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Dear Howard, I don't think there is any convention that the cluster-forming threshold should be p= 0.001 and even if there is, there is no mathematical reason behind it. Different cluster-forming thresholds make your analysis sensitive to different kinds of effects, that is why cluster-forming threshold should not be set based on the data. A higher threshold (in terms of p-value) would mean larger clusters with weaker effects inside, and a lower threshold vice-versa. As I said I think p<0.05 or p<0.01 are quite reasonable choices for sensor-level EEG or TF data because we often have the intuition that large chunks of significant voxels are a true physiological effect and cluster-level correction just quantifies that intutition saying how large is large enough. However in fMRI or source data, it is not necessarily true that large clusters are more likely to represent true effects. I used cluster-level correction with that kind of threshold for TF data in my recent paper http://www.ncbi.nlm.nih.gov/pubmed/22855804 There must be plenty of other examples, people of the list might suggest something as well. Best, Vladimir On Tue, Aug 6, 2013 at 2:19 PM, H.Bowman <[log in to unmask]> wrote: > Dear Vladimir, > > Thank you for your valuable responses to my PhD student, Farzad's, > questions > concerning our SPM for EEG analysis. We did actually meet a few years back > when I gave a talk to a method's meeting at the Centre for Neuroimaging > Sciences. > > As Farzad indicated, we wish to plot scalp maps through time for a > cluster-level analysis. As you suggested, we can choose an uncorrected > analysis to avoid peak-level correction. Then you suggest setting an > uncorrected threshold of 0.05 or 0.01. To be clear, this is what would > usually be called the cluster forming threshold; right? That is, it is the > alpha-level that is applied separately at each space-time point? > > I was under the impression that the standard/ default setting of this was > 0.001, at least this certainly seems to be the case in the fMRI domain. > However, I am also aware that setting of this parameter is somewhat > arbitrary. > > So, are there any papers that explore different settings of this parameter > in the EEG setting, or at the least a prior precedent for setting this to > higher than 0.001. > > Many thanks for your continued help in this matter - it is very much > appreciated. > > Howard + Farzad. > > -------------------------------------------- > Professor Howard Bowman (PhD) > Professor of Cognition & Logic > Joint Director of Centre for CNCS > Centre for Cognitive Neuroscience and Cognitive Systems > and the School of Computing, > University of Kent at Canterbury, > Canterbury, Kent, CT2 7NF, United Kingdom > Telephone: +44-1227-823815 Fax: +44-1227-762811 > email: [log in to unmask] > WWW: http://www.cs.kent.ac.uk/people/staff/hb5/ > > > > ---------- Forwarded message ---------- > From: Vladimir Litvak <[log in to unmask]> > Date: Fri, Aug 2, 2013 at 2:51 PM > Subject: Re: [SPM] Cluster level > To: Farzad Beheshti <[log in to unmask]>, "[log in to unmask]" > <[log in to unmask]> > > > > Dear Farzad, > > What you definitely shouldn't do is choose the threshold based on the data > to get significant results. You can either use peak-level correction and > then FWE = 0.05 is a common choice or you choose cluster-level correction, > in which case you would start with an uncorrected threshold of usually 0.05 > or 0.01 and see what clusters are significant. You can then plot just the > significant clusters by using the size of the smallest significant cluster > as the extent threshold. > > Best, > > Vladimir > > > On Fri, Aug 2, 2013 at 2:45 PM, Farzad Beheshti < > [log in to unmask]> > wrote: > > > Dear Vladimir, > > Thank you very much of your answer but to clarify everything: > > I have faced with a condition that nothing is significant at > peak-level. I mean if I choose FWE = 0.05 and extent threshold default > value > of zero, nothing is reported at statistical table, But if I change FWE = > 0.08 with default extent threshold, significant peak at this level (0.08) > appears in table that are actually more significant at cluster level based > on the information in table(0.000) with a big cluster size. Now my question > is that how should I choose an appropriate FWE level? > > Should I choose FWE = 0.9 to become completely free of threshold > and > then apply a limitation on cluster size (extent threshold) to take active > clusters or no? > > Actually I do not have any idea how should I choose the FWE at > first > step, in this case? > > Thanks > > > On Wed, Jul 31, 2013 at 5:18 PM, Vladimir Litvak > <[log in to unmask]> wrote: > > > Dear Farzad, > > Both SPM8 and SPM12 present cluster-level p-values for > t-tests but only SPM12 does it also for F-tests. You can present a MIP of > significant clusters by noting the the size of the smallest significant > cluster and using it as your extent threshold. > > Best, > > Vladimir > > > > On Tue, Jul 30, 2013 at 10:46 AM, Farzad Beheshti > <[log in to unmask]> wrote: > > > Dear SPMs > > As far as I know SPM8 does not do any EEG cluster > analyses but it does Peak-level analyses. It has been tried to solve this > problem somehow in SPM12 beta version. > > My reason is that the statistical table at version > 12 reports significant clusters in addition to significant peaks but not in > version 8 (only peak level is reported). > > However, when we try to show statistical MIPs both > versions take into account the peak-level statistics and MIPs are the same > in both of the versions. > > Is there any way to show significant clusters > instead of peaks on the top of the scalp (or actually both of them) in > SPM12? > > Thank you. > > >