AFNI Notes: 7/15/2016

Types of Connectivity

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

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.