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Neuroprediction of future rearrest
Eyal Aharonia,b,1,2, Gina M. Vincentc, Carla L. Harenskia, Vince D. Calhouna,d, Walter Sinnott-Armstronge,
Michael S. Gazzanigaf, and Kent A. Kiehla,b,2
a
Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106; Departments of bPsychology and dElectrical
and Computer Engineering, University of New Mexico, Albuquerque, NM 87131; cDepartment of Psychiatry, University of Massachusetts Medical School,
Worcester, MA 01604; eDepartment of Philosophy, and Kenan Institute for Ethics, Duke University, Durham, NC 27708; and fDepartment of Psychological
and Brain Sciences, University of California, Santa Barbara, CA 93106

Identiﬁcation of factors that predict recurrent antisocial behavior
is integral to the social sciences, criminal justice procedures, and
the effective treatment of high-risk individuals. Here we show that
error-related brain activity elicited during performance of an inhibitory task prospectively predicted subsequent rearrest among
adult offenders within 4 y of release (N = 96). The odds that an
offender with relatively low anterior cingulate activity would be
rearrested were approximately double that of an offender with
high activity in this region, holding constant other observed risk
factors. These results suggest a potential neurocognitive biomarker
for persistent antisocial behavior.
impulsivity

| recidivism | risk assessment

R

isk assessment is a major component of criminal justice and
treatment decisions. One crucial application of such predictions is the ability to identify, manage, and remediate antisocial behavior. Decisions that rely on antisocial risk prediction
pervade the justice system, beginning with recommendations for
bail, jail, and probation to sentencing, civil commitment, parole
decisions, diversion, and treatment program assignments, to
name a few. Initial attempts to predict future antisocial behavior
based purely on clinicians’ opinions have been shown to be highly
inaccurate (1). Subsequent research that used evidence-based
static (e.g., age, sex, criminal history) and dynamic (e.g., impulsivity, drug use, social support) risk factors have led to signiﬁcant
improvements in predicting future antisocial behavior (2–4).
One of the strongest and most widely studied risk factors for
recidivism is impulsivity, or behavioral disinhibition, the persistent lack of restraint and consideration of consequences (3). Risk
assessments, personality tests, and neuropsychological measures
have been used to assess impulsivity and have demonstrated
the ability to predict future antisocial behavior. However, these
latter measures serve only as proxies for direct measurement
of the brain’s inhibitory and cognitive control systems. Indeed,
neuroscientists have suggested that endophenotypes carry the
potential to characterize underlying traits and abnormalities independently of behavioral phenotypes (5). This stance has been
supported by recent functional MRI (fMRI) studies that have,
for instance, accurately predicted choices in a motor-decision
task (6), substance abuse relapse (7–10), and consumer purchases (i.e., neuromarketing) (11). These results raise the possibility that more direct measures of brain activity associated with
impulse control may lend incremental utility to the prediction of
future antisocial behavior.
The brain regions associated with impulse control have been
well characterized. Consistent among these regions is the anterior cingulate cortex (ACC), a limbic region associated with error
processing, conﬂict monitoring, response selection, and avoidance learning (12–16). Neurobiological models suggest that the
ACC is central to an error-monitoring circuit wherein it relays
error information from the basal ganglia and inferior frontal
cortex to motor areas. These motor areas then update behavioral
plans and feed back to the basal ganglia and frontal cortex,
facilitating learning (12). Animal lesion studies have shown
that focal damage to the anterior cingulate results in difﬁculty
www.pnas.org/cgi/doi/10.1073/pnas.1219302110

