Cortical Network Models of Firing Rates in the Resting and Active States Predict BOLD Responses

Bennett, Maxwell R.; Farnell, L.; Gibson, W.G.; Lagopoulos, Jim

doi:10.1371/journal.pone.0144796

Cited by 5 publications

(5 citation statements)

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“…This extension allows the Balloon model to be used to simulate a change in the BOLD signal due to a change in some type of simulated neuronal activity (hereafter, more generally referred to as input signal). In this form, the Balloon model has been used in several studies to compute simulated BOLD signals from network models (Friston et al, 2003 ; Smith et al, 2011 ; Deco and Jirsa, 2012 ; Van Hartevelt et al, 2014 ; Bennett et al, 2015 ; Maith et al, 2021 ). The individual components of the extended Balloon model and their dynamics following a rectangular input signal change are shown in Figure 1A .…”

Section: The Balloon Modelmentioning

confidence: 99%

“…The underlying neural processes influencing CBF and CMRO2, and thus ultimately the BOLD signal, are still rather unclear (Howarth et al, 2021 ). Many different hypotheses and modeling approaches can be found in the literature (Smith et al, 2011 ; Van Hartevelt et al, 2014 ; Bennett et al, 2015 ; Heikkinen et al, 2015 ; Schmidt et al, 2018 ). The flexible BOLD monitor in ANNarchy allows us to easily create and compare BOLD models implementing different hypotheses on spiking or rate-coded network models.…”

Section: Example Use Casesmentioning

confidence: 99%

“…For this, we use the conductance variable of the excitatory synapses of the neurons as source variable for the BOLD monitor (BOLD monitor B). The third hypothesis is that the CBF or the BOLD signal is driven by the neuronal output of the neurons, for example, the mean firing rate (as in Smith et al, 2011 ; Bennett et al, 2015 ). Thus, for this BOLD monitor (BOLD monitor C), we use the mean firing rate of the neurons as source variable, as in Section 3.3.…”

Section: Example Use Casesmentioning

confidence: 99%

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BOLD Monitoring in the Neural Simulator ANNarchy

Maith

Dinkelbach

Baladron

et al. 2022

Front. Neuroinform.

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Multi-scale network models that simultaneously simulate different measurable signals at different spatial and temporal scales, such as membrane potentials of single neurons, population firing rates, local field potentials, and blood-oxygen-level-dependent (BOLD) signals, are becoming increasingly popular in computational neuroscience. The transformation of the underlying simulated neuronal activity of these models to simulated non-invasive measurements, such as BOLD signals, is particularly relevant. The present work describes the implementation of a BOLD monitor within the neural simulator ANNarchy to allow an on-line computation of simulated BOLD signals from neural network models. An active research topic regarding the simulation of BOLD signals is the coupling of neural processes to cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO2). The flexibility of ANNarchy allows users to define this coupling with a high degree of freedom and thus, not only allows to relate mesoscopic network models of populations of spiking neurons to experimental BOLD data, but also to investigate different hypotheses regarding the coupling between neural processes, CBF and CMRO2 with these models. In this study, we demonstrate how simulated BOLD signals can be obtained from a network model consisting of multiple spiking neuron populations. We first demonstrate the use of the Balloon model, the predominant model for simulating BOLD signals, as well as the possibility of using novel user-defined models, such as a variant of the Balloon model with separately driven CBF and CMRO2 signals. We emphasize how different hypotheses about the coupling between neural processes, CBF and CMRO2 can be implemented and how these different couplings affect the simulated BOLD signals. With the BOLD monitor presented here, ANNarchy provides a tool for modelers who want to relate their network models to experimental MRI data and for scientists who want to extend their studies of the coupling between neural processes and the BOLD signal by using modeling approaches. This facilitates the investigation and model-based analysis of experimental BOLD data and thus improves multi-scale understanding of neural processes in humans.

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Section: The Balloon Modelmentioning

confidence: 99%

Section: Example Use Casesmentioning

confidence: 99%

Section: Example Use Casesmentioning

confidence: 99%

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BOLD Monitoring in the Neural Simulator ANNarchy

Maith

Dinkelbach

Baladron

et al. 2022

Front. Neuroinform.

