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Linking connectomics and dynamics in the human brain

Big data need big theories!

Verknüpfung von Struktur und Aktivität im menschlichen Gehirn

Theorien helfen, aus komplexen Daten Wissen zu generieren

  • Review article
  • Published:
e-Neuroforum

Abstract

To understand human cognition, it is essential to study the brain on multiple levels, from microscopic to macroscopic scales. Computational connectomics is a new area of neuroscience where scientists seek to combine empirical observations within a computational theory of the brain. The whole-brain network modeling and simulation platform, The Virtual Brain (TVB), is a remarkable innovation in the field of computational connectomics. By combining the connectivity of individual persons with local biologically realistic population models, TVB allows simulation and prediction of the local activity of neuronal populations and the global activity unfolding along the gray matter, both of which can be linked to empirical measures of electrical, hemodynamic, and structural aspects of the brain. TVB is currently used to study the structural, functional, and computational alterations in the diseased brain with reported successes in stroke and epilepsy. Subject-specific brain models provided by TVB will result in robust and efficient personalized diagnostics, prognostics, and treatment.

Zusammenfassung

Um die menschliche Kognition wirklich zu verstehen, ist es von essentieller Bedeutung, das Gehirn in all seinen multiplen Ebenen zu studieren. Das Gebiet der Computational Connectomics eröffnet einen neuen Zweig in den Neurowissenschaften, in dem versucht wird, verschiedene empirische Beobachtungen mit einem mathematischen Modell des Gehirns zu erklären. Eine bemerkenswerte Innovation stellt hier die Plattform „The Virtual Brain“ (TVB) dar. Sie ermöglicht die Modellierung und Simulation des vollständigen menschlichen Gehirns. Dabei wird die individuelle Konnektivität einer Person, also ein Gerüst von langen Nervenfaserbündeln im Gehirn, mit biologisch realistischen Modellen der lokalen Neuronenpopulationen kombiniert. Das Virtual Brain erlaubt die Simulation und Vorhersage der globalen neuronalen Aktivität, die sich in der gesamten kortikalen und subkortikalen grauen Substanz entfaltet. Dabei werden auch diejenigen Signale simuliert, die wir bei individuellen Personen mit invasiven und nichtinvasiven Methoden messen können. TVB wird aktuell genutzt, um strukturelle und funktionelle Veränderungen im erkrankten Gehirn zu untersuchen. Es wurden bereits Erfolge in der Erforschung des Schlaganfalls und der Epilepsie verbucht. Die durch TVB ermöglichten personenbezogenen neuronalen Modelle eröffnen neue Möglichkeiten zur sicheren und effizienten personalisierten Diagnostik, Prognostik und Therapie bei verschiedenen neurologischen und kognitiven Erkrankungen.

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References

  1. Amadi U, Ilie A, Johansen-Berg H, Stagg CJ (2014) Polarity-specific effects of motor transcranial direct current stimulation on fMRI resting state networks. Neuroimage 88:155

    Article  PubMed  PubMed Central  Google Scholar 

  2. Antonenko D, Faxel M, Grittner U, Lavidor M, Flöel A et al (2016) Effects of Transcranial Alternating Current Stimulation on Cognitive Functions in Healthy Young and Older Adults. Neural Plast 2016:1–13. doi:10.1155/2016/4274127

    Article  Google Scholar 

  3. Becker R, Knock S, Ritter P, Jirsa V, da Silva FL, Van Lierop T, Jirsa V (2015) Relating alpha power and phase to population firing and hemodynamic activity using a thalamo-cortical neural mass model. PLOS Comput Biol 11(9):e1004352. doi:10.1371/journal.pcbi.1004352

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bezgin G, Vakorin VA, van Opstal AJ, McIntosh AR, Bakker R (2012) Hundreds of brain maps in one atlas: registering coordinate-independent primate neuro-anatomical data to a standard brain. Neuroimage 62(1):67–76. doi:10.1016/j.neuroimage.2012.04.013

    Article  PubMed  Google Scholar 

  5. Bohland JW, Wu C, Barbas H, Bokil H, Bota M, Breiter HC, Mitra PP (2009) A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale. PLOS Comput Biol 5(3):e1000334. doi:10.1371/journal.pcbi.1000334

    Article  PubMed  PubMed Central  Google Scholar 

  6. Brunoni AR, Machado-Vieira R, Sampaio-Junior B, Vieira ELM, Valiengo L, Benseñor IM, Teixeira AL (2015) Plasma levels of soluble TNF receptors 1 and 2 after tDCS and sertraline treatment in major depression: results from the SELECT-TDCS trial. J Affect Disord. doi:10.1016/j.jad.2015.07.006

    Google Scholar 

  7. Deco G, Jirsa VK, McIntosh AR (2011) Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci 12(1):43–56. doi:10.1038/nrn2961

    Article  CAS  PubMed  Google Scholar 

  8. Deco G, Martí D, Ledberg A, Reig R, Sanchez Vives MV (2009) Effective Reduced Diffusion-Models: A Data Driven Approach to the Analysis of Neuronal Dynamics. PLOS Comput Biol 5(12):e1000587. doi:10.1371/journal.pcbi.1000587

    Article  PubMed  PubMed Central  Google Scholar 

  9. Deco G, Tononi G, Boly M, Kringelbach ML (2015) Rethinking segregation and integration: contributions of whole-brain modelling. Nat Rev Neurosci 16(7):430–439. doi:10.1038/nrn3963

    Article  CAS  PubMed  Google Scholar 

  10. Falcon MI, Riley JD, Jirsa V, McIntosh AR, Chen EE, Solodkin A (2016) Functional mechanisms of recovery after chronic stroke: modeling with the virtual brain. eNeuro. doi:10.1523/ENEURO.0158-15.2016

    PubMed  PubMed Central  Google Scholar 

  11. Falcon MI, Riley JD, Jirsa V, McIntosh AR, Shereen AD, Chen EE, Solodkin A (2015) The virtual brain: modeling biological correlates of recovery after chronic stroke. Front Neurol 6:228. doi:10.3389/fneur.2015.00228

    Article  PubMed  PubMed Central  Google Scholar 

  12. Freyer F, Aquino K, Robinson PA, Ritter P, Breakspear M (2009) Bistability and non-Gaussian fluctuations in spontaneous cortical activity. J Neurosci 29(26):8512–8524. doi:10.1523/JNEUROSCI.0754-09.2009

    Article  CAS  PubMed  Google Scholar 

  13. Freyer F, Becker R, Dinse HR, Ritter P (2013) Behavioral/cognitive state-dependent perceptual learning. J Neurosci. doi:10.1523/JNEUROSCI.4039-12.2013

    PubMed  Google Scholar 

  14. Freyer F, Roberts JA, Ritter P, Breakspear M (2012) A canonical model of multistability and scale-Invariance in biological systems. PLOS Comput Biol 8(8):e1002634. doi:10.1371/journal.pcbi.1002634

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Helmstaedter M (2015) The mutual inspirations of machine learning and neuroscience. Neuron 86:25–28. doi:10.1016/j.neuron.2015.03.031

    Article  CAS  PubMed  Google Scholar 

  16. Helmstaedter M, Briggman KL, Turaga SC, Jain V, Seung HS, Denk W (2013) Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500(7461):168–174. doi:10.1038/nature12346

    Article  CAS  PubMed  Google Scholar 

  17. Hofmann-Apitius M, Alarcón-Riquelme ME, Chamberlain C, McHale D (2015) Towards the taxonomy of human disease. Nat Rev Drug Discov 14(2):75–76. doi:10.1038/nrd4537

    Article  CAS  PubMed  Google Scholar 

  18. Iyappan A, Gündel M, Shahid M, Wang J, Li H, Mevissen H‑T, Hofmann-Apitius M (2016) Towards a Pathway Inventory of the Human Brain for Modeling Disease Mechanisms Underlying Neurodegeneration. J Alzheimers Dis 52(4):1343–1360. doi:10.3233/JAD-151178

    Article  PubMed  Google Scholar 

  19. Jirsa VK, Proix T, Perdikis D, Woodman MM, Wang H, Gonzalez-Martinez J, Bartolomei F (2016) The virtual epileptic patient: individualized whole-brain models of epilepsy spread. Neuroimage. doi:10.1016/j.neuroimage.2016.04.049

    PubMed Central  Google Scholar 

  20. Jirsa VK, Stacey WC, Quilichini PP, Ivanov AI, Bernard C, Andrew R, Schwartz T (2014) On the nature of seizure dynamics. Brain 137(8):2210–2230. doi:10.1093/brain/awu133

    Article  PubMed  PubMed Central  Google Scholar 

  21. Jones DT, Vemuri P, Murphy MC, Gunter JL, Senjem ML, Machulda MM, Seeley W (2012) Non-Stationarity in the “resting brain’s” modular architecture. PLOS ONE 7(6):e39731. doi:10.1371/journal.pone.0039731

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kringelbach ML, McIntosh AR, Ritter P, Jirsa VK, Deco G (2015) The rediscovery of slowness: exploring the timing of cognition. Trends Cogn Sci (Regul Ed) 19(10):616–628. doi:10.1016/j.tics.2015.07.011

    Article  Google Scholar 

  23. Ritter P, Born J, Brecht M, Dinse HR, Heinemann U, Pleger B, Kempter R (2015) State-dependencies of learning across brain scales. Front Comput Neurosci. doi:10.3389/fncom.2015.00001

