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Diffusion magnetic resonance imaging for Brainnetome: A critical review

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Abstract

Increasing evidence shows that the human brain is a highly self-organized system that shows attributes of smallworldness, hierarchy and modularity. The “connectome” was conceived several years ago to identify the underpinning physical connectivities of brain networks. The need for an integration of multi-spatial and -temporal approaches is becoming apparent. Therefore, the “Brainnetome” (brain-net-ome) project was proposed. Diffusion magnetic resonance imaging (dMRI) is a non-invasive way to study the anatomy of brain networks. Here, we review the principles of dMRI, its methodologies, and some of its clinical applications for the Brainnetome. Future research in this field is discussed.

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References

  1. Jiang T, He Y, Zang Y, Weng X. Modulation of functional connectivity during the resting state and the motor task. Hum Brain Mapp 2004, 22: 63–71.

    PubMed  Google Scholar 

  2. Fornito A, Zalesky A, Pantelis C, Bullmore ET. Schizophrenia, neuroimaging and connectomics. Neuroimage 2012. [Epub ahead of print]

  3. Sporns O. The human connectome: a complex network. Ann N Y Acad Sci 2011, 1224: 109–125.

    PubMed  Google Scholar 

  4. Hulvershorn LA, Cullen K, Anand A. Toward dysfunctional connectivity: a review of neuroimaging findings in pediatric major depressive disorder. Brain Imaging Behav 2011, 5: 307–328.

    PubMed  Google Scholar 

  5. Calhoun VD, Sui J, Kiehl K, Turner J, Allen E, Pearlson G. Exploring the psychosis functional connectome: aberrant intrinsic networks in schizophrenia and bipolar disorder. Front Psychiatry 2011, 2: 75.

    PubMed  Google Scholar 

  6. ** the Alzheimer’s brain with connectomics. Front Psychiatry 2011, 2: 77.

    PubMed  Google Scholar 

  7. Tymofiyeva O, Hess CP, Ziv E, Tian N, Bonifacio SL, McQuillen PS, et al. Towards the “baby connectome”: map** the structural connectivity of the newborn brain. PLoS One 2012, 7: e31029.

    PubMed  CAS  Google Scholar 

  8. Wahl M, Barkovich AJ, Mukherjee P. Diffusion imaging and tractography of congenital brain malformations. Pediatr Radiol 2010, 40: 59–67.

    PubMed  Google Scholar 

  9. Madden DJ, Bennett IJ, Song AW. Cerebral white matter integrity and cognitive aging: contributions from diffusion tensor imaging. Neuropsychol Rev 2009, 19: 415–435.

    PubMed  Google Scholar 

  10. Sporns O, Tononi G, Kotter R. The human connectome: A structural description of the human brain. PLoS Comput Biol 2005, 1: e42.

    PubMed  Google Scholar 

  11. The Human Connectome Project. http://www.humanconnectome.org.

  12. Milham MP. Open neuroscience solutions for the connectome-wide association era. Neuron 2012, 73: 214–218.

    PubMed  CAS  Google Scholar 

  13. Yao Z, Zhang Y, Lin L, Zhou Y, Xu C, Jiang T. Abnormal cortical networks in mild cognitive impairment and Alzheimer’s disease. PLoS Comput Biol 2010, 6: e1001006.

    PubMed  Google Scholar 

  14. Brainnetome. http://www.brainnetome.org/.

  15. Heng S, Song AW, Sim K. White matter abnormalities in bipolar disorder: insights from diffusion tensor imaging studies. J Neural Transm 2010, 117: 639–654.

    PubMed  Google Scholar 

  16. Li TQ, Wahlund LO. The search for neuroimaging biomarkers of Alzheimer’s disease with advanced MRI techniques. Acta Radiol 2011, 52: 211–222.

    PubMed  Google Scholar 

  17. Peters BD, Blaas J, de Haan L. Diffusion tensor imaging in the early phase of schizophrenia: what have we learned? J Psychiatr Res 2010, 44: 993–1004.

    PubMed  Google Scholar 

  18. Gross DW. Diffusion tensor imaging in temporal lobe epilepsy. Epilepsia 2011, 52(Suppl 4): 32–34.

    PubMed  Google Scholar 

  19. Zalesky A, Fornito A, Harding IH, Cocchi L, Yucel M, Pantelis C, et al. Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 2010, 50: 970–983.

    PubMed  Google Scholar 

  20. Descoteaux M. High Angular Resolution Diffusion MRI: from Local Estimation to Segmentation and Tractography. Graduate School of Information and Communication Sciences 2008, PhD.

