Abstract
Parcellations used in resting-state fMRI (rs-fMRI) analyses are derived from group-level information, and thus ignore both subject-level functional differences and the downstream task. In this paper, we introduce RefineNet, a Bayesian-inspired deep network architecture that adjusts region boundaries based on individual functional connectivity profiles. RefineNet uses an iterative voxel reassignment procedure that considers neighborhood information while balancing temporal coherence of the refined parcellation. We validate RefineNet on rs-fMRI data from three different datasets, each one geared towards a different predictive task: (1) cognitive fluid intelligence prediction using the HCP dataset (regression), (2) autism versus control diagnosis using the ABIDE II dataset (classification), and (3) language localization using an rs-fMRI brain tumor dataset (segmentation). We demonstrate that RefineNet improves the performance of existing deep networks from the literature on each of these tasks. We also show that RefineNet produces anatomically meaningful subject-level parcellations with higher temporal coherence.
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
B. Biswal, F. Zerrin Yetkin, V. M. Haughton, and J. S. Hyde, "Functional connectivity in the motor cortex of resting human brain using echo-planar mri," Magnetic resonance in medicine, vol. 34, no. 4, pp. 537–541, 1995
van Oort, E.S., et al.: Functional parcellation using time courses of instantaneous connectivity. Neuroimage 170, 31–40 (2018)
Khosla, M., Jamison, K., Kuceyeski, A., Sabuncu, M.R.: Ensemble learning with 3d convolutional neural networks for functional connectome-based prediction. Neuroimage 199, 651–662 (2019)
Fischl, B., et al.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3), 341–355 (2002)
Glasser, M.F., et al.: A multi-modal parcellation of human cerebral cortex. Nature 536(7615), 171–178 (2016)
Wang, D., et al.: Parcellating cortical functional networks in individuals. Nat. Neurosci. 18(12), 1853 (2015)
Chong, M., et al.: Individual parcellation of resting FMRI with a group functional connectivity prior. Neuroimage 156, 87–100 (2017)
Nandakumar, N., et al.: Defining patient specific functional parcellations in Lesional Cohorts via Markov random fields. In: Wu, G., Rekik, I., Schirmer, M.D., Chung, A.W., Munsell, B. (eds.) CNI 2018. LNCS, vol. 11083, pp. 88–98. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00755-3_10
Esposito, F., et al.: Independent component model of the default-mode brain function: combining individual-level and population-level analyses in resting-state fmri. Magn. Reson. Imaging 26(7), 905–913 (2008)
Tessitore, A., et al.: Default-mode network connectivity in cognitively unimpaired patients with parkinson disease. Neurology 79(23), 2226–2232 (2012)
Calhoun, V.D., Adali, T.: Multisubject independent component analysis of FMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery. IEEE Rev. Biomed. Eng. 5, 60–73 (2012)
Sair, H.I., et al.: Presurgical brain mapping of the language network in patients with brain tumors using resting-state FMRI: comparison with task f MRI. Hum. Brain Mapp. 37(3), 913–923 (2016)
Kazemivash, B., Calhoun, V.D.: A novel 5d brain parcellation approach based on spatio-temporal encoding of resting FMRI data from deep residual learning. J. Neurosci. Methods, 109478 (2022)
Van Essen, D.C., et al.: The wu-minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)
Di Martino, A., et al.: Enhancing studies of the connectome in autism using the autism brain imaging data exchange ii. Sci. Data 4(1), 1–15 (2017)
Dsouza, N.S., Nebel, M.B., Crocetti, D., Robinson, J., Mostofsky, S., Venkataraman, A.: M-GCN: a multimodal graph convolutional network to integrate functional and structural connectomics data to predict multidimensional phenotypic characterizations. In: Medical Imaging with Deep Learning, pp. 119–130, PMLR (2021)
Smith, S.M., et al.: Resting-state FMRI in the human connectome project. Neuroimage 80, 144–168 (2013)
Zhang, J., Feng, F., Han, T., Gong, X., Duan, F.: Detection of autism spectrum disorder using FMRI functional connectivity with feature selection and deep learning. Cognitive Computation, pp. 1–12 (2022)
Craddock, C., et al.: Towards automated analysis of connectomes: the configurable pipeline for the analysis of connectomes (C-PAC). Front. Neuroinform. 42, 10–3389 (2013)
Nandakumar, N., et al.: A novel graph neural network to localize eloquent cortex in brain tumor patients from resting-state fMRI connectivity. In: Schirmer, M.D., Venkataraman, A., Rekik, I., Kim, M., Chung, A.W. (eds.) CNI 2019. LNCS, vol. 11848, pp. 10–20. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32391-2_2
Behzadi, Y., Restom, K., Liau, J., Liu, T.T.: A component based noise correction method (compcor) for bold and perfusion based FMRI. Neuroimage 37(1), 90–101 (2007)
Penny, W.D., Friston, K.J., Ashburner, J.T., Kiebel, S.J., Nichols, T.E.: Statistical parametric mapping: the analysis of functional brain images. Elsevier (2011)
Fan, L., et al.: The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb. Cortex 26(8), 3508–3526 (2016)
Craddock, R.C., James, G.A., Holtzheimer, P.E., III., 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)
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)
Bouckaert, R.R., Frank, E.: Evaluating the replicability of significance tests for comparing learning algorithms. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 3–12. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24775-3_3
Acknowledgements
This work was supported by the National Science Foundation CAREER award 1845430 (PI: Venkataraman) and the Research & Education Foundation Carestream Health RSNA Research Scholar Grant RSCH1420.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nandakumar, N., Manzoor, K., Agarwal, S., Sair, H.I., Venkataraman, A. (2022). RefineNet: An Automated Framework to Generate Task and Subject-Specific Brain Parcellations for Resting-State fMRI Analysis. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-16431-6_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16430-9
Online ISBN: 978-3-031-16431-6
eBook Packages: Computer ScienceComputer Science (R0)
