This analysis of neuroimaging (M/EEG) compares data from patients with schizophrenia (n=29) and healthy volunteers under the influence of LSD (75μg, n=17) or ketamine (n=19). It finds that although both show increased neural signal diversity, only for those with schizophrenia did it increase the precision (weighting) of sensory information. Both groups increase 'bottom-up' signalling, but of a different kind.
Schizophrenia and states induced by certain psychotomimetic drugs may share some physiological and phenomenological properties, but they differ in fundamental ways: one is a crippling chronic mental disease, while the others are temporary, pharmacologically-induced states presently being explored as treatments for mental illnesses. Building towards a deeper understanding of these different alterations of normal consciousness, here we compare the changes in neural dynamics induced by LSD and ketamine (in healthy volunteers) against those associated with schizophrenia, as observed in resting-state M/EEG recordings. While both conditions exhibit increased neural signal diversity, our findings reveal that this is accompanied by an increased transfer entropy from the front to the back of the brain in schizophrenia, versus an overall reduction under the two drugs. Furthermore, we show that these effects can be reproduced via different alterations of standard Bayesian inference applied on a computational model based on the predictive processing framework. In particular, the effects observed under the drugs are modelled as a reduction of the precision of the priors, while the effects of schizophrenia correspond to an increased precision of sensory information. These findings shed new light on the similarities and differences between schizophrenia and two psychotomimetic drug states, and have potential implications for the study of consciousness and future mental health treatments.
Rajpal and colleagues situate this study within two converging lines of interest: the recent scientific and clinical resurgence in research on classical serotonergic psychedelics, and the longstanding idea that some psychoactive drugs can produce “psychotomimetic” effects that resemble aspects of schizophrenia. Previous empirical work shows that both psychedelics (notably LSD) and dissociatives (notably ketamine) alter large-scale neural dynamics, including increases in measures of signal diversity and changes in inter-regional information transfer, and EEG work has reported similar increases in signal diversity in schizophrenia. However, a parsimonious account that explains both the commonalities and the divergences between drug-induced states and schizophrenia at a neurophysiological level remains lacking. This paper aims to compare resting-state neuroimaging data from healthy subjects under LSD and ketamine with EEG data from patients with schizophrenia, and to interpret the empirical patterns through a computational model grounded in the predictive processing (PP) framework. Specifically, the investigators test whether observed changes in Lempel–Ziv complexity (LZ, a measure of signal diversity) and transfer entropy (TE, a directed measure of information transfer) can be reproduced by perturbations to a single Bayesian state-space model, instantiated as changes in the precision of priors versus sensory inputs. The overarching goal is to refine psychotomimetic models of psychosis and to clarify how different disruptions to Bayesian inference map onto distinct neural signatures in resting-state activity.
Papers cited by this study that are also in Blossom
Carhart-Harris, R. L., Kaelen, M., Bolstridge, M. et al. · Psychological Medicine (2016)
Frohlich, J, Van Horn, J. D. · Journal of Psychopharmacology (2013)
Carhart-Harris, R. L., Brugger, S., Nutt, D. J. et al. · Journal of Psychopharmacology (2013)
Schartner, M., Carhart-Harris, R. L., Barrett, A. B. et al. · Scientific Reports (2017)
The empirical component combined three datasets. Schizophrenia data were obtained from the BSNIP database and comprised 64-channel EEG recordings from 29 patients and 38 age-matched healthy controls, sampled at 1000 Hz during eyes-closed rest; demographic metadata, PANSS symptom scores, and number of antipsychotics per patient (used as a proxy for medication load) were available. Drug datasets came from prior studies: 17 subjects for LSD and 19 for ketamine, each scanned with a CTF 275-channel MEG system at 600 Hz in two eyes-closed sessions (drug and placebo). Subjects were selected to match an age range of 20–40 years. Preprocessing steps were harmonised across datasets when possible. Continuous recordings were epoched into 2 s segments, visually screened to remove gross artefacts, and cleaned using independent component analysis to remove muscle and ocular artefacts. Source reconstruction used a linearly constrained minimum variance (LCMV) beamformer to project sensor data onto centroids of Automated Anatomical Labeling (AAL) regions. Source time series were bandpass filtered between 1 and 100 Hz and downsampled (to 250 Hz for EEG and 300 Hz for MEG). AAL sources were grouped into five regions of interest (ROIs): frontal, parietal, occipital, temporal and sensorimotor. Two principal outcome metrics were computed. Lempel–Ziv complexity (LZ) was calculated on binarised source time series (values above the epoch mean mapped to 1, below to 0), resulting in an entropy-rate estimate per AAL source and then averaged per ROI. Transfer entropy (TE), estimated via state-space Gaussian models implemented with the MVGC toolbox, provided a conditional, directed 5×5 TE network between the ROIs, with each ROI treated as a multivariate vector of its constituent AAL sources. Statistical testing used within-subject one-sample t-tests for the drug datasets (drug versus placebo differences per subject) and linear models for the schizophrenia dataset, with LZ or TE as dependent variables and predictors including condition, age, gender and number of antipsychotics; quadratic age terms were considered and selected when a likelihood-ratio test favoured them. Multiple comparisons across TE edges were controlled using a directed-network adaptation of the Network-Based Statistic (NBS) cluster-permutation approach. A complementary computational model was implemented to interpret empirical findings. Building on predictive processing, the authors constructed a linear stochastic Bayesian state-space model comprising a low-level (sensory) variable s_t, a higher-level internal state h_t, top-down predictions ŝ_t = b h_t, prediction errors ξ_t, and two precision parameters: λ_p for prior precision and λ_s for sensory precision. The control model was calibrated using placebo visual cortex data via expectation–maximisation to estimate baseline parameters (a_con, b_con, λ_con^p, λ_con^s). Schizophrenia was modelled by increasing sensory precision (λ_s scaled by a factor η > 1), yielding stronger bottom-up influence and a more precise posterior; the drug condition was modelled by reducing prior precision (λ_p scaled down), consistent with the REBUS hypothesis. After parameter adjustments, a and b were retrained, and LZ of ξ_t and top-down TE (ŝ_t → ξ_t) were computed from the model-generated time series to compare with empirical results. Additional control variations of precision parameters were also explored.
Lempel–Ziv complexity increased robustly across all three datasets. Rajpal and colleagues report significant and widespread LZ increases for LSD and ketamine, and an increase in the schizophrenia cohort relative to controls that was most pronounced in frontal and parietal regions. A simple unadjusted comparison of patient versus control LZ showed no significant difference (t = -0.38, p = 0.70), but when linear models corrected for age, gender and number of antipsychotics, the number of antipsychotics had a negative association with LZ (β = -0.016, t = -2.3, p = 0.021). Using those covariate-corrected LZ values, a two-sample t-test between patients and controls revealed a significant difference (t = 3.4, p = 0.001). The authors emphasise that the schizophrenia LZ result is sensitive to medication adjustment and to the proxy used for dose, so it should be considered preliminary. Transfer entropy showed divergent patterns across conditions. Both LSD and ketamine produced widespread decreases in TE between most ROI pairs compared with placebo. By contrast, schizophrenia patients exhibited localised increases in TE relative to healthy controls and no decreases; these increases were predominantly frontal in origin and strongest in frontal→occipital connections, i.e. a front-to-back pattern. The negative correlation between antipsychotic count and some TE edges was small and did not survive multiple-comparison correction. Within the schizophrenia cohort, neither LZ nor TE differences correlated with PANSS symptom scores in the extracted analyses. The computational model reproduced the empirical pattern when precision parameters were perturbed in specific ways. Increasing sensory precision (model variant for schizophrenia) or decreasing prior precision (model variant for the drug state) both led to increased LZ in the simulated prediction-error signals. However, these two perturbations produced opposite effects on top-down TE: increased sensory precision yielded an increase in top-down TE, whereas decreased prior precision yielded a decrease. Control variations that moved precision parameters in the alternative directions (reducing sensory precision or increasing prior precision) did not reproduce the empirical findings. The authors therefore highlight that similar apparent strengthening of bottom-up influence can be implemented by different mechanistic parameter changes, and that TE changes alone do not unambiguously specify which precision parameter is altered.
The authors interpret their results as showing a shared increase in spontaneous neural signal diversity across psychotomimetic drug states (LSD and ketamine) and schizophrenia, accompanied by opposite changes in directed inter-regional information flow: decreases in TE under both drugs versus increases in TE in schizophrenia. Using a parsimonious predictive processing model, Rajpal and colleagues argue that both classes of state can be framed as favouring increased bottom-up influence over top-down priors, but realised through different mechanistic perturbations: psychedelic drugs via reduced precision-weighting of priors (consistent with the REBUS hypothesis), and schizophrenia via increased precision of sensory inputs. This asymmetric implementation explains how the same high-level bias (relative strengthening of sensory information) can produce divergent signatures in TE while yielding similar increases in signal diversity. They caution against interpreting TE changes as straightforward evidence for increased top-down control, because TE quantifies directed information flow but is agnostic about functional role. The modelling results show that strengthening bottom-up influence can produce either increases or decreases in top-down TE depending on whether prior or sensory precision is altered. The authors therefore recommend more nuanced, multidimensional approaches to characterise top-down versus bottom-up dynamics; decomposing TE into finer information modes is suggested as a promising direction. Several limitations are acknowledged. The computational model assumes weakened priors for schizophrenia, whereas other task-based work has argued for stronger priors to explain some hallucinations; the present model is a resting-state formulation and may not generalise to task contexts. Empirically, differences in imaging modality (MEG for drugs, EEG for schizophrenia), sampling rates, and experimental design (within-subject for drugs, between-subject for schizophrenia) complicate direct comparisons. Spatial resolution was limited to 60 AAL sources across five ROIs, which may mask finer-scale PP effects. Crucially, antipsychotic dosage data were unavailable, so the number of antipsychotics was used as an imperfect proxy; this proxy correlated negatively with PANSS positive scores and with some neural metrics, but confounding cannot be disentangled without detailed medication information. The authors also note that their statistical models were linear and might miss non-linear relationships between medication load and neural dynamics. For future work, the investigators propose richer hierarchical modelling that could bridge resting-state and task-based findings, finer-grained analyses of directed connectivity (including band-limited approaches), decomposition of TE into constituent information modes, and studies that link neural dynamics to symptom subtypes and medication dosage. They emphasise that more detailed datasets—including dosage and temporally resolved symptom measures—are needed to clarify how antipsychotics modulate neural dynamics and to characterise heterogeneity within the schizophrenia spectrum.
