Segmenting hypothalamic subunits in human newborn magnetic resonance imaging data

Jerod M Rasmussen; Yun Wang; Alice M Graham; Damien A Fair; Jonathan Posner; Thomas G O'Connor; Hyagriv N Simhan; Elizabeth Yen; Neel Madan; Sonja Entringer; Pathik D Wadhwa; Claudia Buss; program collaborators for Environmental influences on Child Health Outcomes

doi:10.1002/hbm.26582

Segmenting hypothalamic subunits in human newborn magnetic resonance imaging data

Hum Brain Mapp. 2024 Feb 1;45(2):e26582. doi: 10.1002/hbm.26582.

Authors

Jerod M Rasmussen^{1

2}, Yun Wang^{3

4}, Alice M Graham⁵, Damien A Fair⁶, Jonathan Posner^{3

4}, Thomas G O'Connor⁷, Hyagriv N Simhan⁸, Elizabeth Yen⁹, Neel Madan¹⁰, Sonja Entringer^{1

2

11}, Pathik D Wadhwa^{1

2

12

13

14}, Claudia Buss^{1

2

11}; program collaborators for Environmental influences on Child Health Outcomes

Affiliations

¹ Development, Health and Disease Research Program, University of California, Irvine, California, USA.
² Department of Pediatrics, University of California, Irvine, California, USA.
³ Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, USA.
⁴ New York State Psychiatric Institute, New York, New York, USA.
⁵ Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA.
⁶ Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, Minnesota, USA.
⁷ Departments of Psychiatry, Psychology, Neuroscience and Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, New York, USA.
⁸ Department of Obstetrics and Gynecology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
⁹ Department of Pediatrics, Tufts Medical Center, Boston, Massachusetts, USA.
¹⁰ Department of Radiology, Tufts Medical Center, Boston, Massachusetts, USA.
¹¹ Department of Medical Psychology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
¹² Department of Psychiatry and Human Behavior, University of California, Irvine, California, USA.
¹³ Department of Obstetrics and Gynecology, University of California, Irvine, California, USA.
¹⁴ Department of Epidemiology, University of California, Irvine, California, USA.

Abstract

Preclinical evidence suggests that inter-individual variation in the structure of the hypothalamus at birth is associated with variation in the intrauterine environment, with downstream implications for future disease susceptibility. However, scientific advancement in humans is limited by a lack of validated methods for the automatic segmentation of the newborn hypothalamus. N = 215 healthy full-term infants with paired T1-/T2-weighted MR images across four sites were considered for primary analyses (mean postmenstrual age = 44.3 ± 3.5 weeks, n_male /n_female = 110/106). The outputs of FreeSurfer's hypothalamic subunit segmentation tools designed for adults (segFS) were compared against those of a novel registration-based pipeline developed here (segATLAS) and against manually edited segmentations (segMAN) as reference. Comparisons were made using Dice Similarity Coefficients (DSCs) and through expected associations with postmenstrual age at scan. In addition, we aimed to demonstrate the validity of the segATLAS pipeline by testing for the stability of inter-individual variation in hypothalamic volume across the first year of life (n = 41 longitudinal datasets available). SegFS and segATLAS segmentations demonstrated a wide spread in agreement (mean DSC = 0.65 ± 0.14 SD; range = {0.03-0.80}). SegATLAS volumes were more highly correlated with postmenstrual age at scan than segFS volumes (n = 215 infants; R_segATLAS ² = 65% vs. R_segFS ² = 40%), and segATLAS volumes demonstrated a higher degree of agreement with segMAN reference segmentations at the whole hypothalamus (segATLAS DSC = 0.89 ± 0.06 SD; segFS DSC = 0.68 ± 0.14 SD) and subunit levels (segATLAS DSC = 0.80 ± 0.16 SD; segFS DSC = 0.40 ± 0.26 SD). In addition, segATLAS (but not segFS) volumes demonstrated stability from near birth to ~1 years age (n = 41; R² = 25%; p < 10^-3 ). These findings highlight segATLAS as a valid and publicly available (https://github.com/jerodras/neonate_hypothalamus_seg) pipeline for the segmentation of hypothalamic subunits using human newborn MRI up to 3 months of age collected at resolutions on the order of 1 mm isotropic. Because the hypothalamus is traditionally understudied due to a lack of high-quality segmentation tools during the early life period, and because the hypothalamus is of high biological relevance to human growth and development, this tool may stimulate developmental and clinical research by providing new insight into the unique role of the hypothalamus and its subunits in shaping trajectories of early life health and disease.

Keywords: MRI; growth; hypothalamus; infant; newborn; segmentation; subunit.

MeSH terms

Adult
Female
Humans
Hypothalamus / diagnostic imaging
Image Processing, Computer-Assisted* / methods
Infant
Infant, Newborn
Magnetic Resonance Imaging* / methods
Male

Abstract

MeSH terms

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