The experiences induced by psychedelics share a wide variety of subjective features, related to the complex changes in perception and cognition induced by this class of drugs. A remarkable increase in introspection is at the core of these altered states of consciousness. Self-oriented mental activity has been consistently linked to the Default Mode Network (DMN), a set of brain regions more active during rest than during the execution of a goal-directed task. Here we used fMRI technique to inspect the DMN during the psychedelic state induced by Ayahuasca in ten experienced subjects. Ayahuasca is a potion traditionally used by Amazonian Amerindians composed by a mixture of compounds that increase monoaminergic transmission. In particular, we examined whether Ayahuasca changes the activity and connectivity of the DMN and the connection between the DMN and the task-positive network (TPN). Ayahuasca caused a significant decrease in activity through most parts of the DMN, including its most consistent hubs: the Posterior Cingulate Cortex (PCC)/Precuneus and the medial Prefrontal Cortex (mPFC). Functional connectivity within the PCC/Precuneus decreased after Ayahuasca intake. No significant change was observed in the DMN-TPN orthogonality. Altogether, our results support the notion that the altered state of consciousness induced by Ayahuasca, like those induced by psilocybin (another serotonergic psychedelic), meditation and sleep, is linked to the modulation of the activity and the connectivity of the DMN.

The wake-sleep cycle, a spontaneous succession of global brain states that correspond to major overt behaviors, occurs in all higher vertebrates. The transitions between these states, at once rapid and drastic, remain poorly understood. Here, intracranial local field potentials (LFPs) recorded in the cortex, hippocampus, striatum, and thalamus were used to characterize the neurophysiological correlates of the rat wake-sleep cycle. By way of a new method for the objective classification and quantitative investigation of all major brain states, we demonstrate that global brain state transitions occur simultaneously across multiple forebrain areas as specific spectral trajectories with characteristic path, duration, and coherence bandwidth. During state transitions, striking changes in neural synchronization are effected by the prominent narrow-band LFP oscillations that mark state boundaries. Our results demonstrate that distant forebrain areas tightly coordinate the processing of neural information during and between global brain states, indicating a very high degree of functional integration across the entire wake-sleep cycle. We propose that transient oscillatory synchronization of synaptic inputs, which underlie the rapid switching of global brain states, may facilitate the exchange of information within and across brain areas at the boundaries of very distinct neural processing regimens.

Dopamine depletion is involved in the pathophysiology of Parkinson's disease, whereas hyperdopaminergia may play a fundamental role in generating endophenotypes associated with schizophrenia. Sleep disturbances are known to occur in both schizophrenia and Parkinson's disease, suggesting that dopamine plays a role in regulating the sleep–wake cycle. Here, we show that novelty-exposed hyperdopaminergic mice enter a novel awake state characterized by spectral patterns of hippocampal local field potentials that resemble electrophysiological activity observed during rapid-eye-movement (REM) sleep. Treatment with haloperidol, a D₂dopamine receptor antagonist, reduces this abnormal intrusion of REM-like activity during wakefulness. Conversely, mice acutely depleted of dopamine enter a different novel awake state characterized by spectral patterns of hippocampal local field potentials that resemble electrophysiological activity observed during slow-wave sleep (SWS). This dopamine-depleted state is marked by an apparent suppression of SWS and a complete suppression of REM sleep. Treatment with D₂(but not D₁) dopamine receptor agonists recovers REM sleep in these mice. Altogether, these results indicate that dopamine regulates the generation of sleep–wake states. We propose that psychosis and the sleep disturbances experienced by Parkinsonian patients result from dopamine-mediated disturbances of REM sleep.

Scale-invariant neuronal avalanches have been observed in cell cultures and slices as well as anesthetized and awake brains, suggesting that the brain operates near criticality, i.e. within a narrow margin between avalanche propagation and extinction. In theory, criticality provides many desirable features for the behaving brain, optimizing computational capabilities, information transmission, sensitivity to sensory stimuli and size of memory repertoires. However, a thorough characterization of neuronal avalanches in freely-behaving (FB) animals is still missing, thus raising doubts about their relevance for brain function. To address this issue, we employed chronically implanted multielectrode arrays (MEA) to record avalanches of action potentials (spikes) from the cerebral cortex and hippocampus of 14 rats, as they spontaneously traversed the wake-sleep cycle, explored novel objects or were subjected to anesthesia (AN). We then modeled spike avalanches to evaluate the impact of sparse MEA sampling on their statistics. We found that the size distribution of spike avalanches are well fit by lognormal distributions in FB animals, and by truncated power laws in the AN group. FB data surrogation markedly decreases the tail of the distribution, i.e. spike shuffling destroys the largest avalanches. The FB data are also characterized by multiple key features compatible with criticality in the temporal domain, such as 1/f spectra and long-term correlations as measured by detrended fluctuation analysis. These signatures are very stable across waking, slow-wave sleep and rapid-eye-movement sleep, but collapse during anesthesia. Likewise, waiting time distributions obey a single scaling function during all natural behavioral states, but not during anesthesia. Results are equivalent for neuronal ensembles recorded from visual and tactile areas of the cerebral cortex, as well as the hippocampus. Altogether, the data provide a comprehensive link between behavior and brain criticality, revealing a unique scale-invariant regime of spike avalanches across all major behaviors.

Psychosis has various causes, including mania and schizophrenia. Since the differential diagnosis of psychosis is exclusively based on subjective assessments of oral interviews with patients, an objective quantification of the speech disturbances that characterize mania and schizophrenia is in order. In principle, such quantification could be achieved by the analysis of speech graphs. A graph represents a network with nodes connected by edges; in speech graphs, nodes correspond to words and edges correspond to semantic and grammatical relationships. To quantify speech differences related to psychosis, interviews with schizophrenics, manics and normal subjects were recorded and represented as graphs. Manics scored significantly higher than schizophrenics in ten graph measures. Psychopathological symptoms such as logorrhea, poor speech, and flight of thoughts were grasped by the analysis even when verbosity differences were discounted. Binary classifiers based on speech graph measures sorted schizophrenics from manics with up to 93.8% of sensitivity and 93.7% of specificity. In contrast, sorting based on the scores of two standard psychiatric scales (BPRS and PANSS) reached only 62.5% of sensitivity and specificity. The results demonstrate that alterations of the thought process manifested in the speech of psychotic patients can be objectively measured using graph-theoretical tools, developed to capture specific features of the normal and dysfunctional flow of thought, such as divergence and recurrence. The quantitative analysis of speech graphs is not redundant with standard psychometric scales but rather complementary, as it yields a very accurate sorting of schizophrenics and manics. Overall, the results point to automated psychiatric diagnosis based not on what is said, but on how it is said.