Deep Brain Stimulation for Psychiatric Disorders

Widge, Alik S.; Arulpragasam, Amanda R.; Deckersbach, Thilo; Dougherty, Darin D.

doi:10.1002/9781118900772.etrds0103

Cited by 8 publications

(7 citation statements)

References 42 publications

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“…The detected brain signals from one or more brain regions would be processed by a controller using a real-time algorithm, and the electrical stimulation titrated based on the recorded signals. A simpler version of such a device might be “human in the loop”, where the device does not automatically self-adjust, but clinicians can observe the recorded brain signals and provide manual adjustment of the stimulation (Widge, Arulpragasam, Deckersbach, & Dougherty, 2015). Closed-loop stimulation technology has shown promise as a treatment for various diseases.…”

Section: What Is a Closed-loop Neurostimulator?mentioning

confidence: 99%

“…Furthermore, this approach also links between human and the animal research communities. It is difficult to screen psychiatric treatments in animals, as commonly used animal behavioral tests are only partly analogous to human emotion (Widge, Arulpragasam, et al, 2015). RDoC constructs and similar domains are designed to align better to those common laboratory assays.…”

Section: Ongoing Challenges and Limitations Of Closed-loop Approachesmentioning

confidence: 99%

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Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness

Widge

2017

International Review of Psychiatry

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Despite deep brain stimulation’s positive early results in psychiatric disorders, well-designed clinical trials have yielded inconsistent clinical outcomes. One path to more reliable benefit is closed-loop therapy: stimulation that is automatically adjusted by a device or algorithm in response to changes in the patient’s electrical brain activity. These interventions may provide more precise and patient-specific treatments. In this article, we first introduce the available closed-loop neuromodulation platforms, which have shown clinical efficacy in epilepsy and strong early results in movement disorders. We discuss the strengths and limitations of these devices in the context of psychiatric illness. We then describe emerging technologies to address these limitations, including pre-clinical developments such as wireless deep neurostimulation and genetically targeted neuromodulation. Finally, we discuss ongoing challenges and limitations for closed-loop psychiatric brain stimulation development, most notably the difficulty of identifying meaningful biomarkers for titration. We consider this in the recently-released Research Domain Criteria (RDoC) framework and describe how neuromodulation and RDoC are jointly very well suited to address the problem of treatment-resistant illness.

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Section: What Is a Closed-loop Neurostimulator?mentioning

confidence: 99%

Section: Ongoing Challenges and Limitations Of Closed-loop Approachesmentioning

confidence: 99%

Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness

Widge

2017

International Review of Psychiatry

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“…Deep brain stimulation (DBS) is an established therapeutic solution for treatment‐resistant movement disorders and epilepsy, and it has been proposed for the treatment of psychiatric disorders . Unfortunately, the deployment of DBS in a wide array of mental disorders is hindered by a lack of understanding of the mechanisms of action of DBS . MRI is an ideal imaging modality to shed light on such mechanisms.…”

Section: Introductionmentioning

confidence: 99%

“…[8][9][10][11][12][13][14][15] Unfortunately, the deployment of DBS in a wide array of mental disorders is hindered by a lack of understanding of the mechanisms of action of DBS. [16][17][18][19] MRI is an ideal imaging modality to shed light on such mechanisms. Unfortunately, MRI is not widely used in this patient cohort because of the potential safety risks, including heating of tissue due to RF currents induced on the DBS implant.…”

Section: Introductionmentioning

confidence: 99%

Parallel transmission to reduce absorbed power around deep brain stimulation devices in MRI: Impact of number and arrangement of transmit channels

