Abstract
Region of interest (ROI) determination is necessary when using functional near-infrared spectroscopy (fNIRS) data to detect brain activity. To extract ROIs from multiple fNIRS channels, we investigated the validity of applying decision mode analysis to the fNIRS dataset. This classifies a dataset into clusters with similar features. For each cluster, the dataset is decomposed into a mean vector and a linear combination of eigenvectors. Applying this to fNIRS signals, the mean vector can be used to represent change in hemoglobin (Hb), and the eigenvectors interpreted as a signal component constructing the arbitrary signal. Characterizing these vectors by correlating them with a theoretical model of brain function aids our understanding of where Hb changes occur and what type of Hb changes reflect brain activity in fNIRS data. Decision mode analysis of fNIRS data measured during viewing stereoscopic images identified ROIs around the right inferior frontal gyrus associated with attentional control, and frontal association area associated with decision on action and prediction. Our experimental results showed that information obtained from decision mode analysis can aid quantitative and qualitative ROI determination.
Similar content being viewed by others
References
Tak S, Ye JC (2014) Statistical analysis of fNIRS data: a comprehensive review. Neuroimage 85 Part 1:72. doi:10.1016/j.neuroimage.2013.06.016. http://www.sciencedirect.com/science/article/pii/S1053811913006538 [celebrating 20 years of functional near infrared spectroscopy (fNIRS)]
Medvedev AV, Kainerstorfer JM, Borisov SV, VanMeter J (2011) Functional connectivity in the prefrontal cortex measured by near-infrared spectroscopy during ultrarapid object recognition. J Biomed Opt 16(1):016008. doi:10.1117/1.3533266
Lee MH, Fazli S, Lee SW (2013) In: 2013 international winter workshop on brain–computer interface (BCI), pp 95–97. doi:10.1109/IWW-BCI.2013.6506643
Sasai S, Homae F, Watanabe H, Taga G (2011) Frequency-specific functional connectivity in the brain during resting state revealed by NIRS. Neuroimage 56(1):252. doi:10.1016/j.neuroimage.2010.12.075. http://www.sciencedirect.com/science/article/pii/S1053811910016782
Sasai S, Homae F, Watanabe H, Sasaki AT, Tanabe HC, Sadato N, Taga G (2012) A NIRS-fMRI study of resting state network. Neuroimage 63(1):179. doi:10.1016/j.neuroimage.2012.06.011. http://www.sciencedirect.com/science/article/pii/S1053811912005903
Sohn WS, Yoo K, Lee YB, Seo SW, Na DL, Jeong Y (2015) Influence of ROI selection on resting state functional connectivity: an individualized approach for resting state fMRI analysis. Front Neurosci 9:280. doi:10.3389/fnins.2015.00280
Homae F, Watanabe H, Otobe T, Nakano T, Go T, Konishi Y, Taga G (2010) J Neurosci 30:4877
Zhang H, Zhang YJ, Lu CM, Ma SY, Zang YF, Zhu CZ (2010) Functional connectivity as revealed by independent component analysis of resting-state fNIRS measurements. Neuroimage 51(3):1150. doi:10.1016/j.neuroimage.2010.02.080. http://www.sciencedirect.com/science/article/pii/S1053811910002673
Margulies DS, Kelly AC, Uddin LQ, Biswal BB, Castellanos FX, Milham MP (2007) Mapping the functional connectivity of anterior cingulate cortex. Neuroimage 37(2):579. doi:10.1016/j.neuroimage.2007.05.019. http://www.sciencedirect.com/science/article/pii/S1053811907004090
Joel SE, Caffo BS, van Zijl PCM, Pekar JJ (2011) On the relationship between seed-based and ICA-based measures of functional connectivity. Magn Reson Med 66(3):644. doi:10.1002/mrm.22818
Hale JR, Mayhew SD, Mullinger KJ, Wilson RS, Arvanitis TN, Francis ST, Bagshaw AP (2015) Comparison of functional thalamic segmentation from seed-based analysis and ICA. Neuroimage 114:448. doi:10.1016/j.neuroimage.2015.04.027. http://www.sciencedirect.com/science/article/pii/S1053811915003171
Brodmann K (1909) Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. Leipzig: Johann Ambrosius Barth.
