1 Overview

BOLD signal 能夠回答的三種問題

  1. Brain activation to the corresponding task.

    • Similarity between local BOLD signal and task design.

    • “Where is the motion area?”

    • “Where is the face recognition area?”

    • 分析工具:General Linear Model (GLM)。

  2. Brain encoding to the representation of stimulus classes.

    • Classification or similarity analysis.

    • “What are the varying brain states in an area?”

    • “How do brain cortices encode different types of information?”

    • 分析工具:

      • Multivariate Pattern Analysis (MVPA).

      • Classifier‐based MVPA, pattern similarity analysis.

  3. Brain connectivity to the integration of neural networks.

    • Dependencies of BOLD signals between brain regions.

    • “How do neurons and neural networks process information?”

    • 分析工具:

      • Statistical dependency.

      • Independent Component Analysis (ICA).

      • Network analysis

BOLD‐fMRI Preprocessing

fMRI 資料前處理的步驟。

Figure 1.1: fMRI 資料前處理的步驟。

  1. Slice timing:校正時間的差異。

  2. Realignment:校正腦的位置。

  3. Co‐registration (with anatomical images):fMRI 犧牲掉的空間解析度,在此步驟透過解剖影像可以部分挽回。

  4. Normalization:把頭轉換到統一的座標上。

  5. Smoothing:會降低空間解析度,但可以降低 noise(增進 single noise ratio)。

  6. Segment (tissue classification; optional)


***Block design.*** 10 tasking blocks (duration = 20s) with resting interval (20s).

Figure 1.2: Block design. 10 tasking blocks (duration = 20s) with resting interval (20s).

SPM T‐test

  • 虛無假設:\(\beta_1 = 0\)

  • \(t = \dfrac{c^T\hat{\beta}}{SD(c^T\hat{\beta})}\).

Decoding Activity Pattern of Brain

MVPA: A Classification Problem