There was an error in my previous post. I used 4-4-4mm FWHM filter for pre-smoothing, not 6-6-6mm. (I was confused with my other FLB study that is done in the PET-CT). So, the estimated smoothness by SPM (before doing additional smoothing with 10mm FWHM) actually made sense… Now, everything is clear! Thanks! Ji Hyun _____ From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Ji Hyun Ko Sent: Tuesday, May 25, 2010 4:14 PM To: [log in to unmask] Subject: Re: partial correlation for ligand PET study in SPM5 Dear Alexander, That explains a lot. Thank you so much for your quick feedback! Best, Ji Hyun _____ From: Alexander Hammers [mailto:[log in to unmask]] Sent: Tuesday, May 25, 2010 4:09 PM To: Ji Hyun Ko Cc: [log in to unmask] Subject: Re: [SPM] partial correlation for ligand PET study in SPM5 Dear Ji Hyun, Ah, you hadn't mentioned the HRRT before - I didn't know we were talking Formula One PET scanning ;-)! In that case, if initially you didn't apply any smoothing after normalisation but prior to analysis, the FWHM does look as if it could conceivably be derived from HRRT images. By pre-smoothing the 4D images, you would simply have removed some of the noise, but the BPND images would still be as rough as normal PET images. As I'm sure you know, for PET parametric image correlation analyses as for any other SPM analysis, smoothing is necessary for statistical reasons as Tom already said, as well as neuroanatomical ones (to get similar regions in register) - so not only justifiable as per your question, but necessary. In the event of being interested in really small areas, you could also try to preserve the HRRT advantages by doing an ROI analysis, too, and correlate the mean BPNDs from those. Hope this helps, Alexander On 25 May 2010, at 20:43, Ji Hyun Ko wrote: Dear Alexander, Thanks for your comment. Actually, our raw PET images (4D) look somewhat similar to the figure that you referred, because we used HRRT scanner (it’s really noisy). However, smoothing makes the image much better, and the BP image actually looks fine without additional smoothing. Anyway, that’s one of the reasons why we smoothed before making BP image. I thought that pre-smoothing may help with motion correction, coregistration with MRI and SRTM fitting because there is really a lot of noise in HRRT image. There are several modeling papers tested SRTM for FLB, and most of them suggest that it is good enough although some suggest some degree of bias. The reason why we used SRTM for FLB was to use the residual t-test (Aston et al., Human Brain Mapping, 2000) as we previously did in Ko et al. (NeuroImage, 2009, vol 46(2), pp 516-521). We used PET-CT in the 2009 paper, so the image was much less noisy. In the paired t-test using residuals (Aston et al., 2000), there was no problem with t-values, and we verified it by post-hoc VOI analysis. Here, since there is no validated way to do the voxel-based correlation analysis using residuals of fitting (Aston et al., 2000; and we are currently working on it!), we used SPM for the correlation analysis. So, the possible reason that I can think of is pre-smoothing of raw PET images did not end up with smooth BP image which I cannot explain why... If we assume that we didn’t have any other quantification errors, is it justifiable to do smoothing on the BP image before we do SPM correlation analysis? Best, Ji Hyun _____ From: Alexander Hammers [mailto:[log in to unmask]] Sent: Tuesday, May 25, 2010 1:32 PM To: Ji Hyun Ko Cc: [log in to unmask] Subject: Re: [SPM] partial correlation for ligand PET study in SPM5 Dear Ji Hyun, Ok, there's the likely solution - your PET model probably doesn't quite work! The images didn't "loose smoothness" but can become very "rough" by aberrant high and low values in neighbouring pixels. What do your images look like - something like the top left image in Fig 3A in Neuroimage 38 (2007) 82 – 94? I've never worked with FLB. As a D2 ligand it's possible that the SRTM would work - but here it presumably produced aberrant values. This could have a large variety of reasons: - subject motion - unsuitable model - quantification errors (e.g. different scalefactors from frame to frame) - reconstruction errors (e.g. emission - transmission mismatch) - and, quite possibly, mixing different kinetic classes by smoothing your 4D prior to modelling. I'd start by looking for subject motion and leaving out the pre-modelling smoothing (plus of course prior modelling work - do others use SRTM for this ligand, and, if so, using which model?). I definitely wouldn't accept the images (and results) as they are now - you likely had a major quantification error in there which you've now simply smoothed into oblivion. Hope this helps! All the best, Alexander --------------------------------- Alexander Hammers, MD PhD <image002.jpg> NEW Affiliation: Chair in Functional Neuroimaging Fondation Neurodis http://www.fondation-neurodis.org/ NEW Postal Address: CERMEP – Imagerie du Vivant Hôpital Neurologique Pierre Wertheimer 59 Boulevard Pinel 69003 Lyon France Telephone +33-(0)4-72 68 86 34 Fax +33-(0)4-72 68 86 10 Email [log in to unmask] <x-msg:[log in to unmask]> ;[log in to unmask] <x-msg:[log in to unmask]> Web (London lab) http://www1.imperial.ac.uk/medicine/people/alexander.hammers.html