Deconstructing arousal into wakeful, autonomic and affective varieties
January 31, 2018

In this article, we review neuroscience findings for three of the most common origins of arousal: wakeful arousal, autonomic arousal, and affective arousal. Our review makes two overarching points. First, research conducted primarily in non-human animals underscores the importance of several subcortical nuclei that contribute to various sources of arousal, motivating the need for an integrative framework. We outline an integrative neural reference space as a key first step in developing a more systematic understanding of central nervous system contributions to arousal. Second, there is a translational gap between research on non-human animals, which emphasizes subcortical nuclei, and research on humans using non-invasive neuroimaging techniques, which focuses more on gross anatomical characterizations of cortical (e.g. network architectures including the default mode network) and subcortical structures. We forecast the importance of high-field neuroimaging in bridging this gap to examine how the various networks within the neural reference space for arousal operate across varieties of arousal-related phenomena.

Satpute, A. B., Kragel, P. A., Barrett, L. F., Wager, T. D., & Bianciardi, M. (2018). Deconstructing arousal into wakeful, autonomic and affective varieties. Neuroscience Letters

Historical Pitfalls in Emotion Research
November 1, 2017

In this article, we offer a brief history summarizing the last century of neuroscientific study of emotion, highlighting dominant themes that run through various schools of thought. We then summarize the current state of the field, followed by six key points for scientific progress that are inspired by a multi-level constructivist theory of emotion

Barrett LF, Satpute AB (2017) Historical pitfalls and new directions in the neuroscience of emotion.

Contextual Connectivity
July 26, 2017

Investigations of the human brain’s connectomic architecture have produced two alternative models: one describes the brain’s spatial structure in terms of static localized networks, and the other describes the brain’s temporal structure in terms of dynamic whole-brain states. Here, we used tools from connectivity dynamics to develop a synthesis that bridges these models. Using resting fMRI data, we investigated the assumptions undergirding current models of the human connectome. Consistent with state-based models, our results suggest that static localized networks are superordinate approximations of underlying dynamic states. Furthermore, each of these localized, dynamic connectivity states is associated with global changes in the whole-brain functional connectome. By nesting localized dynamic connectivity states within their whole-brain contexts, we demonstrate the relative temporal independence of brain networks. Our assay for functional autonomy of coordinated neural systems is broadly applicable, and our findings provide evidence of structure in temporal state dynamics that complements the well-described static spatial organization of the brain.

Screen Shot 2017-11-01 at 9.29.53 AM

Ciric R, Nomi JS, Uddin LQ, Satpute AB (2017) Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks. Scientific Reports 7:6537.