پیش‌بینی بیماری آلزایمر با استفاده از الگوریتم‌های انتخاب ویژگی محاسبات نرم و بر پایه rs-fMRI و sMRI

نوع مقاله : علمی-پژوهشی

نویسندگان

1 دانشکده مهندسی برق و کامپیوتر - دانشگاه صنعتی نوشیروانی بابل

2 دانشکده مهندسی برق و کامپیوتر - دانشگاه بجنورد

3 دانشکده آناتومی و نوروبیولوژی- دانشگاه مرکز علوم بهداشت تنسی– ممفیس - آمریکا

چکیده

بیماری آلزایمر (AD)، یک بیماری پیشرفته و غیرقابل‌برگشت است که اغلب در افراد مسن رخ می‌دهد و به‌تدریج مناطق مغز را که مسئول حافظه، تفکر، یادگیری و رفتار هستند، از بین می‌برد. در این مقاله پیش‌بینی AD  بر اساس تصاویر rs-fMRI و sMRI بررسی می‌شود. در این مطالعه سه الگوریتم انتخاب ویژگی بر اساس روش محاسبات نرم ارائه شده، که طبقه‌بندی MCI-C از MCI-NC با آموزش و آزمایش الگوریتم SVM انجام می‌شود. این اولین مطالعه‌ای است که از ادغام rs-fMRI و sMRI برای پیش‌بینی AD استفاده کرده است. نتایج حاصل از این مطالعه می‌تواند به مناطق شناخته شده مغز )عملکردی و ساختاری( که در بیماری آلزایمر دچار اختلال شده‌اند، منجر شود. علاوه بر این، روش NBS بر روی تقسیم‌بندی‌های عملکردی مغز، برای جداسازی MCI-C از MCI-NC و تشخیص زیر شبکه‌هایی که دارای قابلیت تشخیصی برای پیش‌بینی AD هستند، به کار گرفته شده است.

کلیدواژه‌ها

عنوان مقاله [English]

Predicting Alzheimer's Disease using Soft Computing Feature selection algorithms and Based on rs-fMRI and sMRI

نویسندگان [English]

  • S. H. Hojjati 1
  • A. Ebrahimzadeh 1
  • A. Khazaee 2
  • A. Babajani-Feremi 3
1 Department of Electrical Engineering, Babol University of Technology, Babol, Iran
2 Department of Electrical Engineering, University of Bojnord, Bojnord, Iran
3 Department of Pediatrics, Division of Clinical Neurosciences, University of Tennessee Health Science Center, Memphis, TN, USA
چکیده [English]

Alzheimer’s disease (AD), a progressive, irreversible neurodegenerative disorder, occurs most frequently in older adults and gradually destroys regions of the brain that are responsible for memory, thinking, learning, and behavior. In this paper, AD prediction is investigated based on rs-fMRI and sMRI analysis. Three feature selection algorithms based on soft computing method has been proposed to classify MCI-C from MCI-NC through training SVM. This is the first study used to integrate rs-fMRI and sMRI for AD prediction. The results refer to the significant brain areas (functional and structural) impaired in AD. Furthermore, NBS method on brain functional parcellations has been utilized for separating MCI-C from MCI-NC and detecting the discriminative ability networks for AD prediction. 

کلیدواژه‌ها [English]

  • Alzheimer’s disease
  • predicting
  • graph theory
  • statistical information
  • sMRI
  • network based analysis

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