CRCNS: Resolving human face perception with novel MEG source localization methods

CRCNS：利用新颖的 MEG 源定位方法解决人脸感知问题

• 批准号: 10397180
• 负责人: Dimitrios Pantazis
• 金额: $ 24.96万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10397180
• 关键词: Achievement; Address; Age; Algorithms; Anatomy; Anterior; Area; Artificial Intelligence; Basic Science; Brain; Brain imaging; Code; Communication; Complex; Computer software; Data; Dimensions; Face; Face Processing; Familiarity; Functional Magnetic Resonance Imaging; Gender; Gender Identity; Human; Lead; Link; Machine Learning; Magnetic Resonance Imaging; Maps; Measurement; Medical Imaging; Methods; Modeling; Nature; Outcome; Pathway interactions; Persons; Process; Property; Public Health; Research; Resolution; Signal Transduction; Social Perception; Source; System; Temporal Lobe; Testing; Time; Training; Vision; Visual Pathways; Visual impairment; analytical method; base; cohort; cortex mapping; deep learning; experimental study; face perception; fusiform face area; human data; human model; imaging modality; implementation tool; improved; innovation; insight; large scale data; learning strategy; millimeter; novel; operation; reconstruction; relating to nervous system; response; simulation; source localization; spatiotemporal; statistical learning; temporal measurement; theories; visual processing; volunteer

A brief glimpse at a face quickly reveals rich multi-dimensional information about the person in front of us. How is this impressive computational feat accomplished? A recently revised neural framework for face processing suggests perception of face form information, i.e. face invariant features such as gender, age, and identity, are processed through the ventral visual pathway, comprising the occipital face area, fusiform face area, and anterior temporal lobe face area. However, evidence from fMRI remains equivocal about when, where, and how specific face dimensions of age, gender, and identity, are extracted. A key property of a complex computation is that it proceeds via stages and hence unfolds over time. We recently investigated the computational stages of face perception in a MEG study (Dobs et al., Nature Comms, 2019) and found that gender and age are extracted before identity information. However, this temporal information has yet to be linked to the spatial information available from fMRI because of limitations in current methods for spatial localization of MEG sources. Here, we propose to overcome these limitations and provide the full picture of how face computations unfold over both time and space in the brain by developing novel methods for localizing MEG sources, leveraging our team’s expertise in MEG and machine learning. In Aim 1 we will develop a new analytical MEG localization method called Alternating Projections that iteratively fits focal sources to the MEG data. In Aim 2 we will develop a novel data-driven MEG localization method based on geometric deep learning that reconstructs distributed cortical maps by learning statistical relationships in the non-Euclidean space of the cortical manifold. In Aim 3, we will first identify which method is most suitable to model human MEG face responses using fMRI face localizers as ground truth. We will then extract spatially and temporally accurate face processing maps to characterize the computational steps entailed in extracting age, gender, and identity information along the ventral visual pathway. A computationally precise characterization of the neural basis of face processing would be a landmark achievement for basic research in vision and social perception in humans. Insights into how face perception is accomplished in humans may further yield clues for how to improve AI systems conducting similar tasks. Further, the methods developed here may increase the power of MEG data to answer questions about the spatiotemporal trajectory of neural computation in the human brain.

对一张脸的短暂一瞥很快就揭示了我们面前这个人的丰富多维信息。这个令人印象深刻的计算壮举是如何完成的？最近修订的面部处理的神经框架表明，面部形状信息的感知，即面部不变的特征，如性别，年龄和身份，是通过腹侧视觉通路，包括枕面部区域，梭状面部区域，和前颞叶面部区域进行处理。然而，来自fMRI的证据仍然模棱两可，关于何时，何地以及如何提取年龄，性别和身份的特定面部尺寸。复杂计算的一个关键属性是它通过阶段进行，因此随着时间的推移而展开。我们最近在MEG研究中调查了面部感知的计算阶段（Dobs等人，Nature Comms，2019），发现性别和年龄在身份信息之前被提取。然而，由于目前MEG源空间定位方法的局限性，这种时间信息尚未与fMRI提供的空间信息联系起来。在这里，我们建议克服这些限制，并通过开发定位MEG源的新方法，利用我们团队在MEG和机器学习方面的专业知识，提供面部计算如何在大脑中的时间和空间上展开的全貌。在目标1中，我们将开发一种新的分析MEG定位方法，称为交替投影，迭代地将焦点源拟合到MEG数据。在目标2中，我们将开发一种基于几何深度学习的新型数据驱动MEG定位方法，该方法通过学习皮层流形的非欧几里得空间中的统计关系来重建分布式皮层地图。在目标3中，我们将首先确定哪种方法最适合使用fMRI面部定位器作为基础事实来模拟人类MEG面部反应。然后，我们将提取空间和时间上准确的人脸处理地图，以表征提取年龄，性别和身份信息沿着腹侧视觉通路的计算步骤。人脸处理的神经基础的计算精确表征将是人类视觉和社会感知基础研究的里程碑式成就。深入了解人类如何完成面部感知，可能会进一步为如何改进执行类似任务的人工智能系统提供线索。此外，这里开发的方法可以提高MEG数据的能力，以回答有关人脑神经计算时空轨迹的问题。