Prediction of recidivism in a long-term follow-up of forensic psychiatric patients : Incremental effects of neuroimaging data
(2019) In PLoS ONE 14(5).- 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... (More)
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.
(Less)
- author
- Delfin, Carl ; Krona, Hedvig LU ; Andiné, Peter ; Ryding, Erik LU ; Wallinius, Märta LU and Hofvander, Björn LU
- organization
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- in
- PLoS ONE
- volume
- 14
- issue
- 5
- article number
- e0217127
- publisher
- Public Library of Science (PLoS)
- external identifiers
-
- scopus:85065921492
- pmid:31095633
- ISSN
- 1932-6203
- DOI
- 10.1371/journal.pone.0217127
- language
- English
- LU publication?
- yes
- id
- 9b283b4a-7060-423f-a3e6-61aadb43eeca
- date added to LUP
- 2019-05-21 19:12:49
- date last changed
- 2024-05-01 07:48:08
@article{9b283b4a-7060-423f-a3e6-61aadb43eeca,
abstract = {{<p>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.</p>}},
author = {{Delfin, Carl and Krona, Hedvig and Andiné, Peter and Ryding, Erik and Wallinius, Märta and Hofvander, Björn}},
issn = {{1932-6203}},
language = {{eng}},
number = {{5}},
publisher = {{Public Library of Science (PLoS)}},
series = {{PLoS ONE}},
title = {{Prediction of recidivism in a long-term follow-up of forensic psychiatric patients : Incremental effects of neuroimaging data}},
url = {{http://dx.doi.org/10.1371/journal.pone.0217127}},
doi = {{10.1371/journal.pone.0217127}},
volume = {{14}},
year = {{2019}},
}