Skip to main content
SpringerLink
Log in

RDLPFC area of the brain encodes sentence polarity: a study using fMRI

  • Original Research
  • Published:
Brain Imaging and Behavior Aims and scope Submit manuscript

Abstract

In this study, we use functional magnetic resonance imaging (fMRI) in combination with multivoxel pattern analysis to address the question of how mental activities that correspond to sentence polarity (affirmative or negative sentences) are encoded in the brain. This approach allows us to investigate the role of left/right dorsolateral prefrontal cortex (DLPFC) in predicting the neural activity of fMRI associated with sentence polarities. Subjects in the experiment were asked to judge the matching of the presented picture with the meaning of affirmative and negative sentences. Our results highlight the role of RDLPFC in encoding of the related mental activity to sentence polarities such that the right hemisphere (RDLPFC) can predict sentence polarity with high accuracy as compared to the left hemisphere (LDLPFC), and that the negative sentences are decoded with high performance as compared to affirmative sentences from the RDLPFC across subjects. In addition, this experiment’s results show that negative sentences involve more syntactic structure than affirmative sentences.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Behroozi, M., & Daliri, M. R. (2012). Software tools for the analysis of functional magnetic resonance imaging. Basic and Clinical Neuroscience, 3, 19–20.

    Google Scholar 

  • Behroozi, M., Daliri, M. R., & Boyaci, H. (2011). Statistical analysis methods for the fMRI data. Basic and Clinical Neuroscience, 2, 67–74.

    Google Scholar 

  • Ben-Hur, A., & Weston, J. (2010). A user’s guide to support vector machines. Methods in Molecular Biology, 609, 223–239.

    CAS  PubMed  Google Scholar 

  • Brennan, J., & Pylkkänen, L. (2012). The time-course and spatial distribution of brain activity associated with sentence processing. Neuroimage, 60, 1139–1148.

    Article  PubMed  Google Scholar 

  • Buckner, R. L. (1998). Event-related fMRI and the hemodynamic response. Human Brain Mapping, 6, 373–377.

    Article  CAS  PubMed  Google Scholar 

  • Caplan, D., Alpert, N., & Waters, G. (1998). Effects of syntactic structure and propositional number on patterns of regional cerebral blood flow. Journal of Cognitive Neuroscience, 10, 541–552.

    Article  CAS  PubMed  Google Scholar 

  • Chang, C.-C., Lin, C.-J., (2011). LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2, 27:1–27:27.

    Google Scholar 

  • Chen, Y. W., & Lin, C. J. (2006). Combining SVMs with various feature selection strategies. Feature Extraction, 207, 315–324.

    Article  Google Scholar 

  • Christensen, K. R. (2009). Negative and affirmative sentences increase activation in different areas in the brain. Journal of Neurolinguistics, 22, 1–17.

    Article  Google Scholar 

  • Cox, D. D., & Savoy, R. L. (2003). Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage, 19, 261–270.

    Article  PubMed  Google Scholar 

  • Daliri, M. R. (2011). Predicting the cognitive states of the subjects in functional magnetic resonance imaging signals using the combination of feature selection strategies. Brain Topography, 25, 129–135.

    Article  PubMed  Google Scholar 

  • Daliri, M. R., & Behroozi, M. (2012). FMRI: clinical and research applications. OMICS Journal of Radiology, 01, 4.

    Google Scholar 

  • Démonet, J.-F., & Thierry, G. (2001). Language and brain: what is up? What is coming up? Journal of Clinical and Experimental Neuropsychology, 23, 49–73.

    Article  PubMed  Google Scholar 

  • Devroye, L., Györfi, L., & Lugosi, G. (1996). A probabilistic theory of pattern recognition. New York: Springer.

    Book  Google Scholar 

  • Domingos, P., & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 103–130.

    Article  Google Scholar 

  • Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification (2nd ed.). New York: Wiley-Interscience.

    Google Scholar 

  • Eddy, W. F., Fitzgerald, M., Genovese, C., Lazar, N., Mockus, A., & Welling, J. (1999). The challenge of functional magnetic resonance imaging. Journal of Computational and Graphical Statistics, 8, 545–558.

