|
95 | 95 | ====================
|
96 | 96 | The title for the final grouped-network connectome file is dependent on the group names. The resulting file for this example
|
97 | 97 | is 'parkinsons-controls.cff'. The following code implements the format a-b-c-...x.cff for an arbitary number of groups.
|
98 |
| -""" |
99 |
| - |
100 |
| -""" |
101 | 98 |
|
102 | 99 | .. warning::
|
103 | 100 |
|
104 |
| - The 'info' dictionary below is used to define the input files. In this case, the diffusion weighted image contains the string 'dwi'. |
| 101 | + The 'info' dictionary below is used to define the input files. In this case, the diffusion weighted image contains the string 'dti'. |
105 | 102 | The same applies to the b-values and b-vector files, and this must be changed to fit your naming scheme.
|
106 | 103 |
|
107 |
| -""" |
108 |
| - |
109 |
| -""" |
110 | 104 | The workflow is created given the information input about the groups and subjects.
|
111 | 105 |
|
112 | 106 | .. seealso::
|
|
115 | 109 | * nipype/workflows/dmri/mrtrix/connectivity_mapping.py
|
116 | 110 | * :ref:`dmri_connectivity_advanced`
|
117 | 111 |
|
118 |
| -""" |
119 |
| - |
120 |
| -""" |
121 | 112 | We set values for absolute threshold used on the fractional anisotropy map. This is done
|
122 | 113 | in order to identify single-fiber voxels. In brains with more damage, however, it may be necessary
|
123 | 114 | to reduce the threshold, since their brains are have lower average fractional anisotropy values.
|
124 |
| -""" |
125 | 115 |
|
126 |
| -""" |
127 |
| -We invert the b-vectors in the encoding file, and set the |
128 |
| -maximum harmonic order of the pre-tractography spherical deconvolution step. This is |
129 |
| -done to show how to set inputs that will affect both groups. |
130 |
| -""" |
| 116 | +We invert the b-vectors in the encoding file, and set the maximum harmonic order |
| 117 | +of the pre-tractography spherical deconvolution step. This is done to show |
| 118 | +how to set inputs that will affect both groups. |
131 | 119 |
|
132 |
| -""" |
133 | 120 | Next we create and run the second-level pipeline. The purpose of this workflow is simple:
|
134 | 121 | It is used to merge each subject's CFF file into one, so that there is a single file containing
|
135 | 122 | all of the networks for each group. This can be useful for performing Network Brain Statistics
|
|
0 commit comments