Anatomical brain structures normalization for deep brain stimulation in movement disorders

Vogel, Dorian; Shah, Ashesh; Coste, Jérôme; Lemaire, Jean‐Jacques; Wårdell, Karin; Hemm, Simone

doi:10.1016/j.nicl.2020.102271

Cited by 29 publications

(44 citation statements)

References 37 publications

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“…Several prior phantom studies have examined the accuracy and precision of electrode localization based on postoperative magnetic resonance or CT imaging, confirming that these modalities typically yield submillimeter reconstruction errors (∼0.2–0.8mm) 104–108 . Normalization of patient images to MNI space was accomplished using the “low variance” ANTs SyN algorithm, which was the top performer for subcortical image registrations in recent studies that compared a variety of normalization techniques, and has been shown to produce automated STN and GPi segmentations that compare favorably to gold standard manual segmentations in both low‐ and high‐quality datasets 15,109 . The mean surface distance (MSD) between manually segmented labels and “low variance” ANTs SyN‐transformed STN/GPi labels was ∼0.4–0.7mm (lower than the MSD between manually segmented labels of different raters), indicating that this normalization approach has submillimeter subcortical accuracy 15 .…”

Section: Discussionmentioning

confidence: 99%

Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy

Elias

Boutet

Joel³

et al. 2020

Annals of Neurology

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Deep brain stimulation (DBS) depends on precise delivery of electrical current to target tissues. However, the specific brain structures responsible for best outcome are still debated. We applied probabilistic stimulation mapping to a retrospective, multidisorder DBS dataset assembled over 15 years at our institution (ntotal = 482 patients; nParkinson disease = 303; ndystonia = 64; ntremor = 39; ntreatment‐resistant depression/anorexia nervosa = 76) to identify the neuroanatomical substrates of optimal clinical response. Using high‐resolution structural magnetic resonance imaging and activation volume modeling, probabilistic stimulation maps (PSMs) that delineated areas of above‐mean and below‐mean response for each patient cohort were generated and defined in terms of their relationships with surrounding anatomical structures. Our results show that overlap between PSMs and individual patients' activation volumes can serve as a guide to predict clinical outcomes, but that this is not the sole determinant of response. In the future, individualized models that incorporate advancements in mapping techniques with patient‐specific clinical variables will likely contribute to the optimization of DBS target selection and improved outcomes for patients. ANN NEUROL 2021;89:426–443

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Section: Discussionmentioning

confidence: 99%

Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy

Elias

Boutet

Joel³

et al. 2020

Annals of Neurology

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“…17 As shown in a recent study, the precision of this normalization protocol may lead to results comparable with manual expert segmentations of the STN and GPi and the method was top-performer in two comparative studies for normalizations of the subcortex. 18,19 Subsequently, DBS electrodes were localized using the PaCER algorithm for CT volumes or the TRAC/CORE method for MRI volumes. 15,20 Results were carefully inspected and manually refined, if necessary, using Lead-DBS.…”

Section: Methodsmentioning

confidence: 99%

Effective subthalamic and pallidal deep brain stimulation – are we modulating the same network?

Sobesky

Goede

et al. 2021

Preprint

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The subthalamic nucleus and internal pallidum are main target sites for deep brain stimulation in Parkinsons disease. Multiple trials that investigated subthalamic versus pallidal stimulation were unable to settle on a definitive optimal target between the two. One reason could be that the effect is mediated via a common network. To test this hypothesis, we calculated connectivity profiles seeding from deep brain stimulation electrodes in 94 patients that underwent subthalamic treatment and 28 patients with pallidal treatment based on a normative connectome atlas calculated from 1,000 healthy subjects. In each cohort, we calculated connectivity profiles that were associated with optimal clinical improvements. The two maps showed striking similarity and were able to cross-predict outcomes in the respective other cohort (R = 0.38 at p < 0.001 & R = 0.35 at p = 0.027). Next, we calculated an agreement map which retained regions common of both target sites. Crucially, this map was able to explain an additional amount of variance in clinical improvements of either cohort when compared to the maps calculated on the two cohorts alone. Finally, we tested profiles and predictive utility of connectivity maps calculated from different motor symptom subscores with a specific focus on bradykinesia and rigidity. While our study is based on retrospective data and indirect connectivity metrics, it delivers empirical data to support the hypothesis of a largely overlapping network associated with effective deep brain stimulation in Parkinsons disease irrespective of the specific target.

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“…Even if an optimal target for an individual patient were identified, ensuring accurate electrode placement, especially in areas with poor intrinsic MRI contrast, such as the thalamus, remains challenging [171][172][173]. Furthermore, contact localization remains a challenge, with many available tools but no consensus on assessing anatomic accuracy, especially in patients with preexisting structural brain injury causing distorted anatomy [174][175][176][177]. Similarly, for noninvasive brain stimulation techniques, the stimulation site should account for the individual patient's underlying brain lesions and their associated network disconnections [178].…”

Section: Gaps In Knowledgementioning

confidence: 99%

Therapies to Restore Consciousness in Patients with Severe Brain Injuries: A Gap Analysis and Future Directions

et al. 2021

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Background/Objective For patients with disorders of consciousness (DoC) and their families, the search for new therapies has been a source of hope and frustration. Almost all clinical trials in patients with DoC have been limited by small sample sizes, lack of placebo groups, and use of heterogeneous outcome measures. As a result, few therapies have strong evidence to support their use; amantadine is the only therapy recommended by current clinical guidelines, specifically for patients with DoC caused by severe traumatic brain injury. To foster and advance development of consciousness-promoting therapies for patients with DoC, the Curing Coma Campaign convened a Coma Science Work Group to perform a gap analysis. Methods We consider five classes of therapies: (1) pharmacologic; (2) electromagnetic; (3) mechanical; (4) sensory; and (5) regenerative. For each class of therapy, we summarize the state of the science, identify gaps in knowledge, and suggest future directions for therapy development. Results Knowledge gaps in all five therapeutic classes can be attributed to the lack of: (1) a unifying conceptual framework for evaluating therapeutic mechanisms of action; (2) large-scale randomized controlled trials; and (3) pharmacodynamic biomarkers that measure subclinical therapeutic effects in early-phase trials. To address these gaps, we propose a precision medicine approach in which clinical trials selectively enroll patients based upon their physiological receptivity to targeted therapies, and therapeutic effects are measured by complementary behavioral, neuroimaging, and electrophysiologic endpoints. Conclusions This personalized approach can be realized through rigorous clinical trial design and international collaboration, both of which will be essential for advancing the development of new therapies and ultimately improving the lives of patients with DoC.

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Anatomical brain structures normalization for deep brain stimulation in movement disorders

Cited by 29 publications

References 37 publications

Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy

Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy

Effective subthalamic and pallidal deep brain stimulation – are we modulating the same network?

Therapies to Restore Consciousness in Patients with Severe Brain Injuries: A Gap Analysis and Future Directions

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