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Classifying the valence of autobiographical memories from fMRI data

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Abstract

We show that fMRI analysis using machine learning tools are sufficient to distinguish valence (i.e., positive or negative) of freely retrieved autobiographical memories in a cross-participant setting. Our methodology uses feature selection (ReliefF) in combination with boosting methods, both applied directly to data represented in voxel space. In previous work using the same data set, Nawa and Ando showed that whole-brain based classification could achieve above-chance classification accuracy only when both training and testing data came from the same individual. In a cross-participant setting, classification results were not statistically significant. Additionally, on average the classification accuracy obtained when using ReliefF is substantially higher than previous results - 81% for the within-participant classification, and 62% for the cross-participant classification. Furthermore, since features are defined in voxel space, it is possible to show brain maps indicating the regions of that are most relevant in determining the results of the classification. Interestingly, the voxels that were selected using the proposed computational pipeline seem to be consistent with current neurophysiological theories regarding the brain regions actively involved in autobiographical memory processes.

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Acknowledgements

The CiNet Research Institute hosted LM (twice) and AF (once) to work with NEN on this work. We thank its staff and especially Director Takahisa Taguchi for their support and encouragement of this work. This work was performed under an MOU between CiNet and the University of Haifa. Some of the computations were performed under an equipment grant of NVIDIA corporation to the Neurocomputation Laboratory at the Cesarea Research Institute, University of Haifa. NEN was partially supported by the Japan Society for the Promotion of Science under a Grant-in-Aid for Scientific Research (JPS KAKENHI grant number JP17K00220).

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Authors and Affiliations

  1. Neurocomputation Laboratory, Department of Computer Science, University of Haifa, Haifa, Israel

    Alex Frid & Larry M. Manevitz

  2. The Laboratory of Clinical Neurophysiology, Technion Faculty of Medicine, Haifa, Israel

    Alex Frid

  3. Department of Computer Science, Ariel University, Ariel, Israel

    Larry M. Manevitz

  4. Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Tokyo, Japan

    Norberto Eiji Nawa

  5. Graduate School of Frontiers Biosciences, Osaka University, Suita, Japan

    Norberto Eiji Nawa

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  1. Alex Frid
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  2. Larry M. Manevitz
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  3. Norberto Eiji Nawa
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Correspondence to Alex Frid.

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Frid, A., Manevitz, L.M. & Nawa, N.E. Classifying the valence of autobiographical memories from fMRI data. Ann Math Artif Intell 88, 1261–1274 (2020). https://doi.org/10.1007/s10472-020-09705-3

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