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Recent findings have shown that psychedelics reliably enhance brain entropy (understood as neural signal diversity), and this effect has been associated with both acute and long-term psychological outcomes, such as personality changes. These findings are particularly intriguing, given that a decrease of brain entropy is a robust indicator of loss of consciousness (e.g., from wakefulness to sleep). However, little is known about how context impacts the entropy-enhancing effect of psychedelics, which carries important implications for how it can be exploited in, for example, psychedelic psychotherapy. This article investigates how brain entropy is modulated by stimulus manipulation during a psychedelic experience by studying participants under the effects of lysergic acid diethylamide (LSD) or placebo, either with gross state changes (eyes closed vs open) or different stimuli (no stimulus vs music vs video). Results show that while brain entropy increases with LSD under all of the experimental conditions, it exhibits the largest changes when subjects have their eyes closed. Furthermore, brain entropy changes are consistently associated with subjective ratings of the psychedelic experience, but this relationship is disrupted when participants are viewing a video─potentially due to a "competition" between external stimuli and endogenous LSD-induced imagery. Taken together, our findings provide strong quantitative evidence of the role of context in modulating neural dynamics during a psychedelic experience, underlining the importance of performing psychedelic psychotherapy in a suitable environment.
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http://dx.doi.org/10.1021/acschemneuro.3c00289 | DOI Listing |
IEEE Trans Biomed Eng
September 2025
Signal complexity analysis plays a crucial role in biomedical research, particularly in electroencephalography (EEG), for early disease diagnosis and cognitive monitoring. However, traditional entropy-based methods lack robustness, suffer from limitations such as sensitivity to noise, and fail to capture the multi-frequency structure of brain signals. To address these challenges, this study introduces Multivariate Multiscale Multi-Frequency Entropy (M3FrEn), a novel complexity metric that simultaneously incorporates multiscale dynamics, multichannel dependencies, and multi-frequency structure into a unified entropy-based framework.
View Article and Find Full Text PDFBiol Psychiatry
September 2025
Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China. Electronic address:
Background: Major depressive disorder (MDD) has been increasingly understood as a disorder of network-level functional dysconnectivity. However, previous brain connectome studies have primarily relied on node-centric approaches, neglecting critical edge-edge interactions that may capture essential features of network dysfunction.
Methods: This study included resting-state functional MRI data from 838 MDD patients and 881 healthy controls (HC) across 23 sites.
Adv Sci (Weinh)
September 2025
Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China.
Early-warning signals of delicate design are used to predict critical transitions in complex systems, which makes it possible to render the systems far away from the catastrophic state by introducing timely interventions. Traditional signals including the dynamical network biomarker (DNB), based on statistical properties such as variance and autocorrelation of nodal dynamics, overlook directional interactions and thus have limitations in capturing underlying mechanisms and simultaneously sustaining robustness against noise perturbations. This study therefore introduces a framework of causal network markers (CNMs) by incorporating causality indicators, which reflect the directional influence between variables.
View Article and Find Full Text PDFUnlabelled: Adaptive behavior requires integrating information from multiple sources. These sources can originate from distinct channels, such as internally maintained latent cognitive representations or externally presented sensory cues. Because these signals are often stochastic and carry inherent uncertainty, integration is challenging.
View Article and Find Full Text PDFBehav Neurol
September 2025
Department of Electronics and Communication Engineering, Chettinad Academy of Research and Education, Manamai Campus, Chennai, Tamil Nadu, India.
Temporary disturbances in brain function are caused by epilepsy, a chronic disorder resulting from sudden abnormal firing of brain neurons. This research introduces an innovative real-time methodology representing detecting epileptic spasms from electroencephalogram (EEG) data. It employs a support vector machine (SVM) alongside embedded zero tree wavelet (EZW) transform.
View Article and Find Full Text PDF