Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders

Area of Science:

• Neuroscience
• Network Science
• Computational Psychiatry

Background:

• Connectomics studies human brain networks using in vivo neuroimaging.
• Network science and graph theory enhance understanding of healthy brain topology and function.

Purpose of the Study:

• Focus on pathoconnectomics: disruptions in neuropsychiatric disorders.
• Explore whole-brain computational models for predicting brain network dynamics.

Main Methods:

• Reviewing methods for mapping and predicting brain network dynamics.
• Utilizing computational connectomics and whole-brain modeling.

Main Results:

• Emerging results show high accuracy in mapping healthy brain dynamics.
• Whole-brain models can generate and predict network interactions across timescales.

Conclusions:

• Computational connectomics offers a mechanistic understanding of brain disorders.
• Computational neuropsychiatry can drive novel therapeutic interventions, including drug discovery and deep brain stimulation targets.