SPM12手册：脑成像数据分析的关键工具

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SPM (Statistical Parametric Mapping) 是一种基于 MATLAB 的高级软件包，专为医学影像处理和统计分析而设计，特别是针对功能性磁共振成像（fMRI）、正电子发射断层扫描（PET）和单光子发射计算机断层扫描（SPECT）等脑成像技术。该软件由伦敦大学学院（UCL）功能成像实验室（Functional Imaging Laboratory）的 Friston 教授团队开发，包括 John Ashburner、Gareth Barnes 等多名荣誉成员。 SPM12 Manual 主要涵盖了两个核心部分：时间序列处理和空间处理。 1. **时间序列处理**： - **Slice Timing**：这是对不同层面扫描时间校准的过程，确保在统计分析前的时间一致性。涉及的因素包括每个扫描中的session、图像层数、重复时间（TR）、回波时间（TE），以及扫描顺序和参考层面的选择。例如，通过指定每个slice采集的时间，可以纠正因机器操作导致的信号延迟。 - **Realign**：此步骤包括估计运动校准和图像重采样，目的是消除由于头部运动引起的信号漂移。它提供了不同的选项，如数据输入、估计参数调整（如平滑度、旋转速度等）和重采样设置，以提高数据的稳定性。 2. **空间处理**： - **Realign: Estimate**：这一阶段主要负责对整个扫描序列进行空间配准，通过对不同时间点的数据进行比对，找出并纠正头部的位置变化。 - **Realign: Reslice**：接着进行图像的空间重采样，将所有时间点的图像转换到统一的空间模板上，便于后续的分析。 - **Realign: Estimate & Reslice**：结合了前面两个步骤，一次性完成配准和重采样，提供灵活的选项以适应特定的分析需求。 SPM12 Manual 提供了详细的步骤和参数设置指南，确保用户能够正确地预处理数据，减少噪声，补偿运动伪影，并准备数据进行统计分析。此外，该手册还包含了日期（2020年1月13日）和链接（https://www.fil.ion.ucl.ac.uk/spm/），显示它是一个最新的专业工具，反映了当时的技术状态和最佳实践。 SPM12是医学成像研究中不可或缺的工具，它通过强大的统计方法处理和分析数据，为深入理解大脑功能和结构提供了强有力的支持。通过掌握其操作流程和参数设置，研究人员能更准确地解读和解释脑成像结果。

16 CONTENTS

45 DCM for Cross Spectral Densities: Anaesthesia Depth in Rodent Data 457

45.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457

45.2 Main Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458

45.3 Using the Graphical User Interface to Obtain those Results . . . . . . . . . . . . . 458

45.3.1 The data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458

45.3.2 Dynamic Causal Modelling of Cross Spectral Densities . . . . . . . . . . . . 458

45.3.3 Comparing models using Bayesian Model Selection . . . . . . . . . . . . . . 462

46 DCM for fNIRS 465

46.1 Example: Motor Execution and Imagery Data . . . . . . . . . . . . . . . . . . . . . 466

46.2 SPM Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466

46.3 Specifying and Estimating the DCM . . . . . . . . . . . . . . . . . . . . . . . . . . 466

47 Using Dartel 473

47.1 Using Dartel for VBM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473

47.1.1 Using Spatial→Segment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474

47.1.2 Using Dartel Tools→Run Dartel (create Template) . . . . . . . . . . . . . . 474

47.1.3 Using Dartel Tools→Normalise to MNI Space . . . . . . . . . . . . . . . . . 476

47.2 Spatially normalising functional data to MNI space . . . . . . . . . . . . . . . . . . 476

47.3 Warping Images to Existing Templates . . . . . . . . . . . . . . . . . . . . . . . . . 479

47.4 Warping one individual to match another . . . . . . . . . . . . . . . . . . . . . . . 480

47.5 Warping the Dartel template to match an individual . . . . . . . . . . . . . . . . . 480

VIII Batch Interface 483

48 Batch tutorial 485

48.1 Single subject . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485

48.1.1 Study speciﬁc input data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486

48.1.2 Necessary processing steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486

48.1.3 Add modules to the batch . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486

48.1.4 Conﬁgure subject-independent data . . . . . . . . . . . . . . . . . . . . . . 487

48.1.5 Data ﬂow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 488

48.1.6 Entering subject-speciﬁc data . . . . . . . . . . . . . . . . . . . . . . . . . . 491

48.2 Advanced features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493

48.2.1 Multiple sessions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493

48.2.2 Processing multiple subjects in GUI . . . . . . . . . . . . . . . . . . . . . . 493

48.2.3 Command line interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494

48.2.4 Modifying a saved job . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495

49 Developer’s guide 497

49.1 SPM and Matlabbatch code organisation . . . . . . . . . . . . . . . . . . . . . . . . 497

49.1.1 Code organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497

49.1.2 Interfaces between SPM and Matlabbatch . . . . . . . . . . . . . . . . . . . 497

49.2 Conﬁguration Code Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497

49.2.1 Virtual Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498

49.2.2 SPM Startup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498

49.2.3 Defaults Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498

49.3 Utilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499

49.3.1 Batch Utilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499

49.3.2 MATLAB Code Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . 499

49.3.3 Conﬁguration Management . . . . . . . . . . . . . . . . . . . . . . . . . . . 499

IX Bibliography 501

Chapter 1

Slice Timing

Contents

1.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.1.1 Session . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.2 Number of Slices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.3 TR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.4 TA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.5 Slice order . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.6 Reference Slice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1.7 Filename Preﬁx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Correct diﬀerences in image acquisition time between slices.

