An Alignment Framework For Mapping Brain Dynamics and Substrates of Human Cognition Across Species

摘要 非人灵长类动物(NHP)模型对翻译神经科学的进步至关重要，因为它允许 研究人员将对人类大尺度脑动力学和认知的观察与潜在的 在人类身上不能完全研究的中微尺度现象。重要的是，终极价值 NHP为人类模型提供信息的研究结果取决于跨物种方法的充分性 解剖和功能对齐。在这方面，基于解剖学标志的异种脑方法 配对在低阶感觉和运动区表现较好，但在记录异形波方面受到限制。 联合区，那里地标很少。作为回应，我们开发了一种基于fMRI的交叉 物种比对框架，利用网络组织表示方面的最新进展来 生成一个公共坐标空间(称为“关节嵌入”)。我们对此函数的初步应用- 基于大脑皮层比对的方法使我们能够量化人类和猕猴之间的同源性 皮质层次所有级别的详细信息。在本提案中，我们将延长关节嵌入对齐 使用多模式核磁共振数据在人类和非人类灵长类动物之间对齐大脑的方法(目标1)。我们会 利用公开提供的大样本多模式磁共振数据集(Human Connectome Project [HCP]，可靠性和再生性联盟[Corr])，以及最近公开分享的非人类 灵长类数据(灵长类数据交换)，以纳入物种内和物种间的差异。我们将评估 与传统的基于地标的配准和单峰联合配准性能进行了比较。 嵌入路线。使用最高性能的比对来转换物种之间的脑图，我们 将量化人类和猕猴大脑网络的时空相似性和差异性 基于自回归、准周期模式和共激活模式分析(目标2)。此外，我们还将 将人类认知本体地图转换到猕猴空间，并评估对应的相似性 人类和猕猴之间每个认知成分的大脑网络。我们还将构建一个交互式的 网页浏览者分享猕猴的翻译人类认知本体(目标3)。所有数据、数据产品 (例如跨物种翻译)和生成的代码将通过开放的科学资源公开共享- PRIME-DE。

ABSTRACT The non-human primate (NHP) model is critical to the advancement of translational neuroscience, as it allows researchers to link observations regarding macroscale brain dynamics and cognition in the human to underlying meso- and microscale phenomena that cannot be fully investigated in humans. Importantly, the ultimate value of findings from the NHP for informing human models relies on the adequacy of methods for cross-species anatomical and functional alignment. In this regard, anatomical landmark-based methods for interspecies brain alignment have excelled in lower order sensory and motor areas, but faced limitation in registering heteromodal association areas, which have a paucity of landmarks. In response, we have developed an fMRI-based cross- species alignment framework that leverages recent advances in representation of network organization to generate a common coordinate space (referred to as “joint-embedding”). Our initial application of this function- based method for cortical alignment allowed us to quantify homologies between human and macaque in high detail at all levels of the cortical hierarchy. In this proposal, we will extend the joint-embedding alignment approach to align brain between human and non-human primates using multimodal MRI data (Aim 1). We will make use of the publicly availability of large sample multimodal MRI datasets (Human Connectome Project [HCP], Consortium for Reliability and Reproducibility [CoRR]), as well as recent openly shared non-human primate data (PRIMatE Data Exchange) to incorporate of within- and between species variations. We will assess the alignment performance by comparing to traditional landmark-based registration and unimodal joint- embedding alignments. Using the highest performing alignment to transform brain maps between species, we will quantify spatiotemporal similarities and divergence of brain network between human and macaque monkey based on autoregressive, quasi-periodic pattern, and coactivation pattern analysis (Aim 2). Additionally, we will transform the human cognitive ontology maps to macaque space and assess the similarities of corresponding brain networks for each cognitive component between human and macaque. We will also build an interactive webpage viewer to share the translation human cognition ontology in macaque (Aim 3). All data, data products (e.g. cross-species translation) and code generated will be openly shared through the open science resource - PRIME-DE.