TReND: Transformer derived features and Regularized NMF for neonatal functional network Delineation
Journal:
arXiv
Published Date:
Mar 4, 2025
Abstract
Precise parcellation of functional networks (FNs) of early developing human
brain is the fundamental basis for identifying biomarker of developmental
disorders and understanding functional development. Resting-state fMRI
(rs-fMRI) enables in vivo exploration of functional changes, but adult FN
parcellations cannot be directly applied to the neonates due to incomplete
network maturation. No standardized neonatal functional atlas is currently
available. To solve this fundamental issue, we propose TReND, a novel and fully
automated self-supervised transformer-autoencoder framework that integrates
regularized nonnegative matrix factorization (RNMF) to unveil the FNs in
neonates. TReND effectively disentangles spatiotemporal features in voxel-wise
rs-fMRI data. The framework integrates confidence-adaptive masks into
transformer self-attention layers to mitigate noise influence. A self
supervised decoder acts as a regulator to refine the encoder's latent
embeddings, which serve as reliable temporal features. For spatial coherence,
we incorporate brain surface-based geodesic distances as spatial encodings
along with functional connectivity from temporal features. The TReND clustering
approach processes these features under sparsity and smoothness constraints,
producing robust and biologically plausible parcellations. We extensively
validated our TReND framework on three different rs-fMRI datasets: simulated,
dHCP and HCP-YA against comparable traditional feature extraction and
clustering techniques. Our results demonstrated the superiority of the TReND
framework in the delineation of neonate FNs with significantly better spatial
contiguity and functional homogeneity. Collectively, we established TReND, a
novel and robust framework, for neonatal FN delineation. TReND-derived neonatal
FNs could serve as a neonatal functional atlas for perinatal populations in
health and disease.
