Altered global brain signal in schizophrenia

Abstract

Neuropsychiatric conditions like schizophrenia display a complex neurobiology, which has long been associated with distributed brain dysfunction. However, no investigation has tested whether schizophrenia shows alterations in global brain signal (GS), a signal derived from functional MRI and often discarded as a meaningless baseline in many studies. To evaluate GS alterations associated with schizophrenia, we studied two large chronic patient samples (n = 90, n = 71), comparing them to healthy subjects (n = 220) and patients diagnosed with bipolar disorder (n = 73). We identified and replicated increased cortical power and variance in schizophrenia, an effect predictive of symptoms yet obscured by GS removal. Voxel-wise signal variance was also increased in schizophrenia, independent of GS effects. Both findings were absent in bipolar patients, confirming diagnostic specificity. Biologically informed computational modeling of shared and nonshared signal propagation through the brain suggests that these findings may be explained by altered net strength of overall brain connectivity in schizophrenia.

Keywords: global signal; psychiatric illness; resting-state.

Fig. 1.

Power and variance of CGm signal in SCZ and BD. (A) Power of CGm signal in 90 SCZ patients (red) relative to 90 HCS (black) (see SI Appendix, Table S1 for demographics). (B) Mean power across all frequencies before and after GSR indicating an increase in SCZ [F(1, 178) = 7.42, P < 0.01], and attenuation by GSR [F(1, 178) = 5.37, P < 0.025]. (C) CGm variance also showed increases in SCZ [F(1, 178) = 7.25, P < 0.01] and GSR-induced reduction in SCZ [F(1, 178) = 5.25, P < 0.025]. (D–F) Independent SCZ sample (see SI Appendix, Table S2 for demographics), confirming increased CGm power [F(1, 143) = 9.2, P < 0.01] and variance [F(1, 143) = 9.25, P < 0.01] effects, but also the attenuating impact of GSR on power [F(1, 143) = 7.75, P < 0.01] and variance [F(1, 143) = 8.1, P < 0.01]. (G–I) Results for BD patients (n = 73) relative to matched HCS (see SI Appendix, Table S3 for demographics) did not reveal GSR effects observed in SCZ samples [F(1, 127) = 2.89, P = 0.092, n.s.] and no evidence for increase in CGm power or variance. All effects remained when examining all gray matter voxels (SI Appendix, Fig. S1). Error bars mark ± 1 SEM. ***P < 0.001 level of significance. n.s., not significant.

Fig. 2.

Relationship between SCZ symptoms and CGm BOLD signal power. We extracted average CGm power for each patient with available symptom ratings (n = 153). (A) Significant positive relationship between CGm power and symptom ratings in SCZ (r = 0.18, P < 0.03), verified using Spearman’s ρ given somewhat nonnormally distributed data (ρ = 0.2, P < 0.015). (B and C) Results held across SCZ samples, increasing confidence in the effect (i.e., joint probability of independent effects P < 0.002, marked in blue boxes). All identified relationships held when examining Gm variance (SI Appendix, Fig. S4). Notably, all effects were no longer significant after GSR, suggesting GS carries clinically meaningful information. The shaded area marks the 95% confidence interval around the best-fit line.

Fig. 3.

Voxel-wise variance differs in SCZ independently of GS effects. Removing GS via GSR may alter within-voxel variance for SCZ. Given similar effects, we pooled SCZ samples to maximize power (n = 161). (A and B) Voxel-wise between-group differences; yellow-orange voxels indicate greater variability for SCZ relative to HCS (whole-brain multiple comparison protected; see SI Appendix), also evident after GSR. These data are movement-scrubbed reducing the likelihood that effects were movement-driven. (C and D) Effects were absent in BD relative to matched HCS, suggesting that local voxel-wise variance is preferentially increased in SCZ irrespective of GSR. Of note, SCZ effects were colocalized with higher-order control networks (SI Appendix, Fig. S13).

Fig. 4.

rGBC results qualitatively change when removing a large GS component. We tested if removing a larger GS from one of the groups, as is typically done in connectivity studies, alters between-group inferences. We computed rGBC focused on PFC, as done previously (17), before (A and B) and after GSR (C and D). Red-yellow foci mark increased PFC rGBC in SCZ, whereas blue foci mark reductions in SCZ relative to HCS. Bars graphs highlight effects with standard between-group effect size estimates. Error bars mark ± 1 SEM. (E) GSR could uniformly/rigidly transform between-group difference maps. Because of larger GS variability in SCZ (purple arrow) the pattern of between-group differences is shifted, rendering increased connectivity in SCZ as the dominant profile (red signal above the 95% confidence interval indicated by green planes). If GSR shifts the distribution uniformly, then the increased connectivity is now within the 95% 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 different pattern before and after GSR). We conducted focused analyses to arbitrate between these possibilities, suggesting that the effect is predominantly uniform (SI Appendix, Fig. S8). Note: topographies in E and F represent a conceptual illustration, and do not reflect specific patient data. ***P < .001.

Fig. 5.

Computational modeling simulation of BOLD signal variance illustrates a biologically grounded hypothetical mechanism for increased global and local 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 are the strength of local, recurrent self-coupling (w) within nodes (solid lines), and the strength of long-range, global coupling (G) between 66 nodes in total (dashed lines), adapted from prior work (19) (B and C) Simulations indicate increased variance of local BOLD signals originating from each node, in response to increased w or G. (D and E) The GS, computed as the spatial average across all nodes, also showed increased variance by elevating w or G. Shading represents the SD at each value of w or G computed across four realizations with different starting noise, illustrating model stability. Dotted lines indicate effects after in silico GSR. (F) Two-dimensional parameter space, capturing the positive relationship between w/G and variance of the BOLD signal at the local node level (squares, far right color bar) and the GS level (circles in each square, the adjacent color bar). The blue area marks regimes where the model baseline is associated with unrealistically elevated firing rates of simulated neurons. Model simulations illustrate how alterations in biophysically based parameters (rather than physiological noise) can increase GS and local variance observed empirically in SCZ. Of note in B–E, when w is modulated, G = 1.25. Conversely, when G is modulated, w = 0.531. For permutations of anatomical connectivity matrixes, mean trends and complete GSR effects, see SI Appendix, Figs. S9–S11.