Abstract
The neuroimaging field has newly become a drift with multimodal neuroimaging findings to study brain connectivity through the combination of High Angular Resolution Diffusion Imaging (HARDI) based tractography, and functional activation maps using functional Magnetic Resonance Imaging (fMRI) task-related. The reconstruction of Working Memory (WM) fibers bundle is an important goal for many clinical applications such as brain tumors, gliomas, traumatic brain injury, schizophrenia, and Alzheimer Disease.
In the present paper, we propose to study the anatomical variability of the WM bundle by focusing on the quantification of the track and testing for correlations with sex and local reconstruction methods.
The dataset used for the experiment is based on in vivo data from the Human Connectome Project (HCP). We used fifty normal volunteers (25 males, 25 females; mean age = 25 ± 2.81 years). We computed the shape similarity, the volume, the mean length, and the number of streamlines, to evaluate the variability of macro structural measures between subjects.
Firstly, this study demonstrates that there is a significant difference between the reconstruction of WM fiber bundle using the Diffusion Tensor Imaging (DTI) model and the High Angular Resolution Diffusion Imaging (HARDI) model, with a mean correlation coefficient r of 0.43 and a p-value of 0.09 for all measured parameters. Also, the WM bundle has a complex architecture with crossing fibers. Therefore, we can conclude that the HARDI model is the most relevant model for the reconstruction of the WM fiber bundle. Secondly, there were a slight sex differences in WM fiber bundles morphology between healthy subjects, with a 0.95 shape similarity average. Finally, we find a slight difference between the measured macrostructural parameters inter-subjects (Normalized Volume, Mean Length of streamlines (MLS), and Number of streamlines). We conclude that the statistical measures show that the MLS is the well-correlated parameter.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Baddeley, A.: Working Memory Components of Working Memory Individual Differences in Working Memory The Slave Systems of Working Memory, vol. 255
Chai, W.J., Abd Hamid, A.I., Abdullah, J.M.: Working memory from the psychological and neurosciences perspectives: a review. Front. Psychol. 9, 1–16 (2018)
Baddeley, A.: Working memory. Curr. Biol. 20(4), 136–140 (2010)
Charlton, R.A., Barrick, T.R., Lawes, I.N.C., Markus, H.S., Morris, R.G.: White matter pathways associated with working memory in normal aging. Cortex 46(4), 474–489 (2010)
Lapointe, S., Perry, A., Butowski, N.A.: Primary brain tumours in adults. Lancet 392(10145), 432–446 (2018)
Acharya, S., et al.: Neuro-oncology survivors of childhood or adolescent low-grade glioma: a 10-year neurocognitive longitudinal study 21, 1175–1183 (2019)
Guberman, G.I., Houde, J., Ptito, A., Gagnon, I.: Structural abnormalities in thalamo-prefrontal tracks revealed by high angular resolution diffusion imaging predict working memory scores in concussed children (2019)
Sugranyes, G., et al.: Multimodal analyses identify linked functional and white matter abnormalities within the working memory network in schizophrenia. Schizophr. Res. 138(2–3), 136–142 (2012)
Huntley, J.D., Howard, R.J.: Working memory in early Alzheimer’s disease: a neuropsychological review. Int. J. Geriatr. Psychiatry 25(2), 121–132 (2010)
Cahn, A.J., Little, G., Beaulieu, C., Tétreault, P.: Diffusion properties of the fornix assessed by deterministic tractography shows age , sex, volume , cognitive , hemispheric , and twin relationships in young adults from the Human Connectome Project. Brain Struct. Funct. (2021)
Yeatman, J.D., Dougherty, R.F., Myall, N.J., Wandell, B.A., Feldman, H.M.: Tract profiles of white matter properties: automating fiber-tract quantification. PLoS One 7(11) (2012)
Boukadi, M., et al.: Test-retest reliability of diffusion measures extracted along white matter language fiber bundles using Hardi-based tractography. Front. Neurosci. 13 (2019)
Sinke, M.R.T., et al.: Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics. Brain Struct. Funct. 223(5), 2269–2285 (2018). https://doi.org/10.1007/s00429-018-1628-y
