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r Human Brain Mapping 34:1921–1930 (2013) r
Intrinsic Limbic and Paralimbic Networks Are Associated With Criminal Psychopathy
Michelle Juárez,1,2 Kent A. Kiehl,1,3 and Vince D. Calhoun1,2*
1The Mind Research Network, Albuquerque, New Mexico 2Department of ECE, University of New Mexico, Albuquerque, New Mexico
3Department of Psychology, University of New Mexico, Albuquerque, New Mexico
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Abstract: Background: Psychopathy is a personality disorder associated with impairments in decision- making, empathy, and impulsivity. Recent brain imaging studies suggest that psychopathy is associated with abnormalities in limbic/paralimbic brain regions. To date, no studies have examined functional brain connectivity measures using independent component analyses (ICA) in adults with psychopathy. Here, we test hypotheses regarding paralimbic connectivity in adult incarcerated individuals stratified by psychopa- thy scores. Methods: One hundred and two prison inmates were rated using the Hare Psychopathy Checklist-Revised (PCL-R). FMRI data were collected while subjects performed an auditory target detection ‘‘oddball’’ task. FMRI data were analyzed using group ICA to identify functional networks responding to the oddball task correlating with psychopathy scores. Results: Components demonstrating significant correlations with psychopathy included a default mode network, a frontoparietal component, and a visual/ posterior cingulate component. Modulation trends correlated strongly with factor 2 (impulsivity) and total PCL-R scores in the frontoparietal and visual/posterior cingulate networks, and with factor 1 (affective) scores within the default mode network. The posterior cingulate region factored significantly in the modulation trends observed. Conclusion: Consistent with the hypothesis of limbic/paralimbic abnormalities associated with psychopathy, modulation trends correlated strongly with PCL-R scores. There is strong evidence to implicate the posterior cingulate in aberrant functional connectivity associated with the manifestation of psychopathic symptoms. Future investigations comparing functional trends associated with the posterior cingulate in psychopathic subjects may provide further insight into the manifestation of this disorder.Hum Brain Mapp 34:1921–1930, 2013. VC 2012Wiley Periodicals Inc.
Keywords: psychopathy; criminal; auditory oddball; fMRI; independent component analysis; functional connectivity; limbic; paralimbic; posterior cingulate
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INTRODUCTION
Psychopathy is a personality disorder that affects �1% of the general population, but about 20% of the prison population [Hare, 2003]. Psychopathy manifests itself through a variety of symptoms, from aggressive narcis- sism and lack of guilt and remorse to severe patterns of irresponsibility, lifelong delinquency, prolific substance abuse, and socially deviant lifestyles. Diagnosis is cur- rently based upon a thorough review of these symptoms that meet a scaled, diagnostic threshold based on the Hare Psychopathy Checklist-Revised (PCL-R) [Hare, 1991, 2003].
It is widely known that psychopathy is difficult to treat and that the afflicted are at high risk for resuming criminal
Contract grant sponsor: National Institute of Health; Contract grant numbers: R01 MH072681, 2R01 EB000840.
*Correspondence to: Vince D. Calhoun, The Mind Research Network, 1101 Yale Boulevard, Albuquerque, NM 87106. E-mail: vcalhoun@mrn.org
Received for publication 1 July 2011; Revised 19 December 2011; Accepted 20 December 2011
DOI: 10.1002/hbm.22037 Published online 19 March 2012 in Wiley Online Library (wileyonlinelibrary.com).
VC 2012 Wiley Periodicals Inc.
behavior following release from prison. Given the cur- rently limited options for treatment, there is increasing research into the neurological models of psychopathy. The symptoms that are exhibited in psychopathy may be asso- ciated with a disruption in the communication between brain regions, resulting in the diversity of symptoms that present.
Previous studies suggest that individuals who exhibit psychopathic symptoms may differ in affective and cogni- tive function from healthy populations [Kiehl et al., 1999, 2004]. There have been noted affective/cognitive differen- ces observed within the limbic and paralimbic structures in individuals with psychopathy [Kiehl, 2006; Kiehl et al., 2001]. These regions include amygdala, hippocampus, and adjacent parahippocampal regions, anterior and posterior cingulate, parahippocampal gyrus, insula, temporal pole, and orbital frontal cortex (see Fig. 1; Kiehl, 2006; and Fig. 1, Anderson and Kiehl, in press]. The limbic network, in particular, has been implicated with the existence of psychopathic behaviors [Bechara et al., 1999; Patrick, 1994; Patrick et al., 1993; Tranel and Damasio, 1994]. However, large-scale investigations into the neural correlates within these structures in criminal psychopaths have been limited.
