Abstract
Neuro-Feedback (NF) is a particular form of bio-feedback, which feeds back brain activity to the individual in real-time, to allow for training of controlled regulation of the brain in order to improve performance. Functional magnetic resonance imaging (fMRI) spatial localization allows for a high-quality real-time targeting of sub-cortical brain regions. Yet, until now, real-time-fMRI-NF treatments were limited to training brain activity localized within a region of interest. Conversely, since most mental functions are associated with functional integration of networks, limiting the treatment to one region may be a serious hurdle for an effective treatment. Thus, broader network perspective features can be of great value. In this study we developed a novel network-based rt-fMRI-NF procedure that obtains feedback derived from networks’ influence features as constructed by a graph theory method entitled the dependency network analysis (D\(_\mathrm{{EP}}\)NA), thus training the subject to control an explicit brain region’s influence on the network. In a proof-of-concept pilot study conducted on ten healthy subjects we demonstrated the feasibility of such a network based probe to be volitionally up-regulated. We further propose that this approach will ultimately provide a clinical therapeutic tool for an individually-tailored intervention protocol aimed at improving different mental processes and cognitive abilities.
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Jacob, Y., Or-Borichev, A., Jackont, G., Lubianiker, N., Hendler, T. (2018). Network Based fMRI Neuro-Feedback for Emotion Regulation; Proof-of-Concept. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_101
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DOI: https://doi.org/10.1007/978-3-319-72150-7_101
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