The adaptive stochasticity hypothesis: Modeling equifinality, multifinality, and adaptation to adversity

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Computational Neuroscience

Background:

• Neural phenotypes arise from probabilistic developmental processes, with stochasticity being an intrinsic aspect of brain self-organization.
• Genomic and environmental factors shape the developing nervous system, but randomness from probability distributions is a significant, often overlooked, contributor.

Purpose of the Study:

• To develop a framework for investigating the role of stochasticity in neurodevelopmental diversity using generative modeling of brain networks.
• To identify the critical determinants and effects of stochasticity on brain network organization and robustness.

Main Methods:

• Utilized generative modeling starting from the medio-posterior neonatal rich club scaffold (Developing Human Connectome Project, n=630).
• Implemented Hebbian-like wiring processes to generate variable brain network topologies across >10^7 simulations.
• Empirically tested the framework using a cohort of neurodiverse children (Centre for Attention, Learning and Memory, n=357) to assess stochasticity in relation to early-life deprivation.

Main Results:

• Stochastic variation significantly impacts brain organization, especially under weaker developmental constraints.
• Increased stochasticity enhances brain network robustness against attacks but can lead to non-normative phenotypic outcomes.
• Low socioeconomic status was found to predict more stochastic brain wiring in neurodiverse children.

Conclusions:

• Stochasticity is an underappreciated contributor to neurodevelopmental diversity and outcomes.
• The study provides a framework for probing stochasticity's role and makes specific predictions for future research.
• Early-life experiences, such as deprivation, can modulate the level of stochasticity in brain wiring.