Connectomic analysis refers to the study and mapping of the brain's structural and functional networks using neuroimaging and computational tools. In the context of neurosurgery and deep_brain_stimulation (DBS), it enables the identification of specific fiber pathways and network hubs associated with clinical outcomes.
Connectomic analysis integrates data from:
Diffusion-weighted imaging (DWI) and tractography — to reconstruct white matter fiber pathways
Resting-state fMRI — to examine functional connectivity
Normative connectomes — population-averaged brain networks
Patient-specific connectomes — derived from individual imaging data
Common platforms include:
Lead-DBS
MRtrix
FSL
BrainSuite
Allows clinicians and researchers to map volumes of activated tissue (VAT) onto brain networks.
Identifies fiber tracts whose modulation correlates with clinical response (e.g., the ocd_response_tract in treatment-resistant_obsessive-compulsive_disorder).
Supports target refinement and the concept of “sweet spots” in subcortical stimulation.
Moves beyond anatomical landmarks to network-based neurosurgery.
Enables hypothesis-driven selection of DBS targets.
Can aid in personalized treatment planning by identifying individual network disruptions.
Heavily reliant on image quality and accurate coregistration.
Normative connectomes may not capture patient-specific anatomy, especially in diseased brains.
Causal inferences from correlational data remain challenging.
In the 2025 study by Coenen et al. (Mol Psychiatry), connectomic analysis was used to:
Compare the connectivity profiles of DBS targets (e.g., anteromedial_subthalamic_nucleus, superolateral_medial_forebrain_bundle).
Demonstrate that the ocd_response_tract is embedded within slMFB fibers.
Suggest that symptom improvement in OCD relates to modulation of convergent sub-networks projecting to the dorsomedial_prefrontal_cortex.