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Intra-Operative Behavioral Tasks in Awake Humans Undergoing Deep Brain Stimulation Surgery
Published on: January 6, 2011
Pre-operative neural substrates of deep brain stimulation outcomes: A systematic review and cross-disorder network
Yutong Bai1, Tiffany A Rodrigues2, Andrew Z Yang3
1Department of Neurosurgery, Beijing Tiantan Hospital, Beijing, China; Department of Neurosurgery, Toronto Western Hospital, Toronto, Ontario, Canada.
Background:
Deep brain stimulation (DBS) is an established therapy for movement and psychiatric disorders, yet its effects vary substantially across individuals. The pre-operative brain provides the individual circuitry upon which DBS acts, but how this circuitry shapes surgical outcomes remains poorly characterized.
Methods:
We conducted a systematic review (CRD42024567015) of studies relating pre-operative neuroimaging (structural, functional, or molecular) to DBS outcomes. Reported regions generated disease-specific frequency maps, which informed two normative connectivity analyses: (1) identifying central regions driving the network via graph theory metrics (internal network) and (2) assessing brain-wide circuit engagement (external network).
Results:
Fifty-three studies (n = 1758 patients) were included. Movement disorders comprised 73.6%, primarily Parkinson's disease (PD), while psychiatric disorders comprised 20.8%, mainly major depressive disorder (MDD). Frequency maps showed disease-specific involvement, most commonly the primary motor cortex (PD) and anterior cingulate cortex (MDD). Internal network analysis identified the primary motor cortex, left thalamus, brainstem, and right subthalamic nucleus as central in PD, and the bilateral amygdala, middle frontal gyrus, and frontal operculum cortex in MDD. External networks showed basal ganglia and limbic engagement in PD and MDD, respectively, plus shared higher-order networks (salience, cerebellar, and default mode).
Conclusions:
Across movement and psychiatric disorders, DBS outcomes were associated with the pre-operative organization of disease-specific circuits and shared higher-order control networks. We propose that individual variation within these networks is a key determinant of surgical benefit. Upon further validation, these findings may open the door to imaging-informed, network-level patient selection.
