Resting-State Functional Connectivity Analysis Using an Independent Component Approach

Authors: Eric Jacob Bacon, Chaoyang Jin, Dianning He, Shuaishuai Hu, Lanbo Wang, Han Li, Shouliang Qi

Abstract:

Refractory epilepsy is a complicated type of epilepsy that can be difficult to diagnose. Recent technological advancements have made resting-state functional magnetic resonance (rsfMRI) a vital technique for studying brain activity. However, there is still much to learn about rsfMRI. Investigating rsfMRI connectivity may aid in the detection of abnormal activities. In this paper, we propose studying the functional connectivity of rsfMRI candidates to diagnose epilepsy. 45 rsfMRI candidates, comprising 26 with refractory epilepsy and 19 healthy controls, were enrolled in this study. A data-driven approach known as Independent Component Analysis (ICA) was used to achieve our goal. First, rsfMRI data from both patients and healthy controls were analyzed using group ICA. The components that were obtained were then spatially sorted to find and select meaningful ones. A two-sample t-test was also used to identify abnormal networks in patients and healthy controls. Finally, based on the fractional amplitude of low-frequency fluctuations (fALFF), a chi-square statistic test was used to distinguish the network properties of the patient and healthy control groups. The two-sample t-test analysis yielded abnormal in the default mode network, including the left superior temporal lobe and the left supramarginal. The right precuneus was found to be abnormal in the dorsal attention network. In addition, the frontal cortex showed an abnormal cluster in the medial temporal gyrus. In contrast, the temporal cortex showed an abnormal cluster in the right middle temporal gyrus and the right fronto-operculum gyrus. Finally, the chi-square statistic test was significant, producing a p-value of 0.001 for the analysis. This study offers evidence that investigating rsfMRI connectivity provides an excellent diagnosis option for refractory epilepsy.

Keywords: Independent Component Analysis, Resting State Network, refractory epilepsy, rsfMRI.

References:

[1] Singh, Anuradha, and Stephen Trevick. "The epidemiology of global epilepsy." Neurologic clinics, (2016). 837:847-34.4. DOI: 10.1016/j.ncl.2016.06.015.
[2] https://www.who.int/news-room/fact-sheets/detail/epilepsy
[3] Kwan P, Brodie MJ. Early identification of refractory epilepsy. N Engl J Med. (2000) 342:314–9. DOI: 10.1056/NEJM200002033420503.
[4] Ravi SN, Borghei A, Bledi CB, Lynn F, Garibay-Pulido D, Byrne RW, et al. Responsive neuro-stimulation of the mesial temporal white matter in bilateral temporal lobe epilepsy. Neurosurgery. (2021) 88:261–7.DOI: 10.1093/neuros/nyaa381.
[5] Gary H. Glover. Overview of Functional Magnetic Resonance Imaging. Neurosurg Clin N Am (2011) 22:133–139. DOI: 10.1016/j.nec.2010.11.001.
[6] Herold, Fabian, et al. "The contribution of functional magnetic resonance imaging to the understanding of the effects of acute physical exercise on cognition." Brain sciences 10.3 (2020): 175. DOI: https://doi.org/10.3390/brainsci10030175.
[7] Gaillard F, Sciacca F, Murphy A, et al. BOLD imaging. Reference article, Radiopaedia.org (Accessed on 16 Jan 2023) DOI: https://doi.org/10.53347/rID-958.
[8] Nikos K Logothetis What we can do and what we cannot do with fMRI. Nature (2008) 12: 69-78. DOI: 10.1038/nature06976.
[9] Oschmann, Meike, Jodie R. Gawryluk, and Alzheimer's Disease Neuroimaging Initiative. "A longitudinal study of changes in resting-state functional magnetic resonance imaging functional connectivity networks during healthy aging." Brain Connectivity 10.7 (2020): 377-384.DOI: https://doi.org/10.1089/brain.2019.0724.
[10] Chakraborty, Arpan R., et al. "Resting‐state functional magnetic resonance imaging with independent component analysis for presurgical seizure onset zone localization: A systematic review and meta‐analysis." Epilepsia (2020). 1958:1968-61.9. DOI: https://doi.org/10.1111/epi.16637.
[11] McKeown M J, Hansen L K, Sejnowsk T J. Independent component analysis of functional MRI: what is signal and what is noise? Current opinion in neurobiology (2003).13:620-629. DOI: https://doi.org/10.1016/j.conb.2003.09.012.
[12] Zhang, Huayu, et al. "Aberrant functional connectivity in resting state networks of ADHD patients revealed by independent component analysis." BMC neuroscience 21 (2020): 1-11. DOI: https://doi.org/10.1016/j.pscychresns.2010.03.010.
[13] Yameng G, Feng H, Lucas ES, Margeaux MSet al. Simultaneous EEG and fMRI signals during sleep from humans. OpenNeuro. (2021). Doi:10.18112/openneuro. ds003768.v1.0.7.
[14] Flandin, Guillaume, and Karl J. Friston. "Statistical parametric mapping (SPM)." Scholarpedia 3.4 (2008). `DOI: 10.4249/scholarpedia.6232.
[15] Henson, Richard N., et al. "Multimodal Integration of M/EEG and f/MRI Data in SPM12." Frontiers in neuroscience 13 (2019): 300.DOI: https://doi.org/10.3389/fnins.2019.00300
[16] http://store.elsevier.com/product.jsp?isbn=9780123725608
[17] Bazay FEZ, Benabdallah FZ, El Maliani AD. Preprocessing FMRI Data in SPM12. 9th International Conference on Wireless Networks and Mobile Communications (2022). 1—5. DOI: 10.1109/WINCOM55661.2022.9966463.
[18] V. D. Calhoun and N. de Lacy, “Ten key observations on the analysis of resting-state functional MR imaging data using independent component analysis,” Neuroimaging Clinics of North America, vol. 27, no. 4, pp. 561:579-2017. DOI: 10.1016/j.nic.2017.06.012.
[19] V. D. Calhoun, T. Adali, G. D. Pearlson, and J. J. Pekar, “A method for making group inferences from functional MRI data using independent component analysis,” Human Brain Mapping, (2001). 140–151-14.DOI: https://doi.org/10.1002/hbm.1048.
[20] C. F. Beckmann, “Modelling with independent components,” Neuroimage, (2012).891:901-62. DOI: https://doi.org/10.1016/j.neuroimage.2012.02.020.
[21] Smitha, K. A., et al. "Resting-state fMRI: A review on methods in resting state connectivity analysis and resting state networks." The neuroradiology journal (2017): 305:317-30.4. DOI: https://doi.org/10.1177/1971400917697342.
[22] Wei, Gao-Xia, et al. "Mind-body practice changes the fractional amplitude of low-frequency fluctuations in intrinsic control networks." Frontiers in Psychology (2017). 1049-8. DOI: https://doi.org/10.3389/fpsyg.2017.01049.
[23] Lv H, Wang Z, Tong E, Williams LM, Zaharchuk G, et al. Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know. AJNR Am J Neuroradiol(2018).1390:1399-30. DOI: 10.3174/ajnr.A5527.
[24] Boerwinkle, V L, Mohanty D, Foldes S T, Guffey D, et al. Correlating resting-state functional magnetic resonance imaging connectivity by independent component analysis-based epileptogenic zones with intracranial electroencephalogram localized seizure onset zones and surgical outcomes in prospective paediatric intractable epilepsy study. Brain Connectivity (2017). 7: 424-442. DOI: https://doi.org/10.1089/brain.2016.0479.
[25] Ebrahimzadeh, Elias, et al. "Localizing epileptic foci using simultaneous EEG-fMRI recording: template component cross-correlation." Frontiers in Neurology 12 (2021): 695997. DOI: 10.3389/fneur.2021.695997
[26] Egorova, Natalia, et al. "Fractional amplitude of low-frequency fluctuations (fALFF) in post-stroke depression." NeuroImage: Clinical 16 (2017): 116-124. DOI: https://doi.org/10.1016/j.nicl.2017.07.014
[27] Wang, J., Shan, Y., Dai, J., Cui, B., Shang, et al. "Altered coupling between resting‐state glucose metabolism and functional activity in epilepsy." Annals of Clinical and Translational Neurology (2020). 1831:1842-7. DOI: 10.1002/acn3.51168
[28] Chang C, Glover G Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI. Neuroimage (2009). 1381:139347. DOI: 10.1016/j.neuroimage.2009.04.048.
[29] McKeown MJ, Jung TP, Makeig S, Brown G, Kindermann SS, et al. Spatially independent activity patterns in functional MRI data during the stroop color-naming task. Proc Natl Acad Sci USA, (1998). 803:810-95. DOI: https://doi.org/10.1073/pnas.95.3.803.
[30] Chawla, M. P. S. "PCA and ICA processing methods for removal of artifacts and noise in electrocardiograms: A survey and comparison." Applied Soft Computing, (2011). 11.2:2216-2226. DOI: https://doi.org/10.1016/j.asoc.2010.08.001.