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Brief Overview of Functional Imaging Principles

  • C. Habas
  • G. de Marco
Chapter
Part of the Contemporary Clinical Neuroscience book series (CCNE)

Abstract

Functional imaging enables to detect and to localize brain areas specifically involved in networks subserving a given mental activity. The two main techniques used in routine consist in functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). We will focus on fMRI. fMRI is based on the local and transient increase of blood oxygenation in the cortical and deep nuclear microvasculature, caused by neuronal activation. This hemodynamic phenomenon called blood-oxygenation level-dependent (BOLD) response only indirectly reflects the amount of neuronal activity (Raichle ME, Proc Natl Acad Sci USA 93:765–772, 1998). This local blood hyperoxygenation produces microscopic magnetic field alterations measurable by appropriate T2*-weighted MRI sequence. A complex image and statistical post-processing is then applied to raw data to generate final “brain activation maps.” These functional maps can be acquired during active stimulation protocols, or “at rest” (resting-state functional connectivity, rsfMRI). fMRI and rsfMRI resort to different algorithmic processing: linear model-related algorithms and nonlinear, model-free, data-driven algorithms, respectively, even if these latter methods can also be applied to fMRI data. This functional connectivity can be complemented by effective connectivity which seeks causality relationships between brain areas participating to a same circuit. Finally, advanced algorithms can also explore the topology of the neural networks and provide new and richer graph-theoretic related information, and machine learning will improve normal and aberrant structural and functional brain pattern recognition and classification.

Keywords

Functional MRI Neurovascular coupling BOLD ASL GLM ICA Brain resting state Functional connectivity Topology Graph theory 

