Increased global integration in the brain after psilocybin therapy for depression

This fMRI study assessed the impact of psilocybin on brain function in two clinical trials of depression. In both trials, the antidepressant response to psilocybin was rapid and sustained, correlating with decreases in fMRI brain network modularity. Network cartography analyses indicated that serotonin (5-HT) 2A receptor-rich higher-order functional networks became more functionally interconnected and flexible after a psilocybin treatment. Together, the findings from both studies point to global increases in brain network integration as an antidepressant mechanism in psilocybin therapy.

Abstract

“Psilocybin therapy shows antidepressant potential, but its therapeutic actions are not well understood. We assessed the subacute impact of psilocybin on brain function in two clinical trials of depression. The first was an open-label trial of orally administered psilocybin (10 mg and 25 mg, 7 d apart) in patients with treatment-resistant depression. Functional magnetic resonance imaging (fMRI) was recorded at baseline and 1 d after the 25-mg dose. Beck’s depression inventory was the primary outcome measure (MR/J00460X/1). The second trial was a double-blind phase II randomized controlled trial comparing psilocybin therapy with escitalopram. Patients with major depressive disorder received either 2 × 25 mg oral psilocybin, 3 weeks apart, plus 6 weeks of daily placebo (‘psilocybin arm’) or 2 × 1 mg oral psilocybin, 3 weeks apart, plus 6 weeks of daily escitalopram (10–20 mg) (‘escitalopram arm’). fMRI was recorded at baseline and 3 weeks after the second psilocybin dose (NCT03429075). In both trials, the antidepressant response to psilocybin was rapid, sustained and correlated with decreases in fMRI brain network modularity, implying that psilocybin’s antidepressant action may depend on a global increase in brain network integration. Network cartography analyses indicated that 5-HT2A receptor-rich higher-order functional networks became more functionally interconnected and flexible after psilocybin treatment. The antidepressant response to escitalopram was milder and no changes in brain network organization were observed. Consistent efficacy-related brain changes, correlating with robust antidepressant effects across two studies, suggest an antidepressant mechanism for psilocybin therapy: global increases in brain network integration.”

Authors: Richard E. Daws, Christopher Timmerman, Bruna Giribaldi, James D. Sexton, Matthew B. Wall, David Erritzoe, Leor Roseman, David J. Nutt & Robin L. Carhartt-Harris

Notes

Psilocybin has demonstrated significant efficacy in alleviating the symptoms of depression. A number of theories, such as increased functional connectivity in the brain and the role of the default mode network (DMN), have been put forward to explain the antidepressant effects psilocybin has, yet the exact mechanisms underlying this effect remain speculative at best.

The present paper, from the research team at Imperial College London, assessed the effects psilocybin has on brain function using fMRI in order to better understand the mechanism through which psilocybin elucidates its antidepressant effect. The paper includes data from two separate trials exploring the effects psilocybin therapy has in depressed patients: 1) an open-label trial of orally administered psilocybin (10 mg and 25 mg, 7 d apart) in patients with treatment-resistant depression (TRD) where fMRI was recorded at baseline and 1-day after the 25mg dose, and 2) a double-blind placebo-controlled trial comparing the effects of psilocybin to the SSRI escitalopram in patients with major depressive disorder (MDD). In this second trial, patients received 2 × 25 mg oral psilocybin, 3 weeks apart, plus 6 weeks of daily placebo (psilocybin arm) or 2 × 1 mg oral psilocybin, 3 weeks apart, plus 6 weeks of daily escitalopram (10–20 mg) (escitalopram arm) and fMRI was recorded at baseline and 3 weeks after the second psilocybin dose.

Using the Beck Depression Inventory as the primary outcome measure, the researchers hypothesized that the well-replicated finding of brain network disintegration and desegregation under psychedelics would be apparent in post-treatment fMRI data and that this effect, consistent with a flatter energy landscape, will relate to improved depression outcomes and will not be observed after a course of escitalopram.

What’s going on in the brain?

• In the open-label trial, brain modularity decreased one day after psilocybin therapy implying that there is a global increase in functional connectivity between the brain’s main intrinsic networks. This decreased modularity was predictive of improved therapeutic outcomes for TRD.
• In the RCT, psilocybin decreased brain modularity but escitalopram did not while the response to psilocybin correlated with enhanced network flexibility.
• Taken together, decreased brain modularity was observed and correlated with improvements in depressive symptomatology and this may be specific to psilocybin.
• Network cartography analyses indicated that 5-HT2A receptor-rich higher-order functional networks became more functionally interconnected and flexible after the psilocybin treatment.
• The liberating effects of psilocybin (e.g. emotional release, increased cognitive & psychological flexibility) in depressed patients may be a result of the effect psilocybin has on cortical 5-HT2A receptors, dysregulating activity in regions rich in their expression.

Overall, the findings of the present study suggest that a global increase in brain network integration may underlie the therapeutic effects psilocybin has in people with depression. Going forward, the authors note that large scale trials are needed to establish the generalizability, reliability and specificity of psilocybin’s antidepressant response. In terms of brain imaging studies, the authors recommend the use of network modularity analysis to elegantly summarize global changes in the brain’s functional network organization. Ultimately, findings expand on the potential mechanisms through which psilocybin elicits a rapid and sustained antidepressant effect.

The findings of this paper have been covered in an article from the BBC.

A written response to this paper can be found here (earlier pre-print link here).

The main critiques presented in this response include:

• The original studies that were analysed in the above paper original used the Quick Inventory of Depressive Symptoms (QIDS) as their primary outcome measures. However, the above study used the Beck Depression Inventory (BDI) as the primary measure. This raises issues of multiple comparisons as “psilocybin was not found to outperform citalopram on the QIDS in the more recent trial, but its superiority to citalopram was more apparent on the BDI.”
• The use of a one-tailed test in the statistical analysis does not provide adequate statistical power to infer psilocybin induces global increases in network integration.
• In terms of changes in modularity, regression to the mean may have a role to play which underlines the important clarifying role of a placebo intervention.
• Inconsistency within the open-label datasets. In the original report, psilocybin therapy increased default mode network (DMN) functional connectivity whereas Daws et al. reported in these same data a decrease in DMN connectivity using a different measure.
• The omission of the only other published fMRI investigation of the effects of psilocybin therapy in patients with depression. “Consistent with the findings of the prior study, Daws et al. find post-psilocybin increases in their measure of neural flexibility and speculate that these increases could be related to enhanced cognitive flexibility.“

The original authors have responded to these critiques. Check it out here.

Summary

Depression is a highly prevalent mental health condition, with modest efficacy, non-negligible side effects, discontinuation problems and high relapse rates.

Patients with a diagnosis of depression often exhibit a negative cognitive bias.

The default mode network (DMN), executive network (EN) and salience network (SN) are associated with introspection and self-referential thinking, and are often overactive in depression. The serotonin 2A (5-HT2A) receptor subtype is most densely expressed in this pattern of cortex.

In the last 15 years, six separate clinical trials have reported impressive improvements in depressive symptoms with psilocybin therapy. Two of these trials included pre- and post-treatment fMRI.

Psilocybin and related psychedelics cause a temporary ‘disintegration’ of intrinsic functional brain networks, which is linked to a broadening of the brain’s functional repertoire of states.

Results

16 patients with treatment-resistant depression participated in a single-arm, open-label psilocybin therapy clinical trial. The treatment had a rapid antidepressant effect on the Beck Depression Inventory (BDI-1A), which may be an important target of psilocybin therapy.

Baseline BDI scores indicated severe depression. After treatment, significant reductions in depression severity were observed at 1 week and still evident at 6 months.

Brain network modularity was significantly reduced 1 day after psilocybin therapy, and this decrease predicted improved clinical outcomes. The relationship between decreased brain network modularity and depression severity was strong at 6 months.

Pre-treatment changes in brain modularity were negatively correlated with change in BDI score at 6 months, but these results did not survive correction.

We tested for evidence of attenuated hyperconnectivity of the DMN and hypoconnectivity of the DMN with other higher-order ‘cognitive’ networks after psilocybin therapy. We observed significant reductions in DMN network recruitment and increased between-network integration of the DMN and EN and SN 1 day after treatment.

Psilocybin therapy for TRD causes a decrease in brain network modularity, which is underpinned by increased connectivity with higher order networks.

In this double-blind randomized controlled trial, patients with major depressive disorder were randomly allocated to receive either 25 mg psilocybin or a presumed inactive 1 mg psilocybin dose. They took daily capsules for 6 weeks and 1 day in total.

The researchers recruited 59 patients with MDD and randomly allocated 29 to the escitalopram arm and 30 to the psilocybin arm. 22 patients were included in the escitalopram imaging sample and 22 were included in the psilocybin imaging sample.

