Transcriptomics-informed large-scale cortical model captures topography of pharmacological neuroimaging effects of LSD

Abstract

Psychoactive drugs can transiently perturb brain physiology while preserving brain structure. The role of physiological state in shaping neural function can therefore be investigated through neuroimaging of pharmacologically induced effects. Previously, using pharmacological neuroimaging, we found that neural and experiential effects of lysergic acid diethylamide (LSD) are attributable to agonism of the serotonin-2A receptor (Preller et al., 2018). Here, we integrate brain-wide transcriptomics with biophysically based circuit modeling to simulate acute neuromodulatory effects of LSD on human cortical large-scale spatiotemporal dynamics. Our model captures the inter-areal topography of LSD-induced changes in cortical blood oxygen level-dependent (BOLD) functional connectivity. These findings suggest that serotonin-2A-mediated modulation of pyramidal-neuronal gain is a circuit mechanism through which LSD alters cortical functional topography. Individual-subject model fitting captures patterns of individual neural differences in pharmacological response related to altered states of consciousness. This work establishes a framework for linking molecular-level manipulations to systems-level functional alterations, with implications for precision medicine.

Classic Psychedelic Drugs: Update on Biological Mechanisms

“…A similar approach employing a transcriptomics-informed large-scale cortical model, including the expression level of various serotonergic and dopaminergic genes also found that modulation of pyramidal cell gain by 5-HT2A receptor activation accurately captures the LSD-induced GBC changes 119 144, 145 . In addition, fitting to GBC in individual subjects revealed that the model also captures patterns of individual differences in LSD response that predict different aspects of the psychedelic experience 144 . Thus, it appears that the integration of bio-physical modeling and empirical neuroimaging data provides a promising framework to further unravel circuit mechanisms through which psychedelics alter cortical functional topography.…”

“…Notably, a recent whole-brain model using the dynamical mean-field quantitative description of excitatory and inhibitory neuronal populations as well as the associated synaptic gain function suggests that the effect of LSD on global brain connectivity can be best explained by the regional distribution and density of 5-HT2A receptors located on cortical pyramidal neurons 174 . A similar approach employing a transcriptomics-informed large-scale cortical model, including the expression level of various serotonergic and dopaminergic genes also found that modulation of pyramidal cell gain by 5-HT2A receptor activation accurately captures the LSD-induced GBC changes 119 144, 145 . In addition, fitting to GBC in individual subjects revealed that the model also captures patterns of individual differences in LSD response that predict different aspects of the psychedelic experience 144 .…”

Psychedelic Therapy's Transdiagnostic Effects: A Research Domain Criteria (RDoC) Perspective

“…A precise mechanistic understanding of psychedelics is challenging because of the synergistic action of pharmacotherapy and psychotherapy, together with the induction of a wide range of complex subjective experiences with marked individual variation (19). The primary initial pharmacological target of the classical psychedelics appears to be activation of 5-HT2A receptors (Box 1) particularly in cortical layer 5 pyramidal cells (20)(21)(22)(23)(24)(25)(26)(27). A contemporary explanatory model-the Relaxed Beliefs under Psychedelics and the Anarchic Brain (REBUS)-proposes that psychedelics via action at 5-HT2A receptors in higherorder cortical regions (27) relax the typical constraints that higher order brain systems impose on emotions, cognitions, and sensory perceptions.…”

Neural mass modelling for the masses: Democratising access to whole-brain biophysical modelling with FastDMF

“…In general, the former are faster to simulate and easier to tune and control, while the latter provide more realistic explanations and can incorporate additional neurobiological information, such as receptor densities obtained using positron emission tomography (PET) or transcriptomic data. 26 One of the most prominent biophysically-grounded whole-brain models is the Dynamic Mean-Field (DMF) model 10,27 , which results from applying a mean-field approach to single neuron models, allowing to model the dynamics of the mean firing rate of a macroscopic brain region 28,29 . This approach allows to model the local dynamics of each brain region as two interacting neural populations: one excitatory (E) and another inhibitory (I) (see Fig.…”

“…In general, the former are faster to simulate and easier to tune and control, while the latter provide more realistic explanations and can incorporate additional neurobiological information, such as receptor densities obtained using positron emission tomography (PET) or transcriptomic data. 26…”