Elucidating the underlying components of food valuation in the human orbitofrontal cortex

The valuation of food is a fundamental component of our decision-making. Yet little is known about how value signals for food and other rewards are constructed by the brain. Using a food-based decision task in human participants, we found that subjective values can be predicted from beliefs about constituent nutritive attributes of food: protein, fat, carbohydrates and vitamin content. Multivariate analyses of functional MRI data demonstrated that, while food value is represented in patterns of neural activity in both medial and lateral parts of the orbitofrontal cortex (OFC), only the lateral OFC represents the elemental nutritive attributes. Effective connectivity analyses further indicate that information about the nutritive attributes represented in the lateral OFC is integrated within the medial OFC to compute an overall value. These findings provide a mechanistic account for the construction of food value from its constituent nutrients

Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments

Humans possess an exceptional aptitude to efficiently make decisions from high-dimensional sensory observations. However, it is unknown how the brain compactly represents the current state of the environment to guide this process. The deep Q-network (DQN) achieves this by capturing highly nonlinear mappings from multivariate inputs to the values of potential actions. We deployed DQN as a model of brain activity and behavior in participants playing three Atari video games during fMRI. Hidden layers of DQN exhibited a striking resemblance to voxel activity in a distributed sensorimotor network, extending throughout the dorsal visual pathway into posterior parietal cortex. Neural state-space representations emerged from nonlinear transformations of the pixel space bridging perception to action and reward. These transformations reshape axes to reflect relevant high-level features and strip away information about task-irrelevant sensory features. Our findings shed light on the neural encoding of task representations for decision-making in real-world situations

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Treatment of Alaska-produced food products by ionizing radiation may benefit the seafood and agricultural industries and the Alaskan consumer. A feasibility study to evaluate the potential social and economic benefits and risks as well as the costs of using the process in Alaska on Alaskan products is being coordinated by the Institute of Northern Engineering. A research and development project to determine effects on the quality o f Alaskan products could be the next phase in the introduction o f a new food-preservation technique to Alaska

Novel use of stir bar sorptive extraction (SBSE) as a tool for isolation of oviposition site attractants for gravid Culex quinquefasciatus

Mosquitoes such as Culex quinquefasciatus Say (Diptera: Culicidae) are important vectors of organisms that cause disease in humans. Research into the development of effective standardized odour baits for blood-fed females (oviposition attractants), to enable entomological monitoring of vector populations, is hampered by complex protocols for extraction of physiologically active volatile chemicals from natural breeding site water samples, which have produced inconsistent results. Air entrainment and solvent extraction are technically demanding methods and are impractical for use in resource poor environments where mosquito-borne disease is most prevalent. This study reports the first use of a simple, robust extraction technique, stir bar sorptive extraction (SBSE), to extract behaviourally active small lipophilic molecules (SLMs) present in water samples collected from Cx. quinquefasciatus breeding sites in Tanzania. Extracts from a pit latrine and from a cess pool breeding site attracted more gravid Cx. quinquefasciatus in pair choice bioassays than control extracts, and coupled gas chromatography-electroantennography (GC-EAG) allowed tentative identification of 15 electrophysiologically active chemicals, including the known oviposition attractant, skatole (3-methylindole). Here, we have demonstrated, using simple pair choice bioassays in controlled laboratory conditions, that SBSE is effective for the extraction of behaviourally and electrophysiologically active semiochemicals from mosquito breeding site waters. Further research is required to confirm that SBSE is an appropriate technique for use in field surveys in the search for oviposition cues for Cx. quinquefasciatus

Characteristics of Evoked Potential Multiple EEG Recordings in Patients with Chronic Pain by Means of Parallel Factor Analysis

This paper presents an alternative method, called as parallel factor analysis (PARAFAC) with a continuous wavelet transform, to analyze of brain activity in patients with chronic pain in the time-frequency-channel domain and quantifies differences between chronic pain patients and controls in these domains. The event related multiple EEG recordings of the chronic pain patients and non-pain controls with somatosensory stimuli (pain, random pain, touch, random touch) are analyzed. Multiple linear regression (MLR) is applied to describe the effects of aging on the frequency response differences between patients and controls. The results show that the somatosensory cortical responses occurred around 250 ms in both groups. In the frequency domain, the neural response frequency in the pain group (around 4 Hz) was less than that in the control group (around 5.5 Hz) under the somatosensory stimuli. In the channel domain, cortical activation was predominant in the frontal region for the chronic pain group and in the central region for controls. The indices of active ratios were statistical significant between the two groups in the frontal and central regions. These findings demonstrate that the PARAFAC is an interesting method to understanding the pathophysiological characteristics of chronic pain