To the top

Page Manager: Webmaster
Last update: 9/11/2012 3:13 PM

Tell a friend about this page
Print version

Prediction of recidivism … - University of Gothenburg, Sweden Till startsida
Sitemap
To content Read more about how we use cookies on gu.se

Contact form








 


Note! If you want an answer on a question you must specify your email address




Prediction of recidivism in a long-term follow-up of forensic psychiatric patients: Incremental effects of neuroimaging data

Journal article
Authors Carl Delfin
Hedvig Krona
Peter Andiné
Erik Ryding
Märta Wallinius
Björn Hofvander
Published in PloS One
Volume 14
Issue 5
Pages e0217127
ISSN 1932-6203
Publication year 2019
Published at Institute of Neuroscience and Physiology
Centre for Ethics, Law, and Mental Health
Pages e0217127
Language en
Links dx.doi.org/10.1371/journal.pone.021...
www.ncbi.nlm.nih.gov/entrez/query.f...
Subject categories Psychiatry, Neuroscience, Neurobiology

Abstract

One of the primary objectives in forensic psychiatry, distinguishing it from other psychiatric disciplines, is risk management. Assessments of the risk of criminal recidivism are performed on a routine basis, as a baseline for risk management for populations involved in the criminal justice system. However, the risk assessment tools available to clinical practice are limited in their ability to predict recidivism. Recently, the prospect of incorporating neuroimaging data to improve the prediction of criminal behavior has received increased attention. In this study we investigated the feasibility of including neuroimaging data in the prediction of recidivism by studying whether the inclusion of resting-state regional cerebral blood flow measurements leads to an incremental increase in predictive performance over traditional risk factors. A subsample (N = 44) from a cohort of forensic psychiatric patients who underwent single-photon emission computed tomography neuroimaging and clinical psychiatric assessment during their court-ordered forensic psychiatric investigation were included in a long-term (ten year average time at risk) follow-up. A Baseline model with eight empirically established risk factors, and an Extended model which also included resting-state regional cerebral blood flow measurements from eight brain regions were estimated using random forest classification and compared using several predictive performance metrics. Including neuroimaging data in the Extended model increased the area under the receiver operating characteristic curve (AUC) from .69 to .81, increased accuracy from .64 to .82 and increased the scaled Brier score from .08 to .25, supporting the feasibility of including neuroimaging data in the prediction of recidivism in forensic psychiatric patients. Although our results hint at potential benefits in the domain of risk assessment, several limitations and ethical challenges are discussed. Further studies with larger, carefully characterized clinical samples utilizing higher-resolution neuroimaging techniques are warranted.

Page Manager: Webmaster|Last update: 9/11/2012
Share:

The University of Gothenburg uses cookies to provide you with the best possible user experience. By continuing on this website, you approve of our use of cookies.  What are cookies?