0939

A deep dive into hippocampus growth: Unveiling neurite and soma development with in vivo diffusion MRI
Bradley G Karat1, Sila Genc2, Erika Raven3, Marco Palombo4, Ali R Khan1,5, and Derek K Jones4
1Robarts Research Institute, Western University, London, ON, Canada, 2Department of Neurosurgery, The Royal Children's Hospital, Melbourne, Australia, 3Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 4Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom, 5Department of Medical Biophysics, Western University, London, ON, Canada

Synopsis

Keywords: Microstructure, Aging, Hippocampus; High-field MRI

Motivation: The hippocampus serves multiple cognitive functions, yet little is known about its microstructural development.

Goal(s): To leverage recent MRI hardware and microstructure modeling advances to capture hippocampal cell-body (soma) and projection (neurite) development during late-childhood and adolescence.

Approach: Diffusion MRI data was acquired in 88 participants aged 8-18 years using a 3T Connectom scanner (with 300mT/m gradients), and analyzed using the Soma and Neurite Density Imaging model.

Results: For the first time, we identified distinct developmental patterns of hippocampal microstructural subcomponents. Specifically, we found an age-related increase in neurite fraction and concurrent decrease in extracellular fraction and soma radius.

Impact: We report, for the first time, distinct neurite and soma developmental profiles in the hippocampus during late childhood/adolescence. This forms a crucial baseline for understanding developmental disorders, and opens new avenues for corroborating in vivo diffusion with histology.

Introduction

The hippocampus is a widely studied yet enigmatic cortical region, and is classically divided into subfields based on cytoarchitecture or function1. Research using structural MRI has suggested that the hippocampal subfields undergo volume changes in childhood and adolescence, representing a likely expansion of cognitive capacity2. However, less is known about the development of the glial cells, neurites, and soma that form the microstructure of the hippocampus.

The development of gray matter is characterized by microstructural changes including neurite (projection) growth, increased myelination, and a proliferation and subsequent pruning of synaptic connections3. Previous studies using diffusion tensor imaging4 to capture microstructural development of the hippocampus have found decreased diffusivity across childhood5. However, this finding is not specific to any particular microstructural property. Recent MRI advances including stronger gradients6 and new modeling approaches7 appear promising to disentangle soma and neurite contributions to the diffusion MRI signal in vivo.

Here we examined age-related changes of hippocampal microstructure using diffusion MRI (dMRI) data acquired with a 300 mT/m gradient in a sample of children and adolescents.

Methods

88 participants aged 8-18 years (42 male, mean age=12.6, SD=2.9) were scanned on a 3T Siemens Connectom system with 300 mT/m gradients8. T1-weighted images (1mm3) and dMRI (2mm3; b-values (directions)=0 (14), 500 (30), 1200 (30), 2400 (60), 4000 (60), 6000 (60) s/mm2) data were acquired8,9. HippUnfold10, was used for surface-based hippocampal modeling (Figure 1). The soma and neurite density imaging (SANDI)7 model was fitted to provide metrics of soma (fsoma), neurite (fneurite), and extracellular (fextracellular) signal fractions, and soma radius (Rsoma, in um). The neurite orientation dispersion and density imaging (NODDI)11 model was fitted to estimate the intracellular fraction (Vic) and orientation dispersion index (ODI). Finally, the DTI model was fitted using b-values of 0 and 6000 s/mm2 to derive mean diffusivity (MD in mm2/s), where higher b-values may increase sensitivity to cortical microstructure12. To interrogate diffusion orientations, cosine similarities were calculated as the dot product between hippocampal axis vectors (radial, tangential, and long-axis) and the primary diffusion orientation (Figure 5)13. All metrics were sampled on the midthickness surface from HippUnfold (Figure 1).

Linear regression was performed across the subfields and long-axis to test for the effects of age, sex, and their interactions.

Results

Distinct age-related microstructural patterns were observed within subfields for fneurite (mean R=0.51), fextracellular (mean R=-0.35), Rsoma in the subiculum and CA1 (R=-0.31, -0.41, respectively), Vic (mean R=0.50), and MD (mean R=-0.47), which is in-line with observations in the cortex8 (Figure 2). Furthermore the age-related changes were significantly different across subfields for fneurite (p<0.0005), Rsoma (p<0.005), and Vic (p<0.05), suggesting that different subfields have unique patterns of neurite and soma radius development.

Significant within-subfield sex differences were found for development patterns of fneurite (males>females), fsoma (females>males), vic (males>females), and MD (females>males) (Figure 3).