    Google Scholar 

  24. Ritter P, Schirner M, McIntosh AR, Jirsa VK (2013) The virtual brain integrates computational modeling and multimodal neuroimaging. Brain Connect. doi:10.1089/brain.2012.0120

    PubMed  PubMed Central  Google Scholar 

  25. Dipanjan R, Rodrigo S, Breakspear M, McIntosh RA, Deco G, Ritter P (2014) Using the virtual brain to reveal the role of oscillations and plasticity in shaping brain’s dynamical landscape. Brain Connect. doi:10.1089/brain.2014.0252

    Google Scholar 

  26. Sanz-Leon P, Knock SA, Spiegler A, Jirsa VK (2015) Mathematical framework for large-scale brain network modeling in the virtual brain. Neuroimage 111:385–430. doi:10.1016/j.neuroimage.2015.01.002

    Article  PubMed  Google Scholar 

  27. Schirner M, Rothmeier S, Jirsa VK, McIntosh AR, Ritter P (2015) An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data. Neuroimage 117:343–357. doi:10.1016/j.neuroimage.2015.03.055

    Article  PubMed  Google Scholar 

  28. Sigala R, Haufe S, Roy D, Dinse HR, Ritter P (2014) The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models. Front Comput Neurosci 8:36. doi:10.3389/fncom.2014.00036

    Article  PubMed  PubMed Central  Google Scholar 

  29. Sporns O, Tononi G, Kötter R (2005) The human connectome: a structural description of the human brain. PLOS Comput Biol 1(4):e42. doi:10.1371/journal.pcbi.0010042

    Article  PubMed  PubMed Central  Google Scholar 

  30. Swanson LW, Lichtman JW (2016) From cajal to connectome and beyond. Annu Rev Neurosci. doi:10.1146/annurev-neuro-071714-033954

    PubMed  Google Scholar 

  31. Rex DE, Ma JQ, Toga AW (2003) The LONI pipeline processing environment. Neuroimage 19(3):1033–1048. doi:10.1016/S1053-8119(03)00185-X

    Article  PubMed  Google Scholar 

  32. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Collins R (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Med 12(3):e1001779. doi:10.1371/journal.pmed.1001779

    Article  PubMed  PubMed Central  Google Scholar 

  33. Uhlhaas PJ, Singer W, Abeles A, Allen PJ, Fish DR, Smith SJ, Gielen S (2006) Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology. Neuron 52(1):155–168. doi:10.1016/j.neuron.2006.09.020

    Article  CAS  PubMed  Google Scholar 

  34. Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TEJ, Bucholz R, Yacoub E (2012) The human connectome project: a data acquisition perspective. Neuroimage 62(4):2222–2231. doi:10.1016/j.neuroimage.2012.02.018

    Article  PubMed  PubMed Central  Google Scholar 

  35. Vemuri P, Jones DT, Jack CR, Moore G, Perkel D, Segundo J, Greicius M (2011) Resting state functional MRI in alzheimer’s disease. Alzheimers Res Ther 4(1):2. doi:10.1186/alzrt100

    Article  Google Scholar 

  36. Younesi E, Hofmann-Apitius M (2013) From integrative disease modeling to predictive, preventive, personalized and participatory (P4) medicine. Epma J 4(1):23. doi:10.1186/1878-5085-4-23

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Neurology, Charité – University Medicine, Charitéplatz 1, 10117, Berlin, Germany

    Leon Stefanovski, Amna Ghani & Petra Ritter

  2. Berlin School of Mind and Brain & Mind and Brain Institute, Humboldt University, Berlin, Germany

    Amna Ghani & Petra Ritter

  3. Machine Learning Group, Technical University of Berlin, Berlin, Germany

    Amna Ghani

  4. Rotman Research Institute of Baycrest Centre, University of Toronto, Toronto, Canada

    Anthony Randal McIntosh

  5. Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, Germany

    Petra Ritter

Authors
  1. Leon Stefanovski
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  2. Amna Ghani
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  3. Anthony Randal McIntosh
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  4. Petra Ritter
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Correspondence to Leon Stefanovski or Petra Ritter.

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Conflict of interest

L. Stefanovski, A. Ghani, A.R. McIntosh, and P. Ritter state that they have no competing interest.

This article does not contain any studies with experiments on animals. Studies on human participants were performed by the authors (neuroradiological data acquirement) and respected all ethical guidelines.

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Stefanovski, L., Ghani, A., McIntosh, A.R. et al. Linking connectomics and dynamics in the human brain. e-Neuroforum 7, 64–70 (2016). https://doi.org/10.1007/s13295-016-0027-1

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  • DOI: https://doi.org/10.1007/s13295-016-0027-1

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