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Thank you. Regards On Sun, Aug 6, 2017 at 4:03 PM, Anderson M. Winkler <[log in to unmask]> wrote: > Hi VM, > > I would read this as indicating that modalities 1 and 3 aren't adding much > information regarding the effect being sought not already provided by > modality 2. I would still not argue for not collecting any of the other > modalities based on this test alone. A reason is that, in the complete > absence of signal in modalities 1 and 3, we would expect the result from > NPC to be less strong than with modality 2 alone (that is as if modalities > 1 and 3 were "diluting" the signal from modality 2). Another reason is that > the effect for that partial test may be too weak, requiring large samples > to be seen. > > A great case for use of NPC is to combine different views from the same > data. For example, using DTI metrics we can compute FA, MD, AD, RD, etc. > Some of these may have different sensitivity profiles, yet all reflect some > changes in the water diffusion. Combining them with NPC gives that extra > strength to find differences that otherwise might have remained unseen. > > Another case is to combine cortical thickness with surface area: both may > change modestly in opposite directions, leading to no net volume changes. > NPC is a good replacement for grey matter volume in this case, for keeping > the sensitivity (we have a biorXiv paper > <http://www.biorxiv.org/content/early/2017/04/02/074666> on this). > > One more case for NPC is to combine results of temporal ICA, investigating > whether independent temporal time courses share a common spatial overlap > across subjects. > > I would argue that NPC shouldn't be used to suggest absence of effects for > a partial test on the grounds that that test didn't change the results of > the combination. > > Hope this clarifies! > > All the best, > > Anderson > > > On 6 August 2017 at 11:38, neuroimage analyst < > [log in to unmask]> wrote: > >> Thanks Anderson for the insight. >> >> I was expecting some additional regions to show up using npc which was >> not found using independent statistical tests with each modality because >> then one could make the argument of increase in power due to increase in >> modalities. >> >> However, in my case, the cluster that showed up overlapped with the >> cluster using modality 2 alone with no additional region showing >> significance. >> >> Given such a scenario, my question is still very basic in terms of >> whether or not future studies should continue to acquire the 3rd modality >> as 2nd modality alone seems to lend enough power to find those regions that >> are different between the groups. >> >> But, again, may be that is not how it supposed to be interpreted and one >> could still argue for the acquisition of the 3rd modality. If I have to >> make the case for the acquisition of the 3rd modality; then how should I go >> ahead and make it? >> >> I will greatly appreciate your response. >> >> Thanks >> >> Regards >> >> VM >> >> On Sun, Aug 6, 2017 at 5:00 AM, Anderson M. Winkler < >> [log in to unmask]> wrote: >> >>> Hi VM, >>> >>> I think the question is a bit ill posed... NPC combines the evidence >>> against the null from the separate (partial) tests. It can be understood as >>> a meta-analysis, except that, instead of summary results from different >>> studies, we use the the actual subject-level data, and further, we use the >>> same subjects, while taking into account, through permutation, the >>> non-independence between the various measurements obtained per subject. >>> >>> NPC can also be seen as a non-parametric counterpart to MANOVA with some >>> nicer properties, such as the ability to identify the direction of the >>> effects, both jointly or separately, and higher power, particularly as the >>> number of modalities being combined increase. >>> >>> The exact profile with which each partial test contribute to the final >>> test is known from the combining function used with the test. See Figure 3 >>> from our NPC paper >>> <http://onlinelibrary.wiley.com/doi/10.1002/hbm.23115/full>. As you can >>> see, for some functions, even non-significant results can contribute >>> (together with others) to a significant joint effect. This isn't the same >>> as "how much each has contributed" because there are non-linearities in the >>> way these functions become significant, even more so when they aren't >>> independent. >>> >>> Hope this helps! >>> >>> All the best, >>> >>> Anderson >>> >>> >>> On 5 August 2017 at 13:58, neuroimage analyst < >>> [log in to unmask]> wrote: >>> >>>> Hi, >>>> >>>> I would greatly appreciate if anyone could provide me some input on how >>>> to interpret the results of multivariate statistics: >>>> >>>> Briefly, I have 3 modalities and 2 groups and I ran statistics on each >>>> of these modalities separately and identified regions that are >>>> significantly different between the groups in each modality separately >>>> (FWER corrected). >>>> >>>> Then I ran the 3 modalities using -npc in PALM and found a cluster >>>> (FWER) that overlaps with the significant cluster obtained using modality >>>> 2. >>>> >>>> Is there a way to understand how much has each modality contributed to >>>> the cluster obtained to be significant using -npc way? Or can this be >>>> interpreted as the future studies could only focus on modality 2 and no >>>> need to test for modality1 and modality3? >>>> >>>> Thanks >>>> >>>> Regards >>>> -VM >>>> >>>> >>>> >>> >> >