Department of Psychology, University of Southern California
Department of Research, Hillside Hospital (North Shore Long Island Jewish Health System)
Department of Psychology, University of Southern California
Department of Radiology, University of Southern California School of Medicine, USA
Correspondence: DrYalingYang, Department of Psychology, University of Southern California, Los Angeles, CA 900891061, USA.Tel: +1 213 720 2220; fax: +1 213 740 0897; e-mail: yalingy{at}usc.edu
Declaration of interest None. Funding detailed in Acknowledgements.
See invited commentary, pp.
326327, this issue.
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ABSTRACT |
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Aims To assess whether deceitful individuals show structural abnormalities in prefrontal grey and white matter volume.
Method Prefrontal grey and white matter volumes were assessed using structural magnetic resonance imaging in 12 individuals who pathologically lie, cheat and deceive (liars),16 antisocial controls and 21 normal controls.
Results Liars showed a 2226% increase in prefrontal white matter and a 3642% reduction in prefrontal grey/white ratios compared with both antisocial controls and normal controls.
Conclusions These findings provide the first evidence of a structural brain deficitinliars, they implicate the prefrontal cortex as an important (but not sole) component in the neural circuitry underlying lying and provide an initial neurobiological correlate of a deceitful personality.
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INTRODUCTION |
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METHOD |
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Assessment of lying
Participants were defined as liars if they fulfilled:
The term liars is intended as a short-hand specifically to denote the above four symptoms. A symptom-based orientation was employed because it has a number of significant advantages over a more traditional syndromal approach (Bentall et al, 1988; Costello, 1992; Halligan & David, 2001), especially in this particular field, which lacks diagnostic boundaries.
Normal controls (n=21) were selected from the remaining pool on the basis that they fulfilled none of the four criteria for lying. They also failed to meet criteria for either DSMIV antisocial personality disorder or DSMIV conduct disorder, and were matched as closely as possible to the 12 liars with respect to gender and ethnicity.
Because the liar group was significantly antisocial, any structural brain differences could be an artefact of antisocial personality, which has been associated with an 11% reduction in prefrontal grey matter in this group (Raine et al, 2000). Consequently, an antisocial control group (n=16) was formed by matching liars with individuals who did not fulfil criteria for lying, but who scored as highly as liars on DSMIV measures of antisocial personality disorder and conduct disorder.
All clinical ratings and diagnoses were performed by clinical PhD graduate research assistants who had both been trained and supervised by A.R. and also had undergone a standardised training and quality assurance programme for diagnostic assessment (Ventura et al, 1998). Pathological lying and conning/manipulative characteristics were assessed using the PCLR, which was supplemented by five sources of collateral data (Raine et al, 2000). These were the Interpersonal Measure of Psychopathy (IMP; Kosson et al, 1997), which provides an interviewers ratings of the participants interpersonal behaviours and which has been validated for use with incarcerated and non-incarcerated samples; self-reported crime as assessed by an adult extension (Raine et al, 2000) of the National Youth Survey self-report delinquency measure (Elliott et al, 1983); official criminal records; data derived from, and behavioural observations made during, the Structured Clinical Interview for DSMIV Mental Disorders (SCIDI; First et al, 1995a) and the Structured Clinical Interview for DSMIV Axis II Personality Disorders (SCIDII; First et al, 1995b). The deceitfulness trait of antisocial personality disorder was ascertained using the SCIDII, whereas malingering (telling lies to obtain sickness benefits) was self-reported on the adult extension of the National Youth Survey self-report delinquency measure.
