Deficiencies in Theory of Mind in patients with schizophrenia, bipolar disorder, and major depressive disorder: A systematic review of secondary literature

Deficiencies in Theory of Mind (ToM) are consistently found in individuals with schizophrenia (SZ), major depressive disorder (MDD), and bipolar disorder (BD). However, the character of these deficits and their role in the pathogenesis of mental illness remains poorly understood. This systematic review synthesizes the available secondary literature pertaining to ToM functioning in individuals with MDD, BD, or SZ, and their respective spectrum disorders in order to delineate disorder or symptom specific patterns of ToM impairment. Literature suggests that ToM deficits increase in severity along the affective-psychotic spectrum, with mild deficits in patients with MDD, and severe deficits in patients with mania or psychosis. Furthermore, ToM deficits appear to be part of a broader developmental phenotype associated with SZ and BD, as suggested by findings of attenuated impairments in ToM in remitted patients with SZ or BD, unaffected first-degree relatives of patients, and clinical high-risk groups. Future psychiatric research on ToM should aim to disentangle relationships between ToM deficits and specific symptom dimensions transdiagnostically, and employ standardized, construct-specific ToM tasks

A two-factor structure of first rank symptoms in patients with a psychotic disorder

Kurt Schneider defined 'first rank symptoms' (FRS) of psychosis. Previous research found two clusters of FRS: 'loss of ego bound' symptoms (e.g., delusions of external control) and auditory hallucinations (e.g, commenting voices). In patients with a psychosis we investigated whether FRS are a separate cluster within the group of positive symptoms, consisting of two underlying factors that are stable over time. We conducted a principal axis factor analysis (PAF) at baseline (n = 857) and a confirmative factor analysis (CFA) at three-year follow-up (n = 414) on (FRS) symptom score. Also, we investigated the stability of the two-factor structure of FRS over the interval. PAF on 16 items representing positive symptoms at baseline revealed two factors with eigenvalues > 1. FRS-delusional self experience (thought withdrawal, thought broadcasting, thought insertion, and beliefs that impulses and/or actions are controlled by an outside force) clustered in one factor and FRS-auditory hallucinations (auditory hallucinations, conversational voices, and voices commenting on one's actions) in the second factor. Furthermore, CFA on the FRS-items at follow-up confirmed the two-factor structure of FRS. FRS delusional self experience and FRS-auditory hallucinations at baseline were significantly associated with the same factors at three-year follow-up (FRS-delusional self experience: r = 0.38; FRS-auditory hallucinations r = 0.47). Hence, our findings confirm a two-factor structure of first rank symptoms, i.e. FRS-delusional self experience and FRS-auditory hallucinations, with a moderate to large internal coherence within each factor and relative stability over time. Future studies on self-processes may contribute to our understanding of the pathophysiology of first rank symptoms.status: publishe

Remission criteria and functional outcome in patients with schizophrenia, a longitudinal study

The Remission in Schizophrenia Working Group (RSWG) has proposed remission criteria for schizophrenia, which were shown to be valid in terms of functional and clinical outcomes. However, studies investigating the association between dynamics in remission status in relation to longitudinal functional and clinical outcome are scarce.status: publishe

White matter disruptions in patients with bipolar disorder

Bipolar disorder (BD) patients show aberrant white matter microstructure compared to healthy controls but little is known about the relation with clinical characteristics. We therefore investigated the relation of white matter microstructure with the main pharmacological treatments as well its relation with IQ. Patients with BD (N = 257) and controls (N = 167) underwent diffusion tensor imaging (DTI) and comprehensive clinically assessments including IQ estimates. DTI images were analyzed using tract-based spatial statistics. Fractional anisotropy (FA) and Mean Diffusivity (MD) were determined. Patients had significantly lower FA and higher MD values throughout the white matter skeleton compared to controls. Within the BD patients, lithium use was associated with higher FA and lower MD. Antipsychotic medication use in the BD patients was not associated with FA but, in contrast to lithium, was associated with higher MD. IQ was significantly positively correlated with FA and negatively with MD in patients as well as in controls. In this large DTI study we found evidence for marked differences in FA and MD particularly in (but not restricted to) corpus callosum, between BD patients and controls. This effect was most pronounced in lithium-free patients, implicating that lithium affects white matter microstructure and attenuates differences associated with bipolar disorder. Effects of antipsychotic medication intake were absent in FA and only subtle in MD relative to those of lithium. The abnormal white matter microstructure was associated with IQ but not specifically for either group

