AFNI Notes: 7/15/2016

Types of Connectivity

Overview: AFNI Connectivity Handout

Resting

Seed-based:

1)      Prepare your data

You should consider bandwidth filtering your data.

                              uber_subject.py/afni_proc.py: preprocess [consider adding -regress_bandpass 0.01 0.1]

                              Or add 3dFourier

3dFourier -prefix output file name output

-lowpass f           low pass filter with a cutoff of f Hz

-highpass f          high pass filter with a cutoff of f Hz

-ignore n              ignore the first n images [default = 1]

-retrend               remove trends [polort 1], filter, then restore trends

List input at end

3dFourier -prefix $out -lowpass .1 -highpass .01 -ignore 2 -retrend $in

2)      Pull seed Timeseries

                              3dROIstats: example.txt

3)      Deconvolve

                              3dDeconvolve: example.txt

4)      Transform data for group analysis

                              R2-to-r-to-fisherz

ICA: AFNI does have 3dICA.R but in general melodic or GiFT have more to offer for this kind of analysis

               Fsl:melodic          use raw nii.gz files

               Spm~GiFT            use prepped files (afni_proc.py is okay for use if not SPM) just split nii.gz files to 1 per time series and have placed in a subject directory (i.e., Study/Subject1/T1.hdr…) use fslsplit

Graph: beyond the scope of today but may add later if we want

 

Task based

PPI-seed:

1)      Prepare your data

You should consider bandwidth filtering your data.

                              uber_subject.py/afni_proc.py: preprocess [consider adding -regress_bandpass 0.01 0.1]

                              Or add 3dFourier

2)      Pull seed Timeseries

                              3dROIstats: example.txt

3)      Splice file using 3dTfitter: example.txt

4)      Deconvolve

                              3dDeconvolve: example.txt 

5)      Transform data for group analysis

                              R2-to-r-to-fisherz

Granger: This is a causal modeling much like DCM (see powerpoint here) in that temporal sequence is analyzed. Granger is carried out in afni on vector or brick files.