  Charting the baby connectome evolution trajectory during the first year after
birth plays a vital role in understanding dynamic connectivity development of
baby brains. Such analysis requires acquisition of longitudinal connectomic
datasets. However, both neonatal and postnatal scans are rarely acquired due to
various difficulties. A small body of works has focused on predicting baby
brain evolution trajectory from a neonatal brain connectome derived from a
single modality. Although promising, large training datasets are essential to
boost model learning and to generalize to a multi-trajectory prediction from
different modalities (i.e., functional and morphological connectomes). Here, we
unprecedentedly explore the question: Can we design a few-shot learning-based
framework for predicting brain graph trajectories across different modalities?
To this aim, we propose a Graph Multi-Trajectory Evolution Network (GmTE-Net),
which adopts a teacher-student paradigm where the teacher network learns on
pure neonatal brain graphs and the student network learns on simulated brain
graphs given a set of different timepoints. To the best of our knowledge, this
is the first teacher-student architecture tailored for brain graph
multi-trajectory growth prediction that is based on few-shot learning and
generalized to graph neural networks (GNNs). To boost the performance of the
student network, we introduce a local topology-aware distillation loss that
forces the predicted graph topology of the student network to be consistent
with the teacher network. Experimental results demonstrate substantial
performance gains over benchmark methods. Hence, our GmTE-Net can be leveraged
to predict atypical brain connectivity trajectory evolution across various
modalities. Our code is available at https: //github.com/basiralab/GmTE-Net.
