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

摘要

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.