Functional connectivity is defined as the study of the correlations between neuronal activations between spa- tially remote regions within the brain [Friston, 1994]. Advances in applied functional neuroimaging techniques have been shown to quantify functional connections between regions [Calhoun et al., 2001a; McIntosh, 1999]. The application of group independent component analysis (ICA) to fMRI data has been used in order to identify spa- tially distinct and temporally coherent components of brain activity [Calhoun et al., 2001a]. When applied in con- junction with a specific task, ICA provides a measure of both functional connectivity and task-relatedness. This allows for the identification of brain networks involving multiple brain regions as well as the ability to test for which networks may be implicated by the psychopathol- ogy being investigated [Erhardt et al., 2010].
The purpose of this investigation was to document the manner in which functional networks are affected by psy- chopathy and how the hemodynamic responses occurring within these networks correlate with psychopathic symp- toms. Based on prior studies on psychopathy in prison inmates, we hypothesized that we would find evidence of aberrant connectivity within the limbic/paralimbic brain network [Kiehl, 2006]. We also hypothesized significant correlations with psychopathy-induced symptoms and dif- ferences in connectivity between inmates who have low PCL-R scores and those who score high on the PCL-R scale.
We implemented group ICA to study the collected fMRI data from a large group of inmates serving time in a North American medium-security prison. We present results collected with the Mind Research Network’s Mobile MRI System from 102 prisoners, each of whom
performed an auditory oddball task. The auditory oddball task was selected for these analyses, because prior research has shown that abnormal brain electrical responses are observed in individuals with psychopathy during this task [Kiehl et al., 2006].
METHODS
Participants
Blood oxygenation level-dependent fMRI data were col- lected from a total of 102 prison inmates. Participants were recruited from North American prison populations and scanned remotely at the prison site using the MRN Mobile MRI System (http://www.mrn.org/mobile-mri-scanning- facility/index.php).
Participants underwent a clinical interview and exten- sive collateral institutional file review to determine their level of psychopathy. Diagnoses were based on the PCL-R [Hare, 1991]. The PCL-R is an expert rating scale used to assess the type and degree of severity of psychopathy within an individual. This scale contains 20 items, each of which is scored on a three-point scale. These 20 items are divided into two groups or ‘‘factors,’’ each factor being further subdivided into two ‘‘facets’’ each [Hare, 2003]. The PCL-R total score provides information on the severity of psychopathy within an individual. Those with a total PCL-R score of 30 or greater qualify for a diagnosis of psychopathy.
The individual factor scores provide insight into the dif- ferent components of psychopathy. Factor 1 scores corre- spond to affective/interpersonal characteristics, whereas factor 2 scores correspond to antisocial characteristics and criminality associated with impulsive violence and socially deviant lifestyles. A complete listing of the 20 items and their breakdown in the PCL-R are given in Table I.
Measures of the degree of severity of psychopathy symp- toms were obtained using the PCL-R list. There are differ- ent views on whether psychopathy should be treated as a continuous or a categorical variable. In this work, we per- form both continuous and categorical analyses. For the con- tinuous analysis, a correlation of the fMRI data with the psychopathy measures was performed. For the categorical analysis, we made the assumption that subjects with a PCL-R total score of 20 or less (low scorers) could be com- parably classified as ‘‘incarcerated control’’ subjects for this study, whereas those with a total PCL-R score of 30 or greater (high scorers), we classified as psychopathy sub- jects. The low-scoring PCL-R group consisted of 48 sub- jects; the mid-scoring PCL-R group consisted of 37 subjects; and the high-scoring PCL-R group consisted of 17 subjects. Participants ranged in age from 18 to 61 years of age, with an average age of 34.6 years (standard deviation ¼ 10 years), and had an average IQ of 96.7 (standard deviation ¼ 15.1). All subjects were untreated by medication for at least a month before the scan. Substance use/abuse data were also collected for all study participants. Subjects were
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rated for the substance use/abuse of nine different types of substances (alcohol, sedative-hypnotic-anxiolytic, cannabis, stimulants, opioid, cocaine, hallucinogen/PCP, poly-drug, and other substances) in their entire life history and for the month before the scan and designated a severity value as follows: ‘‘0’’ for no diagnosis of use/abuse, ‘‘1’’ for basic abuse of a substance/s, and ‘‘2’’ for actual substance de- pendency, the rating with the highest severity. We verified that almost all subjects had not used/abused any con- trolled substances for at least a month before the scan (only one subject appeared to have been dependent upon some controlled substances during the month before the scan even though all participants were incarcerated). To better visualize how these subjects compared in their abuse of substances, we summed the values for abuse severity for each participant to reach a total substance abuse severity score. The average substance use/abuse severity for all subjects was 5.2 (standard deviation ¼ 3.2). Also, any sub- jects who were found unable to correctly perform the task during practice were excluded from participating in the study. All subjects were completely fluent in English. A breakdown of the demographic and clinical data is pro- vided in Table II.
