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: 10686286
• 负责人: Dimitrios Pantazis
• 金额: $ 23.98万
• 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/10686286
• 关键词: Achievement; Address; Age; Algorithms; Anatomy; Anterior; Area; Artificial Intelligence; Basic Science; Brain; Brain imaging; Code; Communication; Complex; Computer software; Data; Dimensions; Dissociation; Face; Face Processing; Familiarity; Functional Magnetic Resonance Imaging; Gender; Human; 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; cohort; cortex mapping; data-driven model; deep learning; deep learning model; experimental study; face perception; fusiform face area; human data; human model; imaging modality; implementation tool; improved; innovation; insight; large scale data; learning strategy; millimeter; neural; novel; operation; reconstruction; 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.

只要瞟一眼对方的脸，就能迅速揭示这个人丰富的多维信息。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神经框架表明，面部形状信息的感知，即面部不变特征，如性别、年龄和身份，是通过腹侧视觉通路处理的，包括枕部面部区、梭状回面部区和前颞叶面部区。然而，功能磁共振成像的证据对于何时、何地以及如何提取年龄、性别和身份的特定面部维度仍然模棱两可。复杂计算的一个关键特性是它分阶段进行，因此随着时间的推移而展开。我们最近在一项MEG研究中研究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随时间和空间展开的全貌。在目标1中，我们将开发一种新的分析性MEG定位方法，称为交替投影，迭代地将震源拟合到MEG数据中。在Aim 2中，我们将开发一种基于几何深度学习的新的数据驱动的MEG定位方法，该方法通过学习皮质流形的非欧几里得空间中的统计关系来重建分布式皮质映射。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真值来模拟人类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图，以表征沿腹侧视觉通路提取年龄、性别和身份信息所需的计算步骤。对人脸处理的神经基础进行精确的计算表征将是人类视觉和社会感知基础研究的里程碑式成就。对人类如何完成面部感知的深入了解，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，本文开发的方法可能会增加MEG数据的能力，以回答有关人脑中神经计算的时空轨迹的问题。