Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
Neuroimage. 2021 Jan 1;224:117429. doi: 10.1016/j.neuroimage.2020.117429. Epub 2020 Oct 7.
Human cognition is dynamic, alternating over time between externally-focused states and more abstract, often self-generated, patterns of thought. Although cognitive neuroscience has documented how networks anchor particular modes of brain function, mechanisms that describe transitions between distinct functional states remain poorly understood. Here, we examined how time-varying changes in brain function emerge within the constraints imposed by macroscale structural network organization. Studying a large cohort of healthy adults (n = 326), we capitalized on manifold learning techniques that identify low dimensional representations of structural connectome organization and we decomposed neurophysiological activity into distinct functional states and their transition patterns using Hidden Markov Models. Structural connectome organization predicted dynamic transitions anchored in sensorimotor systems and those between sensorimotor and transmodal states. Connectome topology analyses revealed that transitions involving sensorimotor states traversed short and intermediary distances and adhered strongly to communication mechanisms of network diffusion. Conversely, transitions between transmodal states involved spatially distributed hubs and increasingly engaged long-range routing. These findings establish that the structure of the cortex is optimized to allow neural states the freedom to vary between distinct modes of processing, and so provides a key insight into the neural mechanisms that give rise to the flexibility of human cognition.
人类认知是动态的，随着时间的推移，在外部关注状态和更抽象、通常是自我产生的思维模式之间交替。尽管认知神经科学已经记录了网络如何锚定特定的大脑功能模式，但描述不同功能状态之间转换的机制仍知之甚少。在这里，我们研究了大脑功能在宏观结构网络组织限制下如何产生时变变化。通过研究一大群健康成年人（n=326），我们利用多种学习技术来识别结构连接组组织的低维表示，并使用隐马尔可夫模型将神经生理活动分解为不同的功能状态及其转换模式。结构连接组组织预测了以感觉运动系统为基础的动态转换，以及感觉运动和跨模态状态之间的转换。连接组拓扑分析表明，涉及感觉运动状态的转换跨越了短距离和中间距离，并且强烈遵循网络扩散的通信机制。相反，跨模态状态之间的转换涉及空间分布的集线器，并越来越多地采用远程路由。这些发现表明，皮层的结构被优化为允许神经状态在不同的处理模式之间自由变化，从而为产生人类认知灵活性的神经机制提供了关键见解。
