Pathophysiological regulation of lung function by the free fatty acid receptor FFA4.

Increased prevalence of inflammatory airway diseases including asthma and chronic obstructive pulmonary disease (COPD) together with inadequate disease control by current frontline treatments means that there is a need to define therapeutic targets for these conditions. Here, we investigate a member of the G protein-coupled receptor family, FFA4, that responds to free circulating fatty acids including dietary omega-3 fatty acids found in fish oils. We show that FFA4, although usually associated with metabolic responses linked with food intake, is expressed in the lung where it is coupled to Gq/11 signaling. Activation of FFA4 by drug-like agonists produced relaxation of murine airway smooth muscle mediated at least in part by the release of the prostaglandin E2 (PGE2) that subsequently acts on EP2 prostanoid receptors. In normal mice, activation of FFA4 resulted in a decrease in lung resistance. In acute and chronic ozone models of pollution-mediated inflammation and house dust mite and cigarette smoke-induced inflammatory disease, FFA4 agonists acted to reduce airway resistance, a response that was absent in mice lacking expression of FFA4. The expression profile of FFA4 in human lung was similar to that observed in mice, and the response to FFA4/FFA1 agonists similarly mediated human airway smooth muscle relaxation ex vivo. Our study provides evidence that pharmacological targeting of lung FFA4, and possibly combined activation of FFA4 and FFA1, has in vivo efficacy and might have therapeutic value in the treatment of bronchoconstriction associated with inflammatory airway diseases such as asthma and COPD

Hippocampus Leads Ventral Striatum in Replay of Place-Reward Information

While the brain is asleep, the hippocampus plays first fiddle in the orchestra of memory; spatial information is reactivated in the hippocampus shortly in advance of emotional memory traces in the vental striatum

Connectivity-based parcellation of the human frontal polar cortex

The frontal pole corresponds to Brodmann area (BA) 10, the largest single architectonic area in the human frontal lobe. Generally, BA10 is thought to contain two or three subregions that subserve broad functions such as multitasking, social cognition, attention, and episodic memory. However, there is a substantial debate about the functional and structural heterogeneity of this large frontal region. Previous connectivity-based parcellation studies have identified two or three subregions in the human frontal pole. Here, we used diffusion tensor imaging to assess structural connectivity of BA10 in 35 healthy subjects and delineated subregions based on this connectivity. This allowed us to determine the correspondence of structurally based subregions with the scheme previously defined functionally. Three subregions could be defined in each subject. However, these three subregions were not spatially consistent between subjects. Therefore, we accepted a solution with two subregions that encompassed the lateral and medial frontal pole. We then examined resting-state functional connectivity of the two subregions and found significant differences between their connectivities. The medial cluster was connected to nodes of the default-mode network, which is implicated in internally focused, self-related thought, and social cognition. The lateral cluster was connected to nodes of the executive control network, associated with directed attention and working memory. These findings support the concept that there are two major anatomical subregions of the frontal pole related to differences in functional connectivity

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison