A comprehensive macaque fMRI pipeline and hierarchical atlas

Abstract

Functional neuroimaging research in the non-human primate (NHP) has been advancing at a remarkable rate. The increase in available data establishes a need for robust analysis pipelines designed for NHP neuroimaging and accompanying template spaces to standardize the localization of neuroimaging results. Our group recently developed the NIMH Macaque Template (NMT), a high-resolution population average anatomical template and associated neuroimaging resources, providing researchers with a standard space for macaque neuroimaging (Seidlitz, Sponheim et al., 2018). Here, we release NMT v2, which includes both symmetric and asymmetric templates in stereotaxic orientation, with improvements in spatial contrast, processing efficiency, and segmentation. We also introduce the Cortical Hierarchy Atlas of the Rhesus Macaque (CHARM), a hierarchical parcellation of the macaque cerebral cortex with varying degrees of detail. These tools have been integrated into the neuroimaging analysis software AFNI (Cox, 1996) to provide a comprehensive and robust pipeline for fMRI processing, visualization and analysis of NHP data. AFNI’s new @animal_warper program can be used to efficiently align anatomical scans to the NMT v2 space, and afni_proc.py integrates these results with full fMRI processing using macaque-specific parameters: from motion correction through regression modeling. Taken together, the NMT v2 and AFNI represent an all-in-one package for macaque functional neuroimaging analysis, as demonstrated with available demos for both task and resting state fMRI.HighlightsThe NMT v2, a stereotaxically aligned symmetric macaque template, is introduced.A new atlas (CHARM), defined on NMT v2, parcellates the cortex at six spatial scales.AFNI’s @animal_warper aligns and maps data between monkey anatomicals and templates.AFNI’s afni_proc.py facilitates monkey fMRI analysis with automated scripting and QC.Demos of macaque task and resting state fMRI analysis with these tools are provided.

U-Net Model for Brain Extraction: Trained on Humans for Transfer to Non-human Primates

“…AFNI @animal_warper uses 3dAllineate and 3dQwarp to compute affine and nonlinear alignments. The same NIMH Macaque Template (NMT) was used in Flirt+ANTs and AFNI @animal_warper pipelines (Jung et al, 2020; Seidlitz et al, 2018). The Macaque Dataset I and II were manually edited by well-trained experts (J.W.C, A.K, and T.X.)…”

“…To compare our deep learning models with state-of-the-art methods for brain extraction, we employed five widely-used skull stripping pipelines implemented in commonly used MRI packages (AFNI, ANTs, FSL, and FreeSurfer) (Avants et al, 2009; Cox, 1996; Fischl, 2012; Jenkinson et al, 2012). Specifically, we tested three intensity-based approaches (FSL BET, FreeSurfer HWA, and AFNI 3dSkullStrip) and two template-driven pipelines (Flirt+ANTS and AFNI @animal_warper) (Jung et al, 2020; Seidlitz et al, 2018; Tustison et al, 2020). The command and parameters of intensity-based approaches were selected based on the experiments and suggestions from the prior studies as follows (Xu et al, 2019; Zhao et al, 2018).…”

“…In recent years, registration-based label transferring (i.e. template-driven) approaches have been proposed as a potential solution for NHP brain extraction (Jung et al, 2020; Lohmeier et al, 2019; Seidlitz et al, 2018; Tasserie et al, 2020). These approaches start by registering an individual’s anatomical image to the template in order to establish the deformation between the subject-specific head and template head.…”

Selective prefrontal-amygdala circuit interactions underlie social and nonsocial valuation in rhesus macaques

“…We developed a semi-automated lesion mapping procedure to assess the location and extent of the lesions. For all T2-weighted scans, volume estimates were taken by performing rigid (6-parameter rigid body transformation) and affine (diffeomorphic -allowing for 12parameter local warps in structure) warps on each scan to the NIMH Macaque Template version 2.0 (NMT; 0.5 mm 3 resolution) (Seidlitz et al, 2018;Jung et al, 2020) using AFNI's 3dAllineate function for the rigid transform (Cox, 1996;Saad et al, 2009) and antsRegistrationSyN in ANTs for the affine transform (Avants et al, 2011). We then applied thresholding to identify the area of hyperintensity on the transformed T2-weighted scans to generate a binary mask that corresponded to the area of damage.…”

“…For the T1 scans showing MFC damage, we used AFNI's @animal_warper (Saad et al, 2009;Jung et al, 2020) to perform a rigid and affine alignment for each subject to the standard NMT version 2.0. For MFC damage, because the aspiration lesion resulted in collapse of nearby tissue into the space created by the lesion, we interpreted the extent of the damage in the transformed T1 scans by comparing the area of damage with the comparable region in the intact contralateral hemisphere, using anatomical and sulcal landmarks of the NMT as references.…”