Aberrant brain functional and structural developments in MECP2 duplication rats

R-Baclofen Treatment Corrects Autistic-like Behavioral Deficits in the RjIbm(m):FH Fawn-Hooded Rat Strain

The Fawn-hooded rat has long been used as a model for various peripheral and central disorders and the data available indicate that the social behavior of this strain may be compromised. However, a thorough description of the Fawn-hooded rat is unavailable in this regard. The objective of the present study was to investigate various aspects of the Fawn-hooded rat's social behavior in depth. Our results show that several facets of socio-communicational behavior are impaired in the RjIbm(m):FH strain, including defective ultrasonic vocalizations in pups upon maternal deprivation, reduced social play in adolescence and impaired social novelty discrimination in adulthood. In addition, Fawn-hooded rats exhibited heightened tactile sensitivity and hyperactivity. The defects observed were comparable to those induced by prenatal valproate exposure, a widely utilized model of autism spectrum disorder. Further on, the pro-social drug R-baclofen (0.25-1 mg/kg) reversed the autistic-like defects observed in Fawn-hooded rats, specifically the deficiency in ultrasonic vocalization, tactile sensitivity and social novelty discrimination endpoints. In conclusion, the asocial, hypersensitive and hyperactive phenotype as well as the responsivity to R-baclofen indicate this variant of the Fawn-hooded rat strain may serve as a model of autism spectrum disorder and could be useful in the identification of novel drug candidates.

Gut microbiome and metabolic profiles of mouse model for MeCP2 duplication syndrome

The extra copy of the methyl-CpG-binding protein 2 (MeCp2) gene causes MeCP2 duplication syndrome (MDS), a neurodevelopmental disorder characterized by intellectual disability and autistic phenotypes. However, the disturbed microbiome and metabolic profiling underlying the autistic-like behavioral deficits of MDS are rarely investigated. Here we aimed to understand the contributions of microbiome disruption and associated metabolic alterations, especially the disturbed neurotransmitters in MDS employing a transgenic mouse model with MeCP2 overexpression. We analyzed metabolic profiles of plasma, urine, and cecum content and microbiome profiles by both 16 s RNA and shotgun metagenomics sequence technology. We found the decreased levels of Firmicutes and increased levels of Bacteroides in the single MeCP2 gene mutation autism-like mouse model, demonstrating the importance of the host genome in a selection of microbiome, leading to the heterogeneity characteristics of microbiome in MDS. Furthermore, the changed levels of several neurotransmitters (such as dopamine, taurine, and glutamate) implied the excitatory-inhibitory imbalance caused by the single gene mutation. Concurrently, a range of microbial metabolisms of aromatic amino acids (such as tryptophan and phenylalanine) were identified in different biological matrices obtained from MeCP2 transgenic mice. Our investigation revealed the importance of genetic variation in accounting for the differences in microbiomes and confirmed the bidirectional regulatory axis of microbiota-gut-brain in studying the role of microbiome on MDS, which could be useful in deeply understanding the microbiome-based treatment in this autistic-like disease.

Data-driven multimodal fusion: approaches and applications in psychiatric research

In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.