Design and Performance Assessment of a Solid-State Microcooler for Thermal Neuromodulation

Fernandes, J. M.; Vendramini, Estelle; Miranda, Alice; Silva, Cristiana J.; Dinis, Hugo; Coizet, Véronique; David, Olivier; Mendes, Paulo

doi:10.3390/mi9020047

Cited by 11 publications

(6 citation statements)

References 40 publications

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“…Further complications can follow from parasitic Joule heating and associated unintended effects on the targeted tissue (Elwassif et al, 2006). Thermal Recent studies demonstrate that careful control of thermal effects can be leveraged as an alternative methodology for neu-romodulation, with relevance in treating neuropathic pain (Brito et al, 2014;Patapoutian et al, 2009), epilepsy (Fernandes et al, 2018), and peripheral neuropathy (Xing et al, 2007). The underlying mechanisms follow from temperature induced changes in the cell membrane capacitance and/or changes in the conductance of thermosensitive transient receptor potential ion channels (e.g., TRPV1 with activation temperature of >$41 C, TRPM8 with activation temperature of <$30 C), both resulting in ionic transport (Luan et al, 2014).…”

Section: Neuromodulation Modalitiesmentioning

confidence: 99%

Emerging Modalities and Implantable Technologies for Neuromodulation

Won

Song

Reeder

et al. 2020

Cell

199

149

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Techniques for neuromodulation serve as effective routes to care of patients with many types of challenging conditions. Continued progress in this field of medicine will require (1) improvements in our understanding of the mechanisms of neural control over organ function and (2) advances in technologies for precisely modulating these functions in a programmable manner. This review presents recent research on devices that are relevant to both of these goals, with an emphasis on multimodal operation, miniaturized dimensions, biocompatible designs, advanced neural interface schemes, and battery-free, wireless capabilities. A future that involves recording and modulating neural activity with such systems, including those that exploit closed-loop strategies and/or bioresorbable designs, seems increasingly within reach.

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Section: Neuromodulation Modalitiesmentioning

confidence: 99%

Emerging Modalities and Implantable Technologies for Neuromodulation

Won

Song

Reeder

et al. 2020

Cell

199

149

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“…[44] Peltier elements, which carry out thermoelectric cooling, have also been shown theoretically to reduce the temperature of brain regions as much as 15 ºC. [45]…”

Section: Thermalmentioning

confidence: 99%

“…By contrast, there are a number of possible explanations [ 43 ] for IR neural inhibition, culminating in a reproducible effect termed “heat block.” [ 44 ] Peltier elements, which carry out thermoelectric cooling, have also been shown theoretically to reduce the temperature of brain regions as much as 15 ºC. [ 45 ]…”

Section: Development Of Neurostimulation Techniques: From Macroscale ...mentioning

confidence: 99%

Magneto‐Optogenetic Deep‐Brain Multimodal Neurostimulation

Walton

McGlynn

Das

et al. 2021

Advanced Intelligent Systems

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Electrical neurostimulation has been used successfully as a technique in both research and clinical contexts for over a century. Despite significant progress, inherent problems remain, hence there has been a drive for novel neurostimulation modalities including ultrasonic, magnetic, and optical, which have the potential to be less invasive, have enhanced biointegration, deeper stimulus penetration from the probe, and higher spatiotemporal resolution. Optogenetics—the optical stimulation of genetically photosensitized neurons, enables highly precise genetic targeting of the stimulus. Specifically, it allows for selective optical excitation and inhibition via different wavelengths. As such, optogenetics has become a prominent tool for neuroscience. Herein, the complementarity between different forms of neurostimulation is explored with a focus on cranial magnetic and optogenetic stimulation. Magnetic stimulation is complementary to optogenetics in that it does not require an electrochemical tissue interface like in the case of electrical stimulation. Furthermore, if incorporated onto the same probe as one with light emitters, its stimulation field can be orthogonal to the light emission field—allowing for complementary stimulus fields. Herein, dual optogenetic and magnetic modalities are proposed that can unite to yield a powerful and versatile tool for neural engineering.

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“…We need a therapeutic method, and thus, the therapeutic model should be capable of recognizing seizures at their onset stage. This model is grouped by the treatment used to slow the progression of seizures: local electrode stimulation (Li & Cook, 2018), thermal stimulation (Fernandes et al., 2018), or neurochemical stimulation (Wang et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

A medium‐weight deep convolutional neural network‐based approach for onset epileptic seizures classification in EEG signals

Nemati

Meshgini

2022

Brain and Behavior

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Introduction Epileptic condition can be detected in EEG data seconds before it occurs, according to evidence. To overcome the related long‐term mortality and morbidity from epileptic seizures, it is critical to make an initial diagnosis, uncover underlying causes, and avoid applicable risk factors. Progress in diagnosing onset epileptic seizures can ensure that seizures and destroyed damages are detectable at the time of manifestation. Previous seizure detection models had problems with the presence of multiple features, the lack of an appropriate signal descriptor, and the time‐consuming analysis, all of which led to uncertainty and different interpretations. Deep learning has recently made tremendous progress in categorizing and detecting epilepsy. Method This work proposes an effective classification strategy in response to these issues. The discrete wavelet transform (DWT) is used to breakdown the EEG signal, and a deep convolutional neural network (DCNN) is used to diagnose epileptic seizures in the first phase. Using a medium‐weight DCNN (mw‐DCNN) architecture, we use a preprocess phase to improve the decision‐maker method. The proposed approach was tested on the CHEG‐MIT Scalp EEG database's collected EEG signals. Result The results of the studies reveal that the mw‐DCNN algorithm produces proper classification results under various conditions. To solve the uncertainty challenge, K‐fold cross‐validation was used to assess the algorithm's repeatability at the test level, and the accuracies were evaluated in the range of 99%–100%. Conclusion The suggested structure can assist medical specialistsin analyzing epileptic seizures' EEG signals more precisely.

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Design and Performance Assessment of a Solid-State Microcooler for Thermal Neuromodulation

Cited by 11 publications

References 40 publications

Emerging Modalities and Implantable Technologies for Neuromodulation

Emerging Modalities and Implantable Technologies for Neuromodulation

Magneto‐Optogenetic Deep‐Brain Multimodal Neurostimulation

A medium‐weight deep convolutional neural network‐based approach for onset epileptic seizures classification in EEG signals

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