Examining microstructural development across the hippocampal long-axis, fneurite, vic, and posterior ODI were significantly higher with increasing age (Figure 4). Metrics of fextracellular, anterior and body Rsoma, and MD were significantly lower with increasing age. However, only ODI had different (p<0.05) age-related changes across the long-axis levels. The microstructural metrics themselves appear to vary across the long-axis (Figure 4 inset), suggesting that its microstructural environment is distinct.

A significant increase in long-axis oriented diffusion and significant decrease in radial diffusion in the subiculum and CA1 was observed with increasing age (Figure 5).

Discussion

Through a combination of novel diffusion MRI data and microstructural modelling, we have uncovered distinct developmental patterns in hippocampal microstructural sub-components. Subfield and long-axis neurite fraction increased significantly with age, potentially reflecting increased myelination14 and increased dendrite/astrocyte branching15. Age-related patterns differed across subfields for neurite fraction and soma radius, supporting the idea that the subfields have unique microstructural developmental patterns16. As well, no significant age-effects on soma fraction were found, partly in-line with an ex vivo study that found no difference in neuron number across development in any subfield except for the DG17.

Radial diffusivity was also lower with age in the subiculum and CA1, which may be attributable to increased myelination of the perforant path in adolescence18, which itself is largely orthogonal to the hippocampal radial neurites.

Overall, our study uncovers unique patterns of hippocampal neurite and soma development in vivo. Future work should compare these results with histology to validate the age-dependent spatial patterns we report here.

Acknowledgements

The authors are grateful to the participants and their families for their participation in this study. We would like to thank Umesh Rudrapatna and John Evans for their support with MR acquisition, and Greg Parker and Chantal Tax for contributions to data preprocessing and model fitting pipelines.

References

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  7. Palombo M, Ianus A, Guerreri M, et al. SANDI: A compartment-based model for non-invasive apparent Soma and neurite imaging by Diffusion MRI. NeuroImage. 2020;215:116835.

  8. Genc S, Chamberland M, Ball G, et al. Disentangling neurite and soma contributions to developmental patterns of cortical microstructure in vivo. ISMRM Annual Meeting. 2021.

  9. Genc S, Tax CM, Raven EP, et al. Impact of b‐value on estimates of apparent fibre density. Hum. Brain. Mapp. 2020;41(10):2583–95.

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  11. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage. 2012;61(4):1000–16.

  12. Mournet S, Okubo G, Koubiyr I, Zhang B, Kusahara H, Prevost VH, et al. Higher B-values improve the correlation between diffusion MRI and the cortical microarchitecture. Neuroradiology. 2020;62(11):1411–9. doi:10.1007/s00234-020-02462-4

  13. Karat BG, DeKraker J, Hussain U, et al. Mapping the macrostructure and microstructure of the in vivo human hippocampus using diffusion MRI. Hum. Brain. Mapp. 2023;44(16):5485–503.

  14. Ábrahám H, Vincze A, Jewgenow I, et al. Myelination in the human hippocampal formation from Midgestation to adulthood. Int. J. Dev. Neurosci. 2010;28(5):401–10.

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  18. Benes FM. Myelination of cortical-hippocampal relays during late adolescence. Schizoph. Bull. 1989;15(4):585–93.

Figures

Figure 1. Workflow for the current study. Aligned T1w and DWI data was used for HippUnfold and diffusion model fitting, respectively. All diffusion metrics were mapped to the midthickness surface from HippUnfold. Finally, subfield and anterior-posterior averaged values were extracted from the midthickness maps.

Figure 2. Relationship between age and microstructure, coloured by hippocampal subfield and quantified using pearson’s correlation coefficient. CA - cornu ammonis; DG - dentate gyrus; SRLM - stratum radiatum lacunosum moleculare; f - fraction; R - radius (um); ODI - orientation dispersion index; Vic - intracellular signal fraction; MD - mean diffusivity (mm2/s).

Figure 3. Relationship between age and microstructure by hippocampal subfield and stratified by sex. Asterisks represent where age-related trends were significantly different by sex.

Figure 4. Relationship between age and microstructure across the hippocampal long-axis and quantified with pearson's R. Bottom right inset shows the separability of the long-axis levels using fneurite and ODI.

Figure 5. Relationship between age and diffusion orientations (cosine similarities) across the hippocampal subfields and quantified with pearson's R. Top row shows the calculation of the cosine similarity, where the HippUnfold axes are used to generate long-axis, tangential, and radial vectors which are then compared with the primary diffusion orientation (mu from the NODDI model).

Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)
0939
DOI: https://doi.org/10.58530/2024/0939