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.
NeuroImage
April 4, 2019
Ottavia Dipasquale, Pierluigi Selvaggi, Mattia Veronese et al.
79 citations
A double-blind, placebo-controlled study combined resting-state fMRI with a molecular atlas of serotonin receptors to examine how MDMA alters functional connectivity. Using the REACT method, the researchers found that MDMA-induced connectivity changes were specifically linked to brain regions rich in the serotonin transporter (5-HTT) and the 5-HT1A receptor, the drug's primary targets. Changes in 5-HT1A-enriched maps correlated with MDMA blood levels, while changes in 5-HT2A-enriched maps correlated with spiritual experiences reported by participants. The approach shows that MDMA's effects on brain connectivity can be explained by the distribution of its serotonergic targets, offering a new way to characterize psychoactive compounds.
Neuroscience & Biobehavioral Reviews
May 5, 2015
Federico Turkheimer, Robert Leech, Paul Expert et al.
64 citations
The brain appears to use repeated computational building blocks—canonical computational motifs—that are similar across species, brain areas, and sensory modalities. These motifs, grounded in stereotyped neuronal circuits and inhibitory interneurons, operate at micro-, meso-, and macro-scales to form a multiplexing information system capable of encoding and transmitting increasingly complex information. Similar activation patterns are observed in primary sensory cortices via electrophysiology and in large-scale networks measured with fMRI. The authors apply this canonical model to unify evidence on the pathophysiology of schizophrenia and suggest it may extend to other brain disorders involving GABA interneuron dysfunction.
NeuroImage
July 1, 2022
Fran Hancock, Joana Cabral, Andrea I. Luppi et al.
40 citations
Dynamic functional connectivity (dFC) in resting-state fMRI is promising for clinical biomarkers, but its reliability and interpretability are debated. This study combined phase-based dFC metrics from dynamical systems, stochastic processes, and information dynamics to assess their interrelationships and reliability. Novel relationships between metrics allowed building a predictive model for integrated information. Global metastability, reflecting simultaneous coupling and decoupling tendencies, was the most representative and stable metric in brain parcellations including cerebellar regions. Spatiotemporal patterns of phase-locking changed slowly and continuously over time. The findings suggest that most resting-state fMRI dynamics reflect an interrelated complexity profile unique to each acquisition, challenging cross-sectional designs for neuromarker discovery and indicating individual life-trajectories may be more informative.
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.
Preprints.org
July 21, 2023
Fran Hancock, Fernando E. Rosas, Mengsen Zhang et al.
16 citations
preprint
Healthy brain function requires a balance between stable integration across brain areas for coordinated activity and brief periods of desynchronization that allow subsystems to reconfigure and express specialized functions. Metastability, a concept from statistical physics and dynamical systems theory, has been proposed as a key signature of this balance. Neuroscience research has used markers of metastability to study cognitive performance, healthy aging, meditation, sleep, responses to drugs, and to characterize psychiatric conditions and disorders of consciousness. However, the term is often used heuristically or inaccurately, making the literature difficult to navigate. This paper provides a comprehensive review of metastability in neuroscience, covering its scientific and historical foundations, practical estimators, and a critical analysis of recent theoretical developments to clarify misconceptions.
bioRxiv : the preprint server for biology
June 12, 2026
Timothy Lawn, Johan Nakuci, Steve Cr Williams et al.
Spatial overlap among brain receptor maps derived from PET imaging can distort analyses that model multiple receptors together. Using test-retest fMRI data, the authors show that as more receptors are included in a multivariate model, the reliability of the resulting functional connectivity networks decreases, and this degradation is driven by collinearity among the receptor maps. A univariate approach, modeling each receptor independently, produces more reliable networks and, in a study comparing LSD to placebo, better captured the known role of the 5HT-2A receptor. Spatial collinearity is a fundamental constraint on multivariate molecular-enriched network estimation, and univariate modeling is recommended as a more robust default.