Transient receptor potential melastatin-3 (TRPM3) is a broadly expressed Ca
2+-permeable nonselective cation channel. Previous work has demonstrated robust activation of TRPM3 by the neuroactive ...steroid pregnenolone sulfate (PS), but its in vivo gating mechanisms and functions remained poorly understood. Here, we provide evidence that TRPM3 functions as a chemo- and thermosensor in the somatosensory system. TRPM3 is molecularly and functionally expressed in a large subset of small-diameter sensory neurons from dorsal root and trigeminal ganglia, and mediates the aversive and nocifensive behavioral responses to PS. Moreover, we demonstrate that TRPM3 is steeply activated by heating and underlies heat sensitivity in a subset of sensory neurons. TRPM3-deficient mice exhibited clear deficits in their avoidance responses to noxious heat and in the development of inflammatory heat hyperalgesia. These experiments reveal an unanticipated role for TRPM3 as a thermosensitive nociceptor channel implicated in the detection of noxious heat.
► TRPM3 is functionally expressed in a large subpopulation of sensory neurons ► The TRPM3 agonist evokes pain and aversion in WT, but not in TRPM3-deficient mice ► TRPM3 is steeply activated by heat ►
TRPM3
−/−
mice exhibit specific deficiencies in their response to noxious heat stimuli
In this paper, we revisit the problem of Bayesian model selection (BMS) at the group level. We originally addressed this issue in Stephan et al. (2009), where models are treated as random effects ...that could differ between subjects, with an unknown population distribution. Here, we extend this work, by (i) introducing the Bayesian omnibus risk (BOR) as a measure of the statistical risk incurred when performing group BMS, (ii) highlighting the difference between random effects BMS and classical random effects analyses of parameter estimates, and (iii) addressing the problem of between group or condition model comparisons. We address the first issue by quantifying the chance likelihood of apparent differences in model frequencies. This leads to the notion of protected exceedance probabilities. The second issue arises when people want to ask “whether a model parameter is zero or not” at the group level. Here, we provide guidance as to whether to use a classical second-level analysis of parameter estimates, or random effects BMS. The third issue rests on the evidence for a difference in model labels or frequencies across groups or conditions. Overall, we hope that the material presented in this paper finesses the problems of group-level BMS in the analysis of neuroimaging and behavioural data.
•Some conceptual issues with group-level Bayesian Model Selection are still outstanding.•We provide a complete picture of the statistical risk incurred when performing BMS.•We address the problem of between-group and between-condition comparisons.•We examine the difference between BMS and classical random effects analyses.
Three-dimensional (3D) tumor cell cultures grown in laminin-rich-extracellular matrix (lrECM) are considered to reflect human tumors more realistic as compared to cells grown as monolayer on plastic. ...Here, we systematically investigated the impact of ECM on phenotype, gene expression, EGFR signaling pathway, and on EGFR inhibition in commonly used colorectal cancer (CRC) cell lines. LrECM on-top (3D) culture assays were performed with the CRC cell lines SW-480, HT-29, DLD-1, LOVO, CACO-2, COLO-205 and COLO-206F. Morphology of lrECM cultivated CRC cell lines was determined by phase contrast and confocal laser scanning fluorescence microscopy. Proliferation of cells was examined by MTT assay, invasive capacity of the cell lines was assayed using Matrigel-coated Boyden chambers, and migratory activity was determined employing the Fence assay. Differential gene expression was analyzed at the transcriptional level by the Agilent array platform. EGFR was inhibited by using the specific small molecule inhibitor AG1478. A specific spheroid growth pattern was observed for all investigated CRC cell lines. DLD-1, HT-29 and SW-480 and CACO-2 exhibited a clear solid tumor cell formation, while LOVO, COLO-205 and COLO-206F were characterized by forming grape-like structures. Although the occurrence of a spheroid morphology did not correlate with an altered migratory, invasive, or proliferative capacity of CRC cell lines, gene expression was clearly altered in cells grown on lrECM as compared to 2D cultures. Interestingly, in KRAS wild-type cell lines, inhibition of EGFR was less effective in lrECM (3D) cultures as compared to 2D cell cultures. Thus, comparing both 2D and 3D cell culture models, our data support the influence of the ECM on cancer growth. Compared to conventional 2D cell culture, the lrECM (3D) cell culture model offers the opportunity to investigate permanent CRC cell lines under more physiological conditions, i.e. in the context of molecular therapeutic targets and their pharmacological inhibition.
In Bayesian brain theories, hierarchically related prediction errors (PEs) play a central role for predicting sensory inputs and inferring their underlying causes, e.g., the probabilistic structure ...of the environment and its volatility. Notably, PEs at different hierarchical levels may be encoded by different neuromodulatory transmitters. Here, we tested this possibility in computational fMRI studies of audio-visual learning. Using a hierarchical Bayesian model, we found that low-level PEs about visual stimulus outcome were reflected by widespread activity in visual and supramodal areas but also in the midbrain. In contrast, high-level PEs about stimulus probabilities were encoded by the basal forebrain. These findings were replicated in two groups of healthy volunteers. While our fMRI measures do not reveal the exact neuron types activated in midbrain and basal forebrain, they suggest a dichotomy between neuromodulatory systems, linking dopamine to low-level PEs about stimulus outcome and acetylcholine to more abstract PEs about stimulus probabilities.
