Investigating cortical networks from vibrotactile stimulation in young adults using independent component analysis: an fMRI study
DOI:
https://doi.org/10.31117/neuroscirn.v6i3.194Keywords:
Functional Magnetic Resonance Imaging (fMRI), Somatosensory, Vibrotactile, Functional Connectivity , Independent component analysisAbstract
This study investigated the functional connectivity of the neural networks when vibrotactile stimulation is applied to the fingertips of young adults. Twenty healthy, right-handed subjects were stimulated with vibrotactile stimulation whilst being scanned with a 3.0 T magnetic resonance imaging scanner. The subjects were stimulated at 30 Hz – 240 Hz using a piezoelectric vibrator attached to the subjects' bilateral index fingers. The scanned data were processed with independent component analysis (ICA), while the temporal configuration and spatial localisation of the component were investigated. The activation locations were tabulated and compared with regions of somatosensory in the brain. Using ICA, somatosensory regions and their neighbouring areas identified one or more of these components mapped to the common significant regions in the medial frontal gyrus (MFG), paracentral lobule (PaCL), precentral gyrus (PrG), postcentral gyrus (PoG), inferior parietal lobule (IPL), and cingulate gyrus (CgG). Using Neuromark as a reference, six significant networks with the highest correlation values, r>0.5, were identified: the visual network (VIN), sensorimotor network (SMN), cognitive-control network (CCN), subcortical network (SCN), default-mode network (DMN), and auditory network (AUN). It showed that VIN and SMN were the most activated during the vibrotactile stimulation. A comparison of the network volumes and peak activations during the conditions demonstrated changes in volume and corresponding peak activation during vibrotactile stimulation. This study contributes to a better understanding of the underlying mechanisms of the somatosensory areas. Other than that, not only this study highlighted the underlying effect of vibrotactile stimulation towards the functional brain connectivity at network levels, but it also highlighted the impact of frequencies in somatosensory studies. In the future, we suggest that exploring the change in the range of frequencies and examining its differences will allow us to comprehend aspects of somatosensory networks and their connectivity.
References
Akselrod, M., Martuzzi, R., Serino, A., van der Zwaag, W., Gassert, R., & Blanke, O. (2017). Anatomical and functional properties of the foot and leg representation in areas 3b, 1 and 2 of primary somatosensory cortex in humans: A 7T fMRI study. NeuroImage, 159, 473–487. https://doi.org/10.1016/j.neuroimage.2017.06.021
Ali, K., & Al-Hameed, A. (2022). Spearman's correlation coefficient in statistical analysis. International Journal of Nonlinear Analysis and Applications, 13, 2008–6822. https://doi.org/10.22075/ijnaa.2022.6079
Araneda, R., Moura, S. S., Dricot, L., & De Volder, A. G. (2021). Beat detection recruits the visual cortex in early blind subjects. Life, 11(4), 296. https://doi.org/10.3390/life11040296
Behler, O., & Uppenkamp, S. (2020). Activation in human auditory cortex in relation to the loudness and unpleasantness of low-frequency and infrasound stimuli. PLoS ONE, 15(2), e0229088. https://doi.org/10.1371/journal.pone.0229088
Beugels, J., van den Hurk, J., Peters, J. C., Heuts, E. M., Tuinder, S. M. H., Goebel, R., & van der Hulst, R. R. W. J. (2020). Somatotopic mapping of the human breast using 7 T functional MRI. NeuroImage, 204, 116201. https://doi.org/10.1016/j.neuroimage.2019.116201
