<i>Caenorhabditis elegans</i>
            and the network control framework—FAQs

Towlson, Emma K.; Vértes, Petra E.; Yan, Gang; Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert‐László

doi:10.1098/rstb.2017.0372

Cited by 35 publications

(23 citation statements)

References 72 publications

(151 reference statements)

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“…The design and analysis of optimal, robust, adaptive, and many other forms of control are much better understood in the context of linear systems than nonlinear ones. This contrast in tractability only grows for large-scale systems like the brain, thus motivating the recent surge of interest and advancements in using linear control theory in neuroscience [40][41][42]. Nonlinear models also present additional challenges beyond network control, including analytical and mechanistic understanding of their functionality, obtaining provable guarantees on their performance, and even hardware requirements for their use in chronic implantable devices.…”

Section: Discussionmentioning

confidence: 99%

Is the brain macroscopically linear? A system identification of resting state dynamics

Nozari

Stiso

Caciagli

et al. 2020

Preprint

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A central challenge in the computational modeling of neural dynamics is the trade-off between accuracy and simplicity. At the level of individual neurons, nonlinear dynamics are both experimentally established and essential for neuronal functioning. One may therefore expect the collective dynamics of massive networks of such neurons to only increase in their complexity, thereby supporting an expanded repertoire of nonlinear behaviors. An implicit assumption has thus formed that an “accurate” computational model of whole-brain dynamics must inevitably be nonlinear whereas linear models may provide a first-order approximation. To what extent this assumption holds, however, has remained an open question. Here, we provide a rigorous and data-driven answer at the level of whole-brain blood-oxygen-level-dependent (BOLD) and macroscopic field potential dynamics by leveraging the theory of system identification. Using functional magnetic resonance imaging (fMRI) and intracranial electroencephalography (iEEG), we model the spontaneous, resting state activity of 700 subjects in the Human Connectome Project (HCP) and 122 subjects from the Restoring Active Memory (RAM) project using state-of-the-art linear and nonlinear model families. We assess relative model fit using predictive power, computational complexity, and the extent of residual dynamics unexplained by the model. Contrary to our expectations, linear auto-regressive models achieve the best measures across all three metrics, eliminating the trade-off between accuracy and simplicity. To understand and explain this linearity, we highlight four properties of macroscopic neurodynamics which can counteract or mask microscopic nonlinear dynamics: averaging over space, averaging over time, observation noise, and limited data samples. Whereas the latter two are technological limitations and can improve in the future, the former two are inherent to aggregated macroscopic brain activity. Our results demonstrate the discounted potential of linear models in accurately capturing macroscopic brain dynamics. This, together with the unparalleled interpretability of linear models, can greatly facilitate our understanding of macroscopic neural dynamics, which in turn may facilitate the principled design of model-based interventions for the treatment of neuropsychiatric disorders.

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Section: Discussionmentioning

confidence: 99%

Is the brain macroscopically linear? A system identification of resting state dynamics

Nozari

Stiso

Caciagli

et al. 2020

Preprint

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“…Such linear estimation of the dynamics makes available powerful computational tools in matrix and linear systems theory, and allowed us to capitalize on recent advances in network control [39,47]. Network control theory is a formal approach to modeling, predicting, and tuning the response of a networked system to exogenous input, and has been recently applied to neural systems at both the cellular [73,78,80] and regional [13,22,31,67] scales (for a recent review, see [66]). In these previous efforts, linear dynamics have been assumed, whereas here such dynamics have been proven, to be relevant for the neural system under study.…”

Section: Discussionmentioning

confidence: 99%

Network topology of neural systems supporting avalanche dynamics predicts stimulus propagation and recovery

Kim

Bassett

2018

Preprint

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Many neural systems display avalanche behavior characterized by uninterrupted sequences of neuronal firing whose distributions of size and durations are heavy-tailed. Theoretical models of such systems suggest that these dynamics support optimal information transmission and storage. However, the unknown role of network structure precludes an understanding of how variations in network topology manifest in neural dynamics and either support or impinge upon information processing. Here, using a generalized spiking model, we develop a mechanistic understanding of how network topology supports information processing through network dynamics. First, we show how network topology determines network dynamics by analytically and numerically demonstrating that network topology can be designed to propagate stimulus patterns for long durations. We then identify strongly connected cycles as empirically observable network motifs that are prevalent in such networks. Next, we show that within a network, mathematical intuitions from network control theory are tightly linked with dynamics initiated by node-specific stimulation and can identify stimuli that promote long-lasting cascades. Finally, we use these network-based metrics and control-based stimuli to demonstrate that long-lasting cascade dynamics facilitate delayed recovery of stimulus patterns from network activity, as measured by mutual information. Collectively, our results provide evidence that cortical networks are structured with architectural motifs that support long-lasting propagation and recovery of a few crucial patterns of stimulation, especially those consisting of activity in highly controllable neurons. Broadly, our results imply that avalanching neural networks could contribute to cognitive faculties that require persistent activation of neuronal patterns, such as working memory or attention.keywords linear dynamical systems, network control, mutual information 2/25

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“…First, S. ven metabolite toxicity mimics idiopathic PD in a way that is reminiscent to other environmental compounds that also cause stress in broad, diverse, cellular pathways. The cellular response to S. ven exemplifies the concept whereby genetic pathways function as interactome networks [96,97]. In these networks, distant genetic components (UPS, mitochondrial dysfunction, etc.)…”

Section: Summary and Future Studiesmentioning

confidence: 99%

No Country for Old Worms: A Systematic Review of the Application of C. elegans to Investigate a Bacterial Source of Environmental Neurotoxicity in Parkinson’s Disease

2018

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While progress has been made in discerning genetic associations with Parkinson’s disease (PD), identifying elusive environmental contributors necessitates the application of unconventional hypotheses and experimental strategies. Here, we provide an overview of studies that we conducted on a neurotoxic metabolite produced by a species of common soil bacteria, Streptomyces venezuelae (S. ven), indicating that the toxicity displayed by this bacterium causes stress in diverse cellular mechanisms, such as the ubiquitin proteasome system and mitochondrial homeostasis. This dysfunction eventually leads to age and dose-dependent neurodegeneration in the nematode Caenorhabditis elegans. Notably, dopaminergic neurons have heightened susceptibility, but all of the neuronal classes eventually degenerate following exposure. Toxicity further extends to human SH-SY5Y cells, which also degenerate following exposure. Additionally, the neurons of nematodes expressing heterologous aggregation-prone proteins display enhanced metabolite vulnerability. These mechanistic analyses collectively reveal a unique metabolomic fingerprint for this bacterially-derived neurotoxin. In considering that epidemiological distinctions in locales influence the incidence of PD, we surveyed soils from diverse regions of Alabama, and found that exposure to ~30% of isolated Streptomyces species caused worm dopaminergic neurons to die. In addition to aging, one of the few established contributors to PD appears to be a rural lifestyle, where exposure to soil on a regular basis might increase the risk of interaction with bacteria producing such toxins. Taken together, these data suggest that a novel toxicant within the Streptomyces genus might represent an environmental contributor to the progressive neurodegeneration that is associated with PD.

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Caenorhabditis elegans and the network control framework—FAQs

Cited by 35 publications

References 72 publications

Is the brain macroscopically linear? A system identification of resting state dynamics

Is the brain macroscopically linear? A system identification of resting state dynamics

Network topology of neural systems supporting avalanche dynamics predicts stimulus propagation and recovery

No Country for Old Worms: A Systematic Review of the Application of C. elegans to Investigate a Bacterial Source of Environmental Neurotoxicity in Parkinson’s Disease

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