Abstract
We propose \(\delta \)-MAPS, a spatio-temporal data analysis method that identifies functionally distinct, possibly overlapping, spatially contiguous regions in the brain, referred to as “domains”, and infers the functional (i.e., correlation-based) connections between them. The proposed network inference method examines the statistical significance of each lagged cross-correlation between two domains, infers a range of lag values for each edge, and assigns a weight to each edge based on the covariance of the signal of the two domains. We illustrate the application of \(\delta \)-MAPS on cortical resting-state fMRI data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
A heuristic to infer \(\delta \) is proposed in [13].
- 2.
The definition of a domain’s signal may vary per application. For example, in climate data it makes sense to define it as the cumulative anomaly, normalized by the size of each grid cell, over the domain’s scope [13].
- 3.
We have experimented with other pruning thresholds between 20%–50% and the results are very similar at the first two hierarchy levels.
- 4.
Grid cells are referred to as voxels in the fMRI literature.
- 5.
MELODIC ICA has an option to automatically estimate the number of ICs to return. Choosing this option yielded approximately 200–250 components in each scan. Activations were much lower than the ones shown in Fig. 3(G,H) both in strength and spatial extent. We could not identify RSNs similar to those shown here.
References
Alvarez-Hamelin, J.I., Dall’Asta, L., Barrat, A., Vespignani, A.: Large scale networks fingerprinting and visualization using the k-core decomposition. Adv. Neural Infor. Process. Syst. 41–50 (2006)
Tirabassi, G., Masoller, C: Unravelling the community structure of the climate system by using lags and symbolic time-series analysis. Sci. Rep. 6 (2016)
Yamasaki, K., Gozolchiani, A., Havlin, S.: Climate networks around the globe are significantly affected by El Nino. Phys. Rev. Lett. 100(22), 228501 (2008)
Goncalves, B., Perra, N.: Vespignani, A.: Modeling users’ activity on twitter networks: Validation of dunbar’s number. PLoS ONE 6(8), e22656 (2011)
Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186 (2009)
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)
Donges, J.F., Zou, Y., Marwan, N., Kurths, J.: The backbone of the climate network. EPL 87(4), 48007 (2009)
Palla, G., Dernyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)
Steinbach, M., Tan, P.N., Kumar, V., Klooster, S., Potter, C: Discovery of climate indices using clustering. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 446-455 (2003)
Steinhaeuser, K., Chawla, N.V., Ganguly, A.R.: Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science. Stat. Anal. Data Min. ASA Data Sci. J. 4(5), 497–511 (2011)
Rummel, C., Muller, M., Baier, G., Amor, F., Schindler, K.: Analyzing spatio-temporal patterns of genuine cross-correlations. J. Neurosci. Methods 191(1), 94–100 (2010)
Dommenget, D., Latif, M.: A cautionary note on the interpretation of EOFs. J. Clim. 15(2), 216–225 (2002)
Fountalis, I., Bracco A., Dilkina, B., Dovrolis, C., Keilholz, S.: From spatio-temporal data to a weighted and lagged network between functional domains. arXiv preprint arXiv:1602.07249. (2016)
Beckmann, C.F., Smith, S.M.: Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23(2), 137–152 (2004)
Hyvarinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Networks 10(3), 626–634 (1999)
Blumensath, T., Behrens, T., Smith, S.M.: Resting-state fMRI single subject cortical parcellation based on region growing. In: Proceeding of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 188-195. Springer, Berlin, Heidelberg (2012)
Lu, Y., Jiang, T., Zang, Y.: Region growing method for the analysis of functional MRI data. NeuroImage 20(1), 455–465 (2003)
Craddock, R.C., James, G.A., Holtzheimer, P.E., Hu, X.P., Mayberg, H.S.: A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33(8), 1914–1928 (2012)
Van Den Heuvel, M., Mandl, R., Pol, H.H.: Normalized cut group clustering of resting-state FMRI data. PloS one 3(4) (2008)
Baldassano, C., Beck, D.M., Fei-Fei, L.: Parcellating connectivity in spatial maps. Peer J 3 (2015)
Blumensath, T., Jbabdi, S., Glasser, M.F., Van Essen, D.C., Ugurbil, K., Behrens, T.E., Smith, S.M.: Spatially constrained hierarchical parcellation of the brain with resting-state fMRI. Neuroimage 76, 313–324 (2013)
Thirion, B., Varoquaux, G., Dohmatob, E., Poline, J.B.: Which fMRI clustering gives good brain parcellations?. Frontiers Neurosci 8(2014)
Kramer, M.A., Eden, U.T., Cash, S.S., Kolaczyk, E.D.: Network inference with confidence from multivariate time series. Phys. Rev. E 79(6) (2009)
Van Den Heuvel, M.P., Sporns, O.: Rich-club organization of the human connectome. J. Neurosci. 31(44) (2011)
Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761–764 (2010)
Lancichinetti, A., Radicchi, F., Ramasco, J.J., Fortunato, S.: Finding statistically significant communities in networks. PloS one 6(4) (2011)
Palla, G., Dernyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)
Power, J.D., Cohen, A.L., Nelson, S.M., Wig, G.S., Barnes, K.A., Church, J.A., Vogel, A.C., Laumann, T.O., Miezin, F.M., Schlaggar, B.L., Petersen, S.E.: Functional network organization of the human brain. Neuron 72(4), 665–678 (2011)
Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, New York (2015)
Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Royal Stat. Soc. B (Methodological), 289–300 (1995)
Reiner, A., Yekutieli, D., Benjamini, Y.: Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19(3), 368–375 (2003)
Martin, E.A., Davidsen, J.: Estimating time delays for constructing dynamical networks. Nonlinear Process. Geophys. 21(5), 929–937 (2014)
Rummel, C., Muller, M., Baier, G., Amor, F., Schindler, K.: Analyzing spatio-temporal patterns of genuine cross-correlations. J. Neurosci. Methods 191(1), 94–100 (2010)
Sporns, O.: Networks of the Brain. MIT press (2010)
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, E.J., Yacoub, E., Ugurbil, K., WU-Minn HCP Consortium.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)
Smith, S.M., Beckmann, C.F., Andersson, J., Auerbach, E.J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D.A., Griffanti, L., Harms, M.P.: Resting-state fMRI in the human connectome project. Neuroimage 80, 144–168 (2013)
Glasser, M.F., Sotiropoulos, S.N., Wilson, J.A., Coalson, T.S., Fischl, B., Andersson, J.L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J.R.: The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013)
Van Essen, D.C., Glasser, M.F., Dierker, D.L., Harwell, J., Coalson, T.: Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cereb. Cortex 22(10), 2241–2262 (2012)
Yeo, B.T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M., Roffman, J.L., Smoller, J.W., Zllei, L., Polimeni, J.R., Fischl, B.: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106(3), 1125–1165 (2011)
Ebert-Uphoff, I., Deng, Y.: Causal discovery for climate research using graphical models. J. Clim. 25(17), 5648–5665 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Fountalis, I., Dovrolis, C., Dilkina, B., Keilholz, S. (2018). \(\delta \)-MAPS: From fMRI Data to Functional Brain Networks. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_100
Download citation
DOI: https://doi.org/10.1007/978-3-319-72150-7_100
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72149-1
Online ISBN: 978-3-319-72150-7
eBook Packages: EngineeringEngineering (R0)