Abstract
The degree to which gray matter morphology constrains brain function remains an elusive target of investigation due to the lack of a gold-standard against which to argue for a better or worse metric of neurobiological significance. Therefore, we sought to compare the output of state-of-the-art morphological and functional covariance decomposition methods directly to one another. Specifically, we compared the spatial network organization produced by non-negative matrix factorization of T1-weighted images and probabilistic functional modes of resting state functional MRI scans from 1297 UK Biobank subjects. We measured the cosine similarity of matched networks across 2 to 140 rank decompositions. Our findings revealed strong commonality between morphological and functional networks at the lowest rank (2). Morphology-function network commonality was retained across all ranks in the visual cortex, but broader network organization diverged between morphology and function at higher ranks.
A. Sotiras and J. Bijsterbosch—Shared senior author.
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References
Beckmann, C.F., DeLuca, M., Devlin, J.T., Smith, S.M.: Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. B Biol. Sci. 360, 1001–1013 (2005). https://doi.org/10.1098/rstb.2005.1634
Bielczyk, N.Z., et al.: Thresholding functional connectomes by means of mixture modeling. Neuroimage 171, 402–414 (2018). https://doi.org/10.1016/j.neuroimage.2018.01.003
Farahibozorg, S.-R., et al.: Hierarchical modelling of functional brain networks in population and individuals from big fMRI data (2021). https://doi.org/10.1101/2021.02.01.428496
Power, J.D., et al.: Functional network organization of the human brain. Neuron 72, 665–678 (2011). https://doi.org/10.1016/j.neuron.2011.09.006
Yeo, B.T.T., et al.: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011). https://doi.org/10.1152/jn.00338.2011
Alexander-Bloch, A., Raznahan, A., Bullmore, E., Giedd, J.: The convergence of maturational change and structural covariance in human cortical networks. J. Neurosci. 33, 2889–2899 (2013). https://doi.org/10.1523/JNEUROSCI.3554-12.2013
Carmon, J., et al.: Reliability and comparability of human brain structural covariance networks. Neuroimage 220, 117104 (2020). https://doi.org/10.1016/j.neuroimage.2020.117104
Sotiras, A., Resnick, S.M., Davatzikos, C.: Finding imaging patterns of structural covariance via Non-Negative Matrix Factorization. Neuroimage 108, 1–16 (2015). https://doi.org/10.1016/j.neuroimage.2014.11.045
Suárez, L.E., Markello, R.D., Betzel, R.F., Misic, B.: Linking structure and function in macroscale brain networks. Trends Cogn. Sci. 24, 302–315 (2020). https://doi.org/10.1016/j.tics.2020.01.008
Llera, A., Wolfers, T., Mulders, P., Beckmann, C.F.: Inter-individual differences in human brain structure and morphology link to variation in demographics and behavior. eLife 8, e44443 (2019). https://doi.org/10.7554/eLife.44443
Huntenburg, J.M., Bazin, P.-L., Margulies, D.S.: Large-scale gradients in human cortical organization. Trends Cogn. Sci. 22, 21–31 (2018). https://doi.org/10.1016/j.tics.2017.11.002
Kelly, C., et al.: A convergent functional architecture of the insula emerges across imaging modalities. Neuroimage 61, 1129–1142 (2012). https://doi.org/10.1016/j.neuroimage.2012.03.021
Zhang, Z., et al.: Resting-state brain organization revealed by functional covariance networks. PLoS ONE 6, e28817 (2011). https://doi.org/10.1371/journal.pone.0028817
Segall, J.M., et al.: Correspondence between structure and function in the human brain at rest. Front. Neuroinform. 6, 10 (2012). https://doi.org/10.3389/fninf.2012.00010
Sotiras, A., Toledo, J.B., Gur, R.E., Gur, R.C., Satterthwaite, T.D., Davatzikos, C.: Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion. Proc. Natl. Acad. Sci. 114, 3527–3532 (2017). https://doi.org/10.1073/pnas.1620928114
Yang, Z., Oja, E.: Linear and nonlinear projective nonnegative matrix factorization. IEEE Trans. Neural Netw. 21, 734–749 (2010). https://doi.org/10.1109/TNN.2010.2041361
Kaczkurkin, A.N., et al.: Evidence for dissociable linkage of dimensions of psychopathology to brain structure in youths. Am. J. Psychiatry 176, 1000–1009 (2019). https://doi.org/10.1176/appi.ajp.2019.18070835
Harrison, S.J., et al.: Large-scale Probabilistic Functional Modes from resting state fMRI. Neuroimage 109, 217–231 (2015). https://doi.org/10.1016/j.neuroimage.2015.01.013
Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20, 45–57 (2001). https://doi.org/10.1109/42.906424
Alfaro-Almagro, F., et al.: Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018). https://doi.org/10.1016/j.neuroimage.2017.10.034
Boutsidis, C., Gallopoulos, E.: SVD based initialization: A head start for nonnegative matrix factorization. Pattern Recognit. 41, 1350–1362 (2008). https://doi.org/10.1016/j.patcog.2007.09.010
Ha, S.M., Bani, A., Sotiras, A.: Scalable NMF via linearly optimized data compression. In: Medical Imaging 2023: Image Processing, pp. 170–176. SPIE (2023). https://doi.org/10.1117/12.2654282
Papenberg, M., Klau, G.W.: Using anticlustering to partition data sets into equivalent parts. Psychol. Methods 26, 161–174 (2021). https://doi.org/10.1037/met0000301
Crouse, D.F.: On implementing 2D rectangular assignment algorithms. IEEE Trans. Aerosp. Electron. Syst. 52, 1679–1696 (2016). https://doi.org/10.1109/TAES.2016.140952
Alexander-Bloch, A., Giedd, J.N., Bullmore, E.: Imaging structural co-variance between human brain regions. Nat. Rev. Neurosci. 14, 322–336 (2013). https://doi.org/10.1038/nrn3465
Pang, J.C., et al.: Geometric constraints on human brain function. Nature 618, 566–574 (2023). https://doi.org/10.1038/s41586-023-06098-1
Bijsterbosch, J.D., et al.: The relationship between spatial configuration and functional connectivity of brain regions. eLife 7, e32992 (2018). https://doi.org/10.7554/eLife.32992
Harrison, S.J., et al.: Modelling subject variability in the spatial and temporal characteristics of functional modes. Neuroimage 222, 117226 (2020). https://doi.org/10.1016/j.neuroimage.2020.117226
Sydnor, V.J., et al.: Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology. Neuron 109, 2820–2846 (2021). https://doi.org/10.1016/j.neuron.2021.06.016
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This project was funded by the McDonnell Center for Systems Neuroscience at Washington University in St. Louis.
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Lenzini, P., Earnest, T., Ha, S.M., Bani, A., Sotiras, A., Bijsterbosch, J. (2023). Morphological Versus Functional Network Organization: A Comparison Between Structural Covariance Networks and Probabilistic Functional Modes. In: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2023. Lecture Notes in Computer Science, vol 14312. Springer, Cham. https://doi.org/10.1007/978-3-031-44858-4_16
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