Classic serotonergic psychedelic drugs have seen a blooming resurgence among the public and the scientific community in recent years, largely driven by promising clinical research into their therapeutic potential. At the same time, and somewhat paradoxically, psychedelics are known to elicit effects that mimic some symptoms of psychosisearning them the label of 'psychotomimetic drugs'. In this context, our aims with this study are twofold: First, to explore the limits of psychotomimetic models of psychosis 10 at a neurophysiological level, thus helping us refine these models. Second, to further our understanding of extended and acute alterations to normal consciousness, which may help the design better mental health therapies. Email addresses: h.rajpal15@imperial.ac.uk (Hardik Rajpal), pam83@cam.ac.uk (Pedro A.M. Mediano) 1 H.R. and P.M. contributed equally to this work. To contrast these conditions in an empirical manner, we compare neuroimaging data from patients suffering from schizophrenia and healthy subjects under the effects of two psychoactive substances: the classical psychedelic lysergic acid diethylamide (LSD)and the dissociative drug ketamine (KET).
Using standardised assessments, it has been claimed that KET reproduces both positive and negative symptoms of schizophrenia in humans, and its mechanism of action -NMDA receptor antagonism -is thought to reproduce a key element of the molecular pathophysiology 25 of schizophrenia. LSD -in common with all classical psychedelics -is a potent agonist of a number of serotonin receptors, but its characteristic effects depend primarily on 5-HT 2A. These neurotransmitter systems have been linked to symptoms of early acute schizophrenic stages, 30 such as "ego-disorders, affective changes, loosened associations and perceptual alterations"(see Ref.for a quantitative analysis of these associations). Both psychotomimetic drug states and schizophrenia are also associated with marked changes in large-scale neural dynamics. For both LSD and KET, previous studies have found increased signal diversity in subjects' neural dynamicsand reduced information transfer between brain regions. However, in the case of KET, evidence from intracranial recordings in cats suggests a much more complicated picture than that of LSD, with very high variability across individuals, brain regions, and dose levels. In a separate line of enquiry, work on EEG data from patients with schizophrenia has also found increased signal diversity, akin to the effect found under these drugs. Nonetheless, a parsimonious account explaining the similarities and differences between the two states is still lacking. A promising approach to gain insights into the mechanisms driving the core similarities and differences between 50 psychotomimetic drug states and schizophrenia is to leverage principles from the predictive processing (PP) framework of brain function. A key postulate of the PP framework is that the dynamics of neural populations can be viewed as engaged in processes of inference involving top-down and bottom-up signals. Under this framework, brain activity can be viewed as resulting from a continuous modelling process in which a prior distribution interacts with new observations via incoming sensory information. In accordance with principles of Bayesian inference, discrepancies between the prior distribution and incoming signals (called 'prediction errors') carried by the bottom-up signals drive revisions to the top-down activity, so as to minimize future surprise. The PP framework has been used to explain perceptual alterations observed in both psychotomimetic drug statesas well as in psychiatric illnesseswith a focus on schizophrenia. Most of these accounts of PP are task-based studies, which manipulate stimuli in order to modulate prediction errors. In contrast, here we extend this approach to the resting state, focusing on spontaneous "prediction errors" that arise from naturally occurring neural activity. PP has also been used to understand the action of psychedelics, most notably through the 'relaxed beliefs under psychedelics' (or REBUS) modelwhich posits that psychedelics reduce the precision of prior beliefs encoded in spontaneous brains activity. REBUS has also been used to inform thinking on the therapeutic mechanisms of psychedelics, where symptomatology can be viewed as pathologically over-weighted beliefs or assumptions encoded in the precision weighting of brain activity encoding them. To deepen our understanding of the similarities and differences between these conditions, in this paper we replicate and extend findings on neural diversity and information transfer under the two psychotomimetic drugs (LSD and KET) and in schizophrenia using EEG and MEG recordings, and we reproduce these experimental findings as perturbations to a single PP model. Our modelling results reveal that the effects observed under the drugs are indeed 90 reproduced by decreasing the precision-weighting of the priors, while the effects observed under schizophrenia are reproduced by increased precision-weighting of the bottomup sensory information. Overall, this study puts forward a more nuanced understanding of the relationship between 95 two different psychotomimetic drug states and schizophrenia, and offers a new model-based perspective on how these conditions alter conscious experience.
