Brain Structure and Function

, Volume 222, Issue 8, pp 3761–3774 | Cite as

Track-weighted dynamic functional connectivity (TW-dFC): a new method to study time-resolved functional connectivity

  • Fernando Calamante
  • Robert E. Smith
  • Xiaoyun Liang
  • Andrew Zalesky
  • Alan Connelly
Original Article


Interest in the study of brain connectivity is growing, particularly in understanding the dynamics of the structural/functional connectivity relation. Structural and functional connectivity are most often analysed independently of each other. Track-weighted functional connectivity (TW-FC) was recently proposed as a means to combine structural/functional connectivity information into a single image. We extend here TW-FC in two important ways: first, all the functional data are used without having to define a prior functional network (cf. TW-FC generates a map for a pre-specified network); second, we incorporate time-resolved connectivity information, thus allowing dynamic characterisation of functional connectivity. We refer to this technique as track-weighted dynamic functional connectivity (TW-dFC), which fuses structural/functional connectivity data into a four-dimensional image, providing a new approach to investigate dynamic connectivity. The structural connectivity information effectively ‘constrains’ the extremely large number of possible connections in the functional connectivity data (i.e. each voxel’s connection to every other voxel), thus providing a way of reducing the problem’s dimensionality while still maintaining key data features. The methodology is demonstrated in data from eight healthy subjects, and independent component analysis was subsequently applied to parcellate the corpus callosum, as an illustration of a possible application. TW-dFC maps demonstrate that different white matter pathways can have very different temporal characteristics, corresponding to correlated fluctuations in the grey matter regions they link. A realistic parcellation of the corpus callosum was generated, which was qualitatively similar to topography previously reported. TW-dFC, therefore, provides a complementary new tool to investigate the dynamic nature of brain connectivity.


Structural connectivity Functional connectivity Fibre-tracking Parcellation Sliding window Networks 



We are grateful to the National Health and Medical Research Council (NHMRC) of Australia, the Australian Research Council (ARC), and the Victorian Government’s Operational Infrastructure Support Grant for their support. AZ is supported by the NHMRC CDF (GNT1047648).

Compliance with ethical standards


This study was funded by the National Health and Medical Research Council (NHMRC) of Australia, the Australian Research Council (ARC), and the Victorian Government (Australia).

Conflicts of interest

The authors declare that they have no conflict of interest.

Research involving Human Participants

Informed written consent was obtained in accordance with ethical approval from the local Human Research Ethics Committee and has been performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Supplementary material

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Supplementary material 4 (PDF 525 kb)


