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To what extent are structural and functional abnormalities in the prefrontal cortex and amygdala associated with psychopathic behavior, as discussed in James H. Fallon’s “The Psychopath Inside”?

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Studying the Cognitive and Behavioural Dynamics of Psychopathy

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The relevance of studying the cognitive and behavioral dynamics of psychopathy lies in its importance for improving diagnosis, therapeutic interventions, and reducing crime, thereby contributing to public safety. This study aimed to investigate how cognitive and behavioral mechanisms influence psychopathy. A combination of methods was employed, including the Levenson Self-Report Psychopathy Scale, semi-structured interviews, and the analysis of non-verbal reactions. The sample consisted of eight participants (four men and four women) aged 23 to 45, selected based on high scores in both primary and secondary psychopathy traits. The findings indicate that psychopathic traits, such as reduced sensitivity to affiliation, emotional dysregulation, impulsivity, and low empathy, complicate social adaptation, leading to social isolation and conflict. These traits increase the risk of antisocial behavior and recidivism, posing challenges to public safety and rehabilitation. Cognitive mechanisms in individuals with psychopathic characteristics showed selective attention, with concentration driven by personal gain, and memory focused on the practical value of information. Decision-making was marked by quick, rational choices aimed at control and efficiency, with impulsivity arising from the desire for immediate results. In stressful situations or when plans were threatened, participants exhibited reactive aggression and managed their impulses using strategies like distraction or physical activity. Manipulativeness was a key strategy for goal achievement, with honesty seen as a tool used only when beneficial. This study contributes to understanding the cognitive and behavioral dynamics of psychopathy, offering insights for developing more effective interventions and rehabilitation strategies. Future research should involve more extensive, diverse samples to validate these findings and explore broader implications for public policy and criminal justice interventions.