Long-term course of negative symptom subdomains and relationship with outcome in patients with a psychotic disorder (vol 193, pg 173, 2018)

© 2017 Elsevier B.V. The authors regret that the GROUP-investigators were not acknowledged for their work due to a missing affiliation in the original publication. The new authorship line is: Annemarie P.M. Stiekema a,b Md Atiqul Islam c,d,e Edith J. Liemburg c,d,f Stynke Castelein c,g Edwin R. van den Heuvel h Jaap van Weeghel i,j André Aleman f,k Richard Bruggeman c,d Lisette van der Meer a,c GROUP-investigators la Lentis Psychiatric Institute, Department of Rehabilitation, Zuidlaren, The Netherlands b School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University Medical Centre, Maastricht, The Netherlands c University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Groningen, The Netherlands d University of Groningen, University Medical Center Groningen, Groningen, Rob Giel Research center, The Netherlands e Department of Statistics, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh f University of Groningen, University Medical Center Groningen, Department of Neuroscience, Groningen, The Netherlands g Lentis Psychiatric Institute, Research Department, Groningen, The Netherlands h Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands i Parnassia Group, Dijk en Duin Mental Health Center, Castricum, The Netherlands j Department of TRANZO, Tilburg School of Social and Behavioral Sciences, Tilburg University, The Netherlands k University of Groningen, Department of Clinical Psychology and Experimental Psychopathology, Groningen, The Netherlands l GROUP investigators are: Behrooz Z. Alizadeh m , Agna A. Bartels-Velthuis n , Nico J. van Beveren o,p,q , Richard Bruggeman n , Wiepke Cahn r , Lieuwe de Haan s , Philippe Delespaul t , Carin J. Meijer s , Inez Myin-Germeys u , Rene S. Kahn r , Frederike Schirmbeck s , Claudia J.P. Simons t,v , Neeltje E.M. van Haren r,w , Jim van Os t,x , Ruud van Winkel um University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands n University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Rob Giel Research center, Groningen, The Netherlands o Antes Center for Mental Health Care, Rotterdam, The Netherlands p Erasmus MC, Department of Psychiatry, Rotterdam, The Netherlands q Erasmus MC, Department of Neuroscience, Rotterdam, The Netherlands r University Medical Center Utrecht, Department of Psychiatry, Brain Centre Rudolf agnus, Utrecht, The Netherlands s Academic Medical Center, University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands t Maastricht University Medical Center, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht, The Netherlands u KU Leuven, Department of Neuroscience, Research Group Psychiatry, Center for Clinical Psychiatry, Leuven, Belgium v GGzE, Institute for Mental Health Care Eindhoven and De Kempen, Eindhoven, The Netherlands w Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre, Rotterdam, Netherlands x King's College London, King's Health Partners, Department of Psychosis Studies, Institute of Psychiatry, London, United Kingdom The authors apologise for any inconvenience caused.status: publishe

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data

Beyond the Global Brain Differences: Intra-individual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers

BACKGROUND: The 1q21.1 distal and 15q11.2 BP1-BP2 CNVs exhibit regional and global brain differences compared to non-carriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intra-individual variability measures can be used to test for regional differences beyond global differences in brain structure. METHODS: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n=30) and duplication (n=27), and 15q11.2 BP1-BP2 deletion (n=170) and duplication (n=243) carriers and matched non-carriers (n=2,350). Regional intra-deviation (RID) scores i.e., the standardized difference between an individual's regional difference and global difference, were used to test for regional differences that diverge from the global difference. RESULTS: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate and temporal pole differed less, and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex and temporal pole differed less, and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness. CONCLUSION: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 CNVs. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 CNVs, with the potential to increase our understanding of mechanisms involved in altered neurodevelopment

Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium.

BackgroundThe profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group.MethodsThe study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11-78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10-87 years; 53% male) assessed with standardized methods at 39 centers worldwide.ResultsCompared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset.ConclusionsThe findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia