Toward personalized medicine in connectomic deep brain stimulation. (March 2022)

• Record Type: Journal Article
• Authors: Hollunder, Barbara; Rajamani, Nanditha; Siddiqi, Shan H.; Finke, Carsten; Kühn, Andrea A.; Mayberg, Helen S.; Fox, Michael D.; Neudorfer, Clemens; Horn, Andreas
• Is Part Of: Progress in neurobiology. Volume 210(2022)
• Publication Date: 2022-03
• Subjects: ALIC Anterior Limb of the Internal Capsule -- BOLD signal Blood Oxygenation Level Dependent signal -- DBS Deep Brain Stimulation -- dMRI diffusion-weighted Magnetic Resonance Imaging -- DRT Dentato-Rubro-Thalamic tract -- ET Essential Tremor -- GPi internal pallidum (Globus Pallidus internus) -- MDD Major Depressive Disorder -- OCD Obsessive Compulsive Disorder -- OFC Orbitofrontal Cortex -- PD Parkinson's Disease -- RDoC Research Domain Criteria -- rs-fMRI resting state-functional Magnetic Resonance Imaging -- STN Subthalamic Nucleus -- SCC Subgenual Cingulate Cortex -- UF Uncinate Fasciculus -- VTA Volume of Tissue Activated; Circuitopathies -- Connectomics -- Deep brain stimulation (DBS) -- Neuromodulation -- Personalized medicine -- Research Domain Criteria; Neurobiology -- Periodicals; Neurology -- Periodicals; Neurology -- Periodicals; Neurobiologie -- Périodiques; 612.8
• DOI: 10.1016/j.pneurobio.2021.102211
• Languages: English
• ISSNs: 0301-0082

Abstract

Graphical abstract: Highlights: We advocate a shift from disease- to symptom-centric connectomic DBS. We propose personalization to individual symptom profiles to improve effectiveness. Symptom network targets can be derived via group-level DBS analyses. Relevant symptom network templates could then be matched to individual circuitry. Stimulation targets may be optimized via individualized blends of symptom networks. Abstract: At the group-level, deep brain stimulation leads to significant therapeutic benefit in a multitude of neurological and neuropsychiatric disorders. At the single-patient level, however, symptoms may sometimes persist despite "optimal" electrode placement at established treatment coordinates. This may be partly explained by limitations of disease-centric strategies that are unable to account for heterogeneous phenotypes and comorbidities observed in clinical practice. Instead, tailoring electrode placement and programming to individual patients' symptom profiles may increase the fraction of top-responding patients. Here, we propose a three-step, circuit-based framework with the aim of developing patient-specific treatment targets that address the unique symptom constellation prevalent in each patient. First, we describe how a symptom network target library could be established by mapping beneficial or undesirable DBS effects to distinct circuits based on (retrospective) group-level data. Second, we suggest ways of matching the resulting symptom networks to circuits defined in the individual patient ( template matching ). Third, we introduce network blending as a strategy to calculate optimal stimulation targets and parameters by selecting and weighting a set of symptom-specific networks based on the symptom profile and subjective priorities of the individual patient. We integrate the approach with published literature and conclude by discussing limitations and future challenges.