Whole-Brain Modelling: Past, Present, and Future

Griffiths, John; Bastiaens, Sorenza P; Kaboodvand, Neda

doi:10.1007/978-3-030-89439-9_13

Cited by 7 publications

(5 citation statements)

References 174 publications

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“…The large-scale spiking neuron model of (Izhikevich and Edelman, 2008) is an interesting early example of whole-brain modelling Griffiths et al (2022), a sub-field of computational neuro-science that emerged in the mid 2000s, drawing strongly on developments in neuroimaging connectomics.…”

Section: Discussionmentioning

confidence: 99%

“…For a more detailed timeline and review on the development of NPMs and whole brain modelling in general, we refer the reader to Griffiths et al (2022) and Chow and Karimipanah (2020). The early mathematical models reviewed there and above laid the groundwork for most NPM formulations used in theoretical neuroscience today.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, in voltage-based models, the state variable corresponds to the membrane potential of the neurons in the population. This means that changes in the state parameters represent changes in the electrical potentials (Griffiths et al, 2022). Therefore, NMMs are classified based on the mathematical operators used and the biological representation of the output state variable.…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive investigation of intracortical and corticothalamic models of the alpha rhythm

Bastiaens,

Momi,

Griffiths

2024

Preprint

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Alpha rhythms are a robust phenomenon prominently observed in posterior resting state electroencephalogram (EEG) that has been shown to play a key role in a number of cognitive processes. However, the underlying mechanisms behind their generation is poorly understood. Here, we showcase the most concrete, mathematically-expressed theoretical foundations for understanding the neural mechanisms underlying the alpha rhythmogenesis. The neural population models of interest are Jansen-Rit (JR), Moran-David-Friston (MDF), Robinson-Rennie-Wright (RRW), and Liley-Wright (LW). Common elements between all models are identified, such as the description of each neural population in the form of a second-order differential equation with a potential-to-rate operator represented as a sigmoid and a rate-to-potential operator usually expressed as an impulse response. Even though these models have major differences, they can be meaningfully compared by associating parameters of analogous biological significance, which we summarize with a unified parameter table. With these correspondences, rate constants and connectivity parameter space is explored to identify common patterns between similar behaviors, such as the role of excitatory-inhibitory interactions in the generation of oscillations. Through stability analysis, two different alpha generation mechanisms were identified: one noise-driven and one self-sustaining oscillation in the form of a limit cycle emerging due to a Andronov-Hopf bifurcation. This work contributes to improving our mechanistic and theoretical understanding on candidate theories of alpha rhythmogenesis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A comprehensive investigation of intracortical and corticothalamic models of the alpha rhythm

Bastiaens,

Momi,

Griffiths

2024

Preprint

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“…We employed a whole-brain modeling 51 approach to analyze hd-EEG data and study the physiological mechanisms of network excitability. The specific model we used here incorporated 200 distinct brain regions (as defined by the Schaefer 200 parcellation), connected with a set of inter-regional weights derived from the anatomical connectome.…”

Section: Overview Of Computational Modeling Approachmentioning

confidence: 99%

Stimulation mapping and whole-brain modeling reveal gradients of excitability and recurrence in cortical networks

Momi,

Wang,

Parmigiani

et al. 2024

Preprint

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The human brain exhibits a modular and hierarchical structure, spanning low-order sensorimotor to high-order cognitive/affective systems. What is the causal significance of this organization for brain dynamics and information processing properties? We investigated this question using rare simultaneous multimodal electrophysiology (stereotactic and scalp EEG) recordings in patients during presurgical intracerebral electrical stimulation (iES). Our analyses revealed an anatomical gradient of excitability across the cortex, with stronger iES-evoked EEG responses in high-order compared to low-order regions. Mathematical modeling further showed that this variation in excitability levels results from a differential dependence of recurrent feedback from non-stimulated regions across the anatomical hierarchy, and could be extinguished by suppressing those connections in-silico. High-order brain regions/networks thus show a more functionally integrated processing style than low-order ones, which manifests as a spatial gradient of excitability that is emergent from, and causally dependent on, the underlying hierarchical network structure.

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“…Accordingly, the time and cost of investigating alternative stimulation protocols for each person hamper the development of tailored therapies. We argue that extensive whole-brain computational modeling alongside multimodal neuroimaging can help us better characterize depression and facilitate stratified therapy design [17,18]. Relatedly, whole-brain modeling has been found to be a useful tool for finding the hidden dichotomy in the neural dynamics of a seemingly coherent cohort, like attention deficit hyperactivity disorder, with a great potential for guiding stratified neurostimulation therapies [18,19].…”

Section: Competing Interestsmentioning

confidence: 99%

Macroscopic resting state model predicts theta burst stimulation response: A randomized trial

et al. 2023

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Repetitive transcranial magnetic stimulation (rTMS) is a promising alternative therapy for treatment-resistant depression, although its limited remission rate indicates room for improvement. As depression is a phenomenological construction, the biological heterogeneity within this syndrome needs to be considered to improve the existing therapies. Whole-brain modeling provides an integrative multi-modal framework for capturing disease heterogeneity in a holistic manner. Computational modelling combined with a probabilistic nonparametric fitting was applied to the resting-state fMRI data from 42 patients (21 women), to parametrize baseline brain dynamics in depression. All patients were randomly assigned to two treatment groups, namely active (i.e., rTMS, n = 22) or sham (n = 20). The active treatment group received rTMS treatment with an accelerated intermittent theta burst protocol over the dorsomedial prefrontal cortex. The sham treatment group underwent the identical procedure but with the magnetically shielded side of the coil. We stratified the depression sample into distinct covert subtypes based on their baseline attractor dynamics captured by different model parameters. Notably, the two detected depression subtypes exhibited different phenotypic behaviors at baseline. Our stratification could predict the diverse response to the active treatment that could not be explained by the sham treatment. Critically, we further found that one group exhibited more distinct improvement in certain affective and negative symptoms. The subgroup of patients with higher responsiveness to treatment exhibited blunted frequency dynamics for intrinsic activity at baseline, as indexed by lower global metastability and synchrony. Our findings suggested that whole-brain modeling of intrinsic dynamics may constitute a determinant for stratifying patients into treatment groups and bringing us closer towards precision medicine.

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Whole-Brain Modelling: Past, Present, and Future

Cited by 7 publications

References 174 publications

A comprehensive investigation of intracortical and corticothalamic models of the alpha rhythm

A comprehensive investigation of intracortical and corticothalamic models of the alpha rhythm

Stimulation mapping and whole-brain modeling reveal gradients of excitability and recurrence in cortical networks

Macroscopic resting state model predicts theta burst stimulation response: A randomized trial

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