  21. Mori S. Introduction to Diffusion Tensor Imaging. 1st ed. Amesterdam, The Netherlands: Elsevier, 2007.

    Google Scholar 

  22. Stejskal E, Tanner J. Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J Chem Phys 1965, 42: 288–292.

    CAS  Google Scholar 

  23. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 1986, 161: 401–407.

    PubMed  Google Scholar 

  24. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J 1994, 66: 259–267.

    PubMed  CAS  Google Scholar 

  25. Moseley M, Cohen Y, Mintorovitch J, Kucharczyk J, Tsuruda J, Weinstein P, et al. Evidence of anisotropic self-diffusion. Radiology 1990, 176: 439–445.

    PubMed  CAS  Google Scholar 

  26. Douek P, Turner R, Pekar J, Patronas N, Le Bihan D. MR color map** of myelin fiber orientation. J Comput Assist Tomogr 1991, 15: 923–929.

    PubMed  CAS  Google Scholar 

  27. Tschumperle D, Deriche R. Variational frameworks for DT-MRI estimation, regularization and visualization. In: Proceedings of the Ninth International Conference on Computer Vision. Nice, France: IEEE Computer Society, IEEE Computer Society Press, 2003: 116–121.

    Google Scholar 

  28. Koay CG, Chang LC, Carew JD, Pierpaoli C, Basser PJ. A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging. J Magn Reson 2006, 182: 115–125.

    PubMed  CAS  Google Scholar 

  29. Fillard P, Pennec X, Arsigny V, Ayache N. Clinical DT-MRI estimation, smoothing, and fiber tracking with log-Euclidean metrics. IEEE Trans Med Imaging 2007, 26: 1472–1482.

    PubMed  Google Scholar 

  30. Jones DK, Horsfield MA, Simmons A. Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn Reson Med 1999, 42: 515–525.

    PubMed  CAS  Google Scholar 

  31. Alexander DC, Barker GJ. Optimal imaging parameters for fiberorientation estimation in diffusion MRI. Neuroimage 2005, 27: 357–367.

    PubMed  Google Scholar 

  32. Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med 1996, 36: 893–906.

    PubMed  CAS  Google Scholar 

  33. Johansen-Berg H, Behrens TEJ. Diffusion MRI: From Quantitative Measurement to In vivo Neuroanatomy. Amsterdam: Academic Press, 2009.

    Google Scholar 

  34. Boespflug EL, Storrs JM, Allendorfer JB, Lamy M, Eliassen JC, Page S. Mean diffusivity as a potential diffusion tensor biomarker of motor rehabilitation after electrical stimulation incorporating task specific exercise in stroke: a pilot study. Brain Imaging Behav 2011. [Epub ahead of print]

  35. Callaghan PT. Principles of Nuclear Magnetic Resonance Microscopy. Oxford: Oxford University Press, 1991.

    Google Scholar 

  36. Tuch D, Weisskoff R, Belliveau J, Wedeen V. High angular resolution diffusion imaging of the human brain. The 7th Annual Meeting of ISMRM 1999.

  37. Tuch DS, Reese TG, Wiegell MR, Makris N, Belliveau JW, Wedeen VJ. High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn Reson Med 2002, 48: 577–582.

    PubMed  Google Scholar 

  38. Liu C, Bammer R, Acar B, Moseley ME. Characterizing non-Gaussian diffusion by using generalized diffusion tensors. Magn Reson Med 2004, 51: 924–937.

    PubMed  Google Scholar 

  39. Assemlal HE, Tschumperle D, Brun L. Efficient and robust computation of PDF features from diffusion MR signal. Med Image Anal 2009, 13: 715–729.

    PubMed  Google Scholar 

  40. Özarslan E, Koay C, Shepherd T, Blackband S, Basser PJ. Simple harmonic oscillator based reconstruction and estimation for three-dimensional q-space MRI. In: ISMRM 17th Annual Meeting and Exhibition, Honolulu, Hawai’i. April 18–24. 2009: 1396.

  41. Descoteaux M, Deriche R, Le Bihan D, Mangin JF, Poupon C. Multiple q-shell diffusion propagator imaging. Med Image Anal 2011, 15: 603–621.

    PubMed  Google Scholar 

  42. Wedeen VJ, Hagmann P, Tseng WY, Reese TG, Weisskoff RM. Map** complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn Reson Med 2005, 54: 1377–1386.

    PubMed  Google Scholar 

  43. Tuch DS. Q-ball imaging. Magn Reson Med 2004, 52: 1358–1372.

    PubMed  Google Scholar 

  44. Descoteaux M, Angelino E, Fitzgibbons S, Deriche R. Regularized, fast, and robust analytical Q-ball imaging. Magn Reson Med 2007, 58: 497–510.

    PubMed  Google Scholar 

  45. Tournier JD, Calamante F, Gadian DG, Connelly A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage 2004, 23: 1176–1185.

    PubMed  Google Scholar 

  46. Tournier JD, Calamante F, Connelly A. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 2007, 35: 1459–1472.

    PubMed  Google Scholar 

  47. Aganj I, Lenglet C, Sapiro G, Yacoub E, Ugurbil K, Harel N. Reconstruction of the orientation distribution function in single- and multiple-shell q-ball imaging within constant solid angle. Magn Reson Med 2010, 64: 554–566.

    PubMed  Google Scholar 

  48. Ozarslan E, Shepherd TM, Vemuri BC, Blackband SJ, Mareci TH. Resolution of complex tissue microarchitecture using the diffusion orientation transform (DOT). Neuroimage 2006, 31: 1086–1103.

    PubMed  Google Scholar 

  49. Prckovska V, Roebroeck AF, Pullens WL, Vilanova A, ter Haar Romeny BM. Optimal acquisition schemes in high angular resolution diffusion weighted imaging. Med Image Comput Comput Assist Interv 2008, 11: 9–17.

    PubMed  CAS  Google Scholar 

  50. Cheng J, Ghosh A, Deriche R, Jiang T. Model-free, regularized, fast, and robust analytical orientation distribution function estimation. Med Image Comput Comput Assist Interv 2010, 13: 648–656.

    PubMed  Google Scholar 

  51. Cheng J, Ghosh A, Jiang T, Deriche R. Model-free and analytical EAP reconstruction via spherical polar Fourier diffusion MRI. Med Image Comput Comput Assist Interv 2010, 13: 590–597.

    PubMed  Google Scholar 

  52. Wedeen VJ, Rosene DL, Wang R, Dai G, Mortazavi F, Hagmann P, et al. The geometric structure of the brain fiber pathways. Science 2012, 335: 1628–1634.

    PubMed  CAS  Google Scholar 

  53. Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A. In vivo fiber tractography using DT-MRI data. Magn Reson Med 2000, 44: 625–632.

    PubMed  CAS  Google Scholar 

  54. Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 1999, 45: 265–269.

    PubMed  CAS  Google Scholar 

  55. Kreher BW, Schneider JF, Mader I, Martin E, Hennig J, Il’yasov KA. Multitensor approach for analysis and tracking of complex fiber configurations. Magn Reson Med 2005, 54: 1216–1225.

    PubMed  CAS  Google Scholar 

  56. Chao YP, Chen JH, Cho KH, Yeh CH, Chou KH, Lin CP. A multiple streamline approach to high angular resolution diffusion tractography. Med Eng Phys 2008, 30: 989–996.

    PubMed  Google Scholar 

  57. Wedeen VJ, Wang RP, Schmahmann JD, Benner T, Tseng WY, Dai G, et al. Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. Neuroimage 2008, 41: 1267–1277.

    PubMed  CAS  Google Scholar 

  58. Jbabdi S, Woolrich MW, Andersson JL, Behrens TE. A Bayesian framework for global tractography. Neuroimage 2007, 37: 116–129.

    PubMed  CAS  Google Scholar 

  59. Iturria-Medina Y, Canales-Rodriguez EJ, Melie-Garcia L, Valdes-Hernandez PA, Martinez-Montes E, Aleman-Gomez Y, et al. Characterizing brain anatomical connections using diffusion weighted MRI and graph theory. Neuroimage 2007, 36: 645–660.

    PubMed  CAS  Google Scholar 

  60. Reisert M, Mader I, Anastasopoulos C, Weigel M, Schnell S, Kiselev V. Global fiber reconstruction becomes practical. Neuroimage 2011, 54: 955–962.

    PubMed  Google Scholar 

  61. Staempfli P, Jaermann T, Crelier GR, Kollias S, Valavanis A, Boesiger P. Resolving fiber crossing using advanced fast marching tractography based on diffusion tensor imaging. Neuroimage 2006, 30: 110–120.

    PubMed  CAS  Google Scholar 

  62. Parker GJ, Wheeler-Kingshott CA, Barker GJ. Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging. IEEE Trans Med Imaging 2002, 21: 505–512.

    PubMed  Google Scholar 

  63. Zalesky A, Fornito A. A DTI-derived measure of cortico-cortical connectivity. IEEE Trans Med Imaging 2009, 28: 1023–1036.

    PubMed  Google Scholar 

  64. Friman O, Farneback G, Westin CF. A Bayesian approach for stochastic white matter tractography. IEEE Trans Med Imaging 2006, 25: 965–978.

    PubMed  Google Scholar 

  65. Sherbondy AJ, Dougherty RF, Ben-Shachar M, Napel S, Wandell BA. ConTrack: finding the most likely pathways between brain regions using diffusion tractography. J Vis 2008, 8: 1–16.

    Google Scholar 

  66. Behrens TE, Woolrich MW, Jenkinson M, Johansen-Berg H, Nunes RG, Clare S, et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med 2003, 50: 1077–1088.

    PubMed  CAS  Google Scholar 

  67. Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage 2007, 34: 144–155.

    PubMed  CAS  Google Scholar 

  68. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002, 15: 273–289.

    PubMed  CAS  Google Scholar 

  69. Kennedy DN, Lange N, Makris N, Bates J, Meyer J, Caviness VS Jr. Gyri of the human neocortex: an MRI-based analysis of volume and variance. Cereb Cortex 1998, 8: 372–384.

    PubMed  CAS  Google Scholar 

  70. Makris N, Meyer JW, Bates JF, Yeterian EH, Kennedy DN, Caviness VS. MRI-Based topographic parcellation of human cerebral white matter and nuclei II. Rationale and applications with systematics of cerebral connectivity. Neuroimage 1999, 9: 18–45.

    PubMed  CAS  Google Scholar 

  71. Dosenbach NU, Nardos B, Cohen AL, Fair DA, Power JD, Church JA, et al. Prediction of individual brain maturity using fMRI. Science 2010, 329: 1358–1361.

    PubMed  CAS  Google Scholar 

  72. Dosenbach NU, Visscher KM, Palmer ED, Miezin FM, Wenger KK, Kang HC, et al. A core system for the implementation of task sets. Neuron 2006, 50: 799–812.

    PubMed  CAS  Google Scholar 

  73. Zhu D, Li K, Faraco CC, Deng F, Zhang D, Guo L, et al. Optimization of functional brain ROIs via maximization of consistency of structural connectivity profiles. Neuroimage 2012, 59: 1382–1393.

    PubMed  Google Scholar 

  74. Li K, Guo L, Faraco C, Zhu D, Chen H, Yuan Y, et al. Visual analytics of brain networks. Neuroimage 2012, 61: 82–97.

    PubMed  CAS  Google Scholar 

  75. Cullen KR, Klimes-Dougan B, Muetzel R, Mueller BA, Camchong J, Houri A, et al. Altered white matter microstructure in adolescents with major depression: a preliminary study. J Am Acad Child Adolesc Psychiatry 2010, 49: 173–183.e1.

    PubMed  Google Scholar 

  76. Bae JN, MacFall JR, Krishnan KR, Payne ME, Steffens DC, Taylor WD. Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry 2006, 60: 1356–1363.

    PubMed  Google Scholar 

  77. Versace A, Almeida JR, Hassel S, Walsh ND, Novelli M, Klein CR, et al. Elevated left and reduced right orbitomedial prefrontal fractional anisotropy in adults with bipolar disorder revealed by tractbased spatial statistics. Arch Gen Psychiatry 2008, 65: 1041–1052.

    PubMed  Google Scholar 

  78. Vulliemoz S, Vollmar C, Koepp MJ, Yogarajah M, O’Muircheartaigh J, Carmichael DW, et al. Connectivity of the supplementary motor area in juvenile myoclonic epilepsy and frontal lobe epilepsy. Epilepsia 2011, 52: 507–514.

    PubMed  Google Scholar 

  79. Wu X, **e M, Zhou J, Anderson AW, Gore JC, Ding Z. Globally optimized fiber tracking and hierarchical clustering — a unified framework. Magn Reson Imaging 2012, 30 (4): 485–495.

    Google Scholar 

  80. Barbieri S, Bauer MH, Klein J, Moltz J, Nimsky C, Hahn HK. DTI segmentation via the combined analysis of connectivity maps and tensor distances. Neuroimage 2012, 60: 1025–1035.

    PubMed  Google Scholar 

  81. Zhang T, Guo L, Li K, Zhu D, Cui G, Liu T. Predicting functional brain ROIs via fiber shape models. Med Image Comput Comput Assist Interv 2011, 14: 42–49.

    PubMed  Google Scholar 

  82. Ge B, Guo L, Lv J, Hu X, Han J, Zhang T, et al. Resting state fMRI-guided fiber clustering. Med Image Comput Comput Assist Interv 2011, 14: 149–156.

    PubMed  Google Scholar 

  83. Gong G, Rosa-Neto P, Carbonell F, Chen ZJ, He Y, Evans AC. Age- and gender-related differences in the cortical anatomical network. J Neurosci 2009, 29: 15684–15693.

    PubMed  CAS  Google Scholar 

  84. Yan C, Gong G, Wang J, Wang D, Liu D, Zhu C, et al. Sex- and brain size-related small-world structural cortical networks in young adults: a DTI tractography study. Cereb Cortex 2011, 21: 449–458.

    PubMed  Google Scholar 

  85. Li Y, Liu Y, Li J, Qin W, Li K, Yu C, et al. Brain anatomical network and intelligence. PLoS Comput Biol 2009, 5: e1000395.

    PubMed  Google Scholar 

  86. Friston KJ, Frith CD. Schizophrenia: a disconnection syndrome? Clin Neurosci 1995, 3: 89–97.

    PubMed  CAS  Google Scholar 

  87. Wee CY, Yap PT, Zhang D, Denny K, Browndyke JN, Potter GG, et al. Identification of MCI individuals using structural and functional connectivity networks. Neuroimage 2012, 59: 2045–2056.

    PubMed  Google Scholar 

  88. Thomason ME, Thompson PM. Diffusion imaging, white matter, and psychopathology. Annu Rev Clin Psychol 2011, 7: 63–85.

    PubMed  Google Scholar 

  89. Smits M, Visch-Brink EG, van de Sandt-Koenderman ME, van der Lugt A. Advanced magnetic resonance neuroimaging of language function recovery after aphasic stroke: a technical review. Arch Phys Med Rehabil 2012, 93: S4–14.

    PubMed  Google Scholar 

  90. Jang SH. A review of diffusion tensor imaging studies on motor recovery mechanisms in stroke patients. NeuroRehabilitation 2011, 28: 345–352.

    PubMed  CAS  Google Scholar 

  91. Maller JJ, Thomson RH, Lewis PM, Rose SE, Pannek K, Fitzgerald PB. Traumatic brain injury, major depression, and diffusion tensor imaging: making connections. Brain Res Rev 2010, 64: 213–240.

    PubMed  Google Scholar 

  92. Sharp DJ, Ham TE. Investigating white matter injury after mild traumatic brain injury. Curr Opin Neurol 2011, 24: 558–563.

    PubMed  Google Scholar 

  93. Wang F, Sun Z, Cui L, Du X, Wang X, Zhang H, et al. Anterior cingulum abnormalities in male patients with schizophrenia determined through diffusion tensor imaging. Am J Psychiatry 2004, 161: 573–575.

    PubMed  Google Scholar 

  94. Mielke MM, Kozauer NA, Chan KC, George M, Toroney J, Zerrate M, et al. Regionally-specific diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease. Neuroimage 2009, 46: 47–55.

    PubMed  CAS  Google Scholar 

  95. Bae MS, Jahng GH, Ryu CW, Kim EJ, Choi WS, Yang DM. Effect of intravenous gadolinium-DTPA on diffusion tensor MR imaging for the evaluation of brain tumors. Neuroradiology 2009, 51: 793–802.

    PubMed  Google Scholar 

  96. Awasthi R, Verma SK, Haris M, Singh A, Behari S, Jaiswal AK, et al. Comparative evaluation of dynamic contrast-enhanced perfusion with diffusion tensor imaging metrics in assessment of corticospinal tract infiltration in malignant glioma. J Comput Assist Tomogr 2010, 34: 82–88.

    PubMed  Google Scholar 

  97. Trivedi R, Husain N, Rathore RK, Saksena S, Srivastava S, Malik GK, et al. Correlation of diffusion tensor imaging with histology in the develo** human frontal cerebrum. Dev Neurosci 2009, 31: 487–496.

    PubMed  CAS  Google Scholar 

  98. Pal D, Trivedi R, Saksena S, Yadav A, Kumar M, Pandey CM, et al. Quantification of age- and gender-related changes in diffusion tensor imaging indices in deep grey matter of the normal human brain. J Clin Neurosci 2011, 18: 193–196.

    PubMed  Google Scholar 

  99. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A 2001, 98: 676–682.

    PubMed  CAS  Google Scholar 

  100. Teipel SJ, Bokde AL, Meindl T, Amaro E Jr, Soldner J, Reiser MF, et al. White matter microstructure underlying default mode network connectivity in the human brain. Neuroimage 2010, 49: 2021–2032.

    PubMed  Google Scholar 

  101. Greicius MD, Supekar K, Menon V, Dougherty RF. Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb Cortex 2009, 19: 72–78.

