Figures
Abstract
The purpose of this study was to investigate the relationship between cognitive insight and cerebral metabolism in patients suffering from psychosis. The Beck Cognitive Insight Scale (BCIS) was administered to 63 patients with psychosis undergoing Positron Emission Tomography investigation. The sample was divided into two groups considering the BCIS score. Data were analyzed using Statistical Parametric Mapping. Results: patients with low insight, compared to those with high insight, showed decreased metabolism in the right fusiform gyrus, left precuneus, superior temporal gyrus and insula bilaterally, as well as increased metabolism in the left orbito-frontal gyrus (all p<0.005). Our results suggest that reduced posterior (occipito-temporo-insulo-parietal) and increased anterior (orbitofrontal) cerebral metabolism may sustain low cognitive insight in psychosis.
Citation: Caletti E, Marotta G, Del Vecchio G, Paoli RA, Cigliobianco M, Prunas C, et al. (2017) The metabolic basis of cognitive insight in psychosis: A positron emission tomography study. PLoS ONE 12(4): e0175803. https://doi.org/10.1371/journal.pone.0175803
Editor: Jason R. Tregellas, University of Colorado Denver School of Medicine, UNITED STATES
Received: June 30, 2016; Accepted: March 31, 2017; Published: April 17, 2017
Copyright: © 2017 Caletti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
In psychiatric lexicon the term insight was introduced in order to define the degree of awareness in psychosis [1]. Specifically, the lack of insight is an essential and complex concept for diagnosis and treatment of psychosis [2,3,4,5,6]. Insight has been described as the patient’s ability to assess the nature and severity of his disease [7] and a multidimensional construct that can be related exclusively to some aspects of the disease [8]. Poor insight in schizophrenia (SZ) has been associated with bad compliance [9,10] and worse long term consequences of psychopathology [5,11]. However, lower insight level can also be found during manic episodes in bipolar disorder (BD) [12,13,14,15].
Previous studies explored the association between insight and cognitive performance in SZ [16,17], since these patients demonstrate significant deficits in many important cognitive domains [18,19,20,21]. Indeed, it has been consistently shown that a negative correlation exists between high need of medical care and low cognitive functions in severe psychiatric illness [22]. Neuroimaging studies have shown frontal lobe dysfunction related to illness unawareness in psychosis [23,24,25]. Sapara and colleagues [26] observed smaller prefrontal gray matter volume, while Gerretsen and colleagues [27] found that a cerebral asymmetry correlated with insight [a lower volume of dorsolateral prefrontal cortex (DLPFC), angular gyrus (AG), parietal lobe and insular cortex in the right hemisphere].
Recently, Nair and colleagues [28] highlighted the complexity of the insight construct, revealing some associations between neurocognition, “clinical” insight and “cognitive” insight. The latter is the metacognitive ability to reflect on our and others mental states to predict appropriate behaviour [29,30]. It is also described as the ability to use mental states in problem solving and interpersonal strategies, in addition to abilities of inferring emotions from others facial expressions, behaviors and actions [31]. Many previous studies found insight to be associated with metacognition deficits [32,33,34,35,36]; particularly self-reflectivity, empathetic perspective-taking and mastery were shown to be connected to awareness of illness and social functioning [37,38]. Buchy and colleagues [39] defined cognitive insight as “…an individual’s ability to understand their own reasoning processes, beliefs and judgments” and showed that healthy subjects have better cognitive insight (higher R and lower C) compared to psychotic patients. This deficiency also refers to a limited ability in evaluating anomalous experiences and erroneous inferences [40] (e.g. impaired objectivity about cognitive distortions, loss of ability to put into perspective, difficulties in correcting feedback and overvaluation of conclusions).
The Beck Cognitive Insight Scale (BCIS) [41] has been developed to evaluate patients’ self-reflectiveness and overconfidence about their interpretations of experiences. Indeed, these authors showed that the BCIS R-C index (the difference between self-reflectiveness score “R” and self-certainty score “C") is correlated to the Unawareness of Mental Disorder Scale (SUMD) subscale “awareness of having a mental disorder”. Indeed subjects with better cognitive insight (higher R-C index) show a higher awareness of illness, but the two measures are not equivalent, because they represent two different dimensions of insight [42]. The BCIS R-C index could represent a satisfactory measure of cognitive insight in patients with psychosis, as several studies have previously demonstrated [41,43,44,45]. Interestingly, reduced self-reflectiveness and overconfidence of experiences interpretation may be a form of neuropsychological deficit [46] that, from a neurobiological point of view, could be related to neuroimaging abnormalities.
However, only few imaging studies investigated the anatomical correlates of cognitive insight in psychosis [43]. Specifically, Buchy and colleagues [47] found a direct association between cognitive insight and left hippocampal volume and an inverse correlation between C scores and bilateral hippocampal volumes. Moreover, R scores positively correlated with ventrolateral prefrontal cortex (VLPFC) gray matter volume in patients with psychosis [48] and with VLPFC activation in non-clinical subjects [39].
Therefore, the current study aimed to investigate the metabolic correlates of cognitive insight by using 18F-Fluoro-2-deoxyglucose Positron Emission Tomography (FDG-PET) scan and BCIS scores in patients with major psychosis, in order to further elucidate the anatomical bases of cognitive insight.
