Anticipatory dynamics in the human brain guide foraging for primary rewards

Foraging is a fundamental food-seeking behavior in a wide range of species that enables survival in an uncertain world. During foraging, behavioral agents constantly face a trade-off between staying in their current location or exploring another. Despite ethological generality and importance of foraging, it remains unclear how the human brain guides continuous decision in such situations. Here we show that anticipatory activity dynamics in the anterior prefrontal cortex (aPFC) and hippocampus underpin foraging for primary rewards. While functional MRI was performed, humans foraged for real liquid rewards available after tens of seconds, and continuous decision during foraging was tracked by a dynamic pattern of brain activity that reflected anticipation of a future reward. When the dynamic anticipatory activity in the aPFC was enhanced, humans remained in their current environment, but when this activity diminished, they explored a new environment. Moreover, the anticipatory activity in the aPFC and hippocampus was associated with distinct decision strategies: aPFC activity was enhanced in humans adopting an exploratory strategy, whereas those remaining stationary showed enhanced activity in the hippocampus. Our results suggest that anticipatory dynamics in the fronto-hippocampal mechanisms underlie continuous decision-making during human foraging.

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Fluctuations in central and peripheral temperatures associated with feeding behavior in rats

AJP Regulatory Integrative and Comparative Physiology ◽

10.1152/ajpregu.90636.2008 ◽

2008 ◽

Vol 295 (5) ◽

pp. R1415-R1424 ◽

Cited By ~ 4

Author(s):

Michael S. Smirnov ◽

Eugene A. Kiyatkin

Keyword(s):

Feeding Behavior ◽

Skin Temperature ◽

Brain Activity ◽

Temperature Fluctuations ◽

Temporal Muscle ◽

Temperature Changes ◽

Food Container ◽

Seeking Behavior ◽

Lower Temperature ◽

Food Seeking

We examined the pattern of temperature fluctuations in the nucleus accumbens (NAcc), temporal muscle, and skin, along with locomotion in food-deprived and nondeprived rats following the presentation of an open or closed food container and during subsequent eating or food-seeking behavior without eating. Although rats in food-deprived, quiet resting conditions had more than twofold lower spontaneous locomotion and lower temperature values than in nondeprived conditions, after presentation of a container, they consistently displayed food-seeking behavior, showing much larger and longer temperature changes. When the container was open, rats rapidly retrieved food and consumed it. Food consumption was preceded and accompanied by gradual increases in brain and muscle temperatures (∼1.5°C) and a weaker, delayed increase in skin temperature (∼0.8°C). All temperatures began to rapidly fall immediately after eating was completed, but NAcc and muscle temperatures returned to baseline after ∼35 min. When the container was closed and rats were unable to obtain food, they continued food-seeking activity during the entire period of presentation. Similar to eating, this activity was preceded and accompanied by gradual temperature increases in the brain and muscle, which were somewhat smaller than those during eating (∼1.2°C), with no changes in skin temperature. In contrast to trials with eating, NAcc and muscle temperatures continued to increase for ∼10 min after the container was removed from the cage and the rat continued food-seeking behavior, with a return to baselines after ∼50 min. These temperature fluctuations are discussed with respect to alterations in metabolic brain activity associated with feeding behavior, depending upon deprivation state and food availability.

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Meaning of goodness-of-fit in dipole approximation of human brain activity

Electroencephalography and Clinical Neurophysiology ◽

10.1016/s0013-4694(97)88932-1 ◽

1997 ◽

Vol 103 (1) ◽

pp. 196

Author(s):

J Hara

Keyword(s):

Human Brain ◽

Goodness Of Fit ◽

Brain Activity ◽

Dipole Approximation

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The Attenuation of Very Low Frequency Brain Oscillations in Transitions from a Rest State to Active Attention

Journal of Psychophysiology ◽

10.1027/0269-8803.23.4.191 ◽

2009 ◽

Vol 23 (4) ◽

pp. 191-198 ◽

Cited By ~ 26

Author(s):

Suzannah K. Helps ◽

Samantha J. Broyd ◽

Christopher J. James ◽

Anke Karl ◽

Edmund J. S. Sonuga-Barke

Keyword(s):

Brain Activity ◽

Sampling Rate ◽

Low Frequency ◽

Future Research ◽

Adhd Symptoms ◽

Default Mode ◽

Very Low Frequency ◽

Wide Range ◽

Interference Hypothesis ◽

Mode Interference

Background: The default mode interference hypothesis ( Sonuga-Barke & Castellanos, 2007 ) predicts (1) the attenuation of very low frequency oscillations (VLFO; e.g., .05 Hz) in brain activity within the default mode network during the transition from rest to task, and (2) that failures to attenuate in this way will lead to an increased likelihood of periodic attention lapses that are synchronized to the VLFO pattern. Here, we tested these predictions using DC-EEG recordings within and outside of a previously identified network of electrode locations hypothesized to reflect DMN activity (i.e., S3 network; Helps et al., 2008 ). Method: 24 young adults (mean age 22.3 years; 8 male), sampled to include a wide range of ADHD symptoms, took part in a study of rest to task transitions. Two conditions were compared: 5 min of rest (eyes open) and a 10-min simple 2-choice RT task with a relatively high sampling rate (ISI 1 s). DC-EEG was recorded during both conditions, and the low-frequency spectrum was decomposed and measures of the power within specific bands extracted. Results: Shift from rest to task led to an attenuation of VLFO activity within the S3 network which was inversely associated with ADHD symptoms. RT during task also showed a VLFO signature. During task there was a small but significant degree of synchronization between EEG and RT in the VLFO band. Attenuators showed a lower degree of synchrony than nonattenuators. Discussion: The results provide some initial EEG-based support for the default mode interference hypothesis and suggest that failure to attenuate VLFO in the S3 network is associated with higher synchrony between low-frequency brain activity and RT fluctuations during a simple RT task. Although significant, the effects were small and future research should employ tasks with a higher sampling rate to increase the possibility of extracting robust and stable signals.

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Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare

Applied Sciences ◽

10.3390/app9224749 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4749

Author(s):

Lingyun Jiang ◽

Kai Qiao ◽

Linyuan Wang ◽

Chi Zhang ◽

Jian Chen ◽

...

Keyword(s):

Deep Learning ◽

Human Brain ◽

Brain Activity ◽

Feature Space ◽

Training Data ◽

Reconstruction Method ◽

Learning Method ◽

Training Samples ◽

Visual Reconstruction ◽

Relationship Of

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.

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