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Fei Wang

3 papers in the library · 51 citations · publishing 2024-2025

Papers

Alterations in brain network connectivity and subjective experience induced by psychedelics: a scoping review

Frontiers in Psychiatry May 14, 2024 Zijia Yu, Lisa Burback, Olga Winkler et al. 38 citations

A scoping review of 24 articles found that four psychedelic drugs—ayahuasca, psilocybin, LSD, and the entactogen MDMA—consistently alter brain functional connectivity in healthy individuals. The drugs decreased connectivity within the default mode network and increased sensory and thalamocortical connectivity. These neurophysiological changes correlated with subjective experiences such as altered consciousness, mood elevation, and mystical experiences, suggesting a brain network basis for the drugs' psychological effects. The review provides a potential neural mechanism for psychedelics' subjective effects but notes that direct clinical evidence is needed to advance therapeutic outcomes.

Exploring the Neural Correlates of Flow Experience with Multifaceted Tasks and a Single-Channel Prefrontal EEG Recording.

Sensors (Basel, Switzerland) March 15, 2024 Yuqi Hang, Buyanzaya Unenbat, Shiyun Tang et al. 13 citations

Deep immersion in tasks, known as flow experience, has significant psychological benefits. In a study involving 28 participants, six multifaceted tasks—including mindfulness and varying levels of Tetris—were used to induce flow. EEG recordings revealed strong positive correlations between subjective flow scores and brain activity in delta, gamma, and theta bands, peaking around two minutes after task onset. The analysis indicated a maximum R² of 0.163, showcasing the effectiveness of portable EEG technology for objectively measuring flow experiences in real-world settings.

Neural oscillations predict flow experience.

Cognitive Neurodynamics December 1, 2025 Bingxin Lin, Baoshun Guo, Lingyun Zhuang et al.

During flow, a state of deep immersion in an activity, the brain shows higher theta power, moderate alpha power, and lower beta power compared to non-flow states, suggesting a focused yet effortless neural pattern. Machine learning (Lasso regression) predicted individuals' subjective flow scores from EEG data with a correlation of 0.571, indicating that flow can be objectively quantified from neural oscillations.