Data from 29 patients diagnosed with schizophrenia and 38 age-matched healthy control subjects were obtained from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (BSNIP) database. The subjects were selected within an age range of 20-40 years to match the 105 psychedelic datasets described below. Data included 64channel EEG recordings sampled at 1000 Hz of each subject in eyes-closed resting state, along with metadata about demographics (age and gender), patients' medications and their PANSS symptom scores. The strength of the 110 medication was estimated using the number of antipsychotics taken by each patient (mean: 2.7, range: 0-8), as the dosage of each medication was not available. Data from healthy subjects under the effects of both drugs was obtained from previous studies with LSD115 (N = 17) and ketamine(N = 19). Data included MEG recordings from a CTF 275-channel axial gradiometer system with a sampling frequency of 600 Hz. Each subject underwent two scanning sessions in eyes-closed resting state: one after drug administration and another after a placebo 120 (PLA). Preprocessing steps for all datasets were kept as consistent as possible, and were performed using the Fieldtripand EEGLABlibraries. First, the data was segmented into epochs of 2 seconds, and epochs with strong artefacts 125 were removed via visual inspection. Next, muscle and eye movement artefacts were removed using ICA. Then, a LCMV beamformerwas used to reconstruct activity of sources located at the centroids of regions in the Automated Anatomical Labelling (AAL) brain atlas.
Finally, source-level data was bandpass-filtered between 1-100 Hz, and downsampled with phase correction to 250 Hz (EEG) and 300 Hz (MEG), and AAL areas were grouped into 5 major Regions of Interest (ROIs): frontal, parietal, occipital, temporal and sensorimotor (see Figureand 135 Table.2 in the Appendix). In the rest of the paper we refer to these 5 areas as "ROIs" and to the AAL regions as "sources."
Our analyses are focused on two complementary met- mathematical framework of information theory, and provide characterisations of different but complementary aspects of neural dynamics: LZ captures aspects of the temporal dynamics of single regions, while TE quantifies how different regions influence each other. Both metrics have a long history, and have been used and robustly validated across a wide range of states of consciousness, including psychedelic states. Lempel-Ziv complexity (LZ) is a measure of the diversity of patterns observed in a discrete -typically binarysequence. When applied to neuroimaging data, lower LZ (with respect to wakeful rest) has been associated with unconscious states such as sleepor anaesthesia, 155 and higher LZ with states of richer phenomenal content under psychedelics, ketamineand states of flow during musical improvisation. To calculate LZ, first one needs to transform a given signal of length T into a binary sequence. For a given epoch of univariate M/EEG data, we do this by calculating the mean value and transforming each data point above the mean to 1 and each point below to 0. Then, the resulting binary sequence is scanned sequentially using the LZ76 algorithm presented by Kaspar and Schuster, which counts the number of distinct "patterns" in the signal. Finally, following results by Ziv, the number of patterns is divided by log 2 (T )/T to yield an estimate of the signal's entropy rate, which we refer to generically as LZ. This process is applied separately to each source time series (i.e. to each AAL region), and the resulting values are averaged according to the grouping in Table.2 to yield an average LZ value per ROI. In addition to LZ, our analyses also consider transfer entropy (TE)-an information-theoretic version of Granger causality-to assess the dynamical interdependencies between ROIs. The TE from a source region to a target region quantifies how much better one can predict the activity of the target after the activity of the source is known. This provides a notion of directed functional 180 connectivity, which can be used to analyse the structure of large-scale brain activity. Mathematically, TE is defined as follows. Denote the activity of two given ROIs at time t by the vectors X t and Y t , and the activity of the rest of the brain by Z t . Note that X t , Y t , and Z t have one component for each AAL source in the corresponding ROI(s). TE is computed in terms of Shannon's mutual information, I, as the information about the future state of the target, Y t+1 , provided by X t over and above the information in Y t and Z t : where X - t refers to the (possibly infinite) past of X t , up to and including time t (and analogously for Y t and Z t ). This quantity can be accurately estimated using state-space 185 models with Gaussian innovations, implemented using the MVGC toolbox. Note that, when calculating the TE between ROIs, we consider each ROI as a vectorwithout averaging the multiple AAL sources into a single number. The result is a directed 5×5 network of conditional 190 TE values between pairs of ROIs, which can be tested for statistical differences across groups.
For both LSD and KET datasets, since the same subjects were monitored under both drug and placebo condi-195 tions, average subject-level differences (either in LZ or TE) were calculated for each subject, and one-sample t-tests were used on those differences to estimate the effect of the drug. For the data of patients and controls in the schizophre-200 nia dataset, group-level differences were estimated via linear models. These models used either LZ or TE as target variable, and condition (schizophrenia or healthy), age, gender, and number of antipsychotics (set to zero for healthy controls) as predictors. Motivated by previous work suggesting 205 a quadratic relationship between complexity and age, each model was built with either a linear or quadratic dependence on age, and the quadratic model was selected if it was preferred over a linear model by a log-likelihood ratio test (with a critical level of 0.05).
Finally, multiple comparisons when comparing TE values across all pairs of ROIs were addressed by using the Network-Based Statistic (NBS)method, which identifies 'clusters' of differences -i.e. connected components where a particular null hypothesis is consistently rejected 215 while controlling for family-wise error rate. Our analysis used an in-house adapted version of NBS that works on directed networks, such as the ones provided by TE analyses.