Boslaugh, S., & Watters, P. A. (2008). Statistics in a Nutshell: A Desktop Quick Reference. O’Reilly Media. https://books.google.com/books/about/Statistics_in_a_Nutshell.html?id=ZnhgO65Pyl4C
Burton, H., Agato, A., & Sinclair, R. J. (2012). Repetition learning of vibrotactile temporal sequences: An fMRI study in blind and sighted individuals. Brain Research, 1433, 69–79. https://doi.org/10.1016/j.brainres.2011.11.039
Burton, H., Sinclair, R. J., & McLaren, D. G. (2008). Cortical network for vibrotactile attention: a fMRI study. Human Brain Mapping, 29(2), 207–221. https://doi.org/10.1002/hbm.20384
Cassady, K., Ruitenberg, M. F. L., Reuter-Lorenz, P. A., Tommerdahl, M., & Seidler, R. D. (2020). Neural Dedifferentiation across the Lifespan in the Motor and Somatosensory Systems. Cerebral Cortex, 30(6), 3704–3716. https://doi.org/10.1093/cercor/bhz336
Cerliani, L., Mennes, M., Thomas, R. M., Di Martino, A., Thioux, M., & Keysers, C. (2015). Increased functional connectivity between subcortical and cortical resting-state networks in Autism spectrum disorder. JAMA Psychiatry, 72(8), 767–777. https://doi.org/10.1001/jamapsychiatry.2015.0101
Chakravarty, M. M., Rosa-Neto, P., Broadbent, S., Evans, A. C., & Collins, D. L. (2009). Robust S1, S2, and thalamic activations in individual subjects with vibrotactile stimulation at 1.5 and 3.0 T. Human Brain Mapping, 30(4), 1328–1337. https://doi.org/10.1002/hbm.20598
Choi, M. H., Kim, S. P., Kim, H. S., & Chung, S. C. (2016). Inter-and Intradigit Somatotopic Map of High-Frequency Vibration Stimulations in Human Primary Somatosensory Cortex. Medicine (United States), 95(20), 1–9. https://doi.org/10.1097/MD.0000000000003714
Chung, Y. G., Kim, J., Han, S. W., Kim, H. S., Choi, M. H., Chung, S. C., Park, J. Y., & Kim, S. P. (2013). Frequency-dependent patterns of somatosensory cortical responses to vibrotactile stimulation in humans: A fMRI study. Brain Research, 1504, 47–57. https://doi.org/10.1016/j.brainres.2013.02.003
Cole, D. M., Stämpfli, P., Gandia, R., Schibli, L., Gantner, S., Schuetz, P., & Meier, M. L. (2022). In the back of your mind: Cortical mapping of tactile and proprioceptive paraspinal afferent inputs. Human Brain Mapping. 43(16), 4943–4953. https://doi.org/10.1002/hbm.26052
Corbetta, M., Burton, H., Sinclair, R. J., Conturo, T. E., Akbudak, E., & McDonald, J. W. (2002). Functional sreorganisation and stability of somatosensory-motor cortical topography in a tetraplegic subject with late recovery. Proceedings of the National Academy of Sciences of the United States of America, 99(26), 17066–17071. https://doi.org/10.1073/pnas.262669099
Deuchert, M., Ruben, J., Schwiemann, J., Meyer, R., Thees, S., Krause, T., Blankenburg, F., Villringer, K., Kurth, R., Curio, G., & Villringer, A. (2002). Event-related fMRI of the somatosensory system using electrical finger stimulation. NeuroReport, 13(3), 365–369. https://doi.org/10.1097/00001756-200203040-00023
Ding, H., Ming, D., Wan, B., Li, Q., Qin, W., & Yu, C. (2016). Enhanced spontaneous functional connectivity of the superior temporal gyrus in early deafness. Scientific Reports, 6, 23239. https://doi.org/10.1038/srep23239
Du, Y., & Fan, Y. (2013). Group information guided ICA for fMRI data analysis. NeuroImage, 69, 157–197. https://doi.org/10.1016/j.neuroimage.2012.11.008
Du, Y., Fu, Z., Sui, J., Gao, S., Xing, Y., Lin, D., Salman, M., Abrol, A., Rahaman, M. A., Chen, J., Hong, L. E., Kochunov, P., Osuch, E. A., & Calhoun, V. D. (2020). NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. NeuroImage: Clinical, 28, 102375. https://doi.org/10.1016/j.nicl.2020.102375
Du, Y., He, X., & Calhoun, V. D. (2021). SMART (splitting-merging assisted reliable) Independent Component Analysis for Brain Functional Networks. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 3263–3266. https://doi.org/10.1109/EMBC46164.2021.9630284
Eickhoff, S. B., & Müller, V. I. (2015). Functional Connectivity. In Brain Mapping: An Encyclopedic Reference, 2, 187–201. https://doi.org/10.1016/B978-0-12-397025-1.00212-8
Elseoud, A. A., Littow, H., Remes, J., Starck, T., Nikkinen, J., Nissilä, J., Timonen, M., Tervonen, O., & Kiviniemi, V. (2011). Group-ICA model order highlights patterns of functional brain connectivity. Frontiers in Systems Neuroscience, 5, 37. https://doi.org/10.3389/fnsys.2011.00037
Francis, S. T., Kelly, E. F., Bowtell, R., Dunseath, W. J. R., Folger, S. E., & McGlone, F. (2000). fMRI of the responses to vibratory stimulation of digit tips. NeuroImage, 11(3), 188–202. https://doi.org/10.1006/nimg.2000.0541
Friedrich, J., Mückschel, M., & Beste, C. (2018). Specific properties of the SI and SII somatosensory areas and their effects on motor control: a system neurophysiological study. Brain Structure and Function, 223(2), 687–699. https://doi.org/10.1007/s00429-017-1515-y
Golaszewski, S. M., Siedentopf, C. M., Baldauf, E., Koppelstaetter, F., Eisner, W., Unterrainer, J., Guendisch, G. M., Mottaghy, F. M., & Felber, S. R. (2002). Functional magnetic resonance imaging of the human sensorimoto cortex using a novel vibrotactile stimulator. NeuroImage, 17(1), 421–430. https://doi.org/10.1006/nimg.2002.1195
Golaszewski, S. M., Siedentopf, C. M., Koppelstaetter, F., Fend, M., Ischebeck, A., Gonzalez-Felipe, V., Haala, I., Struhal, W., Mottaghy, F. M., Gallasch, E., Felber, S. R., & Gerstenbrand, F. (2006). Human brain structures related to plantar vibrotactile stimulation: A functional magnetic resonance imaging study. NeuroImage, 29(3), 923–929. https://doi.org/10.1016/j.neuroimage.2005.08.052
Goltz, D., Pleger, B., Thiel, S., Villringer, A., & Müller, M. M. (2013). Sustained spatial attention to vibrotactile stimulation in the flutter range: Relevant brain regions and their interaction. PLoS ONE, 8(12), 1–12. https://doi.org/10.1371/journal.pone.0084196
Hagen, M. C., & Pardo, J. V. (2002). PET studies of somatosensory processing of light touch. Behavioural Brain Research, 135(1-2), 133–140. https://doi.org/10.1016/s0166-4328(02)00142-0
Hegner, Y. L., Lee, Y., Grodd, W., & Braun, C. (2010). Comparing Tactile Pattern and Vibrotactile Frequency Discrimination: A Human fMRI Study. Journal of Neurophysiology, 103(6), 3115–3122. https://doi.org/10.1152/jn.00940.2009
Hegner, Y. L., Saur, R., Veit, R., Butts, R., Leiberg, S., Grodd, W., & Braun, C. (2007). BOLD adaptation in vibrotactile stimulation: Neuronal networks involved in frequency discrimination. Journal of Neurophysiology, 97(1), 264–271. https://doi.org/10.1152/jn.00617.2006
Iandolo, R., Bellini, A., Saiote, C., Marre, I., Bommarito, G., Oesingmann, N., Fleysher, L., Mancardi, G. L., Casadio, M., & Inglese, M. (2018). Neural correlates of lower limbs proprioception: An fMRI study of foot position matching. Human Brain Mapping, 39(5), 1929–1944. https://doi.org/10.1002/hbm.23972
Jaatela, J., Aydogan, D. B., Nurmi, T., Vallinoja, J., & Piitulainen, H. (2022). Identification of Proprioceptive Thalamocortical Tracts in Children: Comparison of fMRI, MEG, and Manual Seeding of Probabilistic Tractography. Cerebral Cortex, 32(17), 37366–3751. https://doi.org/10.1093/cercor/bhab444