Although the lack of PET studies investigating the metabolic correlates associated with insight, several neuroimaging studies reported fronto-temporal alterations, especially in the DLPFC, VLPFC as well as in the inferior and superior temporal regions, as putative biomarkers of poor insight or weakness in cognition [49,50,51,52,53,54,55,56]. Therefore, we expected that our group of patients will show altered metabolism in the same fronto-temporal areas, based on the hypothesis that morphological deficits might also result in metabolic dysfunctions.
2. Methods and materials
2.1 Participants
Sixty-three inpatients (17 women and 46 men), aged between 18 and 65 years, with acute psychosis according to the DSM-IV-TR (APA, 2000) criteria were enrolled in the current study. Thirty-six were diagnosed with affective psychosis (all with bipolar disorder type I) and twenty-seven with non-affective psychosis (10 with schizophrenia, 17 with psychosis NOS) (for details see Tables 1 and 2).
Exclusion criteria were cognitive deterioration (score lower than 24) as assessed by the Mini-Mental State Examination (MMSE) [57], consistent with normative data in the Italian population [58], or comorbidity with other neurological conditions involving Central Nervous System (e.g. cerebral tumors) and pregnancy or breastfeeding.
The study was approved by the local Ethical Committee (Comitato Etico, Fondazione “IRCCS Ca’ Granda” Policlinico di Milano) and informed written consent was obtained from all subjects. Capacity to consent was determined through clinical interview and MMSE, in order to assess the absence of cognitive deterioration.
2.2. Clinical assessment
Diagnoses were conducted at baseline with the Structured Clinical Interview for DSM-IV-TR, Axis I [59]. Socio-demographic data were collected by an interview completed by experts. Clinical assessment was carried out with the Brief Psychiatric Rating Scale (BPRS) to evaluate severity and change of psychotic and mood symptoms [60]. Cognitive insight was quantified with the BCIS self-report inventory [41]. The subscales “self-reflectiveness” (formed by 9 items) and “self-certainty” (formed by 6 items) are defined to measure separate aspects of cognitive insight. They rate the patients' ability to observe their mental productions and to reflect on alternative explanations and overconfidence in the validity of their beliefs. Accordingly, among psychotic patients an optimal cognitive insight is given by the results of high self-reflectiveness and low self-certainty; a composite index reflecting cognitive insight is the resulting difference between the two series. In the absence of cognitive insight, this index will have negative values [41,61].
2.3. FDG-PET scanning
FDG-PET scanning was performed within 7 days of insight and symptom assessment. Patients underwent FDG-PET at rest, after intravenous injection of 170 MBq. Each acquisition included a Computed Tomography (CT) transmission scan of the head (50mAs lasting 16 seconds) followed by a three-dimensional (3D) static emission of 15 minutes using a Biograph Truepoint 64 PET/CT scanner (Siemens). FDG-PET sections were reconstructed using an iterative algorithm (OS-EM), corrected for scatter and for attenuation using density coefficients derived from the low dose CT scan of the head obtained with the same scanner, with the proprietary software. Images were reconstructed in the form of transaxial images of 128×128 pixels of 2 mm, using an iterative algorithm, ordered-subset expectation maximization (OSEM). The resolution of the PET system was 4–5 mm FWHM.
2.4. Statistical analysis
The statistical analyses were performed using Statistical Parametric Mapping (SPM8, Wellcome Department of Cognitive Neurology, London, UK). FDG PET data were subjected to affine and nonlinear spatial normalization into the MNI space. The spatially normalized set of images was smoothed with an 8mm isotropic Gaussian filter to blur for individual variations in gyral anatomy and increase the signal-to-noise ratio. Images were globally normalized to 50 using proportional scaling to remove confounding effects to global cerebral glucose consumption changes, with a masking threshold of 0.8.
Subjects were divided into four groups considering the BCIS score (low insight with R-C = <5 and high insight with R-C>5, see Table 3) and the diagnosis (affective vs non affective psychosis). The choice of the cut-off of 5 to divide the two FDG-PET groups with different BCIS composite index score (= <5 and >5) was motivated by the average value (4,64) shown in psychotic patients in the literature [61], similar to the median BCIS scores of our group of patients. We performed two-sample t-test between the low and the high insight groups to determine if the data were significantly different, considering P values < 0.05 as statistically significant. We found no statistical differences between the two groups in any of the demographic or clinical variables employed in this study, including age (p = 0,335), sex (χ2 = 0,544, p = 0,461) duration of illness (p = 0,123) and the BPRS scores (p = 0,142). Finally, we found no significant differences between the two groups for the diagnosis (χ2 = 0,049, p = 0,825).
Significant differences in brain metabolism among the four groups (non affective psychosis with low insight, non affective psychosis with high insight, affective psychosis with low insight, and affective psychosis with high insight) were estimated by means of single-subject, conditions and covariates design, with the 4 groups of psychosis modeled as conditions, with insight score as covariate variable of all patients and age as the nuisance variable for group analyses. First, we performed a one-way analysis of variance (ANOVA) between the four groups. Then, we performed two-sample t-test between pair of groups with low insight compared to those with high insight.
All voxel-based statistical analyses of brain glucose metabolism images were performed by applying a statistical threshold of uncorrected p<0.005 at voxel level for F-test and p<0.005 corrected at cluster level for t-test, with an extent threshold of at least 35 contiguous voxels. P values < 0.005 were considered statistically significant in analysis of covariance to correlate the cognitive insight score with the metabolism.