Guérin

Angelone

Dougherty

et al. 2019

Magnetic Resonance in Med

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Purpose To assess the mean and variance performance of parallel transmission (pTx) coils for reduction of the absorbed power around electrodes (APAE) in patients implanted with deep brain stimulation (DBS) devices. Methods We simulated 4 pTx coils (8 and 16 channels, head and body coils) and a birdcage body coil. We characterized the RF safety risk using the APAE, which is the integral of the deposited power (in Watts) in a small cylindrical volume of brain tissue surrounding the electrode tips. We assessed the APAE mean and variance by simulation of 5 realistic DBS patient models that include the full DBS implant length, extracranial loops, and implanted pulse generator. Results PTx coils with 8 (16) channels were able to reduce the APAE by >18× (>169×) compared to the birdcage coil in average for all patient models, at no cost in term of flip angle uniformity or global specific absorption rate (SAR). Moreover, local pTx coils performed significantly better than body arrays. Conclusion PTx is a possible solution to the problem of RF heating of DBS patients when performing MRI, but the large interpatient variability of the APAE indicates that patient‐specific safety monitoring may be needed.

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“…Although the clinical benefits of DBS are well established for movement disorders, its mechanisms of action are not well understood (McIntyre et al , 2004; Widge et al , 2015; Widge and Dougherty, 2015; Herrington et al , 2016). DBS has been suggested as a possible therapy for intractable psychiatric disorders such as treatment-resistant depression (TRD) (Mayberg et al , 2005; Malone Jr et al , 2009; Schlaepfer et al , 2007; Bewernick et al , 2010; Puigdemont et al , 2011) and obsessive-compulsive disorder (Greenberg et al , 2006; Denys et al , 2010; Huff et al , 2010) but the lack of a clear understanding of its mechanisms of action is a major hurdle to large-scale clinical adoption (Widge et al , 2015; Widge and Dougherty, 2015). Of all the clinical imaging modalities, MRI is unique in that it allows mapping both the anatomical (diffusion MRI) and functional (functional MRI, fMRI) brain networks.…”

Section: Introductionmentioning

confidence: 99%

Realistic modeling of deep brain stimulation implants for electromagnetic MRI safety studies

et al. 2018

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We propose a framework for electromagnetic (EM) simulation of deep brain stimulation (DBS) patients in radiofrequency (RF) coils. We generated a model of a DBS patient using post-operative head and neck computed tomography (CT) images stitched together into a “virtual CT” image covering the entire length of the implant. The body was modeled as homogeneous. The implant path extracted from the CT data contained self-intersections, which we corrected automatically using an optimization procedure. Using the CT-derived DBS path, we built a model of the implant including electrodes, helicoidal internal conductor wires, loops, extension cables, and the implanted pulse generator. We also built four simplified models with straight wires, no extension cables and no loops to assess the impact of these simplifications on safety predictions. We simulated EM fields induced by the RF birdcage body coil in the body model, including at the DBS lead tip at both 1.5 Tesla (64 MHz) and 3 Tesla (123 MHz). We also assessed the robustness of our simulation results by systematically varying the EM properties of the body model and the position and length of the DBS implant (sensitivity analysis). The topology correction algorithm corrected all self-intersection and curvature violations of the initial path while introducing minimal deformations (open-source code available at http://ptx.martinos.org/index.php/Main_Page). The unaveraged lead-tip peak SAR predicted by the five DBS models (0.1 mm resolution grid) ranged from 12.8 kW/kg (full model, helicoidal conductors) to 43.6 kW/kg (no loops, straight conductors)at 1.5 T (3.4-fold variation) and 18.6 kW/kg (full model, straight conductors) to 73.8 kW/kg (no loops, straight conductors)at 3 T (4.0-fold variation). At 1.5 T and 3 T, the variability of lead-tip peak SAR with respect to the conductivity ranged between 18% and 30%. Variability with respect to the position and length of the DBS implant ranged between 9.5% and 27.6%.

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Deep Brain Stimulation for Psychiatric Disorders

Cited by 8 publications

References 42 publications

Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness

Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness

Parallel transmission to reduce absorbed power around deep brain stimulation devices in MRI: Impact of number and arrangement of transmit channels

Realistic modeling of deep brain stimulation implants for electromagnetic MRI safety studies

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