  1. Andersson JLR, Skare S, Ashburner J (2003) How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage 20:870–888. doi: 10.1016/S1053-8119(03)00336-7 CrossRefPubMedGoogle Scholar
  2. Basser PJ (1995) Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed 8:333–344CrossRefPubMedGoogle Scholar
  3. Behrens TEJ, Johansen-Berg H, Woolrich MW et al (2003) Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci 6:750–757. doi: 10.1038/nn1075 CrossRefPubMedGoogle Scholar
  4. Bowman FD, Zhang L, Derado G, Chen S (2012) Determining functional connectivity using fMRI data with diffusion-based anatomical weighting. NeuroImage 62:1769–1779. doi: 10.1016/j.neuroimage.2012.05.032 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Calamante F (2017) Track-weighted imaging methods: extracting information from a streamlines tractogram. Magn Reson Mater Phy. doi: 10.1007/s10334-017-0608-1 Google Scholar
  6. Calamante F, Tournier J-D, Jackson GD, Connelly A (2010) Track-density imaging (TDI): super-resolution white matter imaging using whole-brain track-density mapping. NeuroImage 53:1233–1243CrossRefPubMedGoogle Scholar
  7. Calamante F, Tournier J-D, Heidemann RM et al (2011) Track density imaging (TDI): validation of super resolution property. NeuroImage 56:1259–1266CrossRefPubMedGoogle Scholar
  8. Calamante F, Tournier J-D, Smith RE, Connelly A (2012) A generalised framework for super-resolution track-weighted imaging. NeuroImage 59:2494–2503CrossRefPubMedGoogle Scholar
  9. Calamante F, Masterton RAJ, Tournier J-D et al (2013) Track-weighted functional connectivity (TW-FC): a tool for characterizing the structural-functional connections in the brain. NeuroImage 70:199–210CrossRefPubMedGoogle Scholar
  10. Calhoun VD, Miller R, Pearlson G, Adalı T (2014) The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 84:262–274. doi: 10.1016/j.neuron.2014.10.015 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Chang C, Glover GH (2010) Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50:81–98. doi: 10.1016/j.neuroimage.2009.12.011 CrossRefPubMedGoogle Scholar
  12. Chao Y-P, Cho K-H, Yeh C-H et al (2009) Probabilistic topography of human corpus callosum using cytoarchitectural parcellation and high angular resolution diffusion imaging tractography. Hum Brain Mapp 30:3172–3187. doi: 10.1002/hbm.20739 CrossRefPubMedGoogle Scholar
  13. Cho ZH, Calamante F, Chi JG (2015) 7.0 Tesla MRI brain white matter atlas, Second. Springer, New YorkGoogle Scholar
  14. Cribben I, Haraldsdottir R, Atlas LY et al (2012) Dynamic connectivity regression: determining state-related changes in brain connectivity. NeuroImage 61:907–920. doi: 10.1016/j.neuroimage.2012.03.070 CrossRefPubMedPubMedCentralGoogle Scholar
  15. Eickhoff SB, Thirion B, Varoquaux G, Bzdok D (2015) Connectivity-based parcellation: critique and implications. Hum Brain Mapp 36:4771–4792. doi: 10.1002/hbm.22933 CrossRefPubMedGoogle Scholar
  16. Filippini N, MacIntosh BJ, Hough MG et al (2009) Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc Natl Acad Sci USA 106:7209–7214. doi: 10.1073/pnas.0811879106 CrossRefPubMedPubMedCentralGoogle Scholar
  17. Franco AR, Ling J, Caprihan A et al (2008) Multimodal and multi-tissue measures of connectivity revealed by joint independent component analysis. IEEE J Sel Top Signal Process 2:986–997. doi: 10.1109/JSTSP.2008.2006718 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Ge B, Guo L, Zhang T et al (2013) Resting state fMRI-guided fiber clustering: methods and applications. Neuroinformatics 11:119–133. doi: 10.1007/s12021-012-9169-7 CrossRefPubMedPubMedCentralGoogle Scholar
  19. Honey CJ, Sporns O, Cammoun L et al (2009) Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci USA 106:2035–2040. doi: 10.1073/pnas.0811168106 CrossRefPubMedPubMedCentralGoogle Scholar
  20. Huang H, Zhang J, Jiang H et al (2005) DTI tractography based parcellation of white matter: application to the mid-sagittal morphology of corpus callosum. NeuroImage 26:195–205. doi: 10.1016/j.neuroimage.2005.01.019 CrossRefPubMedGoogle Scholar
  21. Hutchison RM, Womelsdorf T, Allen EA et al (2013) Dynamic functional connectivity: promise, issues, and interpretations. NeuroImage 80:360–378. doi: 10.1016/j.neuroimage.2013.05.079 CrossRefPubMedGoogle Scholar
  22. Ji B, Li Z, Li K et al (2016) Dynamic thalamus parcellation from resting-state fMRI data. Hum Brain Mapp 37:954–967. doi: 10.1002/hbm.23079 CrossRefPubMedGoogle Scholar
