Common functional localizers to enhance NHP & cross-species neuroscience imaging research

NeuroImage

Qi Zhu¹  Christopher I Petkov²  Sze Chai Kwok³  Suliann Ben Hamed⁴  Pascal Belin⁵  Brian E Russ⁶  Wim Vanduffel⁷ 

[1] Corresponding authors.;Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, NY, United States;Department of Psychiatry, New York University at Langone, New York City, NY, United States;Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, Jiangsu, China;Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, United Kingdom;Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, United States;Shanghai Key Laboratory of Brain Functional Genomics, Key Laboratory of Brain Functional Genomics Ministry of Education, Shanghai Key Laboratory of Magnetic Resonance, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China;

关键词: fMRI;    Non-human primate;    Brain;    Localizers;    Retinotopy;    Face;   

DOI  :  

来源: DOAJ

【 摘 要 】

Functional localizers are invaluable as they can help define regions of interest, provide cross-study comparisons, and most importantly, allow for the aggregation and meta-analyses of data across studies and laboratories. To achieve these goals within the non-human primate (NHP) imaging community, there is a pressing need for the use of standardized and validated localizers that can be readily implemented across different groups. The goal of this paper is to provide an overview of the value of localizer protocols to imaging research and we describe a number of commonly used or novel localizers within NHPs, and keys to implement them across studies. As has been shown with the aggregation of resting-state imaging data in the original PRIME-DE submissions, we believe that the field is ready to apply the same initiative for task-based functional localizers in NHP imaging. By coming together to collect large datasets across research group, implementing the same functional localizers, and sharing the localizers and data via PRIME-DE, it is now possible to fully test their robustness, selectivity and specificity. To do this, we reviewed a number of common localizers and we created a repository of well-established localizer that are easily accessible and implemented through the PRIME-RE platform.

【 授权许可】

Unknown