    Google Scholar 

  • Fiez, J. A., & Petersen, S. E. (1998). Neuroimaging studies of word reading. PNAS, 95, 914–921.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D. H., et al. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14, 11–22.

    Article  PubMed  Google Scholar 

  • Friedland, R. P., & Iadecola, C. (1991). Roy and Sherrington (1890) A centennial reexamination of “On the Regulation of the Blood‐Supply of the Brain”. Neurology, 41, 10–10.

    Article  CAS  PubMed  Google Scholar 

  • Friston, K. J., Rotshtein, P., Geng, J. J., Sterzer, P., & Henson, R. N. (2006). A critique of functional localisers. NeuroImage, 30, 1077–1087.

    Article  CAS  PubMed  Google Scholar 

  • Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293, 2425–2430.

    Article  CAS  PubMed  Google Scholar 

  • Huettel, S. A., Song, A. W., & McCarthy, G. (2008). Functional magnetic resonance imaging, second edition (2nd ed.). Sunderland: Sinauer Associates.

    Google Scholar 

  • Hutchinson, R. A., Niculescu, R. S., Keller, T. A., Rustandi, I., & Mitchell, T. M. (2009). Modeling fMRI data generated by overlapping cognitive processes with unknown onsets using hidden process models. NeuroImage, 46, 87–104.

    Article  PubMed  Google Scholar 

  • Jezzard, P., Matthews, P. M., & Smith, S. M. (Eds.). (2002). Functional MRI: An introduction to methods (1st ed.). USA: Oxford University Press.

    Google Scholar 

  • Just, M. A., & Carpenter, P. A. (1996). Brain activation modulated by sentence comprehension. Science (New York, N.Y.), 274, 114–6.

    Article  CAS  Google Scholar 

  • Kamitani, Y., & Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature Neuroscience, 8, 679–685.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Keller, T. A., Carpenter, P. A., & Just, M. A. (2001). The neural bases of sentence comprehension: a fMRI examination of syntactic and lexical processing. Cerebral Cortex, 11, 223–237.

    Article  CAS  PubMed  Google Scholar 

  • Kuncheva, L. I., & Rodríguez, J. J. (2010). Classifier ensembles for fMRI data analysis: an experiment. Magnetic Resonance Imaging, 28, 583–593.

    Article  PubMed  Google Scholar 

  • Lancaster, J. L., Woldorff, M. G., Parsons, L. M., Liotti, M., Freitas, C. S., Rainey, L., et al. (2000). Automated Talairach atlas labels for functional brain mapping. Human Brain Mapping, 10, 120–131.

    Article  CAS  PubMed  Google Scholar 

  • Lemm, S., Blankertz, B., Dickhaus, T., & Müller, K. R. (2011). Introduction to machine learning for brain imaging. NeuroImage, 56, 387–399.

    Article  PubMed  Google Scholar 

  • Manenti, R., Cappa, S. F., Rossini, P. M., & Miniussi, C. (2008). The role of the prefrontal cortex in sentence comprehension: an rTMS study. Cortex, 44, 337–344.

    Article  PubMed  Google Scholar 

  • Martín-Loeches, M., Fernández, A., Schacht, A., Sommer, W., Casado, P., Jiménez-Ortega, L., et al. (2012). The influence of emotional words on sentence processing: electrophysiological and behavioral evidence. Neuropsychologia, 50(14), 3262–72.

    Article  PubMed  Google Scholar 

  • Matthews, P. M., & Jezzard, P. (2004). Functional magnetic resonance imaging. Journal of Neurology, Neurosurgery, and Psychiatry, 75, 6–12.

    PubMed Central  CAS  PubMed  Google Scholar 

  • Mitchell, T. M., Hutchinson, R., Niculescu, R. S., Pereira, F., Wang, X., Just, M., et al. (2004). Learning to decode cognitive states from brain images. Machine Learning, 57, 145–175.