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Schizophrenia Bulletin vol. 42 no. 4 pp. 1056-1067, 2016 doi:10.1093/schbul/sbw012 Advance Access publication February 23, 2016 Disconnection Between Amygdala and Medial Prefrontal Cortex in Psychotic Disorders Downloaded from http://schizophreniabulletin.oxfordjournals.org/ at Health Sciences Library, Stony Brook University on September 13, 2016 Prerona Mukherjee1, Amri Sabharwal2, Roman Kotov3, Akos Szekely2, Ramin Parsey3, Deanna M. Barch4, and Aprajita Mohanty*,2 University of California Davis MIND Institute, UC Davis Medical Center, Sacramento, CA; 2Department of Psychology, Stony Brook 1 University, Stony Brook, NY; 3Department of Psychology, Stony Brook University, Stony Brook, NY; 4Departments of Psychology, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, MO *To whom correspondence should be addressed; Department of Psychology, Stony Brook University, Stony Brook, NY 11794-2500, US; tel: 631-632-7872, fax: 631-632-7876, e-mail: Aprajita.Mohanty@stonybrook.edu Distracting emotional information impairs attention more Introduction in schizophrenia (SCZ) than in never-psychotic individu- Psychotic disorders including schizophrenia (SCZ) and als. However, it is unclear whether this impairment and bipolar disorder with psychosis (BDP) are characterized its neural circuitry is indicative generally of psychosis, or by deficits in cognitive control.1 When multiple input specifically of SCZ, and whether it is even more specific dimensions compete for processing access, cognitive con- to certain SCZ symptoms (eg, deficit syndrome). It is also trol biases attention selectively towards the task-relevant unclear if this abnormality contributes to impaired behav- dimension.2-4 Both selective attention to task relevant ioral performance and real-world functioning. Functional aspects of stimuli and inhibition of distractor interfer- imaging data were recorded while individuals with SCZ, ence are impaired in psychotic disorders.5-9 Emotionally bipolar disorder with psychosis (BDP) and no history of salient stimuli that signal potential danger or reward con- psychotic disorders (CON) attended to identity of faces stitute a particularly potent class of distractors that can while ignoring their emotional expressions. We examined hijack attentional resources and impair task-relevant pro- group differences in functional connectivity between amyg- cessing.10-13 Neural and behavioral evidence from several dala, involved in emotional evaluation, and sub-regions studies indicates that emotional (vs neutral) stimuli inter- of medial prefrontal cortex (MPFC), involved in emotion fere with processing of task relevant stimuli in psychotic regulation and cognitive control. Additionally, we exam- disorders,14-17 more so than in healthy individuals. ined correlation of this connectivity with deficit syndrome While mounting evidence indicates that the interaction and real-world functioning. Behaviorally, SCZ showed the of emotion and attention may play an important role in worst accuracy when matching the identity of emotional SCZ,18-20 the specificity and clinical utility of measures vs neutral faces. Neurally, SCZ showed lower amygdala- of emotion-related attentional impairment and their MPFC connectivity than BDP and CON. BPD did not underlying neural circuitry remains unclear. For exam- differ from CON, neurally or behaviorally. In patients, ple, studies have not compared disorders directly to test reduced amygdala-MPFC connectivity during emotional whether emotion interference deficits are unique to SCZ distractors was related to worse emotional vs neutral accu- or reflect deficits shared by SCZ and BDP. Furthermore, racy, greater deficit syndrome severity, and unemployment. it is not known whether behavioral or neural markers of Thus, reduced amygdala-MPFC functional connectivity emotion-cognition interactions relate to specific symp- during emotional distractors reflects a deficit that is spe- tom dimensions or impaired real-world functioning. An cific to SCZ. This reduction in connectivity is associated examination of these questions will help shed light on with worse clinical and real-world functioning. Overall, the shared vs distinct etiology of SCZ and BDP, and also these findings provide support for the specificity and clini- determine the potential clinical utility of behavioral and cal utility of amygdala-MPFC functional connectivity as a neural measures of emotion-cognition interaction. Here, potential neural marker of SCZ. we used functional magnetic resonance imaging (fMRI) to examine neural mechanisms of emotional interference Key words:  emotion/attention/psychosis/schizophrenia/ in SCZ, BDP, and a never-psychotic group during a task bipolar/fMRI/connectivity © The Author 2016. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com 1056 Amygdala Connectivity in Psychotic Disorders that required attention to a task-relevant dimension (face from a seed region to the whole brain in response to identity) while ignoring the task-irrelevant dimension changes in psychological conditions.40-42 Compared to (emotional expression). controls, individuals with SCZ and BDP show greater Maintaining attentional control in the presence of deficits in attention towards the task-relevant dimension distracting emotional information involves a network in the presence of the task-irrelevant emotional dimen- of brain regions acting in concert. These regions (fig- sion that is an integral part of the stimulus.15,43,44 A recent ure 1) include the amygdala, which is involved in process- study found no difference between SCZ and controls for ing saliency of emotional stimuli,10,21 as well as anterior interference from emotional pictures while maintaining cingulate cortex (ACC) and medial prefrontal cortex object information.18 However, in this prior study the dis- Downloaded from http://schizophreniabulletin.oxfordjournals.org/ at Health Sciences Library, Stony Brook University on September 13, 2016 (MPFC), which are involved in emotion appraisal and traction was from a separate stimulus rather than an inte- regulation.22 More specifically, the dorsal-caudal regions gral dimension of the target stimulus. Interference effects of ACC (dACC) and MPFC (dMPFC) are involved in are much smaller when competing information is sepa- monitoring difficulty posed due to competition between rated vs integrated in the same stimulus.45,46 Hence, we choices,4,23-25 including monitoring of emotion-related used a task in which one dimension (emotional expres- competition.25-28 In contrast, the ventral-rostral por- sion) interferes with attention to another dimension tions of ACC (rACC) and MPFC (rMPFC) are involved (identity) of the same facial stimulus.47-51 in regulating emotional conflict,27,29 directing attention Typically, deficits in the ability to manage current away from irrelevant emotional information,30,31 and goals and execute cognitive control have been reported modulating emotional response via strategies such as in both SCZ and BDP, but are more pronounced in the reappraisal.32-34 former.1,52 Thus, one might expect impaired regulation of Anatomically, the amygdala is well connected with ros- emotional interference in both disorders, potentially with tral and dorsal portions of the ACC, particularly the ros- greater severity in SCZ. Weaker amygdala-prefrontal tral portion.35-39 However anatomical connections to the cortical coupling during distracting emotional informa- adjacent regions of dMPFC and rMPFC are fewer.36,37,39 tion has been noted in SCZ14,18 however, studies directly Functionally, amygdala shows strong connectivity with the comparing neural mechanisms in SCZ to BDP are sparse. ACC and MPFC during tasks that require appraisal or reg- A recent study that compared resting state connectivity ulation of emotional stimuli22 and the degree of this func- between the 2 disorders provides evidence of specificity.53 tional coupling is positively correlated with effectiveness of This study showed altered resting state connectivity of self-reported emotion regulation.32 Evaluating functional amygdala with the ACC and MPFC in SCZ compared connectivity of amygdala with the dorsal and rostral ACC to BDP and healthy controls. Hence, in the present study, and MPFC, thus, provides an ideal focus for examining the we hypothesized that, compared to BDP and CON, disruption of task-relevant processing due to distracting SCZ patients will show greater impairment in task-rele- emotional information in psychotic disorders. vant attention due to emotional distraction, along with In the present study, we used psychophysiological inter- reduced functional connectivity of amygdala with rostral action (PPI) analysis to investigate whether amygdala and dorsal portions of the ACC and MPFC. functionally couples with the rostral and dorsal ACC and Additionally, cognitive and emotional impairments MPFC when participants attend to face identity while have been related to a range of functional consequences ignoring facial expressions. PPI estimates connectivity in SCZ.54-57 However, few studies have examined whether neural measures of emotion-related disruption in task rel- evant processing are linked to real-world functioning. We hypothesized that emotional interference-related altera- tions in functional connectivity would be related to social and occupational functioning. Finally, the deficit syndrome is thought to be a particularly severe form of SCZ, char- acterized by primary and enduring negative symptoms,58 impaired emotion and social processing,59 worse func- tional outcomes and unemployment,60,61 as well as altera- tions in anatomical connectivity.62,63 Thus, we hypothesized that alterations in functional connectivity would also be associated with severity of the deficit syndrome. Fig. 1.  Hypothetical connectivity model between amygdala and Methods dorsal-caudal regions of the anterior cingulate cortex (dACC) Participants and medial prefrontal cortex (dMPFC), as well as ventral- rostral portions of the ACC (rACC) and MPFC (rMPFC). The participants (SCZ = 26, BDP = 21, CON = 29) were drawn from the Suffolk County Mental Health Project 1057 P. Mukherjee et al (SCMHP): a longitudinal investigation of first-admission Syndrome),75 and (4) socioeconomic status (SES) based on psychosis.64,65 Inclusion criteria were first admission for primary bread winner's occupation. These assessments were psychosis within the previous 6 months, between ages 15 conducted by master-level mental health professionals. to 60 years, having IQ > 70, speaking English, and judged as being able to give informed consent. Experimental Tasks Since differential diagnosis between psychotic disor- ders is prone to misclassification,66 diagnosis was made Participants completed a modified version of the emo- by longitudinal consensus of study psychiatrists who tional face assessment paradigm76 in which they matched reviewed 6 assessments spanning 20 years, which included the identity of faces while ignoring their emotional Downloaded from http://schizophreniabulletin.oxfordjournals.org/ at Health Sciences Library, Stony Brook University on September 13, 2016 face-to-face assessments by masters-level clinicians using expressions (fear, anger or neutral; figure 2). For details, the Structured Clinical Interview for DSM-IV (SCID67), see supplementary methods. interviews with significant others, and review of medical records collected at 5 time points spanning 10 years (first Image Acquisition and Processing admission, 6-month, 2-year, 4-year, and 10-year). The Functional images were acquired on a 3 Tesla Siemens imaging analysis was performed at 20-year follow-up. The TIM Trio scanner with an interleaved echo-planar SCZ group included 16 individuals with SCZ and 10 indi- viduals with schizoaffective disorder. These 2 diagnoses were combined based on previous work in our cohort,68,69 as well as other work70,71 showing that schizoaffective dis- order and SCZ are very similar and characterized by more severe symptoms, worse course, and greater cognitive impairment than other psychotic disorders. People with schizoaffective disorder experience substantial burden of mood symptoms, which overlaps with bipolar disorder to some degree. However, this also is true of many people with SCZ, as substantial majority have comorbid mood disorders although lesser in duration than those seen in schizoaffective disorder.72,73 With regard to cognitive func- tioning and long-term outcome, schizoaffective disorder in this cohort is qualitatively different from mood disorder with psychosis but indistinguishable from SCZ.68,69 CON, the comparison group of age and gender matched indi- viduals with no history of psychosis was selected from the same neighborhoods as the cases. The present study was approved by Stony Brook University institutional review board. Participants gave informed consent and were screened for MRI contraindi- cations. For behavioral data 8 participants were excluded, 6 due to accuracy less than 60% and 2 due to technical problems in data acquisition resulting in a total of 68 participants for the present study (SCZ = 20, BDP = 21, CON = 27). For fMRI, 10 participants were excluded due to past brain surgery, excessive movement, technical problems, and behavioral accuracy less than 60% result- ing in a total of 66 participants for the present study (SCZ = 22, BDP = 15, CON = 29; see supplementary table 1 for details). Measures Measures used for assessment of functioning included (1) social functioning (sum of social activity, social initiative, Fig. 2.  (A) Modified emotional face assessment task. From the and socio-sexual relations ratings on the Quality of Life pair of faces at the bottom, participants chose the face whose identity best matched the identity of the face at the top and Scale),74 (2) employment status (gainfully employed or not (B) Mean accuracy while matching identity of emotional and at the time of the scan), (3) severity of the deficit syndrome neutral faces for no history of psychotic disorders (CON), bipolar (rated according to criteria of the Schedule for the Deficit disorder with psychosis (BDP) and schizophrenia (SCZ) groups. 1058 Amygdala Connectivity in Psychotic Disorders imaging sequence. Thirty-four slices parallel to the ante- Associations With Real-World Outcomes rior and posterior commissures (field of view: 210 mm, Next, using voxelwise multiple regression, we investigated slice thickness: 3.5 mm, gap: 0.3 mm [distance factor whether functional connectivity differences are associated 10%], in-plane resolution: 3.1 × 3.1 mm, repetition time with task accuracy for all participants in our ROI mask. [TR]: 2 s, echo time [TE]: 30 ms) were collected per vol- We also examined whether functional connectivity differ- ume. Structural images were acquired via a sagittal mag- ences are associated with social functioning, employment netization prepared rapid gradient echo (MPRAGE) status, as well as severity of deficit syndrome and positive sequence (TR: 1900 ms, TE: 2.53 ms, flip angle: 9°, slice symptoms across patients, while controlling for SCID thickness: 1 mm, in-plane resolution: 1 × 1 mm). diagnosis. For details see supplementary methods. Downloaded from http://schizophreniabulletin.oxfordjournals.org/ at Health Sciences Library, Stony Brook University on September 13, 2016 Connectivity Analysis Replication Analyses Functional data were slice-time and motion-corrected, We conducted a replication of the present study using and spatially normalized via SPM8 (http://www.fil.ion. the same sample but with a different task (supplementary ucl.ac.uk/spm). Functional connectivity was estimated by material 2). In the present study, the task involved ignor- PPI,40 using the generalized form of context-dependent ing emotional distractors while attending to task-relevant gPPI (http://brainmap.wisc.edu/PPI).42,77 Amygdala vox- face identity. In the replication study, the task involved els used for connectivity analyses were selected using a ignoring emotional distractors while maintaining task- combination of functional and anatomical approaches.78 relevant information in working memory. As in the pres- Voxels showing the strongest task-related effect across ent study we used PPI to examine group differences in groups were identified using univariate fMRI analyses amygdala connectivity. and results were examined within an amygdala region of interest (ROI) generated using the automated anatomical Results labeling (AAL) atlas.79 The seed was defined as a 6 mm sphere around the peak Montreal Neurological Institute Behavior (MNI) coordinates thus obtained (−18, −2, −18). For Group differences in accuracy were examined with a 3 each subject, the PPI term was computed as a product (BPD, SCZ, CON) × 2 (emotional and neutral) repeated of the task-related regressor and amygdala seed region measures ANOVA. There was a main effect of group, activity. This interaction term was regressed against activ- F (2, 65) = 4.732, P = .012, η2 = 0.16, condition, F (2, ity in the whole brain generating the per-voxel parameter 65) = 126.13, P < .001, η2 = 0.66; and group × condition estimate (β) maps representing the magnitude of func- interaction, F (2, 65) = 3.69, P = .030, η2 = 0.10 on accu- tional connectivity with the seed region (ie, amygdala) as racy of face identity matching (figure 2). Simple effects function of task. To assess how functional connectivity tests showed no significant difference between the groups varies between groups, as well as between the emotional for the neutral condition (P > .05). All 3 groups showed and neutral conditions, these β maps were subjected to a lower accuracy in matching identity of emotional com- 3 (groups: SCZ, BDP, CON) by 2 (conditions: emotional pared to neutral faces. Additionally, the SCZ group dem- and neutral) flexible factorial ANOVA implemented in onstrated lower accuracy than CON (d = 0.95, P = .004) SPM8.80 Since our hypothesis focus on the connectivity while matching identity of emotional faces, indicating of amygdala with rostral and dorsal ACC and MPFC, that impaired task-relevant processing due to emotional we examined the ANOVA results using an anatomical distractors is most pronounced in SCZ. There was no dif- ROI mask that included these regions (for details see ference between SCZ and BDP (d = 0.40, P = .129) or supplementary methods). Post hoc analyses with pairwise between BDP and CON (d = 0.42, P = .202; figure 2). and independent t tests were performed within SPM for clusters of voxels showing significant group × condition interaction in the ANOVA. All results were corrected for Functional Connectivity multiple comparisons using the AlphaSim toolbox.81 For To examine whether greater interference from emotional details see supplementary methods. expressions in psychotic disorders is associated with reduced functional connectivity of amygdala with rostral and dorsal portions of ACC and MPFC, we first con- Mediation Analysis firmed that the amygdala was sensitive to task-related Given that amygdala shows relatively poorer anatomical modulation. There was greater amygdala activation for connectivity with dMPFC82,83 compared to dACC,84-87 emotional vs neutral distractor condition, and this activa- we performed a mediation analyses to examine if dACC tion did not differ by group. Next, we examined connec- mediates the connectivity between amygdala and dMPFC tivity of the task-sensitive amygdala ROI to subregions and if this mediation differs in patients. See supplemen- of the MPFC. PPI analyses showed a significant group × tary methods for details. condition interaction effect in amygdala connectivity 1059 P. Mukherjee et al Downloaded from http://schizophreniabulletin.oxfordjournals.org/ at Health Sciences Library, Stony Brook University on September 13, 2016 Fig. 3.  Amygdala connectivity with (a) dorsal-caudal regions of the anterior cingulate cortex (dACC) (b) dorsal-caudal regions of the medial prefrontal cortex (dMPFC) and (c) ventral-rostral portions of the MPFC (rMPFC) is reduced for schizophrenia (SCZ) compared to bipolar disorder with psychosis (BDP) and no history of psychotic disorders (CON) when matching identity of the emotional compared to neutral faces. Left panel depicts regions showing significant group (CON, BDP, SCZ) × condition (emotional vs neutral face) interaction for amygdala-MPFC connectivity (P < .05, corrected for multiple comparisons). Right panel shows corresponding mean parameter estimates of amygdala-MPFC connectivity for the 3 groups for display purposes only. *Indicates group differences that were significant in voxelwise post hoc analyses conducted in SPM8 (P < .05, corrected for multiple comparisons). with dACC (peak coordinates: 8, 38, 26; cluster size 161; BPD did not differ significantly from CON. Furthermore, peak F 9.81; peak Z 3.71), dMPFC (peak coordinates only SCZ showed lower amygdala connectivity with −11, 48, 32; cluster size 1453; peak F 12.77; peak Z 4.32), dACC, dMPFC, and rMPFC for emotional distractors and rMPFC (peak coordinates −7, 59, 29; cluster size vs neutral distractors. 192; peak F 10.89; peak Z 3.95), but not rACC (figure 3). To ensure group differences in connectivity are not Voxelwise post hoc tests conducted to further investi- confounded by task performance, we conducted a post gate these group × condition interactions showed that, hoc voxelwise ANCOVA with diagnosis (SCZ, BDP while the 3 groups did not differ from each other for the and CON) as the between group factor, face type (emo- neutral condition, SCZ showed lower amygdala connec- tional and neutral) as the within group factor and behav- tivity with dACC, dMPFC, and rMPFC than BDP and ioral accuracy scores as a covariate. Our results showed CON, when matching identity of emotional faces (P < that the group differences in amygdala connectivity to .05, corrected; figures 3a-c). Amygdala connectivity for dACC, dMPFC, and rMPFC remain significant (P < 1060 Amygdala Connectivity in Psychotic Disorders trials was associated with greater amygdala-dACC con- nectivity (peak coordinates: −6, 20, 32; cluster size 73; peak T 3.61; peak Z 3.29) indicating that worse perfor- mance in SCZ may be related to weaker amygdala-dACC connectivity in this group (figure 5a). We also examined the relationship of connectivity differences with symp- toms and real-world functioning. Results showed that amygdala-dACC/DMPFC connectivity correlated posi- tively with employment status (peak coordinates: 20, 36, Downloaded from http://schizophreniabulletin.oxfordjournals.org/ at Health Sciences Library, Stony Brook University on September 13, 2016 41; cluster size 66; peak T 2.78; peak Z 2.64; figure 5b), and correlated negatively with deficit syndrome severity, a strong predictor of poor functional outcomes (peak coordinates: 10, 37, 16; cluster size 171; peak T 2.32; peak Z 2.24; figure 5c). No significant correlations of social functioning or positive symptoms with connectivity or behavioral measures were observed. Fig. 4.  Mediating variable analyses shows that dorsal-caudal regions of the anterior cingulate cortex (dACC) mediates the relationship between amygdala and dorsal-caudal regions of the Replication Study medial prefrontal cortex (dMPFC) across all participants. We found that lower amygdala connectivity with dACC again differentiated the SCZ group from the bipolar with .05, corrected) even after controlling for differences in psychoses and the control group. Additionally, we rep- behavioral accuracy. Post hoc comparisons between the licated our findings regarding the deficit syndrome such SCZ and BDP groups also showed that connectivity that lower amygdala-dACC connectivity was associated differences remain significant even after controlling for with more severe deficit syndrome symptoms even when behavioral accuracy differences. Finally, we simultane- controlling for group-related differences (see supplemen- ously entered performance in task (accuracy) and con- tary material 2 for details). nectivity during the task in logistic regression predicting group membership (SZ vs BPD), we found that accuracy did not independently distinguish the groups (P = .84), Discussion whereas amygdala-MPFC connectivity did distinguish We examined whether psychological and neural mecha- them (P = .02). nisms of emotional interference are specific to SCZ, or whether they are transdiagnostic markers of psychotic Mediation Analysis disorders. On a task in which participants matched iden- While the amygdala has direct anatomical connections tity of faces, SCZ showed the worst accuracy when the with rMPFC, it has little direct anatomical connectivity faces were emotional but performed similar to CON to dMPFC.82,83 Furthermore, there is stronger anatomi- when they were neutral. Neurally, compared to BDP and cal connectivity of amygdala to dACC38 than the adja- CON, SCZ showed reduced amygdala connectivity to cent dMPFC. Hence, we investigated whether the dACC dACC, dMPFC, and rMPFC, indicating that the func- mediates the connectivity between amygdala and dMPFC tional connectivity impairment is specific to SCZ and and whether this differs by group. Results showed a sig- specific to the emotional condition. BPD did not differ nificant indirect connectivity between amygdala and from CON either for accuracy or amygdala connectiv- dMPFC mediated by dACC (r = .14, P = .006). A sig- ity. Additionally, impaired amygdala-dACC functional nificant relationship was found between amygdala and connectivity during emotional distractors was related to dMPFC (r = .36, P = .009), amygdala and dACC (r = .35, poorer behavioral performance on the task across all par- P = .005), as well as between dACC and dMPFC (r = .41, ticipants, as well as worse clinical and real-world func- P = .016). Further, once the effect of dACC was factored tioning in patients. out, the relationship between dMPFC and amygdala was The task used in the present study involves competition no longer significant (r = .22, P = .090). This mediation between dimensions of facial identity and facial expres- effect did not differ by group (figure 4). sion. Emotional expressions tend to interfere with pro- cessing of facial identity,47,48,50,51 an effect we replicated in our study, with all participants showing worse accuracy Association of Connectivity With Task Performance when matching identity of emotional vs neutral faces. and Real-World Functioning Emotional expressions may make it difficult to identify Voxelwise multiple regression across all participants faces due to facial contortion. However, threatening and showed that greater accuracy for emotional vs neutral happy faces both produce distortions of facial features 1061 P. Mukherjee et al A. dACC B. dACC / dMPFC C. dACC Downloaded from http://schizophreniabulletin.oxfordjournals.org/ at Health Sciences Library, Stony Brook University on September 13, 2016 Fig. 5.  