Personalizing neuromodulation.

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Psychiatry

Background:

• Big data provides unprecedented insights into individual neuroanatomy and function.
• Current methods for optimizing mental functions in health and disease lack precision.
• The need for mechanistic models to advance personalized brain interventions is critical.

Purpose of the Study:

• To explore optimal approaches for enhancing individual mental functions.
• To demonstrate how multimodal analysis and mechanistic modeling can guide neuromodulation.
• To improve predictions of treatment effects and refine models of brain health and disease.

Main Methods:

• Integrating anatomical network data, functional signal analysis, and cognitive neuroscience paradigms.
• Developing and applying mechanistic models beyond statistical fits.
• Utilizing transcranial magnetic stimulation (TMS) as a case study for method integration.

Main Results:

• Multimodal approaches can guide personalized neuromodulation strategies.
• Mechanistic models offer superior predictive power compared to statistical models for TMS effects.
• Integrated methods enhance understanding of individual brain function and dysfunction.

Conclusions:

• Advancing from statistical to mechanistic models is crucial for personalized neuroscience.
• Integrated multimodal analysis holds promise for optimizing mental functions in health and disease.
• This framework can refine treatments and improve outcomes for neurological and psychiatric conditions.