Putting age-related task activation into large-scale brain networks: A meta-analysis of 114 fMRI studies on healthy aging

Normal aging is associated with cognitive decline and underlying brain dysfunction. Previous studies concentrated less on brain network changes at a systems level. Our goal was to examine these age-related changes of fMRI-derived activation with a common network parcellation of the human brain function, offering a systems-neuroscience perspective of healthy aging. We conducted a series of meta-analyses on a total of 114 studies that included 2035 older adults and 1845 young adults. Voxels showing significant age-related changes in activation were then overlaid onto seven commonly referenced neuronal networks. Older adults present moderate cognitive decline in behavioral performance during fMRI scanning, and hypo-activate the visual network and hyper-activate both the frontoparietal control and default mode networks. The degree of increased activation in frontoparietal network was associated with behavioral performance in older adults. Age-related changes in activation present different network patterns across cognitive domains. The systems neuroscience approach used here may be useful for elucidating the underlying network mechanisms of various brain plasticity processes during healthy aging.     

Flavonoids and stilbenoids as a promising arsenal for the management of chronic arsenic toxicity

Rapid industrial and technological development has impacted ecosystem homeostasis strongly. Arsenic is one of the most detrimental environmental toxins and its management with chelating agents remains a matter of concern due to associated adverse effects. Thus, safer and more effective alternative therapy is required to manage arsenic toxicity. Based on existing evidence, native and indigenous plant-based active biomolecules appear as a promising strategy to mitigate arsenic-induced toxicity with an acceptable safety profile. In this regard, various phytochemicals (flavonoids and stilbenoids) are considered important classes of polyphenolic compounds with antioxidant and chelation effects, which may facilitate the removal of arsenic from the body more effectively and safely with regard to conventional approaches. This review presents an overview of conventional chelating agents and the potential role of flavonoids and stilbenoids in ameliorating arsenic toxicity. This report may provide a roadmap for identifying novel prophylactic/therapeutic strategies for managing arsenic toxicity.     

Tri-allelic patterns of STRs and partially homologous non-sister chromatid crossover observed in a parentage test

A maternity testing case is reported, in which the child showed tri-allelic patterns in two short tandem repeat (STR) loci. The genotypes of Penta D of the mother and the child were 9,13 and 9,10,13, respectively. Those of D21S11 were 32.2,35 and 29,35, respectively, but intensity ratio of alleles 29 and 35 of the child was 1:2. These results suggested the copy number variations (CNVs) or trisomy of chromosome 21. By further examination using STR-based chromosome aneuploidy detection kit, three alleles were detected in D21S1411, LFG21 and Penta D, and 2 alleles with intensity ratio of 1:2 were observed in D21S2502, D21S1435, D21S11 and D21S1246. Karyotype and whole-genome SNP array analyses showed that the child had a free trisomy 21. In addition, partially homologous non-sister chromatid crossover occurred at the region 19181770-39499178 on the long arm of chromosome 21.     

Multi-site clustering and nested feature extraction for identifying autism spectrum disorder with resting-state fMRI

Brain functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been widely employed to study neuropsychiatric disorders such as autism spectrum disorder (ASD). Existing studies usually suffer from (1) significant data heterogeneity caused by different scanners or studied populations in multiple sites, (2) curse of dimensionality caused by millions of voxels in each fMRI scan and a very limited number (tens or hundreds) of training samples, and (3) poor interpretability, which hinders the identification of reproducible disease biomarkers. To this end, we propose a Multi-site Clustering and Nested Feature Extraction (MC-NFE) method for fMRI-based ASD detection. Specifically, we first divide multi-site training data into ASD and healthy control (HC) groups. To model inter-site heterogeneity within each category, we use a similarity-driven multiview linear reconstruction model to learn latent representations and perform subject clustering within each group. We then design a nested singular value decomposition (SVD) method to mitigate inter-site heterogeneity and extract FC features by learning both local cluster-shared features across sites within each category and global category-shared features across ASD and HC groups, followed by a linear support vector machine (SVM) for ASD detection. Experimental results on 609 subjects with rs-fMRI from the ABIDE database with 21 imaging sites suggest that the proposed MC-NFE outperforms several state-of-the-art methods in ASD detection. The most discriminative FCs identified by the MC-NFE are mainly located in default mode network, salience network, and cerebellum region, which could be used as potential biomarkers for fMRI-based ASD analysis.