Abstract
Investigating human brain activity during expressing emotional states provides deep insight into complex cognitive functions and neurological correlations inside the brain. To be able to resemble the brain function in the best manner, a complex and natural stimulus should be applied as well, the method used for data analysis should have fewer assumptions, simplifications, and parameter adjustment. In this study, we examined a functional magnetic resonance imaging dataset obtained during an emotional audio-movie stimulus associated with human life. We used Jackknife Correlation (JC) method to derive a representation of time-varying functional connectivity. We applied different binary measures and thoroughly investigated two weighted measures to study different properties of binary and weighted temporal networks. Using this approach, we indicated different aspects of human brain function during expressing different emotions. The findings of global and nodal measures could demonstrate a significant difference between emotions and significant regions in each emotion, respectively. Also, the temporal centrality properties of nodes were different in emotional states. Ultimately, we showed that the resulting measures of temporal snapshots created by JC method can distinguish between different emotions.
Similar content being viewed by others
References
Acevedo BP, Aron A, Fisher HE, Brown LL (2012) Neural correlates of long-term intense romantic love. Soc Cogn Affect Neurosci 7(2):145–159. https://doi.org/10.1093/scan/nsq092
Allen EA et al (2014) Tracking whole-brain connectivity dynamics in the resting state. Cerb Cor 24(3):663–676. https://doi.org/10.1093/cercor/bhs352
Alluri V et al (2012) Large-scale brain networks emerge from dynamic processing of musical timbre, key and rhythm. NeuroImage 59:3677–3689. https://doi.org/10.1016/j.neuroimage.2011.11.019
Bartels A, Zeki S (2004) The neural correlates of maternal and romantic love. NeuroImage 21(3):1155–1166. https://doi.org/10.1016/j.neuroimage.2003.11.003
Brattico E et al (2011) A functional MRI study of happy and sad emotions in music with and without lyrics. Front Psychol 2:308. https://doi.org/10.3389/fpsyg.2011.00308
Brennan J et al (2010) Syntactic structure building in the anterior temporal lobe during natural story listening. Brain Lang 120:163–173. https://doi.org/10.1016/j.bandl.2010.04.002
Cacioppo S, Bianchi-Demicheli F, Hatfield E, Rapson RL (2012) Social neuroscience of love. Clinical. Neuropsychiatry 9(1):3–13
Dasdemir Y, Yildirim E, Yildirim S (2017) Analysis of functional brain connections for positive–negative emotions using phase locking value. Cogn Neurodyn 11(6):487–500. https://doi.org/10.1007/s11571-017-9447-z
Eldar E, Ganor O, Admon R, Bleich A, Hendler T (2007) Feeling the real world: limbic response to music depends on related content. Cerb Cor 17(12):2828–2840. https://doi.org/10.1093/cercor/bhm011
Emerson RW, Short SJ, Lin W, Gilmore JH, Gao W (2015) Network-level connectivity dynamics of movie watching in 6-year-old children. Front Hum Neurosci 9:631. https://doi.org/10.3389/fnhum.2015.00631
Evans AC, Janke AL, Collins DL, Baillet S (2012) Brain templates and atlases. NeuroImage 62:911–922. https://doi.org/10.1016/j.neuroimage.2012.01.024
Farahani N, Fatemizadeh E, Nasrabadi AM (2019) Using rDCM method in the mixed model in order to inference effective connectivity in emotions. Frontiers Biomed Technol 6(2):106–113. https://doi.org/10.18502/fbt.v6i2.1692
Fransson P, Schiffler BC, Thompson WH (2018) Brain network segregation and integration during an epoch-related working memory fMRI experiment. NeuroImage 178:147–161. https://doi.org/10.1016/j.neuroimage.2018.05.040
Fulwiler CE, King JA, Zhang N (2012) Amygdala-orbitofrontal resting state functional connectivity is associated with trait anger. NeuroReport 23(10):606–610. https://doi.org/10.1097/WNR.0b013e3283551cfc
Fusar-Poli P et al (2009) Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies. J Psychiatry Neurosci 34(6):418–432
Ghahari S, Fatemizadeh E, Nasrabadi AM (2019) Studying the distinction between emotions in fMRI data by using temporal network theory. Frontiers Biomed Technol 6(2):87–93. https://doi.org/10.18502/fbt.v6i2.1689
Glerean E, Salmi J, Lahnakoski JM, Jaaskelainen IP, Sams M (2012) Functional magnetic resonance imaging phase synchronization as a measure of dynamic functional connectivity. Brain Connect 2(2):91–101. https://doi.org/10.1089/brain.2011.0068
Gu S et al (2019) An integrative way for studying neural basis of basic emotions with fMRI. Front Neurosci 13:628. https://doi.org/10.3389/fnins.2019.00628
Hanke M et al (2014) A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie. Sci Data 1:140003. https://doi.org/10.1038/sdata.2014.3
Hindriks R et al (2016) Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? NeuroImage 127:242–256. https://doi.org/10.1016/j.neuroimage.2015.11.055
Holme P, Saramaki J (2012) Temporal networks. Phys Rep 519(3):97–125. https://doi.org/10.1016/j.physrep.2012.03.001
Hutchison RM et al (2013) Dynamic functional connectivity: promise, issues, and interpretations. NeuroImage 80:360–378. https://doi.org/10.1016/j.neuroimage.2013.05.079
Johnstone T, Reekum CMv, Oakes TR, Davidson RJ (2006) The voice of emotion: an fMRI study of neural responses to angry and happy vocal expressions. Soc Cogn Affect Neurosci 1(3):242–249. https://doi.org/10.1093/scan/nsl027
Kiviniemi V et al (2011) A sliding time-window ICA reveals spatial variability of the default mode network in time. Brain Connect 1(4):339–347. https://doi.org/10.1089/brain.2011.0036
Koelsch S (2014) Brain correlates of music-evoked emotions. Nat Rev Neurosci 15(3):170–180. https://doi.org/10.1038/nrn3666
Koelsch S, Fritz T, Cramon DYv, Muller K, Friederici AD (2006) Investigating emotion with music: an fMRI study. Hum Brain Mapp 27(3):239–250. https://doi.org/10.1002/hbm.20180
Koelsch S et al (2013) The roles of superficial amygdala and auditory cortex in music-evoked fear and joy. NeuroImage 81:49–60. https://doi.org/10.1016/j.neuroimage.2013.05.008
Kotz SA, Kalberlah C, Bahlmann J, Friederici AD, Haynes JD (2012) Predicting vocal emotion expressions from the human brain. Hum Brain Mapp 34(8):1971–1981. https://doi.org/10.1002/hbm.22041
Mitterschiffthaler MT, Fu CHY, Dalton JA, Andrew CM, Williams SCR (2007) A functional MRI study of happy and sad affective states induced by classical music. Hum Brain Mapp 28(11):1150–1162. https://doi.org/10.1002/hbm.20337
Murphy FC, Nimmo-Smith I, Lawrence AD (2003) Functional neuroanatomy of emotions: a meta-analysis. Cogn Affect Behav Neurosci 3(3):207–233. https://doi.org/10.3758/CABN.3.3.207
Nguyen VT et al (2016) The integration of the internal and external milieu in the insula during dynamic emotional experiences. NeuroImage 124:455–463. https://doi.org/10.1016/j.neuroimage.2015.08.078
Nichols TE, Holmes AP (2001) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15:1–25. https://doi.org/10.1002/hbm.1058
Okuya T et al (2017) Investigating the type and strength of emotion with music: an fMRI study. Acoust Sci Tech 38(3):120–127. https://doi.org/10.1250/ast.38.120
Park J-Y et al (2010) Integration of cross-modal emotional information in the human brain: an fMRI study. Cortex 46(2):161–169. https://doi.org/10.1016/j.cortex.2008.06.008
Pohl A, Anders S, Schulte-Ruther M, Mathiak K, Kircher T (2013) Positive facial affect—an fMRI study on the involvement of insula and amygdala. PLoS ONE 8(8):e69886. https://doi.org/10.1371/journal.pone.0069886
Purves D et al (2012) Principles of cognitive neuroscience, 2nd edn. Oxford University Press, Sunderland
Purves D et al (2017) Neuroscience, 6th edn. Oxford University Press, Sunderland
Sato W, Kochiyama T, Yoshikawa S, Naito E, Matsumura M (2004) Enhanced neural activity in response to dynamic facial expressions of emotion: an fMRI study. Cogn Brain Res 20(1):81–91. https://doi.org/10.1016/j.cogbrainres.2004.01.008
Schaefer HE (2017) Music-evoked emotions—current studies. Front Neurosci 11:600. https://doi.org/10.3389/fnins.2017.00600
Shine JM et al (2015) Estimation of dynamic functional connectivity using multiplication of temporal derivatives. NeuroImage 122:399–407. https://doi.org/10.1016/j.neuroimage.2015.07.064
Thompson WH (2017) Brain networks in time: deriving and quantifying dynamic functional connectivity. Dissertation, Department of Clinical Neruoscience, Karolinska Institutet, Stockholm, Sweden
Thompson WH, Fransson P (2015a) The frequency dimension of fMRI dynamic connectivity: network connectivity, functional hubs and integration in the resting brain. NeuroImage 121:227–242. https://doi.org/10.1016/j.neuroimage.2015.07.022
Thompson WH, Fransson P (2015b) The mean-variance relationship reveals two possible strategies for dynamic brain connectivity analysis in fMRI. Front Hum Neurosci 9:398. https://doi.org/10.3389/fnhum.2015.00398
Thompson WH, Fransson P (2016a) Bursty properties revealed in large-scale brain networks with a point-based method for dynamic functional connectivity. Sci Rep 6:39156. https://doi.org/10.1038/srep39156
Thompson WH, Fransson P (2016b) On stabilizing the variance of dynamic functional brain connectivity time series. Brain Connect 6(10):735–746. https://doi.org/10.1089/brain.2016.0454
Thompson WH, Brantefors P, Fransson P (2017) From static to temporal network theory: applications to functional brain connectivity. Netw Neurosci 1(2):69–99. https://doi.org/10.1162/netn_a_00011
Thompson WH, Richter CG, Plaven-Sigray P, Fransson P (2018) Simulations to benchmark time-varying connectivity methods for fMRI. PLoS Comput Biol 14(5):e1006196. https://doi.org/10.1371/journal.pcbi.1006196
Zhang D, Zhou Y, Yuan J (2018) Speech prosodies of diferent emotional categories activate diferent brain regions in adult cortex: an fNIRS study. Sci Rep 8:218. https://doi.org/10.1038/s41598-017-18683-2
Author information
Authors and Affiliations
Contributions
S.G. and N.F. conceived and designed the research. S.G. and N.F. selected and analyzed the data. S.G. proposed and developed the methodology, wrote the codes for analyzing, and prepared all figures. S.G. wrote the original manuscript and N.F. contributed to the original manuscript. S.G., N.F., E.F., and A.M.N. reviewed and edited the final manuscript, provided critical feedback and helped shape the research.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Ghahari, S., Farahani, N., Fatemizadeh, E. et al. Investigating time-varying functional connectivity derived from the Jackknife Correlation method for distinguishing between emotions in fMRI data. Cogn Neurodyn 14, 457–471 (2020). https://doi.org/10.1007/s11571-020-09579-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11571-020-09579-5