نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی برق، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی نوشیروانی بابل، مازندران، ایران

2 استاد، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی نوشیروانی بابل، مازندران، ایران

3 استادیار، دانشکده مهندسی برق و کامپیوتر - دانشگاه بجنورد– بجنورد، ایران

4 استادیار، دانشکده آناتومی و نوروبیولوژی- دانشگاه مرکز علوم بهداشت تنسی– ممفیس - امریکا

10.22041/ijbme.2017.73172.1280

چکیده

پیش­بینی بیماری آلزایمر بر‌اساس تجزیه و تحلیل شبکة مغز، موضوع بسیاری از مطالعات شده است. هدف ما شناسایی تغییرات در مغز بیمارانی است که از اختلال خفیف شناختی، دچار آلزایمر شده­اند یا دچار آلزایمر نشده­اند، برای ارائة الگوریتمی برای طبقه­بندی این بیماران با استفاده از روش تئوری گراف و اطلاعات آماری. در این الگوریتم، تجزیه و تحلیل همبستگی متمایز را پیشنهاد کردیم و روش ادغام در سطح ویژگی برای تشخیص بیومتریک اعمال شد. با توجه به نتایج شبیه‌سازی، دقت 167/87 درصد برای پیش‌بینی بیماری آلزایمر با استفاده از تجزیه و تحلیل همبستگی متمایز و طبقه‌بندی‌کنندة ماشین بردار پشتیبان به‌دست آمد. همچنین تجزیه و تحلیل روی گره­های مهم مغز (هاب­ها) را انجام دادیم و تعدادی از نقاط مهم مغز در بیماران آلزایمری پیشرونده را پیدا کردیم. در حقیقت، این پژوهش، اولین مطالعة­ شناختی با استفاده از ادغام تصویر‌برداری تشدید مغناطیسی حالت استراحت (rs-fMRI) و تصویربرداری تشدید مغناطیسی ساختاری (sMRI) برای تشخیص تبدیل از اختلال شناختی خفیف به بیماری آلزایمر است. روش پیشنهادی، بر پتانسیل استفاده از داده­های تصویر‌برداری rs-fMRI و sMRI، را برای تشخیص پیشرفت بیماری در مراحل اولیه تأکید می­کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Predicting Alzheimer’s Disease using DCA Fusion Algorithm based on rs-fMRI and sMRI

نویسندگان [English]

  • Seyed Hani Hojjati 1
  • Ataollah Ebrahimzadeh 2
  • Ali Khazaee 3
  • Abbas Babajani-Fermi 4

1 Ph.D Student, Electrical Engineering, Department of Electrical Engineering, Babol University of Technology, Babol, Iran

2 Professor, Department of Electrical Engineering, Babol University of Technology, Babol, Iran,

3 Assistant Professor, Department of Electrical Engineering, University of Bojnord, Bojnord, Iran

4 Assistant Professor, Department of Pediatrics, Division of Clinical Neurosciences, University of Tennessee Health Science Center, Memphis, TN, USA

چکیده [English]

Predicting AD based on Brain network analysis has been the subject of much investigation. Here, we aim to identify the changes in brain in patients that conversion from (Mild Cognitive Impariment) MCI to AD (MCI-C) and non conversion from MCI to AD (MCI-NC), to provide an algorithm for classification of these patients by using a graph theoretical approach. In this algorithm we proposed Discriminant Correlation Analysis (DCA), feature level fusion for multimodal biometric recognition method were applied to the original feature sets. An accuracy of 86/167% was achieved for predicting AD using the DCA and the support vector machine classifier. We also performed a hub node analysis and found the number of hubs in progressive AD patients. Indeed, this is the first neuroimaging study that integrates rs-fMRI with sMRI for detecting conversion from MCI to AD. The proposed classification method highlights the potential of using both resting state fMRI and MRI data for identification of the early stage of AD.

کلیدواژه‌ها [English]

  • Predicting Alzheimer’s Disease
  • Graph Theory
  • Statistical Information
  • MRI
  • Hub Node
  • DCA

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