Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex

Mejías, Jorge F.; Murray, John D.; Kennedy, Henry; Wang, Xiao Jing

doi:10.1126/sciadv.1601335

Cited by 177 publications

(140 citation statements)

References 70 publications

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“…However, a recent study that analyzed the fly connectome has demonstrated a number of organizational similarities between the fly and mammalian brains, such as hierarchical structure and other graph theoretical characteristics (e.g., small-world and rich-club organization; Shih et al, 2015). These structural similarities may translate to similar dynamic characteristics of FB and FF, as recently demonstrated by analytical and computational studies (Moon et al, 2015, 2017; Mejias et al, 2016). …”

Section: Discussionsupporting

confidence: 59%

“…Like mammals, wakeful insect brains also face functional challenges in adjusting fast sensory inputs based on prior experience on a slower time scale (Card and Dickinson, 2008; Combes, 2015; Kim et al, 2015; Mischiati et al, 2015). In addition, recent work suggests that the topological organization of the mammalian brain, which shares a number of similarities with the topological organization of the fly brain (Shih et al, 2015), influences the direction of information flow (Moon et al, 2015; Mejias et al, 2016; Moon et al, 2017). This evidence suggests that similar dynamic characteristics of FF and FB processing will be present in the insect brain.…”

Section: Introductionmentioning

confidence: 99%

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Isoflurane Impairs Low-Frequency Feedback but Leaves High-Frequency Feedforward Connectivity Intact in the Fly Brain

Cohen

Swinderen

Tsuchiya

2018

eNeuro

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Hierarchically organized brains communicate through feedforward (FF) and feedback (FB) pathways. In mammals, FF and FB are mediated by higher and lower frequencies during wakefulness. FB is preferentially impaired by general anesthetics in multiple mammalian species. This suggests FB serves critical functions in waking brains. The brain of Drosophila melanogaster (fruit fly) is also hierarchically organized, but the presence of FB in these brains is not established. Here, we studied FB in the fly brain, by simultaneously recording local field potentials (LFPs) from low-order peripheral structures and higher-order central structures. We analyzed the data using Granger causality (GC), the first application of this analysis technique to recordings from the insect brain. Our analysis revealed that low frequencies (0.1–5 Hz) mediated FB from the center to the periphery, while higher frequencies (10–45 Hz) mediated FF in the opposite direction. Further, isoflurane anesthesia preferentially reduced FB. Our results imply that the spectral characteristics of FF and FB may be a signature of hierarchically organized brains that is conserved from insects to mammals. We speculate that general anesthetics may induce unresponsiveness across species by targeting the mechanisms that support FB.

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Section: Discussionsupporting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Isoflurane Impairs Low-Frequency Feedback but Leaves High-Frequency Feedforward Connectivity Intact in the Fly Brain

Cohen

Swinderen

Tsuchiya

2018

eNeuro

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“…These spectral properties of hierarchical interactions were recently simulated in a laminar circuit model of monkey cortex (122), and future modeling can extend such phenomena to human neuroimaging findings. Experimentally constrained extensions of local cortical circuit models, embedded in large-scale network simulations, will expand the range of computational psychiatry questions these models can address.…”

Section: Future Directionsmentioning

confidence: 99%

Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry

Murray

Demirtaş

Antičević

2018

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

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Noninvasive neuroimaging has revolutionized the study of the organization of the human brain and how its structure and function are altered in psychiatric disorders. A critical explanatory gap lies in our mechanistic understanding of how systems-level neuroimaging biomarkers emerge from underlying synaptic-level perturbations associated with a disease state. We describe an emerging computational psychiatry approach leveraging biophysically based computational models of large-scale brain dynamics and their potential integration with clinical and pharmacological neuroimaging. In particular, we focus on neural circuit models, which describe how patterns of functional connectivity observed in resting-state functional magnetic resonance imaging emerge from neural dynamics shaped by inter-areal interactions through underlying structural connectivity defining long-range projections. We highlight the importance of local circuit physiological dynamics, in combination with structural connectivity, in shaping the emergent functional connectivity. Furthermore, heterogeneity of local circuit properties across brain areas, which impacts large-scale dynamics, may be critical for modeling whole-brain phenomena and alterations in psychiatric disorders and pharmacological manipulation. Finally, we discuss important directions for future model development and biophysical extensions, which will expand their utility to link clinical neuroimaging to neurobiological mechanisms.