Slice-time corrected ﬁles are prepended with an ’a’.

Note: The sliceorder arg that speciﬁes slice acquisition order is a vector of N numbers, where

N is the number of slices per volume. Each number refers to the position of a slice within the

image ﬁle. The order of numbers within the vector is the temporal order in which those slices

were acquired. To check the order of slices within an image ﬁle, use the SPM Display option and

move the cross-hairs to a voxel co-ordinate of z=1. This corresponds to a point in the ﬁrst slice

of the volume.

The function corrects diﬀerences in slice acquisition times. This routine is intended to correct

for the staggered order of slice acquisition that is used during echo-planar scanning. The correction

is necessary to make the data on each slice correspond to the same point in time. Without

correction, the data on one slice will represent a point in time as far removed as 1/2 the TR from

an adjacent slice (in the case of an interleaved sequence).

This routine "shifts" a signal in time to provide an output vector that represents the same

(continuous) signal sampled starting either later or earlier. This is accomplished by a simple shift

of the phase of the sines that make up the signal. Recall that a Fourier transform allows for a

representation of any signal as the linear combination of sinusoids of diﬀerent frequencies and

phases. Eﬀectively, we will add a constant to the phase of every frequency, shifting the data in

time.

Shifter - This is the ﬁlter by which the signal will be convolved to introduce the phase shift.

It is constructed explicitly in the Fourier domain. In the time domain, it may be described as

an impulse (delta function) that has been shifted in time the amount described by TimeShift.

The correction works by lagging (shifting forward) the time-series data on each slice using sinc-

interpolation. This results in each time series having the values that would have been obtained

had the slice been acquired at the same time as the reference slice. To make this clear, consider

a neural event (and ensuing hemodynamic response) that occurs simultaneously on two adjacent

slices. Values from slice "A" are acquired starting at time zero, simultaneous to the neural event,

20 CHAPTER 1. SLICE TIMING

while values from slice "B" are acquired one second later. Without correction, the "B" values

will describe a hemodynamic response that will appear to have began one second EARLIER on

the "B" slice than on slice "A". To correct for this, the "B" values need to be shifted towards

the Right, i.e., towards the last value.

This correction assumes that the data are band-limited (i.e. there is no meaningful information

present in the data at a frequency higher than that of the Nyquist). This assumption is support

by the study of Josephs et al (1997, Human Brain Mapping) [71] that obtained event-related data

at an eﬀective TR of 166 msecs. No physio-logical signal change was present at frequencies higher

than our typical Nyquist (0.25 HZ).

When using the slice timing correction it is very important that you input the correct slice

order, and if there is any uncertainty then users are encouraged to work with their physicist to

determine the actual slice acquisition order.

One can also consider augmenting the model by including the temporal derivative in the

informed basis set instead of slice timing, which can account for +/- 1 second of changes in

timing.

Written by Darren Gitelman at Northwestern U., 1998. Based (in large part) on ACQCOR-

RECT.PRO from Geoﬀ Aguirre and Eric Zarahn at U. Penn.

1.1 Data

Subjects or sessions. The same parameters speciﬁed below will be applied to all sessions.

1.1.1 Session

Select images to slice-time correct.

1.2 Number of Slices

Enter the number of slices.

1.3 TR

Enter the TR (in seconds).

1.4 TA

Enter the TA (in seconds). It is usually calculated as TR-(TR/nslices).

You can simply enter this equation with the variables replaced by appropriate numbers.

If the next two items are entered in milliseconds, this entry will not be used and can be set

to 0.

1.5 Slice order

Enter the slice order.

Bottom slice = 1. Sequence types and examples of code to enter are given below:

ascending (ﬁrst slice=bottom): [1:1:nslices]

descending (ﬁrst slice=top): [nslices:-1:1]

interleaved (middle-top):

for k = 1:nslices

round((nslices-k)/2 + (rem((nslices-k),2) * (nslices - 1)/2)) + 1,

end

interleaved (bottom -> up): [1:2:nslices 2:2:nslices]

interleaved (top -> down): [nslices:-2:1, nslices-1:-2:1]

Alternatively you can enter the slice timing in ms for each slice individually.

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