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系統識別號 U0026-0507201623153900
論文名稱(中文) 空間廣義線性混合效應模型應用於西蒙任務之功能性磁振影像研究
論文名稱(英文) Spatial Generalized Linear Mixed Effect Models to an fMRI Study of Simon Task
校院名稱 成功大學
系所名稱(中) 統計學系
系所名稱(英) Department of Statistics
學年度 104
學期 2
出版年 105
研究生(中文) 林璟
研究生(英文) Ching Lin
學號 r26034022
學位類別 碩士
語文別 英文
論文頁數 43頁
口試委員 指導教授-李國榮
口試委員-陳瑞彬
口試委員-張欣民
中文關鍵字 西蒙任務  功能性磁振影像資料  階層模型 
英文關鍵字 Simon task  fMRI data  hierarchical model 
學科別分類
中文摘要 本篇論文提出貝氏階層模型去分析功能性磁振影像資料。功能性磁振影像資料通常具有巨大的資料量且同時存在時間與空間的特性。一些研究指出,空間的相依性不只存在於信號變化的大小,同時也發生在時間相關上。然而,在現有大多數研究中為了計算效率而不考慮時間相關上的空間相依性。本篇論文應用空間隨機效果模型(spatial random effect model) 同時考慮信號和時間相關的空間相依性。透過模擬,我們發現提出的方法可以增加識別大腦區域對刺激反應的準確率。最後,我們透過模擬的結果及一個實際的事件相關功能性磁振影像資料來探討模型的特性。
英文摘要 A spatial Bayesian hierarchical model is proposed to analyze functional magnetic resonance imaging (fMRI) data. Typical fMRI experiments generate massive datasets with complex spatial and temporal structures. Several studies have found that the spatialdependence not only appears in signal changes but also in temporal correlations among voxels; however, current statistical approaches ignore the spatial dependence of temporal correlations to gain computational efficiency. We incorporated the spatial random effects model to simultaneously model spatial dependence arising from both signal changes and temporal correlations. Through simulation studies to demonstrate that the proposed approach increases the accuracy of the detection of brain activities while keeping computationally feasible. Finally, we apply a real event-related fMRI data to further illustrate the usefulness of the proposed model.
論文目次 摘要............................................................................I
Abstract........................................................................II
Acknowledgements................................................................III
Contents........................................................................IV
List of Figures.................................................................VI
List of Tables..................................................................VII
1 Introduction..................................................................1
2 Statistical Modelling.........................................................4
2.1 Bayesian Formulation........................................................5
2.2 Spatial Models..............................................................7
2.3 Priors......................................................................8
2.4 Posterior and Monte Carlo Estimates.........................................10
2.5 Activation Classification...................................................11
3 Simulation Studies............................................................13
3.1 Benchmark Example...........................................................13
3.2 Different Spatial Dependence Structures.....................................17
3.3 Parcellation Effect.........................................................19
3.4 Task Contrasts..............................................................21
4 Application...................................................................24
4.1 Design and procedures.......................................................25
4.2 Activation Detection........................................................27
4.3 Contrast Effect.............................................................30
4.4 Activated region............................................................30
5 Conclusion and Discussion.....................................................32
References......................................................................34
Appendix A Posterior Distribution and Full Conditionals.........................39
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