数据分析
Introduction
To improve the original ALFF approach, a ratio of the power of each frequency at the low-frequency range (0.01-0.08Hz) to that of the entire frequency range (0-0.25Hz), i.e., fractional ALFF (fALFF) was used. (Zou et al., 2008)
After the linear trend was removed, the time series for each voxel were transformed to a frequency domain without band-pass filtering. The square root was calculated at each frequency of the power spectrum. The sum of amplitude across 0.01-0.08Hz was divided by that across the entire frequency range, i.e., 0-0.25Hz. (Zou et al., 2008)
After the linear trend was removed, the time series for each voxel were transformed to a frequency domain without band-pass filtering. The square root was calculated at each frequency of the power spectrum. The sum of amplitude across 0.01-0.08Hz was divided by that across the entire frequency range, i.e., 0-0.25Hz. (Zou et al., 2008)
Process Stream
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
fALFF (conventional) calculation
Statistical Analysis
Statistical modeling (group):
two-sample t-test/paired t-test…
Statistical inference (group):
FDR/GRF…
Correlation Analysis
fALFF and clinical variables
part 3
Reporting and visualizing
Example 1
（Zhou et al., 2020）
Example 2
（Yamamura et al., 2016）
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