Biology subjects

Ziv, M.

Publications and source records attributed to Ziv, M..

3 recordsLinked to original sources

bioRxiv2026

Developmental links between play behavior and brain network integration

Play is a fundamental aspect of childhood and plays a crucial role in the development of creativity, yet its neural mechanisms remain poorly understood. We tested the hypothesis that more frequent play is associated with stronger functional integration among the default mode network (DMN), executive control network (CN), and salience network (SAL), as these cortical networks have been implicated in creativity in adults. In a preregistered study of infants and toddlers (Study 1; N = 143, 10 months-3 years, 67 boys, Baby Connectome Project), parent-reported play and imitation behaviors increased sharply from 1 to 2 years, and were associated with stronger within-DMN connectivity and DMN-CN coupling, controlling for age, sex, and head motion. In middle childhood (Study 2; N = 108, ages 4-11 years, 52 boys), parent-reported play frequency declined with age, as did cross-network coupling involving SAL. However, children who engaged more frequently in play showed higher DMN-SAL and CN-SAL connectivity. Finally, in a quasi-experimental comparison (Study 3; N = 45; ages 4-12 years, 20 boys), children enrolled in a curriculum that includes guided play (Montessori) showed higher DMN-SAL and DMN-CN connectivity than peers in traditional schools, suggesting that pedagogies that center child-led exploration might enable protracted brain network integration. Across these three studies, play was consistently associated with greater integration among DMN, SAL, and CN, a pattern previously linked to creativity in adults. Our findings offer a potential mechanism linking childhood play to later creativity through its role in supporting brain integration during development. Public Significant StatementO_LIPlay is widely believed to nurture childrens creativity, yet the brain mechanisms behind this link are not well understood. C_LIO_LIAcross three studies from infancy to middle childhood, we found that more frequent play was associated with stronger integration among brain networks tied to imagination, attention, and control. C_LIO_LIThese findings suggest that play may help build the neural foundation for later creative thinking. C_LI

neuroscience↗

bioRxiv2023

sNucConv: A bulk RNA-seq deconvolution method trained on single-nucleus RNA-seq data to estimate cell-type composition of human subcutaneous and visceral adipose tissues

Deconvolution algorithms rely on single-cell RNA-sequencing (scRNA-seq) data applied onto bulk RNA-sequencing (bulk RNA-seq) to extract information on the cell-types composition and proportions comprising a certain tissue. Adipose tissues cellular composition exhibits enormous plasticity in response to weight changes and high variance at different anatomical locations (depots). However, adipocytes - the functionally unique cell type of adipose tissue, are not amenable to scRNA-seq, a challenge recently met by applying single-nucleus RNA-sequencing (snRNA-seq). Here we aimed to develop a deconvolution method to estimate the cellular composition of human visceral and subcutaneous adipose tissues (hVAT and hSAT, respectively) using snRNA-seq to assess the true cell-type proportions. To correlate deconvolution-estimated cell-type proportions to true (snRNA-seq -derived) proportions, we analyzed seven hVAT and 5 hSAT samples by both bulk RNA-seq and snRNA-seq. snRNA-seq uncovered 15 distinct cell types in hVAT and 13 in hSAT. Deconvolution tools - SCDC, MuSiC, and Scaden exhibited low performance in estimating cell-type proportions (median |R|= 0.12 for estimated vs. true correlations). Notably, estimation accuracy somewhat improved by decreasing the number of cell-types groups, which nevertheless remained low (|R|<0.42). We therefore developed sNuConv, a novel method that employs Scaden, a deep-learning tool, trained using snRNA-seq - based data corrected by i. snRNA-seq/bulk RNA-seq highly-correlated genes, ii. corrected estimated cell-type proportions based on individual cell-type regression models. Applying sNuConv on our bulk RNA-seq data resulted in cell-type proportion estimation accuracy with median R=0.93 (range:0.76-0.97) for hVAT, and median R=0.95 (range:0.92-0.98) for hSAT. The resulting model was depot-specific, reflecting depot-differences in gene expression patterns. Thus, we present sNuConv, a novel, AI-based, method to deduce the cellular landscape of hVAT and hSAT from bulk RNA-seq data, providing proof-of-concept for producing validated deconvolution algorithms for tissues un-amenable to single-cell RNA sequencing.

bioinformatics↗

bioRxiv2021

A tissue-aware machine learning framework enhances the mechanistic understanding and genetic diagnosis of Mendelian and rare diseases

Genetic studies of Mendelian and rare diseases face the critical challenges of identifying pathogenic gene variants and their modes-of-action. Previous efforts rarely utilized the tissue-selective manifestation of these diseases for their elucidation. Here we introduce an interpretable machine learning (ML) platform that utilizes heterogeneous and large-scale tissue-aware datasets of human genes, and rigorously, concurrently and quantitatively assesses hundreds of candidate mechanisms per disease. The resulting tissue-aware ML platform is applicable in gene-specific, tissue-specific, or patient-specific modes. Application of the platform to selected Mendelian disease genes pinpointed mechanisms that lead to tissue-specific disease manifestation. When applied jointly to diseases that manifest in the same tissue, the models revealed common known and previously underappreciated factors that underlie tissue-selective disease manifestation. Lastly, we harnessed our ML platform toward genetic diagnosis of tissue-selective rare diseases. Patient-specific models of candidate disease-causing genes from 50 patients successfully prioritized the pathogenic gene in 86% of the cases, implying that the tissue-selectivity of rare diseases aids in filtering out unlikely candidate genes. Thus, interpretable tissue-aware ML models can boost mechanistic understanding and genetic diagnosis of tissue-selective heritable diseases. A webserver supporting gene prioritization is available at https://netbio.bgu.ac.il/trace/.

genetics↗