learning to regulate behavior (17). In humans, cingulate damage
has been shown to produce changes in disinhibition, apathy, and
aggressiveness. Indeed, ACC-damaged patients have been classed
in the “acquired psychopathic personality” genre (18). Moreover,
engagement of the ACC during error conﬂicts in healthy adults
has been shown to prospectively predict improvements in cognitive control (19).
Here we evaluate the hypothesis that ACC activity associated
with a go/no-go (GNG) impulse control task will contribute to
the prediction of future antisocial behavior (i.e., rearrest) in
a longitudinal, prospective study of released criminal offenders.
Several brain regions in addition to the ACC have been associated with impulse control (e.g., basal ganglia, dorsolateral prefrontal cortex). However, studies using Kiehl et al.’s (13) GNG
task generally ﬁnd the ACC is the most robustly engaged region
during inhibitory control; thus we focus exclusively on the ACC
region in this study.
Results
Association Between ACC and Error Rate. By using hierarchical linear regression, we examined the association between ACC response and the percentage of commission errors in the GNG task.
As expected, lower ACC activity entered at step 2 corresponded
to a higher rate of commission errors, controlling for variance
attributable to age at step 1 (R2 = 0.08, ΔR2 = 0.04, β = −0.21, P <
0.05). Mean commission error and hit rates on the GNG task were
25.04 (13.00) and 96.56 (6.40), respectively.
Survival Analysis. First, a Kaplan–Meier survival function was
computed to describe the proportion of participants surviving
any felony rearrest over the 4-y follow-up period, ignoring the
inﬂuence of any particular risk factor (Fig. S1). Cox proportionalhazards regression was then used to examine (i) the zero-order
effects of ACC activity on months to rearrest for any crime, (ii)
the shared and unique inﬂuence of the ACC and other potential
risk factors on months to rearrest for any crime, (iii) for nonviolent crimes, and (iv) the shared and unique inﬂuence of the
medial prefrontal cortex (mPFC) control region and other potential risk factors on months to rearrest for any crime. Reliability
of the β-coefﬁcients was evaluated by resampling each Cox distribution in a bootstrapping sequence with 9,999 iterations.
Cox model A examined the zero-order effect of the errorrelated ACC response parameter on months to any felony rearrest before entering other covariates into the model (Tables 1

Author contributions: E.A., W.S.-A., M.S.G., and K.A.K. designed research; E.A., C.L.H., and
K.A.K. performed research; V.D.C. and K.A.K. contributed new reagents/analytic tools;
E.A. and G.M.V. analyzed data; and E.A. wrote the paper.
The authors declare no conﬂict of interest.
This article is a PNAS Direct Submission.
1

Present address: RAND Corporation, Santa Monica, CA 90401.

2

To whom correspondence may be addressed. E-mail: eaharoni@rand.org or kkiehl@mrn.
org.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1219302110/-/DCSupplemental.

PNAS | April 9, 2013 | vol. 110 | no. 15 | 6223–6228

PSYCHOLOGICAL AND
COGNITIVE SCIENCES

Edited by Robert Desimone, Massachusetts Institute of Technology, Cambridge, MA, and approved February 27, 2013 (received for review November 7, 2012)