  23. Jia H, Hu X, Deshpande G (2014) Behavioral relevance of the dynamics of the functional brain connectome. Brain Connect 4:741–759. doi: 10.1089/brain.2014.0300 CrossRefPubMedPubMedCentralGoogle Scholar
  24. Kim J-H, Lee J-M, Jo HJ et al (2010) Defining functional SMA and pre-SMA subregions in human MFC using resting state fMRI: functional connectivity-based parcellation method. NeuroImage 49:2375–2386. doi: 10.1016/j.neuroimage.2009.10.016 CrossRefPubMedGoogle Scholar
  25. Kim J-E, Oh JS, Sung J-J et al (2014) Diffusion tensor tractography analysis of the corpus callosum fibers in amyotrophic lateral sclerosis. J Clin Neurol Seoul Korea 10:249–256. doi: 10.3988/jcn.2014.10.3.249 CrossRefGoogle Scholar
  26. Kivelä M, Arenas A, Barthelemy M et al (2014) Multilayer networks. J Complex Netw 2:203–271CrossRefGoogle Scholar
  27. Leonardi N, Van De Ville D (2015) On spurious and real fluctuations of dynamic functional connectivity during rest. NeuroImage 104:430–436. doi: 10.1016/j.neuroimage.2014.09.007 CrossRefPubMedGoogle Scholar
  28. Li X, Lim C, Li K et al (2013) Detecting brain state changes via fiber-centered functional connectivity analysis. Neuroinformatics 11:193–210. doi: 10.1007/s12021-012-9157-y CrossRefPubMedPubMedCentralGoogle Scholar
  29. Liang X, Connelly A, Calamante F (2013) Graph analysis of resting-state ASL perfusion MRI data: nonlinear correlations among CBF and network metrics. NeuroImage 87:265–275CrossRefPubMedGoogle Scholar
  30. Liang X, Connelly A, Calamante F (2015) Voxel-wise functional connectomics using arterial spin labeling functional magnetic resonance imaging: the role of denoising. Brain Connect 5:543–553. doi: 10.1089/brain.2014.0290 CrossRefPubMedGoogle Scholar
  31. Liang X, Connelly A, Calamante F (2016) A novel joint sparse partial correlation method for estimating group functional networks. Hum Brain Mapp 37:1162–1177. doi: 10.1002/hbm.23092 CrossRefPubMedGoogle Scholar
  32. Lindquist MA, Xu Y, Nebel MB, Caffo BS (2014) Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach. NeuroImage 101:531–546. doi: 10.1016/j.neuroimage.2014.06.052 CrossRefPubMedPubMedCentralGoogle Scholar
  33. Lu H, Golay X, Pekar JJ, Van Zijl PCM (2003) Functional magnetic resonance imaging based on changes in vascular space occupancy. Magn Reson Med Off J Soc Magn Reson Med Soc Magn Reson Med 50:263–274. doi: 10.1002/mrm.10519 CrossRefGoogle Scholar
  34. Lv J, Guo L, Li K et al (2011) Activated fibers: fiber-centered activation detection in task-based FMRI. Inf Process Med Imaging Proc Conf 22:574–587Google Scholar
  35. Miao X, Gu H, Yan L et al (2014) Detecting resting-state brain activity by spontaneous cerebral blood volume fluctuations using whole brain vascular space occupancy imaging. NeuroImage 84:575–584. doi: 10.1016/j.neuroimage.2013.09.019 CrossRefPubMedGoogle Scholar
  36. Mori S, Oishi K, Jiang H et al (2008) Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. NeuroImage 40:570–582. doi: 10.1016/j.neuroimage.2007.12.035 CrossRefPubMedPubMedCentralGoogle Scholar
  37. Park H-J, Kim JJ, Lee S-K et al (2008) Corpus callosal connection mapping using cortical gray matter parcellation and DT-MRI. Hum Brain Mapp 29:503–516. doi: 10.1002/hbm.20314 CrossRefPubMedGoogle Scholar
  38. Raffelt D, Tournier J-D, Rose S et al (2012) Apparent Fibre Density: a novel measure for the analysis of diffusion-weighted magnetic resonance images. NeuroImage 59:3976–3994. doi: 10.1016/j.neuroimage.2011.10.045 CrossRefPubMedGoogle Scholar
  39. Raichle ME, MacLeod AM, Snyder AZ et al (2001) A default mode of brain function. Proc Natl Acad Sci USA 98:676–682. doi: 10.1073/pnas.98.2.676 CrossRefPubMedPubMedCentralGoogle Scholar
  40. Reese TG, Heid O, Weisskoff RM, Wedeen VJ (2003) Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo. Magn Reson Med 49:177–182. doi: 10.1002/mrm.10308 CrossRefPubMedGoogle Scholar
  41. Sadaghiani S, Poline J-B, Kleinschmidt A, D’Esposito M (2015) Ongoing dynamics in large-scale functional connectivity predict perception. Proc Natl Acad Sci USA 112:8463–8468. doi: 10.1073/pnas.1420687112 CrossRefPubMedPubMedCentralGoogle Scholar
  42. Schmahmann JD, Pandya D (2009) Fiber pathways of the brain. Oxford University Press, USAGoogle Scholar
