Abstract
In neuroscience research, there has been an increasing interest in multimodal analysis, combining the strengths of unimodal analysis while reducing some of its drawbacks. However, this increases complexity in data processing and analysis, requiring a big amount of technical knowledge in image manipulation and a lot of iterative processes requiring user intervention. In this work we present a framework that incorporates some of this technical knowledge and enables the automation of most of the processing in the context of combined resting-state functional Magnetic Resonance Imaging (rs-fMRI) and Diffusion Tensor Imaging (DTI) data processing and analysis. The proposed framework presents an object-oriented architecture and its structure reflects the nature of three levels of data processing (i.e. acquisition level, subject level and study level). This framework opens the door to more intelligent and scalable systems for neuroimaging data processing and analysis that ultimately will lead to the dissemination of such advanced techniques.
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
References
Gong, G., He, Y., Concha, L., et al.: Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex 19(3), 524–536 (2009). doi:10.1093/cercor/bhn102
Damoiseaux, J.S., Rombouts, S.A., Barkhof, F., et al.: Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. U.S.A. 103, 13848–13853 (2006). doi:10.1073/pnas.0601417103
Basser, P.J., Pajevic, S., Pierpaoli, C., et al.: In vivo fiber tractography using DT? MRI data. Magn. Reson. Med. 44(4), 625–632 (2000)
van den Heuvel, M.P., Mandl, R., Luigjes, J., et al.: Microstructural organization of the cingulum tract and the level of default mode functional connectivity. J. Neurosci. 28, 1084410851 (2008). doi:10.1523/JNEUROSCI.2964-08.2008
Hasan, K.M., Walimuni, I.S., Abid, H., et al.: A review of diffusion tensor magnetic resonance imaging computational methods and software tools. Comput. Biol. Med. 41, 10621072 (2011). doi:10.1016/j.compbiomed.2010.10.008
Haller, S., Bartsch, A.J.: Pitfalls in FMRI. Eur. Radiol. 19, 2689–2706 (2009). doi:10.1007/s00330-009-1456-9
Vasilakos, A., Witold, P.: Ambient Intelligence, Wireless Networking, and Ubiquitous Computing. Artech House, Inc (2006)
Rech, J., Klaus-Dieter, A.: Artificial intelligence and software engineering: Status and future trends. KI 18(3), 5–11 (2004)
Digital imaging and communications in medicine (DICOM): National Electrical Manufacturers Association (1998)
Cox, R.W., Ashburner, J., Breman, H., et al.: A (sort of) new image data format standard: nifti-1. Human Brain Mapp. 25, 33 (2004)
Penny, W.D., Friston, K.J., Ashburner, J.T. et al (2011) Statistical Parametric Mapping: The Analysis of Functional Brain Images: The Analysis of Functional Brain Images. Academic press
Smith, S.M., Jenkinson, M., Woolrich, M.W., et al.: Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23, S208–S219 (2004)
Cox, R.W.: AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res., Int. J. 29(3), 162–173 (1996)
Goebel, R.: Brainvoyager: a program for analyzing and visualizing functional and structural magnetic resonance data sets. Neuroimage 3(3), S604 (1996)
Jiang, H., van Zijl, P.C., Kim, J., et al.: DtiStudio:resource program for diffusion tensor computation and fiber bundle tracking. Comput. Methods Programs Biomed. 81(2), 106–116 (2006)
Wang R, Benner T, Sorensen AG et al (2007) Diffusion toolkit: a software package for diffusion imaging data processing and tractography. Proc. Intl. Soc. Mag. Reson. Med. 15(3720)
Pieper S, Halle M, Kikinis R (2004) 3D Slicer. In: IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2004, pp. 632–635. IEEE
Marques, P., Soares, J.M., Alves, V. et al. (2013) BrainCAT-a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis. Frontiers Human Neurosci. 7
Rorden, C., Brett, M.: Stereotaxic display of brain lesions. Behav. Neurol. 12, 191200 (2000)
Acknowledgments
This work has been supported by FCT—Fundao para a Cincia e Tecnologia within the Project Scope UID/CEC/00319/2013. PM was supported by the SWITCHBOX project through the grant SwitchBox-FP7-HEALTH-2010-grant 259772-2 and RM is supported by the Portuguese North Regional Operational Program (ON.2 O Novo Norte) under the National Strategic Reference Framework (QREN), through the European Regional Development Fund (FEDER) by a fellowship from the project FCT-ANR/NEU-OSD/0258/2012 funded by FCT/MEC (www.fct.pt) and by FEDER.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Marques, P., Soares, J.M., Magalhaes, R., Sousa, N., Alves, V. (2016). A Framework for the Automation of Multimodalbrain Connectivity Analyses. In: Novais, P., Camacho, D., Analide, C., El Fallah Seghrouchni, A., Badica, C. (eds) Intelligent Distributed Computing IX. Studies in Computational Intelligence, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-25017-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-25017-5_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25015-1
Online ISBN: 978-3-319-25017-5
eBook Packages: EngineeringEngineering (R0)