A General Approach for the Modelling of Negative Feedback Physiological Control Systems

Ponsiglione, Alfonso Maria; Montefusco, Francesco; Donisi, Leandro; Tedesco, Annarita; Cosentino, Carlo; Merola, Alessio; Romano, Maria; Amato, Francesco

doi:10.3390/bioengineering10070835

Cited by 4 publications

(3 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our goal is to construct a model for the BOLD-rsfMRI time series data of each brain region, using a differential equation framework that accounts for inter-regional interactions. Within this model, the time series for the i-th brain area, denoted as y i ( t ), is defined as follows: The term = ™ a i y i ( t ) with a i > 0 represents linear negative feedback, ensuring the signal damping which is a common homeostatic feature in physiological systems [23]. The nonlinear term counterbalances the grow of y i ( t ) thereby introducing a factor that captures the stabilizing effects observed in neural responses as activities reach higher levels [24].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Parameter Estimation in Brain Dynamics Models from Resting-State fMRI Data using Physics-Informed Neural Networks

Sotero,

Sanchez-Bornot,

Shaharabi-Farahani

2024

Preprint

View full text Add to dashboard Cite

Conventional modeling of the Blood-Oxygen-Level-Dependent (BOLD) signal in resting-state functional Magnetic Resonance Imaging (rsfMRI) struggle with parameter estimation due to the complexity of brain dynamics. This study introduces a novel brain dynamics model (BDM) that directly captures BOLD signal variations through differential equations. Unlike dynamic causal models or neural mass models, we integrate hemodynamic responses into the signal dynamics, considering both direct and network-mediated neuronal activity effects. We utilize Physics-Informed Neural Networks (PINNs) to estimate the parameters of this BDM, leveraging their ability to embed physical laws into the learning process. This approach simplifies computational demands and increases robustness against data noise, providing a comprehensive tool for analyzing rsfMRI data. Leveraging the functional connectivity matrices scaled by the estimated parameters, we apply a state-of-the-art community detection method to elucidate the network structure. Our analysis reveals significant differences in the participation coefficients of specific brain regions when comparing neurotypical individuals to those with Autism Spectrum Disorder (ASD), with distinct patterns observed between male and female cohorts. These differences are consistent with regions implicated in previous studies, reinforcing the role of these areas in ASD. By integrating PINNs with advanced network analysis, we demonstrate a robust approach for dissecting the complex neural signatures of ASD, providing a promising direction for future research in neuroimaging and the broader field of computational neuroscience.

show abstract

Section: Methodsmentioning

confidence: 99%

“…(𝑡) with 𝑎 ! > 0 represents linear negative feedback, ensuring the signal damping which is a common homeostatic feature in physiological systems [23]. The nonlinear term −𝑏 !…”

Section: B Rsfmri-based Brain Dynamics Modelmentioning

confidence: 99%

Parameter Estimation in Brain Dynamics Models from Resting-State fMRI Data using Physics-Informed Neural Networks

Sotero,

Sanchez-Bornot,

Shaharabi-Farahani

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, mathematical methods has been put forward based on either linear [ 32 , 33 ] or nonlinear [ 34 ] generic models to describe and analyze physiological control systems. These more mathematically inclined methods differ from the approach taken here, which is based on mass action kinetics.…”

Section: Methodsmentioning

confidence: 99%

Coherent feedback leads to robust background compensation in oscillatory and non-oscillatory homeostats

Nygård,

Ruoff

2023

PLoS ONE

View full text Add to dashboard Cite

When in a reaction kinetic integral controller a step perturbation is applied besides a constant background, the concentration of a controlled variable (described as A) will generally respond with decreased response amplitudes ΔA as backgrounds increase. The controller variable E will at the same time provide the necessary compensatory flux to move A back to its set-point. A typical example of decreased response amplitudes at increased backgrounds is found in retinal light adaptation. Due to remarks in the literature that retinal light adaptation would also involve a compensation of backgrounds we became interested in conditions how background compensation could occur. In this paper we describe novel findings how background influences can be robustly eliminated. When such a background compensation is active, oscillatory controllers will respond to a defined perturbation with always the same (damped or undamped) frequency profile, or in the non-oscillatory case, with the same response amplitude ΔA, irrespective of the background level. To achieve background compensation we found that two conditions need to apply: (i) an additional set of integral controllers (here described as I1 and I2) have to be employed to keep the manipulated variable E at a defined set-point, and (ii), I1 and I2 need to feed back to the A-E signaling axis directly through the controlled variable A. In analogy to a similar feedback applied in quantum control theory, we term these feedback conditions as ‘coherent feedback’. When analyzing retinal light adaptations in more detail, we find no evidence of the presence of background compensation mechanisms. Although robust background compensation, as described theoretically here, appears to be an interesting regulatory property, relevant biological or biochemical examples still need to be identified.

show abstract

Coherent feedback leads to robust background compensation in oscillatory and non-oscillatory homeostats

Nygård

Ruoff

2023

Preprint

View full text Add to dashboard Cite

When in an integral feedback controller a step perturbation is applied at a constant background, the controlled variable (described here as A) will in general respond with decreased response amplitudes Δ A as backgrounds increase. The controller variable E will at the same time provide the necessary compensatory flux to move A back to its set-point. A typical example of decreased response amplitudes at increased backgrounds is found in retinal light adaptation. Due to remarks in the literature that retinal light adaptation would also involve a compensation of backgrounds we became interested in conditions how background compensation could occur. In this paper we describe how background influences can be robustly eliminated. When such a background compensation is active, oscillatory controllers will respond to a defined perturbation with always the same (damped or undamped) frequency profile, or in the non-oscillatory case, with the same response amplitude Δ A, irrespective of the background level. To achieve background compensation we found that two conditions need to apply: (i) an additional set of integral controllers (here described as I_1 and I_2) have to be employed to keep the manipulated variable E at a defined set-point, and (ii), I_1 and I_2 need to feed back to the A-E signaling axis directly through the controlled variable A. In analogy to a similar feedback applied in quantum control theory, we term these feedback conditions as 'coherent feedback'. When analyzing retinal light adaptations in more detail, we find no evidence in the presence of background compensation mechanisms. Although robust background compensation, as described theoretically here, appears to be an interesting regulatory property, relevant biological or biochemical examples still need to be identified.

show abstract

A General Approach for the Modelling of Negative Feedback Physiological Control Systems

Cited by 4 publications

References 62 publications

Parameter Estimation in Brain Dynamics Models from Resting-State fMRI Data using Physics-Informed Neural Networks

Parameter Estimation in Brain Dynamics Models from Resting-State fMRI Data using Physics-Informed Neural Networks

Coherent feedback leads to robust background compensation in oscillatory and non-oscillatory homeostats

Coherent feedback leads to robust background compensation in oscillatory and non-oscillatory homeostats

Contact Info

Product

Resources

About