Investigating cognitive ability using action-based models of structural brain networks

by   Viplove Arora, et al.

Recent developments in network neuroscience have highlighted the importance of developing techniques for analyzing and modeling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use wiring rules to synthesize networks closely resembling the topology of a given connectome. Successful models can highlight the principles by which a network is organized (identify structural features that arise from wiring rules versus those that emerge) and potentially uncover the mechanisms by which it grows and develops. Previous research has shown that such models can validate the effectiveness of spatial embedding and other (non-spatial) wiring rules in shaping the network topology of the human connectome. In this research, we propose variants of the action-based model that combine a variety of generative factors capable of explaining the topology of the human connectome. We test the descriptive validity of our models by evaluating their ability to explain between-subject variability. Our analysis provides evidence that geometric constraints are vital for connectivity between brain regions, and an action-based model relying on both topological and geometric properties can account for between-subject variability in structural network properties. Further, we test correlations between parameters of subject-optimized models and various measures of cognitive ability and find that higher cognitive ability is associated with an individual's tendency to form long-range or non-local connections.


Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection

The cognitive mechanisms needed to account for the English past tense ha...

Autism Classification Using Brain Functional Connectivity Dynamics and Machine Learning

The goal of the present study is to identify autism using machine learni...

Bayesian sense of time in biological and artificial brains

Enquiries concerning the underlying mechanisms and the emergent properti...

Characterizing normal perinatal development of the human brain structural connectivity

Early brain development is characterized by the formation of a highly or...

Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity

The study of functional brain connectivity (FC) is important for underst...

Multi-task Collaborative Pre-training and Individual-adaptive-tokens Fine-tuning: A Unified Framework for Brain Representation Learning

Structural magnetic resonance imaging (sMRI) provides accurate estimates...

Critical elements for connectivity analysis of brain networks

In recent years, new and important perspectives were introduced in the f...

Please sign up or login with your details

Forgot password? Click here to reset