Comparisons of the study groups are given in Table 1. The two antisocial groups did not differ with respect to rates of antisocial personality disorder and conduct disorder, but rates for both were significantly higher than for normal controls. The same pattern was observed for total psychopathy scores and total antisocial personality scores (the latter created by summing SCID scores on the seven features of antisocial personality disorder). All three groups did not differ significantly with respect to social class, ethnicity, IQ, handedness, history of head injury, height, head circumference and DSMIV diagnoses of alcohol/drug misuse/dependence. However, groups differed significantly with respect to age, with a higher mean age in the liar group than both control groups. Liars also had significantly higher verbal relative to performance IQ compared with both control groups. There were also trends for group differences in ethnicity (P=0.056) and total IQ (P=0.056), with antisocial controls tending to have lower total IQ and a greater representation of individuals from Black and minority ethnic groups than normal controls.
Demographic, cognitive and physical measures
Estimated IQ was based on five sub-tests (vocabulary, arithmetic, digit
span, digit symbol, block design) of the Wechsler Adult Intelligence Scale
Revised (WAISR; Wechsler,
1981), with verbalperformance discrepancy scores computed
by subtracting performance IQ from verbal IQ. Right v. left hand
preference was assessed using the abbreviated Oldfield Inventory
(Bryden, 1977), with high
scores indicating a stronger preference for right-handedness. History of head
injury was defined as head trauma resulting in hospitalisation and the amount
of time (in minutes) the subject was rendered unconscious from any head
injury. Social class was measured using the Hollingshead classification system
(Hollingshead, 1975). A
physical examination was conducted to derive measures of height and head
circumference.
Magnetic resonance imaging
Structural MRI was conducted on a Philips S15/ACS scanner (Selton,
Connecticut, USA) with a magnet of 1.5 Tesla field strength. Following an
initial alignment sequence of one midsagittal and four parasagittal scans
(spin-echo T1-weighted image acquisition, time to repetition=600
ms, echo time 20 ms) to identify the anterior commissure/posterior commissure
(AC/PC) plane, 128 three-dimensional T1-weighted gradientecho
coronal images (time to repetition = 34 ms, echo time=12.4 ms, flip angle
35°, thickness= 1.7 mm, 256 x 256 matrix, field of view=23 cm) were
taken in the plane directly orthogonal to the AC/PC line.
Brain images were reconstructed in three dimensions using a SPARC workstation and semi-automated CAMRA S200 ALLEGRO software (Sun Microsystems Inc., Santa Clara, California, USA) was used for grey/white cerebrospinal fluid segmentation. The prefrontal region was defined as all cortex anterior to the genu of the corpus callosum, and divided into left and right hemispheres along the longitudinal fissure (Raine et al, 2000). Segmentation of grey and white matter was performed using a thresholding algorithm, with the operator unaware of group membership, and applying a cut-off value to the signal intensity histogram to optimally differentiate white from grey matter, areas of which were defined using an automated seeding algorithm on each slice. Whole brain volume was defined as all cerebral cortex, excluding the ventricles, pons and cerebellum. The pons was excluded by drawing a straight line between the two innermost points that form the superior border. Colliculi were excluded when no longer attached to the cerebral hemispheres. For volume measures, areas on each slice (mm2) were multiplied by slice thickness (1.7 mm) and added to provide volumes in cubic centimetres. Interrater reliability (intraclass correlation coefficient) based on 23 scans (raters unaware of each others ratings and group membership) were as follows: total brain volume (0.99), left prefrontal grey (0.99), right prefrontal grey (0.99), left prefrontal white (0.93), right prefrontal white (0.94) and total brain volume (0.99). Volumes of grey and white matter were calculated separately for each hemisphere and a grey/white ratio was calculated for each hemisphere, with lower scores indicating increased white matter compared with grey.
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RESULTS |
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Prefrontal grey/white ratio
Liars had relatively more prefrontal white than grey matter. A multiple
analysis of variance (MANOVA) on grey/white ratios showed a significant main
effect for group (F(2,46)=10.25, P = 0.0001,
2=0.308). Liars had lower prefrontal grey/white ratios
(mean=1.15, s.d.=0.21) than antisocial controls (mean = 1.56, s.d.=0.38,
t=3.6, P=0.001) or normal controls (mean=1.63, s.d.=0.27,
t=5.3, P=0.0001). Liars had 35.7% decrease (0.41) a in
prefrontal grey/white ratio compared with antisocial controls and a 41.7%
decrease (0.48) compared with normal controls
(Fig. 2).