The BDI was a primary outcome measure for the open-label trial and a secondary outcome measure for the DB-RCT. The BDI showed significant differences in reductions in depressive symptoms between psilocybin and escitalopram, with psilocybin showing greater reductions at all time points.

Psilocybin significantly reduced brain network modularity at the trial’s primary end point, and this decrease was correlated with improvements in depression symptom severity. However, escitalopram did not change brain network modularity from baseline, and this relationship was not significant.

Response to psilocybin therapy correlates with network flexibility. Increased dynamic flexibility in the EN and other lateral frontoparietal networks correlated with greater symptom improvement at the 6-week primary end point for the psilocybin arm.

Discussion

Psilocybin therapy may improve depressive symptoms by decreasing brain modularity, a finding that was not observed with conventional SSRI therapy.

Research into the acute brain action of psychedelics has revealed well-replicated changes in global brain function, including increased inter-regional and between-network functional connectivity. This study shows robust evidence that increases to global brain network integration accompanies the antidepressant efficacy of psilocybin therapy.

Time-averaged within-network and between-network FC analyses showed that depression patients had increased global FC and a broadened dynamic state space, resembling brain dynamics associated with the acute action of psychedelics.

Previous research has found that heightened network modularity and elevated FC between limbic regions and high-level cortical regions correlate with ruminative symptoms in depression. Psilocybin therapy seems to reduce these symptoms by increasing the brain’s ability to visit a broader state space.

Psilocybin may have a liberating effect on cortical activity via its direct agonist action on cortical 5-HT2A receptors. Escitalopram may not have the same effect.

We observed functional changes in the DMN, EN and SN dynamics after psilocybin therapy that are consistent with neurobiological models of depression. These changes are associated with decreased modularity or increased flexibility of the networks, which may be a key component of the therapeutic mechanism of action.

The neuroimaging data were consistent with previous research regarding the acute action of psychedelics, and the decreased modularity was sufficient for response to psilocybin therapy.

Psilocybin therapy requires successful phase III DB-RCTs, but pragmatic trials may better address questions regarding treatment practicability, specificity and optimization. fMRI datasets are complex and susceptible to noise, so network modularity analyses may be a useful approach.

The present study suggests that the early phase of the response to psilocybin therapy is predictive of the long-term response to psilocybin therapy.

A robust fMRI preprocessing pipeline was employed alongside strict head motion criteria for patient inclusion. An analysis of head motion bolstered the present findings, and there was no evidence that head motion differed between sessions or treatment arms or that it correlated with network modularity.

This study’s primary hypothesis was confirmed despite substantial differences in the design of the two trials. The open-label trial had greater baseline depression severity and a shorter post-treatment fMRI scan.

Although the main findings of the open-label trial were robustly replicated, the finer-grained cartography analyses were not. This limits the ability to make network-specific inferences, but the observed network effects directly follow predictions from the depression literature.

Dynamic analyses can be challenging to conduct, but previous research guided our selection of parameters and a sufficiently broad window of time was used to estimate Pearson correlation FC.

The inferences from both cartography analyses converge on the brain’s higher-order networks, and this converges with previous research14.

Depression is a major public health problem, and psilocybin therapy holds promise as a new treatment option.

Online content

References include Depression and Other Common Mental Disorders: Global Health Estimates (World Health Organization, 2017), COVID-19 related depression and anxiety among quarantined respondents, Surging trends in prescriptions and costs of antidepressants in England amid COVID-19, Hofmann, S. G., Curtiss, J., Carpenter, J. K. & Kind, S., Locher, C. Beck, A. T., Clark, D. A., Rolls, E. T., Hamilton, J. P., Kendler, K. S., Goodman, Z., Margulies, D. S., and others have written about anxiety and depression, and the brain’s default mode network. The default network is the brain’s default mode network, which controls self-generated thought. The default network is also connected to the fronto-parietal and salience networks. A meta-analysis of brain regions associated with task switching found that task-positive neural systems are reduced with the passage of time.

Psilocybin induces schizophrenia-like psychosis in humans via a serotonin-2 agonist action, and fMRI-measured brain mechanisms may explain the therapeutic effects of classic serotonergic psychedelics. Psilocybin and LSD alter dynamic integration and segregation in the human brain, and the 52 symptoms of major depression have no overlap among seven common depression scales. A functional cartography of cognitive systems, a community structure in time-dependent, multiscale, and multiplex networks, and altered emotions and brain function are among the results of a single high dose of psilocybin. Psychedelic ayahuasca alters the salience and default mode networks in the brain, and changes of functional brain networks in major depressive disorder may be reflected in resting-state fMRI.

