此篇論文研究哪種幾何架構較適合fMRI資料，我們使用DCG tree做分析，使用的資料為POP課題的紅與綠實驗數據，此資料的表現形式由Beta-series相關係數矩陣所呈現。在分析幾何形式時為了考慮變數分組之情形，使用了雙層距離的方法計算了個體間的距離。為避免太多變數導致有多餘的雜訊，使用了獨立雙樣本t檢定、主成份分析、個別區域之預測結果篩選出部分變數。我們使用交叉驗證的方式去算出我們的準確率，由DCG tree得到的分群結果，再使用cos⁡θ值去預測測試集的分類，為了使結果更好，我們提高DCG tree中的門檻值與將資料標準化。為了確認DCG tree較適合拿來做這類型研究，也使用SVM、LDA、KNN、K-means和HC tree這些演算法來與其做比較。最後得出使用歐幾里德雙層距離與t檢定篩選變數並提高門檻值能有最好的分類結果，且與其它方法比較後，也得出確實DCG tree有較精確的分類預測。

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