Algorithm 1.

Enhanced longitudinal multishape evolution prediction from baseline

1:	INPUTS: The longitudinal mean atlases ${\mathcal{A}}_i$, the set of training baseline vertices $\mathcal{V}$, the baseline testing multishape M0 = (S0; F0), and ${\pi }^{{\mathcal{A}}_0}(F_0)$.
2:	Initialize ${\stackrel{\sim }{S}}_i\leftarrow {\mathcal{A}}_i$ and ${\stackrel{\sim }{F}}_i=\left\{\right\}$ for i ∈ 2 {0; …; N}.
3:	Initialize ε as the mean distance between S0 and ${\mathcal{A}}_0$ plus its standard deviation.
4:	for every vertex μ in the reconstructed baseline shape ${\stackrel{\sim }{S}}_0$ do
5:	if its 3D position x is located outside the ε–neighborhood from S0 then
	Update x using surface topography-based selection criteria.
	* For each unchecked adjacent face ξ to μ, use the fiber-to-surface 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 ${\stackrel{\sim }{S}}_i(x)=\varphi (x,t_i)$ at each timepoint.
	* Retrieve the spatiotemporal connectivity features for the selected deforming training face (set of fibers ${ℱ}_i(\varphi (\xi ,t_i))$ that hit $\varphi (\xi ,t_i)$ at timepoint, then ${\stackrel{\sim }{F}}_i={\stackrel{\sim }{F}}_i\cup {ℱ}_i(\varphi (\xi ,t_i))$.
6:	else
	Implement * while using projections of both training and testing fibers on their corresponding surfaces (no need to use the atlas for mutliprojections in this case).
7:	end if
8:	end for
9:	OUTPUT: Set of predicted multishapes $\left\{{\stackrel{\sim }{M}}_i=({\stackrel{\sim }{S}}_i,{\stackrel{\sim }{F}}_i)\right\}$ at timepoints ti.