ACE of space: estimating genetic components of high-dimensional imaging data

Latent factor model for multivariate functional data

For multivariate functional data, a functional latent factor model is proposed, extending the traditional latent factor model for multivariate data. The proposed model uses unobserved stochastic processes to induce the dependence among the different functions, and thus, for a large number of functions, may provide a more parsimonious and interpretable characterization of the otherwise complex dependencies between the functions. Sufficient conditions are provided to establish the identifiability of the proposed model. The performance of the proposed model is assessed through simulation studies and an application to electroencephalography data.

Spatial-extent inference for testing variance components in reliability and heritability studies

Clusterwise inference is a popular approach in neuroimaging to increase sensitivity, but most existing methods are currently restricted to the General Linear Model (GLM) for testing mean parameters. Statistical methods for testing variance components, which are critical in neuroimaging studies that involve estimation of narrow-sense heritability or test-retest reliability, are underdeveloped due to methodological and computational challenges, which would potentially lead to low power. We propose a fast and powerful test for variance components called CLEAN-V (CLEAN for testing Variance components). CLEAN-V models the global spatial dependence structure of imaging data and computes a locally powerful variance component test statistic by data-adaptively pooling neighborhood information. Correction for multiple comparisons is achieved by permutations to control family-wise error rate (FWER). Through analysis of task-fMRI data from the Human Connectome Project across five tasks and comprehensive data-driven simulations, we show that CLEAN-V outperforms existing methods in detecting test-retest reliability and narrow-sense heritability with significantly improved power, with the detected areas aligning with activation maps. The computational efficiency of CLEAN-V also speaks of its practical utility, and it is available as an R package.

Single-cell RNA sequencing reveals that VIM and IFITM3 are vital targets of Dengzhan Shengmai capsule to protect against cerebral ischemic injury

ETHNOPHARMACOLOGICAL RELEVANCE

Ischemic stroke is one of the leading causes of mortality, but therapies are limited. Dengzhan Shengmai capsule (DZSM) was included by the Chinese Pharmacopoeia 2020 and has been broadly used for the treatment of ischemic stroke. However, the mechanism of DZSM against ischemic stroke is unclear.

AIM OF THE STUDY

This study used RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) to investigate the mechanism of action of DZSM against ischemic stroke.

MATERIALS AND METHODS

The rats were randomly divided into six groups: the Sham, I/R (water), I/R + DZSM-L (0.1134g/kg), I/R + DZSM-H (0.4536g/kg), I/R + NMDP (20mg/kg), and I/R + Ginaton (20mg/kg). The rats were administrated drugs for 5 days then followed by the ischemic brain injury caused by middle cerebral artery occlusion (MCAO). The neuroprotective effect was assessed by infraction rate, neurological deficit scores, regional cerebral blood flow (rCBF), hematoxylin and eosin (H&E) staining, and Nissl staining. Based on RNA-seq and scRNA-seq, the vital biological processes and core targets of DZSM against cerebral ischemia were revealed. Enzyme-linked immunosorbent assay (ELISA) and immunofluorescence (IF) staining were used to investigate the vital biological processes and core targets of DZSM against ischemic stroke.

RESULTS

Administration of DZSM significantly reduced the infarction rate and Zea Longa score, Garcia JH score, and ameliorated the reduction in rCBF. And alleviated the neuronal damage, such as increased neuronal density level and Nissl bodies density level. RNA-seq analysis revealed that DZSM played important roles in inflammation and apoptosis. ELISA and IF straining validation confirmed that DZSM significantly decreased the expression of IL-6, IL-1β, TNF-α, ICAM-1, IBA-1, MMP9, and Cleaved caspase-3 in MCAO rats. ScRNA-seq analysis identified 8 core targets in neurons including HSPB1, SPP1, MT2A, GFAP, IFITM3, VIM, CRIP1, and GPD1, and VIM and IFITM3 was verified to be decreased by DZSM in neurons.

CONCLUSION

Our study illustrates the neuroprotective effect of DZSM against ischemia stroke, and VIM and IFITM3 were identified as vital targets in neurons of DZSM in protecting against MCAO-induced I/R injury.

Heritability Estimation of Cognitive Phenotypes in the ABCD Study® Using Mixed Models

Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study® sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study® sample.

Surface-Based Connectivity Integration: An atlas-free approach to jointly study functional and structural connectivity

There has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. Previous connectome integration studies almost exclusively required predefined atlases. However, there are many potential atlases to choose from and this choice heavily affects all subsequent analyses. To avoid such an arbitrary choice, we propose a novel atlas-free approach, named Surface-Based Connectivity Integration (SBCI), to more accurately study the relationships between SC and FC throughout the intra-cortical gray matter. SBCI represents both SC and FC in a continuous manner on the white surface, avoiding the need for prespecified atlases. The continuous SC is represented as a probability density function and is smoothed for better facilitation of its integration with FC. To infer the relationship between SC and FC, three novel sets of SC-FC coupling (SFC) measures are derived. Using data from the Human Connectome Project, we introduce the high-quality SFC measures produced by SBCI and demonstrate the use of these measures to study sex differences in a cohort of young adults. Compared with atlas-based methods, this atlas-free framework produces more reproducible SFC features and shows greater predictive power in distinguishing biological sex. This opens promising new directions for all connectomics studies.