    PubMed  Google Scholar 

  102. Camchong J, MacDonald AW 3rd, Bell C, Mueller BA, Lim KO. Altered functional and anatomical connectivity in schizophrenia. Schizophr Bull 2011, 37: 640–650.

    PubMed  Google Scholar 

  103. Soldner J, Meindl T, Koch W, Bokde AL, Reiser MF, Moller HJ, et al. Structural and functional neuronal connectivity in Alzheimer’s disease: A combined DTI and fMRI study. Nervenarzt 2011. [Epub ahead of print] (Article in German)

  104. Bokde AL, Ewers M, Hampel H. Assessing neuronal networks: understanding Alzheimer’s disease. Prog Neurobiol 2009, 89: 125–133.

    PubMed  Google Scholar 

  105. Bozzali M, Padovani A, Caltagirone C, Borroni B. Regional grey matter loss and brain disconnection across Alzheimer disease evolution. Curr Med Chem 2011, 18: 2452–2458.

    PubMed  CAS  Google Scholar 

  106. Liao W, Zhang Z, Pan Z, Mantini D, Ding J, Duan X, et al. Default mode network abnormalities in mesial temporal lobe epilepsy: a study combining fMRI and DTI. Hum Brain Mapp 2011, 32: 883–895.

    PubMed  Google Scholar 

  107. Gutman DA, Holtzheimer PE, Behrens TE, Johansen-Berg H, Mayberg HS. A tractography analysis of two deep brain stimulation white matter targets for depression. Biol Psychiatry 2009, 65: 276–282.

    PubMed  Google Scholar 

  108. McDonald CR, Ahmadi ME, Hagler DJ, Tecoma ES, Iragui VJ, Gharapetian L, et al. Diffusion tensor imaging correlates of memory and language impairments in temporal lobe epilepsy. Neurology 2008, 71: 1869–1876.

    PubMed  CAS  Google Scholar 

  109. Brauer J, Anwander A, Friederici AD. Neuroanatomical prerequisites for language functions in the maturing brain. Cereb Cortex 2011, 21: 459–466.

    PubMed  Google Scholar 

  110. Ostby Y, Tamnes CK, Fjell AM, Walhovd KB. Morphometry and connectivity of the fronto-parietal verbal working memory network in development. Neuropsychologia 2011, 49: 3854–3862.

    PubMed  Google Scholar 

  111. Supekar K, Menon V. Developmental maturation of dynamic causal control signals in higher-order cognition: a neurocognitive network model. PLoS Comput Biol 2012, 8: e1002374.

    PubMed  CAS  Google Scholar 

  112. Ystad M, Hodneland E, Adolfsdottir S, Haasz J, Lundervold AJ, Eichele T, et al. Cortico-striatal connectivity and cognition in normal aging: a combined DTI and resting state fMRI study. Neuroimage 2011, 55: 24–31.

    PubMed  Google Scholar 

  113. Sexton CE, Mackay CE, Ebmeier KP. A systematic review of diffusion tensor imaging studies in affective disorders. Biol Psychiatry 2009, 66: 814–823.

    PubMed  Google Scholar 

  114. Clark L, Chamberlain SR, Sahakian BJ. Neurocognitive mechanisms in depression: implications for treatment. Annu Rev Neurosci 2009, 32: 57–74.

    PubMed  CAS  Google Scholar 

  115. Drevets WC, Price JL, Furey ML. Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression. Brain Struct Funct 2008, 213: 93–118.

    PubMed  Google Scholar 

  116. Lin JJ, Riley JD, Juranek J, Cramer SC. Vulnerability of the frontaltemporal connections in temporal lobe epilepsy. Epilepsy Res 2008, 82: 162–170.

    PubMed  Google Scholar 

  117. Rimrodt SL, Peterson DJ, Denckla MB, Kaufmann WE, Cutting LE. White matter microstructural differences linked to left perisylvian language network in children with dyslexia. Cortex 2010, 46: 739–749.

    PubMed  Google Scholar 

  118. Gharabaghi A, Kunath F, Erb M, Saur R, Heckl S, Tatagiba M, et al. Perisylvian white matter connectivity in the human right hemisphere. BMC neuroscience 2009, 10: 15.

    PubMed  Google Scholar 

  119. Cheon KA, Kim YS, Oh SH, Park SY, Yoon HW, Herrington J, et al. Involvement of the anterior thalamic radiation in boys with high functioning autism spectrum disorders: a Diffusion Tensor Imaging study. Brain Res 2011, 1417: 77–86.