and 2). A signiﬁcant association was found whereby, for every
one unit increase in ACC activity, there was a 1.39 (i.e., 1/exp[B])
decrease in the probability of rearrest.
For the primary hypothesis test (model B), months to any
felony rearrest was regressed on eight potential predictors including age, Hare Psychopathy Checklist–Revised (PCL-R) factor scores and their interaction, lifetime prevalence of alcohol
or drug abuse or dependence, GNG commission error rate, and
the ACC parameter (Tables 1 and 2). A signiﬁcant effect of the
overall model was obtained. Age and PCL-R factor 2 score
exhibited marginal effects on rearrest with the use of a threshold
of P < 0.05. The ACC was the only unique signiﬁcant predictor
that was robust to bootstrap resampling. For every one unit increase in ACC activity, there was a 1.96 (i.e., 1/exp[B]) decrease
in the probability of rearrest (P < 0.001). For visualization purposes, this model was reconstructed with the use of a median
split of “high” and “low” ACC activity (Fig. 1).
Model C was identical to model B except that the event criterion was deﬁned as the presence of any nonviolent felony
rearrest since release. Here, the predicted pattern of results was
even more pronounced (Tables 1 and 2 and Fig. S2). A signiﬁcant omnibus effect on rearrest was found. As earlier, this effect
was driven by age, PCL-R factor 2, and ACC. For every one unit
increase in ACC activity, there was a 2.44 decrease in the
probability of rearrest after controlling for the other covariates
(P < 0.001). As to be expected, the small number of participants
rearrested for a violent offense (n = 9) was insufﬁcient to justify
an independent survival analysis of this group.
Model D was identical to model B except that the ACC parameter was replaced with that of an a priori control region (an
anterior portion of the mPFC; see also ref. 5) not commonly
implicated in response inhibition (Fig. S3). As expected, changes
in mPFC activity were not associated with the probability of
rearrest (P = 0.20).
Supporting analyses included individual Cox regressions
for the seven observed risk factors (Table S1) and Pearson
correlations (r) describing associations among all nine covariates (Table S2).
Discussion
The present analysis shows that hemodynamic activity within
the brain prospectively predicted rearrest in an offender sample.
The anatomical region associated with rearrest, the ACC, was
deﬁned by an a priori hypothesis related to response inhibition
and error processing in healthy adults whereby increased ACC
activity has been associated with improved inhibitory control
(19). The ACC region demonstrated incremental predictive
validity independent of other known risk factors and the GNG
commission error rate, suggesting a possible predictive advantage over some behavioral and personality risk factors.
This pattern of results raises the possibility that brain activity
in regions such as the ACC, elicited by a simple experimental
task, may lend incremental utility to existing behavioral risk
factors in the ability to predict rearrest. In addition, these results

support existing theories that paralimbic function subserves the
relationship between cognitive control and antisocial behavior
(20) and that the ACC in particular may facilitate inhibitory
learning by feeding error-related information to inhibitory control centers (12). Moreover, this pattern supports the view that
neurocognitive endophenotypes carry the potential to characterize underlying traits and defects independently of behavioral
phenotypes, such as self-report instruments and expert-rater
diagnoses based on client interviews and collateral historical
information. Finally, this work highlights potential neuronal
systems that could be targeted for treatment intervention. One
plausible hypothesis is that interventions that modulate ACC
activity may help to increase cognitive control systems and
thereby reduce future recidivism. Initial support for this hypothesis has already been reported in a recent study implicating
the ACC in treatment responsiveness among children with externalizing problems (21).
Before sufﬁcient conﬁdence in the effects can be established,
test–retest reliability must be demonstrated. Here, reliability of the
ACC result was demonstrated by using bootstrap resampling.
Release age and PCL-R factor 2 score also met this criterion, but
only for nonviolent crimes. Additional research would be required
to determine why the other covariates did not exhibit predictive
effects. Efforts to replicate these results should examine the robustness to variations in task, sample characteristics, sample size,
anatomical region of interest (ROI), and analytic procedures.
Should the neuroimaging effects be robust to replication, they
remain silent on the question of suitability in making individuallevel predictions. Whether neurobiological markers should
ever be used to make predictions about individual offenders’ risk
is a thorny question that, at the least, depends on (i) whether
these estimates can survive particular sensitivity and speciﬁcity
thresholds with the use of large random samples, (ii) whether
they can survive a required legal standard of proof, and (iii)
whether their use would violate offender rights (22, 23). It is
noteworthy that existing clinical risk assessment tools, including
those reliant on personal health information about the offender,
already permeate criminal justice decision settings, such as civil
commitment decisions. We are skeptical that emerging neurobiological markers could ever independently outperform these
existing tools in sensitivity and speciﬁcity, but they could potentially improve overall risk estimates in combination with
known psychosocial risk factors. If so, their potential admissibility in legal settings may be greatest in decisions involving a low
standard of proof such as treatment access decisions rather than
in high-stakes decisions such as sentencing. However, even in
low-stakes decisions, use of neurobiological information would
force us to confront the question of whether it presents any
unique civil rights challenges above and beyond that of existing
practices (23). In this regard, one advantage that functional brain
activity has vs. some other risk factors (e.g., age of ﬁrst offense) is
that the hemodynamic response is relatively dynamic, or amenable to change (24). Future treatment research efforts can
potentially exploit this characteristic in the testing of therapies,

