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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

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Stimulation-Based Control of Dynamic Brain Networks.

Sarah Feldt Muldoon^1,2,3, Fabio Pasqualetti⁴, Shi Gu^1,5

¹Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

Plos Computational Biology

|September 10, 2016

Summary

Targeted brain stimulation can treat disorders and enhance performance. Our computational model reveals how stimulating specific brain regions impacts networks, guiding personalized neuromodulation strategies.

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Area of Science:

Neuroscience
Computational modeling
Network science

Background:

Targeted brain stimulation is a growing neuromodulation technique for neurological disorders and human performance enhancement.
The network-level effects of focal brain stimulation remain largely unknown.
Understanding these effects is crucial for developing effective stimulation protocols.

Purpose of the Study:

To investigate the system-wide impact of regional brain stimulation using a data-driven computational model.
To explore the relationship between regional controllability and stimulation effects.
To map stimulation impacts to cognitive systems and network architecture.

Main Methods:

Employed a data-driven computational model of nonlinear brain dynamics.
Systematically explored the effects of targeted stimulation on brain networks.
Validated predictions from network control theory.
Mapped brain regions to cognitive systems and analyzed structural connectivity.

Main Results:

Uncovered a relationship between regional controllability and the focal versus global impact of stimulation.
Demonstrated how network architecture influences stimulation outcomes.
Observed that the default mode system causes significant global changes despite constrained connectivity.

Conclusions:

This study provides insights into the network-level impact of targeted brain stimulation.
Findings support the development of personalized neuromodulation protocols for therapeutic and enhancement purposes.
Computational models are valuable tools for understanding complex brain dynamics and stimulation effects.