3. Results
The comparison between the four groups (non affective psychosis with low and high insight and affective psychosis with low and high insight), showed significant metabolic differences in the fusiform gyrus, superior temporal gyrus and insula, in the right hemisphere, and in the precuneus and superior temporal gyrus and insula, in the left hemisphere (ANOVA, p<0.005). Several post-hoc analyses were carried out to investigate the direction of these abnormalities across the four different groups (see Table 4).
L, left; R, right.
The post-hoc two-samples t-test analyses, between the entire group with low insight compared to the one with high insight, revealed in the group with low insight decreased metabolism (p<0.005) in fusiform gyrus, superior temporal gyrus and insula, in the right hemisphere, and in precuneus and superior temporal gyrus and insula, in the left hemisphere, and showed increased metabolism in left orbito-frontal gyrus (p<0.005) (see Fig 1).
The post-hoc two-samples t-test analyses, between the non-affective group with low insight compared to the one with high insight, revealed that the group with low insight showed decreased metabolism in right superior temporal gyrus and insula, in left precuneus, in left superior temporal gyrus and insula as well as increased metabolism in left orbito-frontal gyrus (all p<0.005).
The post-hoc two-samples t-test analyses, between the affective group with low insight, compared to the one with high insight, revealed in the group with low insight decreased metabolism in the right inferior temporal gyrus and increased metabolism in right orbito-frontal gyrus (all p<0.005).
Finally, the analysis of covariance showed a positive significant correlation (p<0.005) between the metabolism in right precuneus and insight scores.
4. Discussion
Our discussion focuses first on areas in which we observed hypometabolism and a correlation between insight score and low metabolism. Our results suggest that a reduced posterior (occipito-temporo-insulo-parietal) and increased anterior (orbito-frontal) cerebral metabolism sustains low cognitive insight in psychosis. The analyses were carried out comparing four groups, non affective psychosis with low and high insight and affective psychosis with low and high insight. Moreover we decided to perform several post-hoc analyses to investigate the direction of these abnormalities across the four different groups. All the patients had psychotic symptoms and were in acute phase at recruitment. Additionally, the direct comparison between BD and SZ did not show statistical significant differences on cognitive insight (R-C) scores. Therefore, based on these evidence, patients were considered as one homogeneous sample split into high and low cognitive insight. Indeed, it has been consistently reported that psychosis is a trans-diagnostic dimension between in SZ and BD [62]. Both disorders share extensive similarities in terms of biological, clinical, genetic and neurocognitive factors, suggesting a continuous distribution of symptoms among the two disorders [62,63,64,65,66,67,68].
With regards to our findings, patients reported decreased cerebral metabolism in extensive regions encompassing occipital (fusiform gyrus), parietal (precuneus), and temporal (superior temporal gyrus and insula) areas. Specifically for occipital regions, the fusiform gyrus is thought to be involved in face recognition and seems to be associated with prosopagnosia (impaired facial recognition) [69]. Interestingly, previous studies have also reported that face recognition might be related to insight by showing an association between this capacity and self-awareness which may, therefore, account for the known difficulties of SZ patients in elaborating a coherent self experience [70,71].
Hypometabolism was also found in the superior temporal gyrus bilaterally. The superior temporal cortex has been studied for its main role in receptive language [72,73], social cognition, in the Theory of Mind and processing of social stimuli [74,75,76]. Additionally, Buchy and colleagues [39] hypothesized that cognitive insight is based on accurate memory for specific life experiences which in turn affects everyday beliefs and judgments, suggesting therefore a possible role for temporal areas.
Moreover neuroimaging studies found structural and functional abnormalities in the superior temporal gyrus among SZ patients [77,78,79] and decreased gray matter volume in prefrontal and temporal regions which related to low insight scores in a group of first-episode drug naïve psychotic patients, in comparison with a control group [80]. Nevertheless, to date, the role of this area in insight is still not well elucidated.
In contrast, lack of insight in patients with non affective psychosis has been frequently related to impairment in the prefrontal and parietal cortices [34,81]. Therefore, decreased metabolism, together with the positive correlation between metabolism and insight scores in the precuneus, and increased metabolism in the orbital frontal cortex (OFC) seem to confirm this evidence. Furthermore, these findings are not surprising given that the precuneus, a part of the superior parietal lobule, plays a role in a wide range of integrated tasks, including visuo-spatial imagery, which is particularly related to representation of other's perspective, episodic and autobiographical memory retrieval and self-processing [49,82]. Additionally, this area seems to be also implicated in self-consciousness, through a hypothetical neurobiological pathway of impaired clinical and cognitive insight in SZ, as recently described by Xavier and Vorderstrasse [83].
Indeed, it has been shown that both perfusion and activation of bilateral precuneus correlated to insight both in SZ and in BD patients [84,85].
Therefore, based on this evidence, altered precuneus activation could indirectly reflect a reduced integration between past and current information related to self and other reflective processes and to cognitive insight [85]. It could also support an altered self experience, which could correlate with deficits in metacognitive abilities, shown to be associated to insight [32,33,34,35,36].
