N this model. Consequently, we computed the variance with the simulated regional BOLD signals of nodes (regional node-wise variability) (Fig. 5 B and C), along with the variance in the “global signal” computed because the spatial typical of BOLD signals from all 66 nodes (international modelYang et al.7440 | pnas.org/cgi/doi/10.1073/pnas.GSR PERFORMEDPrefrontal GBC in Schizophrenia (N=161) – NO GSR Conceptually Illustrating GSR-induced Alterations in Between-Group Inference Fig. four. rGBC benefits qualitatively adjust when removing late -L Non-uniform Transform Uniform Transform ral ral -R a big GS element. We tested if removing a bigger GS late Increases with preserved 0.07 Increases with altered *** topography from one of the groups, as is usually carried out in connectivity topography 0.06 Betw een-gr Differ ou ence 0.05 Topo p research, alters between-group inferences. We computed rGBC graphy 0.04 me R dia l0.03 l-L focused on PFC, as done previously (17), before (A and B) and dia me 0.02 just after GSR (C and D). Red-yellow foci mark enhanced PFC rGBC 0.01 0 in SCZ, whereas blue foci mark reductions in SCZ relative to Z-value HCS SCZ -4 4 HCSCON SCZHCS HCS. Bars graphs highlight effects with regular betweenPrefrontal GBC in Schizophrenia (N=161) – GSR group effect size estimates. Error bars mark ?1 SEM. (E) GSR Bet Bet late Differ ween-grou Differ ween-grou ence ence ral Topo p Topo p -R 0.04 could uniformly/rigidly transform between-group difference graphy graphy *** maps. Due to larger GS variability in SCZ (purple arrow) 0.03 d= -.five the pattern of between-group variations is shifted, render0.02 ing elevated connectivity in SCZ as the dominant profile (red 0.01 Z-value -4 four signal above the 95 confidence interval indicated by green SCZHCS SCZHCS 0 Focal Focal HCS SCZ reduction reduction planes). If GSR shifts the distribution uniformly, then the increased connectivity is now within the 95 self-confidence interval, but focal reduction becomes apparent with preserved topography. (F) Alternatively, GSR could differentially impact the spatial pattern (i.e., nonuniformly transforming data, illustrated by a qualitatively distinct pattern before and following GSR). We carried out focused analyses to arbitrate amongst these possibilities, suggesting that the impact is predominantly uniform (SI Appendix, Fig. S8). Note: topographies in E and F represent a conceptual illustration, and don’t reflect certain patient data. ***P .001.ABEF95 CIPFC rGBZ [Fz]95 CIGSR PerformedGSR PerformedGS in SCZCDPFC rGBZ [Fz]GS in SCZ95 CI95 CIDiscussionPower and Variability of BOLD Signals in SCZ. Local cortical computations, and in turn large-scale neural connectivity, are profoundly altered in SCZ (13).Formula of 2-Bromo-4-fluorophenol One particular outcome of such dysconnectivity could be an alteration in the distributed gray matter BOLD signal, reflected in improved variance/power.6-Chlorofuro[3,4-c]pyridin-1(3H)-one structure We identified resultsFig.PMID:23329650 five. Computational modeling simulation of BOLD signal variance illustrates a biologically grounded hypothetical mechanism for elevated international and regional variance. (A) We used a biophysically based computational model of resting-state BOLD signals to explore parameters that could reflect empirical observations in SCZ. The two key parameters will be the strength of regional, recurrent self-coupling (w) within nodes (strong lines), and the strength of long-range, worldwide coupling (G) in between 66 nodes in total (dashed lines), adapted from prior function (19) (B and C) Simulations indicate enhanced variance of local BOLD signals originating fr.