MELODIC - FslWiki (ox.ac.uk)
1 Introduction
1.1 Research Overview
MELODIC ( Multivariate Exploratory Linear Optimized Decomposition into Independent Components ) 3.0 uses Independent Component Analysis to decompose a single or multiple 4D data sets into different spatial and temporal components. For ICA group analysis, MELODIC uses either Tensorial Independent Component Analysis (TICA, where data is decomposed into spatial maps, time courses and subject/session modes) or a simpler temporal concatenation approach. MELODIC can pick out different activation and artefactual components without any explicit time series model being specified.MELODIC （多变量探索线性优化分解成独立成分 ） 3.0 使用独立成分分析将单个或多个 4D 数据集分解成不同的空间和时间成分。对于ICA组分析，MELODIC 使用张量独立成分分析（TICA，其中数据被分解成空间地图、时间课程和主题/会话模式）或更简单的时间串联方法。MELODIC 可以挑选出不同的激活和人工成分，而无需指定任何明确的时间系列模型。
A paper on MELODIC Probabilistic ICA (PICA) has been published in IEEE TMI. For detail, see a technical report on MELODIC PDF. 一篇关于梅洛迪奇概率 ICA （PICA） 的论文已发表在 IEEE TMI 上。有关详细信息，请参阅有关 MELODIC PDF 的技术报告。
A paper on Tensor ICA for multi-session and multi-subject analysis has been published in NeuroImage. For detail, see a technical report on TICA PDF. 一篇关于TensorICA的论文，用于多会话和多主题分析，已经发表在《神经图像》上。有关详细信息，请参阅关于 TICA PDF 的技术报告。
A paper investigating resting-state connectivity using independent component analysis has been published in Philosophical Transactions of the Royal Society. For detail, see a technical report PDF. 英国皇家学会哲学事务所发表了一篇论文，利用独立的成分分析研究休息状态连接。有关详细信息，请参阅技术报告PDF。
The different MELODIC programs are: 不同的旋律程序是：
Melodic - MELODIC GUI
melodic - command-line MELODIC program
fsl_regfilt - command-line tool for removing regressors from data (melodic denoising)
1.2 Melodic GUI
要调用 MELODIC GUI，请在终端中键入MELODIC（在 Mac 上键入Melodic_gui），或运行fsl并按下MELODIC按钮。
在调用 GUI 之前，您需要将每个会话的数据准备为 4D NIFTI 或分析格式图像;
fsl/bin 中有称为fslmerge 和 fslsplit 的实用程序，用于在多个 3D 图像和单个 4D（3D+时间）图像之间转换。
注册中用作"highres"图像的结构图像通常应使用BET进行大脑提取。
1.2.1 GUI datails
Misc
Data
Pre-Stats
Registration
Stats
Post-Stats
Bottom Row of Buttons
MELODIC report output
1.2.2 MISC
Balloon help (the popup help messages in the MELODIC GUI) can be turned off once you are familiar with the GUI.
The Progress watcher button allows you to tell Melodic not to start a web browser to watch the analysis progress. If you are running lots of analyses you probably want to turn this off; you can view the same logging information by looking at the report_log.html or log.txt files in any MELODIC directories instead. ---
1.2.3 Data
First, set the filename of the 4D input image (e.g. /users/sibelius/origfunc.nii.gz) by pressing Select 4D data. You can select multiple files if you want MELODIC to perform a group analysis or if you want to run separate ICAs with the same setup. Results for each input file will be saved in separate .ica directories, the name of which is based on the input data's filename (unless you enter an Output directory name).
首先，通过按下"选择 4D 数据"设置4D输入图像的文件名（比如，/users/sibelius/origfunc.nii.gz）。如果您希望 MELODIC 执行组分析，或者如果您想要使用相同的设置运行单独的 ICA，您可以选择多个文件。每个输入文件的结果将保存在单独的.ica 目录中，其名称基于输入数据的文件名号（除非输入Output directory名称）。
Delete volumes控制在进行进一步处理之前要删除的初始 FMRI 卷的数量。
TR controls the time (in seconds) between scanning successive FMRI volumes. Changes here will not affect the analysis and only change the x-axis units of the final time series plots.
TR控制扫描连续 FMRI 卷之间的时间（秒内）。此处的更改不会影响分析，只会更改最终时间系列图的 x 轴单元。
The High pass filter cutoff controls the longest temporal period that you will allow.
高通滤器截止时间控制您允许的最长时间段。
Delete volumes controls the number of initial FMRI volumes to delete before any further processing.
Delete volumes 控制在执行进一步处理之前要删除的初始的fMRI卷的数量。
1.2.4 Pre-stats 预统计
Low-frequency drifts and motion in the data can adversely affect the decomposition. In most cases, you would want to motion-correct the data, remove these drifts first or perform other types of typical data pre-processing before running the analysis. This can be done from within the Melodic GUI Pre-stats section.
数据中的低频漂移和头动会对分解产生不利影响。在大多数情况下，您希望在运行分析之前对数据进行运动校正、先删除这些漂移或执行其他类型的典型数据预处理。这可以在Melodic GUI Pre-stats section部分内完成。
1.2.5 Registration注册
Before any multi-session or multi-subject analyses can be carried out, the different sessions need to be registered to each other. This is made easy within MELODIC which performs registration on input data as part of an analysis using FEAT functionality. Unlike registration step in FEAT this here needs to be performed before the statistical analysis so that the filtered functional data is transformed into the standard space. For information on using multi-stage registration please consult the FEAT manual.
在进行任何多会话或多被试分析之前，需要相互注册不同的会话。这在 MELODIC 中变得简单，它使用 FEAT 功能对输入数据进行注册，作为分析的一部分。与 FEAT 中的注册步骤不同，此处需要在统计分析之前执行，以便将过滤的功能数据转换为标准空间。有关使用多阶段注册的信息，请参阅 FEAT 手册。
Standard space refers to the standard (reference) image; it should be an image already in standard space, ideally with the non-brain structures already removed.
标准空间是指标准（参考）图像：它应该是一个图像已经在标准空间， 理想情况下， 非大脑结构已经删除。
Resampling resolution (mm) refers to the desired isotropic voxel dimension of the resampled data. In order to save on disk space and on required memory during the analysis it is advisable to resample the filtered data into standard space but keeping the resampled resolution at the FMRI resolution (typically 4mm or 5mm).
重新采样分辨率****（****mm****）是指重新采样数据所需的等热带 voxel 维度。为了在分析过程中节省磁盘空间和所需的内存，建议将过滤过的数据重新注入标准空间，但将重新采样的分辨率保持在 FMRI 分辨率（通常为 4mm 或 5mm）。
Note that any output image can be transformed to a higher resolution space later on - see FAQ 请注意，任何输出图像都可以在以后转换为更高的分辨率空间 -请参阅常见问题解答
1.2.6 Stats统计
The Stats section lets you control some of the options for the decomposition. The default setting will most probably already be set to what you would want most of the time. 统计部分允许您控制分解的一些选项。默认设置很可能已经设置为您大部分时间想要的。
By default, MELODIC will variance-normalise timecourses. 默认情况下，MELODIC 将方差使时间过程正则化。
By default, Melodic will automatically estimate the number of components from the data - you can switch this option off and then can specify the number of components explicitly. 默认情况下，Melodic 会自动估计数据中的成分数量 - 您可以关闭此选项，然后可以明确指定成分数量。
You can now select the type of analysis. MELODIC currently offers three options:现在，您可以选择分析类型。MELODIC目前提供三种选择：
- Single-session ICA:
Single-session ICA.png
This will perform standard 2D ICA on each of the input files. The input data will each be represented as a 2D time x space matrix. MELODIC then de-composes each matrix separately into pairs of time courses and spatial maps. The original data is assumed to be the sum of outer products of time courses and spatial maps. All the different time courses (one per component) will be saved in the mixing matrix melodic_mix and all the spatial maps (one per component) will be saved in the 4D file melodic_IC.
单session ICA：这将执行每个输入文件的标准 2D ICA。输入数据将分别表示为 2D 的时间 × 空间矩阵。然后，MELODIC 将每个矩阵分别分解成一对时间过程和空间地图。原始数据假定为时间课程和空间地图的外部产品的总和。所有不同的时间过程（每个成分一个）将保存在混合矩阵中melodic_mix所有空间地图（每个成分一个）将保存在 4D 文件melodic_IC中。
When using separate analyses, MELODIC will attempt to find components which are relevant and non-Gaussian relative to the residual fixed-effects within session/subject variation. It is recommended to use this option in order to check for session-specific effects (such as MR artefacts). You will need to use this option if you want to perform MELODIC denoising using fsl_regfilt. When using single-session ICA the component are ordered in order of decreasing amounts of uniquely explained variance.
