Attention-dependent modulation of cortical taste circuits revealed by granger causality with signal-dependent noise

We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention

Historical greenhouse gas concentrations for climate modelling (CMIP6)

Atmospheric greenhouse gas (GHG) concentrations are at unprecedented, record-high levels compared to the last 800 000 years. Those elevated GHG concentrations warm the planet and – partially offset by net cooling effects by aerosols – are largely responsible for the observed warming over the past 150 years. An accurate representation of GHG concentrations is hence important to understand and model recent climate change. So far, community efforts to create composite datasets of GHG concentrations with seasonal and latitudinal information have focused on marine boundary layer conditions and recent trends since the 1980s. Here, we provide consolidated datasets of historical atmospheric concentrations (mole fractions) of 43 GHGs to be used in the Climate Model Intercomparison Project – Phase 6 (CMIP6) experiments. The presented datasets are based on AGAGE and NOAA networks, firn and ice core data, and archived air data, and a large set of published studies. In contrast to previous intercomparisons, the new datasets are latitudinally resolved and include seasonality. We focus on the period 1850–2014 for historical CMIP6 runs, but data are also provided for the last 2000 years. We provide consolidated datasets in various spatiotemporal resolutions for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), as well as 40 other GHGs, namely 17 ozone-depleting substances, 11 hydrofluorocarbons (HFCs), 9 perfluorocarbons (PFCs), sulfur hexafluoride (SF6), nitrogen trifluoride (NF3) and sulfuryl fluoride (SO2F2). In addition, we provide three equivalence species that aggregate concentrations of GHGs other than CO2, CH4 and N2O, weighted by their radiative forcing efficiencies. For the year 1850, which is used for pre-industrial control runs, we estimate annual global-mean surface concentrations of CO2 at 284.3 ppm, CH4 at 808.2 ppb and N2O at 273.0 ppb. The data are available at https://esgfnode.llnl.gov/search/input4mips/ and http://www.climatecollege.unimelb.edu.au/cmip6. While the minimum CMIP6 recommendation is to use the global- and annual-mean time series, modelling groups can also choose our monthly and latitudinally resolved concentrations, which imply a stronger radiative forcing in the Northern Hemisphere winter (due to the latitudinal gradient and seasonality)

Policy Adjustment in a Dynamic Economic Game

Making sequential decisions to harvest rewards is a notoriously difficult problem. One difficulty is that the real world is not stationary and the reward expected from a contemplated action may depend in complex ways on the history of an animal's choices. Previous functional neuroimaging work combined with principled models has detected brain responses that correlate with computations thought to guide simple learning and action choice. Those works generally employed instrumental conditioning tasks with fixed action-reward contingencies. For real-world learning problems, the history of reward-harvesting choices can change the likelihood of rewards collected by the same choices in the near-term future. We used functional MRI to probe brain and behavioral responses in a continuous decision-making task where reward contingency is a function of both a subject's immediate choice and his choice history. In these more complex tasks, we demonstrated that a simple actor-critic model can account for both the subjects' behavioral and brain responses, and identified a reward prediction error signal in ventral striatal structures active during these non-stationary decision tasks. However, a sudden introduction of new reward structures engages more complex control circuitry in the prefrontal cortex (inferior frontal gyrus and anterior insula) and is not captured by a simple actor-critic model. Taken together, these results extend our knowledge of reward-learning signals into more complex, history-dependent choice tasks. They also highlight the important interplay between striatum and prefrontal cortex as decision-makers respond to the strategic demands imposed by non-stationary reward environments more reminiscent of real-world tasks

Neural Correlates of Appetite and Hunger-Related Evaluative Judgments

How much we desire a meal depends on both the constituent foods and how hungry we are, though not every meal becomes more desirable with increasing hunger. The brain therefore needs to be able to integrate hunger and meal properties to compute the correct incentive value of a meal. The present study investigated the functional role of the amygdala and the orbitofrontal cortex in mediating hunger and dish attractiveness. Furthermore, it explored neural responses to dish descriptions particularly susceptible to value-increase following fasting. We instructed participants to rate how much they wanted food menu items while they were either hungry or sated, and compared the rating differences in these states. Our results point to the representation of food value in the amygdala, and to an integration of attractiveness with hunger level in the orbitofrontal cortex. Dishes particularly desirable during hunger activated the thalamus and the insula. Our results specify the functions of evaluative structures in the context of food attractiveness, and point to a complex neural representation of dish qualities which contribute to state-dependent value

Surprised at All the Entropy: Hippocampal, Caudate and Midbrain Contributions to Learning from Prediction Errors

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts

Cyclophilin A interacts with diverse lentiviral capsids

BACKGROUND: The capsid (CA) protein of HIV-1 binds with high affinity to the host protein cyclophilin A (CypA). This binding positively affects some early stage of the viral life-cycle because prevention of binding either by drugs that occupy that active site of cyclophilin A, by mutation in HIV-1 CA, or RNAi that knocks down intracellular CypA level diminishes viral infectivity. The closely related lentivirus, SIVcpz also binds CypA, but it was thought that this interaction was limited to the HIV-1/SIVcpz lineage because other retroviruses failed to interact with CypA in a yeast two-hybrid assay. RESULTS: We find that diverse lentiviruses, FIV and SIVagmTAN also bind to CypA. Mutagenesis of FIV CA showed that an amino acid that is in a homologous position to the proline at amino acid 90 of HIV-1 CA is essential for FIV interactions with CypA. CONCLUSION: These results demonstrate that CypA binding to lentiviruses is more widespread than previously thought and suggest that this interaction is evolutionarily important for lentiviral infection

