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Extracting functional connectivity

Computing functional connectivity

First, make sure that the preprocessed fMRI data are available as derivatives in your dataset. An example of data structure should be as follows:

├── derivatives
│   ├── fmriprep-23.1.4
│   │   ├── dataset_description.json
│   │   └── sub-pilot
│   │       ├── anat
│   │       ├── figures
│   │       └── ses-15

Then, the functional connectivity matrices can be computed using the funconn.py script. The simplest call of the script only needs a derivative dataset (usually from fMRIPrep). Following the above data structure, it would be called as follows:

python funconn.py path_to_dataset/derivatives/fmriprep-23.1.4

Default call of the funconn.py script

When using the default options, the pipeline will (in this order and for all functional tasks):

  • Fetch the DiFuMo atlas (64 dimensions)
  • Extract the region-wise averaged timeseries
  • Find high motion volumes that have framewise displacement higher than 0.4 mm or higher than 5 standardized DVAR. Then also flag as outlier the segments that are shorter than 5 timepoints.
  • Interpolate high motion volumes with cubic spline interpolation
  • Apply a low-pass butterworth filter (cutoff frequency of 0.15 Hz)
  • Censor high motion volumes
  • Remove confounds: motions (6 parameters) and discrete cosine transform basis (high-pass filtering)
  • Standardize the timeseries
  • Compute the functional connectivity matrices as the sparse inverse covariance (see this example, using Graphical Lasso CV of scikit-learn)

Most parameters of the pipeline can be specified in the options (see python funconn.py -h for more details).

Finally, the pipeline will save the denoised timeseries and connectivity matrices as well as various visual reports (i.e., figures).

Example of visual report
  • Denoising confounds as a design matrix: Timeseries_denoise
  • Denoised timeseries as a carpet plot: Timeseries_carpet
  • Denoised timeseries as a signal plot: Timeseries_signal
  • Functional connectivity matrix as a heatmap: FC_matrix_heatmap

The outputs will be stored in a functional-connectivity folder in the same parent directory as the preprocessed derivatives dataset. In the end, the data structure will look like this:

├── derivatives
│   ├── fmriprep-23.1.4
│   │   └── sub-pilot
│   │       ├── anat
│   │       ├── figures
│   │       └── ses-15
│   │           ├── anat
│   │           └── func
│   └── functional_connectivity
│       └── DiFuMo64-LP
│           └── sub-pilot
│               ├── figures
│               └── ses-15
│                   └── func

QA/QC of denoised data

  • Navigate to the figures/ folder where the visual reports were saved. Following the above data structure, it would be the folder:
    path_to_dataset/derivatives/functional_connectivity/DiFuMo64-LP/sub-pilot/figures`.
    
  • Open the figure fc_dist.png.
  • Visualize the FC distributions and apply the QA/QC criteria.
  • Open the figure QC-FC.png.
  • Visualize the QC-FC distributions and apply the QA/QC criteria.
  • Open the figure QC-FC_euclidean.png.
  • Visualize the three plots showing QC-FC versus euclidean distance and apply the QA/QC criteria

Immediately report sessions deemed exclude, as an issue in the dataset's repository