在使用单独的分析时，MELODIC 将尝试查找与会话/被试变体中的剩余固定效果相关的成分和非高斯语成分。建议使用此选项来检查会话特定效果（如 MR 人工制品）。如果您想要执行使用fsl_regfilt的 MELODIC 表示，则需要使用此选项。使用单会话 ICA 时，按减少独特解释方差的减少数量来订购该成分。
Multi-session temporal concatenation:
This will perform a single 2D ICA run on the concatenated data matrix (obtained by stacking all 2D data matrices of every single data set on top of each other).
多会话时间串联：这将在串联数据矩阵上执行单个 2D 的 ICA 运行（通过将每个数据集的所有 2D 数据矩阵堆叠在一起获得）。
It is recommended to use this approach in cases where one is looking for common spatial patterns but can not assume that the associated temporal response is consistent between sessions/subjects. Examples include activation studies where the design was randomised between sessions or the analysis of data acquired without stimulation (resting-state FMRI).
建议在人们正在寻找常见空间模式但不能假设相关时间响应在会话/被试之间一致的情况下使用此方法。示例包括激活研究，其中设计在会话之间随机化，或分析在没有刺激的情况下获得的数据（静息态 FMRI）。
This approach does not assume that the temporal response pattern is the same across the population, though the final web report will contain the first Eigenvector of all different temporal responses as a summary time course. Access to all time courses is available: the time series plot is linked to a text file (tXX.txt) which contains the first Eigenvector, the best model fit in case a time series design was specified and all different subject/session-specific time courses as columns. For each component the final mixing matrix melodic_mix contains the temporal response of all different data sets concatenated into a single column vector. The final reported time course will be the best rank-1 approximation to these different responses.
这种方法并不假定整个人群的时间响应模式相同，尽管最终的 Web 报告将包含所有不同时间响应的第一个 Eigenvector 作为总结时间过程。访问所有时间课程可用：时间系列情节链接到文本文件 （tXX.txt）， 其中包含第一个 Eigenvector， 最适合模型的情况下，时间系列设计被指定和所有不同的被试/会话特定时间课程作为列.对于每个成分，最终混合矩阵melodic_mix包含串联到单列向量的所有不同数据集的时间响应。最后报告的时间过程将是这些不同响应的最佳排名-1近似。
- Multi-session Tensor-ICA: This will perform a 3D Tensor-ICA decomposition of the data. All individual data sets will be represented as a single time ×space × sessions/subjects block of data. Tensor-ICA will decompose this block of data into triplets of time courses, spatial maps and session/subject modes, which - for each component - characterise the signal variation across the temporal, spatial and subject/session domain.
多会话 Tensor-ICA****：这将对数据进行 3D 张力-ICA 分解。所有单个数据集将作为单个时间 × 空间 × 会话/被试数据块表示。Tensor-ICA 将把这组数据分解为时间过程、空间地图和会话/被试模式的三胞胎，每个成分的信号变化均为时间、空间和被试/会话域的特征。
It is recommended to use this approach for data where the stimulus paradigm is consistent between session/subjects. Tensor-ICA assumes that the temporal response pattern is the same across the population and provides a single decomposition for all original data sets. MELODIC will attempt to find components which are highly non-Gaussian relative to the full mixed-effects variance of the residuals.
建议使用此方法处理在会话/被试之间刺激范式一致的数据。Tensor-ICA 假定整个人群的时间响应模式相同，并为所有原始数据集提供单个分解。MELODIC 将尝试找到相对于残余物的完整混合效应方差的高度非高斯语的成分。
Estimated components typically fall into 2 classes: components which describe effects common to all or most subjects/sessions, and components which describe effects only contained in a small number of subjects/sessions. The former will have a non-zero estimated effect size while the latter will have an effect size around 0 for most subjects/sessions and only few high non-zero values. These different types of components can be identified easily by looking at the boxplots provided. When using Tensor-ICA the components are ordered in order of decreasing amount of median response amplitude. For details on the decomposition see the technical report TR04CB1
估计成分通常分为两类：
- 描述所有或大多数被试/会话所共有的影响的成分，
- 以及仅描述少数被试/会话中所包含的影响的成分。
前者将有一个非零估计效果大小，而后者将有一个效果大小约0的大多数被试/会话，只有很少的高非零值。通过查看所提供的框图，可以轻松识别这些不同类型的成分。使用 Tensor-ICA 时，按中位响应振幅的递减量顺序订购成分。有关分解的详细信息，请参阅技术报告TR04CB1。
1.2.7 Post-Stats统计后
Melodic will also by default carry out inference on the estimated maps using a mixture model and an alternative hypothesis testing approach. A threshold level of 0.5 in the case of alternative hypothesis testing means that a voxel 'survives' thresholding as soon as the probability of being in the 'active' class (as modelled by the Gamma densities) exceeds the probability of being in the 'background' noise class. This threshold level assumes that you are placing an equal loss on false-positives and false-negatives. If, however, you consider e.g. false-positives as being twice as bad as false-negatives you should change this value to 0.66...
默认情况下，Melodic还将使用混合模型和替代假设测试方法对估计的地图进行推理。在替代假设测试的情况下，阈值水平为 0.5 意味着一旦进入"****活动"****类（以伽马密度为模型）的概率超过处于"背景"噪声类中的概率，voxel 就会 从阈值关卡通过。此阈值级别假定您对误报和假阴性具有同等损失。但是，如果您认为假阳性是假阴性两倍，则应将此值更改为 0.66...
You can select the background image used for the generation of the spatial map overlay images. 您可以选择用于生成空间地图叠加图像的背景图像。
If you select the Output full stats folder option, MELODIC will save thresholded maps and probability maps in a /stats subdirectory within its output folder.
如果您选择输出完整统计文件夹选项，MELODIC 将在其输出文件夹中的 /统计子方向中保存阈值地图和概率图。
You can specify a temporal design matrix (and in the case of a group analysis also, a session/subject design matrix) as well as corresponding contrast matrices. If these matrices are set in the GUI, MELODIC will perform a post-hoc regression analysis on estimated time courses and session/subject modes. This can be a helpful tool in order to identify whether or not a given component is task related. The matrices themselves can be created easily using the Glm GUI.
您可以指定时间设计矩阵（在组分析中，还可以指定会话/被试设计矩阵）以及相应的对比矩阵。如果这些矩阵设置在 GUI 中，MELODIC 将对估计的时间课程和会话/被试模式进行事后回归分析。这可能是一个有用的工具，以便确定给定成分是否与任务相关。矩阵本身可以很容易地创建使用Glm GUI。
1.2.8 Bottom Row of Buttons
When you have finished setting up MELODIC, press Go to run the analysis. Once MELODIC is running, you can either Exit the GUI, or setup further analyses. 当您完成设置 MELODIC 时，请按"GO"以运行分析。一旦 MELODIC 运行，您可以退出GUI 或设置进一步分析。
The Save and Load buttons enable you to save and load the complete MELODIC setup to and from file.保存和加载按钮使您能够保存和加载完整的 MELODIC 设置，往返于文件中。
1.2.9 MELODIC report output
Melodic will then generate the results and your terminal window will tell you where to find the web report. Each IC_XX.html webpage shows one spatial map thresholded and rendered on top of a background image followed by the relevant time-course of the ICA decomposition and the power-spectrum of the time-course. If you click on the thresholded map, you can inspect the raw IC output together with probability maps and the Mixture Model fit. 然后，Melodic将生成结果，您的终端窗口将告诉您在哪里可以找到 Web 报告。每个IC_XX.html网页显示一个空间地图阈值，并在背景图像的顶部呈现，然后是 ICA 分解的相关时间过程和时间过程的功率光谱。如果单击阈值地图，您可以与概率图和混合模型配合一起检查原始 IC 输出。
In the case of TICA or simple time series concatenation the time course plotted is the rank-1 approximation to all the different time courses that correspond to the given spatial map within the population.
在 TICA 或简单的时间系列串联的情况下，绘制的时间过程是与人口内给定空间图对应的所有不同时间过程的rank-1 近似。
If a temporal design was specified in the Post-Stats section then the time series plot will also contain a plot of the total model fit. In addition, a simple GLM table will describe the fit in detail, providing information of the regression parameter estimates (PEs). Furthermore, MELODIC will perform a simple F-test on the estimated time course and the total model fit. For task-related components the model fit will explain large amounts of the variation contained in the estimated time couse. In addition, if a contrast matrix was specified, the table will also contain Z-statistics and p-values for all the contrasts. If a group analysis was carried out then the report page will also include information on the distribution of the effect size across the population. A simple plot and a boxplot show the relative effect size across the different sessions/subjects. If a design matrix was specified in the GUI setup then MELODIC will also include a GLM regression fit table. 如果在"后统计"部分指定了时间设计，则时间系列图也将包含整个模型适合的绘图。此外，一个简单的 GLM 表将详细描述拟合情况，提供回归参数估计 （PEs） 的信息。此外，MELODIC 将对估计的时间路线和总体型号进行简单的 F 测试。对于与任务相关的成分，模型拟合将解释估计时间库中包含的大量变化。此外，如果指定了对比矩阵，表中还将包含所有对比度的 Z 统计和 p 值。如果进行了小组分析，则报告页面还将包含有关影响大小在各人群中分布的信息。一个简单的绘图和一个框图显示不同会话/被试的相对效果大小。如果在 GUI 设置中指定了设计矩阵，则 MELODIC 还将包括 GLM 回归拟合表。
1.3 melodic command-line program
Unlike other FSL tools, the melodic command-line is not equivalent to the GUI. The command-line only performs ICA decomposition. 与其他 FSL 工具不同，command-line不等同于 GUI。command-line只执行 ICA 分解。
Running MELODIC via the GUI will call different preprocessing steps and then use the melodic (command-line) tool to perform ICA decomposition. Similarly, melodic command-line is called within the FEAT GUI to perform ICA decomposition (see FEAT Pre-Stats options). 通过 GUI 运行 MELODIC 将调用不同的预处理步骤，然后使用melodic（命令行）工具执行 ICA 分解。同样，在 FEAT GUI 中调用MELODIC命令行来执行 ICA 分解（参见FEAT 预统计选项）。
The melodic command-line also gives more control on the options for ICA decomposition (MELODIC GUI only allows you to change the basic options) melodic命令行提供了更多的控制ICA分解选项（MELODIC GUI只允许您更改基本选项）
Type melodic --helpto get usage. 输入e melodic --help得到使用。
1.2 fsl_regfilt command-line program
Running MELODIC can be a useful tool for gaining insight into unexpected artefacts or activation in your data. 运行 MELODIC 是了解数据中意外伪影或激活的有用工具。
As well as being a good way to find structured noise (or unexpected activation) in your data, ICA can also be used to remove chosen components (normally obvious scanner-related or physiological artefacts) from your data in order, for example, in order to improve the FEAT results. In order to do this: 除了在数据中查找结构噪声（或意外激活）的好方法外，ICA 还可用于从数据中删除选定的成分（通常与扫描仪相关的明显或生理伪影），例如，以便改进 FEAT 结果。为此：
Run MELODIC single-session ICA on a 4D image file在 4D 图像文件上运行 MELODIC 单会话 ICA
Open the MELODIC report (melodic_output_directory.ica/filtered_func_data.ica/report/00index.html) in a web browser and look through the components to identify those that you wish to remove; record the list of component numbers to remove. 打开 Web 浏览器中的 MELODIC 报告（melodic_output_directory.ica/filtered_func_data.ica/report/00index.html）
查看成分，以确定您想要删除的成分：记录要删除的成分编号列表。
- In a terminal, run the MELODIC denoising, using the commands: 在终端中，使用命令运行 MELODIC 降噪：
cd melodic_output_directory.ica
fsl_regfilt -i filtered_func_data -o denoised_data -d filtered_func_data.ica/melodic_mix -f "2,5,9"
where you should replace the comma-separated list of component numbers with the list that you previously recorded when viewing the MELODIC report.
在查看 MELODIC 报告时，您应该将逗号分离的成分列表替换为您以前录制的列表。
The output file denoised_data.nii.gz then contains the filtered and denoised data set which can be used e.g. within FEAT. When running FEAT on this data make sure that the analysis is set to Stats + Post-stats as you do not want to run the other filtering steps (smoothing etc.) again on this data. Similarly, when running Group ICA on this data, you need to turn off all preprocessing, or use the command line (after transforming the data into a common space using, e.g. featregapply).