Task
This task implemented an event-related auditory oddball paradigm in which the auditory stimuli were pre- sented to each participant over the course of two runs while undergoing the fMRI scan [Kiehl et al., 2005]. Wear- ing headphones to shield from the noise of the scanner,
each participant was presented with a series of pseudoran- dom auditory stimuli (tones). Participants were asked to respond only to target stimuli by pressing a single button of the LumiTouch Reply System [Chang et al., 2001], while ignoring (no button press) the standard tones and novel, computer-generated tones. During a test scan, the volume was calibrated to ensure that all test subjects were able to hear the tones comfortably over the background noise of the actual scanner.
The auditory stimuli consisted of standard, target, and novel tones. In the auditory oddball paradigm, the stand- ard stimuli were presented more frequently (P � 0.80) at a pitch of 1 kHz. The target and novel stimuli were pre- sented more infrequently (P � 0.10). The target stimuli tones were presented at a different pitch from the stand- ard tones (1.5 kHz), whereas the novel stimuli were com- plex, computer-generated sounds that varied in pitch during a single presentation. Tones were presented in pseudorandom order, each stimulus lasting 200 ms. Because of the pseudorandom order in which they were presented, the interstimulus interval between tones ranged from 500 to 2,100 ms each time. In this study, there were a total of two sessions per subject. There were two runs of the paradigm during each scanning session, each run con- sisting of the same number of stimuli (�100). To ensure that hemodynamic responses were not induced by the type of stimuli presented, the target and novel presenta- tion sequences were exchanged between runs in this study to balance their presentation. Before execution of the task in the scanner, all subjects practiced performing this task to ensure capability in completing it correctly. Participants unable to perform the task correctly were excluded from the study.
Imaging Parameters
Functional data were acquired at the remote site with EPI sequences on a Mobile Siemens Avanto 1.5 Tesla (T) MR scanner with advanced SQ gradient engine. The imag- ing sequence parameters are as follows: TR ¼ 2,000 ms, TE ¼ 29 ms, FA ¼ 65�, FOV ¼ 24 � 24 cm, 64 � 64 matrix, 3.4 � 3.4 mm in plane resolution, slice thickness ¼ 5 mm, and 27 slices. This sequence covers the entire brain (150 mm) in 1.5 sec.
Data Analysis
Preprocessing
FMRI data were preprocessed using the SPM5 software package. Images were motion-corrected using INRIalign— an algorithm unbiased by local signal changes [Freire and Mangin, 2001; Freire et al., 2002]. Data were spatially nor- malized into the standard Montreal Neurological Institute space [Friston, 1995] and resampled into 3 � 3 � 3 mm, resulting in 53 � 63 � 46 voxels. Next, the data were spatially smoothed with a 10 � 10 � 10 mm full width at
TABLE I. PCL-R list items broken down by factor
Factor 1: Personality ‘‘Aggressive narcissism’’
Glibness/superficial charm Grandiose sense of self-worth Pathological lying Cunning/manipulative Lack of remorse or guilt Emotionally shallow Callous/lack of empathy Failure to accept responsibility for own actions Traits not correlated with either factor Many short-term marital relationships Criminal versatility Factor 2: Impulsivity/Socially deviant lifestyle Need for stimulation/proneness to boredom Parasitic lifestyle Poor behavioral control Promiscuous sexual behavior Lack of realistic, long-term goals Impulsiveness Irresponsibility Juvenile delinquency Early behavioral problems Revocation of conditional release
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half-maximum Gaussian kernel. The resulting coordinates were converted to the Talairach and Tournoux standard space to assist with anatomical labeling [Talairach and Tournoux, 1988]. However, all (x,y,z) coordinates listed in the manuscript are in Montreal Neurological Institute (MNI), the default coordinate system in SPM.
