Objective To investigate the morphology and relationships with the adjacent structures in the pineal region on the thin sections and to provide anatomic data for imaging diagnosis and surgical treatment of diseases in this region. Methods By CT and MRI examination, one normal head specimen was selected for this study. Using the computerized freezing milling technique, the specimen was sliced from anterior to posterior. The in vivo MR images were obtained from ten normal Chinese male adult volunteers using a 3.0 T GE scanner. The base lines of the sectioning and the MR scan were perpendicular to the AC-PC line. Then primary sections were contrasted with the corresponding MR images. Results By the appearance of the pineal peduncle and the disappearance of the pineal gland, the pineal region could be divided into three parts from anterior to posterior, and the shape changed from an inverted triangle to a trapezoid and a triangle gradually. The first interspace was getting wider in the anterior and middle parts of the pineal region, while in the posterior part of the pineal region, it was getting narrower and disappeared finally. From anterior to posterior, the bilateral internal cerebral veins were always in the midline of the pineal region and descended gradually.Conclusion By the computerized freezing milling technique, the anatomic details and adjacent relationships of the pineal region could be exhibited clearly in the thin serial sections, which could help the imaging diagnosis and surgical treatments for minute diseases in this region.

Biomarkers ; Brain/diagnostic imaging* ; Brain Diseases/diagnostic imaging* ; Humans ; Machine Learning ; Magnetic Resonance Imaging ; Neuroimaging

School-age children are in a specific development stage corresponding to juvenility, when the white matter of the brain experiences ongoing maturation. Diffusion-weighted magnetic resonance imaging (DWI), especially diffusion tensor imaging (DTI), is extensively used to characterize the maturation by assessing white matter properties in vivo. In the analysis of DWI data, spatial normalization is crucial for conducting inter-subject analyses or linking the individual space with the reference space. Using tensor-based registration with an appropriate diffusion tensor template presents high accuracy regarding spatial normalization. However, there is a lack of a standardized diffusion tensor template dedicated to school-age children with ongoing brain development. Here, we established the school-age children diffusion tensor (SACT) template by optimizing tensor reorientation on high-quality DTI data from a large sample of cognitively normal participants aged 6-12 years. With an age-balanced design, the SACT template represented the entire age range well by showing high similarity to the age-specific templates. Compared with the tensor template of adults, the SACT template revealed significantly higher spatial normalization accuracy and inter-subject coherence upon evaluation of subjects in two different datasets of school-age children. A practical application regarding the age associations with the normalized DTI-derived data was conducted to further compare the SACT template and the adult template. Although similar spatial patterns were found, the SACT template showed significant effects on the distributions of the statistical results, which may be related to the performance of spatial normalization. Looking forward, the SACT template could contribute to future studies of white matter development in both healthy and clinical populations. The SACT template is publicly available now ( https://figshare.com/articles/dataset/SACT_template/14071283 ).

Alzheimer's disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.
Humans ; White Matter/diagnostic imaging* ; Diffusion Tensor Imaging/methods* ; Alzheimer Disease/complications* ; Reproducibility of Results ; Cognition ; Cognitive Dysfunction/complications* ; Brain/diagnostic imaging*