然后，输出文件denoised_data.nii.gz包含可在 FEAT 中使用的过滤和降噪数据集。在此数据上运行 FEAT 时，请确保分析设置为统计数据和后期统计，因为您不想在此数据上再次运行其他筛选步骤（平滑等）。同样，在运行此数据上的 ICA 组时，您需要关闭所有预处理程序或使用命令行（在将数据转换为使用公共空间后，例如 featregapply）。
1.3 Using melodic for just doing mixture-modelling
The following explains how to apply melodic's mixture modelling to a statistic image, without actually running ICA. This can be useful when you have a statistic image that is nominally a z-statistic, but where there is a chance that it is not valid - for example if the null central part of the distribution does not have zero mean and unity standard deviation (e.g., because your data was temporally smooth, and that was not taken into account when you ran a GLM and created the z-statistic). The mixture-modelling will fit curves to the null and non-null parts of the image histogram, and force the null part of the adjusted statistic image to have zero mean and unity standard deviation. 下面解释了如何将MELODIC的混合物建模应用于统计图像，而无需实际运行 ICA。当您有名义上为 z 统计的统计图像时，如果有可能无效，则可能有用 - 例如，如果分布的空中心部分没有零平均值和统一标准偏差（例如，因为您的数据在时间上是平滑的，并且在运行 GLM 并创建 z 统计时没有考虑到这一点）。混合建模将适合图像直方图的空和非空部分的曲线，并迫使调整后的统计图像的空部分具有零平均值和统一标准偏差。
First, create a dummy file whose contents are irrelevant - this is necessary in order to make melodic run without the full ICA estimation:
首先，创建一个内容无关紧要的虚拟文件 - 这是必要的，以便使MELODIC运行没有完整的ICA估计：
echo "1" > grot.txt
Then, feed your stats image myZstatinto the mixture-modelling:
然后，将您的统计数据图像myZstat输入混合建模：
melodic -i myZstat --ICs=myZstat --mix=grot.txt -o myZstatMM --Oall --report -v --mmthresh=0
The corrected stats image will be named myZstatMM/stats/thresh_zstat1 - this will be corrected and not thresholded, the latter being because of the option --mmthresh=0 . If you wish to adjust the z-statistic and also apply mixture-model-based thresholding (in the same manner as melodic does in normal ICA usage), then set this to (e.g.) 0.5 to get an equal balance between false positives and false negatives.
更正的统计数据图像将命名为myZstatMM/stats/thresh_zstat1----这将被更正，而不是阈值，后者是因为选项- mmthresh=0。如果您希望调整 z 统计，并且应用基于混合物模型的阈值（与MELODIC在正常 ICA 使用中相同），则将其设置为 （例如） 0.5，以便在误报和假阴性之间取得等于平衡。
2 FAQ
2.1 What is an Independent Component (IC) and how do I know what each one means?
Independent Component Analysis (ICA) attempts to split the 4D functional data into a set of spatial maps, each with an associated time course. This is a way of breaking up the original data set in a way which does not require the experimental paradigm to be specified and hopefully separates out signals of interest from other signals or artefacts. It is particularly useful when examining data where the timecourse of the response is uncertain. Ideally the result of running ICA will be a set of Independent Components (ICs), some of which are clearly related to activation while some are related to other physiological processes (e.g. respiration, resting-state signals, etc) or to imaging artefacts (e.g. motion, ghosting, slice dropout, noise, etc). Examples of a wide range of artefacts can be found in the FIX papers. There is no automatic way of determining which ICs are artefacts and which are not (since the process is model-free) and some knowledge of the experiment (and standard artefacts) is usually required to interpret the results.
