The Development and Validation of a Neural Model of Affective States

dc.contributor.authorMcCurry, Katherine Lorraineen
dc.contributor.committeechairCasas, Brooksen
dc.contributor.committeememberWhite, Susan W.en
dc.contributor.committeememberLaConte, Stephen M.en
dc.contributor.committeememberChiu, Pearl H.en
dc.contributor.departmentPsychologyen
dc.date.accessioned2017-06-13T19:44:26Zen
dc.date.adate2016-01-10en
dc.date.available2017-06-13T19:44:26Zen
dc.date.issued2015-09-23en
dc.date.rdate2016-01-10en
dc.date.sdate2015-09-25en
dc.description.abstractEmotion 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.en
dc.description.degreeMaster of Scienceen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-09252015-165109en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-09252015-165109/en
dc.identifier.urihttp://hdl.handle.net/10919/78166en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectMachine learningen
dc.subjectneurofeedbacken
dc.subjectfMRIen
dc.subjectemotionen
dc.subjectsupport vector machineen
dc.titleThe Development and Validation of a Neural Model of Affective Statesen
dc.typeThesisen
dc.type.dcmitypeTexten
thesis.degree.disciplinePsychologyen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen
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