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When prefrontal white matter was expressed as a function of whole brain volume, groups again differed significantly (F(2,46)=8.031, P 0.001). Liars had significantly higher prefrontal white/whole brain ratios (mean=0.069, s.d. 0.011) compared with both antisocial controls (mean=0.054, s.d.=0.011, t=3.4, P 0.002) and normal controls (mean=0.054, s.d.=0.010, t=3.7, P=0.001).
Potential demographic, cognitive and antisocial confounding variables
Groups differed significantly with respect to age, verbalperformance
IQ discrepancy scores, psychopathy, antisocial personality disorder and
conduct disorder, and also showed trends for differences with respect to
ethnicity and full-scale IQ. To rule out the effect of age, psychopathy and
antisocial personality disorder, these measures were included as covariates in
repeated-measures ANOVA. The grey/white matter x group interaction
remained significanticant after correcting for age (F(2,45)=5.76,
P=0.006), ethnicity (F(2,45)=8.046,P=0.001),
verbalperformance IQ discrepancy scores (F(2,45)=6.605,
P=0.003), full-scale IQ (F(2,45)=9.503, P=0.0001),
psychopathy (F(2,45)=4.826, P=0.01), antisocial personality
disorder (F(2,45)=7.421, P=0.002) and conduct disorder
(F(2,45)=7.372, P=0.002).
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DISCUSSION |
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The result could not be attributed to group differences in age, ethnicity, IQ, head injury or substance misuse/dependence. Furthermore, group differences remained after a strict control for antisocial personality disorder, psychopathy and conduct disorder, again indicating specificity to lying in particular rather than antisocial behaviour in general. Consistent with prior research on pathological liars (Ford et al, 1988), liars had significantly higher verbal relative to performance IQ scores than both control groups, but higher verbal scores could not account for group differences in prefrontal white matter. The results further implicate the prefrontal cortex as an important (but not sole) component in the neural circuitry underlying lying, and provide an initial neurobiological correlate of a deceitful personality.
Neurodevelopmental theory of pathological lying
The most significant finding of this study is the increase of prefrontal
white matter and decrease in grey/white ratio in the liar group. Compared with
normal controls, the liar group had a 22.2% increase in prefrontal white
matter and a 41.7% decrease in grey/white ratio, and compared with antisocial
controls they showed a 25.7% white matter increase and a 35.7% decrease in
prefrontal grey/white ratio.
Children with autism are less capable of lying than normal children (Sodian & Firth, 1992) and, intriguingly, brain neurodevelopmental studies of autism show the converse pattern of grey/white ratios to that shown by the liar group. When 2- to 3-year-old children with autism reach 9.511 years of age, their white matter increases only 13% compared with 45% in normal children (Carper et al, 2002). Similarly, Courchesne et al (2001) found only a 10% white matter increase in children with autism compared with a 59% increase in normal children from 23 years of age to 1216 years, and an increased cortical grey/white ratio in children with autism compared with normal controls (i.e. the reverse of liars). Although autism is a complex condition, these results on children with autism, combined with the previous fMRI findings on lying in normal controls and our current findings on adult liars, suggest that the prefrontal cortex is centrally involved in the capacity to lie.
Why should increased white matter predispose to a deceitful personality? Although a complete explanation inevitably requires more extensive investigation, an initial working hypothesis is that increased prefrontal white matter developmentally provides the individual with the cognitive capacity to lie. From an evolutionary perspective, it is known that deception in primates is correlated with degree of neocortical expansion (Byrne & Corp, 2004). From a neurodevelopmental perspective, brain weight reaches adult values between the ages of 10 and 12 years, with a very significant increase in the absolute volume of white matter (Paus et al, 2001) that exceeds the developmental reduction in grey matter (Sowell et al, 2002). Psychosocial behavioural research also indicates that while young children are poor liars, by 10 years of age they become much more adept at lying (McCann, 1998). Consequently, the neurodevelopmental increase in white matter parallels developmental changes in the ability to lie. It is conceivable therefore that the increased prefrontal white matter found in adult liars predisposes to lying. The relative reduction in prefrontal grey matter relative to white may also predispose to a general antisocial disinhibited tendency which, coupled with increased white matter, results in excessive lying.
