Novel Computational Approaches For Multidimensional Brain Image Analysis
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| Title: | Novel Computational Approaches For Multidimensional Brain Image Analysis |
|---|---|
| Authors: | Raviprakash, Harish |
| Committee Members: | Bagci, Ulas |
| Summary: | The overall goal of this dissertation is focused on addressing challenging problems in 1D, 2D/3D and 4D neuroimaging by developing novel algorithms that combine signal processing and machine learning techniques. One of these challenging tasks is the accurate localization of the eloquent language cortex in brain resection pre-surgery patients. This is especially important since inaccurate localization can lead to diminshed functionalities and thus, a poor quality of life for the patient. The first part of this dissertation addresses this problem in the case of drug-resistant epileptic patients. We propose a novel machine learning based algorithm to establish an alternate electrical stimulation-free approach, electro-corticography (ECoG) as a viable technique for localization of the eloqeunt language cortex. We process the 1D signals in frequency domain to train a classifier and identify language responsive electrodes from the surface of the brain. We then enhance the proposed approach by developing novel multi-modal deep learning algorithms. We test different aspects of the experimental paradigm and identify the best features and models for classification. Another difficult neuroimaging task is that of identifying biomarkers of a disease. This is even more challenging considering that skill acquisition leads to neurological changes. We propose to help understand these changes in the brain of chess masters via a multi-modal approach that combines 3D and 4D imaging modalities in a novel way. The proposed approaches may help narrow the regions to be tested in pre-surgical localization tasks and in better surgery planning. The proposed work may also pave the way for a holistic view of the human brain by combining several modalities into one. Finally, we deal with the problem of learning strong signal representations/features by proposing a novel capsule based variational autoencoder, B-Caps. The proposed B-Caps helps in learning a strong feature representation that can be used with multi-dimensional data. |
| URL: | https://stars.library.ucf.edu/etd2020/618 |
| Database: | OpenDissertations |
| FullText | Text: Availability: 0 |
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| Header | DbId: ddu DbLabel: OpenDissertations An: ddu.oai.stars.library.ucf.edu.etd2020.1617 AccessLevel: 6 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Novel Computational Approaches For Multidimensional Brain Image Analysis – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Raviprakash%2C+Harish%22">Raviprakash, Harish</searchLink> – Name: Author Label: Committee Members Group: Au Data: <searchLink fieldCode="CO" term="%22Bagci%2C+Ulas%22">Bagci, Ulas</searchLink> – Name: Abstract Label: Summary Group: Ab Data: The overall goal of this dissertation is focused on addressing challenging problems in 1D, 2D/3D and 4D neuroimaging by developing novel algorithms that combine signal processing and machine learning techniques. One of these challenging tasks is the accurate localization of the eloquent language cortex in brain resection pre-surgery patients. This is especially important since inaccurate localization can lead to diminshed functionalities and thus, a poor quality of life for the patient. The first part of this dissertation addresses this problem in the case of drug-resistant epileptic patients. We propose a novel machine learning based algorithm to establish an alternate electrical stimulation-free approach, electro-corticography (ECoG) as a viable technique for localization of the eloqeunt language cortex. We process the 1D signals in frequency domain to train a classifier and identify language responsive electrodes from the surface of the brain. We then enhance the proposed approach by developing novel multi-modal deep learning algorithms. We test different aspects of the experimental paradigm and identify the best features and models for classification. Another difficult neuroimaging task is that of identifying biomarkers of a disease. This is even more challenging considering that skill acquisition leads to neurological changes. We propose to help understand these changes in the brain of chess masters via a multi-modal approach that combines 3D and 4D imaging modalities in a novel way. The proposed approaches may help narrow the regions to be tested in pre-surgical localization tasks and in better surgery planning. The proposed work may also pave the way for a holistic view of the human brain by combining several modalities into one. Finally, we deal with the problem of learning strong signal representations/features by proposing a novel capsule based variational autoencoder, B-Caps. The proposed B-Caps helps in learning a strong feature representation that can be used with multi-dimensional data. – Name: URL Label: URL Group: URL Data: <link linkTarget="URL" linkTerm="https://stars.library.ucf.edu/etd2020/618" linkWindow="_blank">https://stars.library.ucf.edu/etd2020/618</link> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ddu&AN=ddu.oai.stars.library.ucf.edu.etd2020.1617 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English Subjects: – SubjectFull: Neuroimaging; Machine learning; Electrocorticography; Biomarkers; Deep learning Type: general – SubjectFull: Biomedical Type: general – SubjectFull: Computer Sciences Type: general – SubjectFull: Electrical and Computer Engineering Type: general – SubjectFull: Brain--Imaging; Neurosciences--Data processing; Brain--Research--Data processing; Neurology--Technological innovations; Brain mapping--Mathematics Type: general Titles: – TitleFull: Novel Computational Approaches For Multidimensional Brain Image Analysis Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Raviprakash, Harish IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2020 |
| ResultId | 1 |
