A framework for focal and connectomic mapping of transiently disrupted brain function

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• Mapping brain function is complex due to the distributed nature of neural substrates and limitations of correlative data.
• Disambiguating local versus global neural dependence requires methods combining anatomical connectivity with functional disruption.

Purpose of the Study:

• To present a comprehensive framework for focal and connective spatial inference using sparse disruptive data.
• To demonstrate the application of this framework in mapping the human medial frontal wall using transient direct electrical stimulation.

Main Methods:

• Developed a framework for voxel-wise mass-univariate inference on sparsely sampled data within the statistical parametric mapping framework.
• Applied transient direct electrical stimulation to the medial frontal wall in epilepsy patients during pre-surgical evaluation.
• Analyzed distributed maps based on connectivity criteria to identify local versus remote neural dependencies.

Main Results:

• The transient dysconnectome approach revealed significant discrepancies between local and distributed associations for motor and sensory behaviors.
• Identified functional differentiation linked to remote connectivity that is missed by purely local analyses.
• Demonstrated the framework's ability to map brain function based on sparse disruptive data with minimal spatial assumptions.

Conclusions:

• The proposed framework enables disruptive mapping of the human brain using sparse data, offering statistical efficiency and flexibility.
• It allows for explicit comparison of local and distributed effects, advancing the understanding of neural dependencies.
• This approach is crucial for disambiguating critical neural activity from coincidental associations in brain function mapping.