    PubMed  CAS  Google Scholar 

  120. Zarei M, Patenaude B, Damoiseaux J, Morgese C, Smith S, Matthews PM, et al. Combining shape and connectivity analysis: an MRI study of thalamic degeneration in Alzheimer’s disease. Neuroimage 2010, 49: 1–8.

    PubMed  Google Scholar 

  121. Dudink J, Counsell SJ, Lequin MH, Govaert PP. DTI reveals network injury in perinatal stroke. Arch Dis Child Fetal Neonatal Ed 2011. [Epub ahead of print]

  122. Forster A, Griebe M, Ottomeyer C, Rossmanith C, Gass A, Kern R, et al. Cerebral network disruption as a possible mechanism for impaired recovery after acute pontine stroke. Cerebrovasc Dis 2011, 31: 499–505.

    PubMed  Google Scholar 

  123. Liu H, Fan G, Xu K, Wang F. Changes in cerebellar functional connectivity and anatomical connectivity in schizophrenia: a combined resting-state functional MRI and diffusion tensor imaging study. J Magn Reson Imaging 2011, 34: 1430–1438.

    PubMed  Google Scholar 

  124. Wang Q, Su TP, Zhou Y, Chou KH, Chen IY, Jiang T, et al. Anatomical insights into disrupted small-world networks in schizophrenia. Neuroimage 2012, 59: 1085–1093.

    PubMed  Google Scholar 

  125. Cammoun L, Gigandet X, Sporns O, Thiran JP, Deppen P, Krieger E, et al. Connectome alterations in schizophrenia. Neuroimage 2009, 47: S157.

    Google Scholar 

  126. Lo CY, Wang PN, Chou KH, Wang J, He Y, Lin CP. Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer’s disease. J Neurosci 2010, 30: 16876–16885.

    PubMed  CAS  Google Scholar 

  127. Li J, Liu Y, Qin W, Jiang J, Qiu Z, Xu J, et al. Age of onset of blindness affects brain anatomical networks constructed using diffusion tensor tractography. Cereb Cortex 2012. [Epub ahead of print]

  128. Weinstein M, Ben-Sira L, Levy Y, Zachor DA, Ben Itzhak E, Artzi M, et al. Abnormal white matter integrity in young children with autism. Hum Brain Mapp 2011, 32: 534–543.

    PubMed  Google Scholar 

  129. Müller RA. The study of autism as a distributed disorder. Ment Retard Dev Disabil Res Rev 2007, 13: 85–95.

    PubMed  Google Scholar 

  130. Bohanna I, Georgiou-Karistianis N, Hannan AJ, Egan GF. Magnetic resonance imaging as an approach towards identifying neuropathological biomarkers for Huntington’s disease. Brain Res Rev 2008, 58: 209–225.

    PubMed  CAS  Google Scholar 

  131. Scholz J, Klein MC, Behrens TE, Johansen-Berg H. Training induces changes in white-matter architecture. Nat Neurosci 2009, 12: 1370–1371.

    PubMed  CAS  Google Scholar 

  132. Sagi Y, Tavor I, Hofstetter S, Tzur-Moryosef S, Blumenfeld-Katzir T, Assaf Y. Learning in the fast lane: new insights into neuroplasticity. Neuron 2012, 73: 1195–1203.

    PubMed  CAS  Google Scholar 

  133. Raj A, Kuceyeski A, Weiner M. A network diffusion model of disease progression in dementia. Neuron 2012, 73: 1204–1215.

    PubMed  CAS  Google Scholar 

  134. Jones DK, Cercignani M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed 2010, 23: 803–820.

    PubMed  Google Scholar 

  135. Jbabdi S, Johansen-Berg H. Tractography: where do we go from here? Brain Connect 2011, 1: 169–183.

    PubMed  Google Scholar 

  136. Van Essen DC, Ugurbil K. The future of the human connectome. Neuroimage 2012. [Epub ahead of print]

  137. Cheng H, Wang Y, Sheng J, Sporns O, Kronenberger WG, Mathews VP, et al. Optimization of seed density in DTI tractography for structural networks. J Neurosci Methods 2012, 203: 264–272.

    PubMed  Google Scholar 

  138. Li L, Rilling JK, Preuss TM, Glasser MF, Hu X. The effects of connection reconstruction method on the interregional connectivity of brain networks via diffusion tractography. Hum Brain Mapp 2011. [Epub ahead of print]

  139. Bassett DS, Brown JA, Deshpande V, Carlson JM, Grafton ST. Conserved and variable architecture of human white matter connectivity. Neuroimage 2011, 54: 1262–1279.

