Multimedia Tools and Applications

, Volume 75, Issue 9, pp 4851–4865 | Cite as

Multivoxel analysis for functional magnetic resonance imaging (fMRI) based on time-series and contextual information: relationship between maternal love and brain regions as a case study

  • Bo-Wei Chen
  • Yang-Yen Ou
  • Chun-Chia Kung
  • Ding-Ruey Yeh
  • Seungmin Rho
  • Jhing-Fa Wang


This study explores the relationship between maternal love and brain regions by using functional magnetic resonance imaging (fMRI). Also, a novel pattern analysis for fMRI based on the discovered brain regions is proposed in this work. Firstly, to identify which region responds to stimuli, a statistical t-test is used after the scan. Based on these preliminary regions of interest, this study develops discriminant features extracted from multivoxels for cognitive modeling. In total, five parameters are used in the time-series and contextual analysis, including the proposed blood-oxygen-level-dependent (BOLD) contrast edge, BOLD contrast centroid, activated voxels, mean, and variance. Furthermore, this study also proposes a test function for examining voxel activation based on variance, so that insignificant voxels and irrelevant outliers can be removed from the features. After the feature extraction from brain regions of interest, the analysis subsequently uses Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for reducing the feature size. Lastly, this study adopts a computer-aided pattern recognizer, the Support Vector Machine (SVM), to facilitate automation of the proposed analysis. A dataset consisting of brain-scanning images from 22 subjects was used for evaluation. The statistical result shows that the neural circuitry associated with maternal bonds indeed appears in the relevant brain regions as indicated by the other research. Such regions are subsequently used for assessment of the proposed analysis. Classification result shows that the proposed approach can effectively identify activated samples. Besides, our system achieves an accuracy rate of as high as 83.33 %. A comparison among different systems reveals that the proposed system is superior to the others and establishes its feasibility.


Maternal love recognition Blood-oxygen-level-dependent (BOLD) contrast edge BOLD contrast centroid Multivoxel pattern analysis Functional magnetic resonance imaging (fMRI) 



This work was supported in part by the National Science Council of the Republic of China under Grant Nos. 102-2811-E-006-005 and 103-2917-I-564-058. The authors appreciate Cheng-Hsun Hsieh for the baseline system.