A computational model was developed in order to interpret the LZ and TE findings observed on the neuroimaging data. Building on predictive processing principles, we constructed a Bayesian state-space model that provides an idealised common ground to contrast the three studied conditions -the psychotomimetic drug states, schizophrenia, and baseline (i.e. healthy controls). Our modelling is based on the postulate that the activity of neuronal populations across the brain can be interpreted as carrying out inference on the causes of their afferent signals. Following this view, the proposed model considers the following elements: • the internal state of a low-level region (i.e. near the sensory periphery), denoted by s t ; • the internal state of neural activity taking place functionally one level above, denoted by h t ; • the signal generated at the high-level region in the form of a prediction of the low-level activity, denoted by ŝt ; • the signal generated at the low-level region in the form of a prediction error ξ t ; and • the precision of the prior λ p and precision of sensory/afferent information λ s . This model represents neural activity within a larger hierarchical processing structure, as illustrated in Figure. The key principle motivating this model is that minimisation of prediction error signals throughout the hierarchy, by updating top-down predictions, implements a tractable approximation to Bayesian inference.Within this model, we represent the schizophrenia and psychedelic conditions as different types of disruption to Bayesian inference. To describe the psychedelic state, we build on the REBUS hypothesis, which posits a reduced precision-weighting of prior beliefs, leading to increased bottom-up influence. Conversely, to describe schizophrenia we build on the canonical predictive processing account of psychosis in schizophrenia, which postulates an increased precision of sensory input, along with decreased precision of prior beliefs. Therefore, both conditions are similar in that there is a relative strengthening of bottom-up influence, although instantiated in different ways -which, as shown in Sec. 3.3, bears important consequences for the behaviour of the model. It is important to note that predictive processing accounts of schizophrenia remain hotly debated, with other works proposing an increase of prior precision (instead of decrease) as a model of auditory and visual hallucinations. Recent reviewshave attempted to reconcile both views by suggesting that sensory hallucinations may be caused by stronger priors, while hallucinations related to self-generated phenomena (like inner speech or self-attention) may stem from weaker priors. Here, we base our modelling of SCZ on the weak prior hypothesis, as described above -we return to this issue in this discussion. To simulate the aberrant dynamics of the inference process, as described above, we consider a given afferent signal (s t ) and construct the corresponding activity of a higher area (h t ), prediction (ŝ t ), and prediction error (ξ t ), building on the rich literature of state-space models in neuroscience. Specifically, we use the linear stochastic process: where a, b are weights, and t , ν t are zero-mean Gaussian terms with precision (i.e. inverse variance) λ p and λ s , respectively. Note that this formulation is equivalent to As we show in the following, λ p corresponds to the precision 275 of the prior and λ s to the precision of sensory/afferent information. The dynamics of this system can be described as a recurrent update between predictions and prediction errors as follows. Eq. (3b) implies that the internal state h t generates a prediction about the low-level activity given by ŝt = bh t . At the same time, the dynamics of the high-level region can be seen as a Bayesian update of h t given s t and h t-1 . Under some simplifying assumptions, the mean of the posterior distribution of h t+1 (denoted by ĥt+1 ) is equal to (see Appendix A) which effectively combines a prior a ĥt (which is the optimal prediction of h t+1 given only ĥt , as seen from Eq. (3a)) and a likelihood given by the prediction error ξ t = s tb ĥt that is precision-weighted via β, a parameter known as the Kalman gain. In our simulations, the model is first calibrated using as afferent signals (i.e. s t ) data from the primary visual cortex, corresponding to epochs randomly sampled from the placebo conditions in the LSD and KET datasets. This calibration results in the estimation of the model parameters a con , b con , λ con p , λ con s for the control condition, which is done using the well-known expectation-maximisation algorithm. With these, the schizophrenia condition is then modelled by setting where η > 1 is referred to as a noise factor. This increase of λ s induces a strengthening of bottom-up prediction errors, and makes the posterior of h t excessively precise. Conversely, the drug condition is modelled by setting Reducing λ p also increases the influence of prediction errors, but reduces the precision of the posterior of h t . Subsequently, for both conditions the parameters a, b are retrained with another pass of the expectation-maximisation algorithm on the placebo trials. Finally, to compare the model with the empirical M/EEG data, the LZ of the neural activity elicited in the low-level area (i.e. the prediction errors, ξ t ) and the top-down transfer entropy (from the high-level activity ŝt towards the low-level activity ξ t ) are calculated for each of these three models (control, schizophrenia, and drug).
