Multivariate pattern classification on BOLD activation pattern induced by deep brain stimulation in motor,...

Area of Science:

• Neuroscience
• Medical Imaging
• Neurosurgery

Background:

• Deep brain stimulation (DBS) is effective for movement disorders and emerging for psychiatric conditions.
• Functional magnetic resonance imaging (fMRI) shows DBS affects brain networks, but conventional methods lack sensitivity for diffuse patterns.
• Characterizing DBS-induced brain network effects is crucial for understanding therapeutic and adverse outcomes.

Purpose of the Study:

• To evaluate the utility of multivariate pattern analysis (MVPA) for characterizing blood-oxygen-level-dependent (BOLD) activation patterns evoked by DBS in swine brain.
• To compare the distinctiveness of BOLD patterns elicited by stimulation of different DBS targets: subthalamic nucleus (STN), globus pallidus internus (GPi), and nucleus accumbens (NAc).

Main Methods:

• Application of MVPA to fMRI data acquired during DBS in swine models.
• Stimulation of three key DBS targets: STN, GPi, and NAc.
• Analysis of evoked BOLD activation patterns to identify target-specific network responses.

Main Results:

• MVPA successfully demonstrated distinctive BOLD activation patterns for each stimulated DBS target.
• STN and GPi stimulation evoked similar motor network activation patterns.
• NAc stimulation elicited distinct associative and limbic network patterns.
• MVPA proved effective for analyzing overlapping or sparse BOLD patterns common in DBS studies.

Conclusions:

• MVPA-fMRI is a sensitive technique for decoding DBS-induced brain network effects, even with complex BOLD patterns.
• The study successfully differentiated network responses to motor (STN, GPi) versus limbic/associative (NAc) DBS targets.
• Future applications of MVPA-fMRI are expected to refine target identification and optimize DBS outcomes by mapping specific brain circuitry.