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

Abstract

Childhood maltreatment (CM) has a long impact on physical and mental health of children. However, the neural underpinnings of CM are still unclear. In this study, we aimed to establish the associations between functional connectome of large‐scale brain networks and influences of CM evaluated through Childhood Trauma Questionnaire (CTQ) at the individual level based on resting‐state functional magnetic resonance imaging data of 215 adults. A novel individual functional mapping approach was employed to identify subject‐specific functional networks and functional network connectivities (FNCs). A connectome‐based predictive modeling (CPM) was used to estimate CM total and subscale scores using individual FNCs. The CPM established with FNCs can well predict CM total scores and subscale scores including emotion abuse, emotion neglect, physical abuse, physical neglect, and sexual abuse. These FNCs primarily involve default mode network, fronto‐parietal network, visual network, limbic network, motor network, dorsal and ventral attention networks, and different networks have distinct contributions to predicting CM and subtypes. Moreover, we found that CM showed age and sex effects on individual functional connections. Taken together, the present findings revealed that different types of CM are associated with different atypical neural networks which provide new clues to understand the neurobiological consequences of childhood adversity.

The role of ascending arousal network in patients with chronic insomnia disorder

“…Indeed, many fields, from oncology and cardiology to internal medicine, have developed biomarkers that indicate specific pathophysiological mechanisms. In recent years, the combination of machine‐learning‐based MVPA approaches and neuroimaging data features has yielded some encouraging diagnostic and predictive models for individual‐level disease diagnosis and efficacy prediction (H. Li et al, 2022 ; Pang et al, 2022 ; Zhang et al, 2022 ). However, it is still in the early stages of brain biomarker development; future applications should extend our predictive model to multiple time‐visits longitudinal experimental designs, such as predicting the effect of pharmacological and non‐pharmacological therapies on insomnia by collecting data from two time points observed before and after treatment.…”

“…from oncology and cardiology to internal medicine, have developed biomarkers that indicate specific pathophysiological mechanisms. In recent years, the combination of machine-learning-based MVPA approaches and neuroimaging data features has yielded some encouraging diagnostic and predictive models for individual-level disease diagnosis and efficacy prediction (H Li et al, 2022;Pang et al, 2022;Zhang et al, 2022)…”

Personalized Functional Network Mapping for Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder

“…Cui and colleagues found that variability of brain functional topography is highly correlated to evolutionary expansion, cortical myelination, and cerebral blood ow (Cui et al, 2020). Recently, we found that individual functional connectivity could effectively predict personalized childhood maltreatment and subtype levels (Zhang et al, 2022). These studies indicated that individualspeci c functional connectivity mapping outperforms group-level methods to better capture cognitions and behaviors.…”

Dynamic effective connectivity among large‐scale brain networks mediates risk of anxiety

“…Cognitive emotion dysregulation is a potential mechanism by which childhood trauma experience increases risk of anxiety (Huh et al, 2017 ). According to previous resting‐state fMRI studies of large‐scale brain networks, childhood trauma is associated with atypical neural networks involved in perceptual, cognitive, and emotional regulation systems (Zhang, Zhao, et al, 2022 ). Further evidence suggests that childhood trauma experiences may alter plasticity processes and affect the regular functional interaction of cortical and subcortical neural networks.…”