数据分析
After preprocessing, the time series for each voxel was filtered (bandpass, 0.01-0.08 Hz) to removethe effects of very-low-frequency drift and high frequency noise, e.g., respiratory and heart rhythms. Next, the filtered time series was transformedto a frequency domain with a fast Fourier transform (FFT) (parameters: taperpercent=0, FFT length=shortest) and the power spectrum was then obtained. Sincethe power of a given frequency is proportional to the square of the amplitudeof this frequency component of the original time series in the time domain, thesquare root was calculated at each frequency of the power spectrum and theaveraged square root was obtained across 0.01-0.08 Hz at each voxel. Thisaveraged square root was taken as the ALFF. (Zang et al., 2007)
Process Stream
Introduction
part 1
Data quality control
part 2
Preprocessing
1. Remove first N time points
2. Slice timing correction
3. Motion correction
4. Spatial normalization
5. Spatial smoothing
6. Detrend
7. Nuisance regression
Approach
ALFF (conventional) calculation
Statistical Analysis
Statistical modeling (group):
two-sample t-test/paired t-test…
Statistical inference (group):
FDR/GRF…
Correlation Analysis
ALFF and clinical variables
part 3
Reporting and visualizing
Example 1
(Liu et al., 2020)
Example 2
(Hui et al., 2020)
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