دورية أكاديمية
أعرض في EDS

Classification of early-MCI patients from healthy controls using evolutionary optimization of graph measures of resting-state fMRI, for the Alzheimer's disease neuroimaging initiative.

التفاصيل البيبلوغرافية
العنوان: Classification of early-MCI patients from healthy controls using evolutionary optimization of graph measures of resting-state fMRI, for the Alzheimer's disease neuroimaging initiative.
المؤلفون: Zamani J; School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran., Sadr A; School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran., Javadi AH; School of Psychology, University of Kent, Canterbury, United Kingdom.; School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran.
المصدر: PloS one [PLoS One] 2022 Jun 21; Vol. 17 (6), pp. e0267608. Date of Electronic Publication: 2022 Jun 21 (Print Publication: 2022).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science
مواضيع طبية MeSH: Alzheimer Disease*/diagnostic imaging , Alzheimer Disease*/pathology , Cognitive Dysfunction*/diagnostic imaging , Cognitive Dysfunction*/pathology, Brain/pathology ; Humans ; Magnetic Resonance Imaging/methods ; Neuroimaging/methods
مستخلص: Identifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer's disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n = 72) and EMCI (n = 68) extracted from the publicly available database of the Alzheimer's disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.
Competing Interests: The authors have declared that no competing interests exist.
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معلومات مُعتمدة: U01 AG024904 United States AG NIA NIH HHS
تواريخ الأحداث: Date Created: 20220621 Date Completed: 20220623 Latest Revision: 20231101
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC9212187
DOI: 10.1371/journal.pone.0267608
PMID: 35727837
قاعدة البيانات: MEDLINE
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0267608