Pragmatic Trial of Remote tDCS and Somatosensory Training for Phantom Limb Pain with Machine Learning to Predict Treatment Response

利用机器学习预测治疗反应的远程 tDCS 和体感训练治疗幻肢痛的实用试验

• 批准号: 10671480
• 负责人: Felipe Fregni
• 金额: $ 61.24万
• 依托单位: SPAULDING REHABILITATION HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10671480
• 关键词: Address; Affect; Anodes; Anterior; Area; Behavior Therapy; Behavioral; Biological Markers; Brain; Characteristics; Chronic; Clinical; Combined Modality Therapy; Conduct Clinical Trials; Control Groups; Data; Devices; Disease; Effectiveness; Esthesia; Force of Gravity; Goals; Hand; Health Professional; Home; Individual; Intervention; Investigation; Knowledge; Life; Machine Learning; Meta-Analysis; Modernization; Motor; Motor Cortex; Movement; Occupational Therapy; Operative Surgical Procedures; Pain; Pain management; Paper; Patients; Phantom Limb; Phantom Limb Pain; Pharmacological Treatment; Phenotype; Physical therapy; Prediction of Response to Therapy; Probability; Protocols documentation; Publishing; Randomized; Rehabilitation therapy; Research; Resistance; Surrogate Markers; Syndrome; Techniques; Technology; Testing; Therapeutic; Training; Underrepresented Populations; Validation; Visit; chronic neuropathic pain; chronic pain; clinical predictors; cognitive training; comparison control; cost; design; effectiveness evaluation; effectiveness testing; efficacy testing; evidence base; experience; functional restoration; gray matter; heart rate variability; improved; indexing; innovation; machine learning algorithm; machine learning method; neural; neural circuit; neuroimaging; neurological rehabilitation; neuronal circuitry; neurophysiology; neuroregulation; noninvasive brain stimulation; novel; pain patient; pain reduction; pain relief; painful neuropathy; pharmacologic; portability; pragmatic trial; predicting response; remote assessment; remote therapy; research study; residual limb; response; response biomarker; somatosensory; spinal cord injury pain; statistical and machine learning; transcranial direct current stimulation; treatment as usual; treatment response; treatment strategy; trial design

PROJECT SUMMARY/ABSTRACT: Phantom limb pain (PLP) is considered an extremely hard-to-treat disorder, given that traditional treatments are not effective in targeting the maladaptive neuronal circuits associated with chronic pain. Transcranial direct current stimulation (tDCS) is a non-invasive, safe brain stimulation technique that has been shown to revert maladaptive plasticity as well as reduce pain in neuropathic pain and other pain syndromes. Our previous R01 on this topic has demonstrated the efficacy of tDCS combined with somatosensory training in a controlled setting to improve pain and that this intervention changes PLP associated cortical plasticity. Our previous R01 also shown pain phenotypes based on PLP characteristics that are more responsive to this treatment. The objective of this renewal is to provide novel data to address critical knowledge gaps such as (i) testing a portable device that would reach underrepresented populations; (ii) validation of this therapy in a more pragmatic setting; (iii) confirmation and testing of predictors of response with statistical and machine learning techniques; and (iv) testing the parasympathetic tone changes (with the remote assessment) as a biomarker of neuropathic pain relief. The central hypothesis is that a combination of home-based tDCS and somatosensory therapy will reduce pain in PLP patients. Our long-term goal is to develop a cheap, efficacious, safe, and practical treatment for PLP. Our rationale is that understanding the effects of tDCS in a real-life setting will validate this treatment for PLP and identify predictors of response to this treatment will help health professionals better target and more precisely treat individuals with this condition. Our specific aims will test the following hypotheses: (Aim 1) tDCS combined with somatosensory therapy will be associated with a significantly larger effect size (of at least 1) compared to the control condition in pain reduction; (Aim 2) identifying predictors of response of this combined treatment using machine learning algorithms will help identify different pain phenotypes in patients with PLP and improve their target treatment; (Aim 3) combined treatment will bolster the parasympathetic tone (as indexed by higher heart rate variability) and reduce sympathetic activation, changes which will be correlated with PLP decreases. This contribution is significant because, although several studies have tested the efficacy of tDCS for chronic pain, there is a need to evaluate its effectiveness in a real-world setting, and this proposal provides critical data to develop a safe and unique intervention to be applied at home, which can therefore increase its access to underrepresented populations and decrease therapeutic costs. This investigation will also provide mechanistic data on predictors of response and changes in parasympathetic tone associated with this intervention. The proposed research is innovative because it offers a pragmatic trial design for PLP treatment and aims to validate a home-based tDCS device that is feasible and able to provide longer treatments remotely. This proposal is also investigating HRV as a surrogate marker for reducing PLP and assessing its feasibility in a real-life setting. Finally, this proposal validates predictors of response to PLP treatment using machine learning algorithms.

