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]> 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]> on behalf of Benjamin > Garzon <[email protected]> > Date: Thursday, October 5, 2017 at 1:39 PM > To: "[email protected]" <[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 > > (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 > > 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 > > 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] | 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] > http://lists.humanconnectome.org/mailman/listinfo/hcp-users > > _______________________________________________ > HCP-Users mailing list > [email protected] > http://lists.humanconnectome.org/mailman/listinfo/hcp-users > _______________________________________________ HCP-Users mailing list [email protected] http://lists.humanconnectome.org/mailman/listinfo/hcp-users