Table 1. Cox model statistics
Overall
Model
A
B
C
D

Description

−2 Log likelihood

χ

Any crime: zero-order
Any crime: ACC
Nonviolent crimes: ACC
Any crime: control region

413.45
327.65
244.33
337.77

4.60
17.35
25.48
8.27

2

Change from previous

df

P value

Δχ

1
8
8
8

*
*
**
0.41

4.67
18.33
28.43
8.21

2

df

P value

1
8
8
8

*
*
***
0.41

Omnibus test of Cox regression model with χ2 statistics showing (model A) the zero-order effect of ACC activity on months to rearrest for any crime, (model B)
the shared and unique inﬂuence of the ACC and other potential risk factors on months to rearrest for any crime, (model C) for nonviolent crimes, and (model D)
the shared and unique inﬂuence of the mPFC control region and other covariates on months to rearrest for any crime. *P < 0.05, **P < 0.01, and ***P < 0.001.

6224 | www.pnas.org/cgi/doi/10.1073/pnas.1219302110

Aharoni et al.

Table 2. Effect of individual predictors on rearrest

Model/predictor
Model A—zero-order: ACC
ACC
Model B—any crime: ACC
Age at release
PCL-R factor 1 score
PCL-R factor 2 score
PCL-R factor interaction
Alcohol abuse/dependence (lifetime)
Drug abuse/dependence (lifetime)
GNG commission error rate
ACC
Model C—nonviolent crimes: ACC
Age at release
PCL-R factor 1 score
PCL-R factor 2 score
PCL-R factor interaction
Alcohol abuse/dependence (lifetime)
Drug abuse/dependence (lifetime)
GNG commission error rate
ACC
Model D—any crime: control region
Age at release
PCL-R factor 1 score
PCL-R factor 2 score
PCL-R factor interaction
Alcohol abuse/dependence (lifetime)
Drug abuse/dependence (lifetime)
GNG commission error rate
mPFC

B

SE (B)

B

SE(B)

P value

exp[B]

95% CI for exp[B]

