Multivariate Analyses with Two-Step Dimension Reduction for an Association Study Between <sup>11</sup>C-Pittsburgh Compound B and Magnetic Resonance Imaging in Alzheimer’s Disease
The neuropathological diagnosis of Alzheimer’s disease (AD) relies on amyloid beta (Aβ) deposition in brain tissues. To study the relationship between Aβ deposition and brain structure, as determined using <sup>11</sup>C-Pittsburgh compound B (PiB) and magnetic resonance imaging (MRI), r...
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MDPI AG
2025-01-01
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| author | Atsushi Kawaguchi Fumio Yamashita |
| author_facet | Atsushi Kawaguchi Fumio Yamashita |
| author_sort | Atsushi Kawaguchi |
| collection | DOAJ |
| description | The neuropathological diagnosis of Alzheimer’s disease (AD) relies on amyloid beta (Aβ) deposition in brain tissues. To study the relationship between Aβ deposition and brain structure, as determined using <sup>11</sup>C-Pittsburgh compound B (PiB) and magnetic resonance imaging (MRI), respectively, we developed a regression model with PiB and MRI data as the predictor and response variables, respectively, and proposed a regression method for studying the association between them based on a supervised sparse multivariate analysis with dimension reduction based on a composite paired basis function. By applying this method to imaging data of 61 patients with AD (age: 55–85), the first component showed the strongest correlation with the composite score, owing to the supervised feature. The spatial pattern included the hippocampal and parahippocampal regions for MRI. The peak value was observed in the posterior cingulate and precuneus for PiB. The differences in PiB scores among the diagnosis groups 12 months after PiB imaging were significant between the normal and AD groups (<i>p</i> = 0.0284), but not between the normal and mild cognitive impairment (MCI) groups or the MCI and AD groups (<i>p</i> = 0.3508). Our method may facilitate the development of a dementia biomarker from brain imaging data. Scoring imaging data allows for visualization and the application of traditional analysis, facilitating clinical analysis for better interpretation of results. |
| format | Article |
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| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-01-01 |
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| series | Bioengineering |
| spelling | doaj-art-fa5cc1af6cfe4c14b913d677ab28063a2025-01-24T13:23:05ZengMDPI AGBioengineering2306-53542025-01-011214810.3390/bioengineering12010048Multivariate Analyses with Two-Step Dimension Reduction for an Association Study Between <sup>11</sup>C-Pittsburgh Compound B and Magnetic Resonance Imaging in Alzheimer’s DiseaseAtsushi Kawaguchi0Fumio Yamashita1Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga 849-8501, JapanDivision of Ultrahigh Field MRI, Iwate Medical University, 1-1-1 Idaidori, Yahaba 028-3694, JapanThe neuropathological diagnosis of Alzheimer’s disease (AD) relies on amyloid beta (Aβ) deposition in brain tissues. To study the relationship between Aβ deposition and brain structure, as determined using <sup>11</sup>C-Pittsburgh compound B (PiB) and magnetic resonance imaging (MRI), respectively, we developed a regression model with PiB and MRI data as the predictor and response variables, respectively, and proposed a regression method for studying the association between them based on a supervised sparse multivariate analysis with dimension reduction based on a composite paired basis function. By applying this method to imaging data of 61 patients with AD (age: 55–85), the first component showed the strongest correlation with the composite score, owing to the supervised feature. The spatial pattern included the hippocampal and parahippocampal regions for MRI. The peak value was observed in the posterior cingulate and precuneus for PiB. The differences in PiB scores among the diagnosis groups 12 months after PiB imaging were significant between the normal and AD groups (<i>p</i> = 0.0284), but not between the normal and mild cognitive impairment (MCI) groups or the MCI and AD groups (<i>p</i> = 0.3508). Our method may facilitate the development of a dementia biomarker from brain imaging data. Scoring imaging data allows for visualization and the application of traditional analysis, facilitating clinical analysis for better interpretation of results.https://www.mdpi.com/2306-5354/12/1/48Alzheimer’s diseasePiB imagingmagnetic resonance imagingmatrix decompositionmultimodal |
| spellingShingle | Atsushi Kawaguchi Fumio Yamashita Multivariate Analyses with Two-Step Dimension Reduction for an Association Study Between <sup>11</sup>C-Pittsburgh Compound B and Magnetic Resonance Imaging in Alzheimer’s Disease Bioengineering Alzheimer’s disease PiB imaging magnetic resonance imaging matrix decomposition multimodal |
| title | Multivariate Analyses with Two-Step Dimension Reduction for an Association Study Between <sup>11</sup>C-Pittsburgh Compound B and Magnetic Resonance Imaging in Alzheimer’s Disease |
| title_full | Multivariate Analyses with Two-Step Dimension Reduction for an Association Study Between <sup>11</sup>C-Pittsburgh Compound B and Magnetic Resonance Imaging in Alzheimer’s Disease |
| title_fullStr | Multivariate Analyses with Two-Step Dimension Reduction for an Association Study Between <sup>11</sup>C-Pittsburgh Compound B and Magnetic Resonance Imaging in Alzheimer’s Disease |
| title_full_unstemmed | Multivariate Analyses with Two-Step Dimension Reduction for an Association Study Between <sup>11</sup>C-Pittsburgh Compound B and Magnetic Resonance Imaging in Alzheimer’s Disease |
| title_short | Multivariate Analyses with Two-Step Dimension Reduction for an Association Study Between <sup>11</sup>C-Pittsburgh Compound B and Magnetic Resonance Imaging in Alzheimer’s Disease |
| title_sort | multivariate analyses with two step dimension reduction for an association study between sup 11 sup c pittsburgh compound b and magnetic resonance imaging in alzheimer s disease |
| topic | Alzheimer’s disease PiB imaging magnetic resonance imaging matrix decomposition multimodal |
| url | https://www.mdpi.com/2306-5354/12/1/48 |
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