Fan, J., Milosevic, R., Li, J., Bai, J., Zhang, Y.: The impact of neuroimaging advancement on neurocognitive evaluation in pediatric brain tumor survivors: a review. Brain Sci. Adv. 5(2), 117–127 (2019)
Azad, T.D., Duffau, H.: Limitations of functional neuroimaging for patient selection and surgical planning in glioma surgery. Neurosurg. Focus 48(2), E12 (2020)
Vassal, F., Schneider, F., Boutet, C., Jean, B., Sontheimer, A., Lemaire, J.J.: Combined DTI tractography and functional MRI study of the language connectome in healthy volunteers: extensive mapping of white matter fascicles and cortical activations. PLoS One 11(3), 1–19 (2016)
Yang, Z., He, P., Zhou, J., Ding, Z., Wu, X.: Functional informed fiber tracking using combination of diffusion and functional MRI. IEEE Trans. Biomed. Eng. 66(3), 794–801 (2019)
Takemura, H., Caiafa, C.F., Wandell, B.A., Pestilli, F.: Ensemble tractography. PLoS Comput. Biol. 12(2), 1–22 (2016)
Descoteaux, M.: High Angular Resolution Diffusion MRI: From Local Estimation to Segmentation and Tractography (2008)
Webster, J.G., Descoteaux, M.: high angular resolution diffusion imaging (HARDI). Wiley Encycl. Electr. Electron. Eng., pp. 1–25 (2015)
Brown, G.G., Perthen, J.E., Liu, T.T., Buxton, R.B.: A primer on functional magnetic resonance imaging. Neuropsychol. Rev. 17(2), 107–125 (2007)
Li, M., Newton, A.T., Anderson, A.W., Ding, Z., Gore, J.C.: Characterization of the hemodynamic response function in white matter tracts for event-related fMRI. Nat. Commun. 10(1), 1–11 (2019)
El Kininy, W., Tcd, S., Roddy, D., Hanlon, E.O., Barry, D.: Magnetic resonance diffusion weighted imaging using constrained spherical deconvolution-based tractography of the extracranial course of the facial nerve. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 130(2), e44–e56 (2020)
Raffelt, D., et al.: Apparent fibre density: a novel measure for the analysis of diffusion-weighted magnetic resonance images. Neuroimage 59(4), 3976–3994 (2012)
Chandio, B.Q., et al.: Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Sci. Rep. 10(1), 1–18 (2020)
Chung, S., et al.: Working memory and brain tissue microstructure : white matter tract integrity based on multi-shell diffusion MRI. Sci. Rep. 1–7 (2018)
Glasser, M.F., et al.: The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013)
Tournier, J.D., Calamante, F., Gadian, D.G., Connelly, A.: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage 23(3), 1176–1185 (2004)
Tournier, J.D., Calamante, F., Connelly, A.: MRtrix: diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol. 22(1), 53–66 (2012)
Smith, R.E., Tournier, J., Calamante, F., Connelly, A.: NeuroImage anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage 62(3), 1924–1938 (2012)
Barch, D.M., et al.: Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013)
Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., Van Der Walt, S.: Dipy, a library for the analysis of diffusion MRI data. 8, 1–17 (2014)
Hardi, D.I.: Moving beyond DTI. Introd. to Diffus. Tensor Imaging, pp. 65–78 (2014)
Descoteaux, M., Deriche, R., Knösche, T.R., Anwander, A.: Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans. Med. Imaging 28(2), 269–286 (2009)
Guberman, G.I., Houde, J.-C., Ptito, A., Gagnon, I., Descoteaux, M.: Structural abnormalities in thalamo-prefrontal tracks revealed by high angular resolution diffusion imaging predict working memory scores in concussed children. Brain Struct. Funct. 225(1), 441–459 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Troudi, A., Alaya, I.B., Labidi, S. (2021). Morphological Characteristics Analysis of Working Memory Tracts Using BOLD-fMRI and HARDI Based Tractography in Healthy Human Brains. In: Rojas, I., Castillo-Secilla, D., Herrera, L.J., Pomares, H. (eds) Bioengineering and Biomedical Signal and Image Processing. BIOMESIP 2021. Lecture Notes in Computer Science(), vol 12940. Springer, Cham. https://doi.org/10.1007/978-3-030-88163-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-88163-4_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88162-7
Online ISBN: 978-3-030-88163-4
eBook Packages: Computer ScienceComputer Science (R0)