Clinical conceptualisation of malingering
The results may have implications for research on the clinical concept of
malingering (i.e feigning illness to obtain benefits). While biomedical models
of malingering have been put forward and debated
(Halligan et al,
2003), there appear to be no studies of the biological
characteristics (Raine, 2003).
Of the 12 liars in this study, 6 would be classified as malingerers in that
they admitted to telling lies to obtain sickness benefits. Comparison of these
malingerers with others in the liar group confirms that they too are
characterised by both relatively increased prefrontal white matter (66.0
cm3 v. 64.3 cm3 in malingering and
non-malingering liars, respectively) and a reduced prefrontal grey/white ratio
(1.09 v. 1.21). Malingering is not currently viewed as a clinical
disorder but is included in DSMIV
(American Psychiatric Association,
1994) as a V code to mark it as a condition
requiring further attention. If the current findings can be replicated and
extended to other populations of malingerers, this could have implications for
a more clinical conceptualisation of malingering.
Symptom-based, neurobiological approach to lying
Several neuroscience paradigms are beginning to converge on an initial
answer to the elusive question of what is the neurobiological basis to lying.
Prior research on normal controls who lie has attempted to identify
psychophysiological correlates of lying
(Patrick & Iacono, 1991).
More recent fMRI research has identified prefrontal activation as a correlate
of lying in normal controls. We have reversed the usual research paradigm by
using a symptom-based approach to address the question of what characterises
individuals who pathologically lie and to provide a provisional answer of
excessive prefrontal white matter. Nevertheless, we caution that the
neurobiological basis of lying is likely to be complex, involving brain
circuits extending well beyond the prefrontal cortex. Future studies are
required to examine changes in brain anatomy during the critical
neurodevelopmental time period in childhood alongside changes in lying ability
to test further our preliminary hypothesis on the link between prefrontal
white matter and lying.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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REFERENCES |
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Bentall, R. P., Jackson, H. F. & Pilgrim, D. (1988) Abandoning the concept of schizophrenia: some implications of validity arguments for psychological research into psychotic phenomena. British Journal of Clinical Psychology, 27, 303 324.[Medline]
Bryden, M. P. (1977) Measuring handedness with questionnaires. Neuropsychologia, 15, 617 624.[CrossRef][Medline]
Byrne, R. W. & Corp, N. (2004) Neocortex size predicts deception rate in primates. Proceedings of the Royal Society of London. Series B: Biological Sciences, 271, 1693 1699.[CrossRef][Medline]
Carper, R. A., Moses, P., Tigue, Z. D., et al (2002) Cerebral lobes in autism: early hyperplasia and abnormal age effects. Neuroimage, 16, 1038 1051.[CrossRef][Medline]
Costello, C. G. (1992) Research on symptoms versus research on syndromes arguments in favour of allocating more research time to the study of symptoms. British Journal of Psychiatry, 160, 304 308.[Abstract]
Courchesne, E., Karns, C. M., Davis, H. R., et al
(2001) Unusual brain growth patterns in early life in
patients with autistic disorder. An MRI study.
Neurology, 57, 245
254.
Elliott, D. S., Ageton, S., Huizinga, D., et al (1983) The Prevalence and Incidence of Delinquent Behavior: 19761980 (National Youth Survey, Report no. 26). Boulder, CO: Behavior Research Institute.
First, M. B., Spitzer, R. L., Gibbon, M., et al (1995a) Structured Clinical Interview for DSMIV AAxis xis I Disorders Patient Edition (SCIDI/P, Version 2.0).New York: Biometrics Research Department, New York State Psychiatric Institute.