Independent Component Analysis
Following the SPM5 preprocessing, a group independent component analysis (ICA) was performed on the prepro- cessed data [Calhoun et al., 2001b]. The methods prescribed by these processes were organized in batch scripts and performed via the group ICA of fMRI (GIFT) MATLAB toolbox version 1.3c (http://icatb.sourceforge. net). FMRI time series data for all participants were first compressed through principal component analysis (PCA). There were two PCA data reduction stages, each with a dimensional- ity of 25, which helped to reduce the impact of noise as well as to make the estimation computationally tractable [Calhoun et al., 2001b, 2009; Schmithorst and Holland, 2004]. The dimensionality/number of components was determined using the modified minimum description length criteria tool built into GIFT [Li et al., 2007). The data reduction was followed by a group spatial ICA, per- formed on the participants’ aggregate data, resulting in the final estimation of our independent components [ICs; Calhoun et al., 2001c; Erhardt, in press]. The algorithm used in this process was the infomax algorithm, which attempts to minimize the mutual information of network outputs [Bellemann et al., 1995].
From the group spatial ICA, we reconstructed spatial maps and their corresponding ICA time courses that rep- resented both the spatial and temporal characteristics of each component, subject, and session [Erhardt et al., 2010]. In all, this resulted in 5,100 IC spatial maps (102 subjects � 2 sessions � 25 ICs), each with an associated ICA time course of the data. These maps and time courses were then subjected to a second-level analysis to determine whether the resultant components were task-related as well as to determine which components reflected plausible nonartifact networks.
Statistical analysis of spatial components
We averaged the spatial maps produced during the spa- tial ICA across the two sessions. The spatial maps were converted to z-score maps and then entered into a second- level one-sample t-test to identify voxels that contributed significantly to a given component for the group [Calhoun et al., 2001b]. Next, these components were analyzed stat- istically and compared to group-specific thresholds to observe trends in task modulation among the subjects.
Statistical analysis of ICA time courses
We performed a temporal sorting of the ICA time courses using an SPM5 design matrix containing three regressors corresponding to the three auditory oddball stimuli (standards, targets, and novels). Temporal sorting is a method by which we compare the model’s time course with the ICA time course. Using a multiple linear regres- sion sorting criteria, the concatenated ICA time courses were fit to the model time course. This resulted in a set of beta weights for each regressor associated with a particu- lar subject and IC. The value of the resulting beta weight indicates the degree to which the component was modu- lated by the task. We also calculated the event-related averages of the time courses for all components. Each plot of the event-related average depicts the level of task- related functional activity for that particular component over the course of the experimental period.
Statistical Analyses
For each IC in this study, we performed a number of statistical analyses on the beta weights resulting from the ICA. These analyses included calculating the mean and standard deviation of the beta weights, one and two-sam- ple t-tests on the beta weights, and a correlation of the beta weights with intake demographic data (age, IQ, and substance abuse severity) and with all PCL-R scores (factor 1, factor 2, and total). The mean and standard deviation for task modulation of the hemodynamic response were calculated for the entire set of subjects and for the score- related subject subgroups. Next, one and two-sample
TABLE II. Subject demographics and PCL-R scores
Age IQ Substance abuse Factor 1 Factor 2 Total PCL-R
Mean (all subjects) 34.6 96.7 5.2 6.7 12.7 21.6 StDev 10.0 15.1 3.2 3.4 4.1 7.1 Mean (total PCL-R � 20) 35.8 96.0 5.0 4.5 9.6 15.6 StDev 10.4 15.8 3.5 2.4 3.3 3.9 Mean (20 < total PCL-R < 30) �34.3 94.5 5.4 7.3 14.7 24.5 StDev 9.9 14.4 3.1 2.4 2.2 2.4 Mean (total PCL-R � 30) 32.1 103.2 5.2 11.3 17.2 32.5 StDev 8.8 13.6 3.0 2.1 1.9 2.5
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t-tests were performed on the beta values obtained for each component to identify significant differences in mod- ulation within and among the various participant groups. The one-sample t-tests (one degree of freedom) provided information on the degree and direction of the task modu- lation for each subject group within particular brain regions, whereas the two-sample t-tests (two degrees of freedom) compared the differences in modulation between the low-scoring PCL-R group and the high-scoring PCL-R group. The two-sample t-tests allowed us the opportunity to compare differences in the degree to which certain brain regions exhibited a hemodynamic response to this task.
The final step in the analysis of the fMRI data collected was to identify any significant correlations of the beta weights with age, IQ, substance abuse severity, and with each of the series of PCL-R scores (factor 1, factor 2, and total). We also examined the intake data to see if any of these data factors correlated significantly with each other, thus potentially affecting the statistical results using the fMRI data. To accomplish this, we conducted Pearson correlations for the entire set of participants. These comparisons were thresholded and corrected for multiple comparisons based on the false discovery rate (FDR) [Genovese et al., 2002].