  43. Smith S (2013) Introduction to the NeuroImage special issue “Mapping the Connectome”. NeuroImage 80:1. doi: 10.1016/j.neuroimage.2013.07.012 CrossRefPubMedGoogle Scholar
  44. Smith SM, Jenkinson M, Woolrich MW et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23(Suppl 1):S208–S219. doi: 10.1016/j.neuroimage.2004.07.051 CrossRefPubMedGoogle Scholar
  45. Smith RE, Tournier J-D, Calamante F, Connelly A (2012a) Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage 62:1924–1938CrossRefPubMedGoogle Scholar
  46. Smith SM, Miller KL, Moeller S et al (2012b) Temporally-independent functional modes of spontaneous brain activity. Proc Natl Acad Sci USA 109:3131–3136. doi: 10.1073/pnas.1121329109 CrossRefPubMedPubMedCentralGoogle Scholar
  47. Sui J, Pearlson G, Caprihan A et al (2011) Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA + joint ICA model. NeuroImage 57:839–855. doi: 10.1016/j.neuroimage.2011.05.055 CrossRefPubMedPubMedCentralGoogle Scholar
  48. Sui J, He H, Pearlson GD et al (2013) Three-way (N-way) fusion of brain imaging data based on mCCA + jICA and its application to discriminating schizophrenia. NeuroImage 66:119–132. doi: 10.1016/j.neuroimage.2012.10.051 CrossRefPubMedGoogle Scholar
  49. Sui J, Pearlson GD, Du Y et al (2015) In search of multimodal neuroimaging biomarkers of cognitive deficits in schizophrenia. Biol Psychiatry 78:794–804. doi: 10.1016/j.biopsych.2015.02.017 CrossRefPubMedPubMedCentralGoogle Scholar
  50. Tournier J-D, Calamante F, Gadian DG, Connelly A (2004) Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23:1176–1185CrossRefPubMedGoogle Scholar
  51. Tournier J-D, Calamante F, Connelly A (2007) Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35:1459–1472CrossRefPubMedGoogle Scholar
  52. Tournier J-D, Calamante F, Connelly A (2010) Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Proc Int Soc Magn Reson Med ISMRM 18th Annu Meet Stock Swed 1670Google Scholar
  53. Tournier J-D, Calamante F, Connelly A (2012) MRtrix: diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol 22:53–66CrossRefGoogle Scholar
  54. Tournier J-D, Calamante F, Connelly A (2013) Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR Biomed 26:1775–1786CrossRefPubMedGoogle Scholar
  55. Tozer DJ, Chard DT, Bodini B et al (2012) Linking white matter tracts to associated cortical grey matter: a tract extension methodology. NeuroImage 59:3094–3102. doi: 10.1016/j.neuroimage.2011.10.088 CrossRefPubMedGoogle Scholar
  56. Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320. doi: 10.1109/TMI.2010.2046908 CrossRefPubMedPubMedCentralGoogle Scholar
  57. Wheeler-Kingshott CAM, Cercignani M (2009) About “axial” and “radial” diffusivities. Magn Reson Med 61:1255–1260. doi: 10.1002/mrm.21965 CrossRefPubMedGoogle Scholar
  58. Xue W, Bowman FD, Pileggi AV, Mayer AR (2015) A multimodal approach for determining brain networks by jointly modeling functional and structural connectivity. Front Comput Neurosci 9:22. doi: 10.3389/fncom.2015.00022 CrossRefPubMedPubMedCentralGoogle Scholar
  59. Zalesky A, Breakspear M (2015) Towards a statistical test for functional connectivity dynamics. NeuroImage 114:466–470. doi: 10.1016/j.neuroimage.2015.03.047 CrossRefPubMedGoogle Scholar
  60. Zalesky A, Fornito A, Bullmore E (2012) On the use of correlation as a measure of network connectivity. NeuroImage 60:2096–2106. doi: 10.1016/j.neuroimage.2012.02.001 CrossRefPubMedGoogle Scholar
  61. Zalesky A, Fornito A, Cocchi L et al (2014) Time-resolved resting-state brain networks. Proc Natl Acad Sci USA 111:10341–10346. doi: 10.1073/pnas.1400181111 CrossRefPubMedPubMedCentralGoogle Scholar
  62. Zhu D, Zhang T, Jiang X et al (2014) Fusing DTI and fMRI data: a survey of methods and applications. NeuroImage 102(Pt 1):184–191. doi: 10.1016/j.neuroimage.2013.09.071 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Florey Institute of Neuroscience and Mental HealthMelbourne Brain CentreHeidelbergAustralia
  2. 2.Florey Department of Neuroscience and Mental HealthUniversity of MelbourneMelbourneAustralia
  3. 3.Department of Medicine, Austin Health and Northern HealthUniversity of MelbourneMelbourneAustralia
  4. 4.Melbourne Neuropsychiatry CentreUniversity of MelbourneMelbourneAustralia
  5. 5.Department of Electrical and Electronic EngineeringUniversity of MelbourneMelbourneAustralia

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