Repository logo
  • English
  • 中文
  • Log In
    New user? Click here to register.Have you forgotten your password?
Repository logo
    Communities & Collections
    Research Outputs
    Fundings & Projects
    People
    Organizations
    Statistics
  • English
  • 中文
  • Log In
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. TMU Publications / 北醫出版品
  3. .博碩士學位論文
  4. 110學年度
  5. 利用神經心理測驗結果來分析阿茲海默失智症族群的異質性
 
  • Details
Options

利用神經心理測驗結果來分析阿茲海默失智症族群的異質性

Other Title
Heterogeneity of Alzheimer's disease identified by Neuropsychological Test Profiling
Type
thesis
Date Issued
2022-07-01
Author(s)
NGUYEN TRAN THANH TRUC
Advisor
楊維中;林明錦
Subjects
系所名稱:醫學資訊研究所碩士班
Publisher
醫學資訊研究所碩士班
Description
口試委員:林明錦 LIN, MING-CHIN;楊軒佳 YANG, HSUAN-CHIA;陳龍 CHAN, LUNG;胡朝榮 HU, CHAUR-JONG;楊維中 YANG, WEI-CHUNG
網際網路,開放日期為2022-07-06
Abstract
背景:阿茲海默症(Alzheimer's Disease, AD) 是一種具有高度異質性的神經疾病。釐清其變異性將能促進個人化的治療並改善臨床試驗受試者的招募。本研究目的為將AD群體以集群分析辨識其認知功能的差異再進行次分群。
方法:研究收納雙和醫院經診斷為可能的輕度與中度AD 患者(以簡易智能量表[MMSE]分數介於16至27為標準)。神經心理測驗(neuropsychological tests) 係採認知功能障礙篩檢量表(CASI),再以非負矩陣分解(Non-negative Matrix Factorization) 演算法進行集群分類。經集群分類成次分群後,我們探討患者的臨床與神經生物學之不同處,以及其病程發展。
結果:確認符合收納條件之112 位患者納入進行分析。可知次分群能以認知領域上主要缺陷的記憶問題分成記憶 (62.5%) 和非記憶 (37.5%) 兩群。具體而言,記憶群在短期記憶和定向感方面表現較差、但注意力相對較好,另在專注力、長期記憶、語言能力、抽象和判斷方面的分數與非記憶群相似。此外,研究也分別比較兩群之年齡、性別、受教育之年數、發病時間、大腦顳葉內側萎縮程度以及腦血管相關疾病等,經由MMSE、CASI、及臨床失智症評估量表-總分(CDR-SOB)評估後,發現在初期時記憶群的患者整體的認知狀態和失智嚴重度較差。線性混合效應模型的結果顯示兩個次分群在追蹤3 年內其疾病病程無顯著差異。
結論:本研究結果提供阿茲海默症疾病可能存在的認知異質性之探討,並促使神經系統疾病綜合患者登錄資料庫的實施。
URI
https://handle.ncl.edu.tw/11296/m6n792
https://203.71.86.71/handle/123456789/10652

Copyright Notice

● The digital content on this platform is part of the Taipei Medical University Institutional Repository, featuring various academic works and outputs from the institution. It offers free access to academic research and public education for non-commercial use.

● Please use the content appropriately and within legal boundaries to respect copyright owners' rights. For commercial use, please obtain prior authorization from the copyright owner. Users must not use TMUIR for any illegal purposes.

● By utilising the platform, users are deemed to have fully accepted and understood all the regulations set out in this statement, relevant laws of the Republic of China, all international internet regulations, and usage conventions.

● TMUIR is committed to protecting the interests of copyright owners. If you believe that any material on this website infringes copyright, please contact our staff at libirtmu@gmail.com, and we will remove the work from the repository.

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback