 14(4):523–542. https://doi.org/10.1177/1745691619827511
Spreng RN, Stevens WD, Viviano JD, Schacter DL (2016) Attenuated anticorrelation between the default and dorsal attention networks with aging: evidence from task and rest. Neurobiol Aging 45:149–160. https://doi.org/10.1016/j.neurobiolaging.2016.05.020
Supekar K, Menon V (2012) Developmental maturation of dynamic causal control signals in higher-order cognition: a neurocognitive network model. PLoS Comput Biol 8(2):e1002374. https://doi.org/10.1371/journal.pcbi.1002374
Suweis S, Tu C, Rocha RP, Zampieri S, Zorzi M, Corbetta M (2019) Brain controllability: not a slam dunk yet. Neuroimage 200:552–555. https://doi.org/10.1016/j.neuroimage.2019.07.012
Tang E, Bassett DS (2018) Colloquium: control of dynamics in brain networks. Rev Mod Phys 90(3):031003. https://doi.org/10.1103/RevModPhys.90.031003
Tang C, Zhao Z, Chen C, Zheng X, Sun F, Zhang X et al (2016) Decreased functional connectivity of homotopic brain regions in chronic stroke patients: a resting state fMRI study. PLoS One. 11(4):e0152875. https://doi.org/10.1371/journal.pone.0152875
Tang E, Giusti C, Baum GL, Gu S, Pollock E, Kahn AE et al (2017) Developmental increases in white matter network controllability support a growing diversity of brain dynamics. Nat Commun 8(1):1252. https://doi.org/10.1038/s41467-017-01254-4
Tewarie P, van Dellen E, Hillebrand A, Stam CJ (2015) The minimum spanning tree: an unbiased method for brain network analysis. Neuroimage 104:177–188. https://doi.org/10.1016/j.neuroimage.2014.10.015
Tomasi D, Volkow ND (2012) Aging and functional brain networks. Mol Psychiatry 17(5):549–558. https://doi.org/10.1038/mp.2011.81
Tu C, Rocha RP, Corbetta M, Zampieri S, Zorzi M, Suweis S (2018) Warnings and caveats in brain controllability. Neuroimage 176:83–91. https://doi.org/10.1016/j.neuroimage.2018.04.010
van Dellen E, Sommer IE, Bohlken MM, Tewarie P, Draaisma L, Zalesky A et al (2018) Minimum spanning tree analysis of the human connectome. Hum Brain Mapp 39(6):2455–2471. https://doi.org/10.1002/hbm.24014
van den Heuvel MP, Sporns O (2013) Network hubs in the human brain. Trends Cogn Sci 17(12):683–696. https://doi.org/10.1016/j.tics.2013.09.012
Vázquez-Rodríguez B, Suárez LE, Markello RD, Shafiei G, Paquola C, Hagmann P et al (2019) Gradients of structure-function tethering across neocortex. Proc Natl Acad Sci 116(42):21219–21227. https://doi.org/10.1073/pnas.1903403116
Vinayagam A, Gibson TE, Lee HJ, Yilmazel B, Roesel C, Hu Y et al (2016) Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets. Proc Natl Acad Sci. https://doi.org/10.1073/pnas.1603992113
Voss MW, Erickson KI, Chaddock L, Prakash RS, Colcombe SJ, Morris KS et al (2008) Dedifferentiation in the visual cortex: an fMRI investigation of individual differences in older adults. Brain Res 1244:121–131. https://doi.org/10.1016/j.brainres.2008.09.051
Wang XJ (2020) Macroscopic gradients of synaptic excitation and inhibition in the neocortex. Nat Rev Neurosci 21(3):169–178. https://doi.org/10.1038/s41583-020-0262-x
Wilson D, Moehlis J (2015) Clustered desynchronization from high-frequency deep brain stimulation. PLOS Comput Biol 11(12):e1004673. https://doi.org/10.1371/journal.pcbi.1004673
Worrell JC, Rumschlag J, Betzel RF, Sporns O, Mišić B (2017) Optimized connectome architecture for sensory-motor integration. Netw Neurosci 1(4):415–430
Xu X, Yuan H, Lei X (2016) Activation and connectivity within the default mode network contribute independently to future-oriented thought. Sci Rep 6(1):1–10. https://doi.org/10.1038/srep21001
Yan G, Vértes PE, Towlson EK, Chew YL, Walker DS, Schafer WR et al (2017) Network control principles predict neuron function in the Caenorhabditis elegans connectome. Nature. https://doi.org/10.1038/nature24056 (advance online publication)
Zhang M, Savill N, Margulies DS, Smallwood J, Jefferies E (2019) Distinct individual differences in default mode network connectivity relate to off-task thought and text memory during reading. Sci Rep 9(1):16220. https://doi.org/10.1038/s41598-019-52674-9
Zhao T, Cao M, Niu H, Zuo XN, Evans A, He Y et al (2015) Age-related changes in the topological organization of the white matter structural connectome across the human lifespan. Hum Brain Mapp 36(10):3777–3792. https://doi.org/10.1002/hbm.22877
Zhu Y, Bai L, Liang P, Kang S, Gao H, Yang H (2017) Disrupted brain connectivity networks in acute ischemic stroke patients. Brain Imaging Behav 11(2):444–453. https://doi.org/10.1007/s11682-016-9525-6
Acknowledgements
Authors would like to acknowledge Thibault Rolland (fr.linkedin.com/in/thibault-rolland-40b57419a) for the realization of Picture 1. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.
Funding
The research leading to these results has received funding from the French government under management of Agence Nationale de la Recherche as part of the “Investissements d’avenir” program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and reference ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6), and from the Inria Project Lab Program (project Neuromarkers), the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (Grant Agreement no. 864729).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Authors declare no conflict of interest.
Additional information
Publisher's Note
Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Bassignana, G., Lacidogna, G., Bartolomeo, P. et al. The impact of aging on human brain network target controllability. Brain Struct Funct 227, 3001–3015 (2022). https://doi.org/10.1007/s00429-022-02584-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00429-022-02584-w