Dataset Information

Dynamic interactions between large-scale brain networks predict behavioral adaptation after perceptual errors.

ABSTRACT: Failures to perceive visual stimuli lead to errors in decision making. Different theoretical accounts implicate either medial frontal (MF) cognitive control processes or prestimulus occipital (OC) cortical oscillatory dynamics in errors during perceptual tasks. Here, we show that these 2 previously unconnected theoretical accounts can be reconciled, and the brain regions described by the 2 theories have complimentary and interactive roles in supporting error adaptation. Using a perceptual discrimination task and time-frequency network-based analyses of electroencephalography data, we show that perceptual anticipation and posterror top-down control mechanisms recruit distinct but interacting brain networks. MF sites were a hub for theta-band networks and theta-alpha coupling elicited after errors, whereas occipital sites were a network hub during stimulus anticipation and alpha-gamma coupling. Granger causality analyses revealed that these networks communicate in their preferred direction and frequency band: response-related MF ? OC interactions occurred in the theta band, whereas stimulus anticipation-related OC ? MF interactions occurred in the alpha band. Subjects with stronger network interactions were more likely to improve performance after errors. These findings demonstrate that multiple large-scale brain networks interact dynamically and in a directionally specific manner in different frequency bands to support flexible behavior adaptation during perceptual decision making.

SUBMITTER: Cohen MX

PROVIDER: S-EPMC3615344 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Dynamic interactions between large-scale brain networks predict behavioral adaptation after perceptual errors.

Cohen Michael X MX van Gaal Simon S

Cerebral cortex (New York, N.Y. : 1991) 20120418 5

Failures to perceive visual stimuli lead to errors in decision making. Different theoretical accounts implicate either medial frontal (MF) cognitive control processes or prestimulus occipital (OC) cortical oscillatory dynamics in errors during perceptual tasks. Here, we show that these 2 previously unconnected theoretical accounts can be reconciled, and the brain regions described by the 2 theories have complimentary and interactive roles in supporting error adaptation. Using a perceptual discri ...[more]

PMID: 22514250

Similar Datasets

Project description:More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnetic resonance imaging (fMRI) data to prospectively identify individuals most likely to succeed in a behavioral weight loss intervention. Brain imaging was performed in overweight or obese older adults (age: 65-79 years) who participated in an 18-month lifestyle weight loss intervention. Machine learning and functional brain networks were combined to produce multivariate prediction models. The prediction accuracy exceeded 95%, suggesting that there exists a consistent pattern of connectivity which correctly predicts success with weight loss at the individual level. Connectivity patterns that contributed to the prediction consisted of complex multivariate network components that substantially overlapped with known brain networks that are associated with behavior emergence, self-regulation, body awareness, and the sensory features of food. Future work on independent datasets and diverse populations is needed to corroborate our findings. Additionally, we believe that efforts can begin to examine whether these models have clinical utility in tailoring treatment.

Project description:BackgroundMany complex random networks have been found to be scale-free. Existing literature on scale-free networks has rarely considered potential false positive and false negative links in the observed networks, especially in biological networks inferred from high-throughput experiments. Therefore, it is important to study the impact of these measurement errors on the topology of the observed networks.ResultsThis article addresses the impact of erroneous links on network topological inference and explores possible error mechanisms for scale-free networks with an emphasis on Saccharomyces cerevisiae protein interaction networks. We study this issue by both theoretical derivations and simulations. We show that the ignorance of erroneous links in network analysis may lead to biased estimates of the scale parameter and recommend robust estimators in such scenarios. Possible error mechanisms of yeast protein interaction networks are explored by comparisons between real data and simulated data.ConclusionOur studies show that, in the presence of erroneous links, the connectivity distribution of scale-free networks is still scale-free for the middle range connectivities, but can be greatly distorted for low and high connecitivities. It is more appropriate to use robust estimators such as the least trimmed mean squares estimator to estimate the scale parameter gamma under such circumstances. Moreover, we show by simulation studies that the scale-free property is robust to some error mechanisms but untenable to others. The simulation results also suggest that different error mechanisms may be operating in the yeast protein interaction networks produced from different data sources. In the MIPS gold standard protein interaction data, there appears to be a high rate of false negative links, and the false negative and false positive rates are more or less constant across proteins with different connectivities. However, the error mechanism of yeast two-hybrid data may be very different, where the overall false negative rate is low and the false negative rates tend to be higher for links involving proteins with more interacting partners.

Dataset Information

Dynamic interactions between large-scale brain networks predict behavioral adaptation after perceptual errors.

Publications

Dynamic interactions between large-scale brain networks predict behavioral adaptation after perceptual errors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets