Brain Structure and Function

, Volume 223, Issue 8, pp 3801–3812 | Cite as

Distributed cortical structural properties contribute to motor cortical excitability and inhibition

  • Eran DayanEmail author
  • Virginia López-Alonso
  • Sook-Lei Liew
  • Leonardo G. CohenEmail author
Original Article


The link between the local structure of the primary motor cortex and motor function has been well documented. However, motor function relies on a network of interconnected brain regions and the link between the structural properties characterizing these distributed brain networks and motor function remains poorly understood. Here, we examined whether distributed patterns of brain structure, extending beyond the primary motor cortex can help classify two forms of motor function: corticospinal excitability and intracortical inhibition. To this effect, we recorded high-resolution structural magnetic resonance imaging scans in 25 healthy volunteers. To measure corticospinal excitability and inhibition in the same volunteers, we recorded motor evoked potentials (MEPs) elicited by single-pulse transcranial magnetic stimulation and short-interval intracortical inhibition (SICI) in a separate session. Support vector machine (SVM) pattern classification was used to identify distributed multi-voxel gray-matter areas, which distinguished subjects who had lower and higher MEPs and SICIs. We found that MEP and SICI classification could be predicted based on a widely distributed, largely non-overlapping pattern of voxels in frontal, parietal, temporal, occipital, and cerebellar regions. Thus, structural properties distributed over the brain beyond the primary motor cortex relate to motor function.


Cortical excitability Cortical inhibition TMS MRI 



This work was supported by the Intramural Research Program of the National Institute of Neurological Disorders and Stroke, National Institutes of Health. Virginia López-Alonso was supported by an FPU fellowship from Ministerio de Educación, Cultura y Deporte, Spain. Sook-Lei Liew acknowledges funding by the NIH Eunice Kennedy Shriver National Institute of Child Health and Human Development (K01HD091283, HD055929). The study utilized the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, Md. ( We thank Ryan Thompson for assistance in the preparation of this manuscript.

Author contributions

All authors designed the study; VLA and SLL performed the experiments. ED and VLA analyzed the data. All authors wrote and reviewed the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

429_2018_1722_MOESM1_ESM.docx (13 kb)
Supplementary material 1 (DOCX 12 KB)


  1. Alexander GE, DeLong MR, Strick PL (1986) Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci 9:357–381. CrossRefPubMedPubMedCentralGoogle Scholar
  2. Alexander-Bloch A, Giedd JN, Bullmore E (2013) Imaging structural co-variance between human brain regions. Nat Rev Neurosci 14:322–336. CrossRefPubMedPubMedCentralGoogle Scholar
  3. Bestmann S, Krakauer JW (2015) The uses and interpretations of the motor-evoked potential for understanding behaviour. Exp Brain Res 233:679–689CrossRefPubMedCentralGoogle Scholar
  4. Bishop CM (2006) Pattern recognition and machine learning. Springer, BerlinGoogle Scholar
  5. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on computational learning theory. ACM, pp 144–152Google Scholar
  6. Bullmore ET, Bassett DS (2011) Brain graphs: graphical models of the human brain connectome. Annu Rev Clin Psychol 7:113–140. CrossRefPubMedPubMedCentralGoogle Scholar
  7. Conde V, Vollmann H, Sehm B, Taubert M, Villringer A, Ragert P (2012) Cortical thickness in primary sensorimotor cortex influences the effectiveness of paired associative stimulation. Neuroimage 60:864–870. CrossRefPubMedPubMedCentralGoogle Scholar
  8. Darling WG, Wolf SL, Butler AJ (2006) Variability of motor potentials evoked by transcranial magnetic stimulation depends on muscle activation. Exp Brain Res 174:376–385. CrossRefPubMedPubMedCentralGoogle Scholar
  9. Daskalakis ZJ, Paradiso GO, Christensen BK, Fitzgerald PB, Gunraj C, Chen R (2004) Exploring the connectivity between the cerebellum and motor cortex in humans. J Physiol 557:689–700CrossRefPubMedCentralGoogle Scholar
  10. Dayan E, Censor N, Buch ER, Sandrini M, Cohen LG (2013) Noninvasive brain stimulation: from physiology to network dynamics and back. Nat Neurosci 16:838–844. CrossRefPubMedPubMedCentralGoogle Scholar
  11. Dayan E, Hamann JM, Averbeck BB, Cohen LG (2014) Brain structural substrates of reward dependence during behavioral performance. J Neurosci 34:16433–16441. CrossRefPubMedPubMedCentralGoogle Scholar
  12. Ecker C, Marquand A, Mourão-Miranda J, Johnston P, Daly EM, Brammer MJ, Maltezos S, Murphy CM, Robertson D, Williams SC et al (2010a) Describing the brain in autism in five dimensions—magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. J Neurosci 30:10612–10623CrossRefPubMedCentralGoogle Scholar
  13. Ecker C, Rocha-Rego V, Johnston P, Mourao-Miranda J, Marquand A, Daly EM, Brammer MJ, Murphy C, Murphy DG (2010b) Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage 49:44–56. CrossRefPubMedPubMedCentralGoogle Scholar
  14. Fougnie D (2008) The relationship between attention and working memory. New Res Short Term Mem 1:45Google Scholar
  15. Fox MD, Corbetta M, Snyder AZ, Vincent JL, Raichle ME (2006) Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci 103:10046–10051CrossRefPubMedCentralGoogle Scholar
  16. Garry MI, Thomson RHS (2009) The effect of test TMS intensity on short-interval intracortical inhibition in different excitability states. Exp Brain Res 193:267CrossRefPubMedCentralGoogle Scholar
  17. Goetz SM, Luber B, Lisanby SH, Peterchev AV (2014) A novel model incorporating two variability sources for describing motor evoked potentials. Brain Stimul 7:541–552CrossRefPubMedCentralGoogle Scholar
  18. Guye M, Parker GJM, Symms M, Boulby P, Wheeler-Kingshott CAM, Salek-Haddadi A, Barker GJ, Duncan JS (2003) Combined functional MRI and tractography to demonstrate the connectivity of the human primary motor cortex in vivo. Neuroimage 19:1349–1360CrossRefPubMedCentralGoogle Scholar
  19. Hallett M (2007) Transcranial magnetic stimulation: a primer. Neuron 55:187–199. CrossRefPubMedGoogle Scholar
  20. Hasan A, Galea JM, Casula EP, Falkai P, Bestmann S, Rothwell JC (2013) Muscle and timing-specific functional connectivity between the dorsolateral prefrontal cortex and the primary motor cortex. J Cogn Neurosci 25:558–570. CrossRefPubMedPubMedCentralGoogle Scholar
  21. He BJ, Snyder AZ, Vincent JL, Epstein A, Shulman GL, Corbetta M (2007) Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits in spatial neglect. Neuron 53:905–918. CrossRefPubMedPubMedCentralGoogle Scholar
  22. Johansen-Berg H, Della-Maggiore V, Behrens TEJ, Smith SM, Paus T (2007) Integrity of white matter in the corpus callosum correlates with bimanual co-ordination skills. Neuroimage 36:T16–T21. CrossRefPubMedPubMedCentralGoogle Scholar
  23. Jung NH, Delvendahl I, Kuhnke NG, Hauschke D, Stolle S, Mall V (2010) Navigated transcranial magnetic stimulation does not decrease the variability of motor-evoked potentials. Brain Stimul 3:87–94. CrossRefPubMedPubMedCentralGoogle Scholar
  24. Kanai R, Rees G (2011) The structural basis of inter-individual differences in human behaviour and cognition. Nat Rev Neurosci 12:231–242. CrossRefPubMedPubMedCentralGoogle Scholar
  25. Kelly RM, Strick PL (2003) Cerebellar loops with motor cortex and prefrontal cortex of a nonhuman primate. J Neurosci 23:8432–8444CrossRefGoogle Scholar
  26. Kiers L, Cros D, Chiappa KH, Fang J (1993) Variability of motor potentials evoked by transcranial magnetic stimulation. Electroencephalogr Clin Neurophysiol 89:415–423CrossRefPubMedCentralGoogle Scholar
  27. Kujirai T, Caramia MD, Rothwell JC, Day BL, Thompson PD, Ferbert A, Wroe S, Asselman P, Marsden CD (1993) Corticocortical inhibition in human motor cortex. J Physiol 471:501–519CrossRefPubMedCentralGoogle Scholar
  28. Liew S-L, Santarnecchi E, Buch ER, Cohen LG (2014) Non-invasive brain stimulation in neurorehabilitation: local and distant effects for motor recovery. Front Hum Neurosci. CrossRefPubMedPubMedCentralGoogle Scholar
  29. Lu MT, Preston JB, Strick PL (1994) Interconnections between the prefrontal cortex and the premotor areas in the frontal lobe. J Comp Neurol 341:375–392. CrossRefPubMedPubMedCentralGoogle Scholar
  30. Marquand A, Howard M, Brammer M, Chu C, Coen S, Mourão-Miranda J (2010) Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes. Neuroimage 49:2178–2189CrossRefPubMedCentralGoogle Scholar
  31. Mars RB, Bestmann S, Rothwell JC, Haggard P (2007) Effects of motor preparation and spatial attention on corticospinal excitability in a delayed-response paradigm. Exp Brain Res 182:125–129CrossRefPubMedCentralGoogle Scholar
  32. Matsumoto R, Nair DR, LaPresto E, Bingaman W, Shibasaki H, Luders HO (2006) Functional connectivity in human cortical motor system: a cortico-cortical evoked potential study. Brain 130:181–197. CrossRefPubMedPubMedCentralGoogle Scholar
  33. Mourão-Miranda J, Bokde AL, Born C, Hampel H, Stetter M (2005) Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. Neuroimage 28:980–995CrossRefPubMedCentralGoogle Scholar
  34. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9:97–113CrossRefGoogle Scholar
  35. Picard N, Strick PL (1996) Motor areas of the medial wall: a review of their location and functional activation. Cereb Cortex 6:342–353CrossRefPubMedCentralGoogle Scholar
  36. Pitcher JB, Ogston KM, Miles TS (2003) Age and sex differences in human motor cortex input–output characteristics. J Physiol 546:605–613. CrossRefPubMedPubMedCentralGoogle Scholar
  37. Ramnani N (2006) The primate cortico-cerebellar system: anatomy and function. Nat Rev Neurosci 7:511–522. CrossRefPubMedGoogle Scholar
  38. Rosenkranz K, Rothwell JC (2004) The effect of sensory input and attention on the sensorimotor organization of the hand area of the human motor cortex: muscle vibration and sensorimotor organization. J Physiol 561:307–320. CrossRefPubMedPubMedCentralGoogle Scholar
  39. Roshan L, Paradiso GO, Chen R (2003) Two phases of short-interval intracortical inhibition. Exp Brain Res 151:330–337CrossRefPubMedCentralGoogle Scholar
  40. Rothwell JC (1997) Techniques and mechanisms of action of transcranial stimulation of the human motor cortex. J Neurosci Methods 74:113–122CrossRefPubMedCentralGoogle Scholar
  41. Rothwell JC, Hallett M, Berardelli A, Eisen A, Rossini P, Paulus W (1999) Magnetic stimulation: motor evoked potentials. Electroencephalogr Clin Neurophysiol Suppl 52:97–103PubMedPubMedCentralGoogle Scholar
  42. Rothwell JC, Day BL, Thompson PD, Kujirai T (2009) Short latency intracortical inhibition: one of the most popular tools in human motor neurophysiology. Perspect J Physiol 587:11–12. CrossRefGoogle Scholar
  43. Sakai K, Ugawa Y, Terao Y, Hanajima R, Furubayashi T, Kanazawa I (1997) Preferential activation of different I waves by transcranial magnetic stimulation with a figure-of-eight-shaped coil. Exp Brain Res 113:24–32CrossRefGoogle Scholar
  44. Sanger TD, Garg RR, Chen R (2001) Interactions between two different inhibitory systems in the human motor cortex. J Physiol 530:307–317CrossRefPubMedCentralGoogle Scholar
  45. Smolker HR, Depue BE, Reineberg AE, Orr JM, Banich MT (2015) Individual differences in regional prefrontal gray matter morphometry and fractional anisotropy are associated with different constructs of executive function. Brain Struct Funct 220:1291–1306CrossRefPubMedCentralGoogle Scholar
  46. Taylor JL, Gandevia SC (2001) Transcranial magnetic stimulation and human muscle fatigue. Muscle Nerve 24:18–29CrossRefPubMedCentralGoogle Scholar
  47. Temesi J, Rupp T, Martin V, Arnal PJ, FéAsson L, Verges S, Millet GY (2014) Central fatigue assessed by transcranial magnetic stimulation in ultratrail running. Med Sci Sports Exerc 46:1166–1175. CrossRefPubMedPubMedCentralGoogle Scholar
  48. Thomson RHS, Garry MI, Summers JJ (2008) Attentional influences on short-interval intracortical inhibition. Clin Neurophysiol 119:52–62. CrossRefPubMedPubMedCentralGoogle Scholar
  49. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289. CrossRefGoogle Scholar
  50. van Gaal S, Scholte HS, Lamme VAF, Fahrenfort JJ, Ridderinkhof KR (2011) Pre-SMA gray-matter density predicts individual differences in action selection in the face of conscious and unconscious response conflict. J Cogn Neurosci 23:382–390. CrossRefPubMedPubMedCentralGoogle Scholar
  51. Vemuri P, Gunter JL, Senjem ML, Whitwell JL, Kantarci K, Knopman DS, Boeve BF, Petersen RC, Jack CR (2008) Alzheimer’s disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 39, 1186–1197. CrossRefPubMedPubMedCentralGoogle Scholar
  52. Vossel S, Geng JJ, Fink GR (2014) Dorsal and ventral attention systems: distinct neural circuits but collaborative roles. Neuroscientist 20:150–159CrossRefPubMedCentralGoogle Scholar
  53. Westlye LT, Grydeland H, Walhovd KB, Fjell AM (2010) Associations between regional cortical thickness and attentional networks as measured by the attention network test. Cereb Cortex 21:345–356CrossRefPubMedCentralGoogle Scholar
  54. Zatorre RJ, Fields RD, Johansen-Berg H (2012) Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci 15:528–536. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© © This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2018 2018

Authors and Affiliations

  1. 1.Department of Radiology, Biomedical Research Imaging Center and Neuroscience CurriculumUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Department of Physical Activity and Sport Sciences“Center of Higher Education Alberta Giménez (CESAG)” Comillas Pontifical UniversityPalmaSpain
  3. 3.Department of Physical Education, Faculty of Sciences of Sport and Physical EducationUniversity of A CoruñaA CoruñaSpain
  4. 4.Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA
  5. 5.Divisions of Occupational Science and Occupational Therapy, Biokinesiology and Physical TherapyUniversity of Southern CaliforniaLos AngelesUSA
  6. 6.Human Cortical Physiology and Neurorehabilitation SectionNational Institute of Neurological Disorders and Stroke, National Institutes of HealthBethesdaUSA

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