The algorithmic agent perspective and computational neuropsychiatry: from etiology to advanced therapy in major depressive disorder DOI Open Access
Giulio Ruffini, Francesca Castaldo, Edmundo Lopez-Sola

et al.

Published: March 14, 2024

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through mechanistic modeling of this disorder. Using the Kolmogorov Theory consciousness (KT), we develop foundational model where algorithmic agents interact with world to maximize an Objective Function evaluating affective \textit{valence}. Depression, defined in context by state persistently low valence, may arise from various factors---including inaccurate models (cognitive biases), dysfunctional (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, map agent brain circuits functional networks, framing etiological routes linking depression biotypes. Finally, explore how stimulation, psychotherapy, plasticity-enhancing compounds such as psychedelics can synergistically repair neural optimize therapies using personalized computational models.

Language: Английский

Next generation neural population models DOI Creative Commons
Stephen Coombes

Frontiers in Applied Mathematics and Statistics, Journal Year: 2023, Volume and Issue: 9

Published: Feb. 28, 2023

Low-dimensional neural mass models are often invoked to model the coarse-grained activity of large populations neurons and synapses have been used help understand coordination scale brain rhythms. However, they phenomenological in nature and, although motivated by neurobiological considerations, absence a direct link an underlying biophysical reality is weakness that means may not be best suited capturing some rich behaviors seen real neuronal tissue. In this perspective article I discuss simple spiking neuron network has recently shown admit exact mean-field description for synaptic interactions. This many features coupled additional dynamical equation describes evolution population synchrony. next generation ideally understanding patterns ubiquitously neuroimaging recordings. Here review equations, way which synchrony, firing rate, average voltage intertwined, together with their application modeling. As well as natural extensions new approach modeling dynamics open mathematical challenges developing statistical neurodynamics can generalize one discussed here.

Language: Английский

Citations

22
A personalizable autonomous neural mass model of epileptic seizures DOI
Edmundo Lopez-Sola, Roser Sanchez-Todo, Èlia Lleal-Custey

et al.

Journal of Neural Engineering, Journal Year: 2022, Volume and Issue: 19(5), P. 055002 - 055002

Published: Aug. 22, 2022

Work in the last two decades has shown that neural mass models (NMM) can realistically reproduce and explain epileptic seizure transitions as recorded by electrophysiological methods (EEG, SEEG). In previous work, advances were achieved increasing excitation heuristically varying network inhibitory coupling parameters models. Based on these early studies, we provide a laminar NMM capable of reproducing electrical activity SEEG epileptogenic zone during interictal to ictal states. With exception external noise input into pyramidal cell population, model dynamics are autonomous. By setting system at point close bifurcation, seizure-like generated, including pre-ictal spikes, low voltage fast activity, rhythmic activity. A novel element is physiologically motivated algorithm for chloride dynamics: gain GABAergic post-synaptic potentials modulated pathological accumulation cells due high and/or dysfunctional transport. addition, order simulate signals comparison with real recordings, embedded first layered neocortex then realistic physical model. We compare modeling results data from four epilepsy patient cases. key pathophysiological mechanisms, proposed framework captures succinctly phenomenology observed states, paving way robust personalization based NMMs.

Language: Английский

Citations

23
Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks DOI Creative Commons
Pau Clusella, Gustavo Deco, Morten L. Kringelbach

et al.

PLoS Computational Biology, Journal Year: 2023, Volume and Issue: 19(4), P. e1010781 - e1010781

Published: April 12, 2023

Spatiotemporal oscillations underlie all cognitive brain functions. Large-scale models, constrained by neuroimaging data, aim to trace the principles underlying such macroscopic neural activity from intricate and multi-scale structure of brain. Despite substantial progress in field, many aspects about mechanisms behind onset spatiotemporal dynamics are still unknown. In this work we establish a simple framework for emergence complex dynamics, including high-dimensional chaos travelling waves. The model consists network 90 regions, whose structural connectivity is obtained tractography data. each area governed Jansen mass normalize total input received node so it amounts same across areas. This assumption allows existence an homogeneous invariant manifold, i.e., set different stationary oscillatory states which nodes behave identically. Stability analysis these solutions unveils transverse instability synchronized state, gives rise types as chaotic alpha activity. Additionally, illustrate ubiquity route towards next generation models. Altogehter, our results unveil bifurcation landscape that underlies function

Language: Английский

Citations

8
The algorithmic agent perspective and computational neuropsychiatry: from etiology to advanced therapy in major depressive disorder DOI Open Access
Giulio Ruffini, Francesca Castaldo, Edmundo Lopez-Sola

et al.

Published: March 14, 2024

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through mechanistic modeling of this disorder. Using the Kolmogorov Theory consciousness (KT), we develop foundational model where algorithmic agents interact with world to maximize an Objective Function evaluating affective \textit{valence}. Depression, defined in context by state persistently low valence, may arise from various factors---including inaccurate models (cognitive biases), dysfunctional (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, map agent brain circuits functional networks, framing etiological routes linking depression biotypes. Finally, explore how stimulation, psychotherapy, plasticity-enhancing compounds such as psychedelics can synergistically repair neural optimize therapies using personalized computational models.

Language: Английский

Citations

2