Jung, W. M., Ryu, Y., Park, H. J., Lee, H., & Chae, Y. (2018). Brain activation during the expectations of sensory experience for cutaneous electrical stimulation. NeuroImage: Clinical, 19, 982–989. https://doi.org/10.1016/j.nicl.2018.06.022
Kim, J., Chung, Y. G., Chung, S. C., Bulthoff, H. H., & Kim, S. P. (2016). Neural sCategorisation of Vibrotactile Frequency in Flutter and Vibration Stimulations: An fMRI Study. IEEE Transactions on Haptics, 9(4), 455–464. https://doi.org/10.1109/TOH.2016.2593727
Kim, J., Müller, K.-R., Chung, Y. G., Chung, S.-C., Park, J.-Y., Bülthoff, H. H., & Kim, S.-P. (2015). Distributed functions of detection and discrimination of vibrotactile stimuli in the hierarchical human somatosensory system. Frontiers in Human Neuroscience, 8(January), 1–10. https://doi.org/10.3389/fnhum.2014.01070
Maldjian, J. A., Laurienti, P. J., Kraft, R. A., & Burdette, J. H. (2003). An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. NeuroImage, 19(3), 1233–1239. https://doi.org/10.1016/S1053-8119(03)00169-1
Malone, P. S., Eberhardt, S. P., Wimmer, K., Sprouse, C., Klein, R., Glomb, K., Scholl, C. A., Bokeria, L., Cho, P., Deco, G., Jiang, X., Bernstein, L. E., & Riesenhuber, M. (2019). Neural mechanisms of vibrotactile categorisation. Human Brain Mapping, 40(10), 3078–3090. https://doi.org/10.1002/hbm.24581
Moritz, C. H., Haughton, V. M., Cordes, D., Quigley, M., & Meyerand, M. E. (2000). Whole-brain Functional MR Imaging Activation from a Finger-tapping Task Examined with Independent Component Analysis. American Journal of Neuroradiology, 21(9), 1629–1635.
Motovilova, E., & Winkler, S. A. (2022). Overview of Methods for Noise and Heat Reduction in MRI Gradient Coils. Frontiers in Physics, 10, 907619. https://doi.org/10.3389/fphy.2022.907619
Nazarian, B., Caron-Guyon, J., Anton, J. L., Sein, J., Baurberg, J., Catz, N., & Kavounoudias, A. (2022). A new patterned air-flow device to reveal the network for tactile motion coding using fMRI. Journal of Neuroscience Methods, 365, 109397. https://doi.org/https://doi.org/10.1016/j.jneumeth.2021.109397
Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9(1), 97–113. https://doi.org/10.1016/0028-3932(71)90067-4
Park, J. W., Kim, Y. H., Jang, S. H., Chang, W. H., Park, C. H., & Kim, S. T. (2010). Dynamic changes in the cortico-subcortical network during early motor learning. NeuroRehabilitation, 26(2), 95–103. https://doi.org/10.3233/NRE-2010-0540
Pechenkova, E. V., Nosikova, I. N., Rumshiskaya, A. D., Litvinova, L. D., Rukavishnikov, I. V., Mershina, E. A., Sinitsin, V. E., Van Ombergen, A., Jeurissen, B., Jillings, S. D., Laureys, S., Sijbers, J., Grishin, A., Chernikova, L. A., Naumov, I. A., Kornilova, L. N., Wuyts, F. L., Tomilovskaya, E. S., & Kozlovskaya, I. B. (2019). Alterations of functional brain connectivity after long-duration spaceflight as revealed by fMRI. Frontiers in Physiology, 10(May), 761. https://doi.org/10.3389/fphys.2019.00761
Pfannmöller, J. P., Greiner, M., Balasubramanian, M., & Lotze, M. (2016). High-resolution fMRI investigations of the fingertip somatotopy and variability in BA3b and BA1 of the primary somatosensory cortex. Neuroscience, 339, 667–677. https://doi.org/10.1016/j.neuroscience.2016.10.036
Puckett, A. M., Bollmann, S., Barth, M., & Cunnington, R. (2017). Measuring the effects of attention to individual fingertips in somatosensory cortex using ultra-high field (7T) fMRI. NeuroImage, 161, 179–187. https://doi.org/10.1016/j.neuroimage.2017.08.014
