Virtual Connectomic Datasets in Alzheimer’s Disease and Aging Using Whole-Brain Network Dynamics Modelling

Arbabyazd, Lucas; Shen, Kelly; Wang, Zheng; Hofmann‐Apitius, Martin; Ritter, Petra; McIntosh, Anthony R.; Battaglia, Demian; Jirsa, Viktor K.

doi:10.1523/eneuro.0475-20.2021

Cited by 23 publications

(16 citation statements)

References 116 publications

(214 reference statements)

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“…The inferred virtual connectomes's classification of healthy vs. AD as well as different age classes was comparable to the classification done by the empirical connectomes (Arbabyazd et al, 2021). Lastly, a next generation NMM was employed to investigate the role of structural connectivity in regulating the emergent functional dynamics (Gerster et al, 2021).…”

Section: Advances In Coupled Nmmsmentioning

confidence: 90%

Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging

2022

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We review recent advances in using mathematical models of the relationship between the brain structure and function that capture features of brain dynamics. We argue the need for models that can jointly capture temporal, spatial, and spectral features of brain functional activity. We present recent work on spectral graph theory based models that can accurately capture spectral as well as spatial patterns across multiple frequencies in MEG reconstructions.

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Section: Advances In Coupled Nmmsmentioning

confidence: 90%

Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging

2022

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“…Finally, the empirical SC and the PET density maps are not subject-specific and, moreover, were obtained from different populations. We partially solved this problem using SC optimization, similar to [57,58]. However, the unavailability of subject-specific empirical priors limited our work to simulate the effects of nicotine using an averaged observable of brain dynamics, i.e., the averaged FCs across subjects.…”

Section: Discussionmentioning

confidence: 99%

“…Before fitting the model to the empirical fMRI data, we performed an optimization procedure over the human SC matrix M [57,58]. We swept the parameter α ∈ [0.5, 1.8], and chose the value of alpha that minimized the Euclidean distance of the empirical FC matrix in the placebo -RS block.…”

Section: Model Fittingmentioning

confidence: 99%

Whole-brain modeling explains the context-dependent effects of cholinergic neuromodulation

Coronel-Oliveros

Gießing

Medel

et al. 2022

Preprint

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Integration and segregation are accepted as two fundamental principles of brain organization. The brain manages the transitions between different functional states, more segregated or integrated, through neuromodulatory systems. Recently computational and experimental works suggest a pro-segregation effect of cholinergic neuromodulation. Here, we studied the effects of the cholinergic system on brain functional connectivity using both empirical fMRI data and computational modeling. First, we analyzed the effects of nicotine on functional connectivity and network topology of healthy subjects during resting-state conditions and during an attentional task. Then, to find causal mechanisms about these changes, we employed a whole-brain neural mass model embedded into a human connectome for simulating the effects of nicotine. We found that nicotine incremented functional segregation in both empirical and simulated data, and the effects are context-dependent: observed during task, but not in resting-state. In-task performance correlates with functional segregation, discovering a link between functional network topology and behavior. In the model, the drug was modeled decreasing both the global coupling and local feedback inhibition parameters of the model, consistent with the known cellular effects of acetylcholine. Also, we found that the regional density of the nicotinic acetylcholine α4β2 receptor explains better the effects of nicotine at the whole-brain level. Our results confirm that cholinergic neuromodulation promotes functional segregation in a context-dependent fashion, and suggest that this segregation is suited for simple visual-attentional tasks. In addition, we propose biophysical mechanisms to simulate cholinergic neuromodulation in whole-brain models.

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“…Further, their whole-brain approach provides therapeutic insights by accounting for large-scale functional reversibility of EEG alterations by modeling the effect of memantine (NMDA receptor antagonist) on local neural populations (Stefanovski et al, 2019 ). Recently, whole-brain network models have also been utilized for virtual data completion to augment multimodal AD datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (Arbabyazd et al, 2021 ).…”

Section: Clinical Applicationsmentioning

confidence: 99%

Whole-Brain Network Models: From Physics to Bedside

Pathak

Roy

Banerjee

2022

Front. Comput. Neurosci.

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Computational neuroscience has come a long way from its humble origins in the pioneering work of Hodgkin and Huxley. Contemporary computational models of the brain span multiple spatiotemporal scales, from single neuronal compartments to models of social cognition. Each spatial scale comes with its own unique set of promises and challenges. Here, we review models of large-scale neural communication facilitated by white matter tracts, also known as whole-brain models (WBMs). Whole-brain approaches employ inputs from neuroimaging data and insights from graph theory and non-linear systems theory to model brain-wide dynamics. Over the years, WBM models have shown promise in providing predictive insights into various facets of neuropathologies such as Alzheimer's disease, Schizophrenia, Epilepsy, Traumatic brain injury, while also offering mechanistic insights into large-scale cortical communication. First, we briefly trace the history of WBMs, leading up to the state-of-the-art. We discuss various methodological considerations for implementing a whole-brain modeling pipeline, such as choice of node dynamics, model fitting and appropriate parcellations. We then demonstrate the applicability of WBMs toward understanding various neuropathologies. We conclude by discussing ways of augmenting the biological and clinical validity of whole-brain models.

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Virtual Connectomic Datasets in Alzheimer’s Disease and Aging Using Whole-Brain Network Dynamics Modelling

Abstract: Visual Abstract

Cited by 23 publications

References 116 publications

Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging

Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging

Whole-brain modeling explains the context-dependent effects of cholinergic neuromodulation

Whole-Brain Network Models: From Physics to Bedside

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