Rapid antidepressant effects of the psychedelic ayahuasca in treatment-resistant depression: a randomized placebo-controlled trial.

BACKGROUND: Recent open-label trials show that psychedelics, such as ayahuasca, hold promise as fast-onset antidepressants in treatment-resistant depression. METHODS: To test the antidepressant effects of ayahuasca, we conducted a parallel-arm, double-blind randomized placebo-controlled trial in 29 patients with treatment-resistant depression. Patients received a single dose of either ayahuasca or placebo. We assessed changes in depression severity with the Montgomery-Åsberg Depression Rating Scale (MADRS) and the Hamilton Depression Rating scale at baseline, and at 1 (D1), 2 (D2), and 7 (D7) days after dosing. RESULTS: We observed significant antidepressant effects of ayahuasca when compared with placebo at all-time points. MADRS scores were significantly lower in the ayahuasca group compared with placebo at D1 and D2 (p = 0.04), and at D7 (p < 0.0001). Between-group effect sizes increased from D1 to D7 (D1: Cohen's d = 0.84; D2: Cohen's d = 0.84; D7: Cohen's d = 1.49). Response rates were high for both groups at D1 and D2, and significantly higher in the ayahuasca group at D7 (64% v. 27%; p = 0.04). Remission rate showed a trend toward significance at D7 (36% v. 7%, p = 0.054). CONCLUSIONS: To our knowledge, this is the first controlled trial to test a psychedelic substance in treatment-resistant depression. Overall, this study brings new evidence supporting the safety and therapeutic value of ayahuasca, dosed within an appropriate setting, to help treat depression. This study is registered at http://clinicaltrials.gov (NCT02914769).

Characterizing Complex Networks Using Entropy-Degree Diagrams: Unveiling Changes in Functional Brain Connectivity Induced by Ayahuasca.

With the aim of further advancing the understanding of the human brain's functional connectivity, we propose a network metric which we term the geodesic entropy. This metric quantifies the Shannon entropy of the distance distribution to a specific node from all other nodes. It allows to characterize the influence exerted on a specific node considering statistics of the overall network structure. The measurement and characterization of this structural information has the potential to greatly improve our understanding of sustained activity and other emergent behaviors in networks. We apply this method to study how the psychedelic infusion Ayahuasca affects the functional connectivity of the human brain in resting state. We show that the geodesic entropy is able to differentiate functional networks of the human brain associated with two different states of consciousness in the awaking resting state: (i) the ordinary state and (ii) a state altered by ingestion of the Ayahuasca. The functional brain networks from subjects in the altered state have, on average, a larger geodesic entropy compared to the ordinary state. Finally, we discuss why the geodesic entropy may bring even further valuable insights into the study of the human brain and other empirical networks.

Brain complex network analysis by means of resting state fMRI and graph analysis: will it be helpful in clinical epilepsy?

Functional magnetic resonance imaging (fMRI) has just completed 20 years of existence. It currently serves as a research tool in a broad range of human brain studies in normal and pathological conditions, as is the case of epilepsy. To date, most fMRI studies aimed at characterizing brain activity in response to various active paradigms. More recently, a number of strategies have been used to characterize the low-frequency oscillations of the ongoing fMRI signals when individuals are at rest. These datasets have been largely analyzed in the context of functional connectivity, which inspects the covariance of fMRI signals from different areas of the brain. In addition, resting state fMRI is progressively being used to evaluate complex network features of the brain. These strategies have been applied to a number of different problems in neuroscience, which include diseases such as Alzheimer's, schizophrenia, and epilepsy. Hence, we herein aimed at introducing the subject of complex network and how to use it for the analysis of fMRI data. This appears to be a promising strategy to be used in clinical epilepsy. Therefore, we also review the recent literature that has applied these ideas to the analysis of fMRI data in patients with epilepsy.

Effects of Yoga Respiratory Practice (Bhastrika pranayama) on Anxiety, Affect, and Brain Functional Connectivity and Activity: A Randomized Controlled Trial.