  1. 1.
    Baird AA, Gruber SA, Fein DA, Maas LC, Steingard RJ, Renshaw PF, Cohen BM, Yurgelun-Todd DA (1999) Functional magnetic resonance imaging of facial affect recognition in children and adolescents. Am Acad Child Adolesc Psychiatry 38(2):195–199CrossRefGoogle Scholar
  2. 2.
    Bartels A, Zeki S (2004) The neural correlates of maternal and romantic love. Neuroimage 21(3):1155–1166CrossRefGoogle Scholar
  3. 3.
    Batut A-C, Gounot D, Namer IJ, Hirsch E, Kehrli P, Metz-Lutz M-N (2006) Neural responses associated with positive and negative emotion processing in patients with left versus right temporal lobe epilepsy. Epilepsy Behav 9(3):415–423CrossRefGoogle Scholar
  4. 4.
    Bordi A, Rosa GD, Napolitano F, Litterio M, Marino V, Rubino R (1994) Postpartum development of the mother-young relationship in goats. Appl Anim Behav Sci 42(2):145–152CrossRefGoogle Scholar
  5. 5.
    Doi H, Shinohara K (2012) Event-related potentials elicited in mothers by their own and unfamiliar infants’ faces with crying and smiling expression. Neuropsychologia 50(7):1297–1307CrossRefGoogle Scholar
  6. 6.
    Feldman R, Weller A, Leckman JF, Kuint J, Eidelman AI (1999) The nature of the mother’s tie to her infant: maternal bonding under conditions of proximity, separation, and potential loss. J Child Psychol Psychiatryand Allied Relat Discip 40(6):929–939CrossRefGoogle Scholar
  7. 7.
    Gillian NE (2011) Gesture recognition for musician computer interaction Ph.D. dissertation, faculty of arts, humanities and social sciences, school of music and sonic arts. Queen’s University Belfast, BelfastGoogle Scholar
  8. 8.
    Hossein-Zadeh G-A, Ardekani BA, Soltanian-Zadeh H (2003) Activation detection in fMRI using a maximum energy ratio statistic obtained by adaptive spatial filtering. IEEE Trans Med Imaging 22(7):795–805CrossRefGoogle Scholar
  9. 9.
    Hsieh C-H, Wang J-F (2013) A study on the relationship between happiness and brain regions based on multi-voxel pattern analysis (MVPA) and fMRI database, M.S. thesis, department of electrical engineering. National Cheng Kung University, TainanGoogle Scholar
  10. 10.
    Hsu C-W, Lin C-J (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425CrossRefGoogle Scholar
  11. 11.
    Hudson SJ, Mullord MM (1977) Investigations of maternal bonding in dairy cattle. Appl Anim Ethol 3(3):271–276CrossRefGoogle Scholar
  12. 12.
    Huettel SA, Song AW, McCarthy G (2008) Functional Magnetic Resonance Imaging, 2nd ed. Sinauer Associates, SunderlandGoogle Scholar
  13. 13.
    Jansen J, Weerth CD, Riksen-Walraven JM (2008) Breastfeeding and the mother–infant relationship—A review. Dev Rev 28(4):503–521CrossRefGoogle Scholar
  14. 14.
    Kadous MW (2002) Temporal classification: extending the classification paradigm to multivariate time series Ph.D. dissertation, school of computer science and engineering. University of New South Wales, New South WalesGoogle Scholar
  15. 15.
    Kendrick KM, Lévy F, Keverne EB (1991) Importance of vaginocervical stimulation for the formation of maternal bonding in primiparous and multiparous parturient ewes. Physiol Behav 50(3):595–600CrossRefGoogle Scholar
  16. 16.
    Kent JP (1987) A note concerning the use of the maternal bond concept. Appl Anim Behav Sci 17(3):361–363CrossRefGoogle Scholar
  17. 17.
    Laurent HK, Ablow JC (2012) The missing link: mothers’ neural response to infant cry related to infant attachment behaviors. Infant Behav Dev 35(4):761–772CrossRefGoogle Scholar
  18. 18.
    Laurent HK, Stevens A, Ablow JC (2011) Neural correlates of hypothalamic-pituitary-adrenal regulation of mothers with their infants. Biol Psychiatry 70(9):826–832CrossRefGoogle Scholar
  19. 19.
    Leckman JF, Feldman R, Swain JE, Eicher V, Thompson N, Mayes LC (2004) Primary parental preoccupation: circuits, genes, and the crucial role of the environment. J Neural Transm 111(7):753–771CrossRefGoogle Scholar
  20. 20.
    Leckman JF, Mayes LC, Feldman R, Evans DW, King RA, Cohen DJ (1999) Early parental preoccupations and behaviors and their possible relationship to the symptoms of obsessive-compulsive disorder. Acta Psychiatr Scand 100:1–26CrossRefGoogle Scholar
  21. 21.
    Levine A, Zagoory-Sharon O, Feldman R, Weller A (2007) Oxytocin during pregnancy and early postpartum: individual patterns and maternal–fetal attachment. Peptides 28(6):1162–1169CrossRefGoogle Scholar
  22. 22.
    Lorberbaum JP, Newman JD, Dubno JR, Horwitz AR, Nahas Z, Teneback CC, Bloomer CW, Bohning DE, Vincent D, Johnson MR, Emmanuel N, Brawman-Mintzer O, Book SW, Lydiard RB, Ballenger JC, George MS (1999) Feasibility of using fMRI to study mothers responding to infant cries. Depress Anxiety 10(3):99–104CrossRefGoogle Scholar
  23. 23.
    Lorberbaum JP, Newman JD, Horwitz AR, Dubno JR, Lydiard RB, Hamner MB, Bohning DE, George MS (2002) A potential role for thalamocingulate circuitry in human maternal behavior. Biol Psychiatry 51(6):431–445CrossRefGoogle Scholar
  24. 24.
    Medford N, Brierley B, Brammer M, Bullmore E, Andrew C, Williams S, David A, Phillips M (2000) Emotional memory — Content and context: An fMRI study. Neuroimage 11(5):S241CrossRefGoogle Scholar
  25. 25.
    Mukamel R, Gelbard H, Arieli A, Hasson U, Fried I, Malach R (2005) Coupling between neuronal firing, field potentials, and fMRI in human auditory cortex. Science 309(5736):951–954CrossRefGoogle Scholar
  26. 26.
    Musser ED, Kaiser-Laurent H, Ablow JC (2012) The neural correlates of maternal sensitivity: An fMRI study. Dev Cogn Neurosci 2(4):428–436CrossRefGoogle Scholar
  27. 27.
    Ng B, Hamarneh G, Abugharbieh R (2012) Modeling brain activation in fMRI using group MRF. IEEE Trans Med Imaging 31(5):1113–1123CrossRefGoogle Scholar
  28. 28.
    Nishitani S, Doi H, Koyama A, Shinohara K (2011) Differential prefrontal response to infant facial emotions in mothers compared with non-mothers. Neurosci Res 70(2):183–188CrossRefGoogle Scholar
  29. 29.
    Noriuchi M, Kikuchi Y, Senoo A (2008) The functional neuroanatomy of maternal love: mother’s response to infant’s attachment behaviors. Biol Psychiatry 63(4):415–423CrossRefGoogle Scholar
  30. 30.
    Phillips ML, Bullmore ET, Howard R, Woodruff PWR, Wright IC, Williams SCR, Simmons A, Andrew C, Brammer M, David AS (1998) Investigation of facial recognition memory and happy and sad facial expression perception: an fMRI study. Psychiatry Res Neuroimaging 83(3):127–138CrossRefGoogle Scholar
  31. 31.
    Rajapakse JC, Piyaratna J (2001) Bayesian approach to segmentation of statistical parametric maps. IEEE Trans Biomed Eng 48(10):1186–1194CrossRefGoogle Scholar
  32. 32.
    Schneider F, Grodd W, Weiss U, Klose U, Mayer KR, Nägele T, Gur RC (1997) Functional MRI reveals left amygdala activation during emotion. Psychiatry Res Neuroimaging 76(2–3):75–82CrossRefGoogle Scholar
  33. 33.
    Schuller B, Rigoll G, Lang M (2003) Hidden Markov model-based speech emotion recognition in Proc. 2003. IEEE Int Conf Acoust, Speech, Signal Proc II:1–4Google Scholar
  34. 34.
    Skudlarski P, Fulbright R, Gore J, Wexler B (2000) Emotions changes the functional connectivity measured by the fMRI time-course correlations. Neuroimage 11(5):246CrossRefGoogle Scholar
  35. 35.
    Swain JE (2004) Neural substrates and psychology of human parent-infant attachment in the postpartum in Proc. 59th Annual Meeting of the Society of Biological Psychiatry, New YorkGoogle Scholar
  36. 36.
    Swain JE (2008) Baby stimuli and the parent brain: functional neuroimaging of the neural substrates of parent-infant attachment. Psychiatry Res Neuroimaging 5(8):28–36Google Scholar
  37. 37.
    Tu JV (1996) Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 49(11):1225–1231CrossRefGoogle Scholar
  38. 38.
    Wang Y, Rajapakse JC (2006) Contextual modeling of functional MR images with conditional random fields. IEEE Trans Med Imaging 25(6):804–812CrossRefGoogle Scholar
  39. 39.
    Yeh D-R, Kung C-C (2012) The neural substrates of maternal love in shopping: mother’s willingness to pay for her child vs. for herself — An fMRI study,” M.S. thesis, department of psychology. National Cheng Kung University, TainanGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Bo-Wei Chen
    • 1
  • Yang-Yen Ou
    • 1
  • Chun-Chia Kung
    • 2
  • Ding-Ruey Yeh
    • 2
  • Seungmin Rho
    • 3
  • Jhing-Fa Wang
    • 1
  1. 1.Department of Electrical EngineeringNational Cheng Kung UniversityTainanTaiwan
  2. 2.Department of PsychologyNational Cheng Kung UniversityTainanTaiwan
  3. 3.Department of MultimediaSungkyul UniversitySungkyulRepublic of Korea

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