Hi all, Thanks for your comments. Reassuring although somewhat disappointing at the same time. As as far as I know there's no external validation of their fluid intelligence model. They replicated the prediction for their sustained attention model (Rosenberg 2016), but there may be quite different processes at play there. I think it's a great study in any case.
Julien, when you say the method still has predictive value in the large sample 'without confounds', do you mean without removing confounds or after deconfounding? It's also not clear to me whether the scores the Ma study reported are deconfounded or not, but I guess they are not. If one is interested in the added value of fMRI predicting cognition (my case), it makes sense to be conservative, so I would be interested in knowing whether there's something left in the deconfounded space. Best regards, Benjamín Garzón, PhD Department of Neurobiology, Care Sciences and Society Aging Research Center | 113 30 Stockholm | Gävlegatan 16 [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 ________________________________ From: [email protected] [[email protected]] on behalf of Julien Dubois [[email protected]] Sent: Friday, October 06, 2017 5:33 PM To: [email protected] Subject: Re: [HCP-Users] netmats prediction of fluid intelligence Hi all to chime in: we have done extensive work over the past year to replicate and understand the prediction of IQ obtained in the Finn study. Our manuscript is about to be submitted. Take away points: -- the high effect size they find is partly due to small sample size (118 subjects) and to the specific subject sample -- their method still has predictive value in the larger sample of subjects, though the effect size is much reduced (similar to Megatrawl without confounds) -- the specifics of preprocessing/denoising and predictive model don't have a huge effect on the final result (when enough subjects are included) See also the work presented by Feilong Ma at OHBM this year, which took great care in aligning subjects (MSMall + whole-brain hyperalignment) in a much larger sample than the Finn study: https://files.aievolution.com/hbm1701/abstracts/37710/3928_Ma.pdf Happy to discuss further if someone is interested. - Julien Postdoc | Cedars-Sinai Medical Center // Caltech | +1 (310)423-8377 | nigiri.caltech.edu/~jdubois<http://nigiri.caltech.edu/~jdubois> _______________________________________________ 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
