Multisensory signals used for learning

我的研究重点是大脑中多感觉信号的整合，以及它们是如何被用来驱动运动的。为了做到这一点，我利用fMRI、EEG、MEG和眼球跟踪技术来研究舞者和健康对照组。舞蹈是理想的，因为它结合了视觉、听觉、触觉和体感来计划、执行和纠正动作。我的研究考察了专业舞者、业余舞者和为了治疗而跳舞的人。了解多感官信号如何被用来训练大脑，在帮助微调我们所有人学习移动的方式，或者学习绕过受损的运动神经回路方面，可能会有深远的应用。*我观察到舞蹈训练导致的特定神经变化。通过对加拿大国家芭蕾舞团(NBoC)专业舞者的研究，我发现，随着专业舞者在8个月的时间里学习舞蹈序列，补充运动区(SMA)的fMRI活动遵循着一种增加和随后下降的模式。我观察到业余舞者在学习一段短曲时也有类似的模式。这暗示了马达电路与音乐和学习的重新连接。我们的具体目标是更深入地了解这种神经重新连接，这可能会揭示一个尚不清楚的运动学习的神经机制。尚不清楚的是，为什么初学跳舞的人的脑电结果在跳舞后显示出第二个神经动态变化：阿尔法波频率上令人着迷的振荡增加。Cecere等人(2015)认为，这可能表明大脑增加了多感觉输入的整合，如听力和视觉。我想通过进一步的研究来确定这种增加的阿尔法能量的意义。我们的实验室想要确定：*-大脑中这种情况发生在哪里(项目1-功能磁共振成像)*-大脑动力学是如何变化的(项目2-EEG/MEG)*-行为和计算建模(项目3和4)*为了补充我的工作，我计划使用多伦多贝克雷斯特医院罗特曼研究所的虚拟大脑软件。虚拟大脑是一种强大的新工具，可以利用结构和功能动力学的连接体来进行模型预测，来检查个人的大脑。正如我观察到的听觉和SMA与阿尔法波活动的变化所表明的那样，多感觉训练能够改变大脑的连接和输出。有了虚拟大脑，就有可能观察到运动学习连接的变化可能产生的预期结果。与我的研究相结合，这将有助于开发和定制多感官训练，以最好地教授个人，无论他们是运动员还是业余选手，无论是年轻人还是老年人。

My research focuses on the integration of multisensory signals in the brain and how they are used to drive movement. To do this, I utilize fMRI, EEG, MEG and eye-tracking techniques to study dancers and healthy controls. Dance is ideal because it incorporates vision, audition, touch, and somatosensation to plan, execute, and correct movement. My studies examine expert dancers, amateurs, and people dancing for therapy. Learning how multisensory signals may be used to train the brain could have far-reaching applications in helping to fine-tune the way we all learn to move, or learning to circumvent damaged motor circuits.******I have observed specific neural changes resulting from dance training. Through studying professional dancers at the National Ballet of Canada (NBoC), I have shown that fMRI activity in the supplemental motor area (SMA) follows a pattern of increase and subsequent decline as expert dancers learn a dance sequence over 8 months. I observed a similar pattern in amateur dancers learning a short piece. This hints at a rewiring of motor circuits with music and learning. Our specific aims are to achieve a deeper understanding of this neural rewiring which may uncover a yet unknown neural mechanisms of motor learning. What is unknown is why initial EEG results from a novice dancers have shown a second neural dynamic change following dancing: a fascinating increase in power oscillation at the alpha wave frequency. Cecere et al (2015) suggests this may be indicative of the brain's increased integration of multisensory input like audition with vision. I would like to determine the significance of this increased alpha power with further study. Our lab would like to determine: ***-where in the brain this occurs (Project 1 - fMRI)***-how brain dynamics are changing (Project 2 -EEG/MEG)***-behaviour and computation modelling (Projects 3 & 4)******To complement my work, I plan to utilize The Virtual Brain software from Rotman Institute at Baycrest Hospital in Toronto. The Virtual Brain is a powerful new tool to examine individual brains using the connectome of structural and functional dynamics to make model predictions. As suggested by the changes in auditory and SMA with alpha wave activity I have observed, multisensory training is capable of changing the brain's connectivity and output. With The Virtual Brain, it will be possible to observe the expected outcomes that changes in motor learning connections are likely to produce. When combined with my research, this will help develop and customize multisensory training to best teach an individual, whether they are an athlete or an amateur, young or old.