Information

Abstract

Neurofeedback (NF) based on functional magnetic resonance imaging (fMRI) offers promising possibilities for therapeutic approaches in neurological and psychiatric diseases. By providing information over the current activity in a target brain region, conscious control can be learned allowing for counteracting disease-specific symptoms. Social feedback in the form of a face with changing expressions is often chosen as a very intuitive type of feedback. Since the brain regions affected in psychiatric conditions are often involved in the perception and processing of emotions, it is possible that these regions are additionally activated with emotional feedback. In this thesis it is examined whether such an additional activity has a significant influence on the measured activity, as this could lead to inaccurate feedback and, as a result, to suboptimal learning outcomes. For this purpose, the data of a previously published study is reanalysed while particularly taking the potential influence of the feedback signal into account. Using different model approaches, the exact nature of the influence is investigated, as well as whether positive and negative feedback differ in their influence. Given the highly individual aspects of NF and the goal to implement corrections for the training of a single subject in an openly available NF software, the analyses were conducted on an individual but also the group level allowing for tests of generalizability. At the single run level, a significant influence of both the feedback and its change over time was found. Positive feedback more often had a significant impact on the neuronal activation than negative feedback. With regard to the change over time, significant results could more often be found with negative feedback. At the group level, only the change in feedback showed a significant influence on the activation of the target region. In a cross-validation, it was not possible to determine generalizability beyond a single run for any of the models under investigation. The examined effect seems to be very individual both for subjects and measurements and should therefore be treated separately from case to case. In NF studies in which emotional feedback is used while training a brain region involved in emotion processing, accounting for the influence of the feedback signal could improve the accuracy of the presented feedback and, hence, learning performance and therapeutic success.

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BibTeX

@mastersthesis{Caic2021,
  title =      "Modelling the Effect of emotional Feedback as Stimulus in
               fMRI Neurofeedback",
  author =     "Victoria Caic",
  year =       "2021",
  abstract =   "Neurofeedback (NF) based on functional magnetic resonance
               imaging (fMRI) offers promising possibilities for
               therapeutic approaches in neurological and psychiatric
               diseases. By providing information over the current activity
               in a target brain region, conscious control can be learned
               allowing for counteracting disease-specific symptoms. Social
               feedback in the form of a face with changing expressions is
               often chosen as a very intuitive type of feedback. Since the
               brain regions affected in psychiatric conditions are often
               involved in the perception and processing of emotions, it is
               possible that these regions are additionally activated with
               emotional feedback. In this thesis it is examined whether
               such an additional activity has a significant influence on
               the measured activity, as this could lead to inaccurate
               feedback and, as a result, to suboptimal learning outcomes.
               For this purpose, the data of a previously published study
               is reanalysed while particularly taking the potential
               influence of the feedback signal into account. Using
               different model approaches, the exact nature of the
               influence is investigated, as well as whether positive and
               negative feedback differ in their influence. Given the
               highly individual aspects of NF and the goal to implement
               corrections for the training of a single subject in an
               openly available NF software, the analyses were conducted on
               an individual but also the group level allowing for tests of
               generalizability. At the single run level, a significant
               influence of both the feedback and its change over time was
               found. Positive feedback more often had a significant impact
               on the neuronal activation than negative feedback. With
               regard to the change over time, significant results could
               more often be found with negative feedback. At the group
               level, only the change in feedback showed a significant
               influence on the activation of the target region. In a
               cross-validation, it was not possible to determine
               generalizability beyond a single run for any of the models
               under investigation. The examined effect seems to be very
               individual both for subjects and measurements and should
               therefore be treated separately from case to case. In NF
               studies in which emotional feedback is used while training a
               brain region involved in emotion processing, accounting for
               the influence of the feedback signal could improve the
               accuracy of the presented feedback and, hence, learning
               performance and therapeutic success. ",
  month =      oct,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2021/Caic2021/",
}