Similarly, the OFC plays a major role in many neuropsychological processes [86] and it might therefore be also associated with insight. Specifically OFC is described in literature as a polymodal brain region that receives inputs from many different sensory modalities [87] and brain alterations in this area have been linked to several psychiatric disorders [88], including SZ and BD [86,89,90,91,92,93]. Moreover, OFC is considered, with its adjacent areas, a neural substrate of social behavior, critical for decision making and probably for intuitive processes [94,95]. Indeed it has been shown that damage in this area causes cognitive, affective and social impairments [96].
Even though most studies have shown cognitive insight to be related to frontal lobe effectiveness [97,98] the current study highlights that more brain areas, rather than the only prefrontal cortex, are involved in cognitive insight process. Other brain imaging studies have shown the interaction between frontal and posterior cortex in metacognition [99]. Fleming & Dolan [100] suggested a neural combination in which prefrontal regions interact with cingulate and insula to promote accurate judgments of performance; Moran et al. [101] described a self-reflective process network that includes dorsomedial and ventromedial prefrontal cortex, anterior and posterior cingulate, anterior insula, inferior frontal gyrus, temporo-parietal junction/AG and inferior parietal lobule. A latest study in healthy subjects identified a hypothetical cognitive insight’s functional neural basis in the ventrolateral prefrontal cortex, in temporal and occipital cortices [39].
We also assume that self-reflection and reduced self-certainty should be a focal point for poor cognitive insight in psychosis, representing a complex phenotype potentially sustained by a combination of neurobiological vulnerability (genes, neural circuitry formed by prefrontal, cingulate, temporal and parietal cortices, insula, hippocampus) and environment [83]. Understanding cognitive distortions and neural correlates might be of paramount importance in psychosis because they could be a predictor of good prognosis.
The results of our study should be interpreted cautiously due to some limitations. First, our sample was 73% male and did not consent to discover sex effects in cerebral metabolism–cognitive insight associations. Moreover, the large age range could have led to a potential confounding effect, therefore we decided to use this parameter as a covariate in the statistical analysis. Second, a healthy control group was lacking limiting the generalizability of our results. Third, neuropsychological measures were not collected. Therefore future studies should take into account specific assessment of executive functions to identify their impact on cognitive insight. Finally, since BD is characterized by swinging mood episodes, a cross sectional assessment of metabolic correlates of cognitive insight may not be fully generalizable.
In conclusion, our findings suggest reduced posterior (occipito-temporo-parietal cortices and insula) and increased anterior (orbito-frontal) cerebral metabolism supporting low cognitive insight in psychosis, especially in the right emisphere.
This is the first study that takes into account the cognitive insight and brain metabolism of affective and non-affective psychotic patients. Future longitudinal studies should explore cognitive insight over time in psychosis before and after treatment, coupling with comprehensive cognitive evaluation.
Author Contributions
- Conceptualization: EC PB ACA.
- Data curation: EC GM EZ MC FB CP.
- Formal analysis: GM GDV.
- Investigation: EC GM MC CP EZ FB.
- Methodology: EC GM PB RAP.
- Project administration: ACA GM PB RAP.
- Resources: EC RAP GM MC CP PB.
- Supervision: ACA PB GM RAP.
- Visualization: EC GM EZ MC FB CP.
- Writing – original draft: EC GM EZ FB CP.
- Writing – review & editing: EC GM EZ MC FB CP.
References
- 1. Markovà IS, Clare L, Wang M, Romero B, Kenny G. Awareness in dementia: Conceptual issues. Aging Ment Health. 2005;9: 386–393. pmid:16024398
- 2.
Amador XF, David AS. Insight and psychosis. New York Oxford: Oxford University Press; 1998.
- 3.
Amador XF, David AS. Insight and psychosis: Awareness of illness in schizophrenia and relate disorders. Oxford, UK: Oxford University Press; 2004.
- 4. Amador XF, Flaum M, Andreasen NC, Strauss DH, Yale SA, Clark SC, et al. Awareness of illness in schizophrenia and schizoaffective and mood disorders. Arch Gen Psychiatry. 1994;45: 826–836.
- 5. Gilleen J, Greenwood K, David AS. Domains of awareness in schizophrenia. Schizophrenia Bull. 2011; 37:61–72.
- 6. McEvoy JP, Freter S, Everett G, Geller JL, Appelbaum P, Apperson LJ, et al. Insight and the clinical outcome of schizophrenic patients. J Nerv Ment Dis. 1989;177: 48–51. pmid:2535871
- 7.
Jaspers K. General Psychopathology. Manchester: Manchester University Press; 1963.
- 8. Amador XF, Strauss DH, Yale SA, Gorman JM. Awareness of illness in schizophrenia. Schizophrenia Bull. 1991;17: 113–132.
- 9. Amador XF, Strauss DH. Poor insight in schizophrenia. Psychiat Quart. 1993;64: 305–318. pmid:8234544
- 10. Mohamed S, Rosenheck R, McEvoy J, Swartz M, Stroup S, Lieberman JA. Cross-sectional and longitudinal relationships between insight and attitudes toward medication and clinical outcomes in chronic schizophrenia. Schizophrenia Bull. 2009;35: 336–346.
- 11. Lincoln TM, Lüllmann E, Rief W. Correlates and Long-Term Consequences of Poor Insight in Patients With Schizophrenia. A Systematic Review. Schizophrenia Bull. 2007;33: 1324–1342.
