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Enregistrement W4389286985 · doi:10.1002/brx2.50

Sonogenetics as a promising approach for non‐invasive ultrasound neuromodulation of deep neural circuits

2023· article· en· W4389286985 sur OpenAlex

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Notice bibliographique

RevueBrain‐X · 2023
Typearticle
Langueen
DomaineEngineering
ThématiqueUltrasound and Hyperthermia Applications
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésOptogeneticsBiological neural networkNeuroscienceStriatumCre recombinaseYellow fluorescent proteinBiologyDopamineBiophysicsCell biologyTransgeneGenetically modified mouseBiochemistry

Résumé

récupéré en direct d'OpenAlex

Sonogenetics is a non-invasive approach that selectively modulates neural activities using ultrasound-reactive mediators.1 An acoustic pressure gradient is generated by introducing ultrasound waves into tissues. Since optogenetics, which is currently widely used for modulating neural activities, is invasive as it requires surgeries, a physiologically safer modulation technique is in need. Sonogenetics has a high temporal resolution and is non-invasive, accurately targeting the brain region of interest without affecting other tissues.2 A recent landmark study observed several beneficial bio-effects with the G22S mutant of the large conductance mechanosensitive ion channel MscL in mice. MscL sonogenetics could accurately target deep brain circuits such as dopamine (DA) circuits by creating a dual-viral vector strategy: one containing a Cre-recombinase-dependent enhanced yellow fluorescent protein (EYFP) or MscL-G22S-EYFP fragment and the other controlling the tyrosine hydroxylase promoter modulating Cre recombinase expression. The ventral tegmental area reward circuitry was activated to test fiber photometry (FP) recording. The authors then inserted optical fibers into the nucleus accumbens (NAc) to monitor DA activity by measuring Da2m fluorescence changes. There was a rapid increase in DA2m fluorescence in the NAc of mutant McsL-G22S mice after being inserted at a 0.3 MPa pressure, but there was no increase in fluorescence for mutant EYFP mice. Therefore, MscL sonogenetics was effective for inducing DA release in neurons. Another beneficial bio-effect for MscL sonogenetics in MscL-G22S mice was that stimulating the dorsal striatum (dSTR) neurons generated a motor response. By measuring the fluorescence changes of jRGE-CO1a (a genetically encoded calcium sensor with red fluorescence) using FP, results illustrated that applying MscL sonogenetics to the dSTR successfully induced neural activation. Mice were stimulated with ultrasound in an open-field box experiment. The results showed that MscL-G22S mice had significantly increased locomotion activity compared to EYFP mice. In addition, mobility speed and motor activity increased in the MscL-G22S mice but did not change in the EYFP mice. Furthermore, employing MscL sonogentics show alleviation effects of Parkinson's disease (PD) symptoms in freely moving mice by injecting 6-hydroxydopamine (6-OHDA) into their brains to selectively activate neurons in the subthalamus (STN). They showed the alleviation of movement symptoms in PD mice. In baseline experiments, 6-OHDA-treated PD mice showed decreased retention time in the rotarod test. However, after US stimulation, retention time significantly increased for MscL + PD mice but not for EYFP + PD mice (control). Finally, an open-field experiment demonstrated improvement in motor functions for PD mice. The MscL + PD mice showed increased movement distances and longer mobile time. Therefore, the motor symptoms of PD mice could be alleviated by US stimulation of the STN in their brain. However, several challenges remain for current sonogenetics. First, sonogenetics with transcranial ultrasound may automatically activate non-target regions in the peripheral auditory system,3 causing confounding effects between regions of interest and other non-relevant regions. Second, ultrasound waves decay with stimulation depth, making it hard to generate very stable stimulation. Third, it is uncertain whether the targeted area of the brain is activated. Fourth, sonogenetics has a low spatial resolution in axial directions such as the z-axis.4 Nonetheless, sonogenetics is safer than optogenetics, which requires an optical fiber to be inserted, an invasive procedure that requires many surgeries. However, it may not target the region as accurately as optogenetics. The recent development of wireless optogenetics enables wireless LED light sources to be used to accurately stimulate targeted brain regions in freely moving mice. In addition, sonogenetics can activate but not inhibit neural activities, which is an inherent limitation compared to optogenetics. However, as a novel non-invasive approach, sonogenetics is biologically safe, and computational sonogenetics currently enables more suitable ultrasound parameters to accurately target the neural circuits. Scientists have recently attempted to reduce the technological limitations of both sonogenetics and optogenetics by combining their working systems. In order to minimize the invasiveness of optogenetics and the low axial directional resolution of sonogenetics, a novel and less invasive technique, sono-optogenetics, was developed. Sono-optogenetics uses mechanoluminescent nanoparticles as a light source, injecting them into the blood circulation in the intrinsic circulatory system.5 Brain-penetrant-focused ultrasound could activate or inhibit light sources on a millisecond scale. Therefore, sono-optogenetics is another promising technique for effectively modulating the nervous system. Indeed, there are multiple ways to conduct in vivo experiments using a combination of different types of neuromodulation techniques. It has been demonstrated that ultrasound could produce bio-effects on tissue with a resolution on the order of 100 μm and 1 ms.6 It is believed that ultrasound-based neural modulation is a promising technique for treating multiple neurodegenerative diseases since, over the years, ultrasound has developed broad clinical uses. Additional brain disease models could be developed in the future, and other ion channels could be explored for MscL to research ultrasound stimulation mechanisms. Peiyu Liao: Conceptualization, visualization, writing—original draft. Xianglian Jia: Conceptualization, editing, reviewing. None. The authors declare no conflicts of interest. The ethics approval was not needed in this study. Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,779
Score d'incertitude au seuil0,515

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,235
Écart entre enseignants0,215 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle