{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/89093"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/89093","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Classification of tinnitus versus non-tinnitus hearing impaired subject fMRI data","abstract":"Diagnosis of tinnitus, the perception of sound minus external stimulus, depends on self-report and there is currently no objective way to determine if a person has the disorder. Previous examinations of resting-state brain scan data combined with a priori knowledge in the form of suspect regions found from previous research to show differing activity for tinnitus versus non-tinnitus regions of interest have consistently found spatial and temporal differences in electrical activation patterns between tinnitus subjects and controls, implying that development of an objective diagnostic method is possible. However, a whole-head, data-driven, non-a priori method appropriate for diagnostic use has yet to be developed. In the past, clustering algorithms have been used on resting-state data for diagnosis of other conditions, by dividing data into as many groups as desired, and determining each datum's or feature's membership in a cluster group by how similar the feature is to all other features in the cluster, then iterating through subsequent reapplication of this process to the data, until cluster membership converges towards a static set of groups; in the case of this study, the data that has been clustered is resting state functional magnetic resonance imaging (fMRI) data, which consists of brain voxel time-courses, essentially three-dimensional pixel information corresponding to relative levels of electrical activity within the data point over time. Then a linear or non-linear classifier, an algorithm that trains itself by building a probability profile for how likely each feature will be to belong to a cluster group, given a target or control class (in the case of this study, tinnitus or non-tinnitus hearing impaired), is built based on feature cluster label data from each of a subset of subjects from both classes. This classifier is then tested on the remaining subject fMRI data to determine the efficacy of the clustering + classifier system being tested. After a variety of clustering methods and classifiers as well as differing methods for applying these strategies had been tested, the best results indicate that a k-means clustering algorithm paired with a Gaussian maximum-likelihood classifier yields an average classification success rate of close to 60 percent. While this result is not yet good enough to be applied with expectation of a reliable result, it is significantly better than chance, demonstrating that whole-head, (mostly) unsupervised classification is possible, and indicating a direction for further research into the development of a useful tinnitus diagnostic tool.","abstract_html":"Diagnosis of tinnitus, the perception of sound minus external stimulus, depends on self-report and there is currently no objective way to determine if a person has the disorder. Previous examinations of resting-state brain scan data combined with a priori knowledge in the form of suspect regions found from previous research to show differing activity for tinnitus versus non-tinnitus regions of interest have consistently found spatial and temporal differences in electrical activation patterns between tinnitus subjects and controls, implying that development of an objective diagnostic method is possible. However, a whole-head, data-driven, non-a priori method appropriate for diagnostic use has yet to be developed. In the past, clustering algorithms have been used on resting-state data for diagnosis of other conditions, by dividing data into as many groups as desired, and determining each datum's or feature's membership in a cluster group by how similar the feature is to all other features in the cluster, then iterating through subsequent reapplication of this process to the data, until cluster membership converges towards a static set of groups; in the case of this study, the data that has been clustered is resting state functional magnetic resonance imaging (fMRI) data, which consists of brain voxel time-courses, essentially three-dimensional pixel information corresponding to relative levels of electrical activity within the data point over time. Then a linear or non-linear classifier, an algorithm that trains itself by building a probability profile for how likely each feature will be to belong to a cluster group, given a target or control class (in the case of this study, tinnitus or non-tinnitus hearing impaired), is built based on feature cluster label data from each of a subset of subjects from both classes. This classifier is then tested on the remaining subject fMRI data to determine the efficacy of the clustering + classifier system being tested. After a variety of clustering methods and classifiers as well as differing methods for applying these strategies had been tested, the best results indicate that a k-means clustering algorithm paired with a Gaussian maximum-likelihood classifier yields an average classification success rate of close to 60 percent. While this result is not yet good enough to be applied with expectation of a reliable result, it is significantly better than chance, demonstrating that whole-head, (mostly) unsupervised classification is possible, and indicating a direction for further research into the development of a useful tinnitus diagnostic tool.","abstract_has_math":false,"creators":["Nakashima, Corinne Cameron"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engineering","degree_department":null,"school":null,"contributors":["Baryshnikov, Yuliy"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-03-02T19:45:15Z","date_published":"2016-03-02T19:45:15Z","updated_at":"2026-07-22T22:26:32Z","subjects":["clustering","k-means","tinnitus","classifiers","functional magnetic resonance imaging (fMRI)"],"languages":["en"],"rights":["Copyright 2015 Corinne Nakashima"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/89093","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Baryshnikov, Yuliy"]},{"key":"dc:creator","label":"Author","values":["Nakashima, Corinne Cameron"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-03-02T19:45:15Z","2015-12-10","2015-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["clustering","k-means","tinnitus","classifiers","functional magnetic resonance imaging (fMRI)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Corinne Nakashima"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/89093"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Diagnosis of tinnitus, the perception of sound minus external stimulus, depends on self-report and there is currently no objective way to determine if a person has the disorder. Previous examinations of resting-state brain scan data combined with a priori knowledge in the form of suspect regions found from previous