RESULTS
We performed a full statistical analysis on the group ICA results. Twenty-five ICs were estimated through group ICA. We first eliminated 15 components that were related to motion artifacts or correlated with spatial maps for white matter, cerebral spinal fluid, or the ventricular system [Ste- vens et al., 2007]. Next, we tested the remaining components for differences in task modulation between low and high- scoring participants and for significant correlations of the associated target beta weights with psychopathy symptom scores. Please see the Appendix Table A1 for a listing and description of all 25 components along with the results of the correlations of their respective target beta weights with psychopathy symptom scores. Three components met the criteria for statistically significant associations with psy- chopathy symptom scores (P � 0.05). The ICs and their re- spective component numbers are a default mode component (10), a frontoparietal component (14), and a visual/posterior cingulate component (24; see Fig. 1). Table III contains the list ofMNI coordinateswith descriptions of the associated brain regions for the chosen components. A summary of the results is provided in the next section.
Behavioral Results
Only subjects who were capable of correctly performing the task were included in this study. Participants had an average reaction time of 507 ms in response to target stim- uli, with a standard deviation of 107 ms (Table IV).
Correlations With Demographic Data and
With PCL-R Scores
We calculated Pearson correlations of the target beta weights with the following intake data: age, IQ, substance abuse severity, and with all PCL-R scores (factor 1, factor 2, and total). The purpose of this was to see if we could reveal significant correlations of the modulation trends with any of the PCL-R symptom factors and/or overall total PCL-R symptoms, and if any of those results could be moderated by any of the other intake parameters (age, IQ, and substance abuse). Calculations revealed significant (P � 0.05, FDR corrected) negative correlations of the fron- toparietal and medial visual component target regressor beta weights with the factor 2 and total PCL-R scores. We also observed a significant positive correlation of the default mode component and target regressor beta weights with fac- tor 1 scores. Correlations of the intake data parameters with each other revealed a significant negative correlation of age with factor 2 scores as well as a significant positive correla- tion of substance abuse severity with factor 2 scores. Results of these correlations are provided in Table V.
DISCUSSION
Consistent with our hypotheses, analyses revealed several brain networks (components) that included limbic and para- limbic regions that exhibited task-related modulation induced by the target stimuli in the auditory oddball task in the prison inmates. Modulation within these networks cor- related strongly with psychopathy symptoms and showed differences in connectivity between low and high-scoring subjects. These observations are consistent with previous results implicating the limbic/paralimbic networks in psy- chopathy [Kiehl, 2006; Kiehl et al., 2001; Laurens et al., 2005].
Default Mode
The default mode network has been associated with the internal monitoring of functional activity and cognitive processing within the brain, for which the anterior and posterior cingulate are key contributors [Greicius et al., 2003]. Within the default mode component, correlations of the beta weights from all subjects with factor 1 PCL-R scores were significant. For this particular component, the cingulate gyrus and the posterior cingulate, which are regions associated with the limbic network, were signifi- cantly associated with this region, along with the precu- neus, medial frontal gyrus, superior temporal gyrus, middle temporal gyrus, and anterior cingulate. The cingu- late gyrus has been identified as an integral member of the limbic system [Clark et al., 2005] and has been implicated in the cognitive and attentional processing of the brain [Maddock and Buonocore, 1997; Mesulam, 1999]. The pos- terior cingulate has also been implicated in the mediation of emotional and memory-related processes [Maddock et al., 2003]. Its significance in the adaptive reflexive
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processing of external stimuli has been demonstrated through the observation of the associated hemodynamic response to an auditory oddball task [Kiehl et al., 2005]. Observations of the behavioral and cognitive changes asso- ciated with psychopathy have also implicated abnormal- ities within the posterior cingulate, which is a member of the paralimbic network [Kiehl, 2006; Kiehl et al., 2001].
The frontal regions associated with this circuit included the anterior cingulate, which has been shown to be abnor- mal in psychopathy in fMRI studies [Kiehl et al., 2001, 2004]. Also, lesions of the anterior cingulate produce impairments in response inhibition and behavioral disre- gulation [Degos et al., 1993; Hornak et al., 2003; Mesulam, 2000; Tekin and Cummings, 2002]. It has been suggested that decreased functional activity within the limbic pre- frontal circuit, which includes the anterior cingulate, is associated with psychopathic behavioral characteristics [Veit et al., 2002]. Furthermore, criminal psychopaths dem- onstrated no significant hemodynamic activity within the limbic prefrontal circuit in response to a fear conditioning paradigm, suggesting a dissociation in emotional and cog-
nitive processing in the condition [Birbaumer et al., 2005]. Our results suggest that abnormalities in functional con- nectivity in the anterior and posterior cingulate may con- tribute to deficits in the internal monitoring of cognitive and attentional processes of the brain, which appear to be more strongly linked to the interpersonal and affective symptoms (factor 1) of psychopathy.