- 12. Dell'Osso L, Pini S, Cassano GB, Mastrocinque C, Seckinger RA, Saettoni M, et al. Insight into illness in patients with mania, mixed mania, bipolar depression and major depression with psychotic features. Bipolar Disord. 2002;4: 315–322. pmid:12479664
- 13. Varga M, Magnusson A, Flekkoy K, Ronneberg U, Opjordsmoen S. Insight, symptoms, and neurocognition in bipolar I patients. J Affect Disord. 2006;91: 1–9. pmid:16442637
- 14. Yen CF, Chen CS, Ko CH, Yen JY, Huang CF. Changes in insight among patients with bipolar I disorder: a 2-year prospective study. Bipolar Disord. 2007;9: 238–242. pmid:17430298
- 15. Cassidy F. Insight in bipolar disorder: relationship to episode subtypes and symptom dimensions. Neuropsychiatr Dis Trea. 2010;6: 627–631.
- 16. Startup M. Insight and cognitive deficits in schizophrenia: evidence for a curvilinear relationship. Psychol Med. 1996;26: 1277–1281. pmid:8931174
- 17. Rossell SL, Coakes J, Shapleske J, Woodruff PWR, David AS. Insight: its relationship with cognitive function, brain volume and symptoms in schizophrenia. Psychol Med. 2003;33: 111–119. pmid:12537042
- 18. Heinrichs RW, Zakzanis KK. Neurocognitive deficit in schizophrenia: A quantitative review of the evidence. Neuropsychology. 1998;12: 426–445. pmid:9673998
- 19. McClure MM, Bowie CR, Patterson TL, Heaton RK, Weaver C, Anderson H, et al. Correlations of functional capacity and neuropsychological performance in older patients with schizophrenia: Evidence for specificity of relationships? Schizophr Res. 2007;89: 330–338. pmid:16982175
- 20. Green MF, Kern RS, Heaton RK. Longitudinal studies of cognition and functional outcome in schizophrenia: implications for MATRICS. Schizophr Res. 2004;72: 41–51. pmid:15531406
- 21. Wobrock T, Ecker UKH, Scherk H, Schneider-Axmann T, Falkaia P, Gruber O. Cognitive impairment of executive function as a core symptom of schizophrenia. World J Biol Psychia. 2009;10: 442–451.
- 22. Donohoe G, Hayden J, McGlade N, O’Gráda C, Burke T, Barry S, et al. Is “clinical” insight the same as “cognitive” insight in schizophrenia? J Int Neuropsych Soc. 2009;15: 471–475.
- 23. Goldberg RW, Green-Paden LD, Lehman AF, Gold JM. Correlates of insight in serious mental illness. J Nerv Ment Dis. 2001;189: 137–145. pmid:11277349
- 24.
Barr WB. Neurobehavioral disorders of awareness and their relevance to schizophrenia. In: Amador XF, David AS, editors. Insight and psychosis. New York Oxford: Oxford University Press; 1998. pp. 107–141.
- 25. Frith CD, Blakemore S-J, Wolpert DM. Explaining the symptoms of schizophrenia: Abnormalities in the awareness of action. Brain Res Rev. 2000;31: 357–363. pmid:10719163
- 26. Sapara A, Cooke M, Fannon D, Francis A, Buchanan RW, Anilkumar APP, et al. Prefrontal cortex and insight in schizophrenia: A volumetric MRI study. Schizophr Res. 2007;89: 22–34. pmid:17097853
- 27. Gerretsen P, Chakravarty MM, Mamo D, Menon M, Pollock BG, Rajji TK, et al. Frontotemporoparietal asymmetry and lack of illness awareness in schizophrenia. Hum Brain Mapp. 2013;34: 1035–1043. pmid:22213454
- 28. Nair A, Palmer EC, Aleman A, David AS. Relationship between cognition, clinical and cognitive insight in psychotic disorders: A review and meta-analysis. Schizophr Res. 2014;152: 191–200. pmid:24355529
- 29. Baier M. Insight in Schizophrenia: A Review. Curr Psychiatry Rep. 2010;12: 356–361. pmid:20526897
- 30. Alaimo SM, Cascio MI. Adult Attachment, Emotional Dysregulation and Metacognitive Functions in Patients with Personality Disorders. Psychology 2015;6: 1940–1950.
- 31.
Dimaggio G, Lysaker PH. Metacognition and severe adult mental disorders: From research to treatment. New York: Routledge; 2010.
- 32. Corcoran R, Mercer G, Frith CD. Schizophrenia, symptomatology and social inference: investigating “theory of mind” in people with schizophrenia. Schizophr Res. 1995;17: 5–13. pmid:8541250
- 33. Langdon R, Corner T, McLaren J, Ward PB, Coltheart M. Externalizing and personalizing biases in persecutory delusions: the relationship with poor insight and theory-of-mind. Behav Res Ther. 2006;44: 699–713. pmid:16038873
- 34. Lysaker PH, Dimaggio G, Buck KD, Callaway SS, Salvatore G, Carcione A, et al. Poor insight in schizophrenia: links between different forms of metacognition with awareness of symptoms, treatment need and consequences of illness. Compr Psychiatry. 2011;52: 253–260. pmid:21497218
- 35. Bora E, Sehitoglu G, Aslier M, Atabay I,Veznedaroglu B. Theory of mind and unawareness of illness in schizophrenia: is poor insight a mentalizing deficit? Eur Arch Psychiatry Clin Neurosci. 2007;257: 104–111. pmid:17171312
- 36. David AS, Bedford N, Wiffen B, Gilleen J. Failures of metacognition and lack of insight in neuropsychiatric disorders. Philos T R Soc B. 2012;367: 1379–1390.
