Functional Imaging: Magnetic Resonance Imaging

Living reference work entry

Abstract

Magnetic resonance imaging (MRI) has had a substantial clinical impact since the first commercial scanners were introduced in the early 1980s. The ability to image most human tissue and a wide range of pathologies at high-resolution and high anatomic contrast has led to the explosive propagation of MRI scanners worldwide. Anatomic MRI has enabled identification of tumors, lesions, and other pathologies throughout the body and has been particularly effective and suitable for brain imaging due to the predominance of its MR-differentiable soft tissue, lack of motion, and high levels of magnetic field homogeneity relative to the rest of the body.

Keywords

Anatomic MRI Blood oxygenation level dependent contrast (BOLD) Cerebral blood volume Chemical shift imaging Connectivity maps Decoding and multi-variate analysis Diffusion imaging Diffusion tensor imaging Dynamic connectivity Echo–planar imaging (EPI) Functional MRI Blood perfusion contrast Calibration Resting state Signal interpretation issues Susceptibility contrast Higher power gradient coils Independent component analysis (ICA) Magnetic susceptibility Multi-variate approach Naturalistic stimuli studies Neuromarkers Perfusion imaging Resting state fMRI Spin-echo imaging Susceptibility contrast Velocity nulling Voxel-wise subtraction of blood volume map 

References

  1. Alsop DC et al (2010) Arterial spin labeling blood flow MRI: its role in the early characterization of Alzheimer’s disease. J Alzheimers Dis 20(3):871–880PubMedPubMedCentralGoogle Scholar
  2. Bandettini PA (1999) The temporal resolution of functional MRI. In: Moonen C, Bandettini P (eds) Functional MRI. Springer, New York, pp 205–220Google Scholar
  3. Bandettini PA (2012) Sewer pipe, wire, epoxy, and finger tapping: the start of fMRI at the Medical College of Wisconsin. Neuroimage 62(2):620–631PubMedCrossRefGoogle Scholar
  4. Bandettini P, Wong E (1997) A hypercapnia-based normalization method for improved spatial localization of human brain activation with fMRI. NMR Biomed 10(4–5):197–203PubMedCrossRefGoogle Scholar
  5. Bandettini PA et al (1992) Time course Epi of human brain-function during task activation. Magn Reson Med 25(2):390–397PubMedCrossRefGoogle Scholar
  6. Bandettini P et al (1994) Spin-echo and gradient-echo Epi of human brain activation using bold contrast – a comparative-study at 1.5 T. NMR Biomed 7(1–2):12–20PubMedCrossRefGoogle Scholar
  7. Bandettini PA, Petridou N, Bodurka J (2005) Direct detection of neuronal activity with MRI: fantasy, possibility, or reality? Appl Magn Res 29(1):65–88CrossRefGoogle Scholar
  8. Basser PJ, Jones DK (2002) Diffusion-tensor MRI: theory, experimental design and data analysis – a technical review. NMR Biomed 15(7–8):456–467PubMedCrossRefGoogle Scholar
  9. Bellgowan P, Saad Z, Bandettini P (2003) Understanding neural system dynamics through task modulation and measurement of functional MRI amplitude, latency, and width. Proc Natl Acad Sci U S A 100(3):1415–1419PubMedPubMedCentralCrossRefGoogle Scholar
  10. Belliveau JW et al (1991) Functional mapping of the human visual-cortex by magnetic-resonance-imaging. Science 254(5032):716–719PubMedCrossRefGoogle Scholar
  11. Birn RM et al (2008) The respiration response function: the temporal dynamics of fMRI signal fluctuations related to changes in respiration. Neuroimage 40(2):644–654PubMedCrossRefGoogle Scholar
