Diffusion basis spectrum imaging detects pathological alterations in substantia nigra and white matter tracts with early-stage Parkinson's disease

Limbic/paralimbic connection weakening in preschool autism-spectrum disorder based on diffusion basis spectrum imaging

This study aims to investigate the value of basal ganglia and limbic/paralimbic networks alteration in identifying preschool children with ASD and normal controls using diffusion basis spectrum imaging (DBSI). DBSI data from 31 patients with ASD and 30 NC were collected in Hunan Children's Hospital. All data were imported into the post-processing server. The most discriminative features were extracted from the connection, global and nodal metrics separately using the two-sample t-test. To effectively integrate the multimodal information, we employed the multi-kernel learning support vector machine (MKL-SVM). In ASD group, the value of global efficiency, local efficiency, clustering coefficient and synchronization were lower than NC group, while modularity score, hierarchy, normalized clustering coefficient, normalized characteristic path length, small-world, characteristic path length and assortativity were higher. Significant weaker connections are mainly distributed in the limbic/paralimbic networks. The model combining consensus connection, global and nodal graph metrics features can achieve the best performance in identifying ASD patients, with an accuracy of 96.72%.The most specific brain regions connection weakening associated with preschool ASD are predominantly located in limbic/paralimbic networks, suggesting their involvement in abnormal brain development processes. The effective combination of connection, global and nodal metrics information by MKL-SVM can effectively distinguish patients with ASD.

Considering the role of estradiol in the psychoneuroimmunology of perimenopausal depression

In recent years, a burgeoning field of research has focused on women's mental health and psychiatric conditions associated with perinatal and postpartum periods. An emerging trend points to the link between hormone fluctuations during pregnancy and postpartum that have immunologic consequences in cases of perinatal depression and postpartum psychosis. The transition to menopause (or "perimenopause") has garnered comparatively less attention, but existing studies point to the influential interaction of hormonal and immune pathways. Moreover, the role of this cross talk in perturbing neural networks has been implicated in risk for cognitive decline, but relatively less work has focused on the depressed brain during perimenopause. This brief review brings a psychoneuroimmunology lens to depression during the perimenopausal period by providing an overview of existing knowledge and suggestions for future research to intertwine these bodies of work.

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

Cortical-cerebellar circuits changes in preschool ASD children by multimodal MRI

OBJECTIVE

To investigate the alterations in cortical-cerebellar circuits and assess their diagnostic potential in preschool children with autism spectrum disorder using multimodal magnetic resonance imaging.

METHODS

We utilized diffusion basis spectrum imaging approaches, namely DBSI_20 and DBSI_combine, alongside 3D structural imaging to examine 31 autism spectrum disorder diagnosed patients and 30 healthy controls. The participants' brains were segmented into 120 anatomical regions for this analysis, and a multimodal strategy was adopted to assess the brain networks using a multi-kernel support vector machine for classification.

RESULTS

The results revealed consensus connections in the cortical-cerebellar and subcortical-cerebellar circuits, notably in the thalamus and basal ganglia. These connections were predominantly positive in the frontoparietal and subcortical pathways, whereas negative consensus connections were mainly observed in frontotemporal and subcortical pathways. Among the models tested, DBSI_20 showed the highest accuracy rate of 86.88%. In addition, further analysis indicated that combining the 3 models resulted in the most effective performance.

CONCLUSION

The connectivity network analysis of the multimodal brain data identified significant abnormalities in the cortical-cerebellar circuits in autism spectrum disorder patients. The DBSI_20 model not only provided the highest accuracy but also demonstrated efficiency, suggesting its potential for clinical application in autism spectrum disorder diagnosis.