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“…Areas in the networks were composed of mean-field attractors that have been used to model working memory, perceptual decision-making, as well as to model individual areas in multi-area models of cortex (Wong and Wang 2006;Wong, Huk, et al 2007;Murray, Jaramillo, and Wang 2017;Mejias et al 2016). Each area consisted of two populations, such that c = A, B.…”

Section: Cortical Areasmentioning

confidence: 99%

Hierarchical Network Model Excitatory-Inhibitory Tone Shapes Alternative Strategies for Different Degrees of Uncertainty in Multi-Attribute Decisions

Pettine

Louie

Murray

et al. 2020

Preprint

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We investigated two-attribute, two-alternative decision-making in a hierarchical neural network with three layers: an input layer encoding choice alternative attribute values; an intermediate layer of modules processing separate attributes; and a choice layer producing the decision. Depending on intermediate layer excitatory-inhibitory (E/I) tone, the network displays three distinct regimes characterized by linear (I), convex (II) or concave (III) choice indifference curves. In regimes I and II, each option's attribute information is additively integrated. To maximize reward at low environmental uncertainty, the system should operate in regime I. At high environmental uncertainty, reward maximization is achieved in regime III, with each attribute module selecting a favored alternative, and the ultimate decision based upon comparison between outputs of attribute processing modules. We then use these principles to examine multi-attribute decisions with autism-related deficits in E/I balance, leading to predictions of different choice patterns and overall performance between autism and neurotypicals. IntroductionWe are constantly faced with decisions between alternatives defined by multiple attributes. The true value of each attribute is at times clear, and other times uncertain. For example, on Friday one might choose between main courses at a restaurant where the flavor or healthiness attributes of all the dishes are familiar.The following Wednesday might be at a restaurant with an unknown cuisine, where one is highly uncertain as to different items' flavor or healthiness. To ensure the best meal, the brain must be able to optimize choice in both environments.Systems neuroscientists have, for many years, been studying the specific circuits engaged in this kind of multi-attribute decision-making. Based on a robust set of electrophysiology and imaging findings (Xie and Padoa-Schioppa 2016; Raghuraman and Padoa-Schioppa 2014; Padoa-Schioppa and Assad 2006; O'Neill and Schultz 2018; Morrison and Salzman 2009; Conen and Padoa-Schioppa 2015; Chib et al. 2009; Pastor-Bernier, Stasiak, and Schultz 2019), many hold that all attribute signals are available in brain areas proximal to the final decision (Levy and Glimcher 2012; Padoa-Schioppa and Conen 2017). Indeed, when attribute values are clear, multi-attribute choice theoretically is simple: linearly weight and combine all attributes associated with a choice alternative, then select the one with the larger value. Though the subjective value of an attribute might be non-linearly related to the quantity offered, when the final choice is made in an environment without uncertainty, a weighted linear combination of attributes optimizes the choice between options (Nicholson and Snyder 2007).

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Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex

Abstract: A large-scale laminar network model sheds light on frequency-dependent interactions in the primate cortex.

Cited by 177 publications

References 70 publications

Isoflurane Impairs Low-Frequency Feedback but Leaves High-Frequency Feedforward Connectivity Intact in the Fly Brain

Isoflurane Impairs Low-Frequency Feedback but Leaves High-Frequency Feedforward Connectivity Intact in the Fly Brain

Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry

Hierarchical Network Model Excitatory-Inhibitory Tone Shapes Alternative Strategies for Different Degrees of Uncertainty in Multi-Attribute Decisions

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