Expert Financial Advice Neurobiologically “Offloads” Financial Decision-Making under Risk

BACKGROUND: Financial advice from experts is commonly sought during times of uncertainty. While the field of neuroeconomics has made considerable progress in understanding the neurobiological basis of risky decision-making, the neural mechanisms through which external information, such as advice, is integrated during decision-making are poorly understood. In the current experiment, we investigated the neurobiological basis of the influence of expert advice on financial decisions under risk. METHODOLOGY/PRINCIPAL FINDINGS: While undergoing fMRI scanning, participants made a series of financial choices between a certain payment and a lottery. Choices were made in two conditions: 1) advice from a financial expert about which choice to make was displayed (MES condition); and 2) no advice was displayed (NOM condition). Behavioral results showed a significant effect of expert advice. Specifically, probability weighting functions changed in the direction of the expert's advice. This was paralleled by neural activation patterns. Brain activations showing significant correlations with valuation (parametric modulation by value of lottery/sure win) were obtained in the absence of the expert's advice (NOM) in intraparietal sulcus, posterior cingulate cortex, cuneus, precuneus, inferior frontal gyrus and middle temporal gyrus. Notably, no significant correlations with value were obtained in the presence of advice (MES). These findings were corroborated by region of interest analyses. Neural equivalents of probability weighting functions showed significant flattening in the MES compared to the NOM condition in regions associated with probability weighting, including anterior cingulate cortex, dorsolateral PFC, thalamus, medial occipital gyrus and anterior insula. Finally, during the MES condition, significant activations in temporoparietal junction and medial PFC were obtained. CONCLUSIONS/SIGNIFICANCE: These results support the hypothesis that one effect of expert advice is to "offload" the calculation of value of decision options from the individual's brain

Including the public in pandemic planning: a deliberative approach

Background: Against a background of pandemic threat posed by SARS and avian H5N1 influenza, this study used deliberative forums to elucidate informed community perspectives on aspects of pandemic planning. Methods: Two deliberative forums were carried out with members of the South Australian community. The forums were supported by a qualitative study with adults and youths, systematic reviews of the literature and the involvement of an extended group of academic experts and policy makers. The forum discussions were recorded with simultaneous transcription and analysed thematically. Results: Participants allocated scarce resources of antiviral drugs and pandemic vaccine based on a desire to preserve society function in a time of crisis. Participants were divided on the acceptability of social distancing and quarantine measures. However, should such measures be adopted, they thought that reasonable financial, household and psychological support was essential. In addition, provided such support was present, the participants, in general, were willing to impose strict sanctions on those who violated quarantine and social distancing measures. Conclusions: The recommendations from the forums suggest that the implementation of pandemic plans in a severe pandemic will be challenging, but not impossible. Implementation may be more successful if the public is engaged in pandemic planning before a pandemic, effective communication of key points is practiced before and during a pandemic and if judicious use is made of supportive measures to assist those in quarantine or affected by social isolation measures.Annette J Braunack-Mayer, Jackie M Street, Wendy A Rogers, Rodney Givney, John R Moss, Janet E Hiller, Flu Views tea

Dorsal Striatum and Its Limbic Connectivity Mediate Abnormal Anticipatory Reward Processing in Obesity

Obesity is characterized by an imbalance in the brain circuits promoting reward seeking and those governing cognitive control. Here we show that the dorsal caudate nucleus and its connections with amygdala, insula and prefrontal cortex contribute to abnormal reward processing in obesity. We measured regional brain glucose uptake in morbidly obese (n = 19) and normal weighted (n = 16) subjects with 2-[18F]fluoro-2-deoxyglucose ([18F]FDG) positron emission tomography (PET) during euglycemic hyperinsulinemia and with functional magnetic resonance imaging (fMRI) while anticipatory food reward was induced by repeated presentations of appetizing and bland food pictures. First, we found that glucose uptake rate in the dorsal caudate nucleus was higher in obese than in normal-weight subjects. Second, obese subjects showed increased hemodynamic responses in the caudate nucleus while viewing appetizing versus bland foods in fMRI. The caudate also showed elevated task-related functional connectivity with amygdala and insula in the obese versus normal-weight subjects. Finally, obese subjects had smaller responses to appetizing versus bland foods in the dorsolateral and orbitofrontal cortices than did normal-weight subjects, and failure to activate the dorsolateral prefrontal cortex was correlated with high glucose metabolism in the dorsal caudate nucleus. These findings suggest that enhanced sensitivity to external food cues in obesity may involve abnormal stimulus-response learning and incentive motivation subserved by the dorsal caudate nucleus, which in turn may be due to abnormally high input from the amygdala and insula and dysfunctional inhibitory control by the frontal cortical regions. These functional changes in the responsiveness and interconnectivity of the reward circuit could be a critical mechanism to explain overeating in obesity