Linking Brain Structure, Activity, and Cognitive Function through Computation

Amunts, Katrin; DeFelipe, Javier; Pennartz, Cyriel M. A.; Destexhe, Alain; Migliore, Michele; Ryvlin, Philippe; Furber, Steve; Knoll, Alois; Bitsch, Lise; Bjaalie, Jan G.; Ioannidis, Yannis; Lippert, Thomas; Sanchez-Vives, Maria V.; Goebel, Rainer; Jirsa, Viktor K.

doi:10.1523/eneuro.0316-21.2022

Cited by 30 publications

(20 citation statements)

References 157 publications

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“…In conclusion, the cerebellar MF represents the first step toward a new generation of models capable of bearing biological properties into virtual brains that will allow to simulate the healthy and pathological brain towards the overarching aim of a personalized brain representation and ultimately personalized medicine and the technology of brain digital twins (Amunts et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

A multi-layer mean-field model for the cerebellar cortex: design, validation, and prediction

Lorenzi

Geminiani

Zerlaut

et al. 2022

Preprint

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Mean-field (MF) models can be used to summarize in a few statistical parameters the salient properties of an inter-wired neuronal network incorporating different types of neurons and synapses along with their topological organization. MF are crucial to efficiently implement the modules of large-scale brain models maintaining the specificity of local microcircuits. While MFs have been generated for the isocortex, they are still missing for other parts of the brain. Here we have designed and simulated a multi-layer MF of the cerebellar network (including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells) and validated it against experimental data and the corresponding spiking neural network (SNN) microcircuit model. The cerebellar MF was built using a system of equations, where properties of neuronal populations and topological parameters are embedded in inter-dependent transfer functions. The model time constant was optimised using local field potentials recorded experimentally from acute mouse cerebellar slices as a template. The MF satisfactorily reproduced the average dynamics of the different neuronal populations in response to various input patterns and predicted the modulation of Purkinje Cells firing depending on cortical plasticity, which drives learning in associative tasks, and the level of feedforward inhibition. The cerebellar MF provides a computationally efficient tool that will allow to investigate the causal relationship between microscopic neuronal properties and ensemble brain activity in virtual brain models addressing both physiological and pathological conditions.

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Section: Discussionmentioning

confidence: 99%

A multi-layer mean-field model for the cerebellar cortex: design, validation, and prediction

Lorenzi

Geminiani

Zerlaut

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The fact that the inattentive and hyperactive clusters were not found to differ on any behavioural measures could be attributable to the fact that functional connectivity is typically better-associated with behaviour than structural connectivity (Jia et al, 2014; Fjell et al, 2017). In addition, structural brain connectivity has been linked robustly to cognitive ability (Ponsoda et al, 2017; Babaeeghazvini et al, 2021; Amunts et al, 2022). This coupling of structure-cognition and function-behaviour relationships could explain why cognitive differences, rather than behavioural differences, were found to exist between two different profiles of structural brain connectivity.…”

Section: Discussionmentioning

confidence: 99%

Exploring Neural Heterogeneity in Inattention and Hyperactivity

Zdorovtsova

Jones

Akarca

et al. 2022

Preprint

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Inattention and hyperactivity are cardinal symptoms of Attention Deficit Hyperactivity Disorder (ADHD). These characteristics have also been observed across a range of other neurodevelopmental conditions, such as autism and dyspraxia, suggesting that they might best be studied across diagnostic categories. Here, we evaluated the associations between inattention and hyperactivity behaviours and features of the structural brain network (connectome) in a large transdiagnostic sample of children (Centre for Attention, Learning, and Memory; n = 383). In our sample, we found that a single latent factor explains 77.6% of variance in scores across multiple questionnaires measuring inattention and hyperactivity. Partial Least-Squares (PLS) regression revealed that variability in this latent factor could not be explained by a linear component representing nodewise properties of connectomes. We then investigated the type and extent of neural heterogeneity in a subset of our sample with clinically-elevated levels of inattention and hyperactivity. Multidimensional scaling combined with k-means clustering revealed two neural subtypes in children with elevated levels of inattention and hyperactivity (n = 232), differentiated primarily by nodal communicability - a measure which demarcates the extent to which neural signals propagate through specific brain regions. These different clusters had indistinguishable behavioural profiles, which included high levels of inattention and hyperactivity. However, one of the clusters scored higher on multiple cognitive assessment measures of executive function. We conclude that inattention and hyperactivity are so common in children with neurodevelopmental difficulties because they emerge from multiple different trajectories of brain development. In our own data, we can identify two of these possible trajectories, which are reflected by measures of structural brain network topology and cognition.

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“…In neuroscience for example, we can pose this question in the context of the emergence of higher level brain function from the details expressed or simulated at the level of individual neurons. The Human Brain Project (79), attempts to do just this, and others have questioned the limits of this approach (e.g., 80, in the context of vision), or looking at "top-down" effects where the large-scale structure regulates the behavior at the neuronal level (81).…”

Section: What Might a Future Modelling Landscape Look Like?mentioning

confidence: 99%

Are General Circulation Models obsolete?

Balaji¹,

Couvreux²,

Deshayes³

et al. 2022

Preprint

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Traditional general circulation models, or GCMs -i.e. 3D dynamical models with unresolved terms represented in equations with tunable parameters -have been a mainstay of climate research for several decades, and some of the pioneering studies have recently been recognized by a Nobel prize in Physics. Yet, there is considerable debate around their continuing role in the future. Frequently mentioned as limitations of GCMs are the structural error and uncertainty across models with different representations of unresolved scales; and the fact that the models are tuned to reproduce certain aspects of the observed Earth. We consider these shortcomings in the context of a future generation of models that may address these issues through substantially higher resolution and detail, or through the use of machine learning techniques to match them better to observations, theory, and process models. It is our contention that calibration, far from being a weakness of models, is an essential element in the simulation of complex systems, and contributes to our understanding of their inner workings. Models can be calibrated to reveal both fine-scale detail, or the global response to external perturbations. New methods enable us to articulate and improve the connections between the different levels of abstract representation of climate processes, and our understanding resides in an entire hierarchy of models where GCMs will continue to play a central role for the foreseeable future.. climate models | calibration | machine learning | ...

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Linking Brain Structure, Activity, and Cognitive Function through Computation

Abstract: Visual Abstract

Cited by 30 publications

References 157 publications

A multi-layer mean-field model for the cerebellar cortex: design, validation, and prediction

A multi-layer mean-field model for the cerebellar cortex: design, validation, and prediction

Exploring Neural Heterogeneity in Inattention and Hyperactivity

Are General Circulation Models obsolete?

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