I was reading the principal component analysis section of ISLR. The authors define the $m$th principal component score from the $m$th principal loading vector $\phi_1=(\phi_{1m},...,\phi_{pm})$ as $Z_m=\phi_{1m}X_1+...+\phi_{pm}X_m$. On page 387-388 of the pdf, the author states that for an $n$ by $p$ (observations by predictor) design matrix $X$ that is centered, if $M = min(n-1,p)$ (number of principal components where the score has non-zero variance), then one can reconstruct the original design matrix $X$ using $x_{ij}=\sum_{m=1}^M z_{im}\phi_{jm}$ (where $1 \leq i \leq n$ and $1 \leq j \leq p$) which is similar to this answer.
I'm trying to get an intuition as to why we only need the principal components where the score has non-zero variance to reconstruct the original design matrix.