Frontiers in Human Neuroscience
January 1, 2014
Robin Carhart-Harris, Robert Leech, Peter J. Hellyer et al.
1,289 citations
Entropy, a measure of uncertainty or disorder, is applied to brain function and consciousness, focusing on the psychedelic state induced by psilocybin. The psychedelic state is considered a primary or primitive state of consciousness, characterized by elevated entropy in brain function, including a greater repertoire of functional connectivity motifs that form and fragment over time. This suggests primary states may exhibit criticality, a transition zone between order and disorder. Normal waking consciousness suppresses entropy, operating just below criticality, which constrains cognition and enables metacognitive functions like reality-testing and self-awareness. Entry into primary states involves collapse of default-mode network activity and decoupling from medial temporal lobes. These hypotheses can be tested by comparing brain activity in REM sleep, early psychosis, normal waking consciousness, and anesthesia.
Proc Natl Acad Sci U S A
April 11, 2016
Robin Carhart-Harris, Suresh Muthukumaraswamy, Leor Roseman et al.
887 citations
LSD produces marked changes in brain activity that correlate with its psychological effects. Increased blood flow in the visual cortex, decreased alpha power there, and an expanded functional connectivity profile of the primary visual cortex strongly correlated with visual hallucinations, suggesting that intrinsic brain activity influences visual processing more during the psychedelic state. Decreased connectivity between the parahippocampus and retrosplenial cortex correlated strongly with ego-dissolution and altered meaning, indicating this circuit's role in maintaining the self and processing meaning. Different imaging metrics showed strong relationships, allowing firmer inferences about their functional significance.
Journal of The Royal Society Interface
October 29, 2014
Giovanni Petri, Paul Expert, Federico Turkheimer et al.
689 citations
Functional brain networks can be studied through homological cycles—topological objects that capture mesoscopic structure in weighted correlation networks. A new method, homological scaffolds, compactly represents these cycles and makes them amenable to standard network analysis. Applied to resting-state fMRI data from 15 healthy volunteers given placebo or psilocybin, the homological structure of brain activity changed dramatically after psilocybin, producing many transient, low-stability cycles and a few persistent ones absent under placebo.
Journal of Neuroscience
January 8, 2014
Peter J. Hellyer, Murray Shanahan, Gregory Scott et al.
225 citations
During an attentionally demanding task, brain activity becomes more synchronized and less variable over time compared to rest. This shift is linked to increased activity in the frontoparietal control/dorsal attention network and decreased activity in the default mode network. A computational model confirmed that activating the frontoparietal network increases synchrony and reduces variability, while activating the default mode network does the opposite. The balance between these networks may control how the brain shifts between an unfocused, exploratory state with high variability and a focused, constrained state with low variability.
PLoS Computational Biology
August 24, 2017
Peter J. Hellyer, Claudia Clopath, Angie A. Kehagia et al.
21 citations
A simple computational model of spontaneous neural dynamics controlling an agent in a virtual environment shows that brain-environment feedback can rapidly destabilize neural and behavioral dynamics, requiring homeostatic mechanisms. Local homeostatic plasticity, where inhibition adjusts to balance excitation, and global mechanisms, where regional task-negative activity compensates for task-positive sensory input in another region, both stabilize behavior. The results suggest complementary functional roles for local and macroscale homeostatic processes and propose a novel function for macroscopic task-negative activity patterns, such as the default mode network, in maintaining stable neural and behavioral dynamics.
Journal of psychopharmacology (Oxford, England)
June 18, 2025
Maria Bălăeţ, William Trender, Annalaura Lerede et al.
2 citations
During the COVID-19 pandemic, six common patterns of drug use emerged in a large citizen science cohort. Most drug-using groups had worse average mental health scores than drug-naive individuals at all timepoints, and those who increased their drug use saw their mental health worsen over time. However, people who used both psychedelics and cannabis showed average improvements in depression, anxiety, and overall mental health from before the pandemic to January 2022, becoming comparable to the drug-naive group. Cannabis-only users did not show this improvement; their worse mental health scores persisted. These findings suggest that beneficial effects of psychedelics on mood and anxiety may extend beyond controlled conditions.
UNC Libraries
April 22, 2020
Peter J. Hellyer, Luke T. J. Williams, Ben Sessa et al.
1 citation
Lysergic acid diethylamide (LSD) in microgram doses produces profound, sometimes life-changing experiences and is a uniquely powerful psychoactive substance. In the first modern neuroimaging study of LSD, marked changes in brain blood flow, electrical activity, and network communication patterns were observed. These changes correlated strongly with the drug's hallucinatory and consciousness-altering properties. The findings have implications for understanding the neurobiology of consciousness and for potential applications of LSD in psychological research.
Neuroscience and Biobehavioral Reviews
April 1, 2019
Federico Turkheimer, Peter J. Hellyer, Angie A. Kehagia et al.
Emergence describes how complex systems exhibit properties not easily explained by their individual parts, seen in ant colonies, bird flocks, or brain function. This paper clarifies the concept, distinguishing strong emergence (where properties are irreducible to lower-level mechanisms) from weak emergence (where properties arise from but are explainable by lower-level interactions). It argues that models based on strong emergence, such as the free energy principle and integrated information theory, risk metaphysical implausibility and overdetermination, making them only one of many possible explanations. In contrast, weakly emergent computational models like oscillatory networks, which start from biologically plausible elementary units, are ontologically sound and offer a powerful approach for future neuroscientific research.