Resting state functional connectivity correlates of rumination and worry in internalizing psychopathologies, emotional breakthrough and psychedelics, mindfulness and cognitive flexibility in ayahuasca drinkers, and serotonin and brain function: a tale of two receptors. The intrinsic network and cross-network interactions are important for cognitive vulnerability to major depression. Neuroimaging evidence for a network sampling theory of individual differences in human intelligence test performance, brain network dynamics in high-functioning individuals with autism, abnormal salience network in depression, pragmatic research, real-world data and digital technologies aid in the development of psychedelic medicine.

Methods

The open-label31 and DB-RCT32 trials were conducted at the National Institute for Health Research Imperial Clinical Research Facility and received ethical approval, Health Research Authority and Medicines and Healthcare products Regulatory Agency approval.

Participants in both trials had a diagnosis of unipolar MDD and TRD, and were asked whether they had previous experience of using psychedelics. Exclusion criteria included immediate family or personal history of psychosis, risky physical health condition, history of serious suicide attempts, positive pregnancy test and MRI contraindications.

Nineteen patients with TRD attended a 1-d pre-treatment baseline session, followed by two psilocybin therapy DDs, separated by 1 week. They completed further clinical assessment electronically at 3 and 6 months.

Of 59 MDD patients recruited to the DB-RCT, 30 received psilocybin and 29 received escitalopram. Patients took daily capsules for 6 weeks and 1 d in total, with one capsule per day for the first 3 weeks and two thereafter.

In both studies, BDI-1A scores were used to assess depression severity. This patient-rated measure captures a broader range of symptoms, with an additional focus on the cognitive features of depression, compared with other measures such as the QIDS-SR-16.

fMRI was performed on a 3T Siemens Tim Trio at Invicro to acquire resting-state fMRI data. T2*-weighted echo-planar images with 3-mm isotropic voxels were collected with 12-channel head coils in study 1 and 32-channel head coils in study 2.

Patients were excluded if their fMRI scan contained >20% of volumes with a framewise displacement >0.5 mm.

The following preprocessing stages were performed: removal of the first three volumes, de-spiking, slice time correction, motion correction, brain extraction, rigid body registration to anatomical scans, nonlinear registration to the 2 mm Montreal Neurological Institute brain, scrubbing, 0.01 to 0.08 Hz band-pass filtering, linear and quadratic de-trending, and nuisance regression.

Functional connectivity between regions of interest was calculated using a Pearson correlation coefficient between mean signal ‘time courses’. Positive values were retained and Fisher-transformed to z scores.

Brain network modularity was measured by summing each FC matrix with a common Louvain-like community detection algorithm62. The modularity quality function score, Q63, tends to be high when the brain exhibits a high segregation between its functional networks.

Q is the weight of FC between ROI i and j, k2imkj is the expected null FC, ci is the community to which ROI i is assigned, and m is the total FC of the network.

To allow valid comparisons between patients and scans, modularity scores were generated 100 times and normalized by the mean modularity generated from 100 randomly rewired FC matrices65.

The community detection procedure assigns labels to each ROI, and the allegiance matrix represents the probability that two regions are assigned to the same community across 100 iterations.

We summarized how often ROIs formed communities with ROIs from the same functional network or between networks across the partitions, and normalized the results against 1,000 randomly shuffled ROI network assignments.

The short TR used in study 2 generated twice as many time points as the long TR, which allowed for an additional analysis of dynamic flexibility. Multilayer modularity was estimated 100 times from each N NT FC matrix.

Multilayer modularity estimation generates an N – T matrix, and the flexibility metric, fi, can be calculated as the number of times an ROI changes its community allegiance given the number of observations.

Data availability

R.C.H. and D.J.N. promptly review requests for raw and analyzed data and materials, and provide them with this paper.

author contributions

This study was designed and planned by R.C.-H. and D.N., conducted by B.G., M.B.W., D.E. and L.R., and analyzed by R.E.D. and C.T. All authors contributed to the interpretation of the study results.

Competing interests

The authors report receiving consulting fees from Entheon Biomedical and Beckley Psytech, small pharmaceutical fees from SmallPharma, and lecture fees from Takeda and Otsuka and Janssen.