Hi all Yes - I've discussed this with Todd and it's not immediately clear whether the difference is due to: - they used full correlation not partial - they used fewer confound regressors (IIRC) - their prediction method is *very* different (pooling across all relevant features rather than keeping them separate in the multivariate elastic net regression prediction).
Or some combination of all of this. I don't have a strong gut feeling which of these might be the biggest factor, but we should note that the Finn paper took a lot more care over many aspects of their analysis than many studies do, and in particular it was impressive how they got replication of the prediction between completelty separate studies. But yes I would be interested to see this resolved more. With respect to our CCA-based population mode, which covaried more highly with the intelligence measure as you mentioned - I think maybe this points at the main issue possibly being the noisiness of the individual features (netmat edges) and also of the intelligence feature (when all combined together within the elastic net prediction framework). Cheers. > On 6 Oct 2017, at 04:11, Harms, Michael <[email protected]> wrote: > > > In the context of the long resting state runs that we have available, I would > argue that throwing in additional possible confounds is the appropriate thing > to do. Are you suggesting that sex, age, age^2, sex*age, sex*age^2, brain > size, head size, and average motion shouldn’t all be included? > > Regardless, r = 0.21 (without confounds in the MegaTrawl) is a long way from > the r = 0.5 prediction in Finn et al. > > Cheers, > -MH > > -- > Michael Harms, Ph.D. > ----------------------------------------------------------- > Conte Center for the Neuroscience of Mental Disorders > Washington University School of Medicine > Department of Psychiatry, Box 8134 > 660 South Euclid Ave. Tel: 314-747-6173 > St. Louis, MO 63110 Email: > [email protected] <mailto:[email protected]> > > From: <[email protected] > <mailto:[email protected]>> on behalf of Thomas Yeo > <[email protected] <mailto:[email protected]>> > Date: Thursday, October 5, 2017 at 10:01 PM > To: "Glasser, Matthew" <[email protected] <mailto:[email protected]>> > Cc: "[email protected] <mailto:[email protected]>" > <[email protected] <mailto:[email protected]>> > Subject: Re: [HCP-Users] netmats prediction of fluid intelligence > > Certainly one difference is that HCP (i.e., Steve) tends to take the more > conservative approach of regressing a *lot* of potential confounds, which > tends to result in a lower prediction values. You can see that without > confound regression, Steve's prediction is 0.21 versus 0.06. > > Regards, > Thomas > > On Fri, Oct 6, 2017 at 1:44 AM, Glasser, Matthew <[email protected] > <mailto:[email protected]>> wrote: >> Perhaps there is an issue related to data clean up or alignment of brain >> areas across subjects. The Finn study does not appear to have followed the >> recommended approach to either. >> >> Peace, >> >> Matt. >> >> From: <[email protected] >> <mailto:[email protected]>> on behalf of Benjamin Garzon >> <[email protected] <mailto:[email protected]>> >> Date: Thursday, October 5, 2017 at 1:39 PM >> To: "[email protected] <mailto:[email protected]>" >> <[email protected] <mailto:[email protected]>> >> Subject: [HCP-Users] netmats prediction of fluid intelligence >> >> Dear HCP experts, >> >> I'm trying to reconcile the MegaTrawl prediction of fluid intelligence >> (PMAT24_A_CR) >> >> https://db.humanconnectome.org/megatrawl/3T_HCP820_MSMAll_d200_ts2/megatrawl_1/sm203/index.html >> >> <https://db.humanconnectome.org/megatrawl/3T_HCP820_MSMAll_d200_ts2/megatrawl_1/sm203/index.html> >> >> (which shows r = 0.06 between predicted and measured scores) >> >> with the Finn 2015 study >> >> https://www.nature.com/neuro/journal/v18/n11/full/nn.4135.html >> <https://www.nature.com/neuro/journal/v18/n11/full/nn.4135.html> >> >> claiming an r = 0.5 correlation between predicted and measured scores. In >> the article they used a