Granger causality analysis of rat cortical functional connectivity in pain

Guo, Xinling; Zhang, Qiaosheng; Singh, Amrita; Wang, Jing; Chen, Zhe

doi:10.1088/1741-2552/ab6cba

Cited by 16 publications

(7 citation statements)

References 60 publications

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“…Specifically, in a human MEG study, Granger-causal connectivity in the beta-band was found to be strongest for backward top-down connections, whereas the gamma-band was found to be strongest for feed-forward bottom-up connections (Pelt et al, 2016). In our recent Granger causality analysis of rodent S1-ACC LFP data (Guo et al, 2020), we have observed a S1→ACC Granger-causality peak at a higher frequency (~75 Hz), and an ACC→S1 Granger-causality peak at a lower frequency (~55 Hz), supporting this predictive coding theory. This causality analysis result may also be ascribed to the spectral asymmetry in predictive coding (Bastos et al, 2015).…”

Section: Discussionsupporting

confidence: 68%

Predictive coding models for pain perception

Song

Yao

Kemprecos

et al. 2019

Preprint

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Pain is a complex, multidimensional experience that involves dynamic interactions between sensory-discriminative and affective-emotional processes. Pain experiences have a high degree of variability depending on their context and prior anticipation. Viewing pain perception as a perceptual inference problem, we use a predictive coding paradigm to characterize both evoked and spontaneous pain. We record the local field potentials (LFPs) from the primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) of freely behaving rats-two regions known to encode the sensory-discriminative and affective-emotional aspects of pain, respectively. We further propose a framework of predictive coding to investigate the temporal coordination of oscillatory activity between the S1 and ACC. Specifically, we develop a high-level, empirical and phenomenological model to describe the macroscopic dynamics of bottom-up and top-down activity. Supported by recent experimental data, we also develop a mechanistic mean-field model to describe the mesoscopic population neuronal dynamics in the S1 and ACC populations, in both naive and chronic pain-treated animals. Our proposed predictive coding models not only replicate important experimental findings, but also provide new mechanistic insight into the uncertainty of expectation, placebo or nocebo effect, and chronic pain. Author SummaryPain perception in the mammalian brain is encoded through multiple brain circuits.The experience of pain is often associated with brain rhythms or neuronal oscillations at different frequencies. Understanding the temporal coordination of neural oscillatory activity from different brain regions is important for dissecting pain circuit mechanisms and revealing differences between distinct pain conditions. Predictive coding is a general computational framework to understand perceptual inference by integrating bottom-up sensory information and top-down expectation. Supported by experimental data, we propose a predictive coding framework for pain perception, and develop empirical and biologically-constrained computational models to characterize oscillatory dynamics of neuronal populations from two cortical circuits-one for the sensory-discriminative PLOS 2/49 experience and the other for affective-emotional experience, and further characterize their temporal coordination under various pain conditions. Our computational study of biologically-constrained neuronal population model reveals important mechanistic insight on pain perception, placebo analgesia, and chronic pain. Introduction 1Pain is a basic experience that is subjective and multidimensional. Pain processing 2 involves sensory, affective, and cognitive processing across distributed neural 3 circuits [1-5]. However, a complete understanding of pain perception and cortical pain 4 processing has remained elusive. Given the same nociceptive stimuli, the context 5 matters for pain percept. Human neuroimaging studies have shown that among many 6 brain regions, the primary somatosensory cortex (S1) a...

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Section: Discussionsupporting

confidence: 68%

Predictive coding models for pain perception

Song

Yao

Kemprecos

et al. 2019

Preprint

Self Cite

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“…In practice, A ji ( k ) are estimated by minimizing the squared prediction error or by maximizing the likelihood or sparsity-inducing penalized likelihood (Pollonini et al, 2010 ; Basu et al, 2015 ). Granger Causality has been applied toward inference of CFC in the linear Gaussian VAR model setting (Pollonini et al, 2010 ; Schmidt et al, 2016 ; Guo et al, 2020 ). Extensions of GC to categorical random variables, non-linear auto-regressive models and non-parametric models exist (Dhamala et al, 2008 ; Marinazzo et al, 2008 ; Tank et al, 2017 , 2018 ).…”