Zilles K, Amunts K (2010) Centenary of Brodmann’s map–conception and fate. Nat Rev/Neurosci 11:139. doi:10.1038/nrn2776. http://juser.fz-juelich.de/record/8021
Satoru Hiwa TH, Miki M (2014) Design mode analysis of Pareto solution set for decision-making support. J Appl Math. doi:10.1155/2014/520209. http://www.hindawi.com/journals/jam/2014/520209/ (15 pages, article ID 520209)
Kuriyama K, Honma M (2012) Effects of Sleep Debt on Cognitive Performance and Prefrontal Activity in Humans. In: Theophanides Theophile (Ed.) Infrared Spectroscopy - Life and Biomedical Sciences, In Tech. https://www.intechopen.com/books/infraredspectroscopy-life-and-biomedical-sciences/effects-of-sleep-debt-on-cognitive-performance-and-prefrontalactivity-in-humans
Raichle ME (2003) Functional Brain Imaging and Human Brain Function. J Neurosci 23(10):3959–3962
Etzel JA, Gazzola V, Keysers C (2009) An introduction to anatomical ROI-based fMRI classification analysis. Brain Res 1282:114. doi:10.1016/j.brainres.2009.05.090. http://www.sciencedirect.com/science/article/pii/S000689930901110X
Nieto-Castanon A, Ghosh SS, Tourville JA, Guenther FH (2003) Region of interest based analysis of functional imaging data. Neuroimage 19(4):1303. doi:10.1016/S1053-8119(03)00188-5. http://www.sciencedirect.com/science/article/pii/S1053811903001885
Swallow KM, Braver TS, Snyder AZ, Speer NK, Zacks JM (2003) Reliability of functional localization using fMRI. Neuroimage 20(3):1561. doi:10.1016/S1053-8119(03)00436-1. http://www.sciencedirect.com/science/article/pii/S1053811903004361
Fekete T, Rubin D, Carlson JM, Mujica-Parodi LR (2011) The NIRS analysis package: noise reduction and statistical inference. PLoS One 6(9):e24322. doi:10.1371/journal.pone.0024322
MacQueen J (1967) In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, volume 1: statistics, pp 281–297. http://projecteuclid.org/euclid.bsmsp/1200512992
Friston KJ (2011) Functional and effective connectivity: a review. Brain Connect 1(1):13. doi:10.1089/brain.2011.0008. http://citeseerx.ist.psu.edu/viewdoc/download?rep=rep1&type=pdf&doi=10.1.1.222.9471%5Cnonline.liebertpub.com/doi/pdfplus/10.1089/brain.2011.0008www.liebertonline.com/doi/abs/10.1089/brain.2011.0008
Bonomini V, Zucchelli L, Re R, Ieva F, Spinelli L, Contini D, Paganoni A, Torricelli A (2015) Linear regression models and k-means clustering for statistical analysis of fNIRS data. Biomed Opt Express 6(2):615. doi:10.1364/BOE.6.000615. http://www.osapublishing.org/boe/abstract.cfm?URI=boe-6-2-615
Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. Acoust Speech Signal Process IEEE Trans 26(1):43. doi:10.1109/TASSP.1978.1163055
Mishkin M, Ungerleider LG, Macko KA (1983) Object vision and spatial vision: two cortical pathways. Trends Neurosci 6:414. doi:10.1016/0166-2236(83)90190-X. http://www.sciencedirect.com/science/article/pii/016622368390190X
Hampshire A, Chamberlain SR, Monti MM, Duncan J, Owen AM (2010) The role of the right inferior frontal gyrus: inhibition and attentional control. Neuroimage 50(3):1313. doi:10.1016/j.neuroimage.2009.12.109. http://www.sciencedirect.com/science/article/pii/S1053811909013986
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Hiwa, S., Miki, M. & Hiroyasu, T. Validity of decision mode analysis on an ROI determination problem in multichannel fNIRS data. Artif Life Robotics 22, 336–345 (2017). https://doi.org/10.1007/s10015-017-0362-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10015-017-0362-5