First, M. B., Spitzer, R. L., Gibbon, M., et al (1995b) Structured Clinical Interview for DSMIV Axis II Personality Disorders (SCIDII, Version 2.0). NewYork: Biometrics Research Department, New York State Psychiatric Institute.
Ford, C.V., King, B. H. & Hollender, M. H. (1988) Lies and liars: psychiatric aspects of prevarication. American Journal of Psychiatry, 145, 554 562.[Abstract]
Ganis, G., Kosslyn, S. M., Stose, S., et al
(2003) Neural correlates of different types of deception: an
fMRI investigation. Cerebral Cortex,
13, 830
836.
Halligan, P. W. & David, A. S. (2001) Cognitive neuropsychiatry: towards a scientific psychopathology. Nature Reviews Neuroscience, 2, 209 215.[CrossRef][Medline]
Halligan, P.W., Bass, C. & Oakley, D. A. (2003) Malingering and Illness Deception.NewYork: Oxford University Press.
Hare, R. D. (1991) The Hare Psychopathy Checklist Revised (PCLR). Toronto: Multi-Health Systems.
Hollingshead, A. B. (1975) Four Factor Index of Social Status. New Haven, CT:Yale University Department of Sociology.
Kosson, D. S., Steuerwald, B. L., Forth, A. E., et al (1997) A new method for assessing the interpersonal behavior of psychopathic individuals. Preliminary validation studies. Psychological Assessment, 9, 89 101.[CrossRef]
Lee, T. M. C., Liu, H. L., Tan, L. H., et al (2002) Lie detection by functional magnetic resonance imaging. Human Brain Mapping, 15, 157 164.[CrossRef][Medline]
McCann, J. T. (1998) Malingering and Deception in Adolescents: Assessing Credibility in Clinical and Forensic Settings. Washington, DC: American Psychological Press.
Patrick, C. J. & Iacono, W. G. (1991) Acomparison of field and laboratory polygraphs in the detection of deception. Psychophysiology, 28, 632 638.[Medline]
Paus, T., Collins, D. L., Evans, A. C., et al (2001) Maturation of white matter in the human brain. A review of magnetic resonance studies. Brain Research Bulletin, 54, 255 266.[CrossRef][Medline]
Raine, A. (2003) Malingering and criminal behavior as psychopathology. In Malingering and Illness Deception (eds P.W. Halligan, C. Bass & D. A. Oakley), pp. 93 106. Oxford: Oxford University Press.
Raine, A., Lencz, T., Bihrle, S., et al
(2000) Reduced prefrontal gray matter volume and reduced
autonomic activity in antisocial personality disorder. Archives of
General Psychiatry, 57, 119
127.
Rogers, R. (1997) Clinical Assessment of Malingering and Deception (2nd edn). New York: The Guilford Press.
Sodian, B. & Firth, U. (1992) Deception and sabotage in autistic, retarded and normal children. Journal of Child Psychology and Psychiatry, 33, 591 605.[Medline]
Sowell, E. R., Trauner, D. A., Gamst, A., et al (2002) Development of cortical and subcortical brain structures in childhood and adolescence: a structural MRI study. Developmental Medicine and Child Neurology, 44, 4 16.[Medline]
Spence, S. A., Farrow, T. F., Herford, A. E., et al (2001) Behavioral and functional anatomical correlates of deception in humans. Neuroreport, 12, 2349 2353.
Ventura, J., Liberman, R. P., Green, M. F., et al (1998) Training and quality assurance with Structured Clinical Interview for DSMIV (SCID I/P). Psychiatry Research, 79, 163 173.[CrossRef][Medline]
Wechsler, D. (1981) Wechsler Adult Intelligence Scale Revised. San Antonio, TX: Psychological Corporation.
Received for publication January 5, 2004. Revision received November 9, 2004. Accepted for publication November 17, 2004.
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