Most recent paper

Neuroimage. 2024 Apr 2;292:120589. doi: 10.1016/j.neuroimage.2024.120589. Online ahead of print.

ABSTRACT

Child maltreatment can adversely affect brain development, leading to vulnerabilities in brain structure and function and various psychiatric disorders. Among the various types of child maltreatment, neglect has the highest incidence rate (76.0%); however, data on its sole adverse influence on the brain remain limited. This case-control brain magnetic resonance imaging (MRI) study identified the changes in gray matter structure and function that distinguish neglected children with no other type of maltreatment (Neglect group, n = 23) from typically developing children (TD group, n = 140), and investigated the association between these structural and functional differences and specific psychosocial phenotypes observed in neglected children. Our results showed that the Neglect group had a larger right and left anterior cingulate cortex (R/L.ACC) and smaller left angular gyrus (L.AG) gray matter volume. The larger R/L.ACC was associated with hyperactivity and inattention. Resting-state functional analysis showed increased functional connectivity (FC) between the left supramarginal gyrus (L.SMG) in the salience network (SN) and the right middle frontal gyrus (R.MFG) simultaneously with a decrease in FC with the L.ACC for the same seed. The increased FC for the R.MFG was associated with difficulty in peer problems and depressive symptoms; a mediating effect was evident for depressive symptoms. These results suggest that the structural atypicality of the R/L.ACC indirectly contributes to the disturbed FCs within the SN, thereby exacerbating depressive symptoms in neglected children. In conclusion, exposure to neglect in childhood may lead to maladaptive brain development, particularly neural changes associated with depressive symptoms.

PMID:38575041 | DOI:10.1016/j.neuroimage.2024.120589

Interdiscip Sci. 2024 Apr 4. doi: 10.1007/s12539-024-00625-y. Online ahead of print.

ABSTRACT

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.

PMID:38573456 | DOI:10.1007/s12539-024-00625-y

Neurobiol Stress. 2024 Mar 19;30:100623. doi: 10.1016/j.ynstr.2024.100623. eCollection 2024 May.

ABSTRACT

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.

PMID:38572483 | PMC:PMC10987907 | DOI:10.1016/j.ynstr.2024.100623

Heliyon. 2024 Mar 24;10(7):e28559. doi: 10.1016/j.heliyon.2024.e28559. eCollection 2024 Apr 15.

ABSTRACT

BACKGROUND: At present, the diagnosis of post-traumatic stress disorder（PTSD） mainly relies on clinical symptoms and psychological scales, and finding objective indicators that are helpful for diagnosis has always been a challenge in clinical practice and academic research. Neuroimaging is a useful and powerful tool for discovering the biomarkers of PTSD,especially functional MRI (fMRI), structural MRI (sMRI) and Diffusion Weighted Imaging（DTI）are the most commonly used technologies, which can provide multiple perspectives on brain function, structure and its connectivity. Machine learning (ML) is an emerging and potentially powerful method, which has aroused people's interest because it is used together with neuroimaging data to define brain structural and functional abnormalities related to diseases, and identify phenotypes, such as helping physicians make early diagnosis.

OBJECTIVES: According to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) declaration, a systematic review was conducted to assess its accuracy in distinguishing between PTSD patients, TEHC(Trauma-Exposed Healthy Controls), and HC(healthy controls).

METHODS: We searched PubMed, Embase, and Web of Science using common words for ML methods and PTSD until June 2023, with no language or time limits. This review includes 13 studies, with sensitivity, specificity, and accuracy taken from each publication or acquired directly from the authors.

RESULTS: All ML techniques have an diagnostic accuracy rate above 70%，and support vector machine（SVM） are the most commonly used techniques. This series of studies has revealed significant neurobiological differences in key brain regions among individuals with PTSD, TEHC, and HC. The connectivity patterns of regions such as the Insula and Amygdala hold particular significance in distinguishing these groups. TEHC exhibits more normal connectivity patterns compared to PTSD, providing valuable insights for the application of machine learning in PTSD diagnosis.

CONCLUSION: In contrast to any currently available assessment and clinical diagnosis, ML techniques can be used as an effective and non-invasive support for early identification and detection of patients as well as for early screening of high-risk populations.

PMID:38571633 | PMC:PMC10988057 | DOI:10.1016/j.heliyon.2024.e28559

Brain Res Bull. 2024 Apr 2;211:110937. doi: 10.1016/j.brainresbull.2024.110937. Online ahead of print.

ABSTRACT

Adult survivors of childhood brain tumors often present with cognitive deficits that affect their quality of life. Studying brain structure and function in brain tumor survivors can help understand the underlying mechanisms of their cognitive deficits to improve long-term prognosis of these patients. This study analyzed voxel-based morphometry (VBM) derived from T1-weighted MRI and the amplitude of low-frequency fluctuation (ALFF) from resting-state functional magnetic resonance imaging (rs-fMRI) to examine the structural and functional alterations in 35 brain tumor survivors using 35 matching healthy individuals as controls. Compared with healthy controls, brain tumor survivors had decreased gray matter volumes (GMV) in the thalamus and increased GMV in the superior frontal gyrus. Functionally, brain tumor survivors had lower ALFF values in the inferior temporal gyrus and medial prefrontal area and higher ALFF values in the thalamus. Importantly, we found concurrent but negatively correlated structural and functional alterations in the thalamus based on observed significant differences in GMV and ALFF values. These findings on concurrent brain structural and functional alterations provide new insights towards a better understanding of the cognitive deficits in brain tumor survivors.

PMID:38570077 | DOI:10.1016/j.brainresbull.2024.110937

Front Neurosci. 2024 Mar 19;18:1341142. doi: 10.3389/fnins.2024.1341142. eCollection 2024.

ABSTRACT

When faced with a conflict or dilemma, we tend to postpone or even avoid making a decision. This phenomenon is known as decisional procrastination. Here, we investigated the neural correlates of this phenomenon, in particular the parahippocampal gyrus (PHG) that has previously been identified in procrastination studies. In this study, we applied an individual difference approach to evaluate participants' spontaneous neural activity in the PHG and their decisional procrastination levels, assessed outside the fMRI scanner. We discovered that the fractional amplitude of low-frequency fluctuations (fALFF) in the caudal PHG (cPHG) could predict participants' level of decisional procrastination, as measured by the avoidant decision-making style. Importantly, participants' self-esteem mediated the relationship between the cPHG and decisional procrastination, suggesting that individuals with higher levels of spontaneous activity in the cPHG are likely to have higher levels of self-esteem and thus be more likely to make decisions on time. In short, our study broadens the PHG's known role in procrastination by demonstrating its link with decisional procrastination and the mediating influence of self-esteem, underscoring the need for further exploration of this mediation mechanism.

PMID:38567283 | PMC:PMC10986735 | DOI:10.3389/fnins.2024.1341142