Putting Humpty together again: Decoding brain networks during real-world processing

认知神经科学的一项重要任务是了解大脑如何支持日常生活中的广泛功能，如人际交流。通过将这些功能分解为可以与特定大脑区域和网络的活动相关联的离散过程，已经取得了很大的进展。然而，我们不知道的是，这些过程是否以及如何共同运作。因此，我们的首要目标将是了解大脑网络如何在更生态的条件下支持现实世界的行为。 博士项目：目的和描述（限制300字）与会者将观看电影，同时进行功能磁共振成像（fMRI）。总体目标是将结果数据分解为网络，并使用注释和机器学习方法标记这些网络的功能。然后，我们描述了不同的网络是否，何时以及如何与经验相互作用和变化，并测试关于大脑如何支持现实世界处理的新假设。神经图像采集（由Skipper监督）。我们将使用功能磁共振成像技术，从观看三个未切割的电影的人的母语中获取解剖和功能大脑图像。每部电影将由五个以前没有看过这部电影的成年人观看。我们将对数据使用标准的预处理例程。电影注释和解码（由船长和格里芬监督）。电影中的特征应该被详细标记，以解码与这些特征相关的大脑过程。即使是一部电影的特征的手工注释也需要大量的时间。为了减少这一时间，我们将首先使用现有的基于文本的描述和自动标签来提供详细的时间锁定功能。其次，我们将使用大脑数据本身来识别电影时间，以便通过众包方法获得更详细的注释。两者的结果是非常丰富的基于文本的电影描述，可以用于机器学习解码。这将通过将数据划分为训练，验证和测试数据集并使用注释来测试如何有效地使用不同的算法标记网络来完成。可能最成功的机器学习方法（支持向量机，卷积神经网络，随机森林）产生的系统的操作是不平凡的理解。因此，工作的一个组成部分将涉及探索解决方案的方法的开发，以发现用于预测的空间/时间活动模式。这些是理解大脑如何处理自然刺激的基础。

An important task for cognitive neuroscience is to understand how the brain supports broad functions that are engaged in everyday life, like interpersonal communication. Great progress has been made by decomposing these functions into discrete processes that can be associated with activity in particular brain regions and networks. What we do not know, however, is if and how these processes operate together. Thus, our overarching goal will be to understand how brain networks supports real-world behaviours under more ecological conditions. PhD project: aims and description (limit 300 words) Participants will watch films while undergoing functional magnetic resonance imaging (fMRI). The general aim is decompose the resulting data into networks and to label functions of those networks using annotation and machine learning approaches. We then characterize if, when, and how different networks interact and change with experience and test novel hypotheses about how the brain supports real-world processing.YR2. Neuroimage collection (supervised by Skipper). We will use fMRI to acquire anatomical and functional brain images from people watching three uncut fils in their native language. Each movie will be viewed by five adults who have not previously seen the film. We will use standard preprocessing routines on the data.YRS2-3. Film annotation and decoding (supervised by Skipper and Griffin). Features in films should be labelled in detail to decode the brain processes associated with those features. Hand annotation of features for even one film requires an enormous amount of time. To reduce this time, we will, first, use existing text-based descriptions and automated labelling to provide detailed time locked features. Second, we will use the brain data itself to identify film times for acquiring more detailed annotations through crowd-sourced approaches. The result of both is very rich text based descriptions of the films that are ready for machine learning decoding. This will be done by dividing data into training, validation, and test datasets and using the annotations to test how effectively networks can be labelled with different algorithms. Machine-learning approaches that might be most successful (support vector machines, convolutional neural networks, random forests) produce systems whose operations are non-trivial to understand. Thus, a component of the work will involve development of methods to probe solutions, to discover the spatial/temporal activity patterns used for predictions. These serve as the foundation for understanding how the brain processes natural stimuli.