Model based optimal multipolar stimulation without <i>a priori</i> knowledge of nerve structure: application to vagus nerve stimulation

Dali, Mélissa; Rossel, Olivier; Andreu, David; Laporte, Laure; Hernàndez, A.; Laforêt, Jérémy; Marijon, Éloi; Hagège, Albert; Clerc, Maureen; Henry, Christine; Guiraud, David

doi:10.1088/1741-2552/aabeb9

Cited by 39 publications

(42 citation statements)

References 43 publications

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“…Some examples of these clinical trials target chronic heart failure [2] or autoimmune diseases [3] such as Crohn's disease or rheumatoid arthritis. However, the side effects of these therapies still remain; these include neck pain, coughing, voice alteration, and dyspnea [4]. Therefore, the goal of this paper is to explore a new stimulation technique, which has the potential to induce therapeutic effect that targets only one of the previously mentioned diseases, while reducing the side effects by localizing the stimulation to individual fascicles within the nerve.…”

Section: Introductionmentioning

confidence: 99%

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Pressure measurement of geometrically curved ultrasound transducer array for spatially specific stimulation of the vagus nerve

Kawasaki

Giagka

Haas

et al. 2019

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

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Vagus nerve stimulators currently on the market can treat epilepsy and depression. Recent clinical trials show the potential for vagus nerve stimulation (VNS) to treat epilepsy, autoimmune disease, and traumatic brain injury. As we explore the benefits of VNS, it is expected that more possibilities for a new treatment will emerge in the future. However, existing VNS relies on electrical stimulation, whose limited selectivity (due to its poor spatial resolution) does not allow for any control over which therapeutic effect to induce. We hypothesize that by localizing the stimulation to fascicular level within the vagus nerve with focused ultrasound (US), it is possible to induce selective therapeutic effects with less side effects. A geometrically curve US transducer array that is small enough to wrap around the vagus nerve was fabricated. An experiment was conducted in water, with 48 US elements curved in a 1 mm radius and excited at 15 MHz to test the focusing capabilities of the device. The results show that the geometrical curvature focused the US to an area with a width and height of 110 µm and 550 µm. This will be equivalent to only 2.1% of the cross section of the vagus nerve, showing the potential of focused US to stimulate individual neuronal fibers within the vagus nerve selectively.

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Section: Introductionmentioning

confidence: 99%

“…Another option investigated was to use multiple electrodes along the inner walls of a cuff that can be excited independently (i.e. multi-contact electrode [4], [6]). For this method, additional advancement must be made to increase the number of electrodes to achieve better spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Pressure measurement of geometrically curved ultrasound transducer array for spatially specific stimulation of the vagus nerve

Kawasaki

Giagka

Haas

et al. 2019

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

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“…Intra-operative functional brain mapping is performed during tumor excision, while 4 patients are awake, to interrogate the organisation and operation of tissues. As such, 5 functional mapping aids in the identification of non-operational tissues important to 6 excise, and the prevention of neuropsychological complications induced by the excision 7 1/13 of operational tissue and tracts (fasciculi of axons). Despite the demonstrated utility of 8 intra-operative functional mapping, there is no standard approach to choose the 9 electrode parameters that are mostly set empirically [31].…”

Section: Introductionmentioning

confidence: 99%

“…For this purpose, a model was 43 developed to compute the response of a myelinated axons of a tract (either isotropic or 44 anisotropic) to the potential field generated by various electrode parameters: was coupled with a model of mammalian myelinated nerve fiber to study axonal 51 recruitmentment resulting from stimuli. This method builds on models developed to 52 successfully describe the effects of peripheral nerve stimulation [4,5], adapted for white 53 matter stimulation of tracts in the central nervous system. 54…”

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confidence: 99%

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Modeling subcortical white matter stimulation

Dali

Goldman

Pantz³

et al. 2019

Preprint

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Objective. Intracranial electrical stimulation of subcortical axonal tracts is particularly useful during brain surgery, where mapping helps identify and excise dysfunctional tissue while avoiding damage to functional structures. Stimulation parameters are generally set empirically and consequences for the spatial recruitment of axons within subcortical tracts are not well identified. Approach. Computational modeling is employed to study the effects of stimulation parameters on the recruitment of axons: monophasic versus biphasic stimuli induced with monopolar versus bipolar electrodes, oriented orthogonal or parallel to the tract, for isotropic and anisotropic tracts. Main results. The area and depth of axonal activation strongly depend on tissue conductivity and electrode parameters. The largest activation area results from biphasic stimulation with bipolar electrodes oriented orthogonal to axonal fasciculi, for anisotropic and especially isotropic tracts. For anisotropic tracts, the maximal activation depth is similar regardless of whether a monopolar or bipolar electrode is employed. For isotropic tracts, bipolar parallel and monopolar stimulation activate axons deeper than orthogonal bipolar stimulation. Attention is warranted during monophasic stimulation: a blockade of action potentials immediately under cathodes and a propagation of action potentials under anodes are found. Significance. Considering the spatial patterns of blockade and activation present during monophasic stimulation with both monopolar and bipolar electrodes, biphasic stimulation is recommended to explore subcortical axon responses during intraoperative mapping. Finally, the precise effect of electrical stimulation depends on conductivity profiles of tracts, and as such, should be explicitly considered for each individual subject and tract undergoing intracranial mapping.

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Characterization of Machine Learning‐Based Surrogate Models of Neural Activation Under Electrical Stimulation

Toni,

Pierantoni,

Verardo

et al. 2024

Bioelectromagnetics

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Electrical stimulation of peripheral nerves via implanted electrodes has been shown to be a promising approach to restore sensation, movement, and autonomic functions across a wide range of illnesses and injuries. While in principle computational models of neuromodulation can allow the exploration of large parameter spaces and the automatic optimization of stimulation devices and strategies, their high time complexity hinders their use on a large scale. We recently proposed the use of machine learning‐based surrogate models to estimate the activation of nerve fibers under electrical stimulation, producing a considerable speed‐up with respect to biophysically accurate models of fiber excitation while retaining good predictivity. Here, we characterize the performance of four frequently employed machine learning algorithms and provide an illustrative example of their ability to generalize to unseen stimulation protocols, stimulating sites, and nerve sections. We then discuss how the ability to generalize to such scenarios is relevant to different optimization protocols, paving the way for the automatic optimization of neuromodulation applications.

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Model based optimal multipolar stimulation without a priori knowledge of nerve structure: application to vagus nerve stimulation

Cited by 39 publications

References 43 publications

Pressure measurement of geometrically curved ultrasound transducer array for spatially specific stimulation of the vagus nerve

Pressure measurement of geometrically curved ultrasound transducer array for spatially specific stimulation of the vagus nerve

Modeling subcortical white matter stimulation

Characterization of Machine Learning‐Based Surrogate Models of Neural Activation Under Electrical Stimulation

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