The contribution of regional gray/white matter volume in preclinical depression assessed by the Automatic Thoughts Questionnaire: a voxel-based morphometry study

Emotional abuse mediated by negative automatic thoughts impacts functional connectivity during adolescence

Background

Emotional abuse during childhood and adolescence is thought to be associated with the brain; however, the neural mechanism underlying the cognitive process remains unknown. Therefore, we aimed to investigate the mediating effect of negative automatic thoughts on the relationship between emotional abuse and resting-state functional connectivity (rsFC) during adolescence.

Method

Our community sample included 54 adolescents aged 13-17 years in the statistical analysis. Resting-state functional and structural magnetic resonance imaging (MRI) was performed, while emotional abuse and negative automatic thoughts were assessed using self-reported scales. A mediation analysis was used to assess the contributions of early traumatic events and negative automatic thoughts to resting functional connectivity.

Result

Higher negative automatic thoughts were associated with lower connectivity in the context of greater emotional abuse (i.e., suppression effect). Thus, the relationships between emotional abuse and connectivity in the precuneus (pCun)-medial prefrontal cortex, parahippocampal cortex-extrastriate cortex, and temporal cortex-temporal pole were decreased by negative automatic thoughts. In contrast, functional connections in the pCun-pCun, pCun-precuneus/posterior cingulate cortex, and nucleus accumbens-somatomotor areas were strongly mediated when emotionally abused adolescents reported a high tendency for negative automatic thoughts.

Conclusion

Negative automatic thoughts strengthened the relationship between emotional abuse and rsFC. These findings highlight the underlying cognitive processing of the traumatic event-neural system, supporting the use of cognitive therapy for post-traumatic symptoms.

Psychological characteristics associated with the brain volume of patients with fibromyalgia

Fibromyalgia (FM) is a disease characterized by chronic widespread pain concomitant with psychiatric symptoms such as anxiety and depression. It has been reported that FM patients engage in pain catastrophizing. In this study, we investigated characteristics of the brain volume of female FM patients and the association between psychological indices and brain volume. Thirty-nine female FM patients and 25 female healthy controls (HCs) were recruited for the study, and five FM patients were excluded due to white matter lesions. The following analyses were performed: (1) T1-weighted MRI were acquired for 34 FM patients (age 41.6 ± 7.4) and 25 HCs (age 39.5 ± 7.4). SPM12 was used to compare their gray and white matter volumes. (2) Data from anxiety and depression questionnaires (State-Trait Anxiety Inventory and Hospital Anxiety and Depression Scale), the Pain Catastrophizing Scale (subscales rumination, helplessness, magnification), and MRI were acquired for 34 FM patients (age 41.6 ± 7.4). Correlation analysis was done of the psychological indices and brain volume. We found that (1) The white matter volume of the temporal pole was larger in the FM patient group than in the HC group. (2) Correlation analysis of the psychological indices and gray matter volume showed a negative correlation between trait anxiety and the amygdala. For the white matter volume, positive correlations were found between depression and the brainstem and between magnification and the postcentral gyrus. Changes in the brain volume of female FM patients may be related to anxiety, depression, and pain catastrophizing.

Integrating Multimodal and Longitudinal Neuroimaging Data with Multi-Source Network Representation Learning

Uncovering the complex network of the brain is of great interest to the field of neuroimaging. Mining from these rich datasets, scientists try to unveil the fundamental biological mechanisms in the human brain. However, neuroimaging data collected for constructing brain networks is generally costly, and thus extracting useful information from a limited sample size of brain networks is demanding. Currently, there are two common trends in neuroimaging data collection that could be exploited to gain more information: 1) multimodal data, and 2) longitudinal data. It has been shown that these two types of data provide complementary information. Nonetheless, it is challenging to learn brain network representations that can simultaneously capture network properties from multimodal as well as longitudinal datasets. Here we propose a general fusion framework for multi-source learning of brain networks - multimodal brain network fusion with longitudinal coupling (MMLC). In our framework, three layers of information are considered, including cross-sectional similarity, multimodal coupling, and longitudinal consistency. Specifically, we jointly factorize multimodal networks and construct a rotation-based constraint to couple network variance across time. We also adopt the consensus factorization as the group consistent pattern. Using two publicly available brain imaging datasets, we demonstrate that MMLC may better predict psychometric scores than some other state-of-the-art brain network representation learning algorithms. Additionally, the discovered significant brain regions are synergistic with previous literature. Our new approach may boost statistical power and sheds new light on neuroimaging network biomarkers for future psychometric prediction research by integrating longitudinal and multimodal neuroimaging data.