    PubMed  Google Scholar 

  140. Konarski JZ, McIntyre RS, Soczynska JK, Kennedy SH. Neuroimaging approaches in mood disorders: technique and clinical implications. Ann Clin Psychiatry 2007, 19: 265–277.

    PubMed  Google Scholar 

  141. Liu B, Song M, Li J, Liu Y, Li K, Yu C, et al. Prefrontal-related functional connectivities within the default network are modulated by COMT val158met in healthy young adults. J Neurosci 2010, 30: 64–69.

    PubMed  Google Scholar 

  142. Liu B, Li J, Yu C, Li Y, Liu Y, Song M, et al. Haplotypes of catechol-O-methyltransferase modulate intelligence-related brain white matter integrity. Neuroimage 2010, 50: 243–249.

    PubMed  CAS  Google Scholar 

  143. Brown JA, Terashima KH, Burggren AC, Ercoli LM, Miller KJ, Small GW, et al. Brain network local interconnectivity loss in aging APOE-4 allele carriers. Proc Natl Acad Sci U S A 2011, 108: 20760–20765.

    PubMed  CAS  Google Scholar 

  144. Teipel SJ, Pogarell O, Meindl T, Dietrich O, Sydykova D, Hunklinger U, et al. Regional networks underlying interhemispheric connectivity: an EEG and DTI study in healthy ageing and amnestic mild cognitive impairment. Hum Brain Mapp 2009, 30: 2098–2119.

    PubMed  Google Scholar 

  145. Mohamed IS, Otsubo H, Shroff M, Donner E, Drake J, Snead OC 3rd. Magnetoencephalography and diffusion tensor imaging in gelastic seizures secondary to a cingulate gyrus lesion. Clin Neurol Neurosurg 2007, 109: 182–187.

    PubMed  Google Scholar 

  146. Luat AF, Chugani HT. Molecular and diffusion tensor imaging of epileptic networks. Epilepsia 2008, 49(Suppl 3): 15–22.

    PubMed  Google Scholar 

  147. Rowland LM, Spieker EA, Francis A, Barker PB, Carpenter WT, Buchanan RW. White matter alterations in deficit schizophrenia. Neuropsychopharmacology 2009, 34: 1514–1522.

    PubMed  CAS  Google Scholar 

  148. Park B, Kim JI, Lee D, Jeong SO, Lee JD, Park HJ. Are brain networks stable during a 24-hour period? Neuroimage 2012, 59: 456–466.

    PubMed  Google Scholar 

  149. Liang X, Wang J, Yan C, Shu N, Xu K, Gong G, et al. Effects of different correlation metrics and preprocessing factors on smallworld brain functional networks: a resting-state functional MRI study. PLoS One 2012, 7: e32766.

    PubMed  CAS  Google Scholar 

  150. Vaessen MJ, Hofman PA, Tijssen HN, Aldenkamp AP, Jansen JF, Backes WH. The effect and reproducibility of different clinical DTI gradient sets on small world brain connectivity measures. Neuroimage 2010, 51: 1106–1116.

    PubMed  CAS  Google Scholar 

  151. You X, Adjouadi M, Guillen MR, Ayala M, Barreto A, Rishe N, et al. Sub-patterns of language network reorganization in pediatric localization related epilepsy: a multisite study. Hum Brain Mapp 2011, 32: 784–799.

    PubMed  Google Scholar 

  152. Teipel SJ, Reuter S, Stieltjes B, Acosta-Cabronero J, Ernemann U, Fellgiebel A, et al. Multicenter stability of diffusion tensor imaging measures: a European clinical and physical phantom study. Psychiatry Res 2011, 194: 363–371.

    PubMed  Google Scholar 

  153. Wang JH, Zuo XN, Gohel S, Milham MP, Biswal BB, He Y. Graph theoretical analysis of functional brain networks: test-retest evaluation on short- and long-term resting-state functional MRI data. PLoS One 2011, 6: e21976.

    PubMed  CAS  Google Scholar 

  154. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, et al. Map** the structural core of human cerebral cortex. PLoS Biol 2008, 6: e159.

    PubMed  Google Scholar 

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Authors and Affiliations

  1. LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Bei**g, 100190, China

    Nianming Zuo, Jian Cheng & Tianzi Jiang

  2. Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Tianzi Jiang

  3. The Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia

    Tianzi Jiang

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  1. Nianming Zuo
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Correspondence to Tianzi Jiang.

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Zuo, N., Cheng, J. & Jiang, T. Diffusion magnetic resonance imaging for Brainnetome: A critical review. Neurosci. Bull. 28, 375–388 (2012). https://doi.org/10.1007/s12264-012-1245-3

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