AFNI (Analysis of Functional NeuroImages)

1. Preprocess fMRI data (slice timing correction, motion correction, spatial normalization).

2. Perform statistical analysis (GLM) to identify activated brain regions.

3. Visualize results using AFNI's 3D rendering capabilities.

4. Overlay activation maps on anatomical images to localize activity.

5. Perform ROI analysis to quantify activity in specific brain regions.

1. Preprocess fMRI data for each subject.

2. Spatially normalize individual data to a common template space.

3. Perform inter-subject correlation (ISC) analysis to identify shared neural responses.

4. Visualize group-level activation maps.

5. Perform statistical tests to assess the significance of group-level effects.

1. Preprocess DWI data (eddy current correction, motion correction).

2. Estimate diffusion tensors or diffusion kurtosis imaging (DKI) parameters.

3. Perform tractography to reconstruct white matter pathways.

4. Visualize tractographic results in 3D.

5. Quantify white matter properties (e.g., fractional anisotropy, mean diffusivity) along specific tracts.

1. Reconstruct cortical surface models from anatomical MRI data.

2. Map fMRI data onto the cortical surface.

3. Perform statistical analysis on the surface data.

4. Visualize activation maps on the cortical surface using SUMA.

5. Perform surface-based ROI analysis.

1. Spatially normalize fMRI data to a standard template space.

2. Overlay atlas regions onto the normalized data.

3. Extract activity levels within specific atlas regions.

4. Perform statistical analysis to compare activity between different regions or groups.

5. Report results using standardized atlas labels.