Frontoparietal
Correlations of factor 2 and total PCL-R scores with tar- get beta weights for all subjects revealed significant associ- ations in the frontoparietal component (see Fig. 1). Target beta weights also correlated significantly with age for all subjects. Evidence from auditory oddball fMRI studies on the functional characteristics of attentional processing has demonstrated target-related modulation trends associated with the frontoparietal brain region [Fichtenholtz et al., 2004; Strobel et al., 2008]. Furthermore, it has been sug- gested that frontoparietal cortex functionality is associated with the allocation of attentional processes to emotional
Figure 1.
Regions of auditory oddball modulation shown with their associated time courses.
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stimuli [Bentley et al., 2003]. It has been observed that abnormalities in the corticolimbic circuits of the brain, with particular emphasis on the frontoparietal regions, are linked to an increased prevalence in impulsive/reactive aggression [Coccaro et al., 2011]. Factor 2 of psychopathy was more strongly linked to this frontoparietal circuit than factor 1, suggesting that the frontoparietal circuit observed here is more strongly linked to the impulsive dimension of psychopathy than to the affective dimension.
Medial Visual
Correlations between total and factor 2 PCL-R scores with target beta weights for all subjects revealed signifi- cant associations with the medial visual component. Target beta weights also correlated significantly with age for all subjects. The medial visual component includes multiple regions of the visual, parietal, and limbic cortex. We note that limbic contribution to this component was relatively limited and only included tiny aspects of the parahippo- campal complex and anterior and posterior cingulate. The paralimbic dysfunction model [Kiehl, 2006], as well most other models of psychopathy, does not predict abnormal- ities in the visual or parietal lobe in psychopathy. Thus, it is unclear how the visual and parietal aspects of this com- ponent map onto existing theories of psychopathy.
In a recent study on the aggressive behavior of criminal psychopaths inflicting punishment during a retaliation task, aberrant activity was observed within the posterior
cingulate in subjects punished more severely [Veit et al., 2010]. Some of the key behavioral characteristics associated with factor 2 PCL-R scores are poor behavioral controls and impulsivity. Thus, to the extent that this medial visual component reflects aberrant posterior cingulate connectiv- ity, our results are consistent with the prior work.
Limitations of the Present Study
The purpose of the present study was to test the hypotheses that psychopathy was associated with aberrant functional connectivity in the paralimbic system using ICA
TABLE IV. Results from the one and two-sample t-tests
performed on the target beta weights, broken down by
PCL-R subject group
Default mode Frontoparietal
Visual/posterior cingulate
One-sample t-test (df ¼ 101) PCLR � 20 �3.85 5.52 5.41 20 < PCLR < 30 �5.90 1.70 1.52 PCLR � 30 �0.67 1.80 0.85
Two-sample t-test (df ¼ 100) PCLR low versus
high �1.18 1.46 1.99
Highlighted results are classified as statistically significant (P � 0.05).
