Generative network models of altered structural brain connectivity in schizophrenia

Zhang, Xiaolong; Braun, Urs; Harneit, Anais; Zang, Zhenxiang; Geiger, Lena; Betzel, Richard F.; Chen, Junfang; Schweiger, Janina I.; Schwarz, Kristina; Reinwald, Jonathan; Fritze, Stefan; Witt, Stephanie H.; Rietschel, Marcella; Nöthen, Markus M.; Degenhardt, Franziska; Schwarz, Emanuel; Hirjak, Dušan; Meyer‐Lindenberg, Andreas; Bassett, Danielle S.; Tost, Heike

doi:10.1016/j.neuroimage.2020.117510

Cited by 27 publications

(83 citation statements)

References 75 publications

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“…Optimally simulated networks, using this simple wiring equation, are so similar to the actual networks that a support vector machine is unable to distinguish them using the parameters from the energy equation 2(mean accuracy = 50.45%, SD = 2.85%). Replicating previous work, we find that our simulated networks, optimized via the energy equation 2under homophily generative mechanisms, capture the statistical properties of observed networks 24,25,35 . But do these capture crucial network properties not included in the energy equation, like the spatial embedding of their statistical properties?…”

Section: Small Variations In Gnm Parameter Combinations Produce Accursupporting

confidence: 84%

“…We propose that the superordinate goal of any developing brain network is to achieve the optimal computational capacity required of it, given finite biological resources. In this light, we suggest that matching produces the lowest-energetic networks precisely because it provides the closest heuristic estimate (compared to those tested here and in other works 24,25,35 ) of the genuine dynamic reappraisals that occur over developmental time. This is because by virtue of preferentially wiring with nodes with shared neighborhoods modular architectures emerge 44 , and this reflects the brain's overarching structure.…”

Section: Genomic Patterning Of Network Growthsupporting

confidence: 68%

“…Mirroring findings in adult samples 24,25,35 , we found that models driven by geometry and topology outperform the pure geometric spatial model and homophily-based models achieve the lowest energy for our pediatric sample ( Fig. 2e).…”

Section: Small Variations In Gnm Parameter Combinations Produce Accursupporting

confidence: 51%

“…The enrichment analysis that accompanied our GNM takes a very different approach. As far as we are aware, this is the first study aiming to bridge models of whole brain organizational emergence and genetics in this way (for work using generative models, see refs 24,25,35,54,55 and work that integrates Allen Brain Atlas gene data with functional and structural brain imaging, see refs [56][57][58][59][60] ). Nodal costs covaried with genes enriched for highly costly metabolic processes, including catabolic processes, protein transport and cellular components centered around the ribosome and endoplasmic membranes.…”

Section: Genomic Patterning Of Network Growthmentioning

confidence: 99%

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A generative network model of neurodevelopment

Akarca

Vértes

Bullmore

et al. 2020

Preprint

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The emergence of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms govern the diversity of these developmental processes? There are many existing descriptive theories, but to date none are computationally formalized. We provide a mathematical framework that specifies the growth of a brain network over developmental time. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over development. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the developmental simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity of childhood brain development, capable of integrating different levels of analysis – from genes to cognition.

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Section: Small Variations In Gnm Parameter Combinations Produce Accursupporting

confidence: 84%

Section: Genomic Patterning Of Network Growthsupporting

confidence: 68%

Section: Small Variations In Gnm Parameter Combinations Produce Accursupporting

confidence: 51%

Section: Genomic Patterning Of Network Growthmentioning

confidence: 99%

See 2 more Smart Citations

A generative network model of neurodevelopment

Akarca

Vértes

Bullmore

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Replicating previous work, we find that our simulated networks, optimized via the statistical properties included in the energy Eq. (2) via homophily generative mechanisms, accurately capture these properties in observed networks 24,25,36 . But do these capture crucial network properties not included in the energy equation, like their spatial embedding?…”

Section: Resultsmentioning

confidence: 99%

A generative network model of neurodevelopmental diversity in structural brain organization

et al. 2021

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The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis—from genes to cognition.

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Early adversity changes the economic conditions of mouse structural brain network organization

Carozza

Holmes

Vértes

et al. 2023

Developmental Psychobiology

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Early adversity can change educational, cognitive, and mental health outcomes. However, the neural processes through which early adversity exerts these effects remain largely unknown. We used generative network modeling of the mouse connectome to test whether unpredictable postnatal stress shifts the constraints that govern the organization of the structural connectome. A model that trades off the wiring cost of long‐distance connections with topological homophily (i.e., links between regions with shared neighbors) generated simulations that successfully replicate the rodent connectome. The imposition of early life adversity shifted the best‐performing parameter combinations toward zero, heightening the stochastic nature of the generative process. Put simply, unpredictable postnatal stress changes the economic constraints that reproduce rodent connectome organization, introducing greater randomness into the development of the simulations. While this change may constrain the development of cognitive abilities, it could also reflect an adaptive mechanism that facilitates effective responses to future challenges.

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Generative network models of altered structural brain connectivity in schizophrenia

Cited by 27 publications

References 75 publications

A generative network model of neurodevelopment

A generative network model of neurodevelopment

A generative network model of neurodevelopmental diversity in structural brain organization

Early adversity changes the economic conditions of mouse structural brain network organization

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