Regions in dorsal-caudal regions of the medial prefrontal cortex (dMPFC) and anterior cingulate cortex (dACC) significantly correlated with measures of (a) accuracy while matching identity of emotional vs neutral faces, (b) employment, controlling for diagnoses and (c) global deficit symptoms, while controlling for diagnoses. but have differing effects on identification88,89 with posi- with subsequent resolution of interference being attrib- tive expressions sometimes enhancing recognition and uted to rACC inhibiting amygdala activation.27,107-111 negative impairing recognition. Further, identity process- Additionally, dACC itself has been shown to be associ- ing is impacted even by emotions conveyed by body in ated with down-regulation of negative emotions.112,113 which the face is embedded.90 Thus it is likely that, due to Recent theories of dACC function have proposed that their salience, emotional expressions use up a major por- this region connects emotional valence with executive tion of the limited capacity attentional system and leave control processes.91,114,115 The connectivity of dACC with little for task-relevant processing.91 amygdala is considered important for communication While there was no significant difference in accuracy of error signals between the 2 regions.116 In the present between SCZ, BPD, and CON for matching identity of study, stronger dACC-amygdala connectivity in controls neutral faces, SCZ performed significantly worse than may be indexing better monitoring of emotion-related CON when matching identity of emotional faces and interference, whereas reduced connectivity in SCZ may BPD did not differ from CON. These results demon- be indicative of worse monitoring of emotional interfer- strate that, in the presence of strongly competing emo- ence. In fact, our results show that worse performance tional information that requires immediate responses, during emotional vs neutral distractors correlates with SCZ show greater impairment in attentional control. greater dACC-amygdala connectivity across all groups, Although SCZ is characterized by emotional face pro- suggesting that reduction in this connectivity may be con- cessing deficits, these deficits chiefly concern explicit tributing to worse behavioral performance during emo- emotion perception measured via recognition, labeling tional distractors in SCZ. or matching of facial emotional expressions. However, Our connectivity findings for dMPFC were similar to our study involves implicit emotion perception, which is those for the adjacent dACC, showing reduced amygdala- typically intact in SCZ.92-99 Hence individuals with SCZ dMPFC connectivity specifically in SCZ while match- can perceive emotional expressions and these emotional ing identity of emotional vs neutral faces. dMPFC has expressions can interfere with task-relevant processing as been shown to be involved in similar functions of conflict our results indicate. monitoring and cognitive control as dACC.22,117 However, While ignoring emotional distractors the SCZ group while there is dense connectivity between amygdala and showed reduced connectivity with dACC. The dACC dACC,84-87 there is little direct anatomical connectivity is recruited when there is increased demand for cogni- between amygdala and dMPFC.82,83 Using mediation tive control, especially in the presence of distraction analyses, we found that dACC mediates the functional or strongly competing responses (eg, habitual or emo- relationship between amygdala and dMPFC across all tional) that must be overcome to stay task-focused.100-103 subjects in our study. Specifically, dACC is involved in monitoring conflict aris- Finally, our study showed decreased amygdala-rMPFC ing between competing choices.23,104,105 Recently, research- connectivity during emotional interference, more so in ers have hypothesized that dACC plays an important role SCZ than BDP and CON. The anterior portion of the in the evaluation of expected value of control, which rMPFC enables making choices between competing involves integration of payoff expected from implement- aspects of stimuli by predicting the expected outcome, ing control, the amount of control required to achieve or "affective value" of the competing choices118-121 and, that payoff, and the cost using cognitive effort.106 Overall, accordingly, modulating attention.122,123 It has rich con- the role of the dACC is to integrate information to deter- nectivity with amygdala and, thus, together they play mine whether, where, and how much control to allocate. a role when evaluating of subjective value of threaten- While most dACC research is conducted on interfer- ing stimuli.122-126 Contrary to what we hypothesized, we ence due to relatively nonemotional stimuli, research on did not find connectivity differences between amygdala emotional interference monitoring also implicates dACC, and the adjacent rACC. The lack of amygdala-rACC 1062 Amygdala Connectivity in Psychotic Disorders connectivity differences for emotional vs neutral dis- when controlling for unemployment, establishing that they tractors in the present study is inconsistent with previ- are independent. Further, this effect was specific relative to ous studies that showed a negative relationship between social functioning, which did not correlate with amygdala- amygdala and the rACC.111 While the rACC has been MPFC connectivity. shown to be involved in emotion regulation, its role is A limitation of the present study is that we employed a very specific in that it helps in resolving emotion-related block design, which does not allow us to separate the dif- conflict.29 Although our stimuli involve attentional com- ferent components of processing which could have been petition between emotional (facial emotional expression) achieved with an event-related design. However, given vs nonemotional (facial identity) dimensions, they do not our patient sample a faster block design is better suited Downloaded from http://schizophreniabulletin.oxfordjournals.org/ at Health Sciences Library, Stony Brook University on September 13, 2016 involve direct conflict, in that the emotional expression than a longer event related design. does not have any inherent conflict with the identity of a Functional imaging offers a window into the neurobi- face. The absence of direct emotion-related conflict may ology supporting cognitive and emotional processing, as have contributed to the negative findings in our study. well its disruption in SCZ, creating the potential for bet- Although our study shows that amygdala-dACC and ter diagnosis and more targeted treatment development.132 amygdala-rMPFC connectivity deficits are specific to However, this knowledge has not been effectively translated SCZ, amygdala connectivity with other regions in the into diagnostics and treatment development. While increas- PFC has been implicated in many disorders. Amygdala- ing data support the validity and reliability of neuroimaging MPFC connectivity has been found to vary in anxiety markers of cognitive and emotional processing in SCZ, little but the region of the MPFC implicated lies in pregen- is known about the clinical utility of these measures.55,133 By ual ACC or rACC.127-129 Similarly, amygdala-PFC con- leveraging an exquisitely characterized cohort and well- nectivity differences may be seen in depression but the defined neural circuitry identified from animal and human regions implicated are typically in ventral and subgenual research, the present study advances the literature by clari- ACC.130 The one study that examined task-based amyg- fying the diagnostic and clinical utility of neuroimaging dala-MPFC connectivity in bipolar disorder showed no measures of emotion-cognition interactions in SCZ. Our deficits in amygdala-ACC connectivity.131 To our knowl- results demonstrating the specificity of neural measures of edge, no study has compared task-based amygdala- emotion-cognition interactions to SCZ and their relation- MPFC connectivity deficits directly between BDP and ship to clinical and real-world functioning are an important SCZ spectrum disorders. Additionally our findings of step in facilitating the translation of these behavioral and specificity compliment findings showing that resting-state neural measures from basic affective/cognitive neuroscience amygdala-MPFC connectivity deficits are specific to SCZ into brain-based tools for better diagnosis and treatment compared to bipolar disorder.53 development. Although larger studies would be required to We also found a relationship between reduced amyg- establish translation of these promising findings into a reli- dala-dACC connectivity and greater deficit syndrome able biomarker of SCZ, our findings provide the first evi- severity, across both psychotic groups even after controlling dence for specificity and utility in SCZ. Further, our results for diagnosis. Deficit symptoms include restricted affect, highlight the importance of conducting future longitudinal diminished emotional range, diminished social drive or studies to determine whether observed neural processes con- sense of purpose, and in the present study they were higher tribute to the development of symptoms and impairments. for SCZ as compared to BDP group (Cohen's d = 0.80). To examine the possibility that our group behavioral and neu- Supplementary Material ral brain differences are driven by the deficit syndrome, we conducted an ANCOVA with diagnosis (SCZ, BDP and Supplementary material is available at http://schizophre- CON) as the between group factor, face type (emotional niabulletin.oxfordjournals.org. and neutral) as the within group factor and deficit syndrome score as a covariate. Our behavioral and neural results Funding showing differences between SCZ and the other groups remained significant, indicating that they cannot be solely This work was supported by a National Institute of attributed to the deficit syndrome. Additionally, the rela- Mental Health (NIMH) grant to R.K. (MH094398), tionship between deficit syndrome and amygdala-dACC Supplemental NIMH Funding to R.K. and A.M. connectivity remained significant even after controlling for (3RO1MH094398-02S1), and funding from Stony Brook diagnosis, suggesting that the effect is independent of diag- University to A.M. nosis. Finally, we found that reduced amygdala-MPFC connectivity is associated with unemployment, possibly Acknowledgments because this circuitry plays an important role in the abil- ity to selectively attend to task-relevant aspects of stimuli We thank Evelyn Bromet, cohort founder and Rachel while ignoring irrelevant emotional distractors. However, Roger, study coordinator. Authors have no conflicts of group differences in connectivity remained significant even interest to declare. 1063 P. Mukherjee et al References comparing individuals with schizophrenia and healthy adults. J Psychiatr Res. 2011;45:1184-1193. 1. Barch DM, Sheffield JM. Cognitive impairments in psychotic 20. 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Disconnection Between Amygdala and Medial Prefrontal Cortex in Psychotic Disorders

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Distracting emotional information impairs attention more in schizophrenia (SCZ) than in never-psychotic individuals. However, it is unclear whether this impairment and its neural circuitry is indicative generally of psychosis, or specifically of SCZ,

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The role of prefrontal cortex in psychopathy

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Psychopathy is a personality disorder characterized by remorseless and impulsive antisocial behavior. Given the significant societal costs of the recidivistic criminal activity associated with the disorder, there is a pressing need for more ...

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Examining the effect of psychopathic traits on gray matter volume in a community substance abuse sample

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Psychopathy is believed to be associated with brain abnormalities in both paralimbic (i.e., orbitofrontal cortex, insula, temporal pole, parahippocampal gyrus, posterior cingulate) and limbic (i.e., amygdala, hippocampus, anterior cingulate)…

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Abstract

Psychopathy is a personality disorder characterized by callous lack of empathy, impulsive antisocial behavior, and criminal recidivism. Studies of brain structure and function in psychopathy have frequently identified abnormalities in the prefrontal cortex. However, findings have not yet converged to yield a clear relationship between specific subregions of prefrontal cortex and particular psychopathic traits. We performed a multimodal neuroimaging study of prefrontal cortex volume and functional connectivity in psychopathy, using a sample of adult male prison inmates ( N =   124). We conducted volumetric analyses in prefrontal subregions, and subsequently assessed resting-state functional connectivity in areas where volume was related to psychopathy severity. We found that overall psychopathy severity and Factor 2 scores (which index the impulsive/antisocial traits of psychopathy) were associated with larger prefrontal subregion volumes, particularly in the medial orbitofrontal cortex and dorsolateral prefrontal cortex. Furthermore, Factor 2 scores were also positively correlated with functional connectivity between several areas of the prefrontal cortex. The results were not attributable to age, race, IQ, substance use history, or brain volume. Collectively, these findings provide evidence for co-localized increases in prefrontal cortex volume and intra-prefrontal functional connectivity in relation to impulsive/antisocial psychopathic traits.

Keywords: psychopathy, medial orbitofrontal cortex, dorsolateral prefrontal cortex, volume, functional connectivity

Introduction

Psychopathy is a mental health disorder characterized by shallow affect, callous disregard for others, and impulsive antisocial behavior. Present in roughly one-fourth of adult prison inmates, psychopathy is associated with a disproportionately high incidence of violent crime, substance abuse, and recidivism ( Smith and Newman, 1990; Hare, 2003). Identifying the psychological and neurobiological mechanisms underlying these deficits could thus have profound implications for the clinical and legal management of psychopathic criminals, as well as for the basic understanding of human social behavior.

