Multivariate Analysis for Neuroimaging Data:
This book describes methods for statistical brain imaging data analysis from both the perspective of methodology and from the standpoint of application for software implementation in neuroscience research. These include those both commonly used (traditional established) and state of the art methods....
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton, Fl
Taylor & Francis
2023
CRC Press 2023 |
| Schlagwörter: | |
| Zusammenfassung: | This book describes methods for statistical brain imaging data analysis from both the perspective of methodology and from the standpoint of application for software implementation in neuroscience research. These include those both commonly used (traditional established) and state of the art methods. The former is easier to do due to the availability of appropriate software. To understand the methods it is necessary to have some mathematical knowledge which is explained in the book with the help of figures and descriptions of the theory behind the software. In addition, the book includes numerical examples to guide readers on the working of existing popular software. The use of mathematics is reduced and simplified for non-experts using established methods, which also helps in avoiding mistakes in application and interpretation. Finally, the book enables the reader to understand and conceptualize the overall flow of brain imaging data analysis, particularly for statisticians and data-scientists unfamiliar with this area. The state of the art method described in the book has a multivariate approach developed by the authors’ team. Since brain imaging data, generally, has a highly correlated and complex structure with large amounts of data, categorized into big data, the multivariate approach can be used as dimension reduction by following the application of statistical methods. The R package for most of the methods described is provided in the book. Understanding the background theory is helpful in implementing the software for original and creative applications and for an unbiased interpretation of the output. The book also explains new methods in a conceptual manner. These methodologies and packages are commonly applied in life science data analysis. Advanced methods to obtain novel insights are introduced, thereby encouraging the development of new methods and applications for research into medicine as a neuroscience |
| Umfang: | ix, 213 Seiten Illustrationen, Diagramme |
| ISBN: | 9780367752217 |
Internformat
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| 520 | |a This book describes methods for statistical brain imaging data analysis from both the perspective of methodology and from the standpoint of application for software implementation in neuroscience research. These include those both commonly used (traditional established) and state of the art methods. The former is easier to do due to the availability of appropriate software. To understand the methods it is necessary to have some mathematical knowledge which is explained in the book with the help of figures and descriptions of the theory behind the software. In addition, the book includes numerical examples to guide readers on the working of existing popular software. The use of mathematics is reduced and simplified for non-experts using established methods, which also helps in avoiding mistakes in application and interpretation. Finally, the book enables the reader to understand and conceptualize the overall flow of brain imaging data analysis, particularly for statisticians and data-scientists unfamiliar with this area. The state of the art method described in the book has a multivariate approach developed by the authors’ team. Since brain imaging data, generally, has a highly correlated and complex structure with large amounts of data, categorized into big data, the multivariate approach can be used as dimension reduction by following the application of statistical methods. The R package for most of the methods described is provided in the book. Understanding the background theory is helpful in implementing the software for original and creative applications and for an unbiased interpretation of the output. The book also explains new methods in a conceptual manner. These methodologies and packages are commonly applied in life science data analysis. Advanced methods to obtain novel insights are introduced, thereby encouraging the development of new methods and applications for research into medicine as a neuroscience | ||
| 650 | 4 | |a bicssc / Environmental science, engineering & technology | |
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| 650 | 4 | |a bisacsh / SCIENCE / Life Sciences / General | |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-034559314 | |
Datensatz im Suchindex
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|---|---|
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| author | Kawaguchi, Atsushi |
| author_GND | (DE-588)142063134 |
| author_facet | Kawaguchi, Atsushi |
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| author_sort | Kawaguchi, Atsushi |
| author_variant | a k ak |
| building | Verbundindex |
| bvnumber | BV049298018 |
| classification_rvk | ST 600 WC 7000 |
| ctrlnum | (OCoLC)1401180011 (DE-599)BVBBV049298018 |
| discipline | Biologie Informatik |
| format | Book |
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| id | DE-604.BV049298018 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T20:05:52Z |