subset of the HCP data (126 subjects), but the >> measure of fluid intelligence is the same one. What can explain the >> considerable difference? As far as I can see the article did not address >> confounding, but even in that case r = 0.21 for MegaTrawl, which is still >> far from 0.5. And this considering that the model used in the article is a >> much simpler one than the MegaTrawl elastic net regressor. >> >> I've been trying to predict fluid intelligence in an independent sample with >> 300 subjects and a netmats + confounds model does not perform better than a >> confounds-only model, more in agreement with the MegaTrawl results. >> >> In the Smith 2015 paper >> >> http://www.nature.com/neuro/journal/v18/n11/full/nn.4125.html >> <http://www.nature.com/neuro/journal/v18/n11/full/nn.4125.html> >> >> the found mode of covariation with the netmats data correlates with fluid >> intelligence with r = 0.38. >> >> Should I conclude from the Megatrawl analysis (as well as from my own) that >> the single measure of fluid intelligence is not reliable enough to be >> predicted based on connectome data, or am I missing something from the Finn >> paper? >> >> I would be happy to read people 's thoughts about this topic, in view of the >> disparate results in the literature. >> >> Best regards, >> >> Benjamín Garzón, PhD >> Department of Neurobiology, Care Sciences and Society >> Aging Research Center | 113 >> <https://maps.google.com/?q=113%C2%A030+Stockholm+%7C+G%C3%A4vlegatan+16&entry=gmail&source=g> >> 30 Stockholm | Gävlegatan 16 >> <https://maps.google.com/?q=113%C2%A030+Stockholm+%7C+G%C3%A4vlegatan+16&entry=gmail&source=g> >> [email protected] <mailto:[email protected]> | www.ki-su-arc.se >> <https://email.ki.se/owa/redir.aspx?C=LDNa9T7Nak68Br6ZyIC_J4KUwCiWMdEIQwVElfLYlCPLbdpUruOe0XhySwY-dNAYT9JyRT4AtFo.&URL=http%3a%2f%2fwww.ki-su-arc.se%2f> >> ______________________________________ >> Karolinska Institutet – a medical university >> >> >> _______________________________________________ >> HCP-Users mailing list >> [email protected] <mailto:[email protected]> >> http://lists.humanconnectome.org/mailman/listinfo/hcp-users >> <http://lists.humanconnectome.org/mailman/listinfo/hcp-users> >> _______________________________________________ >> HCP-Users mailing list >> [email protected] <mailto:[email protected]> >> http://lists.humanconnectome.org/mailman/listinfo/hcp-users >> <http://lists.humanconnectome.org/mailman/listinfo/hcp-users> > > _______________________________________________ > HCP-Users mailing list > [email protected] <mailto:[email protected]> > http://lists.humanconnectome.org/mailman/listinfo/hcp-users > <http://lists.humanconnectome.org/mailman/listinfo/hcp-users> > > > The materials in this message are private and may contain Protected > Healthcare Information or other information of a sensitive nature. If you are > not the intended recipient, be advised that any unauthorized use, disclosure, > copying or the taking of any action in reliance on the contents of this > information is strictly prohibited. If you have received this email in error, > please immediately notify the sender via telephone or return mail. > _______________________________________________ > HCP-Users mailing list > [email protected] <mailto:[email protected]> > http://lists.humanconnectome.org/mailman/listinfo/hcp-users > <http://lists.humanconnectome.org/mailman/listinfo/hcp-users> --------------------------------------------------------------------------- Stephen M. Smith, Professor of Biomedical Engineering Head of Analysis, Oxford University FMRIB Centre FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK +44 (0) 1865 222726 (fax 222717) [email protected] http://www.fmrib.ox.ac.uk/~steve <http://www.fmrib.ox.ac.uk/~steve> --------------------------------------------------------------------------- Stop the cultural destruction of Tibet <http://smithinks.net/> _______________________________________________ HCP-Users mailing list [email protected] http://lists.humanconnectome.org/mailman/listinfo/hcp-users
