Large-scale brain network dynamics provide a measure of psychosis and anxiety in 22q11.2 deletion syndrome

Abstract

Prodromal positive psychotic symptoms and anxiety are two strong risk factors for schizophrenia in 22q11.2 deletion syndrome (22q11DS). The analysis of large-scale brain network dynamics during rest is promising to investigate aberrant brain function and identify potentially more reliable biomarkers. We retrieved and examined dynamics of large-scale functional brain networks using innovation-driven co-activation patterns (iCAPs) and probed into functional signatures of prodromal psychotic symptoms and anxiety. Patients with 22q11DS had shorter activation in cognitive brain networks and longer activation in emotion processing networks. Functional signatures of prodromal psychotic symptoms confirmed an implication of cingulo-prefrontal salience network activation duration and coupling. Functional signatures of anxiety uncovered an implication of amygdala activation and coupling, indicating differential roles of dorsal and ventral sub-divisions of anterior cingulate and medial prefrontal cortices. These results confirm that the dynamic nature of brain network activation contains essential function to develop clinically relevant imaging markers of psychosis vulnerability.

Structural control energy of resting‐state functional brain states reveals less cost‐effective brain dynamics in psychosis vulnerability

“…FMRI analyses in this study were conducted on the identical dataset as Zöller et al (2019), which included 78 patients with 22q11DS and 85 healthy controls (HCs), aged between 8 and 30 years. For structural connectivity analysis, we used diffusion MRI (dMRI) scans acquired from the same subjects.…”

“…We preprocessed fMRI scans identically as in Zöller et al (2019) in Matlab R2017a using in‐house code and functions of statistical parametric mapping (SPM12, http://www.fil.ion.ucl.ac.uk/spm/), Data Processing Assistant for Resting‐State fMRI (DPARSF; Yan & Yufeng, 2010) and Individual Brain Atlases using Statistical Parametric Mapping (IBASPM; Aleman‐Gomez, Melie‐García, & Valdés‐Hernandez, 2006). Briefly, preprocessing steps included functional realignment (registered to the mean) and spatial smoothing of fMRI frames with a Gaussian kernel of 6 mm full width half maximum, co‐registration of the structural T1‐weighted image to the functional mean, and segmentation of anatomical scans using the Segmentation algorithm in SPM12 (Ashburner & Friston, 2005).…”

“…Finally, a time course was estimated for each iCAP using spatio‐temporal transient‐informed regression with soft assignment factor ξ = 1.3 (Zöller et al, 2018). For a more detailed description of the method, we refer the interested reader to (Zöller et al, 2019), where we used the identical approach on a largely overlapping sample.…”

“…In the only three studies who looked at dynamic features of resting‐state brain function, we found global reductions in variability of blood‐oxygenation level dependent (BOLD) signals (Zöller, Schaer, et al, 2017), and reduced BOLD variability in the dorsal anterior cingulate cortex (a central node of the salience network [SN]) in relationship to higher positive psychotic symptoms (Zöller, Padula, et al, 2017). In another study investigating dynamic properties of resting‐state brain states, rather than voxel‐wise BOLD variability, we found that in 22q11DS higher positive psychotic symptoms come with higher SN activation and coupling, while higher levels of anxiety are related to higher activation of the amygdala and hippocampus (Zöller et al, 2019). In these studies, we demonstrated the implication of resting‐state brain dynamics in psychopathology in 22q11DS.…”

“…Here, we address this question by combining dynamic fMRI analysis with whole‐brain tractography and principles from network control theory to investigate how the brain's white matter connectivity may influence its dynamic behavior and how this relationship is affected in patients with 22q11DS. More specifically, we extracted brain states from resting‐state fMRI scans similarly to our previous study focusing on brain state dynamics (Zöller et al, 2019). Then, we calculated the control energy that is required—based on the structural connectivity of the same subjects—to persist in these specific brain states (Braun et al, 2019; Cornblath et al, 2020).…”