Pranayama refers to a set of yoga breathing exercises. Recent evidence suggests that the practice of pranayama has positive effects on measures of clinical stress and anxiety. This study explored the impact of a Bhastrika pranayama training program on emotion processing, anxiety, and affect. We used a randomized controlled trial design with thirty healthy young adults assessed at baseline and after 4 weeks of pranayama practices. Two functional magnetic resonance imaging (MRI) protocols were used both at baseline and post-intervention: an emotion task as well as a resting-state acquisition. Our results suggest that pranayama significantly decreased states of anxiety and negative affect. The practice of pranayama also modulated the activity of brain regions involved in emotional processing, particularly the amygdala, anterior cingulate, anterior insula, and prefrontal cortex. Resting-state functional MRI (fMRI) showed significantly reduced functional connectivity involving the anterior insula and lateral portions of the prefrontal cortex. Correlation analysis revealed that changes in connectivity between the ventrolateral prefrontal cortex and the right anterior insula were associated with changes in anxiety. Although it should be noted that these analyses were preliminary and exploratory, it provides the first evidence that 4 weeks of B. pranayama significantly reduce the levels of anxiety and negative affect, and that these changes are associated with the modulation of activity and connectivity in brain areas involved in emotion processing, attention, and awareness. The study was registered at https://www.ensaiosclinicos.gov.br/rg/RBR-2gv5c2/(RBR-2gv5c2).

Cortisol Modulation by Ayahuasca in Patients With Treatment Resistant Depression and Healthy Controls.

Major depression is a highly prevalent mood disorder, affecting about 350 million people, and around 30% of the patients are resistant to currently available antidepressant medications. Recent evidence from a randomized controlled trial (RCT) supports the rapid antidepressant effects of the psychedelic ayahuasca in treatment-resistant depression. The aim of this study was to explore the effect of ayahuasca on plasma cortisol and awakening salivary cortisol response, in the same group of treatment-resistant patients (MD) and in healthy volunteers (C). Subjects received a single dose of ayahuasca or placebo (dosing session), and both plasma and awakening salivary cortisol response were measured at baseline (before dosing session) and 48 h after the dosing session. Baseline assessment (D0) showed blunted awakening salivary cortisol response and hypocortisolemia in patients, with respect to healthy controls. Salivary cortisol was also measured during dosing session, and we observed higher increases for both C and MD that ingested ayahuasca than placebo. After 48 h from the dosing session with ayahuasca, patients' awakening salivary cortisol response is similar to the ones detected in controls. No significant changes in plasma cortisol levels were observed 48 h after the sessions. Therefore, these findings point to new evidence on the modulation of salivary cortisol levels as a result of an ayahuasca session, both in healthy and depressive volunteers. Considering that cortisol acts in regulation of distinct physiological pathways, emotional and cognitive processes, it is assumed to be critically involved to the etiology of depression and its regulation seems to be important for the treatment and remission of major depression, ayahuasca use as antidepressant should be further investigated. Moreover, this study highlights the importance of psychedelics in the treatment of human mental disorders.

Shannon entropy of brain functional complex networks under the influence of the psychedelic Ayahuasca.

The entropic brain hypothesis holds that the key facts concerning psychedelics are partially explained in terms of increased entropy of the brain's functional connectivity. Ayahuasca is a psychedelic beverage of Amazonian indigenous origin with legal status in Brazil in religious and scientific settings. In this context, we use tools and concepts from the theory of complex networks to analyze resting state fMRI data of the brains of human subjects under two distinct conditions: (i) under ordinary waking state and (ii) in an altered state of consciousness induced by ingestion of Ayahuasca. We report an increase in the Shannon entropy of the degree distribution of the networks subsequent to Ayahuasca ingestion. We also find increased local and decreased global network integration. Our results are broadly consistent with the entropic brain hypothesis. Finally, we discuss our findings in the context of descriptions of "mind-expansion" frequently seen in self-reports of users of psychedelic drugs.

Application of partial directed coherence to the analysis of resting-state EEG-fMRI data.

The simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data potentially allows measurement of brain signals with both high spatial and temporal resolution. Partial directed coherence (PDC) is a Granger causality measure in the frequency domain, which is often used to infer the intensity of information flow over the brain from EEG data. In the current study, we propose a new approach to investigate functional connectivity in resting-state (RS) EEG-fMRI data by combining time-varying PDC with the analysis of blood oxygenation level-dependent (BOLD) signal fluctuations. Basically, we aim to identify brain circuits that are more active when the information flow is increased between distinct remote neuronal modules. The usefulness of the proposed method is illustrated by application to simultaneously recorded EEG-fMRI data from healthy subjects at rest. Using this approach, we decomposed the nodes of RS networks in fMRI data according to the frequency band and directed flow of information provided from EEG. This approach therefore has the potential to inform our understanding of the regional characteristics of oscillatory processes in the human brain.