Segmenting hypothalamic subunits in human newborn magnetic resonance imaging data

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.

PMID: 38339904
PMCID: PMC10826633
DOI: 10.1002/hbm.26582

Segmenting hypothalamic subunits in human newborn magnetic resonance imaging data

Jerod M Rasmussen et al. Hum Brain Mapp. 2024.

. 2024 Feb 1;45(2):e26582.

doi: 10.1002/hbm.26582.

Authors

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.

PMID: 38339904
PMCID: PMC10826633
DOI: 10.1002/hbm.26582

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.

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Conflict of interest statement

The authors declare the following financial interests/personal relationships which may be considered potential competing interests: Damien Fair reports a relationship with Turing Medical Inc that includes employment and equity or stocks. Damien Fair has a patent issued to Framewise Integrated Real‐Time Motion Monitoring (FIRMM) software. He is also a co‐founder of Turing Medical Inc. The nature of this financial interest and the design of the study have been reviewed by two committees at the University of Minnesota. They have put in place a plan to help ensure that this research study is not affected by the financial interest.

Figures

**FIGURE 1**
Atlas‐based registration pipeline (segATLAS). A registration pipeline was designed to leverage high quality age‐appropriate atlases in combination with a robust brain extraction procedure. Collectively, the proposed pipeline uses an age‐appropriate middle registration point to warp a FreeSurfer compatible definition of hypothalamic subunits into native infant space for segmentation. BCP, Baby Connectome Project; MNI, Montreal Neurological Institute.

**FIGURE 2**
Summary of comparison between Naïve FreeSurfer (segFS) and atlas‐based registration (segATLAS) approaches to segmentation. Segmentation approaches (segFS and segATLAS) varied in their degree of agreement (example of good agreement, a). SegFS had several instances of gross underestimation (e.g., data points below 200mm³, b), and segATLAS volume was more strongly associated with postmenstrual age at scan than segFS (b). Segmentations in disagreement (segFS vs. segATLAS) tended to be younger (c) and underestimated by segFS (d, white border indicates whole‐hypothalamus border in template space).

**FIGURE 3**
Comparison between manual corrections (segMAN) and automatic approaches to segmentation (segFS and segATLAS). Segmentation approaches were compared against manually edited standards. Example segmentations are shown in the top row (note the fornix is properly excluded from the segmentation, white arrow). Bottom left depicts whole hypothalamic volume across methods. SegFS volumes had a relatively widespread encompassing some implausibly small volumes. SegMAN volumes were smaller on average as they were biased (the intersection of segATLAS and segMAS as the initialization point). Bottom right depicts the DSC values from each of the three segmentation approaches in reference to segMAN segmentations.

See this image and copyright information in PMC

References

1. Balsevich, G. , Baumann, V. , Uribe, A. , Chen, A. , & Schmidt, M. V. (2016). Prenatal exposure to maternal obesity alters anxiety and stress coping behaviors in aged mice. Neuroendocrinology, 103(3–4), 354–368. - PubMed
1. Bao, A. M. , & Swaab, D. F. (2019). The human hypothalamus in mood disorders: The HPA axis in the center. IBRO Reports, 6, 45–53. - PMC - PubMed
1. Bethlehem, R. A. I. , Seidlitz, J. , White, S. R. , Vogel, J. W. , Anderson, K. M. , Adamson, C. , Adler, S. , Alexopoulos, G. S. , Anagnostou, E. , Areces‐Gonzalez, A. , & Astle, D. E. (2022). Brain charts for the human lifespan. Nature, 604(7906), 525–533. - PMC - PubMed
1. Billot, B. , Bocchetta, M. , Todd, E. , Dalca, A. V. , Rohrer, J. D. , & Iglesias, J. E. (2020). Automated segmentation of the hypothalamus and associated subunits in brain MRI. NeuroImage, 223, 117287. - PMC - PubMed
1. Bird, H. R. , Davies, M. , Duarte, C. S. , Shen, S. A. , Loeber, R. , & Canino, G. J. (2006). A study of disruptive behavior disorders in Puerto Rican youth: II. Baseline prevalence, comorbidity, and correlates in two sites. Journal of the American Academy of Child and Adolescent Psychiatry, 45(9), 1042–1053. - PubMed

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Segmenting hypothalamic subunits in human newborn magnetic resonance imaging data

Affiliations

Segmenting hypothalamic subunits in human newborn magnetic resonance imaging data

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Medical