Fluctuations in Oscillation Frequency Control Spike Timing and Coordinate Neural Networks

Neuroscience research spans multiple spatiotemporal scales, from subsecond dynamics of individual neurons to the slow coordination of billions of neurons during resting state and sleep. Here it is shown that a single functional principle—temporal fluctuations in oscillation peak frequency ("frequency sliding")—can be used as a common analysis approach to bridge multiple scales within neuroscience. Frequency sliding is demonstrated in simulated neural networks and in human EEG data during a visual task. Simulations of biophysically detailed neuron models show that frequency sliding modulates spike threshold and timing variability, as well as coincidence detection. Finally, human resting-state EEG data demonstrate that frequency sliding occurs endogenously and can be used to identify large-scale networks. Frequency sliding appears to be a general principle that regulates brain function on multiple spatial and temporal scales, from modulating spike timing in individual neurons to coordinating large-scale brain networks during cognition and resting state

Human Frontal–Subcortical Circuit and Asymmetric Belief Updating

How humans integrate information to form beliefs about reality is a question that has engaged scientists for centuries, yet the biological system supporting this process is not well understood. One of the most salient attributes of information is valence. Whether a piece of news is good or bad is critical in determining whether it will alter our beliefs. Here, we reveal a frontal–subcortical circuit in the left hemisphere that is simultaneously associated with enhanced integration of favorable information into beliefs and impaired integration of unfavorable information. Specifically, for favorable information, stronger white matter connectivity within this system, particularly between the left inferior frontal gyrus (IFG) and left subcortical regions (including the amygdala, hippocampus, thalamus, putamen, and pallidum), as well as insular cortex, is associated with greater change in belief. However, for unfavorable information, stronger connectivity within this system, particularly between the left IFG and left pallidum, putamen, and insular cortex, is associated with reduced change in beliefs. These novel results are consistent with models suggesting that partially separable processes govern learning from favorable and unfavorable information

Error blindness and motivational significance: Shifts in networks centering on anterior insula co-vary with error awareness and pupil dilation

This investigation aims to further our understanding of the brain mechanisms underlying the awareness of one's erroneous actions. While all errors are registered as such in the rostral cingulate zone, errors enter awareness only when the anterior insula cortex is activated. Aware but not unaware errors elicit autonomic nervous system reactivity. Our aim is to investigate the hypothesis that activation in the insula during error awareness is related to autonomic arousal and to inter-regional interactions with other areas of the brain. To examine the role of the anterior insula in error awareness, we assessed its functional connectivity to other brain regions along with autonomic nervous system reactivity in young healthy participants who underwent simultaneous pupil-diameter and functional magnetic resonance imaging measurements while performing a complex and error-prone task. Error blindness was associated with failures to engage sufficient autonomic reactivity. During aware errors increased pupil-diameter along with increased task-related activation within, and increased connectivity between anterior insula and task-related networks suggested an increased capacity for action-control information transfer. Increased pupil-diameter during aware errors was furthermore associated with decreased activation of the default-mode network along with decreased insular connectivity with regions of the default mode system, possibly reflecting decreased task-irrelevant information processing. This shifting mechanism may be relevant to a better understanding of how the brain and the autonomic nervous system interact to enable efficient adaptive behavior during cognitive challenge

Frontostriatal anatomical connections predict age- and difficulty-related differences in reinforcement learning

Contains fulltext : 167812.pdf (Publisher’s version ) (Closed access)Reinforcement learning (RL) is supported by a network of striatal and frontal cortical structures that are connected through white-matter fiber bundles. With age, the integrity of these white-matter connections declines. The role of structural frontostriatal connectivity in individual and age-related differences in RL is unclear, although local white-matter density and diffusivity have been linked to individual differences in RL. Here we show that frontostriatal tract counts in young human adults (aged 18-28), as assessed noninvasively with diffusion-weighted magnetic resonance imaging and probabilistic tractography, positively predicted individual differences in RL when learning was difficult (70% valid feedback). In older adults (aged 63-87), in contrast, learning under both easy (90% valid feedback) and difficult conditions was predicted by tract counts in the same frontostriatal network. Furthermore, network-level analyses showed a double dissociation between the task-relevant networks in young and older adults, suggesting that older adults relied on different frontostriatal networks than young adults to obtain the same task performance. These results highlight the importance of successful information integration across striatal and frontal regions during RL, especially with variable outcomes.12 p

Model-based analyses: Promises, pitfalls, and example applications to the study of cognitive control

We discuss a recent approach to investigating cognitive control, which has the potential to deal with some of the challenges inherent in this endeavour. In a model-based approach, the researcher defines a formal, computational model that performs the task at hand and whose performance matches that of a research participant. The internal variables in such a model might then be taken as proxies for latent variables computed in the brain. We discuss the potential advantages of such an approach for the study of the neural underpinnings of cognitive control and its pitfalls, and we make explicit the assumptions underlying the interpretation of data obtained using this approach