Changes in Default-Mode Network Associated With Childhood Trauma in Schizophrenia

Abstract

Background There is considerable evidence of dysconnectivity within the default-mode network (DMN) in schizophrenia, as measured during resting-state functional MRI (rs-fMRI). History of childhood trauma (CT) is observed at a higher frequency in schizophrenia than in the general population, but its relationship to DMN functional connectivity has yet to be investigated. Methods CT history and rs-fMRI data were collected in 65 individuals with schizophrenia and 132 healthy controls. Seed-based functional connectivity between each of 4 a priori defined seeds of the DMN (medial prefrontal cortex, right and left lateral parietal lobes, and the posterior cingulate cortex) and all other voxels of the brain were compared across groups. Effects of CT on functional connectivity were examined using multiple regression analyses. Where significant associations were observed, regression analyses were further used to determine whether variance in behavioral measures of Theory of Mind (ToM), previously associated with DMN recruitment, were explained by these associations. Results Seed-based analyses revealed evidence of widespread reductions in functional connectivity in patients vs controls, including between the left/right parietal lobe (LP) and multiple other regions, including the parietal operculum bilaterally. Across all subjects, increased CT scores were associated with reduced prefrontal-parietal connectivity and, in patients, with increased prefrontal-cerebellar connectivity also. These CT-associated differences in DMN connectivity also predicted variation in behavioral measures of ToM. Conclusions These findings suggest that CT history is associated with variation in DMN connectivity during rs-fMRI in patients with schizophrenia and healthy participants, which may partly mediate associations observed between early life adversity and cognitive performance.

Decreased resting-state neural signal in the left angular gyrus as a potential neuroimaging biomarker of schizophrenia: An amplitude of low-frequency fluctuation and support vector machine analysis

“…Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a powerful tool that offers a potential means of identifying novel, sensitive biomarker signatures associated with specific brain disorders ( 3 – 6 ). A growing body of evidence suggests that abnormal changes in patients with SCH are primarily found in the striatum ( 7 ), temporal lobe ( 8 ), default-mode network (DMN) ( 9 ), and frontoparietal network ( 10 ), although the specific nature of these changes has varied across studies, with some reporting functional signal increases ( 11 – 13 ), decreases ( 14 ), or both ( 15 ). These discrepant findings may be attributable to the differences in sample size, disease course, and the analytical methods employed ( 16 , 17 ).…”

Prediction of childhood maltreatment and subtypes with personalized functional connectome of large‐scale brain networks

“…The relationships between CM and these networks have been documented in previous studies. For example, CM was associated with increased DN deactivation during working memory (Philip et al, 2013), reduced resting‐state FNCs within DN and FPN (Dauvermann et al, 2021), and enhanced morphometric network centrality in DN regions involved in internal emotional perception, self‐awareness (Teicher et al, 2014). Physical abuse/neglect was associated with the FNCs between VIS and cingulo‐opercular network in patients with major depressive disorder (Yu et al, 2019).…”

“…To determine the contribution of each network during prediction, the identified networks through NMF method were classified into eight networks including seven cortical networks (visual network (VS), motor network (Dauvermann et al, 2021), limbic network (LMB, Cheng, Roberts, et al, 2022), default mode network (Dannlowski et al, 2012), ventral attention network (Cui et al, 2020), dordal attention network (Tomoda et al, 2009), frontoparietal network (FPN), and a cerebellum network in according to the Yeo group atlas (Yeo et al, 2011). Feature weights of each FNC were assigned to the corresponding connected networks, and the positive weights which made contribution for prediction were included.…”

Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning

“…The lateral parietal cortex is also a key area of DMN. In a seed-based FC study to explore the association between DMN and schizophrenia with cognitive impairment, Dauvermann et al selected mPFC, PCC, and lateral parietal lobes as the seeds of the DMN and reported patients with cognitive impairment had reductions in FC between the left/right parietal lobe and multiple other regions ( Dauvermann et al, 2021 ). The SN anchored in the insula and anterior cingulate cortex plays a crucial role in identifying the cognitively relevant events that guide attention, detection, emotional information, and orientation ( Menon and Uddin, 2010 ).…”