Dear Tanmay,

Regarding 1, the images generated in the model estimation are:
- mask, a binary image indicating which voxels were considered in the analysis, (see e.g. http://www.ncbi.nlm.nih.gov/pubmed/18848... for more on this with regard to VBM)
- beta, the estimated linear model parameters at each voxel, in your case of a two-sample design, these are just the two group mean images
- con, the contrasts of the parameters, which in your case of a [-1 1] contrast will simply be beta_0002 - beta_0001
- spmT, the t-statistic maps formed by dividing the contrast image by its estimated standard error
- ResMS, the estimated variance (residual mean squares), from which the above-mentioned standard error is derived (in more detail, the standard error is proportional to the square root of ResMS and a term that relates to the design matrix X and contrast c as sqrt(c'*pinv(X'*X)*c), which in your case roughly reflects the fact that the standard error is inversely proportional to the number of subjects you have)
- RPV, the resels-per-voxel image related to the smoothness (roughness) estimate needed for random field theory (see e.g. http://www.ncbi.nlm.nih.gov/pubmed/15193... for more on RPV)

Regarding 2, I think the most likely problem is incorrect orientation of the original images, perhaps reflecting incorrect conversion from DICOM (or other format) to NIfTI. To check this, you can use the "check reg" button, go into the SPM templates directory and pick the T1 template, then pick one or more of your images. If the orientation is very far off, you won't be able to see both the template and the original at the same time, in which case use the "Display" button and just pick one of your images. To fix the orientation, you can either right-click your image after Check Reg and there is a context menu option for reorientation, or you can edit the orientation related parameters in the Display GUI.

Manual reorientation is (somewhat laboriously!) demonstrated in the first 10 minutes of following video:
 http://www.fil.ion.ucl.ac.uk/spm/course/video/#DemoPreproc

I hope that helps, best wishes,
Ged

Originally posted by Tanmay Nath: