2026/04/10 by Alita Jesal D Almeida, Brad A. Hobson, Anelise Caceres +7 · 1 voice
Earth and Planetary Sciences · Psychology · #Evolution and Paleontology Studies #Paleontology and Evolutionary Biology #Primate Behavior and Ecology
paper · doi:10.1016/j.neuroimage.2026.121921
openalex publication_date 2026/04/10 · openalex created_date 2026/04/11 · openalex updated_date 2026/07/16
Introduction The coppery titi monkey ( Plecturocebus cupreus ) is an essential nonhuman primate model for social neuroscience, yet neuroimaging studies have been severely constrained by the paucity of standardized atlases. We address this gap by introducing the first MRI-based atlas package for the titi monkey brain that includes a single-subject atlas (UC Davis Titi monkey Neuroimaging Atlas (UCD-TiNA)), alongside a population atlas (UCD-TiNAgroup) and a manually-segmented atlas compilation (UCD-TiNAmac). Methods UCD-TiNA comprises 74 hierarchically organized regions delineated from the MRI of a representative adult brain, along with whole brain, gray and white matter masks. These segmentations were propagated to the population template (UCD-TiNAgroup, N = 17 monkeys) generated to enhance generalizability. A manually-segmented atlas compilation (UCD-TiNAmac, N = 6 monkeys, 14 regions) was created to enable multi-atlas segmentation approaches. UCD-TiNA and UCD-TiNAmac were evaluated in a [ 11 C]GR103545 PET and MRI study (N = 42 monkeys) to quantify regional kappa opioid binding. Results The warped UCD-TiNA achieved a high concordance with the manually-segmented UCD-TiNAmac labels (median Dice = 0.74). Regional [ 11 C]GR103545 binding potential was consistent with published patterns. Quantitative PET analyses showed <1% median error and a high correlation (Spearman r = 0.99) between the warped and manually-segmented labels. Discussion This work delivers the first in vivo atlas package to enable standardized, reproducible and cross-modal analyses of titi monkey neuroimaging data. By providing a common anatomical reference, this atlas package should facilitate rigorous and harmonized data processing, supporting high-throughput and longitudinal investigations in social neurobiology and informing translational research on social behavior.