Quintana, D. S. (2023). A guide for calculating study-level statistical power for meta-analyses. Advances in Methods and Practices in Psychological Science, 6,(1). https://doi.org/10.1177/25152459221147260
Rabe, F., Kikkert, S., & Wenderoth, N. (2021). Finger representations in primary somatosensory cortex are modulated by a vibrotactile working memory task. BioRxiv, 340(1), 1–33. https://doi.org/10.1101/2021.10.29.466459
Renier, L. A., Anurova, I., De Volder, A. G., Carlson, S., VanMeter, J., & Rauschecker, J. P. (2009). Multisensory Integration of Sounds and Vibrotactile Stimuli in Processing Streams for "What" and "Where." Journal of Neuroscience, 29(35), 10950–10960. https://doi.org/10.1523/JNEUROSCI.0910-09.2009
Rondinoni, C., Amaro, E., Cendes, F., dos Santos, A. C., & Salmon, C. E. G. (2013). Effect of scanner acoustic background noise on strict resting-state fMRI. Brazilian Journal of Medical and Biological Research, 46(4), 359–367. https://doi.org/10.1590/1414-431X20132799
Rosazza, C., Minati, M., Ghielmetti, F., Mandelli, M. L., & Bruzzone, M. G. (2012). Functional connectivity during resting-state functional MR imaging: Study of the correspondence between independent component analysis and region-of-interest - Based methods. American Journal of Neuroradiology, 33(1), 180–187. https://doi.org/10.3174/ajnr.A2733
Sauvage, C., Poirriez, S., Manto, M., Jissendi, P., & Habas, C. (2011). Reevaluating brain networks activated during mental imagery of finger movements using probabilistic Tensorial Independent Component Analysis (TICA). Brain Imaging and Behavior, 5(2), 137–148. https://doi.org/10.1007/s11682-011-9118-3
Schweisfurth, M. A., Frahm, J., & Schweizer, R. (2014). Individual fMRI maps of all phalanges and digit bases of all fingers in human primary somatosensory cortex. Frontiers in Human Neuroscience, 8(SEP), 1–14. https://doi.org/10.3389/fnhum.2014.00658
Schweisfurth, M. A., Frahm, J., & Schweizer, R. (2015). Individual left-hand and right-hand intra-digit representations in human primary somatosensory cortex. European Journal of Neuroscience, 42(5), 2155–2163. https://doi.org/10.1111/ejn.12978
Schweisfurth, M. A., Schweizer, R., & Frahm, J. (2011). Functional MRI indicates consistent intra-digit topographic maps in the little but not the index finger within the human primary somatosensory cortex. NeuroImage, 56(4), 2138–2143. https://doi.org/10.1016/j.neuroimage.2011.03.038
Sengupta, A., Ackerley, R., Watkins, R. H., Panchuelo, R. S., Paul, G., Wessberg, J., & Francis, S. (2019). Global responses to microstimulation at 7T and comparison with vibrotactile stimulation. Proceedings of Joint Annual Meeting ISMRM-ESMRMB, 0149. https://archive.ismrm.org/2018/0149.html
Seri, F. A. S., Abd Hamid, A. I., Abdullah, J. M., Idris, Z., & Omar, H. (2019). Brain responses to frequency changes due to vibratory stimulation of human fingertips: An fMRI study. Journal of Physics: Conference Series, 1248(1), 1–6. https://doi.org/10.1088/1742-6596/1248/1/012029
Seri, F. A. S., Abd Hamid, A. I., Abdullah, J. M., Idris, Z., & Omar, H. (2020). Brain responses to high frequencies (270 Hz-480 Hz) changes due to vibratory stimulation of human fingertips: An fMRI study. Journal of Physics: Conference Series, 1497, 012012. https://doi.org/10.1088/1742-6596/1497/1/012012
Siedentopf, C. M., Heubach, K., Ischebeck, A., Gallasch, E., Fend, M., Mottaghy, F. M., Koppelstaetter, F., Haala, I. A., Krause, B. J., Felber, S., Gerstenbrand, F., & Golaszewski, S. M. (2008). Variability of BOLD response evoked by foot vibrotactile stimulation: Influence of vibration amplitude and stimulus waveform. NeuroImage, 41(2), 504–510. https://doi.org/10.1016/j.neuroimage.2008.02.049
