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Noninvasive Brain Stimulation, Maladaptive Plasticity, and Bayesian Analysis in Phantom Limb Pain

期刊

MEDICAL ACUPUNCTURE
卷 29, 期 4, 页码 220-228

出版社

MARY ANN LIEBERT, INC
DOI: 10.1089/acu.2017.1240

关键词

phantom limb pain; noninvasive brain stimulation; maladaptive plasticity; Bayesian model

资金

  1. Institutional National Research Service Award from National Center for Complementary and Integrative Health grant [T32AT000051]
  2. Ryoichi Sasakawa Fellowship Fund
  3. Program in Placebo Studies at Beth Israel Deaconess Medical Center

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Introduction: Phantom limb pain (PLP) is a common and poorly understood pathology of difficult medical control that progressively takes place after amputation occurs. Objective: This article discusses the multifactorial bases of PLP. These bases involve local changes at the stump level, spinal modifications of excitability, deafferentation, and central sensitization, leading to the development of maladaptive plasticity, and consequentially, defective processing of sensory information by associative neural networks. These changes can be traced by neurophysiology and imaging topographical studies, indicating a degree of cortical reorganization that perpetuates pain and discomfort. Intervention: Noninvasive brain stimulation can be an alternative way to manage PLP. This article discusses two techniquestranscranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS)that have shown promising results for controlling PLP. The modulation that both techniques rely on is based on synaptic mechanisms linked to long-term potentiation and long-term depression phenomena. By applying tDCS or rTMS, clinicians can target processes associated with central sensitization and maladaptive plasticity, while promoting adequate sensory information processing by integrative cognitive behavioral techniques in a comprehensive rehabilitation program. Conclusions: Understanding PLP from a dynamic neurocomputational perspective will help to develop better treatments. Furthermore, Bayesian analysis of sensory information can help guide and monitor therapeutic interventions directed toward PLP resolution.

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