Long term time-lapse imaging reveals that individual synapses undergo significant structural remodeling not only when driven by activity, but also when network activity is absent, raising questions about how reliably individual synapses maintain connections.

Fibromyalgia (FM) is a prevalent syndrome, characterised by chronic widespread pain, fatigue, and impaired sleep, that is challenging to diagnose and difficult to treat. The microbiomes of 77 women with FM and that of 79 control participants were compared using 16S rRNA gene amplification and whole-genome sequencing. When comparing FM patients with unrelated controls using differential abundance analysis, significant differences were revealed in several bacterial taxa. Variance in the composition of the microbiomes was explained by FM-related variables more than by any other innate or environmental variable and correlated with clinical indices of FM. In line with observed alteration in butyrate-metabolising species, targeted serum metabolite analysis verified differences in the serum levels of butyrate and propionate in FM patients. Using machine-learning algorithms, the microbiome composition alone allowed for the classification of patients and controls (receiver operating characteristic area under the curve 87.8%). To the best of our knowledge, this is the first demonstration of gut microbiome alteration in nonvisceral pain. This observation paves the way for further studies, elucidating the pathophysiology of FM, developing diagnostic aids and possibly allowing for new treatment modalities to be explored.

Long-term, repeated measurements of individual synaptic properties have revealed that synapses can undergo significant directed and spontaneous changes over time scales of minutes to weeks. These changes are presumably driven by a large number of activity-dependent and independent molecular processes, yet how these processes integrate to determine the totality of synaptic size remains unknown. Here we propose, as an alternative to detailed, mechanistic descriptions, a statistical approach to synaptic size dynamics. The basic premise of this approach is that the integrated outcome of the myriad of processes that drive synaptic size dynamics are effectively described as a combination of multiplicative and additive processes, both of which are stochastic and taken from distributions parametrically affected by physiological signals. We show that this seemingly simple model, known in probability theory as the Kesten process, can generate rich dynamics which are qualitatively similar to the dynamics of individual glutamatergic synapses recorded in long-term time-lapse experiments in ex-vivo cortical networks. Moreover, we show that this stochastic model, which is insensitive to many of its underlying details, quantitatively captures the distributions of synaptic sizes measured in these experiments, the long-term stability of such distributions and their scaling in response to pharmacological manipulations. Finally, we show that the average kinetics of new postsynaptic density formation measured in such experiments is also faithfully captured by the same model. The model thus provides a useful framework for characterizing synapse size dynamics at steady state, during initial formation of such steady states, and during their convergence to new steady states following perturbations. These findings show the strength of a simple low dimensional statistical model to quantitatively describe synapse size dynamics as the integrated result of many underlying complex processes.

No abstract

The involvement of dopamine in the process of learning, at the cellular and behavioral levels, has been studied extensively. Evidently, dopamine is released from midbrain nuclei neurons on exposure to salient unpredicted stimuli and binds to neurons of cortical and subcortical structures, where its neuromodulatory effects are exerted. The neuromodulatory effects of dopamine at the synaptic and cellular levels are very rich, but it is difficult to extrapolate from these elementary levels what their effect might be at the behaviorally relevant level of neuronal ensembles. Using multi-site recordings from networks of cortical neurons developing ex vivo, we studied the effects of dopamine on connectivity within neuronal ensembles. We found that dopamine disperses correlations between individual neuronal activities while preserving the global distribution of correlations at the network level. Using selective D(1) and D(2) modulators, we show that both receptor types are contributing to dopamine-induced dispersion. Our results indicate that, at the neuronal ensemble level, dopamine acts to enhance changes in network connectivity rather than stabilize such connections.