−0.33

0.15

−0.33

0.14

*

0.72

0.53–0.97

−0.05
−0.03
−2.23
0.05
0.08
−0.31
−0.10
−0.68

0.02
0.07
0.95
0.24
0.20
0.37
0.11
0.21

−0.05
−0.03
−2.23
0.05
0.08
−0.31
−0.10
−0.68

0.03
0.09
1.34
0.33
0.24
0.53
0.15
0.23

0.07
0.73
0.06
0.85
0.72
0.49
0.41
***

0.96
0.97
0.11
1.05
1.08
0.74
0.90
0.51

0.91–1.00
0.85–1.11
0.02–0.70
0.66–1.67
0.72–1.61
0.35–1.53
0.73–1.12
0.34–0.76

−0.07
0.02
−3.68
0.36
0.13
−0.46
−0.06
−0.88

0.03
0.08
1.16
0.33
0.23
0.43
0.13
0.24

−0.07
0.02
−3.70
0.36
0.13
−0.46
−0.06
−0.88

0.04
0.11
1.74
0.45
0.28
0.66
0.19
0.26

*
0.80
**
0.31
0.58
0.37
0.66
***

0.94
1.02
0.03
1.43
1.14
0.63
0.94
0.41

0.89–0.99
0.87–1.20
0.00–0.24
0.76–2.72
0.73–1.79
0.27–1.45
0.73–1.20
0.26–0.67

−0.03
−0.09
−2.15
0.17
−0.01
−0.01
−0.01
−0.15

0.02
0.07
1.00
0.25
0.19
0.38
0.11
0.11

−0.03
−0.09
−2.15
0.17
−0.02
0.00
0.00
−0.14

0.03
0.08
1.31
0.32
0.21
0.52
0.13
0.14

0.19
0.19
0.07
0.55
0.93
0.99
0.96
0.20

0.97
0.91
0.12
1.19
0.99
0.99
0.99
0.20

0.93–1.01
0.80–1.04
0.02–0.83
0.73–1.94
0.68–1.43
0.47–2.10
0.80–1.23
0.69–1.08

Results of Cox regression analyses examining the predictive effect of the ACC and other covariates on rearrest for (model A) any crime, (model B) any crime
controlling for covariates, (model C) nonviolent crimes controlling for covariates, and (model D) the effect of the mPFC control region on rearrest for any
crime controlling for covariates. Table reports unstandardized B, bootstrapped B, and relative risk ratio (exp[B]). *P < 0.05, **P < 0.01, and ***P < 0.001.

programs, and medications aimed at remediating problems in
behavioral disinhibition. In the meantime, functional brain patterns may still be useful at the group level, enabling scientists
to learn how certain cohorts with shared risk factors respond to
different types of treatments to ultimately improve their access to
early intervention options.
Methods
Participants. Participants were 96 adult male offenders ranging in age from
20 to 52 y (Mean, 33.1; SD, 7.78). Ten of them did not complete some of the
assessments. Approximately 6% were left-hand–dominant. Based on National Institutes of Health racial and ethnic classiﬁcation, 36% of the sample
self-identiﬁed as white, 9% as black/African American, 9% as American Indian, 28% as mixed/OTHER, 42% as Hispanic, and 14% chose not to respond.
They were paid $1/h, a rate commensurate with standard pay for work
assignments at their facility.
Participants completed a number of psychological and behavioral assessment measures and an fMRI-based inhibition task using the Mind Research Network’s Mobile MRI system before release from one of two New
Mexico state correctional facilities. All participants were determined to be
free of traumatic brain injury and psychosis, and had a general IQ of greater
than 70. Participants reported having normal hearing, and visual acuity
was normal or corrected to normal with the use of contact lenses or MRIcompatible glasses. After being released, they were tracked from 2007 to
2010. The average follow-up period was 34.5 mo. Participants provided
written informed consent in protocols approved by the institutional review
board of the University of New Mexico.
Nonoffender Sample. An independent sample of 102 healthy adult nonoffenders (49 men) provided functional imaging results from which the
a priori peak voxel could be deﬁned. This sample was drawn from the Olin

Aharoni et al.

Neuropsychiatry Research Center at the Institute of Living Hartford Hospital
and the surrounding community of Hartford, CT, and ranged in age from
23 to 52 y (Mean, 33.92; SD, 9.64). Seven participants (7%) were left-handed.
The sample reﬂected the ethnic nature of the community: 68% of the sample
self-identiﬁed as white, 10% as black/African American, 9% as Hispanic, 8%
as Asian, and 6% as mixed/other racial heritage.
Behavioral Task. Behavioral impulsivity was measured during fMRI using the
GNG task, a widely used procedure that requires participants to inhibit
a prepotent motor response. The task, modeled after the work of Kiehl
et al. (13), presents participants with a frequently occurring target (the
letter “X”; occurrence probability, 0.84) interleaved with a less-frequent
distracter (the letter “K”; occurrence probability, 0.16) on a computer
screen. Participants were instructed to depress a button with their right
index ﬁnger as quickly and accurately as possible whenever they saw the
target (“go” stimulus) and not when they saw the distractor (“no-go”
stimulus). Because targets are more frequent than distracters in this task,
a prepotent response toward the targets is elicited. When a distractor is
presented, participants are required to inhibit their button response,
which increases the rate of commission errors (Fig. S4). The commission
error rate was deﬁned as the proportion of observed commission errors
among total no-go trials. Successful performance on this task requires the
ability to monitor error-related conﬂicts and to selectively inhibit the
prepotent go response on cue. Before scanning, participants completed
a brief practice session of ∼10 trials.
Experimental Design. The present fMRI study comprised an a priori ROI
analysis, and all imaging data used for that ROI analysis are included in
Dataset S1 in the form of a β-value for each subject. Dataset S1 also includes
codes and scores from all other predictors and measures reported.
The experimental design was adopted from Kiehl et al. (13). Two
scanning runs, each composed of 246 visual stimuli, were presented to

PNAS | April 9, 2013 | vol. 110 | no. 15 | 6225

PSYCHOLOGICAL AND
COGNITIVE SCIENCES

Bootstrapped values

hemodynamic response to the stimuli of interest was sampled effectively at
500-ms intervals.
Behavioral responses were recorded by using a commercially available
MRI-compatible ﬁberoptic response device (Lightwave Medical). Correct hits
were deﬁned as go (ie, X-stimuli) events that were followed by a button press
within 1,000 ms of stimulus onset. Correct rejections were deﬁned by an
absence of a motor response within 1,000 ms of the no-go stimulus. Commission errors were deﬁned as the presence of a response within 1,000 ms of
the onset of a no-go stimulus.

Fig. 1. Cox survival function showing proportional rearrest survival rates of
high (solid green) vs. low (dashed red) ACC response groups for any crime
over a 4-y period. Results of this median split analysis were equivalent to
that of the parametric model: bootstrapped B = 0.96; SE = 0.40; P < 0.01;
95% CI, 0.29–1.84. The mean survival times to rearrest for the low and high
ACC activity groups were 25.27 (2.80) mo and 32.42 (2.73) mo, respectively.
The overall probabilities of rearrest were 60% for the low ACC group and
46% for the high ACC group.

participants using Presentation, a computer-controlled visual and auditory
software (Neurobehavioral Systems). Stimuli were displayed on a rearprojection screen mounted at the rear entrance to the magnet bore and
subtended a visual angle of ∼3 × 3.5°. Each stimulus appeared for 250 ms
in white text within a continuously displayed rectangular ﬁxation box.
Participants viewed the screen by means of a mirror system attached to the
head coil.
The stimulus onset asynchrony (SOA) between go stimuli varied pseudorandomly among 1,000, 2,000, and 3,000 ms, subject to the constraint that
three go stimuli were presented within each consecutive 6-s period. The
no-go stimuli were interspersed among the go stimuli in a pseudorandom
manner subject to three constraints: the minimum SOA between a go and
a no-go stimulus was 1,000 ms; the SOA between successive no-go stimuli was
in the range of 10 ± 15 s; and no-go stimuli had an equal likelihood of occurring at 0, 500, or 1,000 ms after the beginning of a 1.5-s acquisition period. By jittering stimulus presentation relative to the acquisition time, the

Image Acquisition and Processing: Offender Sample. MRI acquisition parameters were identical to those of Harenski et al. (25). Images were collected
with a mobile Siemens 1.5-T Avanto system with advanced SQ gradients
(max slew rate, 200T/m/s; 346 T/m/s vector summation, rise time 200 μs)
equipped with a 12-element head coil. The echoplanar image gradient-echo
pulse sequence (repetition/echo times, 2,000/39 ms; ﬂip angle, 75°; ﬁeld of
view, 24 × 24 cm; 64 × 64 matrix; 3.4 × 3.4-mm in-plane resolution; 5-mm
slice thickness; 30 slices) effectively covers the entire brain (150 mm) in 2,000
ms. Head motion was limited by using padding and restraint. Functional
images were reconstructed ofﬂine at 16-bit resolution and manually reoriented to approximately the anterior commissure/posterior commissure
plane. Motion correction was achieved by using a preprocessing pipeline
designed by Mazaika (26). Functional images were analyzed using Statistical
Parametric Mapping software (SPM5). Functional images were spatially
normalized to the Montreal Neurological Institute template via a nineparameter afﬁne transformation by using smooth basis functions to account
for nonlinear differences (27), and spatially smoothed (8 mm full width at
half maximum). High frequency noise was removed by using a low-pass ﬁlter
(cutoff, 128s). Runs were normalized to an in-brain mean of 100 (arbitrary
units) to compensate for intensity variations across runs [note that this is not
the “proportional” scaling procedure that can result in artifactual deactivations when global effects are correlated with the local blood oxygen leveldependent (BOLD) signal] (28). Response types (hits and commission errors)
were modeled as separate events. Event-related responses were modeled
by using a synthetic hemodynamic response function composed of two
γ-functions. The ﬁrst γ-function modeled the hemodynamic response by
using a peak latency of 6 s. A term proportional to the derivative of this
γ-function was included to allow for small variations in peak latency. The
second γ-function and associated derivative was used to model the small
“overshoot” of the hemodynamic response on recovery. A latency variation
amplitude-correction method was used to provide a more accurate estimate
of hemodynamic response for each condition that controlled for differences
between slices in timing and variation across regions in the latency of the
hemodynamic response (29).
Functional images were computed for each participant that represented
hemodynamic responses associated with commission errors and hits. Activity associated with these contrasts was evaluated in a whole-brain
analysis across participants by using a t test in SPM5. All contrasts were
corrected with a family-wise error (FWE) threshold of P < 0.0001 or higher

