Title page for ETD etd-09252015-165109


Type of Document Master's Thesis
Author McCurry, Katherine Lorraine
Author's Email Address kmccurry@vt.edu
URN etd-09252015-165109
Title The Development and Validation of a Neural Model of Affective States
Degree Master of Science
Department Psychology
Advisory Committee
Advisor Name Title
Brooks King-Casas Committee Chair
Pearl H. Chiu Committee Member
Stephen M. LaConte Committee Member
Susan W. White Committee Member
Keywords
  • machine learning
  • neurofeedback
  • fMRI
  • emotion
  • support vector machine
Date of Defense 2015-09-23
Availability restricted
Abstract
Emotion dysregulation plays a central role in psychopathology (B. Bradley et al., 2011) and has been linked to aberrant activation of neural circuitry involved in emotion regulation (Beauregard, Paquette, & Lévesque, 2006; Etkin & Schatzberg, 2011). In recent years, technological advances in neuroimaging methods coupled with developments in machine learning have allowed for the non-invasive measurement and prediction of brain states in real-time, which can be used to provide feedback to facilitate regulation of brain states (LaConte, 2011). Real-time functional magnetic resonance imaging (rt-fMRI)-guided neurofeedback, has promise as a novel therapeutic method in which individuals are provided with tailored feedback to improve regulation of emotional responses (Stoeckel et al., 2014). However, effective use of this technology for such purposes likely entails the development of (a) a normative model of emotion processing to provide feedback for individuals with emotion processing difficulties; and (b) best practices concerning how these types of group models are designed and translated for use in a rt-fMRI environment (Ruiz, Buyukturkoglu, Rana, Birbaumer, & Sitaram, 2014).

To this end, the present study utilized fMRI data from a standard emotion elicitation paradigm to examine the impact of several design decisions made during the development of a whole-brain model of affective processing. Using support vector machine (SVM) learning, we developed a group model that reliably classified brain states associated with passive viewing of positive, negative, and neutral images. After validating the group whole-brain model, we adapted this model for use in an rt-fMRI experiment, and using a second imaging dataset along with our group model, we simulated rt-fMRI predictions and tested options for providing feedback.

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