Predicting their past: Machine language learning can discriminate the brains of chimpanzees with different early-life social rearing experiences

Allyson J. Bennett, Peter J. Pierre, Michael J. Wesley, Robert Latzman, Steven J. Schapiro, Mary Catherine Mareno, Brenda J. Bradley, Chet C. Sherwood, Michele M. Mullholland, William D. Hopkins

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Early life experiences, including separation from caregivers, can result in substantial, persistent effects on neural, behavioral, and physiological systems as is evidenced in a long-standing literature and consistent findings across species, populations, and experimental models. In humans and other animals, differential rearing conditions can affect brain structure and function. We tested for whole brain patterns of morphological difference between 108 chimpanzees reared typically with their mothers (MR; N = 54) and those reared decades ago in a nursery with peers, human caregivers, and environmental enrichment (NR; N = 54). We applied support vector machine (SVM) learning to archival MRI images of chimpanzee brains to test whether we could, with any degree of significant probability, retrospectively classify subjects as MR and NR based on variation in gray matter within the entire brain. We could accurately discriminate MR and NR chimpanzee brains with nearly 70% accuracy. The combined brain regions discriminating the two rearing groups were widespread throughout the cortex. We believe this is the first report using machine language learning as an analytic method for discriminating nonhuman primate brains based on early rearing experiences. In this sense, the approach and findings are novel, and we hope they stimulate application of the technique to studies on neural outcomes associated with early experiences. The findings underscore the potential for infant separation from caregivers to leave a long-term mark on the developing brain.

Original languageEnglish
Article numbere13114
JournalDevelopmental Science
Volume24
Issue number6
DOIs
StatePublished - Nov 2021

Bibliographical note

Funding Information:
This research was supported in part by NIH grants NS‐42867, NS‐73134, HD‐60563, and NSF INSPIRE grant 1542848. The NCCC chimpanzees are supported by Cooperative Agreement U42‐OD011197.The Yerkes Center and NCCC are fully accredited by the AAALAC International. American Psychological Association guidelines for the ethical treatment of animals were adhered to during all aspects of this study. W.D.H. collected neuroimaging data from chimpanzees, with assistance from S.J.S. and M.C.M. M.M.M. processed MRI images. M.J.W. designed and performed the machine learning analysis. W.D.H. performed all other statistical analyses. A.J.B, P.J.P., R.L., C.C.S., B.J.B., and W.D.H. designed the study, interpreted the results, and wrote the paper.

Funding Information:
This research was supported in part by NIH grants NS-42867, NS-73134, HD-60563, and NSF INSPIRE grant 1542848. The NCCC chimpanzees are supported by Cooperative Agreement U42-OD011197.The Yerkes Center and NCCC are fully accredited by the AAALAC International. American Psychological Association guidelines for the ethical treatment of animals were adhered to during all aspects of this study. W.D.H. collected neuroimaging data from chimpanzees, with assistance from S.J.S. and M.C.M. M.M.M. processed MRI images. M.J.W. designed and performed the machine learning analysis. W.D.H. performed all other statistical analyses. A.J.B, P.J.P., R.L., C.C.S., B.J.B., and W.D.H. designed the study, interpreted the results, and wrote the paper.

Publisher Copyright:
© 2021 John Wiley & Sons Ltd.

Keywords

  • MRI
  • adversity
  • brain
  • cortical
  • experience introduction
  • primate

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Cognitive Neuroscience

Fingerprint

Dive into the research topics of 'Predicting their past: Machine language learning can discriminate the brains of chimpanzees with different early-life social rearing experiences'. Together they form a unique fingerprint.

Cite this