Towards a prediction of cognitive deficits based on underlying connectivity differences in schizophrenia
Intrinsic connectivity networks (ICNs) in schizophrenia (SZ) exhibit aberrant resting state connectivity based on regional temporal correlations relative to healthy subjects [Fornito et al., 2012]. We examined ICNs displaying abnormal connectivity in schizophrenia in an rsfMRI study and related them to similar networks extracted from the BrainMap database. BrainMap meta-analyses have identified several reliable and meaningful circuits of coactivations, i.e., areas commonly reported together in various neuroimaging analyses [Laird et al., 2013]. These circuits have been linked to coactivation networks in resting state datasets, leading to the hypothesis that they represent intrinsic networks underlying cognitive processes [Laird et al., 2011]. Here, we provide a new method of linking resting state connectivity differences to predicted cognitive deficits using metadata archived in the BrainMap database.