Analysis of brain microcircuitry with statistical physics and machine learning tools

大约每五个人中就有一个人在他们的一生中会遭受精神健康或神经系统疾病的折磨。由于神经系统的巨大复杂性，了解痴呆、癫痫、抑郁和双相情感障碍等疾病的潜在细胞机制已被证明是一项具有挑战性的任务。为了了解大脑和脊髓疾病的根本原因，下一代诊断成像技术将需要更详细地了解大脑和脊髓不同区域中不同类型的神经元如何共同编码，调节，处理和传递神经系统内的信息。在过去的二十年里，功能成像技术，如fMRI和PET，为我们对大脑功能的理解提供了显着的改善。然而，这些方法和当前的光学设备不允许同时监测涉及疾病并且位于大脑和脊髓深处的多个区域中的大神经元集合中的单个神经元的活动。Neurescence是一家加拿大拥有和经营的公司，是市场上唯一一家能够同时监测多个大脑和脊髓区域神经元群体活动的公司，其第一个可用的产品称为Quartet。Quartet技术的新奇需要适当的新工具来分析它提供的数据，以产生对大脑功能最重要的洞察。有了这些新工具，Neurescence平台可以更好地理解中枢神经系统的系统水平操作，并利用这种理解 ** 进行合理的药物开发。在本提案中，我们将应用最近开发的植根于统计物理学的机器学习工具来分析Quartet获得的数据，并将开发有效的推理和分析场地，其总体目标是将数学分析集成到Neurescence的软硬件包中。这除了Neurescence的产品线允许雇用额外的高 ** 合格的人员，并有助于加拿大经济。

Approximately one in every five people will suffer from a mental health or a neurological condition during**their life time. Due to the enormous complexity of the nervous system, understanding the underlying cellular**mechanisms of diseases such as dementia, epilepsy, depression and bipolar disorder has proven to be a**challenging task. To understand the underlying causes of brain and spinal cord diseases, the next generation of**diagnostic imaging technologies will need to provide more detailed understanding of how distinct types of**neurons in different regions of the brain and the spinal cord work together to encode, regulate, process and**transfer information within the nervous system. In the past two decades, functional imaging technologies, such**as fMRI and PET have provided a significant improvement in our understanding of the brain function.**However, these methods and current optical devices do not allow simultaneous monitoring of activities of**individual neurons in large neuronal assemblies in multiple regions that are involved in a disease and are**located deep in the brain and in the spinal cord. Neurescence, a Canadian owned and operated company, is the**only company in the market that offers the capability of simultaneously monitoring the activities of neuronal**populations in multiple brain and spinal cord regions, with its first available product called Quartet. The**novelty of Quartet technology requires appropriate new tools for the analysis of the data it provides to yield the**most significant insight into brain function. With these new tools, Neurescence platform enables better**understanding of the system level operation of the central nervous system and leveraging this understanding**for rational drug development. In this proposal we will apply recently developed machine learning tools rooted**in statistical physics to the analysis of the data obtained by Quartet and will develop efficient inference and**analysis venues with the overarching goal of integrating the mathematical analysis into a software-hardware**package for Neurescence. This addition to Neurescence's product line allows employment of additional highly**qualified personnel and contribute to the Canadian economy.