One area of the brain that is believed to play a central role in the pathophysiology of psychopathy is the prefrontal cortex. Subregions of the prefrontal cortex mediate a variety of functions that contribute to behavioral control, emotion, social cognition, and value-based decision-making. For instance, ventromedial prefrontal cortex (vmPFC) and medial orbitofrontal cortex (mOFC) are thought to represent the values of potential decision outcomes and update these values based on ongoing experiences of reward or punishment ( Damasio, 1996; Grabenhorst and Rolls, 2011), as well as subserve aspects of moral judgment ( Greene and Haidt, 2002) and emotion states such as guilt, regret, and empathy ( Barrash et al., 2000; Camille et al., 2004; Vollm et al., 2006). Dorsolateral prefrontal cortex (dlPFC), which consists of the middle frontal gyrus (MFG) and superior frontal gyrus (SFG), has been implicated in abstract reasoning ( Greene et al., 2004) and cognitive control ( Miller and Cohen, 2001). Also, the anterior cingulate cortex (ACC), a limbic structure surrounded by and densely interconnected with prefrontal cortex, has been linked to error detection, performance monitoring, cognitive control, goal-directed behavior, and emotion processing ( Devinsky et al., 1995; Shackman et al., 2011). Psychopathic individuals, as well as individuals with acquired damage to the prefrontal cortex, display deficits in many of these functions. Indeed, some of the earliest evidence to suggest involvement of prefrontal cortex dysfunction in psychopathy came from studies of patients who began to display psychopathic-like traits-including conspicuously diminished guilt, shame, and empathy; irritability; poor planning; irresponsibility; and failure to learn from punishment-after acquiring damage to the vmPFC/mOFC ( Eslinger and Damasio, 1985; Damasio, 1994; Bechara et al., 1997; Barrash et al., 2000). Subsequent behavioral studies of psychopathic individuals have expanded on these findings, documenting deficits in reversal learning ( Budhani et al., 2006), response perseveration ( Molto et al., 2007), moral judgment ( Koenigs et al., 2012), and economic decision-making ( Koenigs et al., 2010).

To help resolve some of the inconsistencies and gaps in knowledge reviewed here, we conducted a multimodal neuroimaging investigation of prefrontal cortex structure and function in psychopathy. Using a mobile scanner, we collected magnetic resonance imaging (MRI) from a sample of adult male prison inmates ( N =   124) with a broad range of psychopathy severity. First, we assessed whether the volumes of frontal lobe subregions were correlated with psychopathy severity, in terms of PCL-R Total, Factor 1, and Factor 2 scores. We performed region of interest (ROI)-based analyses in two separate image processing software programs (FreeSurfer and SPM), as well as a voxel-wise analysis in SPM. Next, we analyzed RSFC data from the same participants to determine whether the observed prefrontal structural abnormalities were accompanied by alterations in prefrontal RSFC. This set of analyses comprises the largest study to date to examine both structural and functional features of the prefrontal cortex in psychopathy.

Materials and methods

Participants

Participants ( N =  124) from a medium-security Wisconsin correctional facility were selected based on the following inclusion criteria: (1) <55 years old; (2) IQ >70; (3) no history of psychosis or bipolar disorder; (4) no history of significant head injury or post-concussion symptoms; (5) no current use of psychotropic medications; and (6) completed interview assessments for psychopathy and substance use disorder (SUD) (see below). Of these 124 subjects, RSFC data were obtained for 115 subjects; 8 of these were excluded due to excessive motion in the scanner, leaving a total of 107 subjects for RSFC analysis (see Supplementary Methods for motion exclusion criteria). Informed consent was obtained both orally and in writing. The present participant sample was included in two prior neuroimaging studies ( Philippi et al., 2015; Korponay et al., 2017).

Psychopathy was assessed with the Psychopathy Checklist Revised (PCL-R) ( Hare, 2003) by trained research assistants. The PCL-R is a 20-item scale completed based on a semi-structured interview and file review. Each item is scored as 0, 1, or 2 based on the severity of each trait. Total scores ≥30 indicate psychopathy ( n =  41); scores >20 and <30 are considered intermediate ( n =  48), and scores ≤20 are non-psychopathic ( n =  35) ( Hare, 2003). Inter-rater reliability (intraclass correlation) for PCL-R Total score was 0.98 based on 10 dual ratings. PCL-R Total, Factor 1, and Factor 2 scores were used for separate regression analyses ( Harpur et al., 1989). Cronbach's alpha for the factor scores in this sample was 0.75, and the correlation between factor scores was 0.61 ( P <   0.001).

Substance use severity was assessed with the Addiction Severity Index (ASI) ( McLellan et al., 1992). Following the method used previously in adult male inmates ( Ermer et al., 2012b), years of regular use were summed for each substance (alcohol and drug) that the participant reported using regularly (three or more times per week for a minimum period of 1 month); total scores were then divided by age (to control for opportunity to use) and a square root transformation was applied. Participant characteristics are summarized in Table 1.

Table 1.

All (n = 124)

Non-psychopathic (n = 35)

Intermediate (n = 48)

Psychopathic (n=41)

P a

Mean

SD

Mean

SD

Mean

SD

Mean

SD

%n%n%n%n

MRI acquisition

MRI data were acquired using the Mind Research Network's Siemens 1.5T Avanto Mobile MRI System equipped with a 12-element head coil. All participants underwent scanning on correctional facility grounds. A high-resolution T1-weighted structural image was acquired for each subject using a four-echo magnetization-prepared rapid gradient-echo sequence (TR = 2530 ms; TE = 1.64, 3.5, 5.36, and 7.22 ms; flip angle = 7°; FOV = 256 × 256 mm ²; matrix = 128 × 128; slice thickness = 1.33 mm; no gap; voxel size = 1 × 1 × 1.33 mm ³; 128 interleaved sagittal slices). All four echoes were averaged into a single high-resolution image ( Ly et al., 2012). Resting-state functional images (T2*-weighted gradient-echo functional echo planar images) were collected while subjects lay still and awake, passively viewing a fixation cross for 5.5 min (158 volumes) ( Philippi et al., 2015) and were acquired with the following parameters: TR = 2000 ms; TE = 39 ms; flip angle = 75°; FOV = 24 × 24 cm ²; matrix = 64 × 64; slice thickness = 4 mm; gap = 1 mm; voxel size = 3.75 × 3.75 × 5 mm ³; 27 sequential axial oblique slices.

Preprocessing and analyses of structural MRI data were conducted in both FreeSurfer 5.1 ( Fischl, 2012) in Linux and Statistical Parametric Mapping software (SPM12; http://www.fil.ion.ucl.ac.uk/spm). RSFC data analysis was performed using AFNI ( Cox, 1996) and FSL ( http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/). Refer to Supplemental Methods for structural and RSFC preprocessing details.

Analytic strategy

Because processing pipelines and ROI parcellations differ between image processing software programs, and because studies have reported that results may vary as a function of program ( Rajagopalan and Pioro, 2015), we computed volumes of the prefrontal cortex ROIs (mOFC, lateral orbitofrontal cortex, inferior frontal gyrus, MFG, SFG, and ACC) from two separate programs (FreeSurfer and SPM) and performed separate analyses with both sets of values (see Supplementary Table S1 for correlations between FreeSurfer and SPM for the average volume of each prefrontal cortex subregion). Statistical significance for the ROI analyses was evaluated at a Bonferroni-corrected P <  0.004 that accounted for the 12 analyses conducted in each program (left and right hemisphere of six ROIs). In addition, SPM was used to perform small volume-corrected voxel-wise analyses within the prefrontal cortex. This type of analysis can detect smaller, focal aberrations in volume that may not be detected in the subregion volume analysis, and allows for more specific localization of the areas where volume is most strongly linked to psychopathy severity. These analyses were restricted to a mask encompassing the union of the six bilateral ROIs listed above. Peak height with correction for multiple comparisons using a family-wise error (FWE) rate of P <   0.05 was used to assess statistical significance for these voxel-wise analyses.