Sormaz, M., Murphy, C., Wang, H. T., Hymers, M., Karapanagiotidis, T., Poerio, G., Margulies, D. S., Jefferies, E., & Smallwood, J. (2018). Default mode network can support the level of detail in experience during active task states. Proceedings of the National Academy of Sciences of the United States of America, 115(37), 9318–9323. https://doi.org/10.1073/pnas.1721259115
Sörös, P., Marmurek, J., Tam, F., Baker, N., Staines, W. R., & Graham, S. J. (2007). Functional MRI of working memory and selective attention in vibrotactile frequency discrimination. BMC Neuroscience, 8, 1–10. https://doi.org/10.1186/1471-2202-8-48
Sung, E. J., Yoo, S. S., Yoon, H. W., Oh, S. S., Han, Y., & Park, H. W. (2007). Brain activation related to affective dimension during thermal stimulation in humans: A functional magnetic resonance imaging study. International Journal of Neuroscience, 117(7), 1011–1027. https://doi.org/10.1080/00207450600934432
Tomasi, D., & Volkow, N. D. (2011). Association between functional connectivity hubs and brain networks. Cerebral Cortex, 21(9), 2003–2013. https://doi.org/10.1093/cercor/bhq268
Uluç, I., Schmidt, T. T., Wu, Y. hao, & Blankenburg, F. (2018). Content-specific codes of parametric auditory working memory in humans. NeuroImage, 183, 254–262. https://doi.org/10.1016/j.neuroimage.2018.08.024
Willoughby, W. R., Thoenes, K., & Bolding, M. (2021). Somatotopic Arrangement of the Human Primary Somatosensory Cortex Derived From Functional Magnetic Resonance Imaging. Frontiers in Neuroscience, 14, 598482. https://doi.org/10.3389/fnins.2020.598482
Woolgar, A., & Zopf, R. (2017). Multisensory coding in the multiple-demand regions: vibrotactile task information is coded in frontoparietal cortex. Journal of Neurophysiology, 118, 703–716. https://doi.org/10.1152/jn.00559.2016
World Medical Association. (2013). World Medical Association declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA, 310(20), 2191–2194. https://doi.org/10.1001/jama.2013.281053
Wu, L., Caprihan, A., Bustillo, J., Mayer, A., & Calhoun, V. (2018). An approach to directly link ICA and seed-based functional connectivity: Application to schizophrenia. NeuroImage, 179, 448–470. https://doi.org/10.1016/j.neuroimage.2018.06.024
Wu, Y., Velenosi, L. A., & Blankenburg, F. (2021). Response modality-dependent categorical choice representations for vibrotactile comparisons. NeuroImage, 226, 117592. https://doi.org/10.1016/j.neuroimage.2020.117592
Xu, J., Potenza, M. N., & Calhoun, V. D. (2013). Spatial ICA reveals functional activity hidden from traditional fMRI GLM-based analyses. Frontiers in Neuroscience, 7, 154. https://doi.org/10.3389/fnins.2013.00154
Zhao, W., Li, H., Hu, G., Hao, Y., Zhang, Q., Wu, J., Frederick, B. B., & Cong, F. (2021). Consistency of independent component analysis for FMRI. Journal of Neuroscience Methods, 351, 109013. https://doi.org/10.1016/j.jneumeth.2020.109013
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2023 Faten Anis Syairah Seri, Aini Ismafairus Abd Hamid, Jafri Malin Abdullah, Zamzuri Idris, Hazim Omar, Muhammad Riddha Abdul Rahman
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The observations and associated materials published or posted by NeurosciRN are licensed by the authors for use and distribution in accord with the Creative Commons Attribution license CC BY-NC 4.0 international, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
