# Modeling the Neonatal Brain Development Using Implicit Neural Representations

arXiv: https://arxiv.org/abs/2408.08647 · HTML: https://mlsanity.com/paper/2408.08647/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models](https://mlsanity.com/paper/2204.02641/) (2022)
- [SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields](https://mlsanity.com/paper/2211.17048/) (2022)
- [Medical Image Registration via Neural Fields](https://mlsanity.com/paper/2206.03111/) (2022)

## Built on by (most cited first, 3 of 3)

- [MedFuncta: A Unified Framework for Learning Efficient Medical Neural Fields](https://mlsanity.com/paper/2502.14401/) (2025)
- [TabINR: An Implicit Neural Representation Framework for Tabular Data Imputation](https://mlsanity.com/paper/2510.01136/) (2025)
- [Modelling Geographic Atrophy Progression using Implicit Neural Representations](https://mlsanity.com/paper/2608.10807/) (2026)

## Abstract (arXiv)

The human brain undergoes rapid development during the third trimester of pregnancy. In this work, we model the neonatal development of the infant brain in this age range. As a basis, we use MR images of preterm- and term-birth neonates from the developing human connectome project (dHCP). We propose a neural network, specifically an implicit neural representation (INR), to predict 2D- and 3D images of varying time points. In order to model a subject-specific development process, it is necessary to disentangle the age from the subjects' identity in the latent space of the INR. We propose two methods, Subject Specific Latent Vectors (SSL) and Stochastic Global Latent Augmentation (SGLA), enabling this disentanglement. We perform an analysis of the results and compare our proposed model to an age-conditioned denoising diffusion model as a baseline. We also show that our method can be applied in a memory-efficient way, which is especially important for 3D data.