Connectomic Analysis

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.