- 37. Brüne M, Dimaggio G, Lysaker P.H. Metacognition and social functioning in schizophrenia: evidence, mechanisms of influence and treatment implications. Curr Psychiatry Rev. 2011;7: 239–247.
- 38. Couture SM, Granholm EL, Fish SC. A path model investigation of neurocognition, theory of mind, social competence, negative symptoms and real-world functioning in schizophrenia. Schizophr Res. 2011;125: 152–160. pmid:20965699
- 39. Buchy L, Hawco C, Bodnar M, Izadi S, Dell'Elce J, Messina K, et al. Functional magnetic resonance imaging study of external source memory and its relation to cognitive insight in non-clinical subjects. Psychiat Clin Neuros. 2014;68: 683–691.
- 40.
Beck AT, Warman DM. Cognitive insight: theory and assessment. In: Amador XF, David AS, editors. Insight and Psychosis: Awareness of Illness in Schizophrenia and Related Disorders. 2nd ed. New York, NY: Oxford University Press; 2004. pp. 79–87.
- 41. Beck AT, Baruch E, Balter JM, Steer RA, Warman DM. A new instrument for measuring insight: the Beck Cognitive Insight Scale. Schizophr Res. 2004;68: 319–329. pmid:15099613
- 42. Dam J. Insight in schizophrenia: a review. Nord J Psychiatry. 2006;60: 114–120. pmid:16635929
- 43. Riggs SE, Grant PM, Perivoliotis D, Beck AT. Assessment of cognitive insight: A qualitative review. Schizophrenia Bull. 2010 Aug 6.
- 44. Buchy L, Malla A, Joober R, Lepage M. Delusions are associated with low self-reflectiveness in first-episode psychosis. Schizophr Res 2009;112: 187–191. pmid:19372032
- 45. Kao YC, Wang TS, Lu CW, Liu YP. Assessing cognitive insight in nonpsychiatric individuals and outpatients with schizophrenia in Taiwan: an investigation using the Beck Cognitive Insight Scale. BMC Psychiatry 2011;11: 170–184. pmid:22018413
- 46. Rossell SL, Coakes J, Shapleske J, Woodruff PWR, David AS. Insight: its relationship with cognitive function, brain volume and symptoms in schizophrenia. Psychol Med. 2003;33: 111–119. pmid:12537042
- 47. Buchy L, Michael BD, Lepage C, Ad-Dab'bagh Y, Sergerie K, Malla A, et al. Evidence for widespread thinning of the cerebral cortex in patients with first-episode psychosis with poor insight. Schizophr Res 2010;117: 224.
- 48. Orfei MD, Piras F, Macci E, Caltagirone C, Spalletta G. The neuroanatomical correlates of cognitive insight in schizophrenia. Soc Cogn Affect Neurosci. 2013;8: 418–423. pmid:22287264
- 49. Bergé D, Carmona S, Rovira M, Bulbena A, Salgado P, Vilarroya O. Gray matter volume deficits and correlation with insight and negative symptoms in first‐psychotic‐episode subjects. Acta Psychiat Scand. 2011;123: 431–439. pmid:21054282
- 50. Ha TH, Youn T, Ha KS, Rho KS, Lee JM, Kim IY, et al. Gray matter abnormalities in paranoid schizophrenia and their clinical correlations. Psychiatry Res. 2004;132: 251–260. pmid:15664796
- 51. Flashman LA, McAllister TW, Johnson SC, Rick JH, Green RL, Saykin AJ. Specific frontal lobe subregions correlated with unawareness of illness in schizophrenia: a preliminary study. J Neuropsychiatry Clin Neurosci. 2001;13: 255–257. pmid:11449033
- 52. Tamminga CA, Pearlson G, Keshavan M, Sweeney J, Clementz B, Thaker G. Bipolar and schizophrenia network for intermediate phenotypes: outcomes across the psychosis continuum. Schizophr Bull. 2014;40: 131–137.
- 53. Dresler M, Wehrle R, Spoormaker VI, Steiger A, Holsboer F, Czisch M, Hobson JA. Neural correlates of insight in dreaming and psychosis. Sleep Med Rev. 2015;20: 92–9. pmid:25092021
- 54. Antonius D, Prudent V, Rebani Y, D'Angelo D, Ardekani BA, Malaspina D, et al. White matter integrity and lack of insight in schizophrenia and schizoaffective disorder. Schizophr Res. 2011;128: 76–82. pmid:21429714
- 55. Larøi F, Fannemel M, Rønneberg U, Flekkøy K, Opjordsmoen S, Dullerud R, et al. Unawareness of illness in chronic schizophrenia and its relationship to structural brain measures and neuropsychological tests. Psychiatry Res. 2000;100: 49–58. pmid:11090725
- 56. Mathew I, Gardin TM, Tandon N, Eack S, Francis AN, Seidman LJ, et al. Medial temporal lobe structures and hippocampal subfields in psychotic disorders: findings from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) study. JAMA Psychiatry. 2014;71: 769–777. pmid:24828364
- 57. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiat Res. 1975;12: 189–198. pmid:1202204
- 58. Measso G, Cavarzeran F, Zappala G, Lebowitz BD, Crook TH, Pirozzolo FJ, et al. The mini-mental state examination: normative study of an Italian random sample. Dev Neuropsychol. 1993;9: 77–85.