TABLE III. MNI coordinates for the selected components
Area Brodmann area R/L volume (cc) R/L random effects: max value (x, y, z)
Default mode Precuneus 7, 18, 19, 23, 31, 39 30.6/28.0 51.5 (0, �60, 31)/51.1 (3, �57, 30) Cingulate gyrus 23, 24, 31 13.4/10.8 49.6 (�3, �45, 33)/50.5 (3, �60, 28) Medial frontal gyrus 6, 9, 10, 11 9.7/10.0 27.4 (�3, 49, �8)/27.3 (3, 49, �8) Superior temporal gyrus 13, 22, 39, 41, 42 8.6/4.5 23.6 (�48, �57, 28)/18.7 (48, �60, 28) Middle temporal gyrus 19, 20, 21, 39 7.7/6.5 23.9 (�50, �60, 28)/21.5 (45, �63, 28) Posterior cingulate 23, 29, 30, 31 7.6/8.3 44.7 (�9, �48, 22)/49.6 (6, �51, 25) Anterior cingulate 10, 24, 32 7.0/6.7 26.9 (�3, 46, �5)/27.3 (3, 46, �5) Frontoparietal Precuneus 7, 19, 31 25.5/24.3 48.8 (�9, �70, 50)/50.3 (6, �61, 56) Cingulate gyrus 23, 24, 31, 32 12.4/7.4 17.3 (0, 19, 32)/16.1 (3, 22, 27) Inferior parietal lobule 7, 40 9.5/13.4 11.7 (�45, �47, 55)/18.0 (45, �44, 55) Superior frontal gyrus 6, 9, 10, 11 9.2/10.4 18.5 (�33, 47, 17)/18.6 (33, 45, 17) Middle frontal gyrus 6, 9, 10, 11, 46 8.2/10.5 18.9 (�33, 45, 20)/18.9 (30, 45, 20) Superior temporal gyrus 22, 38, 42 7.4/8.7 20.6 (�50, 14, �8)/18.5 (53, 9, �3) Superior parietal lobule 5, 7 6.9/7.0 51.7 (�3, �64, 56)/52.5 (6, �64, 53) Visual/posterior cingulate
Cuneus 7, 17, 18, 19, 23, 30 15.6/17.0 54.3 (�9, �69, 12)/52.2 (6, �72, 9) Precuneus 7, 19, 23, 31 14.3/10.6 47.1 (0, �72, 23)/42.6 (3, �72, 26) Middle occipital gyrus 18, 19 10.8/9.9 35.1 (�12, �87, 15)/28.8 (18, �87, 15) Lingual gyrus 17, 18, 19 10.4/10.0 48.9 (�12, �67, 1)/49.2 (6, �73, 4) Culmen * 8.3/8.2 39.7 (�9, �61, �2)/41.3 (6, �67, �2) Posterior cingulate 23, 29, 30, 31 7.4/6.7 51.7 (�9, �66, 14)/50.5 (6, �69, 12) Parahippocampal gyrus 19, 27, 30, 35, 36, 37 5.1/3.5 36.0 (�18, �56, �5)/31.4 (21, �56, �5)
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[Kiehl, 2006]. Consistent with the hypotheses, we found that three ICA-derived components that included paralim- bic regions were abnormal in psychopathy. If an ICA spa- tial component showed a relationship to psychopathy and that component included regions of the paralimbic system, we interpreted such findings as support for the paralimbic dysfunction model of psychopathy [Kiehl, 2006]. However, it must be noted that all the components included regions that also fell outside the paralimbic system. Abnormal functional connectivity between nonparalimbic regions is not predicted by the paralimbic dysfunction model of psy- chopathy [Kiehl, 2006]. Future work such as path analyses need to be conducted in order to help elucidate the role each paralimbic may play in the larger network of brain regions observed to be implicated in psychopathy. Never- theless, as has been found in other psychopathological conditions, we have found evidence that psychopathy is associated with impairments in functional connectivity and that these abnormalities appear to include numerous aspects of the paralimbic system.
CONCLUSION
Overall, there is strong evidence to implicate abnormal functional connectivity within the limbic and paralimbic networks in the observable manifestations of abnormal personality symptoms characteristic of psychopathy. The three networks implicated in this study demonstrated aberrant modulation trends that significantly correlated
with psychopathy symptoms within the posterior cingulate region of the brain, a region that has been functionally associated with the limbic/paralimbic network. Investiga- tions demonstrating aberrant modulation trends in distin- guishable functional brain networks of psychopathic subjects using fMRI have been limited. Thus far, this is the first investigation in functional connectivity to demonstrate viable results implicating the limbic and paralimbic net- works, with particular emphasis on the frontoparietal region and posterior cingulate, in the manifestation of psy- chopathic symptoms. Modulation trends within the default mode network correlated significantly with factor 1 symp- tom scores, suggesting that a possible interaction dysfunc- tion between the anterior and posterior cingulate. The frontoparietal network appears to be most strongly linked to the behavioral/impulsive dimension (factor 2) of psy- chopathy. Moreover, the trends observed within the poste- rior cingulate region of the brain, which correlated significantly with PCL-R total and factor 2 symptom scores, as well as with factor 1 scores within the default mode network, suggest that abnormalities within this par- ticular region may directly contribute to the manifestation of psychopathic symptoms. The results from this study further bolster the hypotheses of limblic/paralimbic abnor- malities as being a key contributor in the manifestation of psychopathy, as highlight the relevance of posterior cingu- late to the condition. Future investigations into the neural correlates of the posterior cingulate and its possible dynamic interaction with both the anterior cingulate and the frontoparietal component may provide further clues
TABLE V. Correlation results of the beta weights with age, IQ, substance abuse severity, and all PCL-R scores
(total, factor 1, and factor 2) as well as correlation results of intake data with each other
Default mode Frontoparietal Visual/posterior
cingulate Comparisons
rho P-value rho P-value rho P-value Rho P-value
Age 0.073 0.464 0.223 0.024 0.201 0.043 IQ 0.020 0.844 �0.027 0.789 0.142 0.154 Substance abuse 0.091 0.376 �0.142 0.164 0.013 0.902 Total PCL-R scores 0.140 0.161 �0.207 0.037 �0.201 0.043 Factor 1 scores 0.212 0.033 �0.045 0.656 �0.080 0.421 Factor 2 scores 0.091 0.360 �0.246 0.013 �0.214 0.031 IQ—total PCL-R 0.102 0.310 IQ—factor 1 0.072 0.472 IQ—factor 2 0.058 0.562 Age—total PCL-R �0.162 0.104 Age—factor 1 �0.022 0.824 Age—factor 2 �0.243 0.014 Substance abuse—total PCL-R 0.172 0.093 Substance abuse—factor 1 0.084 0.411 Substance abuse—factor 2 0.259 0.010 IQ—Age �0.077 0.440 IQ—substance abuse 0.013 0.896 Age—substance abuse �0.067 0.516
Highlighted results are classified as statistically significant (P � 0.05).