独立成分分析 （ICA） 尝试将 4D 功能数据拆分为一组空间地图，每个地图都有相关的时间过程。这是一种分解原始数据集的方式，不需要指定实验范式，并希望将感兴趣的信号与其他信号或伪影分开。在检查响应时间过程不确定的数据时，它特别有用。理想情况下，运行ICA的结果将是一组独立成分（ICs），其中一些明显与激活有关，而有些则与其他生理过程（例如呼吸、静息状态信号等）或伪影（例如运动、影像、切片辍学、噪音等）有关。在FIX论文中可以找到各种伪影的例子。没有自动的方法来确定哪些IC是人工制品，哪些不是（因为这个过程是无模型的），并且通常需要一些实验知识（和标准人工制品）来解释结果。
Technically, ICA performs a linear decomposition of the original data, such that when all the Independent Components (ICs) are added together (each one being a 4D signal formed by the outer product of the spatial map and timecourse) they equal the original data. This is a similar concept to PCA but enforces independence between the components spatially while PCA enforces orthogonality both spatially and temporally. Note that in ICA for FMRI no relationship between the timecourses is imposed - they can be very similar. In addition, MELODIC uses a dimensionality estimation technique which separates out much of the noise before performing the ICA, thus reducing the number of purely noise-driven ICs in the output. 从技术上讲，ICA 对原始数据进行线性分解，因此，当将所有独立成分 （ICs） 添加在一起（每个成分均由空间地图和时间过程的外部产品形成的 4D 信号）时，它们等于原始数据。这是一个与 PCA 类似的概念，但在空间上强制执行成分之间的独立性，而 PCA 在空间和时间上都强制执行正交性。请注意，在 ICA 中，FMRI 没有规定时间段之间的关系 - 它们可能非常相似。此外，MELODIC 使用维度估计技术，在执行 ICA 之前将大部分噪声分离出来，从而减少输出中纯噪声驱动的 IC 数量。
2.2 How do I use MELODIC to filter out unwanted components from my functional data?
To filter out unwanted components from the original data using MELODIC you will need (i) the name of the original data, (ii) the mixing matrix that defines the decomposition and (iii) a list of component numbers to remove. This is described more fully in an FSL Course Example. In brief, the required command is: 要使用 MELODIC 从原始数据中筛选出不需要的成分，您需要
（i） 原始数据的名称，
（ii） 定义分解的混合矩阵和
（iii） 要删除的成分编号列表。
这在 FSL 课程示例中进行了更全面的描述。简言之，所需的命令是：
melodic -i inputdata -v -o outputname.ica --mix=inputdata.ica/melodic_mix -f "a,b,c,d,e,f,..."
where inputdata has previously been run through MELODIC, creating the output directory inputdata.ica and a,b,c etc. are the component numbers of the unwanted components found in this ouptut. 其中inputdata 以前已经通过 MELODIC 运行，创建输出目录 inputdata.ica和a，b，c等是在此 ouptut 中发现的不需要的成分的成分编号。
Note: You need those doublequotes so that the entire list of numbers is passed to melodic as the argument of the -f option! 注意：您需要这些双报价，以便将整个数字列表传递给旋律作为-f选项的参数！
2.3 How does MELODIC calculate the number of Independent Components (ICs)?
The number of components is calculated using Bayesian dimensionality estimation techniques, as detailed in the FMRIB technical report TR02CB1. Refer to this report for full details on this and other aspects of the probabilistic ICA method used in MELODIC. This dimensionality estimation is used by default in both the command line and GUI versions. It can be turned off and the number of components specified manually, although this is not recommended for FMRI data.
成分的数量是使用贝叶斯维度估计技术计算的，如FMRIB技术报告TR02CB1所详述的。请参阅本报告，了解有关 MELODIC 中使用的概率 ICA 方法的这一及其他方面的完整详细信息。默认情况下，该尺寸估计用于命令行和 GUI 版本。它可以关闭并手动指定成分的数量，尽管不建议用于 FMRI 数据。
2.4 How do I transform the MELODIC results from a low-resolution standard space to a higher-resolution one?
Transforming an image between different resolution versions of standard space (e.g. 3mm to 2mm) should be done with flirt: 在标准空间的不同分辨率版本（例如 3mm 到 2mm）之间转换图像应用flirt完成：
flirt -in image3mm -ref $FSLDIR/data/standard/MNI152_T1_2mm -applyxfm -usesqform -out image2mm
Note that in this case the flirt command line must be used (not applywarp) since the -usesqform flag aligns the images based on standard space coordinates, and not using a prior transformation matrix or warp. The input image can be at any resolution as long as it is in standard space (as created by MELODIC) and the reference image can be at a higher resolution (e.g. 1mm) if desired. 请注意，在这种情况下，调情命令线必须使用（不应用扭曲），因为-用矩旗根据标准空间坐标对齐图像，而不是使用先前的转换矩阵或扭曲。输入图像可以处于任何分辨率，只要它是在标准空间（由 MELODIC 创建），如果需要，参考图像的分辨率可能更高（例如 1 毫米）。