http://www.csc.mrc.ac.uk/Research/Groups/ECN/Epilepsy/ --------------------------------- Other affiliations: Visiting Reader; Honorary Consultant Neurologist Division of Neuroscience and Mental Health, Faculty of Medicine Imperial College London Room 243, Cyclotron Building Hammersmith Hospital, DuCane Road, London W12 0NN and Honorary Reader in Neurology; Honorary Consultant Neurologist Department of Clinical and Experimental Epilepsy National Hospital for Neurology and Neurosurgery/ Institute of Neurology, UCL 33 Queen Square, London WC1N 3BG On 25 May 2010, at 18:12, Ji Hyun Ko wrote: Dear Tom, I think I found a solution. Originally, I smoothed the dynamic images before producing binding potential (BP) images with 6mm-6mm-6mm FWHM Gaussian filter. When I produced the binding potential images (which is 4D -> 3D), I think it somehow lost its smoothness…<- Does this make sense to you? So, I smoothed the BP images (3D) with 10-10-10mm FWHM filter, and then run the SPM. Then, it gave me estimated smoothness of about 20mm. Now it gives me realistic t-values! So, I guess my problem is “solved,” but I am still curious why the images lost the smoothness when I produced BP images (4D->3D). I used Roger Gunn’s simplified reference tissue model. Best Regards, Ji Hyun _____ To: [log in to unmask] CC: [log in to unmask] From: [log in to unmask] Subject: Re: [SPM] partial correlation for ligand PET study in SPM5 Date: Sun, 23 May 2010 23:29:17 +0100 Dear Ji, Hmm... ~5mm FWHM smoothness... does that surprise you? What sort of reconstruction filter was used? I would have thought the smoothness would be much greater (the estimated smoothness accounts for both intrinsic and applied smoothness, and should at least exceed the size of any applied Gaussian kernel smoothing. -Tom On Fri, May 21, 2010 at 2:56 PM, Ji Hyun Ko <[log in to unmask]> wrote: Dear Tom, Thank you so much for your advice. I will take a look at the SnPM. Anyway, the SPM gave me the following: Degree of freedom = [1.0, 4.0] FWHM = 4.3 4.7 5.3 mm mm mm; 2.1 2.4 2.6 (voxels); Volume: 1156096 = 144512 voxels = 8360.6 resels Voxel size: 2.0 2.0 2.0 mm mm mm; (resel = 13.25 voxels) Best, Ji Hyun _____ From: [log in to unmask] [mailto:[log in to unmask]] On Behalf Of Thomas Nichols Sent: Friday, May 21, 2010 4:16 AM To: Ji Hyun Ko Cc: [log in to unmask] Subject: Re: [SPM] partial correlation for ligand PET study in SPM5 Dear Ji, You are right to be skeptical of amazing results from just 7 subjects. Can you also report your estimated smoothness (FWHM in voxels and mm, and Resel count)? That will help to see if you have a chance of getting reasonable accuracy out of the RFT corrected P-values. With such a tiny amount of data, you also might want to try a nonparametric approach, with variance smoothing in particular. See SnPM toolbox under SPM extensions for more details. -Tom On Thu, May 20, 2010 at 9:14 PM, Ji Hyun Ko <[log in to unmask]> wrote: Dear SPM users, I have two questions regarding how to do the partial correlation for a ligand PET study. I have 7 patients and each of them had one FLB-PET scan. I produced binding potential maps (3D image) using some in-house software. I have two covariates. One of the covariates is the behavioural score and the other is age which should be controlled for (nuisance covariate). So, I have design matrix of X = [behaviour, age, constant]. Of course, Those are column vectors. And, I used C = [1 0 0]. My first question is...is this the right way to do partial correlation? My second question is about extent threshold. I put 0.001 for uncorrected height threshold, and put 10 for extent threshold. And, I think the SPM gives me some unrealistic values. All of the p_corrected in cluster-level was less than 0.001. And, in the left bottom of the result window, it gives: Height threshold: T=7.17, p=0.001 (1.000) {p<0.001 (unc.)} Extent threshold: k = 10 voxels, p=0.000 (0.000) Expected voxels per cluster, <k>=0.156 Expected number of clusters, <c>=0.00 Expected false discovery rate, <=0.15 I think there is something wrong. How can I have such "good" p_corrected values with only 7 scans? Please help... Best, Ji Hyun ____________________________________ Ji Hyun Ko, PhD Post-doctoral Fellow, Centre for Addiction and Mental Health & Toronto Western Research Institute, University of Toronto Physical Address: 455 Spadina Ave., Suite 402 Toronto, Ontario, M5S 2G8 CANADA Mailing Address: PET Centre, CAMH 250 College street Toronto, Ontario, M5T 1R8 CANADA Phone: +1-416-535-8501 ext. 7396 Fax: +1-416-979-3855 -- ____________________________________________ Thomas Nichols, PhD Principal Research Fellow, Head of Neuroimaging Statistics Department of Statistics & Warwick Manufacturing Group University of Warwick Coventry CV4 7AL United Kingdom Email: [log in to unmask] Phone, Stats: +44 24761 51086, WMG: +44 24761 50752 Fax: +44 24 7652 4532 -- ____________________________________________ Thomas Nichols, PhD Principal Research Fellow, Head of Neuroimaging Statistics Department of Statistics & Warwick Manufacturing Group University of Warwick Coventry CV4 7AL United Kingdom Email: [log in to unmask] Phone, Stats: +44 24761 51086, WMG: +44 24761 50752 Fax: +44 24 7652 4532 _____ MSN Dating: Find someone special. 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