We begin the analysis by comparing changes in signal diversity, as measured by LZ, across the LSD, ketamine (KET), and schizophrenia (SCZ) datasets. Our results show strong and significant increases in LZ in all three datasets (Fig.), in line with previous work. In all three cases the LZ increases are widespread throughout the brain, with the effects in schizophrenia patients being more pronounced in frontal and parietal regions. While the t-scores are higher in LSD and KET than schizophrenia, this could be due to the within-subjects design of both drug experiments -which are more statistically powerful than the between-subjects analysis used on the schizophrenia dataset. Interestingly, we found that controlling for the medication status of each schizophrenia patient was crucial to obtain results that match prior work. A direct comparison of LZ values between patients and controls yielded no significant differences (t = -0.38, p = 0.70); however, when using a linear model correcting for age, gender, and number of antipsychotics, the antipsychotics coefficient of 315 the model reveals a negative effect on LZ (β = -0.016, t = -2.3, p = 0.021). Additionally, a two-sample t-test calculated between the corrected LZ values of patients and controls yields a substantial difference (t = 3.4, p = 0.001). Nonetheless, the sensitivity of this result to these pre-320 processing steps, as well as the lack of detailed dosage data for each medication, mean it should be considered preliminary and could only be properly interpreted after further investigation in future research (see the corresponding discussion in Sec. 4.3). 325
We next report the effects of LSD, KET, and schizophrenia on large-scale information flow in the brain, as measured via transfer entropy (TE). The TE between each pair of 330 ROIs (conditioned on all other ROIs) is calculated for each subject, and used to build directed TE networks. The resulting networks were tested for differences between the drug states and placebo conditions (for LSD and KET), and between patients and controls (for SCZ), correcting 335 for multiple comparisons via cluster permutation testing (see Sec. 2.3). We found a ubiquitous decrease in the TE between most pairs of ROIs under LSD and KET (Fig.), which is consistent with previous findings. In contrast, SCZ 340 patients exhibit marked localised increases in TE -and no decreases -with respect to the control subjects. Notably, most increases in TE originated in the frontal ROI, and are strongest between the frontal and occipital ROIs. The increase of information transfer seen in schizophrenia pa-345 tients therefore takes place "front to back" -aligned with the pathways thought to carry top-down information in the brain from highly cognitive, decision-making regions to unimodal regions closer to the sensory periphery. As was the case for LZ, controlling for antipsychotic 350 use was key to revealing differences between the healthy controls and schizophrenia patients. In addition, we found a small negative correlation between antipsychotic use and TE between certain ROI pairs -but, unlike for LZ, this effect did not survive correction for multiple comparisons.
Although we find significant increase in both LZ and TE between certain regions among the schizophrenia patients when compared to healthy controls, these are not correlated with the symptom scores within the schizophrenia cohort. (see the corresponding discussion in Sec. 4.3). 360
So far, we have seen that subjects under the effects of two different psychotomimetic drugs display increased signal diversity and reduced information flow in their neural dynamics. In comparison, schizophrenia patients display in- respect to healthy controls. We now show how complementary perturbations to the precision terms of the predictive processing model introduced in Section 2.4 reproduce these findings. We compared the basline model against the drug and schizophrenia variants by systematically increasing the noise factor η, which results in reduced prior precision in the drug model, and increased sensory precision in the schizophrenia model. We then computed the corresponding LZ and TE based on the model-generated time series ξ t , ŝt as per Sec. 2.4 (Fig.). Results show that the proposed model successfully reproduced the experimental findings of both LZ and TE under the two different psychotomimetic drugs and schizophrenia (Fig.). Interestingly, the model also shows (Fig.) that a relative strengthening of sensory information (via either increased sensory precision, or decreased prior precision) can trigger either an increase or a decrease (respectively) of top-down transfer entropy. This suggests that transfer entropy changes cannot be directly interpreted as revealing the changes in any underlying predictive processing mechanisms (see Discussion). Finally, as a control, we repeated the analysis on the 390 model but exploring the variation of the precision terms in the two unexplored directions -either reducing λ p or increasing λ s (see Section 2.4). Neither of these changes reproduced the experimental findings (Supp.
In this paper we have analysed MEG data from healthy subjects under the effects of the psychotomimetic drugs LSD and ketamine, as well as EEG data from a cohort of schizophrenia patients and healthy control subjects. We 400 focused on signal diversity and information transfer, both widely utilised metrics which provide a complementary Figure: A computational model based on predictive processing principles reproduces experimental findings in the LSD, ketamine and schizophrenia datasets. (a) By increasing the sensory precision (for schizophrenia; blue), or reducing the prior's precision (for LSD and KET; orange) by a given 'noise' factor η, the model can reproduce the experimental findings of (b) increased in LZ in both conditions, and (c) opposite changes in TE in both conditions, compared to a baseline (grey). account of neural dynamics. We found that all datasets show increases in signal diversity, but diverging changes in information transfer, which was higher in schizophrenia patients but lower for subjects under the effects of either drug. In addition to replicating previous results reporting signal diversity and information transfer under the effects of both drugs, we described new findings applying these metrics to schizophrenia. Using a computational model inspired by predictive processing principles, we showed that this combination of effects can be reproduced via specific alterations to prediction updating, which can be interpreted as specific forms of disruption to Bayesian inference. Critically, the effects of both psychotomimetic drugs and schizophrenia, on both signal diversity and information transfer, are explained by a relative strengthening of sensory information over prior beliefs, although triggered by different mechanisms -a decrease in the precision of priors in the case of psychotomimetic drugs (consistent with Ref.), and an increase in the precision of sensory information for schizophrenia.
The idea that the symptoms of schizophrenia can be understood as alterations to processes of Bayesian inference has been particularly fertile in the field of computational psychiatry. In particular, various studies based on PP have related psychosis to decreased precision of prior beliefs and increased precision of the sensory inputs. These computational models have been supported by a growing number of related experimental findings, including an enhanced confirmation bias, impaired reversal learning, and a greater resistance to visual illusions. For instance, schizophrenia patients are less susceptible to the Ebbinghaus illusion, which arises primarily from misleading prior expectations, suggesting that patients do not integrate this prior context with sensory evidence and thus achieve more accurate judgements. 440 Most of the above mentioned studies are task-based, focusing on differentiating perceptual learning behaviours between healthy controls and schizophrenia patients. Though these studies provide a range of experimental markers, the corresponding methodologies cannot be applied to resting-445 state or task-free conditions, under which it is known that certain behavioural alterations (e.g. delusions, anhedonia, and paranoia) persist. The findings presented in this paper provide a step towards bridging this important knowledge gap by providing 450 empirical and theoretical insights into resting-state neural activity under schizophrenia. Although we build on and replicate results related to signal diversity, we are not aware of previous studies of information transfer on schizophrenia in resting state.
The findings presented here link spontaneous brain activity to the PP framework using empirical metrics of signal diversity and information transfer. In the psychotomimetic 460 drug condition, the former increases while the latter decreases; in schizophrenia, both increase -in both cases as compared to baseline placebo or control. The explanation for this pattern of results, articulated by our computational model, is based on the idea that a bias favouring bottom-up 465 over top-down processing can be triggered by changing different precision parameters, which can give rise to opposite effects in specific aspects of the neural dynamics. This observation, we argue, opens the door to more nuanced analyses for future studies. The increased transfer entropy from frontal to posterior brain areas observed under schizophrenia could be naively interpreted as supporting increased top-down regulation; however, neither the empirical analysis nor the computational model warrant this conclusion. Transfer entropy simply indicates information flow and is agnostic about functional role. Our model-based analyses illustrate how aberrant Bayesian inference in which bottom-up influences become stronger can trigger either an increase or a decrease in transfer entropy from frontal to posterior regions, depending on which precision terms are involved. An interesting possible explanation for this divergence between mechanisms and TE is provided by recent results that show that TE is an aggregate of qualitatively different information modes. Future work may explore if resolving TE into its finer constituents might provide a more informative mapping from observed patterns to underlying mechanisms, as well as how these quantities may be related to other consciousness-related electrophysiology metrics. Taken together, these findings suggest that conceiving the bottom-up vs top-down dichotomy as a singledimensional trade-off might be too simplistic, and that multi-dimensional approaches could shed more light on this issue. In particular, our results show how such a simplistic view fails to account for the rich interplay of similarities 495 and differences between schizophrenia and psychosis.
While our empirical and modelling results agree with the canonical PP account of psychosis, some reports have suggested a stronger influence of priors over sensory signals 500 -especially in some cases of hallucinations. It is important to remark that the 'strengthened prior' interpretation put forward by these task-based studies cannot be accounted for by the simple computational modelling developed here. At the same time, the resting-state model 505 presented here relates spontaneous activity, and our results cannot be directly generalised to task-based settings. Future work may investigate whether a richer hierarchical model is able to reproduce both rest and task data, bridging between these results and prior work. Regarding the empirical analyses, it is important to note that our analyses are subject to a few limitations due to the nature of the data used. First, the analyses used only 60 AAL sources across 5 ROIs (due to the spatial resolution limitations of EEG), and therefore may neglect potential 515 PP effects that may exist at smaller spatial scales. In addition, the studies on both drugs and schizophrenia used different imaging methods (MEG vs EEG), sampling rate, and experiment designs (within vs between subjects), complicating direct comparisons. Finally, future work should examine how power spectra across the different conditions relate to the findings presented, in terms of both their effect on LZ, and their relationship with top-down and bottom-up signalling, for example using band-limited Granger causality, as well as how directed functional connectivity measures relate to undirected measures such as mutual information and coherence. Similarly, while the measures discussed here capture significant differences between schizophrenia patients and healthy controls, more work needs to be done to further 530 characterise the differences within the schizophrenia spectrum, which features a heterogenous array of symptoms and states, e.g. at different phases of the so-called 'psychotic process'. A crucial part of this research will be to analyse the clinical symptom scores of the patients and 535 their relationship to both medication and neural dynamics, which was not possible here due to the lack of appropriate metadata on the dosage of antipsychotics. In our preliminary analysis we use the number of antipsychotics as a proxy to the missing dosage data. This proxy measure was 540 found to be negatively correlated with the positive symptom scores of the PANSS scale (see Appendix C) among the schizophrenia patients, suggesting that the symptom scores are confounded by antipsychotic use -but without dosage data it is difficult to disentangle this effect from potential 545 confounds. An interesting possibility is that the neural underpinnings of positive and negative symptoms could be different, and investigating these differences may yield further insight into schizophrenia itself and its relationship with the psychotomimetic drug states. Moreover, both 550 schizophrenia and drug-induced states can be conceived of as dynamic states of consciousness, comprised of several sub-states and/or episodes with hallucinations, delusions and negative symptoms varying widely between and within individuals. Future studies could explore these finer fluc-555 tuations in conscious state, as well as what features or episodes overlap in the neural and psychological levels between psychotomimetic drug states and schizophrenia. Finally, recall that (as described in Sec. 2.1) we used the number of antipsychotic medications being used by each 560 patient as a proxy measure for their medication load. This is a significant oversimplification, as it ignores the specifics of all drugs and their dose-response effects, and future work with richer datasets should explore in more detail the effects of each particular medication -which would potentially 565 bring more nuance to these analyses. Also, the models used for statistical analysis (as per Sec. 2.3) are linear and may not capture possible non-linear dependencies between antipsychotic use and its effect on neural dynamics (in our case, LZ or TE). Bearing this caveat in mind, our 570 tentative results in the schizophrenia group suggest that antipsychotic use may bring the patients' neural dynamics closer to the range of healthy controls. This finding should be replicated with more detailed analyses involving dosage information and clinical symptom scores, and, if robust, 575 could potentially be used to investigate the mechanism of action of current antipsychotic drugs.
In this paper we have contrasted changes in brain activity in individuals with schizophrenia (compared to healthy 580 controls) with changes induced by a classic 5-HT 2A receptor agonist psychedelic, LSD, and an NMDA antagonist dissociative, ketamine (compared to placebo). Empirical analyses revealed that both schizophrenia and drug states show an increase in neural signal diversity, but they have divergent transfer entropy profiles. Furthermore, we proposed a simple computational model based on the predictive processing frameworkthat recapitulates the empirical findings through distinct alterations to optimal Bayesian inference. In doing so, we argued that both schizophrenia and psychotomimetic drugs can be described as inducing a stronger "bottom-up" influence of sensory information, but in qualitatively different ways, thus painting a more nuanced picture of the functional dynamics of predictive processing systems. Crucially, the proposed model differs from others in the literature in that it is a model of restingstate (as opposed to task-based) brain activity, bringing this methodology closer to other approaches to neuroimaging data analysis based on complexity science. Overall, this study illustrates the benefits of combining information-theoretic analyses of experimental data and computational modelling, as well as of integrating datasets from patients with those from healthy subjects. We hope our findings will inspire further work deepening our understanding about the relationship between neural dynamics and high-level brain functions, which in turn may accelerate the development of novel, mechanism-based treatments to foster and promote mental health. Eq. (3a), one can propagate the prediction in Eq. (A.1) to the next step, and obtain a recurrent update equation for ĥt given by ĥt+1 where β t = abλ -1 t F -1 t is known as the Kalman gain parameter] and which, as described in Sec. 2.4, depends only on its previous value ĥt-1 and the prediction error ξ t . Furthermore, from the definition of β t and F t it can be seen that increasing λ s leads to a higher β t , thus increasing the bottom-up influence of prediction errors. A similar argument can be made for the increase of β t with lower λ p , although this requires writing a recurrent expression analogous to Eq. (A.2) for λ t and is more mathematically involved. Interested readers are referred to Sec. 4.3 of Ref.for a detailed derivation.
We explored additional variations of the computational model reported above for completeness of the analysis. In this case, results show that increasing state precision or decreasing sensory precision (both cases of increased topdown influence) lead to decreased LZ in the prediction error signals, as seen in Supp. Fig..6. This is in opposition to the empirical findings reported in both the psychedelicand the schizophrenialiterature. Overall, these variations further support that both schizophrenia and psychedelics can be modelled as increased bottom-up influences in the resting state. Nonetheless, we hypothesise that these variations may be used to model other altered states of consciousness that exhibit a decrease in neural signal diversity in resting state condition.
As discussed in the main text, we use the number of antipsychotics as a proxy measure for strength of antipsychotic medication used by the patients -in absence of the data on the dosage used/prescribed to the patients. This proxy measure works well to differentiate the neural activity in terms of LZ complexity and TE between brain regions. However, it is worth mentioning that this measure is not capable of controlling for differences observed within the schizophrenia patients. For completion, here we present a preliminary analysis of PANSS (Positive And Negative Syndrome Scale) symptom scores, in order to explore their relationship to the number of antipsychotics and the neural measures. Results show that the number of antipsychotics is negatively correlated with the positive scores PANSS scores, but not with any other type of symptoms (see
Raw MEG data for the LSD and KET conditions (and their respective placebo controls) is available in the Harvard Dataverse repository. EEG data from schizophrenia patients and healthy controls was obtained from the BSNIP study, accessed via the NIMH Data Archive. Open-source implementations are available online for all tools used in the study, including LZ (link), TE (link), and NBS (link).
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Mediano, P. A. M., Rosas, F. E., Timmermann, C. et al. · ACS Chemical Neuroscience (2024)
Barnett, L., Muthukumaraswamy, S., Carhart-Harris, R. L. · NeuroImage (2020)
Leptourgos, P., Fortier-Davy, M., Carhart-Harris, R. L. et al. · Schizophrenia Bulletin (2020)
Carhart-Harris, R. L., Friston, K. J. · Pharmacological Reviews (2019)
Brouwer, A., Carhart-Harris, R. L. · Journal of Psychopharmacology (2020)
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Rosas, F. E., Mediano, P. A. M., Timmermann, C. et al. · Biorxiv (2023)
Sadibolova, R., Murray-Lawson, C., Family, N. et al. · Biorxiv (2023)
Hipólito, I., Mago, J., Rosas, F. E. et al. · Psyarxiv (2022)