Abstract
Functional neuroimaging technology offers a new window to snapshot the transient neural activity in-vivo. Although tremendous efforts have been made to characterize spontaneous functional fluctuations, little attention has been paid to the functional mechanisms of neural interactions. Inspired by the notion of holography, we propose an explainable machine learning approach to establishing a novel underpinning of self-organized cross-frequency coupling (CFC) through the lens of brain wave interference on top of the network topology. Specifically, we conceptualize that the interaction between ubiquitous neural activities and a collection of reference harmonic wavelets forms a region-adaptive interference pattern that captures cross-frequency coupling of remarkable neuronal oscillations. In this regard, assembling whole-brain CFC patterns under the constraint of brain wiring mechanisms constitutes a HoloBrain mapping that records a wide spectrum of spontaneous neural activities. Since each local interference pattern is a symmetric and positive-definite (SPD) matrix, we tailor a deep model of HoloBrain (coined DeepHoloBrain) to infer the latent feature representations on the Riemannian manifold of SPD matrices for predicting brain states and recognizing disease connectomes. We have applied DeepHoloBrain to the Human Connectome Project and several dementia-related datasets. Compared with current state-of-the-art deep models, our DeepHoloBrain not only improves the recognition/prediction accuracy but also sheds new light on understanding the neurobiological mechanisms of brain function and cognition.
H. Liu and T. Dan—These authors contributed equally to this work.
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References
Bassett, D.S., Sporns, O.: Network neuroscience. Nat. Neurosci. 20(3), 353–364 (2017)
Airan, R.D., et al.: Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI. Hum. Brain Mapp. 37(5), 1986–1997 (2016)
Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010)
Hutchison, R.M., et al.: Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80, 360–378 (2013)
Canolty, R.T., Knight, R.T.: The functional role of cross-frequency coupling. Trends Cogn. Sci. 14(11), 506–515 (2010)
Hyafil, A., et al.: Neural cross-frequency coupling: connecting architectures, mechanisms, and functions. Trends Neurosci. 38(11), 725–740 (2015)
Gabor, D.: A new microscopic principle. Nature 161(4098), 777 (1948)
Atasoy, S., Donnelly, I., Pearson, J.: Human brain networks function in connectome-specific harmonic waves. Nat. Commun. 7(1), 10340 (2016)
Melzi, S., et al.: Localized manifold harmonics for spectral shape analysis. Comput. Graph. Forum 37(6), 20–34 (2018)
Young, T.: I. The Bakerian lecture. Experiments and calculations relative to physical optics. Philos. Trans. Roy. Soc. London 94, 1–16 (1804)
Defferrard, M. Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 3844–3852. Curran Associates Inc., Barcelona (2016)
Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)
Dan, T., et al.: Learning brain dynamics of evolving manifold functional MRI data using geometric-attention neural network. IEEE Trans. Med. Imaging 41(10), 2752–2763 (2022)
Dan, T., et al.: Uncovering shape signatures of resting-state functional connectivity by geometric deep learning on Riemannian manifold. Hum. Brain Mapp. 43(13), 3970–3986 (2022)
Chakraborty, R., et al.: A statistical recurrent model on the manifold of symmetric positive definite matrices. In: Neural Information Processing Systems (2018)
Shen, X., et al.: Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82, 403–415 (2013)
Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273–289 (2002)
Pruim, R.H.R., et al.: ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage 112, 267–277 (2015)
Koo, T.K., Li, M.Y.: A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15(2), 155–163 (2016)
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Liu, H. et al. (2023). HoloBrain: A Harmonic Holography for Self-organized Brain Function. In: Frangi, A., de Bruijne, M., Wassermann, D., Navab, N. (eds) Information Processing in Medical Imaging. IPMI 2023. Lecture Notes in Computer Science, vol 13939. Springer, Cham. https://doi.org/10.1007/978-3-031-34048-2_3
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DOI: https://doi.org/10.1007/978-3-031-34048-2_3
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