Identification of Alzheimer’s Disease and Mild Cognitive Impairment Using Networks Constructed Based on Multiple Morphological Brain Features
Structural brain markers are important for characterizing the pathology of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Here, we constructed a multifeature-based network (MFN) for each individual using a sparse linear regression performed on six types of morphological features to pr...
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| Published in | Biological psychiatry : cognitive neuroscience and neuroimaging Vol. 3; no. 10; pp. 887 - 897 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.10.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2451-9022 2451-9030 2451-9030 |
| DOI | 10.1016/j.bpsc.2018.06.004 |
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| Abstract | Structural brain markers are important for characterizing the pathology of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Here, we constructed a multifeature-based network (MFN) for each individual using a sparse linear regression performed on six types of morphological features to promote the structure-based autodiagnosis. The categorization performance of the MFN was evaluated in 165 normal control subjects, 221 patients with MCI, and 142 patients with AD. We achieved 96.42% and 96.37% accuracy, respectively, in distinguishing the patients with AD and MCI from the normal control subjects, and reasonable discrimination of the two disease cohorts (70.52%) and prediction of the MCI to AD progression (65.61%). The performance was further improved by combining the properties of the MFN with the morphological features. Our results demonstrate the effectiveness of the MFN in combination with morphological features obtained from single imaging modality, serving as robust biomarkers in the diagnosis of AD and MCI. |
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| AbstractList | Structural brain markers are important for characterizing the pathology of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Here, we constructed a multifeature-based network (MFN) for each individual using a sparse linear regression performed on six types of morphological features to promote the structure-based autodiagnosis. The categorization performance of the MFN was evaluated in 165 normal control subjects, 221 patients with MCI, and 142 patients with AD. We achieved 96.42% and 96.37% accuracy, respectively, in distinguishing the patients with AD and MCI from the normal control subjects, and reasonable discrimination of the two disease cohorts (70.52%) and prediction of the MCI to AD progression (65.61%). The performance was further improved by combining the properties of the MFN with the morphological features. Our results demonstrate the effectiveness of the MFN in combination with morphological features obtained from single imaging modality, serving as robust biomarkers in the diagnosis of AD and MCI.Structural brain markers are important for characterizing the pathology of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Here, we constructed a multifeature-based network (MFN) for each individual using a sparse linear regression performed on six types of morphological features to promote the structure-based autodiagnosis. The categorization performance of the MFN was evaluated in 165 normal control subjects, 221 patients with MCI, and 142 patients with AD. We achieved 96.42% and 96.37% accuracy, respectively, in distinguishing the patients with AD and MCI from the normal control subjects, and reasonable discrimination of the two disease cohorts (70.52%) and prediction of the MCI to AD progression (65.61%). The performance was further improved by combining the properties of the MFN with the morphological features. Our results demonstrate the effectiveness of the MFN in combination with morphological features obtained from single imaging modality, serving as robust biomarkers in the diagnosis of AD and MCI. Structural brain markers are important for characterizing the pathology of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Here, we constructed a multifeature-based network (MFN) for each individual using a sparse linear regression performed on six types of morphological features to promote the structure-based autodiagnosis. The categorization performance of the MFN was evaluated in 165 normal control subjects, 221 patients with MCI, and 142 patients with AD. We achieved 96.42% and 96.37% accuracy, respectively, in distinguishing the patients with AD and MCI from the normal control subjects, and reasonable discrimination of the two disease cohorts (70.52%) and prediction of the MCI to AD progression (65.61%). The performance was further improved by combining the properties of the MFN with the morphological features. Our results demonstrate the effectiveness of the MFN in combination with morphological features obtained from single imaging modality, serving as robust biomarkers in the diagnosis of AD and MCI. |
| Author | Zheng, Weihao Hu, Bin Xie, Yuanwei Fan, Jin Yao, Zhijun |
| Author_xml | – sequence: 1 givenname: Weihao surname: Zheng fullname: Zheng, Weihao organization: School of Information Science and Engineering, Lanzhou University, Lanzhou, China – sequence: 2 givenname: Zhijun surname: Yao fullname: Yao, Zhijun organization: School of Information Science and Engineering, Lanzhou University, Lanzhou, China – sequence: 3 givenname: Yuanwei surname: Xie fullname: Xie, Yuanwei organization: School of Information Science and Engineering, Lanzhou University, Lanzhou, China – sequence: 4 givenname: Jin surname: Fan fullname: Fan, Jin email: jin.fan@qc.cuny.edu organization: Department of Psychology, Queens College, City University of New York, Queens – sequence: 5 givenname: Bin orcidid: 0000-0003-3514-5413 surname: Hu fullname: Hu, Bin email: bh@lzu.edu.cn organization: School of Information Science and Engineering, Lanzhou University, Lanzhou, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30077576$$D View this record in MEDLINE/PubMed |
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| Keywords | MCI AD Alzheimer’s disease MFN Mild cognitive impairment Structural brain markers Sparse linear regression Classification Multifeature-based network |
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| Snippet | Structural brain markers are important for characterizing the pathology of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Here, we constructed a... Structural brain markers are important for characterizing the pathology of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Here, we constructed a... |
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| SubjectTerms | Aged Aged, 80 and over Algorithms Alzheimer Disease - diagnosis Alzheimer Disease - pathology Alzheimer’s disease Biomarkers - analysis Brain - pathology Brain - physiopathology Classification Cognitive Dysfunction - diagnosis Cognitive Dysfunction - pathology Disease Progression Female Humans Image Interpretation, Computer-Assisted - methods Machine Learning Male MCI MFN Mild cognitive impairment Multifeature-based network Nerve Net - pathology Sparse linear regression Structural brain markers |
| Title | Identification of Alzheimer’s Disease and Mild Cognitive Impairment Using Networks Constructed Based on Multiple Morphological Brain Features |
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