Critical phenomena abound in nature, from forest fires and earthquakes to avalanches in sand and neuronal activity. Since the 2003 publication by Beggs & Plenz on neuronal avalanches, a growing body of work suggests that the brain homeostatically regulates itself to operate near a critical point where information processing is optimal. At this critical point, incoming activity is neither amplified (supercritical) nor damped (subcritical), but approximately preserved as it passes through neural networks. Departures from the critical point have been associated with conditions of poor neurological health like epilepsy, Alzheimer's disease, and depression. One complication that arises from this picture is that the critical point assumes no external input. But, biological neural networks are constantly bombarded by external input. How is then the brain able to homeostatically adapt near the critical point? We’ll see that the theory of quasicriticality, an organizing principle for brain dynamics, can account for this paradoxical situation. As external stimuli drive the cortex, quasicriticality predicts a departure from criticality while maintaining optimal properties for information transmission. We’ll see that simulations and experimental data confirm these predictions and describe new ones that could be tested soon. More importantly, we will see how this organizing principle could help in the search for biomarkers that could soon be tested in clinical studies.