Brain Scans: Predicting Future Criminal Behavior?

A new study shows that neuroimaging data can predict the likelihood of whether a criminal will reoffend following release from prison. 

Credit: © jinga80 / Fotolia

The paper, which is to be published in the Proceedings of the National Academy of Sciences (PNAS), studied impulsive and antisocial behaviour and centered on the anterior cingulate cortex (ACC), a portion of the brain that deals with regulating behavior and impulsivity.

The study demonstrated that inmates with relatively low anterior cingulate activity were twice as likely to reoffend than inmates with high-brain activity in this region.

Dr Kent Kiehl

"These findings have incredibly significant ramifications for the future of how our society deals with criminal justice and offenders," said Dr Kent Kiehl, who was senior author on the study and is director of mobile imaging at MRN and an associate professor of psychology at the University of New Mexico.

"Not only does this study give us a tool to predict which criminals may reoffend and which ones will not reoffend, it also provides a path forward for steering offenders into more effective targeted therapies to reduce the risk of future criminal activity."

The study looked at 96 adult male criminal offenders aged 20-52 who volunteered to participate in research studies.

This study population was followed over a period of up to four years after inmates were released from prison.

Walter Sinnott-Armstrong

"These results point the way toward a promising method of neuroprediction with great practical potential in the legal system," said Dr. Walter Sinnott-Armstrong, Stillman Professor of Practical Ethics in the Philosophy Department and the Kenan Institute for Ethics at Duke University, who collaborated on the study.

"Much more work needs to be done, but this line of research could help to make our criminal justice system more effective."

The study used the Mind Research Network's Mobile Magnetic Resonance Imaging (MRI) System to collect neuroimaging data as the inmate volunteers completed a series of mental tests.

"People who reoffended were much more likely to have lower activity in the anterior cingulate cortices than those who had higher functioning ACCs," Kiehl said.

"This means we can see on an MRI a part of the brain that might not be working correctly -- giving us a look into who is more likely to demonstrate impulsive and anti-social behavior that leads to re-arrest."

"The anterior cingulate cortex of the brain is "associated with error processing, conflict monitoring, response selection, and avoidance learning," according to the paper.

"People who have this area of the brain damaged have been shown to produce changes in 'dis-inhibition' (the inability to be inhibited by their socially unacceptable actions), apathy, and aggressiveness. "

"Indeed, ACC-damaged patients have been classed in the 'acquired psychopathic personality' genre." Kiehl says he is working on developing treatments that increase activity within the ACC to attempt to treat the high-risk offenders.

Reference
Neuroprediction of future rearrest. Proceedings of the National Academy of Sciences, 2013; DOI: 10.1073/pnas.1219302110

Smart Security CCTV Detects Criminal Behaviour

WHAT'S the difference between a suicide bomber and a cleaner? It sounds like the opening line of a sick joke, but for computer scientists working on intelligent video-surveillance software, being able to make that distinction is a key goal.

Current CCTV systems can collect masses of data, but little of it is used, says Shaogang Gong, a computer-vision computation researcher at Queen Mary, University of London. "What we really need are better ways to mine that data," he says.

Gong is leading an international team of researchers to develop a next-generation CCTV system, called Samurai, which is capable of identifying and tracking individuals that act suspiciously in crowded public spaces. It uses algorithms to profile people's behaviour, learning about how people usually behave in the environments where it is deployed. It can also take changes in lighting conditions into account, enabling it to track people as they move from one camera's viewing field to another.

To improve the tracking of an individual at an airport, the system can also learn the routes people are likely to take - straight from the entrance to check-in, say. It can even follow a target as they move in a crowd, using the characteristic shape of the person, their luggage and the people they are walking with, to follow them as they walk between different camera views.

Samurai is designed to issue alerts when it detects behaviour that differs from the norm, and adjusts its reasoning based on feedback. So an operator might reassure the system that the person with a mop appearing to loiter in a busy thoroughfare is no threat. When another person with a mop exhibits similar behaviour, it will remember that this is not a situation that needs flagging up.

While video analysis tools already exist, they tend to operate according to rigid, predefined rules, says Gong, and cannot follow a large number of people across multiple cameras situated in busy public spaces.

The Samurai team last month demonstrated the system to commercial partners including BAA Airports in the UK. The researchers claim the prototype system successfully identified potential threats which may have been missed by human operators, using footage collected at Heathrow airport. The Samurai team has funding to continue refining their software until the end of 2011.

"The use of relevant feedback from human operators will be a very important part of these technologies," says Paul Miller, of Queen's University's Centre for Secure Information Technologies in Belfast, UK, who is leading a project to develop a video-analysis system capable of predicting assaults on buses. "The key is developing learning algorithms that work not only in the lab but that are robust in real-world applications."