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to better understanding trends in the functional network connectivity of psychopathy and should prove to be prom- ising avenue for further clarifying the origin and develop- ment of the psychopathy.
ACKNOWLEDGMENTS
The authors thank the Mind Research Network staff for their efforts during the data collection processes. Author- ship contributions: M. Juárez performed the data analyses, interpretation of results, and wrote the manuscript; K. Kiehl designed and implemented the experiment, collected the data, provided input into the interpretation, and contrib- uted to the composition of the manuscript. V. Calhoun provided input into the data analysis and interpretation and contributed to the composition of the manuscript.
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TABLE AI. Correlation results of the target beta weights for all components with all PCL-R scores
(total, factor 1, factor 2)
Component description Component number
Total scores Factor 1 scores Factor 2 scores
rho P-value rho P-value rho P-value
Bilateral frontoparietal 1 �0.137 0.170 �0.187 0.060 �0.045 0.650 Right lateral anterior (motion) 2 �0.162 0.104 �0.073 0.467 �0.165 0.097 (ventricle/white matter) 3 �0.005 0.962 0.056 0.574 �0.041 0.685 Temporal 4 �0.024 0.807 0.120 0.229 �0.088 0.378 (ventricles) 5 �0.131 0.188 �0.031 0.758 �0.165 0.097 (ventricles/midbrain) 6 0.093 0.353 0.097 0.333 0.153 0.125 (motion artifact) 7 0.028 0.778 �0.032 0.749 0.055 0.580 (ventricles) 8 0.045 0.653 0.023 0.816 0.034 0.731 (motion artifact) 9 �0.097 0.333 �0.051 0.608 �0.123 0.220 Default mode 10 0.140 0.161 0.212 0.033 0.091 0.360 (CSF) 11 �0.104 0.297 �0.073 0.466 �0.040 0.689 Similar to default mode 2 12 �0.089 0.371 �0.132 0.187 0.005 0.958 Scattered/left lateral posterior 13 �0.143 0.151 �0.091 0.365 �0.149 0.135 Frontoparietal 14 �0.207 0.037 �0.045 0.656 �0.246 0.013 (motion artifact) 15 �0.080 0.426 0.026 0.792 �0.150 0.132 (motion artifact) 16 0.002 0.982 0.019 0.851 �0.013 0.895 (motion artifact) 17 �0.202 0.041 �0.211 0.033 �0.156 0.118 (motion artifact) 18 �0.159 0.111 �0.141 0.159 �0.099 0.324 (nothing) 19 �0.090 0.369 �0.255 0.010 0.029 0.771 (ventricles) 20 �0.004 0.970 0.002 0.986 0.010 0.917 Default mode 2 21 0.125 0.212 0.062 0.533 0.165 0.097 Deep temporal 22 �0.054 0.590 0.075 0.454 �0.065 0.516 (motor/motion artifact) 23 �0.088 0.381 �0.009 0.926 �0.113 0.256 Visual/posterior cingulate 24 �0.201 0.043 �0.080 0.421 �0.214 0.031 (motion artifact) 25 �0.103 0.305 �0.083 0.406 �0.077 0.443
Highlighted results are classified as statistically significant (P � 0.05).
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