Identifying Biomarkers of Alzheimer's Disease via a Novel Structured Sparse Canonical Correlation Analysis Approach

fMRI-based spatio-temporal parcellations of the human brain

PURPOSE OF REVIEW

Human brain parcellation based on functional magnetic resonance imaging (fMRI) plays an essential role in neuroscience research. By segmenting vast and intricate fMRI data into functionally similar units, researchers can better decipher the brain's structure in both healthy and diseased states. This article reviews current methodologies and ideas in this field, while also outlining the obstacles and directions for future research.

RECENT FINDINGS

Traditional brain parcellation techniques, which often rely on cytoarchitectonic criteria, overlook the functional and temporal information accessible through fMRI. The adoption of machine learning techniques, notably deep learning, offers the potential to harness both spatial and temporal information for more nuanced brain segmentation. However, the search for a one-size-fits-all solution to brain segmentation is impractical, with the choice between group-level or individual-level models and the intended downstream analysis influencing the optimal parcellation strategy. Additionally, evaluating these models is complicated by our incomplete understanding of brain function and the absence of a definitive "ground truth".

SUMMARY

While recent methodological advancements have significantly enhanced our grasp of the brain's spatial and temporal dynamics, challenges persist in advancing fMRI-based spatio-temporal representations. Future efforts will likely focus on refining model evaluation and selection as well as developing methods that offer clear interpretability for clinical usage, thereby facilitating further breakthroughs in our comprehension of the brain.

Association between Circulating T Cells and the Gut Microbiome in Healthy Individuals: Findings from a Pilot Study

Though the microbiome's impact on immune system homeostasis is well documented, the effect of circulating T cells on the gut microbiome remains unexamined. We analyzed data from 50 healthy volunteers in a pilot trial of aspirin, using immunophenotyping and 16S rRNA sequencing to evaluate the effect of baseline T cells on microbiome changes over 6 weeks. We employed an unsupervised sparse canonical correlation analysis (sCCA) and used multivariable linear regression models to evaluate the association between selected T cell subsets and selected bacterial genera after adjusting for covariates. In the cross-sectional analysis, percentages of naïve CD4+ T cells were positively associated with a relative abundance of Intestinimonas, and the percentage of activated CD8+ T cells was inversely associated with Cellulosibacter. In the longitudinal analysis, the baseline percentages of naïve CD4+ T cells and activated CD4+ T cells were inversely associated with a 6-week change in the relative abundance of Clostridium_XlVb and Anaerovorax, respectively. The baseline percentage of terminal effector CD4+ T cells was positively associated with the change in Flavonifractor. Notably, the microbiome taxa associated with T cell subsets exclusively belonged to the Bacillota phylum. These findings can guide future experimental studies focusing on the role of T cells in impacting gut microbiome homeostasis.

Homogeneous-Multiset-CCA-Based Brain Covariation and Contravariance Connectivity Network Modeling

Brain connectivity networks based on functional magnetic resonance imaging (fMRI) have expanded our understanding of brain functions in both healthy and diseased states. However, most current studies construct connectivity networks using averaged regional time courses with the strong assumption that the activities of voxels contained in each brain region are similar, ignoring their possible variations. Additionally, pairwise correlation analysis is often adopted with more attention to positive relationships, while joint interactions at the network level as well as anti-correlations are less investigated. In this paper, to provide a new strategy for regional activity representation and brain connectivity modeling, a novel homogeneous multiset canonical correlation analysis (HMCCA) model is proposed, which enforces sign constraints on the weights of voxels to guarantee homogeneity within each brain region. It is capable of obtaining regional representative signals and constructing covariation and contravariance networks simultaneously, at both group and subject levels. Validations on two sessions of fMRI data verified its reproducibility and reliability when dealing with brain connectivity networks. Further experiments on subjects with and without Parkinson's disease (PD) revealed significant alterations in brain connectivity patterns, which were further associated with clinical scores and demonstrated superior prediction ability, indicating its potential in clinical practice.

Downregulation of miR-23a-3p improves cognitive function in rats after subarachnoid hemorrhage by targeting VCAN

Subarachnoid hemorrhage (SAH) is a complicated and deadly disorder. Dysregulation of miRNAs in SAH has been widely reported. This investigation elucidated the function of miR-23a-3p in the in vivo and in vitro models of SAH. The miR-23a-3p and VCAN levels in SAH rats and sham controls were detected by RT-qPCR. The SAH rats were intracerebrally administrated with miR-23a-3p antagomir. Morphological changes and brain function were assessed. The isolated brain microvascular endothelial cells (BMECs), identified by immunofluorescence staining, were used as the model of SAH in vitro. The viability and apoptosis of BMECs were evaluated using MTT, flow cytometry, and western blotting analyses. Targeted relationship between miR-23a-3p and VCAN was predicted in miRDB and validated by a luciferase reporter assay. We found that the miR-23a-3p level was upregulated in rats after SAH, while VCAN was downregulated. Silencing miR-23a-3p attenuated neurological deficits and neuronal apoptosis in rats after SAH. VCAN was verified to be targeted by miR-23a-3p. Functionally, miR-23a-3p downregulation or VCAN overexpression inhibited BMEC apoptosis and promoted cell activity. Moreover, knockdown of VCAN eliminated the influence of miR-23a-3p inhibition in BMECs. Overall, suppression of miR-23a-3p improves cognitive function after SAH by targeting VCAN.