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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@gm >>> ail.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 >>>> >>> >>> >> >