- 59.
First MB, Spitzer R, Gibbon M, Williams JBW. Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version. Patient Edition (SCID-I/P). New York: Biometrics Research, New York State Psychiatric Institute; 2002.
- 60. Overall JE, Gorham DR. The Brief Psychiatric Rating Scale. Psychol Rep. 1962;10: 799–812.
- 61. Warman DM, Martin JM. Cognitive insight and delusion proneness: An investigation using the Beck Cognitive Insight Scale. Schizophr Res. 2006;84: 297–304. pmid:16545944
- 62. Keshavan MS, Morris DW, Sweeney JA, Pearlson G, Thaker G, Seidman LJ et al. A dimensional approach to the psychosis spectrum between bipolar disorder and schizophrenia: the Schizo-Bipolar Scale. Schizophr Res. 2011;133: 250–254. pmid:21996268
- 63. Maggioni E, Bellani M, Altamura AC, Brambilla P. Neuroanatomical voxel-based profile of schizophrenia and bipolar disorder. Epidemiol Psychiatr Sci. 2016;25: 312–316. pmid:27095442
- 64. Squarcina L, De Luca A, Bellani M, Brambilla P, Turkheimer FE, Bertoldo A. Fractal analysis of MRI data for the characterization of patients with schizophrenia and bipolar disorder. Phys Med Biol. 2015;60:1697–1716. pmid:25633275
- 65. Altamura AC, Bertoldo A, Marotta G, Paoli RA, Caletti E, Dragogna F, Buoli M, Baglivo V, Mauri MC, Brambilla P. White matter metabolism differentiates schizophrenia and bipolar disorder: a preliminary PET study. Psychiatry Res. 2013;214: 410–414. pmid:24144506
- 66. Brambilla P, Perlini C, Bellani M, Tomelleri L, Ferro A, Cerruti S, Marinelli V, Rambaldelli G, Christodoulou T, Jogia J, Dima D, Tansella M, Balestrieri M, Frangou S. Increased salience of gains versus decreased associative learning differentiate bipolar disorder from schizophrenia during incentive decision making. Psychol Med. 2013;43: 571–580. pmid:22687364
- 67. Brambilla P, Cerruti S, Bellani M, Perlini C, Ferro A, Marinelli V, Giusto D, Tomelleri L, Rambaldelli G, Tansella M, Diwadkar VA. Shared impairment in associative learning in schizophrenia and bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2011;35: 1093–1099. pmid:21420463
- 68. Perlini C, Marini A, Garzitto M, Isola M, Cerruti S, Marinelli V, Rambaldelli G, Ferro A, Tomelleri L, Dusi N, Bellani M, Tansella M, Fabbro F, Brambilla P. Linguistic production and syntactic comprehension in schizophrenia and bipolar disorder. Acta Psychiatr Scand. 2012;126: 363–376. pmid:22509998
- 69. Damasio AR, Damasio H, Van Hoesen G.W. Prosopagnosia Anatomic basis and behavioral mechanisms. Neurology. 1982;32: 331–331. pmid:7199655
- 70. Heinisch C, Wiens S, Gründl M, Juckel G, Brüne M. Self-face recognition in schizophrenia is related to insight. Eur Arch Psy Clin N. 2013;263: 655–662.
- 71. Chen X, Duan M, Xie Q, Lai Y, Dong L, Cao W et al. Functional disconnection between the visual cortex and the sensorimotor cortex suggests a potential mechanism for self-disorder in schizophrenia. Schizophr Res. 2015;166: 151–157. pmid:26143483
- 72. Karnath H.O. New insights into the functions of the superior temporal cortex. Nat rev neusci. 2001;2: 568–576.
- 73.
Zigmond MJ, Bloom FE, Landis SC, Roberts JL, Squire LR. Fundamental neuroscience. San Diego CA: Academic Press; 1999.
- 74. Völlm BA, Taylor AN, Richardson P, Corcoran R, Stirling J, Mckie S, et al. Neuronal correlates of the theory of mind and empathy: a functional magnetic resonance imaging study in a nonverbal task. Neuroimage. 2006;29: 90–98. pmid:16122944
- 75. Gallagher HL, Frith CD. Functional imaging of 'theory of mind'. Trends Cogn Sci. 2003;7: 77–83. pmid:12584026
- 76. Bigler ED, Mortensen S, Neeley ES, Ozonoff S, Krasny L, Johnson M. et al. Superior temporal gyrus, language function, and autism. Dev Neuropsychol. 2007;31: 217–238. pmid:17488217
- 77. Wolthusen RP, Hass J, Walton E, Turner JA, Rössner V, Sponheim SR, Ho BC, Holt DJ, Gollub RL, Calhoun V, et al. Genetic underpinnings of left superior temporal gyrus thickness in patients with schizophrenia. World J Biol Psychiatry. 2015;16: 430–440.