项目概要/摘要： 幻肢痛 (PLP) 被认为是一种极难治疗的疾病，因为传统的治疗方法是 不能有效针对与慢性疼痛相关的适应不良神经元回路。经颅直接 电流刺激 (tDCS) 是一种非侵入性、安全的脑部刺激技术，已被证明可以恢复 适应不良可塑性以及减轻神经性疼痛和其他疼痛综合征的疼痛。我们之前的R01 关于这个主题的研究已经证明了在受控环境中 tDCS 与体感训练相结合的功效 改善疼痛，并且这种干预改变了 PLP 相关的皮质可塑性。我们之前的R01也 显示了基于 PLP 特征的疼痛表型，这些特征对这种治疗更敏感。目标 此次更新的目的是提供新颖的数据来解决关键知识差距，例如（i）测试便携式设备 这将惠及代表性不足的人群； (ii) 在更务实的环境中验证该疗法； (三) 使用统计和机器学习技术确认和测试反应预测因子； (四) 测试副交感神经张力变化（通过远程评估）作为神经性疼痛的生物标志物 宽慰。中心假设是家庭经颅直流电刺激 (tDCS) 和体感疗法的结合将减少 PLP 患者的疼痛。我们的长期目标是开发一种廉价、有效、安全、实用的 PLP 治疗方法。 我们的理由是，了解 tDCS 在现实生活中的效果将验证这种 PLP 治疗方法 并确定对该治疗反应的预测因素将帮助卫生专业人员更好地瞄准和更准确地 治疗患有这种疾病的人。我们的具体目标将测试以下假设：（目标 1）结合 tDCS 与体感疗法相比，体感疗法将带来显着更大的效应量（至少 1） 减轻疼痛的控制条件； （目标 2）确定这种联合治疗反应的预测因素 使用机器学习算法将有助于识别 PLP 患者的不同疼痛表型并改善 他们的目标治疗； （目标 3）联合治疗将增强副交感神经张力（以较高的指数表示） 心率变异性）并减少交感神经激活，与 PLP 减少相关的变化。 这一贡献意义重大，因为尽管有几项研究已经测试了经颅直流电刺激 (tDCS) 对慢性 痛苦，需要在现实环境中评估其有效性，并且该提案提供了关键数据 开发一种适用于家庭的安全且独特的干预措施，从而增加其获得治疗的机会 代表性不足的人群并降低治疗费用。这项调查还将提供机制 与此干预相关的反应预​​测因素和副交感神经张力变化的数据。这 拟议的研究具有创新性，因为它为 PLP 治疗提供了务实的试验设计，旨在验证 一种可行且能够远程提供更长时间治疗的家用 tDCS 设备。这个提议也是 研究 HRV 作为减少 PLP 的替代指标，并评估其在现实生活中的可行性。 最后，该提案使用机器学习算法验证了 PLP 治疗反应的预测因子。

项目成果

Digitalized transcranial electrical stimulation: A consensus statement.

数字化经颅电刺激：共识声明。

• 发表时间: 2022-11
• 作者: Brunoni, Andre R;Ekhtiari, Hamed;Antal, Andrea;Auvichayapat, Paradee;Baeken, Chris;Benseñor, Isabela M;Bikson, Marom;Boggio, Paulo;Borroni, Barbara;Brighina, Filippo;Brunelin, Jerome;Carvalho, Sandra;Caumo, Wolnei;Ciechanski, Patrick;Charvet
• 通讯作者: Charvet

Editorial: Bench to Bedside - the translation of intracortical inhibition marker to clinical practice.

社论：从工作台到床边——皮质内抑制标记物到临床实践的转化。

• 发表时间: 2022-12-02
• 作者: Pimenta, Danielle Carolina;Lima, Daniel;Slawka, Eric;Pacheco;Fregni, Felipe
• 通讯作者: Fregni, Felipe

Editorial - Seeking Brain Homeostatic Compensatory Mechanisms for Pain Control.

社论-寻求大脑稳态补偿机制来控制疼痛。

• 发表时间: 2023-09-03
• 作者: Pacheco;Marques, Lucas M;Dodurgali, Mustafa Reha;Martinez;Barbosa, Sara P;De Andrade, Marianna;Márquez, Jorge Ortega;de Melo, Paulo S;Simis, Marcel;Caumo, Wolnei;Fregni, Felipe
• 通讯作者: Fregni, Felipe

Effects of Prefrontal Transcranial Direct Current Stimulation and Motivation to Quit in Tobacco Smokers: A Randomized, Sham Controlled, Double-Blind Trial.

前额经颅直流电刺激和吸烟者戒烟动机的影响：一项随机、假手术对照、双盲试验。

• 发表时间: 2018
• 作者: Vitor de Souza Brangioni, Maria C;Pereira, Danilo A;Thibaut, Aurore;Fregni, Felipe;Brasil;Boechat
• 通讯作者: Boechat

STRATEGIES TO ENHANCE RECRUITMENT METHODS IN PHANTOM LIMB PAIN CLINICAL TRIALS.

加强幻肢痛临床试验中招募方法的策略。

• 发表时间: 2017-04
• 作者: Pinto, Camila Bonin;Vélez, Faddi Ghassan Saleh;French, Melanie N;Zeng, Dian;Crandell, David;Bolognini, Nadia;Merabet, Lotfi B;Fregni, Felipe
• 通讯作者: Fregni, Felipe