  12. Biswal B et al (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537–541PubMedCrossRefGoogle Scholar
  13. Bode S, Haynes JD (2009) Decoding sequential stages of task preparation in the human brain. Neuroimage 45(2):606–613PubMedCrossRefGoogle Scholar
  14. Boxerman JL et al (1995) The intravascular contribution to fmri signal change – Monte-Carlo modeling and diffusion-weighted studies in-vivo. Magn Reson Med 34(1):4–10PubMedCrossRefGoogle Scholar
  15. Boyacioglu R et al (2014) Whole brain, high resolution multiband spin-echo EPI fMRI at 7T: a comparison with gradient-echo EPI using a color-word Stroop task. Neuroimage 97:142–150PubMedCrossRefGoogle Scholar
  16. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198PubMedCrossRefGoogle Scholar
  17. Bulte DP et al (2012) Quantitative measurement of cerebral physiology using respiratory-calibrated MRI. Neuroimage 60(1):582–591PubMedCrossRefGoogle Scholar
  18. Buonocore MH, Maddock RJ (2015) Magnetic resonance spectroscopy of the brain: a review of physical principles and technical methods. Rev Neurosci 26(6):609–632PubMedCrossRefGoogle Scholar
  19. Buxton RB, Frank LR (1997) A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation. J Cereb Blood Flow Metab 17(1):64–72PubMedCrossRefGoogle Scholar
  20. Calhoun VD et al (2008) Temporal lobe and “default” hemodynamic brain modes discriminate between schizophrenia and bipolar disorder. Hum Brain Mapp 29(11):1265–1275PubMedPubMedCentralCrossRefGoogle Scholar
  21. Calhoun VD, Eichele T, Pearlson G (2009) Functional brain networks in schizophrenia: a review. Front Hum Neurosci 3:17PubMedPubMedCentralCrossRefGoogle Scholar
  22. Chang C, Cunningham JP, Glover GH (2009) Influence of heart rate on the BOLD signal: the cardiac response function. Neuroimage 44(3):857–869PubMedCrossRefGoogle Scholar
  23. Chen CM et al (2014) GABA level, gamma oscillation, and working memory performance in schizophrenia. Neuroimage Clin 4:531–539PubMedPubMedCentralCrossRefGoogle Scholar
  24. Cheng K (2012) Revealing human ocular dominance columns using high-resolution functional magnetic resonance imaging. Neuroimage 62(2):1029–1034PubMedCrossRefGoogle Scholar
  25. Choe AS et al (2015) Reproducibility and temporal structure in weekly resting-state fMRI over a period of 3.5 years. PLoS ONE 10(10):e0140134PubMedPubMedCentralCrossRefGoogle Scholar
  26. Christopher DeCharms R (2008) Applications of real-time fMRI. Nat Rev Neurosci 9(9):720–729PubMedCrossRefGoogle Scholar
  27. Cole DM, Smith SM, Beckmann CF (2010) Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front Syst Neurosci 4. doi: 10.3389/fnsys.2010.00008
  28. Cordes D et al (2001) Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR Am J Neuroradiol 22(7):1326–1333PubMedGoogle Scholar
  29. Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29(3):162–173PubMedCrossRefGoogle Scholar
  30. Craddock RC et al (2012) A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum Brain Mapp 33(8):1914–1928PubMedCrossRefGoogle Scholar
  31. Davis TL et al (1998) Calibrated functional MRI: mapping the dynamics of oxidative metabolism. Proc Natl Acad Sci U S A 95(4):1834–1839PubMedPubMedCentralCrossRefGoogle Scholar
  32. Deblaere K et al (2002) Developing a comprehensive presurgical functional MRI protocol for patients with intractable temporal lobe epilepsy: a pilot study. Neuroradiology 44(8):667–673PubMedCrossRefGoogle Scholar
  33. DeCharms RC et al (2004) Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage 21(1):436–443PubMedCrossRefGoogle Scholar
  34. DeCharms RC et al (2005) Control over brain activation and pain learned by using real-time functional MRI. Proc Natl Acad Sci U S A 102(51):18626–18631PubMedPubMedCentralCrossRefGoogle Scholar
  35. Detre JA et al (1992) Perfusion imaging. Magn Reson Med 23(1):37–45PubMedCrossRefGoogle Scholar
  36. Detre JA et al (2009) Arterial spin-labeled perfusion MRI in basic and clinical neuroscience. Curr Opin Neurol 22(4):348–355PubMedCrossRefGoogle Scholar
  37. Detre JA et al (2012) Applications of arterial spin labeled MRI in the brain. J Magn Reson Imaging 35(5):1026–1037PubMedPubMedCentralCrossRefGoogle Scholar
  38. Draganski B et al (2004) Changes in grey, matter induced by training. Nature 427(6972):311–312PubMedCrossRefGoogle Scholar
  39. Du Y et al (2015) A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders. Neuroimage 122:272–280PubMedCrossRefGoogle Scholar
  40. Duyn JH (2012) The future of ultra-high field MRI and fMRI for study of the human brain. Neuroimage 62(2):1241–1248PubMedCrossRefGoogle Scholar
  41. Fan Q et al (2016) MGH-USC human connectome project datasets with ultra-high b-value diffusion MRI. Neuroimage 124:1108–1114PubMedCrossRefGoogle Scholar
  42. Feinberg DA, Setsompop K (2013) Ultra-fast MRI of the human brain with simultaneous multi-slice imaging. J Magn Reson 229:90–100PubMedPubMedCentralCrossRefGoogle Scholar
  43. Feinberg DA, Yacoub E (2012) The rapid development of high speed, resolution and precision in fMRI. Neuroimage 62(2):720–725PubMedPubMedCentralCrossRefGoogle Scholar
  44. Finn ES et al (2015) Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci 18(11):1664–1671PubMedCrossRefGoogle Scholar
  45. Fox MD, Greicius M (2010) Clinical applications of resting state functional connectivity. Front Syst Neurosci 4. doi: 10.3389/fnsys.2010.00019
  46. Fu CHY et al (2008) Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol Psychiatry 63(7):656–662PubMedCrossRefGoogle Scholar
  47. Gabrieli JDE, Ghosh SS, Whitfield-Gabrieli S (2015) Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85(1):11–26PubMedPubMedCentralCrossRefGoogle Scholar
  48. Goense J, Merkle H, Logothetis N (2012) High-resolution fMRI reveals laminar differences in neurovascular coupling between positive and negative BOLD responses. Neuron 76(3):629–639PubMedCrossRefGoogle Scholar
  49. Gonzalez-Castillo J et al (2012) Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis. Proc Natl Acad Sci U S A 109(14):5487–5492PubMedPubMedCentralCrossRefGoogle Scholar
  50. Gonzalez-Castillo J et al (2015) Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns. Proc Natl Acad Sci U S A 112(28):8762–8767PubMedPubMedCentralCrossRefGoogle Scholar
  51. Grafton ST, Tipper CM (2012) Decoding intention: a neuroergonomic perspective. Neuroimage 59(1):14–24PubMedCrossRefGoogle Scholar
  52. Haacke EM et al (1997) In vivo measurement of blood oxygen saturation using magnetic resonance imaging: a direct validation of the blood oxygen level-dependent concept in functional brain imaging. Hum Brain Mapp 5(5):341–346PubMedCrossRefGoogle Scholar
  53. Hall EL et al (2013) The relationship between MEG and fMRI. Neuroimage 102(Pt 1):80–91PubMedGoogle Scholar
  54. Harrison DM et al (2015) Thalamic lesions in multiple sclerosis by 7T MRI: clinical implications and relationship to cortical pathology. Mult Scler 21(9):1139–1150PubMedCrossRefGoogle Scholar
  55. Haxby JV (2012) Multivariate pattern analysis of fMRI: the early beginnings. Neuroimage 62(2):852–855PubMedPubMedCentralCrossRefGoogle Scholar
  56. Haxby JV et al (2001) Distinct, overlapping representations of faces and multiple categories of objects in ventral temporal cortex. Neuroimage 13(6):S891–S891CrossRefGoogle Scholar
  57. Hoge RD (2012) Calibrated fMRI. Neuroimage 62(2):930–937PubMedCrossRefGoogle Scholar
  58. Hoge RD et al (1999) Investigation of BOLD signal dependence on cerebral blood flow and oxygen consumption: the deoxyhemoglobin dilution model. Magn Reson Med 42(5):849–863PubMedCrossRefGoogle Scholar
  59. Horikawa T, Kamitani Y (2014) Exploring dream contents by neuroimaging. Brain Nerve 66(4):461–469PubMedGoogle Scholar
  60. Horovitz SG et al (2008) Low frequency BOLD fluctuations during resting wakefulness and light sleep: a simultaneous EEG-fMRI study. Hum Brain Mapp 29(6):671–682PubMedCrossRefGoogle Scholar
  61. Hutchison RM et al (2013) Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80:360–378PubMedCrossRefGoogle Scholar
  62. Huynh HT, Won Y (2008) Decoding cognitive states from fMRI data using single hidden-layer feed forward neural networks. In: Proceedings – 4th international conference on networked computing and advanced information management, NCM 2008Google Scholar
  63. Jack CR et al (1994) Sensory-motor cortex – correlation of presurgical mapping with functional mr-imaging and invasive cortical mapping. Radiology 190(1):85–92PubMedCrossRefGoogle Scholar
  64. Jochimsen TH et al (2010) Whole-brain mapping of venous vessel size in humans using the hypercapnia-induced BOLD effect. Neuroimage 51(2):765–774PubMedCrossRefGoogle Scholar
  65. Johnston SJ et al (2010) Neurofeedback: a promising tool for the self-regulation of emotion networks. Neuroimage 49(1):1066–1072PubMedCrossRefGoogle Scholar
  66. Kahnt T et al (2010) The neural code of reward anticipation in human orbitofrontal cortex. Proc Natl Acad Sci U S A 107(13):6010–6015PubMedPubMedCentralCrossRefGoogle Scholar
  67. Kamitani Y, Tong F (2005) Decoding the visual and subjective contents of the human brain. Nat Neurosci 8(5):679–685PubMedPubMedCentralCrossRefGoogle Scholar
  68. Kamitani Y, Tong F (2006) Decoding seen and attended motion directions from activity in the human visual cortex. Curr Biol 16(11):1096–1102PubMedPubMedCentralCrossRefGoogle Scholar
  69. Kauppinen RA et al (1993) Applications of magnetic-resonance spectroscopy and diffusion-weighted imaging to the study of brain biochemistry and pathology. Trends Neurosci 16(3):88–95PubMedCrossRefGoogle Scholar
  70. Kay KN et al (2008) Identifying natural images from human brain activity. Nature 452(7185):352–355PubMedPubMedCentralCrossRefGoogle Scholar
  71. Kim SG (1995) Quantification of relative cerebral blood-flow change by flow-sensitive alternating inversion-recovery (fair) technique – application to functional mapping. Magn Reson Med 34(3):293–301PubMedCrossRefGoogle Scholar
  72. Kim T, Hendrich K, Kim SG (2008) Functional MRI with magnetization transfer effects: determination of BOLD and arterial blood volume changes. Magn Reson Med 60(6):1518–1523PubMedPubMedCentralCrossRefGoogle Scholar
  73. Kim J et al (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 124:127–146PubMedCrossRefGoogle Scholar