- 78. Narayanaswamy JC, Kalmady SV, Venkatasubramanian G., Gangadhar BN. Clinical correlates of superior temporal gyrus volume abnormalities in antipsychotic-naive schizophrenia. J Neuropsych Clin N. 2014;27: e128–e133.
- 79. Ohi K, Matsuda Y, Shimada T, Yasuyama T, Oshima K, Sawai K, Kihara H, Nitta Y, Okubo H, Uehara T, Kawasaki Y. Structural alterations of the superior temporal gyrus in schizophrenia: Detailed subregional differences. Eur Psychiatry. 2016;35: 25–31. pmid:27061374
- 80. Buchy L, Ad-Dab’bagh Y, Malla A, Lepage C, Bodnar M, Joober R et al. Cortical thickness is associated with poor insight in first-episode psychosis. J Psychiatr Res. 2011;45: 781–787. pmid:21092987
- 81. Cavanna AE, Trimble MR. The precuneus: a review of its functional anatomy and behavioural correlates. Brain. 2006;129: 564–583. pmid:16399806
- 82. Schurz M, Radua J, Aichhorn M, Richlan F, Perner J. (2014). Fractionating theory of mind: a meta-analysis of functional brain imaging studies. Neurosci Biobehav R. 2014;42: 9–34.
- 83. Xavier RM, Vorderstrasse A. Neurobiological Basis of Insight in Schizophrenia: A Systematic Review. Nurs Res. 2016;65: 224–237. pmid:27124258
- 84. Faget-Agius C, Padovani R, Richieri R. Schizophrenia with preserved insight is associated with increased perfusion of the precuneus. J Psychiatr Neurosci. 2012;37: 297.
- 85. Zhang L, Opmeer EM, Ruhé HG, Aleman A, van der Meer L. Brain activation during self-and other-reflection in bipolar disorder with a history of psychosis: Comparison to schizophrenia. NeuroImage: Clin. 2015;8: 202–209.
- 86. Lacerda AL, Hardan AY, Yorbik O, Vemulapalli M, Prasad KM, Keshavan MS. Morphology of the orbitofrontal cortex in first-episode schizophrenia: relationship with negative symptomatology. Prog Neuro-Psychoph. 2007;31: 510–516.
- 87. Kringelbach ML, Rolls ET. The functional neuroanatomy of the human orbitofrontal cortex: evidence from neuroimaging and neuropsychology. Prog Neurobiol. 2004;72: 341–372. pmid:15157726
- 88. Bellani M, Cerruti S, Brambilla P. Orbitofrontal cortex abnormalities in schizophrenia. Epidemiol Psichiat S. 2010;19: 23–25.
- 89. Blumberg HP, Stern E, Ricketts S, Martinez D, de Asis J, White T et al. Rostral and orbital prefrontal cortex dysfunction in the manic state of bipolar disorder. Am J Psychiat. 1999;156: 1986–1988. pmid:10588416
- 90. Bass D, Aleman A, Vink M, Ramsey NF, Haan EHF, Kahn RS. Evidence of altered cortical and amygdala activation during social decision-making in schizophrenia. Neuroimage. 2008;40: 719–727. pmid:18261933
- 91. Frangou S, Raymont V, Bettany D. The Maudsley bipolar disorder project. A survey of psychotropic prescribing patterns in bipolar I disorder. Bipolar Disord. 2002;4: 378–385. pmid:12519097
- 92. López-Larson MP, DelBello MP, Zimmerman ME, Schwiers ML, Strakowski SM. Regional prefrontal gray and white matter abnormalities in bipolar disorder. Biol Psychiat. 2002;52: 93–100. pmid:12114000
- 93. Cotter D, Hudson L, Landau S. Evidence for orbitofrontal pathology in bipolar disorder and major depression, but not in schizophrenia. Bipolar Disord. 2005;7: 358–369. pmid:16026489
- 94. Volz KG, von Cramon DY. What neuroscience can tell about intuitive processes in the context of perceptual discovery. J Cog Neurosci. 2006;18: 2077–2087.
- 95. Volz KG, von Cramon DY. How the orbitofrontal cortex contributes to decision making—A view from neuroscience. Prog Brain Res. 2009;174: 61–71. pmid:19477330
- 96. Bechara A. The role of emotion in decision-making: evidence from neurological patients with orbitofrontal damage. Brain Cognition; 2004:55, 30–40. pmid:15134841
- 97. Lee KH, Brown WH, Egleston IN, Green RD, Farrow TF, Hunter MD et al. A functional magnetic resonance imaging study of social cognition in schizophrenia during an acute episode and after recovery. Am J Psychiatry. 2006;163:1926–1933. pmid:17074944
- 98. Raffard S, Bortolona C, MacGregor A, Norton J, Boulenger J-H, El Haj M et al. Cognitive insight in schizophrenia patients and their biological parents: a pilot study. Schizophr Res. 2014;159: 471–477. pmid:25242359
- 99. Shimamura AP. Toward a cognitive neuroscience of metacognition. Conscious Cogn. 2000;9: 313–323. pmid:10924251
- 100. Fleming SM, Dolan RJ. The neural basis of metacognitive ability. Phil Trans R Soc B. 2012;367: 1338–1349. pmid:22492751
- 101. Moran JM, Kelley WM, Heatherton TF. What can the organization of the brain’s default mode network tell us about self-knowledge? Front Hum Neurosci. 2013;7: 1–6.