What they did is take a control group that's the same size of the autism group. Then they did what's called leave-one-out cross-validation (which is exactly what it says, train on the entire set leaving one out, then use that one for validation and do so all ways). The measure they chose (Accuracy) is not without its flaws, but it would not be susceptible to the one you mention.
For instance, if you took 100 negative and 100 positive and then ran your test, you'd expect an accuracy of something a little more than 50%.
Under this methodology, how meaningful would having more data points be with respect to statistical meaningfulness? The study only appears to use 17 people in each group (for a total of 34 people).
For instance, if you took 100 negative and 100 positive and then ran your test, you'd expect an accuracy of something a little more than 50%.