References

  1. 1.
    Raichle ME (1998) Behind the scenes of functional brain imaging: a historical and physiological perspective. Proc Natl Acad Sci U S A 93:765–772CrossRefGoogle Scholar
  2. 2.
    Hillman EMC (2014) Coupling mechanism and significance of the BOLD signal: a status report. Annu Rev Neurosci 37:161–181CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Buxton RB (2013) The physics of functional magnetic resonance imaging (fMRI). Rep Prog Phys 76(9):096601CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Drake CT, Iadecola C (2007) The role of neuronal signaling in controlling cerebral blood flow. Brain Lang 102:141–152CrossRefPubMedGoogle Scholar
  5. 5.
    Tallini YN, Brekke JF, Shui B, Doran R, Hwang S-M, Nakai J, Salama G, Segal SS, Kotlikoff MI (2007) Propagated endothelial Ca2+ waves and arteriolar dilation in vivo. Circ Res 101:1300–1309CrossRefPubMedGoogle Scholar
  6. 6.
    Marelli SP (2001) Mechanisms of endothelial P2Y1- and P2Y2-mediated vasodilation involve differential [Ca2+]i responses. Am J Physiol Heart Circ Physiol 28:H1759–H1766CrossRefGoogle Scholar
  7. 7.
    Fergus A, Lee KS (1997) GABAergic regulation of cerebral microvascular tone in the rat. J Cereb Blood Flow Metab 17:992–1003CrossRefPubMedGoogle Scholar
  8. 8.
    Li J, Iadecola C (1994) Nitric oxide and adenosine mediate vasodilatation during functional activation in cerebellar cortex. Neuropharmacology 33:1453–1461CrossRefPubMedGoogle Scholar
  9. 9.
    Magistretti PJ, Pellerin L (1996) Cellular bases of brain metabolism and their relevance to functional brain imaging: evidence for a prominent role of astrocytes. Cereb Cortex 6:50–61CrossRefPubMedGoogle Scholar
  10. 10.
    Magistretti PJ, Pellerin L (1999) Cellular bases of brain metabolism and their relevance to functional brain imaging: evidence for a prominent role of astrocytes. Philos Trans R Soc Lond Ser B Biol Sci 354:1155–1163CrossRefGoogle Scholar
  11. 11.
    Ogawa S, Menon RS, Tank DW, Kim SG, Merkle H, Ellermann H, Ugurbil K (1993) Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. Biophys J 64(3):803–812CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Buxton RB, Griffeth VE, Simon AB, Moradi F (2014) Variability of the coupling of the blood flow and oxygen metabolism responses in the brain: a problem for interpreting BOLD studies but potentially a new window on the underlying neural activity. Front Neurosci 8:139PubMedPubMedCentralGoogle Scholar
  13. 13.
    Davis TL, Kwong KK, Weiskoff RM, Rosen BR (1998) Calibrated functional MRI: mapping the dynamics of oxidative metabolism. Proc Natl Acad Sci U S A 95(4):1834–1839CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Shih Y-YI, Chen C-CV, Lin Z-J, Chiang Y-C, Jaw F-S, Chen Y-Y, Chang C (2009) A new scenario for negative functional magnetic resonance imaging signals: endogenous neurotransmission. J Neurosci 29(10):3036–3044CrossRefPubMedGoogle Scholar
  15. 15.
    Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A (2001) Neurophysiological investigation of the basis of the fMRI signal. Nature 412:150–157CrossRefPubMedGoogle Scholar
  16. 16.
    Wang J, Aguirre GK, Kimberg DY, Roc AC, Li L, Detre JA (2003) Arterial spin labeling perfusion fMRI with very low task frequency. Magn Reson Med 49(5):796–802CrossRefPubMedGoogle Scholar
  17. 17.
    Chen JJ, Jann K, Wang DJJ (2015) Characterizing resting-state brain function using arterial spin labeling. Brain Connect 5(9):527–542CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Buxton RB (2016) Beyond BOLD correlations: a more quantitative approach for investigating brain networks. J Cereb Blood Flow Metab 36(3):461–462CrossRefPubMedGoogle Scholar
  19. 19.
    Dai W, varma G, Scheidegger R, Alsop DC (2016) Quantifying fluctuations of resting state networks using arterial spin labeling perfusion MRI. J Cereb Blood Flow Metab 36(3):463–473CrossRefPubMedGoogle Scholar
  20. 20.
    Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537–541CrossRefPubMedGoogle Scholar
  21. 21.
    Gusnard DA, Raichle MES (2011) Rearching for a baseline: functional imaging and the resting human brain. Nat Rev Neurosci 2:685–694CrossRefGoogle Scholar
  22. 22.
    Raichle EM (2015) The restless brain: how intrinsic activity organizes brain function. Philos Trans B 370:1–11CrossRefGoogle Scholar
  23. 23.
    Fox MD, Raichle M (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8:700–711CrossRefPubMedGoogle Scholar
  24. 24.
    Mantini D, Perrucci MG, Del Gratta C, Romani GL, Corbetta M (2007) Electrophysiological signatures of resting state networks in the human brain. Proc Natl Acad Sci U S A 104:13170–13175CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Brookes MJ, Woolrich M, Luckhoo H, Price D, Hale JR, Stephenson MC, Barnes GR, Smith SM, Moris PG (2011) Investigating the electrophysiological basis of resting state networks using magnetoencephalography. Proc Natl Acad Sci U S A 108(40):16783–16788CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Shmuel A, Leopold DA (2008) Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: implications for functional connectivity at rest. Hum Brain Mapp 29(7):751–761CrossRefPubMedGoogle Scholar
  27. 27.
    Leopold DA, Maier A (2012) Ongoing physiological processes in the cerebral cortex. NeuroImage 62:2190–2200CrossRefPubMedGoogle Scholar
  28. 28.
    Greco G, Corbetta M (2011) The dynamical balance of the brain at rest. Neuroscientist 17:107–123CrossRefGoogle Scholar
  29. 29.
    Margulies DS, Böttger J, Long X, Lv Y, Kelly C, Schäfer A, Goldhahn D, Abbushi A, Milham MP, Lohmann G, Villringer A (2010) Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity. MAGMA 23(5–6):289–307CrossRefPubMedGoogle Scholar
  30. 30.
    Beckmann CF (2012) Modelling with independent components. NeuroImage 62:891–901CrossRefPubMedGoogle Scholar
  31. 31.
    Calhoun VD, Liu J, Adali T (2009) A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic and ERP data. NeuroImage 45:S163–S172CrossRefPubMedGoogle Scholar
  32. 32.
    Zou Q-H, Zhu C-Z, Yang Y, Zuo X-N, Long X-Y, Cao Q-J, Wang Y-F, Zang Y-F (2008) An improved approach to detection of amplitudes of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods 172(1):137–141CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Zang Y, Jiang T, Lu Y, He Y, Tian L (2003) Regional homogeneity approach to fMRI data analysis. NeuroImage 22(1):394–400CrossRefGoogle Scholar
  34. 34.
