Anterior-posterior gradient of plasticity in primate prefrontal cortex

Riley, Mitchell R.; Qi, Xue-Lian; Zhou, Xin; Constantinidis, Christos

doi:10.1038/s41467-018-06226-w

Cited by 42 publications

(75 citation statements)

References 60 publications

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“…Some authors additionally propose a caudal to rostral axis corresponding to levels of hierarchical abstraction ( Badre and D'Esposito 2009 ). Given that higher levels in a hierarchy tend to represent states that evolve over longer time periods ( Hasson et al 2008 ), this is highly consistent with recent findings that training in working memory tasks induces plastic changes in more anterior (i.e., abstract) parts of the prefrontal cortex ( Riley et al 2018 ).…”

Section: Anatomysupporting

confidence: 87%

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Prefrontal Computation as Active Inference

et al. 2019

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The prefrontal cortex is vital for a range of cognitive processes, including working memory, attention, and decision-making. Notably, its absence impairs the performance of tasks requiring the maintenance of information through a delay period. In this paper, we formulate a rodent task—which requires maintenance of delay-period activity—as a Markov decision process and treat optimal task performance as an (active) inference problem. We simulate the behavior of a Bayes optimal mouse presented with 1 of 2 cues that instructs the selection of concurrent visual and auditory targets on a trial-by-trial basis. Formulating inference as message passing, we reproduce features of neuronal coupling within and between prefrontal regions engaged by this task. We focus on the micro-circuitry that underwrites delay-period activity and relate it to functional specialization within the prefrontal cortex in primates. Finally, we simulate the electrophysiological correlates of inference and demonstrate the consequences of lesions to each part of our in silico prefrontal cortex. In brief, this formulation suggests that recurrent excitatory connections—which support persistent neuronal activity—encode beliefs about transition probabilities over time. We argue that attentional modulation can be understood as the contextualization of sensory input by these persistent beliefs.

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Section: Anatomysupporting

confidence: 87%

“…This is an important direction to pursue for 2 reasons. First, this will be crucial in understanding the processes that underwrite plastic changes in prefrontal cortex during working memory training ( Riley et al 2018 ). Second, it will be important in accounting for certain types of error or bias in these kinds of task.…”

Section: Discussionmentioning

confidence: 99%

Prefrontal Computation as Active Inference

et al. 2019

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“…These observations suggest that domain-specific systems, like the perisylvian fronto-temporal language network, are not always amenable for "recycling" by cultural inventions. The fronto-parietal system might be inherently more flexible throughout the lifespan and thus more capable of taking on new cultural skills (Riley, Qi, Zhou, & Constantinidis, 2018).…”

Section: Code Comprehension and Languagementioning

confidence: 99%

Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network

Liu

Kim

Wilson

et al. 2020

Preprint

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Despite the importance of programming to modern society, the cognitive and neural bases of code comprehension are largely unknown. Programming languages might 'recycle' neurocognitive mechanisms originally used for natural languages. Alternatively, comprehension of code could depend on fronto-parietal networks shared with other culturally derived symbol systems, such as formal logic and math. Expert programmers (average 11 years of programming experience) performed code comprehension and memory control tasks while undergoing fMRI. The same participants also performed language, math, formal logic, and executive control localizer tasks. A left-lateralized fronto-parietal network was recruited for code comprehension. Patterns of activity within this network distinguish between "for" loops and "if" conditional code functions. Code comprehension overlapped extensively with neural basis of formal logic and to a lesser degree math. Overlap with simpler executive processes and language was low, but laterality of language and code covaried across individuals. Cultural symbol systems, including code, depend on a distinctive fronto-parietal cortical network.

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“…The present results are timely, as there has recently been increased interest in the global organization of the cortex due to the availability of global imaging techniques for connectivity and myelination [60], function [61], plasticity [62] and gene expression [63]. A global cortical gradient of these various physiological and functional features has been proposed, spanning visual, auditory and somatosensory/motor functions, along with a unimodal/transmodal distinction which appears as gradient with twice the spatial frequency of the global gradient [64].…”

Section: Discussionmentioning

confidence: 99%

Large-scale cortical travelling waves predict localized future cortical signals

et al. 2019

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Predicting future brain signal is highly sought-after, yet difficult to achieve. To predict the future phase of cortical activity at localized ECoG and MEG recording sites, we exploit its predominant, large-scale, spatiotemporal dynamics. The dynamics are extracted from the brain signal through Fourier analysis and principal components analysis (PCA) only, and cast in a data model that predicts future signal at each site and frequency of interest. The dominant eigenvectors of the PCA that map the large-scale patterns of past cortical phase to future ones take the form of smoothly propagating waves over the entire measurement array. In ECoG data from 3 subjects and MEG data from 20 subjects collected during a self-initiated motor task, mean phase prediction errors were as low as 0.5 radians at local sites, surpassing state-of-the-art methods of within-time-series or event-related models. Prediction accuracy was highest in delta to beta bands, depending on the subject, was more accurate during episodes of high global power, but was not strongly dependent on the time-course of the task. Prediction results did not require past data from the to-be-predicted site. Rather, best accuracy depended on the availability in the model of long wavelength information. The utility of large-scale, low spatial frequency traveling waves in predicting future phase activity at local sites allows estimation of the error introduced by failing to account for irreducible trajectories in the activity dynamics.

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Anterior-posterior gradient of plasticity in primate prefrontal cortex

Cited by 42 publications

References 60 publications

Prefrontal Computation as Active Inference

Prefrontal Computation as Active Inference

Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network

Large-scale cortical travelling waves predict localized future cortical signals

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