    Article  Google Scholar 

  • Mitchell, T. M., Shinkareva, S. V., Carlson, A., Chang, K. M., Malave, V. L., Mason, R. A., et al. (2008). Predicting human brain activity associated with the meanings of nouns. Science, 320, 1191–1195.

    Article  CAS  PubMed  Google Scholar 

  • Mourão-Miranda, J., Bokde, A. L. W., Born, C., Hampel, H., & Stetter, M. (2005). Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. NeuroImage, 28, 980–995.

    Article  PubMed  Google Scholar 

  • Ng, A. Y., Jordan, M. I., (2002). On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes.

  • Poldrack, R. A. (2007). Region of interest analysis for fMRI. Social Cognitive and Affective Neuroscience, 2, 67–70.

    Article  PubMed Central  PubMed  Google Scholar 

  • Price, C. J. (2012). A review and synthesis of the first 20 years of PET and fMRI studies of heard speech, spoken language and reading. NeuroImage, 62, 816–847.

    Article  PubMed Central  PubMed  Google Scholar 

  • Rumsey, J. M., Horwitz, B., Donohue, B. C., Nace, K., Maisog, J. M., & Andreason, P. (1997). Phonological and orthographic components of word recognition. A PET-rCBF study. Brain, 120, 739–759.

    Article  PubMed  Google Scholar 

  • Sato, Y., Mori, K., Koizumi, T., Minagawa-Kawai, Y., Tanaka, A., Ozawa, E., et al. (2011). Functional lateralization of speech processing in adults and children who stutter. Frontiers in Psychology, 2, 70.

    Article  PubMed Central  PubMed  Google Scholar 

  • Saxe, R., Brett, M., & Kanwisher, N. (2006). Divide and conquer: a defense of functional localizers. NeuroImage, 30, 1088–1096.

    Article  PubMed  Google Scholar 

  • Stehman, S. V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62, 77–89.

    Article  Google Scholar 

  • Theodoridis, S., & Koutroumbas, K. (2003). Pattern recognition, second edition (2nd ed.). Boston: Academic.

    Google Scholar 

  • Tsoumakas, K. T. G., Kalliris, G., Vlahavas, I., (2008). Multi-label classification of music into emotions, In: ISMIR 2008: Proceedings of the 9th International Conference of Music Information Retrieval. p. 325.

  • Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15, 273–289.

    Article  CAS  PubMed  Google Scholar 

  • Vartanian, O., & Goel, V. (2005). Task constraints modulate activation in right ventral lateral prefrontal cortex. NeuroImage, 27, 927–933.

    Article  PubMed  Google Scholar 

  • Wagner, A. D., Schacter, D. L., Rotte, M., Koutstaal, W., Maril, A., & Dale, A. M. (1998). Building memories: remembering and forgetting of verbal experiences as predicted by brain activity. Science, 281, 1188–1191.

    Article  CAS  PubMed  Google Scholar 

  • Wandell, B. A. (2011). The neurobiological basis of seeing words. Annals of the New York Academy of Sciences, 1224, 63–80.

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgement

The authors would like to thank Prof. Tom Mitchell for providing the data for analysis. This work was funded by the Institute for Research in Fundamental Sciences (IPM), School of Cognitive Sciences.

Informed consent statement

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients for being included in the study.

Conflict of interest

M. Behroozi and M.R. Daliri declare that they have no conflicts of interest.

Author information

Authors and Affiliations

  1. Biomedical Engineering Department, Faculty of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, 16846-13114, Tehran, Iran

    Mehdi Behroozi & Mohammad Reza Daliri

  2. School of Cognitive Sciences (SCS), Institute for Research in Fundamental Science (IPM), Niavaran, P.O. Box 19395-5746, Tehran, Iran

    Mehdi Behroozi & Mohammad Reza Daliri

Authors
  1. Mehdi Behroozi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Reza Daliri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Reza Daliri.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Behroozi, M., Daliri, M.R. RDLPFC area of the brain encodes sentence polarity: a study using fMRI. Brain Imaging and Behavior 9, 178–189 (2015). https://doi.org/10.1007/s11682-014-9294-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11682-014-9294-z

Keywords

Search

Navigation