Psilocybin modulation of time-varying functional connectivity is associated with plasma psilocin and subjective effects.

BACKGROUND: Psilocin, the neuroactive metabolite of psilocybin, is a serotonergic psychedelic that induces an acute altered state of consciousness, evokes lasting changes in mood and personality in healthy individuals, and has potential as an antidepressant treatment. Examining the acute effects of psilocin on resting-state time-varying functional connectivity implicates network-level connectivity motifs that may underlie acute and lasting behavioral and clinical effects. AIM: Evaluate the association between resting-state time-varying functional connectivity (tvFC) characteristics and plasma psilocin level (PPL) and subjective drug intensity (SDI) before and right after intake of a psychedelic dose of psilocybin in healthy humans. METHODS: Fifteen healthy individuals completed the study. Before and at multiple time points after psilocybin intake, we acquired 10-minute resting-state blood-oxygen-level-dependent functional magnetic resonance imaging scans. Leading Eigenvector Dynamics Analysis (LEiDA) and diametrical clustering were applied to estimate discrete, sequentially active brain states. We evaluated associations between the fractional occurrence of brain states during a scan session and PPL and SDI using linear mixed-effects models. We examined associations between brain state dwell time and PPL and SDI using frailty Cox proportional hazards survival analysis. RESULTS: Fractional occurrences for two brain states characterized by lateral frontoparietal and medial fronto-parietal-cingulate coherence were statistically significantly negatively associated with PPL and SDI. Dwell time for these brain states was negatively associated with SDI and, to a lesser extent, PPL. Conversely, fractional occurrence and dwell time of a fully connected brain state partly associated with motion was positively associated with PPL and SDI. CONCLUSION: Our findings suggest that the acute perceptual psychedelic effects induced by psilocybin may stem from drug-level associated decreases in the occurrence and duration of lateral and medial frontoparietal connectivity motifs. We apply and argue for a modified approach to modeling eigenvectors produced by LEiDA that more fully acknowledges their underlying structure. Together these findings contribute to a more comprehensive neurobiological framework underlying acute effects of serotonergic psychedelics.

Combining electro- and magnetoencephalography data using directional archetypal analysis.

Metastable microstates in electro- and magnetoencephalographic (EEG and MEG) measurements are usually determined using modified k-means accounting for polarity invariant states. However, hard state assignment approaches assume that the brain traverses microstates in a discrete rather than continuous fashion. We present multimodal, multisubject directional archetypal analysis as a scale and polarity invariant extension to archetypal analysis using a loss function based on the Watson distribution. With this method, EEG/MEG microstates are modeled using subject- and modality-specific archetypes that are representative, distinct topographic maps between which the brain continuously traverses. Archetypes are specified as convex combinations of unit norm input data based on a shared generator matrix, thus assuming that the timing of neural responses to stimuli is consistent across subjects and modalities. The input data is reconstructed as convex combinations of archetypes using a subject- and modality-specific continuous archetypal mixing matrix. We showcase the model on synthetic data and an openly available face perception event-related potential data set with concurrently recorded EEG and MEG. In synthetic and unimodal experiments, we compare our model to conventional Euclidean multisubject archetypal analysis. We also contrast our model to a directional clustering model with discrete state assignments to highlight the advantages of modeling state trajectories rather than hard assignments. We find that our approach successfully models scale and polarity invariant data, such as microstates, accounting for intersubject and intermodal variability. The model is readily extendable to other modalities ensuring component correspondence while elucidating spatiotemporal signal variability.

Macrolide resistance conferred by rRNA mutations in field isolates of Mannheimia haemolytica and Pasteurella multocida.

OBJECTIVES: To determine how resistance to macrolides is conferred in field isolates of Pasteurella multocida and Mannheimia haemolytica that lack previously identified resistance determinants for rRNA methylation, efflux and macrolide-modifying enzymes. METHODS: Isolates of P. multocida and M. haemolytica identified as being highly resistant (MICs >64 mg/L) to the macrolides erythromycin, gamithromycin, tilmicosin, tildipirosin and tulathromycin were screened by multiplex PCR for the previously identified resistance genes erm(42), msr(E) and mph(E). Strains lacking these determinants were analysed by genome sequencing and primer extension on the rRNAs. RESULTS: Macrolide resistance in one M. haemolytica isolate was conferred by the 23S rRNA mutation A2058G; resistance in three P. multocida isolates were caused by mutations at the neighbouring nucleotide A2059G. In each strain, all six copies of the rrn operons encoded the respective mutations. There were no mutations in the ribosomal protein genes rplD or rplV, and no other macrolide resistance mechanism was evident. CONCLUSIONS: High-level macrolide resistance can arise from 23S rRNA mutations in P. multocida and M. haemolytica despite their multiple copies of rrn. Selective pressures from exposure to different macrolide or lincosamide drugs presumably resulted in consolidation of either the A2058G or the A2059G mutation.