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Dear Sir,

Sorry for late reply, My original intent of my current study is to analyse changes in Gray matter for the patients suffering from Bilateral vestibulopathy , I have 50 subjects ( 25 healthy controls and 25 BLVP patients , I have acquired T1w, rfRMI,DTI imaging data ), after i did VBM on global level i did not notice any significant differences between 2 groups, Then i proceeded to see if there are significant changes in hippocampus region using VBM analysis with Hippocampus as ROI mask, where i noticed specific differences .

But i have also performed analysis using Freesurfer to see if there are changes in cortical thickness between healthy controls and BLVP patients .

My goal is to analyse significant changes in patients with Bilateral vestibulopathy w.r.t healthy controls and further study structural and functional connectivity between Visual system and Vestibular network in general.

Since i noticed some significant differences in Hippocampus ROI i would like to further parcellate Hippocampus and see which specific sub parcellations of Hippocampus gray matter volume show significant differences w.r.t to health controls.

This is my brief idea how i would like to proceed with my task, If you think my approach has some inconsistencies and if i can use any other methods to study Gray matter volume changes in Bilateral vestibulopathy patients i would be really interested to study your ideas , If you have some journals to point in this direction kindly please let me know.

Thanks

Vaudev

On 20 January 2017 at 09:52, Anderson M. Winkler <[log in to unmask]> wrote:

Hi Vasudev,

You probably need "-v" (lower case) in fslstats. Also, instead of dividing, need to multiply if the goal is to have the total amount of GM.

(I have no idea what you will use this for, though.)

All the best,

Anderson

On 19 January 2017 at 12:38, Dev vasu <vasudevamurthy.devulapally@gmail.com> wrote:
Dear sir,

Yes, I would like to compare one ROI with another and see GM in each . I have done following steps let me know if i am right in this

fslmeants –i <GM_mod_merg_s3> –m >Hippocampus_L_clustere_corrp_tstat1.nii.gz >> Hippocampus_L_cluster1.txt
fslmeants –i <GM_mod_merg_s3> –m >Hippocampus_L_clustere_corrp_tstat2.nii.gz >> Hippocampus_L_cluster2.txt
fslmeants –i <GM_mod_merg_s3> –m >Hippocampus_L_clustere_corrp_tstat2.nii.gz >> Hippocampus_L_cluster3.txt
fslmeants –i <GM_mod_merg_s3> –m >Hippocampus_L_clustere_corrp_tstat4.nii.gz >> Hippocampus_L_cluster4.txt

First i calculated mean voxel instensity in each ROI

then

fslstats <GM_mod_merg_s3> –k <Hippocampus_L_clustere_corrp_tstat1.nii.gz> –V >> Volume_Voxels.txt

Calculated the volume and voxels of each respective cluster

the i performed

total volume / n * intensity value for every subject at the fslmeants output ( Hippocampus_L_cluster.txt) with this i can extract volume voxels and then possibly compare which ROI has more volume than the other

( For each ROI, i made a Mask ~ 1 Cluster = 1 mask )

Please let me know if my approach is right or not.

Thanks
Vasudev

On 19 January 2017 at 11:15, Anderson M. Winkler <[log in to unmask]> wrote:
Hi Vasudev,

Please see below:

On 18 January 2017 at 13:29, Dev vasu <vasudevamurthy.devulapally@gmail.com> wrote:
Dear all,

I have run randomise of different ROI masks eg Hippocampus subiculum,dentate gyrus,entorhinal cortex,Cornu Ammonis , I have obtained tstats for each ROI mask,

Ok, so one spatial map per ROI, each with voxelwise t-statistics.

now i would like to compare the intensity of voxels in each ROI and statistically interpret it,

Do you mean comparing one ROI with another to see which have more or less GM? If that is the case, then use fslmeants or fslstats to average the GM within mask, then compute the difference between the ROIs, then run a 1-sample t-test.

For multiple ROIs, there are multiple possible such tests, and it will be necessary to correct across them (either via a "fake image" in randomise, or with "-corrcon" in PALM).

All the best,

Anderson

How could i possibly approach this process, any help appreciated.

Thanks
Vasudev