research to show differing activity for tinnitus versus non-tinnitus regions of interest have consistently found spatial and temporal differences in electrical activation patterns between tinnitus subjects and controls, implying that development of an objective diagnostic method is possible. However, a whole-head, data-driven, non-a priori method appropriate for diagnostic use has yet to be developed. In the past, clustering algorithms have been used on resting-state data for diagnosis of other conditions, by dividing data into as many groups as desired, and determining each datum's or feature's membership in a cluster group by how similar the feature is to all other features in the cluster, then iterating through subsequent reapplication of this process to the data, until cluster membership converges towards a static set of groups; in the case of this study, the data that has been clustered is resting state functional magnetic resonance imaging (fMRI) data, which consists of brain voxel time-courses, essentially three-dimensional pixel information corresponding to relative levels of electrical activity within the data point over time. Then a linear or non-linear classifier, an algorithm that trains itself by building a probability profile for how likely each feature will be to belong to a cluster group, given a target or control class (in the case of this study, tinnitus or non-tinnitus hearing impaired), is built based on feature cluster label data from each of a subset of subjects from both classes. This classifier is then tested on the remaining subject fMRI data to determine the efficacy of the clustering + classifier system being tested. After a variety of clustering methods and classifiers as well as differing methods for applying these strategies had been tested, the best results indicate that a k-means clustering algorithm paired with a Gaussian maximum-likelihood classifier yields an average classification success rate of close to 60 percent. While this result is not yet good enough to be applied with expectation of a reliable result, it is significantly better than chance, demonstrating that whole-head, (mostly) unsupervised classification is possible, and indicating a direction for further research into the development of a useful tinnitus diagnostic tool.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-03-02 without embargo terms","The student, Corinne Nakashima, accepted the attached license on 2015-12-10 at 10:54.","The student, Corinne Nakashima, submitted this Thesis for approval on 2015-12-10 at 11:08.","This Thesis was approved for publication on 2015-12-10 at 15:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9000 on 2016-03-02 at 12:53:11","Made available in DSpace on 2016-03-02T19:45:15Z (GMT). No. of bitstreams: 2 NAKASHIMA-THESIS-2015.pdf: 2412807 bytes, checksum: e7e91fe3cbd06974c8dacf2bae450b4c (MD5) LICENSE.txt: 4214 bytes, checksum: f0302a1116b061e7d8ac7665ddbce963 (MD5) Previous issue date: 2015-12-10"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Classification of tinnitus versus non-tinnitus hearing impaired subject fMRI data"]}]}],"canonical_facts":{"dc:contributor":["Baryshnikov, Yuliy"],"dc:creator":["Nakashima, Corinne Cameron"],"dc:date":["2016-03-02T19:45:15Z","2015-12-10","2015-12"],"dc:description":["Diagnosis of tinnitus, the perception of sound minus external stimulus, depends on self-report and there is currently no objective way to determine if a person has the disorder. Previous examinations of resting-state brain scan data combined with a priori knowledge in the form of suspect regions found from previous research to show differing activity for tinnitus versus non-tinnitus regions of interest have consistently found spatial and temporal differences in electrical activation patterns between tinnitus subjects and controls, implying that development of an objective diagnostic method is possible. However, a whole-head, data-driven, non-a priori method appropriate for diagnostic use has yet to be developed. In the past, clustering algorithms have been used on resting-state data for diagnosis of other conditions, by dividing data into as many groups as desired, and determining each datum's or feature's membership in a cluster group by how similar the feature is to all other features in the cluster, then iterating through subsequent reapplication of this process to the data, until cluster membership converges towards a static set of groups; in the case of this study, the data that has been clustered is resting state functional magnetic resonance imaging (fMRI) data, which consists of brain voxel time-courses, essentially three-dimensional pixel information corresponding to relative levels of electrical activity within the data point over time. Then a linear or non-linear classifier, an algorithm that trains itself by building a probability profile for how likely each feature will be to belong to a cluster group, given a target or control class (in the case of this study, tinnitus or non-tinnitus hearing impaired), is built based on feature cluster label data from each of a subset of subjects from both classes. This classifier is then tested on the remaining subject fMRI data to determine the efficacy of the clustering + classifier system being tested. After a variety of clustering methods and classifiers as well as differing methods for applying these strategies had been tested, the best results indicate that a k-means clustering algorithm paired with a Gaussian maximum-likelihood classifier yields an average classification success rate of close to 60 percent. While this result is not yet good enough to be applied with expectation of a reliable result, it is significantly better than chance, demonstrating that whole-head, (mostly) unsupervised classification is possible, and indicating a direction for further research into the development of a useful tinnitus diagnostic tool.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-03-02 without embargo terms","The student, Corinne Nakashima, accepted the attached license on 2015-12-10 at 10:54.","The student, Corinne Nakashima, submitted this Thesis for approval on 2015-12-10 at 11:08.","This Thesis was approved for publication on 2015-12-10 at 15:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9000 on 2016-03-02 at 12:53:11","Made available in DSpace on 2016-03-02T19:45:15Z (GMT). No. of bitstreams: 2 NAKASHIMA-THESIS-2015.pdf: 2412807 bytes, checksum: e7e91fe3cbd06974c8dacf2bae450b4c (MD5) LICENSE.txt: 4214 bytes, checksum: f0302a1116b061e7d8ac7665ddbce963 (MD5) Previous issue date: 2015-12-10"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/89093"],"dc:language":["en"],"dc:rights":["Copyright 2015 Corinne Nakashima"],"dc:subject":["clustering","k-means","tinnitus","classifiers","functional magnetic resonance imaging (fMRI)"],"dc:title":["Classification of tinnitus versus non-tinnitus hearing impaired subject fMRI data"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:32Z"}