    Kelly RE, Alexopoulos GS, Wang Z, Gunning FM, Murphy CF, Morimoto SS et al (2010) Visual inspection of independent components: defining a procedure for artifact removal from fMRI data. J Neurosci Methods 189:233–245CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Kalcher K, Huf W, Boubela RN, Filzmoser P, Pezawas L, Biswal B et al (2012) Fully exploratory network independent component analysis of the 1000 functional connectomes database. Front Hum Neurosci 6:1–11CrossRefGoogle Scholar
  36. 36.
    Habas C, Kamdar N, Nguyen D, prater K, Beckmann CF, Menon V, Greicius MD (2009) Distinct cerebellar contribution to intrinsic connectivity networks. J Neurosci 29(26):8586–8594CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Chen AC, Oathes DJ, Chang C, Bradley T, Zhou Z-W, Williams LM et al (2013) Causal interactions between fronto-parietal central executive and default-mode networks in humans. Proc Natl Acad Sci U S A 110(49):19944–19949CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Fox MF, Corbetta M, Snyder AZ, Vincent J, Raichle M (2006) Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci U S A 103:10046–10051CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Hoff GEA-J, Van de Heuvel MP, Benders MJNL, Kersbergen KJ, de Vries LSD (2013) On the development of functional brain connectivity in the young brain. Front Hum Neurosci 7:650CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Antonenko D, Flöel A (2014) Healthy aging by staying selectivity connected: a mini-review. Gerontology 60:3–9CrossRefPubMedGoogle Scholar
  41. 41.
    Kelly C, Castallanos FX (2014) Strengthening connections: functional connectivity and brain plasticity. Neuropsychol Rev 24:63–76CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD (2014) Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 24(3):663–676CrossRefPubMedGoogle Scholar
  43. 43.
    Fox MD, Greicius M (2010) Clinical applications of resting state functional connectivity. Front Syst Neurosci 4:1–13Google Scholar
  44. 44.
    Rosazza C, Minati L (2011) Resting-state brain networks: literature review and clinical applications. Neurol Sci 32:773–785CrossRefPubMedGoogle Scholar
  45. 45.
    Friston KJ (2011) Functional and effective connectivity: a review. Brain Connect 1(1):13–36CrossRefPubMedGoogle Scholar
  46. 46.
    Penny WD, Stephan KE, Mechelli A, Friston KJ (2004) Modelling functional integration: a comparison of structural equation and dynamic causal models. NeuroImage 23(Suppl 1):S264–S274CrossRefPubMedGoogle Scholar
  47. 47.
    Friston KJ, Harrison L, Penny W (2003) Dynamic causal modelling. NeuroImage 19(4):1273–1302CrossRefPubMedGoogle Scholar
  48. 48.
    Ed B, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198CrossRefGoogle Scholar
  49. 49.
    Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52:1059–1069CrossRefPubMedGoogle Scholar
  50. 50.
    Guye M, Bettus G, Bartolomei F, Cozzone PJ (2010) Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks. MAGMA 23:409–421CrossRefPubMedGoogle Scholar
  51. 51.
    Redcay E, Moran JM, Mavros PL, Tager-Flusberg H, Gabrieli JDE, Whitfield-Gabrieli S (2013) Intrinsic functional network organization in high-functioning adolescents with autism spectrum disorder. Front Hum Neurosci 7(573):1–11Google Scholar
  52. 52.
    Wang S, Summers RM (2012) Machine learning and radiology. Med Image Anal 16(5):933–951CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Vieira S, Pinaya WHL, Mechelli A (2017) Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci Behavl Rev 74:58–75CrossRefGoogle Scholar
  54. 54.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRefPubMedGoogle Scholar
  55. 55.
    Wernick MN, Yang YY, Braaankov JG, Yourganov G, Strother SC (2010) Machine learning in medical imaging. IEEE Signal Process Mag 27(4):25–38CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Lemm S, Blankertz B, Dickhaus T, Müller K-R (2011) Introduction to machine learning for brain imaging. NeuroImage 56:387–399CrossRefPubMedGoogle Scholar
  57. 57.
    Cox DD, Savoy RL (2003) Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage 19(2):261–270CrossRefPubMedGoogle Scholar
  58. 58.
    Davatzikos C, Ruparel K, Fan Y, Shen D, Acharyya M (2005) Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. NeuroImage 28(8):663–668CrossRefPubMedGoogle Scholar
  59. 59.
    Shen D, Wu G, Suk H-I (2017) Deep learning in medical images analysis. Annu Rev Biomed Eng 19:221–248CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Brosch T, Tam R, Alzheimer’s Disease Neuroimaging Initiative (2013) Manifold learning of brain MRIs by deep learning. In: International conference on medical image computing and computer-assisted intervention, Springer Berlin Heidelberg, pp 633–640Google Scholar
  61. 61.
    Suk H-I, Lee S-W, Shen D (2014) Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage 101:569–582CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Kim J, Calhoun VD, Shim E, Lee J-H (2016) Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. NeuroImage 124(Pt A):127–146CrossRefPubMedGoogle Scholar
  63. 63.
    Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt H, Long JD, Johnson HJ, Paulsen JS, Turner JA, Calhoun VD (2014) Deep learning for neuroimaging: a validation study. Front Neurosci 8:229CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Suk H-I, Lee S-H, Shen D (2015) Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct Funct 220(2):841–859CrossRefPubMedGoogle Scholar
  65. 65.
    Rosa MJ, Seymour B (2014) Decoding the matrix: benefits and limitations of applying machine learning algorithms to pain neuroimaging. Pain 155:864–867CrossRefPubMedGoogle Scholar
  66. 66.
    Pereira F, Mitchell T, Botvinick M (2009) Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 15(1 suppl):S199–S209CrossRefGoogle Scholar
  67. 67.
    Self MW, van Kerkoerle T, Goebel R, Roelfsema PR (2017) Benchmarking laminar fMRI: neuronal spiking and synaptic activity during top-down and bottom-up processing in the different layers of cortex. Neuroimage. 2017 Jun 23. pii: S1053-8119(17)30517-7. doi: 10.1016/j.neuroimage.2017.06.045. [Epub ahead of print]

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Service de NeuroImagerie, CHNO des 15-20ParisFrance
  2. 2.Laboratoire CeRSM (EA-2931), Equipe Analyse du Mouvement en Biomécanique, Physiologie et ImagerieUniversité Paris NanterreNanterreFrance

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