I understand that Multidimensional scaling (MDS) is same as doing Principal Components analysis (PCA) if Euclidean distance is used, this is known as Metric MDS. But I came across this in a book that "it has been shown (Chatfield and Collins 1980) that the eigenvalues of $XX^T$ (unnormalised outer product matrix) are equal to the eigenvalues $X^TX$ (unnormalised inner product matrix) and eigenvectors of $XX^T$ and $X^TX$ are related by a linear transformation. "
Note, that X denotes a matrix of data, with $n$ features (rows) and $m$ instances (columns).
Now I am unable to get this Chattield and Collins book anywhere, and I can understand that the eigenvalues are equal. But how are the eigenvectors of PCA and metric MDS related to each other ?