After performing the volumetric analyses, we then examined whether the identified volume abnormalities were associated with abnormalities in RSFC. We chose seeds centered at the peak coordinates of the focal volume clusters identified in the voxel-wise analyses in order to directly assess whether areas where volume correlated with psychopathy severity also had functional connectivity relationships that correlated with psychopathy severity, as the co-localization of these abnormalities may point to a common underlying pathophysiology ( Korponay et al., 2017). We created a 6mm-radius spherical seed around these peak coordinates, and subsequently assessed RSFC between these seeds and other areas of the brain in relation to psychopathy ratings. While creating seeds based on the exact voxel sizes and shapes of each volume cluster would have provided the greatest degree of precision in assessing the overlap between volume and RSFC metrics, 6-mm radius spheres are the standard seed size and shape for cortical resting-state analyses based on the literature. Seeds were visually inspected to ensure that they did not include volume outside of the whole-brain mask; seeds that did expand beyond the brain mask were excluded from analysis.

Seeds were evaluated in RSFC regressions only in relation to the specific psychopathy score-type (PCL-R Total, Factor 1, and/or Factor 2) for which the focal area (on which the seed was based) had demonstrated a relationship with in the volumetric analysis. To correct for multiple comparisons, we used FWE-correction at the cluster level using a whole-brain mask (3dClustSim in AFNI, using the version updated December 2015) ( Forman et al., 1995; Carp, 2012) and applied cluster extent thresholding. We used the autocorrelation function to calculate the full width at half maximum in order to address the non-Gaussian nature of functional MRI data ( Eklund et al., 2016). The cluster extent threshold corresponded to the statistical probability ( α = 0.05, or 5% chance) of identifying a random noise cluster at a predefined voxel-wise (i.e. whole-brain) threshold of P <  0.001 (uncorrected). Using this whole-brain FWE cluster correction, a cluster-corrected size of ≥47 voxels was significant at P FWE < 0.05.

Covariates

We found the expected ( Walhovd et al., 2005) negative correlations between age and the gray matter volume of all ROIs (significant at P <  0.05 in 10 out of 12 FreeSurfer ROIs and in 3 out of 12 SPM ROIs). Thus, age was included as a covariate in all models. Also, Factor 2 scores had a trending positive relationship with substance use severity as measured by the ASI ( P =  0.055). Because gray matter volume has been shown to relate to substance use ( Franklin et al., 2002; Fein et al., 2006; Makris et al., 2008; Tanabe et al., 2009; Yuan et al., 2009), we included substance use severity using the transformed ASI variable as a covariate in all regression models. Furthermore, we observed a significant group effect ( P <  0.05) of race (between Caucasian and non-Caucasian subjects) on volume of 11 out of 12 ROIs in FreeSurfer and ten out of 12 ROIs in SPM, and thus race was also included as a covariate in all regression models. IQ was not related to psychopathy severity (PCL-R Total: P =  0.405; Factor 1: P =  0.726; Factor 2: P =  0.730) but was significantly positively correlated ( P <  0.05) with the volumes of three ROIs in FreeSurfer and ten ROIs in SPM. However, when race was added to the model testing the main effect of IQ on volume, the relationship between IQ and volume did not remain significant in any ROI in either program. On the other hand, when IQ was added to the model testing the main effect of race on volume, race maintained a significant relationship with the volume of 11 of the 12 ROIs in FreeSurfer and 9 of the 12 ROIs in SPM. These data suggest that race accounts for much of the variance in IQ in this model. Coupled with the observation that race was highly correlated with IQ ( P <  0.001), we excluded IQ from our main analysis models in order to reduce multicollinearity. In addition, in order to account for variation in overall brain size, we included brain volume (gray matter + white matter) as a covariate in all structural analyses. There was no significant relationship between PCL-R Total score and brain volume as measured by either SPM ( P =  0.65) or FreeSurfer ( P =  0.43). We also ensured that problematic levels of multicollinearity were not present between brain volume and age; the variance inflation factor was <1.4 for both brain volume and age in regression models for all of the ROIs, indicating unproblematic levels of multicollinearity ( Hair et al., 1995). In regressions where the main variable of interest was a Factor score, the other Factor score was also included as a covariate. In addition to regression analyses, we calculated zero-order (bivariate) correlations between PCL-R scores and structural volumes.

Results

ROI volume analysis

PCL-R Total scores and Factor 2 scores were positively related to specific subregion volumes throughout the prefrontal cortex (covariates: age, race, substance abuse severity, and brain volume; Factor 1 score was also included as a covariate for Factor 2 analyses). The most robust relationships (surviving a P <  0.004 Bonferroni correction for multiple comparisons) were observed between Factor 2 scores and volume of the right mOFC, left MFG, and left SFG ( Figure 1). Zero-order correlations between Factor 2 scores and each of these three subregions were significant ( P <  0.05) in SPM but were not always significant in FreeSurfer. The directions of findings were consistent for the volume values extracted from both SPM and FreeSurfer, though significance levels differed. See Tables 2-4 for regression data. There were no significant relationships between Factor 1 scores and ROI volumes.

Fig. 1.

Zero-order correlation plots for significant relationships ( P <    0.004) between Factor 2 scores and volume of prefrontal cortex subregions.

Table 2.

Table 3.

Table 4.

In addition to these main analyses, we performed the following Supplementary analyses with different models to assess the sensitivity of the findings to covariates: factor score analyses without covarying for the other factor; replacing the continuous ASI substance use severity covariate with the categorical SUD covariate (None, Abuse, or Dependence) from the Structured Clinical Interview for DSM-IV Axis I disorders (SCID-IV) ( First, 2002); and the original model but analyzed separately by race. Findings remained essentially the same with the alternate factor score and substance abuse models. The analyses split by race showed that findings only reached significance at the Bonferroni-corrected threshold in non-Caucasian subjects, though the direction of findings was the same for both race groups (see Supplementary Tables S2-S9 for full results).

Voxel-wise volume analysis

Voxel-wise regressions identified focal areas within the prefrontal cortex where volume was positively correlated with Factor 2 scores (covariates: age, race, substance abuse severity, brain volume, and Factor 1 score) ( Figure 2). Mirroring the ROI results, these relationships were found in the right mOFC, left MFG, and left SFG, as well as in the right MFG, left mOFC, right SFG, and right ACC. There were no significant relationships between PCL-R Total scores or Factor 1 scores and prefrontal cortex focal volumes. There were also no negative relationships between any psychopathy score-type and prefrontal focal volumes. See Table 5 for complete results.

Fig. 2.

Results of voxel-wise analysis for the relationship between Factor 2 scores and volume in the prefrontal cortex. Positive relationships are shown in yellow.

Table 5.

In addition to the voxel-wise regressions restricted to the prefrontal cortex mask, we also performed whole-brain voxel-wise regressions to assess the specificity of psychopathy severity relations to volumes in the prefrontal cortex. These results show that while relationships between psychopathy severity and volume are not isolated to the prefrontal cortex, consistent with both the main analyses of this study and of a previous study of this sample ( Korponay et al., 2017), the prefrontal cortex and striatum are brain regions where volume is strongly positively correlated with Factor 2 severity. See Supplementary Results and Figure S1 for full results of these analyses.

Resting-state functional connectivity analysis

Increased functional connectivity was observed between multiple regions of the prefrontal cortex in relation to Factor 2 scores (covariates: age, race, substance abuse severity, and Factor 1 scores) ( Figure 3). In particular, Factor 2 scores were positively correlated with functional connectivity between left MFG and right anterolateral prefrontal cortex and between right MFG and right frontal polar cortex. See Table 6 for complete results. RSFC was not evaluated for PCL-R Total scores or Factor 1 scores because there were no focal volumes significantly related to these score-types.

Fig. 3.

RSFC results. Positive relationships between RSFC and Factor 2 scores are shown in yellow.

Table 6.

Focal seed

Seed origin coordinates

RSFC relationship with:

MNI peak coordinates

Cluster size

t-value a

MFG (L)

(−34, 60, −3)

Angular gyrus (L)

(−51.5, −65.5, 33.5)

59

4.49

MFG (R)

(26, 62, 12)

MFG (R)

(41.5, 18.5, 54.5)

90

5.16

Frontal polar cortex (R)

(26.5, 60.5, −5.5)

63

4.48

Discussion

This study used a multimodal neuroimaging approach to examine the relationship between psychopathic traits and structural and functional features of the prefrontal cortex. First, we investigated how volumes of frontal lobe subregions relate to psychopathy severity as measured by PCL-R Total, Factor 1, and Factor 2 scores. We found that across both FreeSurfer and SPM, PCL-R Total scores and Factor 2 scores were exclusively linked to larger prefrontal cortex subregion volumes. The most robust relationships were observed between Factor 2 scores and the volume of right mOFC and left dlPFC (middle and superior frontal gyri). Next, we performed complementary voxel-wise analyses within the prefrontal cortex, which had the potential to reveal volumetric relationships within or across ROIs. This analysis also revealed exclusively positive relationships between Factor 2 severity and volume in specific regions of prefrontal cortex, including mOFC and dlPFC. Finally, we assessed RSFC in areas where volume was related to psychopathy severity. We found that Factor 2 scores were positively correlated with functional connectivity between prefrontal subregions, including between left MFG and right inferior frontal gyrus and between right MFG and right lateral orbitofrontal cortex.

A positive association between prefrontal gray matter volume and Factor 2 trait severity could potentially be the result of aberrant neurodevelopment. Gray matter volume decreases throughout adolescence and early adulthood in several distinct clusters of prefrontal cortex, including the medial and dorsolateral prefrontal subregions ( Gogtay et al., 2004; Giorgio et al., 2010). One possibility is that the observed positive associations between Factor 2 scores and PFC subregion volumes reflects deficient synaptic and neuronal pruning in these areas, resulting in ineffective and/or dysfunctional processing. Our data do not address this level of analysis directly, but they do suggest an interesting avenue for future research.

Factor 2's relevance to prefrontal cortex neurobiology can be understood conceptually by considering the types of experimental tasks on which psychopathic individuals perform poorly and the consequences that these deficits are likely to yield outside the laboratory. For instance, psychopathic individuals have been shown to perform poorly on tasks that require learning from punishment ( Newman, 1987); this function has been shown to involve OFC ( O'Doherty et al., 2001), a region found here to be enlarged in psychopathic individuals. A deficit in this function may increase the likelihood that psychopathic individuals engage in poor decision-making that results in impulsive and criminal behavior-traits that are indexed by Factor 2 score. Consistent with this interpretation, in a previous study with this same inmate sample we found that Factor 2 scores were specifically associated with volume of the nucleus accumbens subnucleus of the striatum ( Korponay et al., 2017). The medial OFC region identified in this study is known to be densely interconnected with the nucleus accumbens ( Sesack et al., 1989; Gabbott et al., 2005), and both regions are central components of the brain circuitry involved in processing value and reward ( Pujara and Koenigs, 2014).

One potential issue of this study that warrants consideration is the substantial rate of SUD in this sample. Multiple studies have linked SUD to structural and functional abnormalities in the frontal lobe. We included a continuous substance use severity variable in our regression models to account for this feature of the study population. Hence, the findings we report here do not appear to be due to individual differences in substance abuse histories. Other substance use metrics such as the SUD measure on the Structured Clinical Interview for DSM-IV Axis I disorders (SCID-IV) ( First, 2002) quantify abuse severity in a categorical manner, though it is not well established which substance abuse measure is best suited for capturing meaningful differences in brain data. In Supplementary analyses, we conducted the regional volume analyses using the SCID-IV's categorical metric for presence of SUD (None, Abuse, or Dependence) instead of the ASI's continuous measure, and findings remained essentially the same. In a future study, we will more fully examine the relationships between substance use characteristics and frontal lobe neurobiology in this sample. A related issue is that this study did not include a non-incarcerated comparison group, which makes it difficult to discern whether the observed findings represent "deficits" or "abnormalities". It is possible that increased prefrontal gray matter and intra-frontal functional connectivity facilitate enhanced function in certain domains and deficits in others. We may only conclude based on the present findings that these neurobiological features are associated with increased impulsive and antisocial traits. Another issue worth addressing in future studies is the relationship between volumetric and RSFC findings. While our approach allowed us to directly assess whether structural and functional abnormalities were co-localized, this method may be considered liable to statistical non-independence ( Kriegeskorte et al., 2009), in that the volume and RSFC of frontal lobe regions may be inherently linked. Future studies, in both clinical and non-clinical samples, could establish whether this is indeed consistently the case. Future studies could also examine frontal lobe structure and function with respect to more specific clusters of psychopathic traits (e.g. using the four-facet model of the PCL-R -Cooke and Michie, 2001). Using this four-facet model in the present study, we found no significant ROI volume relationships at the Bonferroni-corrected threshold ( Supplementary Tables S10-S13). Another potential methodological issue is that the resting-state scans were collected after subjects had performed a variety of functional tasks, which likely introduced noise into the data. Lastly, it should be noted that another study from our group used subjects from the present sample to examine RSFC in psychopathy within and between the default mode network, frontoparietal network and cingulo-opercular network ( Philippi et al., 2015) and some evidence of a positive correlation between Factor 2 scores and intra-frontal RSFC was found. However, as prefrontal RSFC per se was not a primary focus of this previous study, only four frontal lobe seeds (mPFC, left and right dlPFC, and dACC) were assessed, seeds were not chosen in relation to volumetric data, and only clusters falling within masks of the specific brain networks of interest to the study were examined. This study thus reports on new and more comprehensive data in relation to prefrontal RSFC in psychopathy.

In sum, we have analyzed a unique set of multimodal neuroimaging data from a large sample of incarcerated criminal offenders to help clarify the structural and functional characteristics of prefrontal cortex in psychopathy. Our findings provide evidence of co-localized prefrontal cortex enlargement and heightened intra-frontal RSFC related predominantly to the impulsive and antisocial traits of psychopathy.

Supplementary data

Supplementary data are available at SCAN online.

Supplementary Material

Acknowledgements

We thank many individuals at the Wisconsin Department of Corrections who made this research possible, and are especially indebted to Warden Judy Smith, Warden Randy Hepp, and Dr. Kevin Kallas.

Funding

This study was supported by grants from the National Institutes of Health [Grant numbers MH070539, DA026505, MH087525, MH090169].

Conflict of interest. None declared.

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