Over the years, diffusion tractography has seen increasing use for comparing minute differences in connectivity of brain structures in neurodegenerative diseases and treatments. Studies on connectivity between basal ganglia has been a focal point for studying the effects of diseases such as Parkinson's and Alzheimer's, as well as the effects of treatments such as deep brain stimulation. Additionally, in previous studies, diffusion tractography was utilized in disease mouse models to identify white matter alterations, as well as biomarkers that occur in the progression of disease. However, despite the extensive use of mouse models to study model diseases, the structural connectivity of the mouse basal ganglia has been inadequately explored. In this study, we present the methodology of segmenting the basal ganglia of a mouse brain, then generating diffusion tractography between the segmented basal ganglia structures. Additionally, we compare the relative levels of connectivity of connecting fibers between each basal ganglia structure, as well as visualize the shapes of each connection. We believe that our results and future studies utilizing diffusion tractography will be beneficial for properly assessing some of the connectivity changes that are found in the basal ganglia of various mouse models.

Preclinical studies using rodents have been the choice for many neuroscience researchers due totheir close reflection of human biology. In particular, research involving rodents has utilized MRI to accurately identify brain regions and characteristics by acquiring high resolution cavity images with different contrasts non-invasively, and this has resulted in high reproducibility and throughput. In addition, tractographic analysis using diffusion tensor imaging to obtain information on the neural structure of white matter has emerged as a major methodology in the field of neuroscience due to its contribution in discovering significant correlations between altered neural connections and various neurological and psychiatric diseases. However, unlike image analysis studies with human subjects where a myriad of human image analysis programs and procedures have been thoroughly developed and validated, methods for analyzing rat image data using MRI in preclinical research settings have seen significantly less developed. Therefore, in this study, we present a deterministic tractographic analysis pipeline using the SIGMA atlas for a detailed structural segmentation and structural connectivity analysis of the rat brain’s structural connectivity. In addition, the structural connectivity analysis pipeline presented in this study was preliminarily tested on normal and stroke rat models for initial observation.

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Depression in the elderly population has shown increased likelihood of neurological disorders due to structural changes in the subcortical area. However, further investigation into depression related subcortical changes is needed due to mismatches in structural analysis results between studies as well as scarcities in research regarding subcortical connectivity patterns of subclinical depression populations. This study aims to investigate structural differences in subcortical regions of aged participants with subclinical depression using 3Tesla MRI. In structural analysis, volumes of each subcortical region were measured to observe the volumetric difference and asymmetry between groups, but no significant difference was found. In addition, fractional anisotropy (FA) and apparent diffusion coefficient (ADC) did not show any significant differences between groups. Structural analysis using probabilistic tractography indicated that the connection strength between left nucleus accumbens-right hippocampus, and right thalamus-right caudate was higher in the control group than the subclinical depression group. The differences in subcortical connection strength of subclinical depression groups, have shown to correlate with emotional and cognitive disorders, such as anxiety and memory impairment. We believe that the analysis of structural differences and cross-regional network measures in subcortical structures can help identify neurophysiological changes occurring in subclinical depression.

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