山口 淳

The precuneus is an association area in the posteromedial cortex (PMC) that is involved in high-order cognitive functions through integrating multi-modal information. Previous studies have shown that the precuneus is functionally heterogeneous and subdivided into several subfields organized by the anterior-posterior and ventral-dorsal axes. Further, the precuneus forms the structural core of brain connectivity as a rich-club hub and overlaps with the default mode network (DMN) as the functional core. This review summarizes recent research on the connectivity and cognitive functions of the precuneus. We then present our recent tractography-based studies of the precuneus and contextual these results here with respect to possible cognitive functions and resting-state networks.

Z-DNA binding protein 1 (ZBP1) is a cytosolic nucleic acid sensor, functioning as a critical mediator of inflammation and cell death pathways. Since neuroinflammation could occur in response to damage-associated molecular patterns (DAMPs), ZBP1 might be involved in neuroinflammation after stroke. However, the spatiotemporal expression profile of ZBP1 in the post-stroke brain remains to be elucidated. The aim of this study is to demonstrate the spatiotemporal expression patterns of ZBP1 in the post-stroke brain using a mouse photothrombotic stroke model. Real-time PCR assays showed that ZBP1 is induced on days 3-14 post stroke. ZBP1 immunoreactivity was observed in Iba1-positive microglia/macrophages in peri-infarct regions by immunohistochemistry. ZBP1-positive cells were spread in layers surrounding the infarct core by 7-14 days post stroke. Interestingly, ZBP1 immunoreactivity was also detected in CD206-positive border-associated macrophages (BAMs) in the meninges. Furthermore, ZBP1-expressing cells were positive for antibodies against inflammatory mediators such as Toll-like receptor 4 (TLR4), Toll/IL-1R domain-containing adaptor-inducing IFN-β (TRIF), and receptor-interacting serine/threonine-protein kinase 1 (RIPK1). Morphological analysis with confocal microscopy showed that the co-localization signals of ZBP1 and its adaptor, TRIF, are increased by glucose oxidase (GOx) treatment, which has been reported to induce mitochondrial DNA (mtDNA) release. These results suggest that ZBP1 is induced in peri-infarct microglia/macrophages and may be involved in DAMPs-mediated neuroinflammation involving mtDNA in the post-infarct brain.

It is generally hypothesized that functional connectivity (FC) reflects the underlying structural connectivity (SC). The precuneus is associated with highly integrated cognitive functions. However, our understanding of the structural connections that could underlie them is limited. This study aimed to characterize the cortico-cortical connections by probabilistic tractography. The precuneus corresponds to the five cortical areas (7Am, PCV, 7Pm, 7m, POS2) on the HCP MMP atlas. We first conducted the atlas-based probabilistic tractography. The anterior part (7Am) was strongly connected to the sensorimotor region. The dorsal part (7Am, 7Pm) was highly connected with the adjacent parietal and temporal cortex, while the ventral part (PCV, 7m) showed strong connections with the adjacent posterior cingulate and medial prefrontal cortex. The most posterior part (POS2) was explicitly connected to the visual cortex. In addition, there was a correlation between SC and resting-state fMRI connectivity (Spearman's rank correlation coefficient = 0.322 ± 0.019, p < 0.05 corrected at subject level). Collectively, the current study revealed the characteristic connectional profile of precuneus, which could shed light on the structural heterogeneity for the future functional analyses.

The intervention at the stage of mild cognitive impairment (MCI) is promising for preventing Alzheimer's disease (AD). This study aims to search for the optimal machine learning (ML) model to classify early and late MCI (EMCI and LMCI) subtypes using multimodal MRI data. First, the tract-based spatial statistics (TBSS) analyses showed LMCI-related white matter changes in the Corpus Callosum. The ROI-based tractography addressed the connected cortical areas by affected callosal fibers. We then prepared two feature subsets for ML by measuring resting-state functional connectivity (TBSS-RSFC method) and graph theory metrics (TBSS-Graph method) in these cortical areas, respectively. We also prepared feature subsets of diffusion parameters in the regions of LMCI-related white matter alterations detected by TBSS analyses. Using these feature subsets, we trained and tested multiple ML models for EMCI/LMCI classification with cross-validation. Our results showed the ensemble ML model (AdaBoost) with feature subset of diffusion parameters achieved better performance of mean accuracy 70%. The useful brain regions for classification were those, including frontal, parietal lobe, Corpus Callosum, cingulate regions, insula, and thalamus regions. Our findings indicated the optimal ML model using diffusion parameters might be effective to distinguish LMCI from EMCI subjects at the prodromal stage of AD.