In Developmental cognitive neuroscience ; h5-index 46.0
The hippocampus, critical for learning and memory, undergoes substantial changes early in life. Investigating the developmental trajectory of hippocampal structure and function requires an accurate method for segmenting this region from anatomical MRI scans. Although manual segmentation is regarded as the "gold standard" approach, it is laborious and subjective. This has fueled the pursuit of automated segmentation methods in adults. However, little is known about the reliability of these automated protocols in infants, particularly when anatomical scan quality is degraded by head motion or the use of shorter and quieter infant-friendly sequences. During a task-based fMRI protocol, we collected quiet T1-weighted anatomical scans from 42 sessions with awake infants aged 4-23 months. Two expert tracers first segmented the hippocampus in both hemispheres manually. The resulting inter-rater reliability (IRR) was only moderate, reflecting the difficulty of infant segmentation. We then used four protocols to predict these manual segmentations: average adult template, average infant template, FreeSurfer software, and Automated Segmentation of Hippocampal Subfields (ASHS) software. ASHS generated the most reliable hippocampal segmentations in infants, exceeding the manual IRR of experts. Automated methods thus provide robust hippocampal segmentations of noisy T1-weighted infant scans, opening new possibilities for interrogating early hippocampal development.
Fel J T, Ellis C T, Turk-Browne N B
2023-Jan-27
Brain development, Cognitive neuroscience, Learning and memory, MRI, Machine learning, Medial temporal lobe