Fig. 2. (A) A priori seed region (red) for BOLD response to commission errors vs. correct hits in anterior cingulate from a GNG task with an independent
sample of 102 healthy adult nonoffenders; peak voxel x = −3, y = 24, z = 33; radius = 14 mm sphere; t(94) = 13.38, P < 0.0001, FWE. A priori control region
(blue) embodying anterior portion of the medial prefrontal cortex (peak voxel: 0, 51, −6; radius = 14 mm sphere). (B) Mean hemodynamic response change in
offender sample (n = 96) during commission errors vs. correct hits from sagittal (Upper Left), coronal (Right), and axial (Lower Left) orientations. Peak activation located at x = 3, y = 24, z = 33 within the ACC ROI (P < 0.00001, FWE).

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Aharoni et al.

Image Acquisition and Processing: Nonoffender Sample. Imaging data were
collected on a Siemens Allegra 3-T system located at the Olin Neuropsychiatry
Research Center (Hartford, CT). The echoplanar image gradient-echo pulse
sequence (repetition/echo times, 1,500/28 ms; ﬂip angle, 65°) effectively
covered the entire brain (150 mm) in 1.5 s. Functional image runs were
motion-corrected by using an algorithm unbiased by local signal changes
(INRIAlign) (30) as implemented in the SPM2 software (Wellcome Trust
Centre for Neuroimaging, University College London). Normalized images
were smoothed at 9 mm full width at half maximum. All other speciﬁcations
were identical to that of the offender sample.
Functional ROIs. An a priori dorsal–caudal region of the ACC was developed
from previous literature (13, 16, 31) (Fig. 2). This region has been associated
with the “cognitive” as opposed to “affective” functions of the ACC (32).
The seed coordinate was deﬁned by the peak BOLD activity during commission errors vs. correct hits within an independent sample of 102 healthy
adult nonoffenders who underwent the same GNG task (Fig. 1A). Deﬁning
hits as a baseline permitted the examination of commission errors while
controlling for the motor response. A control region was also deﬁned by this
nonoffender dataset (a similar approach is described in ref. 5), namely an
anterior portion of the mPFC, one of several regions that was not engaged
by the GNG task in this nonoffender sample. Fourteen-millimeter radius
spheres were deﬁned around center coordinates derived from the activation
peaks in each region. ROI activity was modeled by computing a mean β-value
for each participant.
Covariate Risk Assessment. Data from several additional potential risk factors
were obtained to examine the incremental predictive validity of the ACC
response. All these variables have been previously found to predict antisocial
behavior in offender populations or are correlated with activity within the
anterior cingulate (19, 33). Indices of behavioral disinhibition included the
commission error rates from the GNG task described earlier and scores from
the Hare PCL-R (34), a detailed archival analysis and semistructured interview
(Mean, 23.49; SD, 6.98). Twenty percent of the assessed sample (n = 90) met
criteria for a diagnosis of psychopathy (score of ≥30). The PCL-R provides
a reliable and valid assessment of psychopathy in incarcerated, forensic,
psychiatric, and normal populations (35–37). To examine the independent
clusters of psychopathic traits, the PCL-R items have also been organized into
separable subfactors. The two-factor model distinguishes between interpersonal/affective attributes, such as glibness and lack of empathy, and
antisocial behavioral attributes, such as early behavioral problems and impulsivity (factors 1 and 2, respectively). PCL-R total score was not entered
into any models because of its collinearity with the entered PCL-R factor
scores. Number of previous arrests was also not included as an independent
predictor because this variable was part of the scoring criteria used on the
assessment of PCL-R factor 2. PCL-R assessments were conducted by trained
raters who exhibited high interrater reliability (intraclass correlation coefﬁcient,0.93) for the PCL-R total score (25).
Additional risk factors included age at release and lifetime prevalence
of drug and alcohol abuse/dependence, which were assessed by using

