Algorithm 1. Hybrid longitudinal multishape evolution prediction from baseline
1:	INPUTS:
	The longitudinal mean atlases Ai, the set of training baseline vertices V, the baseline testing multishape M0 = (S0, F0), and ${\pi }^{A_0}$ (F0).
2:	Initialize S̃i ← Ai and F̃i ={} for i∈ {0, …, N}.
3:	Initialize ε as the mean distance between S0 and A0 plus its standard deviation.
4:	for every vertex μ in the reconstructed baseline shape S̃0 do
5:	if its 3D position x is located outside the ε — neighborhood from S0 then
	Update x using a hierarchically surface topography-based metric.
	⋆ For each unchecked adjacent face ξ to μ, use the fiber-face selection criterion to identify the most similar corresponding training face in fiber properties to the testing face. Mark this face as ‘checked’.
	Retrieve the dynamic feature for μ as S̃i(x) = ø (x, ti) at each timepoint.
	Retrieve the spatiotemporal connectivity features for the selected deforming training face (set of fibers Fi(ø(ξ, ti)) that hit ø(ξ, ti) at timepoint ti), then F̃i = F̃i ∪ Fi(ø(ξ, ti)).
6:	else
	Implement ⋆ while using projections of both training and testing fibers on A0.
7:	end if
8:	end for
9:	OUTPUT:
	Set of predicted multishapes {M̃i = (S̃i, F̃i)} at timepoints ti.