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Ying Yao on Muck Rack

Ying Yao

As seen in: Color Research & Application, Nature, Wiley Online Library, PLOS Biology, Frontiers, MDPI, Taylor & Francis Online, Tobacco Control, ACS Publications, Biomed Central (BMC) and
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Articles

It’s not just the phase: Frequency-dependent tuning of neuronal firing

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By Ying Yao, Simon Hanslmayr
| PLOS Biology Verified
Neural activity in the brain is often organized into rhythmic patterns, commonly referred to as neural oscillations, which are reflected in local field potentials (LFPs) and thought to coordinate communication across neural populations [1]. Neurons may preferentially fire at specific moments within an oscillatory cycle (phase tuning) or during oscillations at particular frequencies (frequency tuning).
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The moderating role of social support in the relationship between drug addiction memory and craving: the mediating effect of meaning in life in a Chinese compulsory rehabilitation context

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By Lishuang Chen, Ying Yao, Mingsheng Xiong
| Nature Verified
Abstract Drug craving represents a persistent challenge in substance use disorder treatment, yet the psychological mechanisms underlying the relationship between drug addiction memory and craving remain insufficiently understood. This study examines whether meaning in life mediates the association between drug addiction memory and drug craving, and whether social support moderates these pathways, specifically within the context of China’s compulsory rehabilitation system.
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The retina-body axis: proteomic mechanisms linking oculomics and clinical traits in a female aging cohort

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By Selena Zhang, Shunming Liu, Yanxian Chen, Jing J. Liu, Ying Yao, Xianwen Shang, Honghua Yu, Yu Huang, Mingguang He
| Nature Verified
Abstract The retina provides a unique window into systemic health, yet molecular mechanisms linking retinal features (oculomics) to clinical traits in aging remain unclear. In this study, we leveraged the homogeneous Canton 70 s Alumni Cohort (N = 258 females aged ~70 years) to minimize socio-demographic confounders and extracted oculomic features from fundus images using AutoMorph.
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