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Aharoni et al.

the structured clinical interview for the Diagnostic and Statistical Manual
of Mental Disorders, Fourth Edition, Research Version (38). Abuse and dependence were deﬁned by diagnostic scores of 2 and 3, respectively. Drug
abuse/dependence included an average of scores from the following drug
classes: sedatives, cannabis, stimulants, opioids, cocaine, and hallucinogens.
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typically a predictor of recidivism, but to ensure that IQ did not moderate
the effect of the ACC in predicting rearrest, we conducted a supplementary
survival analysis with IQ included in the model. In this model, the ACC still
predicted rearrest whereas IQ did not. Model B (any crime): ACC, B = −0.67
(0.21), P < 0.05, exp[B] = 0.99 (95% CI, 0.96–1.01); IQ, B = −0.02 (0.02), P =
0.67, exp[B] = 0.99 (95% CI, 0.96–1.03). Model C (nonviolent crime): ACC, B =
−0.94 (0.27), P < 0.001, exp[B] = 0.39 (95% CI, 0.23–0.66); IQ, B = −0.01 (0.02),
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Follow-Up Procedure. Rearrest data, including arrest date and offense type,
were obtained by a professional criminal background check service (SSC),
which conducted national, state, and county criminal searches following each
participant’s release date. Approximately 53% of the sample was rearrested
at least once between their release date (ranging from 2007 to 2010) and
their follow-up date during July to September 2011. Offense type was
classiﬁed into one of 27 common felony categories by 10 trained raters. In
line with previous literature (41), minor parole and probation violations
were excluded from analysis, and the remaining offenses were further
classiﬁed as violent or nonviolent when warranted (Table S4). A larger
portion of the sample was rearrested for nonviolent offenses (41.7%) than
for violent offenses (9.4%).
Analytic Strategy. The primary hypothesis was evaluated by using Cox proportional-hazards regression. Cox regression is a semiparametric test that
evaluates differences in individuals’ time “at risk” to an event (e.g., rearrest)
while estimating time for cases that have yet to reach that event (censored
cases). The dependent variable is the cumulative survival function, or proportion of cases surviving the event. Hazard ratios (i.e., exp[B]) are computed to characterize each individual’s relative odds of reaching the event
for every one unit change in the risk factor (e.g., brain response), controlling
for other covariates. All predictors were mean-centered.
ACKNOWLEDGMENTS. We thank Prashanth Nyalakanti, Eric Claus, Ed Bedrick,
and Eswar Damaraju for analytic support; Brandi Fink, Vaughn Steele,
and our anonymous reviewers for helpful comments; and the staff and
inmates of the New Mexico Corrections Department, for without their
generous cooperation this work could not have been completed. This work
was supported by the MacArthur Foundation Law and Neuroscience Project;
National Institute of Mental Health (NIMH) Grants 5R01MH070539 and
1R01MH085010 (to K.A.K.); National Institute on Drug Abuse Grants
1R01DA026505 and 1R01DA026964 (to K.A.K.); National Institute of Biomedical Imaging and Bioengineering Grant 2R01EB000840 (to V.D.C.); and
NIMH Postdoctoral Fellowship 1 F32 MH090668-01 (to E.A.).

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