Learning and plasticity in the human brain

这项研究的主要目标是：i）确定学习和经验如何影响大脑的结构和功能; ii）确定学习和可塑性如何被调节。 正在进行的研究主要有两个领域： 1)截肢后皮质手的表征如何改变？ 我们一生中的感觉运动经验被认为塑造了身体的神经表征。当成年人的大脑失去了一个关键的输入源时，比如说，在手臂被截肢后，它会发生什么？我们纵向调查了选择性手截肢前后皮质手代表的稳定性。两名患者在截肢前和截肢后的两个不同时间点（3个月和6个月）接受了两次脑功能成像。此外，我们在相同的时间范围内扫描了15名年龄匹配的健全对照参与者（共60次扫描）。使用映射的数字地形，代表性的相似性分析和解码随着时间的推移，我们显示了一个显着一致的数字间的代表性结构的截肢前的手和截肢后的幻影（失踪）的手。总的来说，这项工作提供了第一个前和截肢后的纵向证据保留代表性的幻影（失踪）截肢后的手。这些证据对于思考脑机接口（BCI）和假体设备的发展至关重要。 2)调节学习-假肢训练对手部表征的影响（NCT 00001360） 在以前的工作中，我们发现，专家工具变得更加区别于手的表示（即较少体现）。在截肢者的假肢上也发现了类似的结果。然而，假体装置的发展强调实施例作为目标，装置的设计和控制变得更加仿生（模仿身体）。在这种情况下，值得注意的是，假肢手的使用率很低，甚至在上肢截肢人群中完全排斥装置也很常见。如果使用一种设备的经验并没有导致它更像一个真实的身体部位，那么也许设计人造手的追求，以同样的方式看和行动，作为人体是误导，实际上可能会阻碍使用。在这项研究中，我们调查了不同的训练方案，特别是直觉（仿生）与任意控制机制，影响假肢（仿生）和真实的手的代表性。我们训练身体健全的参与者使用一种类似于假肢的假手模拟器，这种模拟器可以绑在前臂上，并通过前臂的肌电图（EMG）读数进行控制。参与者接受仿生控制系统（将机器人手部运动与真实的手部运动相匹配）或任意控制系统（将机器人手部运动与任意真实的手部运动相匹配）的训练。对于两个训练组，训练改善了仿生肢体控制，减少了认知依赖，并增加了仿生手的体现。仿生用户在训练早期有更直观和更快的控制。任意用户在稍后的训练中匹配仿生性能。此外，任意用户表现出增加的泛化到一个新的控制策略。总的来说，我们的研究结果表明，仿生和任意控制策略提供了不同的好处。最佳策略可能不是严格的仿生，而是在仿生范围内的任意频谱内的灵活策略，这取决于用户、可用的培训机会和用户要求。 除了行为数据，我们还收集了训练前后的功能性MRI测量。在正在进行的分析中，我们正在研究真实的和仿生手的视觉和运动表征如何在训练过程中发生变化。 确定成人皮质可塑性的性质、程度和后果，为神经系统损伤或功能障碍后的康复性脑治疗提供了重要的见解。

The primary goals of this research are i) to establish how learning and experience impact the structure and function of the brain, and ii) to determine how learning and plasticity can be modulated. There are two main areas of ongoing research: 1) How does the cortical hand representation change following amputation? Sensorimotor experiences throughout our lifespan are thought to shape the neural representation of the body. What happens to the adult brain when it loses a key source of input, for example, following the amputation of an arm? We longitudinally investigated the stability of the cortical hand representation, before and after elective hand amputation. Two patients underwent functional brain imaging twice pre-amputation and at two separate time-points following amputation: 3 months and 6 months. Additionally, we scanned 15 age-matched able-bodied control participants across the same timescale (60 scans in total). Using mapping of digit topography, representational similarity analysis and decoding over time, we show a remarkably consistent inter-digit representational structure of the pre-amputation hand and the post-amputation phantom (missing) hand. Overall, this work provides the first pre- and post-amputation longitudinal evidence for preserved representation of the phantom (missing) hand following amputation. Such evidence is critical for thinking about the development of brain computer interfaces (BCI) and prosthetic devices. 2) Modulating learning - Impact of prosthesis training on hand representations (NCT00001360) In prior work, we found that expert tools become more differentiated from the representation of the hand (i.e. less embodied). A similar result has been found for prosthetic limbs in amputees. However, the development of prosthetic devices emphasizes embodiment as the goal with the design and the control of devices becoming more biomimetic (mimicking the body). In this context it is notable that there are low rates of prosthetic hand usage and even complete device rejection are common in upper-limb amputee populations. If experience with a device does not lead to representing it more like a real body part, then maybe the quest to design artificial hands that look and act in the same way as the human body is misguided and may actually hinder usage. In this study, we investigated how different training regimens, and in particular intuitive (biomimetic) versus arbitrary control mechanisms, impact the representation of prosthetic (bionic) and real hands. We trained able-bodied participants to use an artificial hand simulator, similar to a prosthesis, that can be strapped to the forearm and controlled via electromyographic (EMG) readings from the forearm. Participants were trained with either a biomimetic control system (matching robotic hand movements to real hand movements) or an arbitrary control system (matching robotic hand movements to arbitrary real hand movements). For both trained groups, training improved bionic limb control, reduced cognitive reliance, and increased embodiment over the bionic hand. Biomimetic users had more intuitive and faster control early in training. Arbitrary users matched biomimetic performance later in training. Further, arbitrary users showed increased generalization to a novel control strategy. Collectively, our findings suggest that biomimetic and arbitrary control strategies provide different benefits. The optimal strategy is likely not strictly biomimetic, but rather a flexible strategy within the biomimetic to arbitrary spectrum, depending on the user, available training opportunities and user requirements. In addition to the behavioral data, we also collected functional MRI measures before and after training. In ongoing analyses we are investigating how visual and motor representations of the real and bionic hand change over the course of training. Establishing the nature, degree and consequences of plasticity in the adult cortex provides important insights into the potential for rehabilitative brain therapies following injury or dysfunction in the nervous system.