- 期刊
Source-Based Morphometry Analysis of Group Differences in Schizophrenia
空間信號源形態學分析精神分裂症之組間差異
摘要
以多變量空間信號源為基礎的形態學（source-based morphometry, SBM）是利用獨立成分分析方法（independent component analysis, ICA）解碼大腦灰質結構的空間分佈和負荷係數。負荷係數（loading coefficients）代表在這個兩個群體個體大腦灰質結構空間分佈的體積大小或濃度比例。因此我們假設用SBM分析年齡和性別相近的精神分裂症患者（n =52）和健康對照組（n =31）的資料上，也預期大腦結構在額葉，顳葉與小腦之間會有組間差異，與最近國際研究用voxel-based morphometry, VBM的結果類似。四個在20個獨立成分中的負荷係數有顯著的組間差異，且從研究結果顯示在20個獨立成分中有四個與以往的用體素為基礎的型態學研究分析精神分裂症的結果是一致的。所以用SBM分析可發現空間分佈不相交的大腦區域和多個空間結構模式，這與以往的以體素為基礎的單變量形態學技術的顯著更為有意義。總之，這些結果闡明多變量形態分析方法在研究精神分裂症的重要性。
並列摘要
A multivariate source-based morphometry (SBM) method utilizes independent component analysis (ICA) and decomposes structural grey matter segmentation images into spatial maps and loading coefficients. The loading coefficients represent the relative concentration of each component contributes to a given subject's grey matter structure. We hypothesized that SBM analysis on a dataset of age- and gender-matched patients with schizophrenia (n = 52) and healthy controls (n= 31) and the results would show a similar, specific pattern (network) of frontal, temporal and cerebellar group differences as a recent VBM meta-analysis. From our study, four selected of the twenty components had significant group differences with the loading coefficients. Each component was composed of several grey matter spatial distribution throughout the brain. The results were consistent with regions identified in previous voxel-based studies of schizophrenia. SBM identified several componentsthat covered disjoint brain regions and multiple spatial structure patterns (network) that would not have been possible with previous voxel-based univariate techniques. Overall, these results suggest the importance of utilizing multivariate approaches in morphometrical studies in schizophrenia.
延伸閱讀
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