Neuroprediction [1]
Annual Question:
The Cartesian wall between mind and brain has fallen. Its disintegration has been aided by the emergence of a wealth of new techniques in collecting and analyzing neurobiological data, including neuroprediction, which is the use of human brain imaging data to predict how the brain’s owner will feel or behave in the future. The reality of neuroprediction requires accepting the fact that human thoughts and choices are a reflection of basic biological processes. It also has the potential to transform fields like mental health and criminal justice.
In mental health, the potential for advances in identifying and treating psychopathology has been limited by existing diagnostic practices. In other fields of medicine, new diagnostic techniques like genetic sequencing have led to more targeted treatments for tumors and pathogens and major improvements in patient outcomes. But mental disorders are still diagnosed as they have been for a hundred years: using a checklist of symptoms derived from a patient’s subjective reports or a clinician’s subjective observations. This is like trying to determine whether someone has leukemia or the flu based on subjective evaluations of weakness, fatigue, and fever. The checklist approach not only makes it difficult for a mental health practitioner to determine what afflicts a patient—particularly if he is unwilling, unmotivated, or unable to report his symptoms—it provides no information about what therapeutic approach will be most effective.
In criminal justice, parallel problems persist in sentencing and probation. Making appropriate sentencing and probation decisions is hampered by the difficulty of determining whether a given offender is likely to reoffend after being released—decisions that are also based on largely subjective criteria. As a result, those who likely would not recidivate are often detained for too long, and those who will recidivate are released—both suboptimal outcomes.
Neuroprediction may yield solutions to these problems. One recent study found that the relative efficacy of different treatments for depression could be predicted from a brain scan that measured metabolic activity in the insula. Another found that predictions about whether paroled offenders would recidivate were improved using a brain scan that measured hemodynamic activity in the anterior cingulate cortex. Neither approach is ready for widespread use yet, in part because predictive accuracy at the individual level is still only moderate, but inevitably they—or improvements upon them—will be.
This would be an enormous advance in mental health. Presently, treatment outcomes for disorders like depression remain stubbornly poor; up to 40 percent of depressed patients fail to respond to the first-line treatment, the selection of which still relies more or less on guesswork. Using neuroprediction to improve this statistic could dramatically reduce suffering. Because required brain scans are expensive and their availability limited, however, disparities in access would be a concern.
Neuroprediction of crime presents quite a different scenario, as its primary purpose would be to improve outcomes for society (less crime, fewer resources spent on needless detentions) rather than for the potential offender in question. It is difficult to imagine this becoming accepted practice without concomitant changes in our approach to criminal behavior, namely, shifting the focus away from retribution and toward rehabilitation. In furthering understanding of the biological basis of persistent offending, neuroprediction may actually help in this regard.
Regardless, neuroprediction, at least the beta version of it, is here. Now is the time to consider how to harness its potential.