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“…Being an identical source, neuroenergetics may also govern the network topology of all synaptic connections. The core of those networks, such as the DMN, tends to have high levels of CMRglc (Bennett et al, 2015), displaying a significantly higher level of aerobic glycolysis (Riedl et al, 2016). As a result, a significantly heterogeneous pattern of correlations with PET is found across both functional networks and anatomical regions with the strongest correlation for DMN (Vaishnavi et al, 2010).…”

Section: Neuroenergetics and Fcmentioning

confidence: 99%

How Energy Supports Our Brain to Yield Consciousness: Insights From Neuroimaging Based on the Neuroenergetics Hypothesis

Chen

Zhang

2021

Front. Syst. Neurosci.

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Consciousness is considered a result of specific neuronal processes and mechanisms in the brain. Various suggested neuronal mechanisms, including the information integration theory (IIT), global neuronal workspace theory (GNWS), and neuronal construction of time and space as in the context of the temporospatial theory of consciousness (TTC), have been laid forth. However, despite their focus on different neuronal mechanisms, these theories neglect the energetic-metabolic basis of the neuronal mechanisms that are supposed to yield consciousness. Based on the findings of physiology-induced (sleep), pharmacology-induced (general anesthesia), and pathology-induced [vegetative state/unresponsive wakeful syndrome (VS/UWS)] loss of consciousness in both human subjects and animals, we, in this study, suggest that the energetic-metabolic processes focusing on ATP, glucose, and γ-aminobutyrate/glutamate are indispensable for functional connectivity (FC) of normal brain networks that renders consciousness possible. Therefore, we describe the energetic-metabolic predispositions of consciousness (EPC) that complement the current theories focused on the neural correlates of consciousness (NCC).

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“…This then enables the calculation of the BOLD signal after input of a (scaled) neuronal activity. These equations have been used in a number of investigations of how the BOLD signal arises in neuronal networks (Bennett et al 2015(Bennett et al , 2016Deco et al 2014;Deco and Jirsa 2012;Robinson et al 2006). There is now considerable experimental evidence for negative BOLD signals (Devor et al 2007;Frahm et al 2008;Moraschi et al 2012;Mullinger et al 2014Mullinger et al , 2017Pasley et al 2007;Sadaghiani et al 2009;Shmuel et al 2006), but it is still an open question as to the role played by below-baseline neuronal activity.…”

Section: Introductionmentioning

confidence: 99%

Quantitative relations between transient BOLD responses, cortical energetics, and impulse firing in different cortical regions

Bennett

Farnell

Gibson

2019

Journal of Neurophysiology

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The blood oxygen level-dependent (BOLD) functional magnetic resonance imaging signal arises as a consequence of changes in blood flow (cerebral blood flow) and oxygen usage (cerebral metabolic rate of oxygen) that in turn are modulated by changes in neuronal activity. Much attention has been given to both theoretical and experimental aspects of the energetics but not to the neuronal activity. Here we use our previous theory relating the steady-state BOLD signal to neuronal activity and amalgamate it with the standard dynamic causal model (DCM, Friston) theory to produce a quantitative model relating the time-dependent BOLD signal to the underlying neuronal activity. Unlike existing treatments, this new theory incorporates a nonzero baseline activity in a completely consistent way and is thus able to account for both positive and negative BOLD signals. It can reproduce a wide variety of experimental BOLD signals reported in the literature solely by adjusting the neuronal input activity. In this way it provides support for the claim that the main features of the signals, including poststimulus undershoot and overshoot, are principally a result of changes in neuronal activity. NEW & NOTEWORTHY A previous model relating the steady-state blood oxygen level-dependent (BOLD) signal to neuronal activity, both above and below baseline, is extended to account for transient BOLD signals. This allows for a detailed investigation of the role neuronal activity can play in such signals and also encompasses poststimulus undershoot and overshoot. A wide variety of experimental BOLD signals are reproduced solely by adjusting the neuronal input activity, including recent results regarding the BOLD signal in patients with schizophrenia.

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Cortical Network Models of Firing Rates in the Resting and Active States Predict BOLD Responses

Cited by 5 publications

References 50 publications

BOLD Monitoring in the Neural Simulator ANNarchy

BOLD Monitoring in the Neural Simulator ANNarchy

How Energy Supports Our Brain to Yield Consciousness: Insights From Neuroimaging Based on the Neuroenergetics Hypothesis

Quantitative relations between transient BOLD responses, cortical energetics, and impulse firing in different cortical regions

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