| institution | BVB |
| isbn | 9780367752217 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034559314 |
| oclc_num | 1401180011 |
| open_access_boolean | |
| owner | DE-29T |
| owner_facet | DE-29T |
| physical | ix, 213 Seiten Illustrationen, Diagramme |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Taylor & Francis CRC Press |
| record_format | marc |
| spelling | Kawaguchi, Atsushi Verfasser (DE-588)142063134 aut Multivariate Analysis for Neuroimaging Data Atsushi Kawaguchi Boca Raton, Fl Taylor & Francis 2023 CRC Press 2023 ©2021 ix, 213 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier This book describes methods for statistical brain imaging data analysis from both the perspective of methodology and from the standpoint of application for software implementation in neuroscience research. These include those both commonly used (traditional established) and state of the art methods. The former is easier to do due to the availability of appropriate software. To understand the methods it is necessary to have some mathematical knowledge which is explained in the book with the help of figures and descriptions of the theory behind the software. In addition, the book includes numerical examples to guide readers on the working of existing popular software. The use of mathematics is reduced and simplified for non-experts using established methods, which also helps in avoiding mistakes in application and interpretation. Finally, the book enables the reader to understand and conceptualize the overall flow of brain imaging data analysis, particularly for statisticians and data-scientists unfamiliar with this area. The state of the art method described in the book has a multivariate approach developed by the authors’ team. Since brain imaging data, generally, has a highly correlated and complex structure with large amounts of data, categorized into big data, the multivariate approach can be used as dimension reduction by following the application of statistical methods. The R package for most of the methods described is provided in the book. Understanding the background theory is helpful in implementing the software for original and creative applications and for an unbiased interpretation of the output. The book also explains new methods in a conceptual manner. These methodologies and packages are commonly applied in life science data analysis. Advanced methods to obtain novel insights are introduced, thereby encouraging the development of new methods and applications for research into medicine as a neuroscience bicssc / Environmental science, engineering & technology bicssc / Biomedical engineering bicssc / Probability & statistics bicssc / Medical imaging bicssc / Artificial intelligence bisacsh / SCIENCE / Life Sciences / Neuroscience bisacsh / BUSINESS & ECONOMICS / Statistics bisacsh / COMPUTERS / Artificial Intelligence / General bisacsh / MATHEMATICS / Probability & Statistics / Multivariate Analysis bisacsh / MEDICAL / Epidemiology bisacsh / MEDICAL / Neuroscience bisacsh / SCIENCE / Life Sciences / General Erscheint auch als Druck-Ausgabe, Hardcover 978-0-367-25532-9 |
| spellingShingle | Kawaguchi, Atsushi Multivariate Analysis for Neuroimaging Data bicssc / Environmental science, engineering & technology bicssc / Biomedical engineering bicssc / Probability & statistics bicssc / Medical imaging bicssc / Artificial intelligence bisacsh / SCIENCE / Life Sciences / Neuroscience bisacsh / BUSINESS & ECONOMICS / Statistics bisacsh / COMPUTERS / Artificial Intelligence / General bisacsh / MATHEMATICS / Probability & Statistics / Multivariate Analysis bisacsh / MEDICAL / Epidemiology bisacsh / MEDICAL / Neuroscience bisacsh / SCIENCE / Life Sciences / General |
| title | Multivariate Analysis for Neuroimaging Data |
| title_auth | Multivariate Analysis for Neuroimaging Data |
| title_exact_search | Multivariate Analysis for Neuroimaging Data |
| title_full | Multivariate Analysis for Neuroimaging Data Atsushi Kawaguchi |
| title_fullStr | Multivariate Analysis for Neuroimaging Data Atsushi Kawaguchi |
| title_full_unstemmed | Multivariate Analysis for Neuroimaging Data Atsushi Kawaguchi |
| title_short | Multivariate Analysis for Neuroimaging Data |
| title_sort | multivariate analysis for neuroimaging data |
| topic | bicssc / Environmental science, engineering & technology bicssc / Biomedical engineering bicssc / Probability & statistics bicssc / Medical imaging bicssc / Artificial intelligence bisacsh / SCIENCE / Life Sciences / Neuroscience bisacsh / BUSINESS & ECONOMICS / Statistics bisacsh / COMPUTERS / Artificial Intelligence / General bisacsh / MATHEMATICS / Probability & Statistics / Multivariate Analysis bisacsh / MEDICAL / Epidemiology bisacsh / MEDICAL / Neuroscience bisacsh / SCIENCE / Life Sciences / General |
| topic_facet | bicssc / Environmental science, engineering & technology bicssc / Biomedical engineering bicssc / Probability & statistics bicssc / Medical imaging bicssc / Artificial intelligence bisacsh / SCIENCE / Life Sciences / Neuroscience bisacsh / BUSINESS & ECONOMICS / Statistics bisacsh / COMPUTERS / Artificial Intelligence / General bisacsh / MATHEMATICS / Probability & Statistics / Multivariate Analysis bisacsh / MEDICAL / Epidemiology bisacsh / MEDICAL / Neuroscience bisacsh / SCIENCE / Life Sciences / General |
| work_keys_str_mv | AT kawaguchiatsushi multivariateanalysisforneuroimagingdata |