Learning and plasticity in the human brain

这项研究的主要目标是：i）确定学习如何影响大脑的结构和功能; ii）确定学习如何通过反馈（积极或消极）等因素进行调节。在过去的一年里，我们集中精力实现这两个目标。 1)学习对大脑结构和功能的影响（NCT 00001360） 在过去的几年里，我们一直在对学习不同任务（例如运动序列，空间布局）的参与者进行长期的纵向研究，以确定大脑（灰质，白色物质）的结构特性如何随时间变化。在为期四周的时间里，参与者接受了两项不同任务的培训，我们在培训过程中收集了大量的功能和结构MRI数据。 虽然以前的研究已经确定了与学习相关的结构变化，即使是在几个小时的过程中，我们的初步研究结果强调了一个潜在的混淆，需要在这些研究中加以解释。具体来说，我们已经发现，与MRI获得的结构和功能的措施波动，根据一天中的时间。在结构上，我们观察到皮质厚度的减少和心室容积的增加以及与脑脊液增加相关的游离水体积分数的增加。在功能上，我们观察到在类似的区域，我们观察到的结构变化的静息状态连接的变化。总的来说，这些结果表明，我们检测到的MRI测量值的昼夜波动具有潜在的生理基础，并可能反映了昼夜节律的影响。 有趣的是，这些波动似乎受到训练的调节，我们正试图确定除了这些时间效应之外，训练还会发生哪些额外的结构和功能变化。在运动序列任务中，我们发现，经过训练后，感觉运动网络的功能连接发生了变化。相反，在空间布局任务中，与导航相关的海马网络发生了变化。重要的是，即使我们排除了可能只是反映一天中时间的任何变化，也会观察到这些变化。总的来说，我们的研究为以下观点提供了进一步的支持：静息状态连接可以提供有关离线大脑过程的有意义的见解，这些过程有助于技能的获得，并表明特定网络中特定任务的变化是学习的基础，而不是由于昼夜节律波动而引起的任何变化。此外，我们的研究强调了在学习和可塑性研究中控制时间等因素的重要性。 2)反馈对学习的影响（NCT 00001360） 我们一直在研究反馈（积极的，消极的）对运动学习的影响。各组参与者接受两种不同运动任务之一的训练，并提供积极，消极或中性（无信息）反馈。训练在MRI扫描仪中进行，我们在训练前、训练中和训练后测量了fMRI活动。 行为上，我们发现反馈的影响取决于任务。在序列学习任务中，我们发现惩罚提高了在线性能，而在纯粹的运动力跟踪任务中，我们观察到相反的效果。 在大脑活动方面，我们发现奖励和惩罚以特定于任务的方式不同地影响了前运动皮层（PMC，一个已知对学习排序技能至关重要的区域）的功能连接。对于连续反应时间任务（响应视觉提示按下一系列按钮），奖励训练增加了PMC与小脑和纹状体的连接，而惩罚训练增加了PMC内侧颞叶的连接。对于力跟踪任务（挤压一个酒吧来控制屏幕上光标的移动），控制反馈和奖励的训练增加了PMC与顶叶和颞叶皮层的连接，而惩罚的训练增加了PMC与腹侧纹状体的连接。虽然两个实验的结果在某些大脑区域重叠，包括腹侧苍白球，颞叶和小脑，但这些区域在不同的反馈条件下显示出不同的结果模式。总的来说，这些发现表明，奖励和惩罚会影响训练后的自发大脑活动，并且所涉及的区域取决于所学习的任务。 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 impacts the structure and function of the brain, and ii) to determine how learning can be modulated by factors such as feedback (positive or negative). Over the past year we have focused on both of these goals. 1) Impact of learning on brain structure and function (NCT00001360) Over the past few years, we have been conducting a long-term longitudinal study of participants learning different tasks (e.g. motor sequences, spatial layout) to determine how structural properties of the brain (gray matter, white matter) change over time. Over a period of four weeks, participants were trained in two different tasks and we collected extensive functional and structural MRI data over the course of training. While previous studies have identified structural changes associated with learning, even over the course of a couple of hours, our initial findings have highlighted a potential confound that needs to be accounted for in such studies. Specifically, we have found that the structural and functional measures obtained with MRI fluctuate according to the time of day. Structurally, we observed decreases in cortical thickness and increases in ventricular volume as well as increases in free water volume fraction that is associated with an increase in cerebrospinal fluid. Functionally, we observed changes in resting state connectivity in similar regions to those where we observed structural changes. Collectively, these results suggest that the diurnal fluctuations in MRI measures that we detected have an underlying physiological basis and may reflect the impact of the circadian rhythm. Interestingly, these fluctuations appear to be modulated by training and we are trying to establish what additional structural and functional changes occur with training above and beyond these time-of-day effects. With the motor sequence task we find that, following training, sensorimotor networks show changes in their functional connectivity. In contrast, with the spatial layout task, hippocampal networks that have been associated with navigation change. Critically, these changes are observed even when we factor out any changes that might reflect simply the time of day. Overall, our study provides further support for the idea that resting state connectivity can provide meaningful insights regarding the offline brain processes that contribute to skill acquisition and suggest that task-specific changes in particular networks underlie learning above and beyond any changes due to circadian fluctuations. Further, our study highlights the importance of controlling for factors such as time of day in studies of learning and plasticity. 2) Impact of feedback on learning (NCT00001360) We have been investigating the impact of feedback (positive, negative) on motor learning. Groups of participants were trained on one of two different motor tasks and either provided with positive, negative or neutral (uninformative) feedback. Training occurred in the MRI scanner and we measured fMRI activity before, during and after training. Behaviorally, we found that the impact of feedback is dependent on the task. In a sequence learning task we find that punishment improves online performance, whereas in a purely motoric force tracking task, we observe the opposite effect. In terms of brain activity, we found that reward and punishment differentially affected the functional connectivity of premotor cortex (PMC, a region known to be critical for learning of sequencing skills) in a task-specific manner. For a serial reaction time task (pushing a sequence of buttons in response to visual cues), training with reward increased PMC connectivity with cerebellum and striatum, while training with punishment increased PMC-medial temporal lobe connectivity. For a force tracking task (sqeezing a bar to control the movement of a cursor on the screen), training with control feedback and reward increased PMC connectivity with parietal and temporal cortices, while training with punishment increased PMC connectivity with ventral striatum. While the results from the two experiments overlapped in some brain areas, including ventral pallidum, temporal lobe, and cerebellum, these regions showed diverging patterns of results across the two tasks for the different feedback conditions. Collectively, these findings suggest that reward and punishment influence spontaneous brain activity after training, and that the regions implicated depend on the task learned. 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.