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•Hierarchical precision-weighted prediction errors (PEs) during audio-visual learning•PEs about sensory outcome are widely represented throughout the brain•BOLD signal in midbrain reflects PEs about stimulus outcome•BOLD signal in basal forebrain reflects PEs about stimulus probabilities
Prediction errors (PEs) are crucial teaching signals for learning. Iglesias et al. show that different PEs map onto distinct neurotransmitter systems: the dopaminergic midbrain signals PEs about visual stimulus outcomes, while the cholinergic basal forebrain encodes PEs about stimulus probabilities.
The goal of dynamic causal modelling (DCM) of neuroimaging data is to study experimentally induced changes in functional integration among brain regions. This requires (i) biophysically plausible and ...physiologically interpretable models of neuronal network dynamics that can predict distributed brain responses to experimental stimuli and (ii) efficient statistical methods for parameter estimation and model comparison. These two key components of DCM have been the focus of more than thirty methodological articles since the seminal work of Friston and colleagues published in 2003.
In this paper, we provide a critical review of the current state-of-the-art of DCM. We inspect the properties of DCM in relation to the most common neuroimaging modalities (fMRI and EEG/MEG) and the specificity of inference on neural systems that can be made from these data. We then discuss both the plausibility of the underlying biophysical models and the robustness of the statistical inversion techniques. Finally, we discuss potential extensions of the current DCM framework, such as stochastic DCMs, plastic DCMs and field DCMs.
Summary
The availability of a reliable system to record sleep stage measures easily and automatically in ambulatory settings could be of utility for research and clinical work. The aim of this study ...was to evaluate a novel wireless system (WS) that does not require skilled preparation for the automatic collection and scoring of human sleep. Twenty‐nine healthy adults underwent concurrent sleep measurement via the WS, polysomnography (PSG) and an actigraph (ACT) in a sleep laboratory for one assessment night preceded by an acclimation night. The PSG recordings were scored by two experienced trained technicians from separate laboratories. Each recording was scored by both technicians to Rechtschaffen and Kales (R&K) criteria. The WS and ACT were compared with each of the PSG scores and a consensus PSG score, and the PSG scores were compared with each other. Inter‐rater agreement was assessed for each pair over all pooled epochs by percentage agreement, Cohen’s kappa and intraclass correlation coefficient. The WS agreement with each of the two PSG scores for sleep stages was 75.8 and 74.7%, respectively. WS agreement with each of the two PSG scores for sleep/wakefulness was 92.6 and 91.1%, ACT agreement with PSG was 86.3 and 85.7%. The PSG scorers’ agreement with each other for sleep stages was 83.2%, and for sleep/wakefulness was 95.8%. The findings from the current study indicate that the WS may provide an easy to use and accurate complement to other established technologies for measuring sleep in healthy adults.
Over the last 2 decades, a large number of neurophysiological and neuroimaging studies of patients with schizophrenia have furnished in vivo evidence for dysconnectivity, ie, abnormal functional ...integration of brain processes. While the evidence for dysconnectivity in schizophrenia is strong, its etiology, pathophysiological mechanisms, and significance for clinical symptoms are unclear. First, dysconnectivity could result from aberrant wiring of connections during development, from aberrant synaptic plasticity, or from both. Second, it is not clear how schizophrenic symptoms can be understood mechanistically as a consequence of dysconnectivity. Third, if dysconnectivity is the primary pathophysiology, and not just an epiphenomenon, then it should provide a mechanistic explanation for known empirical facts about schizophrenia. This article addresses these 3 issues in the framework of the dysconnection hypothesis. This theory postulates that the core pathology in schizophrenia resides in aberrant N-methyl-D-aspartate receptor (NMDAR)-mediated synaptic plasticity due to abnormal regulation of NMDARs by neuromodulatory transmitters like dopamine, serotonin, or acetylcholine. We argue that this neurobiological mechanism can explain failures of self-monitoring, leading to a mechanistic explanation for first-rank symptoms as pathognomonic features of schizophrenia, and may provide a basis for future diagnostic classifications with physiologically defined patient subgroups. Finally, we test the explanatory power of our theory against a list of empirical facts about schizophrenia.
Functional neuroimaging has made fundamental contributions to our understanding of brain function. It remains challenging, however, to translate these advances into diagnostic tools for psychiatry. ...Promising new avenues for translation are provided by computational modeling of neuroimaging data. This article reviews contemporary frameworks for computational neuroimaging, with a focus on forward models linking unobservable brain states to measurements. These approaches—biophysical network models, generative models, and model-based fMRI analyses of neuromodulation—strive to move beyond statistical characterizations and toward mechanistic explanations of neuroimaging data. Focusing on schizophrenia as a paradigmatic spectrum disease, we review applications of these models to psychiatric questions, identify methodological challenges, and highlight trends of convergence among computational neuroimaging approaches. We conclude by outlining a translational neuromodeling strategy, highlighting the importance of openly available datasets from prospective patient studies for evaluating the clinical utility of computational models.
Psychiatry lacks diagnostic tools that enable clinical predictions and treatment selection for individual patients. Computational models of neuroimaging data offer a promising new avenue. Focusing on schizophrenia, this article reviews clinical applications, methodological challenges, and future developments of computational neuroimaging.
Highlights • A generic Bayesian framework explains regression, range, and sequential effects. • Weber-Fechner law and Stevens’ power law can be linked via Bayes’ Rule. • Stevens’ power law exponent ...can be reinterpreted as weighting of prior knowledge. • We link Bayesian explanations of magnitude estimation to theories of schizophrenia.
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