Section: From Association To Causationmentioning

confidence: 99%

“…Furthermore, in practice, GC uses a model assumption between the variables, e.g., a linear Gaussian VAR model, and results could differ when this assumption does not hold (Lütkepohl, 2005 ). Notwithstanding the limitations, GC has been a well-known method in the neural time series scenarios, and applications (Qiao et al, 2017 ; Guo et al, 2020 ). GC based on linear VAR model is equivalent to Transfer Entropy for Gaussian variables, while the latter is a non-linear method in its general formulation (Barnett et al, 2009 ).…”

Section: From Association To Causationmentioning

confidence: 99%

Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study

Biswas

Shlizerman

2022

Front. Syst. Neurosci.

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Representation of brain network interactions is fundamental to the translation of neural structure to brain function. As such, methodologies for mapping neural interactions into structural models, i.e., inference of functional connectome from neural recordings, are key for the study of brain networks. While multiple approaches have been proposed for functional connectomics based on statistical associations between neural activity, association does not necessarily incorporate causation. Additional approaches have been proposed to incorporate aspects of causality to turn functional connectomes into causal functional connectomes, however, these methodologies typically focus on specific aspects of causality. This warrants a systematic statistical framework for causal functional connectomics that defines the foundations of common aspects of causality. Such a framework can assist in contrasting existing approaches and to guide development of further causal methodologies. In this work, we develop such a statistical guide. In particular, we consolidate the notions of associations and representations of neural interaction, i.e., types of neural connectomics, and then describe causal modeling in the statistics literature. We particularly focus on the introduction of directed Markov graphical models as a framework through which we define the Directed Markov Property—an essential criterion for examining the causality of proposed functional connectomes. We demonstrate how based on these notions, a comparative study of several existing approaches for finding causal functional connectivity from neural activity can be conducted. We proceed by providing an outlook ahead regarding the additional properties that future approaches could include to thoroughly address causality.

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“…, K. In practice, A ji (k) are estimated by minimizing the squared prediction error or by maximizing the likelihood or sparsity-inducing penalized likelihood [66,69]. Granger Causality has been applied towards inference of CFC in the linear Gaussian VAR model setting [61,69,70]. Extensions of GC to categorical random variables, non-linear auto-regressive models and non-parametric models exist [71][72][73][74].…”

Section: Granger Causalitymentioning

confidence: 99%

“…a linear Gaussian VAR model, and results could differ when this assumption does not hold [68]. Notwithstanding the limitations, GC has been a well-known method in the neural time series scenarios, and applications [70,90]. GC is equivalent to Transfer Entropy for Gaussian variables, while the latter is a non-linear method in its general formulation [91].…”

Section: Granger Causalitymentioning

confidence: 99%

Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study

Biswas,

Shlizerman

2021

Preprint

View full text Add to dashboard Cite

Representation of brain network interactions is fundamental to the translation of neural structure to brain function. As such, methodologies for mapping neural interactions into structural models, i.e., inference of functional connectome from neural recordings, are key for the study of brain networks. While multiple approaches have been proposed for functional connectomics based on statistical associations between neural activity, association does not necessarily incorporate causation. Additional approaches have been proposed to incorporate aspects of causality to turn functional connectomes into causal functional connectomes, however, these methodologies typically focus on specific aspects of causality. This warrants a systematic statistical framework for causal functional connectomics that defines the foundations of common aspects of causality. Such a framework can assist in contrasting existing approaches and to guide development of further causal methodologies. In this work, we develop such a statistical guide. In particular, we consolidate the notions of associations and representations of neural interaction, i.e., types of neural connectomics, and then describe causal modeling in the statistics literature. We particularly focus on the introduction of directed Markov graphical models as a framework through which we define the Directed Markov Property -an essential criterion for examining the causality of proposed functional connectomes. We demonstrate how based on these notions, a comparative study of several existing approaches for finding causal functional connectivity from neural activity can be conducted. We proceed by providing an outlook ahead regarding the additional properties that future approaches could include to thoroughly address causality.Preprint. Under review.

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Granger causality analysis of rat cortical functional connectivity in pain

Cited by 16 publications

References 60 publications

Predictive coding models for pain perception

Predictive coding models for pain perception

Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study

Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study

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