  74. Kohno S et al (2009) Water-diffusion slowdown in the human visual cortex on visual stimulation precedes vascular responses. J Cereb Blood Flow Metab 29(6):1197–1207PubMedCrossRefGoogle Scholar
  75. Kriegeskorte N, Goebel R, Bandettini P (2006) Information-based functional brain mapping. Proc Natl Acad Sci U S A 103(10):3863–3868PubMedPubMedCentralCrossRefGoogle Scholar
  76. Kucyi A, Davis KD (2014) Dynamic functional connectivity of the default mode network tracks daydreaming. Neuroimage 100:471–80PubMedCrossRefGoogle Scholar
  77. Kundu P et al (2012) Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. Neuroimage 60(3):1759–1770PubMedCrossRefGoogle Scholar
  78. Kundu P et al (2013) Integrated strategy for improving functional connectivity mapping using multiecho fMRI. Proc Natl Acad Sci U S A 110(40):16187–16192PubMedPubMedCentralCrossRefGoogle Scholar
  79. Kwong KK (2012) Record of a single fMRI experiment in May of 1991. Neuroimage 62(2):610–612PubMedCrossRefGoogle Scholar
  80. Kwong KK et al (1992) Dynamic magnetic-resonance-imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A 89(12):5675–5679PubMedPubMedCentralCrossRefGoogle Scholar
  81. LaConte SM (2011) Decoding fMRI brain states in real-time. Neuroimage 56(2):440–454PubMedCrossRefGoogle Scholar
  82. LaConte SM, Peltier SJ, Hu XP (2007) Real-time fMRI using brain-state classification. Hum Brain Mapp 28(10):1033–1044PubMedCrossRefGoogle Scholar
  83. Le Bihan D (2003) Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci 4(6):469–480PubMedCrossRefGoogle Scholar
  84. Le Bihan D (2012) Diffusion, confusion and functional MRI. Neuroimage 62(2):1131–1136PubMedCrossRefGoogle Scholar
  85. Le Bihan D, Johansen-Berg H (2012) Diffusion MRI at 25: exploring brain tissue structure and function. Neuroimage 61(2):324–341PubMedCrossRefGoogle Scholar
  86. Linden DEJ et al (2012) Real-time self-regulation of emotion networks in patients with depression. PLoS ONE 7(6), e38115PubMedPubMedCentralCrossRefGoogle Scholar
  87. Lu H, Ge Y (2008) Quantitative evaluation of oxygenation in venous vessels using T2-relaxation-under-spin-tagging MRI. Magn Reson Med 60(2):357–363PubMedPubMedCentralCrossRefGoogle Scholar
  88. Lu H et al (2003) Functional magnetic resonance imaging based on changes in vascular space occupancy. Magn Reson Med 50(2):263–274PubMedCrossRefGoogle Scholar
  89. Maguire EA et al (2000) Navigation-related structural change in the hippocampi of taxi drivers. Proc Natl Acad Sci U S A 97(8):4398–4403PubMedPubMedCentralCrossRefGoogle Scholar
  90. Mantini D et al (2007) Electrophysiological signatures of resting state networks in the human brain. Proc Natl Acad Sci U S A 104(32):13170–13175PubMedPubMedCentralCrossRefGoogle Scholar
  91. McGonigle DJ (2012) Test-retest reliability in fMRI: or how I learned to stop worrying and love the variability. Neuroimage 62(2):1116–1120PubMedCrossRefGoogle Scholar
  92. Menon RS, Kim SG (1999) Spatial and temporal limits in cognitive neuroimaging with fMRI. Trends Cogn Sci 3(6):207–216PubMedCrossRefGoogle Scholar
  93. Menon RS, Luknowsky DC, Gati JS (1998) Mental chronometry using latency-resolved functional MRI. Proc Natl Acad Sci U S A 95(18):10902–10907PubMedPubMedCentralCrossRefGoogle Scholar
  94. Misaki M et al (2010) Comparison of multivariate classifiers and response normalizations for pattern-information fMRI. Neuroimage 53(1):103–118PubMedPubMedCentralCrossRefGoogle Scholar
  95. Misaki M, Luh WM, Bandettini PA (2013) Accurate decoding of sub-TR timing differences in stimulations of sub-voxel regions from multi-voxel response patterns. Neuroimage 66:623–633PubMedCrossRefGoogle Scholar
  96. Mitchell RLC, Ross ED (2008) fMRI evidence for the effect of verbal complexity on lateralisation of the neural response associated with decoding prosodic emotion. Neuropsychologia 46(12):2880–2887PubMedCrossRefGoogle Scholar
  97. Mitchell TM et al. (2003) Classifying instantaneous cognitive states from FMRI data. AMIA. In: Annual symposium proceedings [electronic resource]/AMIA symposium. AMIA symposium, pp 465–469Google Scholar
  98. Mitchell TM et al (2004) Learning to decode cognitive states from brain images. Mach Learn 57(1–2 Spec. iss):145–175CrossRefGoogle Scholar
  99. Mitchell TV et al (2005) Functional magnetic resonance imaging measure of automatic and controlled auditory processing. Neuroreport 16(5):457–461PubMedPubMedCentralCrossRefGoogle Scholar
  100. Moonen CT et al (1990) Functional magnetic resonance imaging in medicine and physiology. Science 250(4977):53–61PubMedCrossRefGoogle Scholar
  101. Mori S, Zhang J (2006) Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron 51(5):527–539PubMedCrossRefGoogle Scholar
  102. Mourão-Miranda J et al (2005) Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. Neuroimage 28(4):980–995PubMedCrossRefGoogle Scholar
  103. Murphy K, Birn RM, Bandettini PA (2013) Resting-state fMRI confounds and cleanup. Neuroimage 80:349–359PubMedPubMedCentralCrossRefGoogle Scholar
  104. Naselaris T et al (2011) Encoding and decoding in fMRI. Neuroimage 56(2):400–410PubMedCrossRefGoogle Scholar
  105. Niazy RK et al (2011) Spectral characteristics of resting state networks. Prog Brain Res 193:259–276PubMedCrossRefGoogle Scholar
  106. Norman KA et al (2006) Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn Sci 10(9):424–430PubMedCrossRefGoogle Scholar
  107. Ogawa S, Lee TM (1990) Magnetic-resonance-imaging of blood-vessels at high fields – invivo and invitro measurements and image simulation. Magn Reson Med 16(1):9–18PubMedCrossRefGoogle Scholar
  108. Ogawa S et al (1990a) Brain magnetic-resonance-imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A 87(24):9868–9872PubMedPubMedCentralCrossRefGoogle Scholar
  109. Ogawa S et al (1990b) Oxygenation-sensitive contrast in magnetic-resonance image of rodent brain at high magnetic-fields. Magn Reson Med 14(1):68–78PubMedCrossRefGoogle Scholar
  110. Ogawa S et al (1992) Intrinsic signal changes accompanying sensory stimulation – functional brain mapping with magnetic-resonance-imaging. Proc Natl Acad Sci U S A 89(13):5951–5955PubMedPubMedCentralCrossRefGoogle Scholar
  111. Ojemann GA, Ramsey NF, Ojemann J (2013) Relation between functional magnetic resonance imaging (fMRI) and single neuron, local field potential (LFP) and electrocorticography (ECoG) activity in human cortex. Front Hum Neurosci 7:34PubMedPubMedCentralCrossRefGoogle Scholar
  112. Posse S et al (2013) MR spectroscopic imaging: principles and recent advances. J Magn Reson Imaging 37(6):1301–1325PubMedCrossRefGoogle Scholar
  113. Power JD et al (2014) Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84:320–341PubMedCrossRefGoogle Scholar
  114. Power JD, Schlaggar BL, Petersen SE (2015) Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage 105:536–551PubMedCrossRefGoogle Scholar
  115. Pruessmann KP et al (1999) SENSE: sensitivity encoding for fast MRI. Magn Reson Med 42(5):952–962PubMedCrossRefGoogle Scholar
  116. Reddy L, Tsuchiya N, Serre T (2010) Reading the mind's eye: decoding category information during mental imagery. Neuroimage 50(2):818–825PubMedCrossRefGoogle Scholar
  117. Reichenbach JR (2012) The future of susceptibility contrast for assessment of anatomy and function. Neuroimage 62(2):1311–1315PubMedCrossRefGoogle Scholar
  118. Richiardi J et al (2011) Decoding brain states from fMRI connectivity graphs. Neuroimage 56(2):616–626PubMedCrossRefGoogle Scholar
  119. Rigolo L et al (2011) Development of a clinical functional magnetic resonance imaging service. Neurosurg Clin N Am 22(2):307–314PubMedPubMedCentralCrossRefGoogle Scholar
  120. Rosazza C, Minati L (2011) Resting-state brain networks: literature review and clinical applications. Neurol Sci 32(5):773–785PubMedCrossRefGoogle Scholar
  121. Rosen BR et al (1991) Susceptibility contrast imaging of cerebral blood-volume – human-experience. Magn Reson Med 22(2):293–299PubMedCrossRefGoogle Scholar
  122. Sarkheil P et al (2015) fMRI feedback enhances emotion regulation as evidenced by a reduced amygdala response. Behav Brain Res 281:326–332PubMedCrossRefGoogle Scholar
  123. Scholz J et al (2009) Training induces changes in white-matter architecture. Nat Neurosci 12(11):1370–1371PubMedPubMedCentralCrossRefGoogle Scholar
  124. Setsompop K et al (2012) Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magn Reson Med 67(5):1210–1224PubMedCrossRefGoogle Scholar
  125. Shulman RG et al (1993) Nuclear-magnetic-resonance imaging and spectroscopy of human brain-function. Proc Natl Acad Sci U S A 90(8):3127–3133PubMedPubMedCentralCrossRefGoogle Scholar
  126. Smith SM (2012) The future of FMRI connectivity. Neuroimage 62(2):1257–1266PubMedCrossRefGoogle Scholar
  127. Smith SM et al (2015) A positive–negative mode of population covariation links brain connectivity, demographics and behavior. Nat Neurosci 18(11):1565–1567PubMedPubMedCentralCrossRefGoogle Scholar
  128. Sodickson DK (2000) Tailored SMASH image reconstructions for robust in vivo parallel MR imaging. Magn Reson Med 44(2):243–251PubMedCrossRefGoogle Scholar
  129. Sommer M et al (2008) Decoding of affective facial expressions in the context of emotional situations. Neuropsychologia 46(11):2615–2621PubMedCrossRefGoogle Scholar
  130. Song AW (2012) Diffusion modulation of the fMRI signal: early investigations on the origin of the BOLD signal. Neuroimage 62(2):949–952PubMedCrossRefGoogle Scholar
  131. Song AW et al (1996) Diffusion weighted fMRI at 1.5 T. Magn Reson Med 35(2):155–158PubMedCrossRefGoogle Scholar
  132. Stoeckel LE et al (2014) Optimizing real time fMRI neurofeedback for therapeutic discovery and development. NeuroImage Clin 5:245–255PubMedPubMedCentralCrossRefGoogle Scholar
  133. Sulzer J et al (2013) Real-time fMRI neurofeedback: progress and challenges. Neuroimage 76:386–399PubMedPubMedCentralCrossRefGoogle Scholar
  134. Thulborn KR (2012) My starting point: the discovery of an NMR method for measuring blood oxygenation using the transverse relaxation time of blood water. Neuroimage 62(2):589–593PubMedCrossRefGoogle Scholar
  135. Tong F (2003) Primary visual cortex and visual awareness. Nat Rev Neurosci 4(3):219–229PubMedCrossRefGoogle Scholar
  136. Tsai YH et al (2014) Disruption of brain connectivity in acute stroke patients with early impairment in consciousness. Front Psychol 4:956PubMedPubMedCentralCrossRefGoogle Scholar
  137. Uǧurbil K (2012) Development of functional imaging in the human brain (fMRI); the University of Minnesota experience. Neuroimage 62(2):613–619PubMedPubMedCentralCrossRefGoogle Scholar
  138. Valkanova V, Eguia Rodriguez R, Ebmeier KP (2014) Mind over matter – What do we know about neuroplasticity in adults? Int Psychogeriatr 26(6):891–909PubMedCrossRefGoogle Scholar
  139. van den Heuvel MP, Hulshoff Pol HE (2010) Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20(8):519–534PubMedCrossRefGoogle Scholar
  140. Van Essen DC et al (2012) The human connectome project: a data acquisition perspective. Neuroimage 62(4):2222–2231PubMedPubMedCentralCrossRefGoogle Scholar
  141. van Zijl PCM et al (1998) Quantitative assessment of blood flow, blood volume and blood oxygenation effects in functional magnetic resonance imaging. Nat Med 4(2):159–167PubMedCrossRefGoogle Scholar
  142. Wang J et al (2003) Arterial spin labeling perfusion fMRI with very low task frequency. Magn Reson Med 49(5):796–802PubMedCrossRefGoogle Scholar
  143. Wang D et al (2015) Parcellating cortical functional networks in individuals. Nat Neurosci 18(12):1853–1860PubMedPubMedCentralCrossRefGoogle Scholar
  144. Weiskopf N et al (2003) Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. Neuroimage 19(3):577–586PubMedCrossRefGoogle Scholar
  145. Weiskopf N et al (2004) Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Trans Biomed Eng 51(6):966–970PubMedCrossRefGoogle Scholar
  146. Williams DS et al (1992) Magnetic-resonance-imaging of perfusion using spin inversion of arterial water. Proc Natl Acad Sci U S A 89(1):212–216PubMedPubMedCentralCrossRefGoogle Scholar
  147. Wong EC (2007) Vessel-encoded arterial spin-labeling using pseudocontinuous tagging. Magn Reson Med 58(6):1086–1091PubMedCrossRefGoogle Scholar
  148. Wong EC, Buxton RB, Frank LR (1999) Quantitative perfusion imaging using arterial spin labeling. Neuroimaging Clin N Am 9(2):333–342PubMedGoogle Scholar
  149. Wu WC, Wong EC (2007) Feasibility of velocity selective arterial spin labeling in functional MRI. J Cereb Blood Flow Metab 27(4):831–838PubMedGoogle Scholar
  150. Yablonskiy DA, Ackerman JJH, Raichle ME (2000) Coupling between changes in human brain temperature and oxidative metabolism during prolonged visual stimulation. Proc Natl Acad Sci U S A 97(13):7603–7608PubMedPubMedCentralCrossRefGoogle Scholar
  151. Yacoub E, Harel N, Uǧurbil K (2008) High-field fMRI unveils orientation columns in humans. Proc Natl Acad Sci U S A 105(30):10607–10612PubMedPubMedCentralCrossRefGoogle Scholar
  152. Yang Y, Gu H, Stein EA (2004) Simultaneous MRI acquisition of blood volume, blood flow, and blood oxygenation information during brain activation. Magn Reson Med 52(6):1407–1417PubMedCrossRefGoogle Scholar
  153. Yang Z et al (2014) Using fMRI to decode true thoughts independent of intention to conceal. Neuroimage 99:80–92PubMedPubMedCentralCrossRefGoogle Scholar
  154. Zilverstand A et al. (2015) fMRI neurofeedback facilitates anxiety regulation in females with spider phobia. Front Behav Neurosci 9(June). doi: 10.3389/fnbeh.2015.00148

Copyright information

© Springer Science+Business Media New York (outside the USA) 2016

Authors and Affiliations

  1. 1.Section on Functional Imaging Method, Laboratory of Brain and CognitionNational Institute of Mental Health, NIHBethesdaUSA

Personalised recommendations