Longitudinal gray matter change in young people who are at enhanced risk of schizophrenia due to intellectual impairment

Assessment of dementia in a clinical sample of persons with intellectual disability

BACKGROUND

Assessment of age-associated disorders has become increasingly important.

METHODS

In a clinical setting, people with intellectual disability with and without dementia were assessed retrospectively using the Neuropsychological Test Battery (NTB) and the Dementia Questionnaire for People with Learning Disabilities (DLD) at two different times to analyse neuropsychological changes and diagnostic validity. One group (n = 44) was assessed with both instruments, while the DLD was applied in 71 patients.

RESULTS

In the NTB (n = 44), only patients with dementia (n = 26) showed a decline in the NTB total score and three subscales. Receiver operating characteristic analysis revealed a diagnostic sensitivity of .67, a specificity of .81, and an area under the curve (AUC) of .767. In the DLD group (n = 71), only those with dementia displayed a decrease in the cognitive and social scale; diagnostic sensitivity and specificity values were low (.61/.63) and the AUC was .704.

CONCLUSIONS

Neuropsychological assessment was sensitive to detect cognitive changes over time. Sensitivity values of both instruments suggest a reassessment at a later time point.

Atypical processing of uncertainty in individuals at risk for psychosis

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Humans at psychosis clinical high risk (CHR) over-estimate environmental volatility.

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Low-level prediction error (PE) signals evoke increased frontal activity in CHR.

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Volatility-related PEs are associated with reduced frontal activity in CHR.

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Frontal cortical activation to low-level PEs reflects impaired clinical functioning.

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Atypical PE learning signal representations may promote delusion formation in CHR.

Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in clinical high risk (CHR) individuals.

Non-medicated CHR individuals (n = 13) and control participants (n = 13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour – with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental ‘volatility’ – and used these computational quantities for analyses of fMRI data.

Computational modelling of CHR individuals’ behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of CHR individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in CHR was negatively associated with clinical measures of global functioning.

Our results suggest a multi-faceted learning abnormality in CHR individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation.

Predictors of psychotic symptoms among young people with special educational needs

BACKGROUND

Psychotic symptoms and psychotic disorders occur at increased rates in adults with intellectual disability, including borderline intellectual functioning, compared with the general population. Little is known about the development of such symptoms in this population.AimsTo examine whether clinical factors predictive of psychotic disorder in a familial study of schizophrenia also apply to those with intellectual disability.

METHOD

Adolescents with special educational needs (SEN) were assessed with the Structured Interview for Schizotypy (SIS) and Childhood Behavioural Checklist (CBCL). These scores were used to prospectively divide participants based on their anticipated risk for psychotic disorder. A subsample were reassessed three times over 6 years, using the Positive and Negative Syndrome Scale (PANSS).

RESULTS

The SEN group were more symptomatic than controls throughout (Cohen's d range for PANSS subscale scores: 0.54-1.4, all P < 0.007). Over 6 years of follow-up, those above the SIS and CBCL cut-off values at baseline were more likely than those below to display morbid positive psychotic symptoms (odds ratio, 3.5; 95% CI 1.3-9.0) and develop psychotic disorder (odds ratio, 11.4; 95% CI 2.6-50.1). Baseline SIS and CBCL cut-off values predicted psychotic disorder with sensitivity of 0.67, specificity of 0.85, positive predictive value of 0.26 and negative predictive value of 0.97.

CONCLUSIONS

Adolescents with SEN have increased psychotic and non-psychotic symptoms. The personality and behavioural features associated with later psychotic disorder in this group are similar to those in people with familial loading. Relatively simple screening measures may help identify those in this vulnerable group who do and do not require monitoring for psychotic symptoms.Declaration of interestNone.

Effect of personal characteristics, victimization types, and family- and school-related factors on psychological distress in adolescents with intellectual disabilities

The purpose of this study was to determine the prevalence of bullying victimization among adolescents with intellectual disabilities and the influence of victimization experience on their mental health in Taiwan. Data on 706 adolescents from the 2011 Special Needs Education Longitudinal Study were analyzed. Multivariate regression analysis was applied to variables comprising 7 items of psychological distress, 4 types of bullying victimization, and family-, school-, and peer-related factors. Approximately 70% of the survey respondents had experienced at least one type of victimization, and 44% of them had experienced at least two types of victimization. Exclusion (50%) and verbal bullying (70%) were the most commonly reported types. In addition, exclusion and verbal bullying were found to be significantly associated with psychological distress in these adolescents. Our findings suggest that victimization is a common experience among adolescents with disabilities, and a notable risk factor for the psychological well-being of adolescents with intellectual disabilities. However, a good relationship with parents and peers can relieve psychological distress and its effect on mental health.

Negative symptoms and longitudinal grey matter tissue loss in adolescents at risk of psychosis: preliminary findings from a 6-year follow-up study

BACKGROUND

Negative symptoms are perhaps the most disabling feature of schizophrenia. Their pathogenesis remains poorly understood and it has been difficult to assess their development over time with imaging techniques.

AIMS

To examine, using tensor-based structural imaging techniques, whether there are regions of progressive grey matter volume change associated with the development of negative symptoms.

METHOD

A total of 43 adolescents at risk of psychosis were examined using magnetic resonance imaging and whole brain tensor-based morphometry at two time points, 6 years apart.

RESULTS

When comparing the individuals with significant negative symptoms with the remaining participants, we identified five regions of significant grey matter tissue loss over the 6-year period. These regions included the left temporal lobe, the left cerebellum, the left posterior cingulate and the left inferior parietal sulcus.

CONCLUSIONS

Negative symptoms are associated with longitudinal grey matter tissue loss. The regions identified include areas associated with psychotic symptoms more generally but also include regions uniquely associated with negative symptoms.

Hippocampus and amygdala volumes in children and young adults at high-risk of schizophrenia: research synthesis

BACKGROUND

Studies have reported hippocampal and amygdala volume abnormalities in schizophrenic patients. It is necessary to explore the potential for these structures as early disease markers in subjects at high risk (HR) of schizophrenia.

METHODS

We performed a review of 29 magnetic resonance imaging (MRI) studies measuring hippocampal and amygdala volumes in subjects at HR for schizophrenia. We reclassified subjects in 3 new HR categories: presence of only risk symptoms (psychotic moderate symptoms), presence of only risk factors (genetic, developmental or environmental), and presence of combined risk symptoms/factors.

RESULTS

Hippocampal volume reductions were detected in subjects with first episode (FE) of psychosis, in all young adults and in adolescents at HR of schizophrenia. The loss of tissue was mainly located in the posterior part of hippocampus and the right side seems more vulnerable in young adults with only risk symptoms. Instead, the anterior sector seems more involved in HR subjects with genetic risks. Abnormal amygdala volumes were found in FE subjects, in children with combined risk symptoms/factors and in older subjects using different inclusion criteria, but not in young adults.

CONCLUSION

Hippocampal and amygdala abnormalities may be present before schizophrenia onset. Further studies should be conducted to clarify whether these abnormalities are causally or effectually related to neurodevelopment. Shape analysis could clarify the impact of environmental, genetic, and developmental factors on the medial temporal structures during the evolution of this disease.

Neurobiology of cognitive remediation therapy for schizophrenia: a systematic review

Cognitive impairment is an important aspect of schizophrenia, where cognitive remediation therapy (CRT) is a promising treatment for improving cognitive functioning. While neurobiological dysfunction in schizophrenia has been the target of much research, the neural substrate of cognitive remediation and recovery has not been thoroughly examined. The aim of the present article is to systematically review the evidence for neural changes after CRT for schizophrenia. The reviewed studies indicate that CRT affects several brain regions and circuits, including prefrontal, parietal, and limbic areas, both in terms of activity and structure. Changes in prefrontal areas are the most reported finding, fitting to previous evidence of dysfunction in this region. Two limitations of the current research are the few studies and the lack of knowledge on the mechanisms underlying neural and cognitive changes after treatment. Despite these limitations, the current evidence suggests that CRT is associated with both neurobiological and cognitive improvement. The evidence from these findings may shed light on both the neural substrate of cognitive impairment in schizophrenia, and how better treatment can be developed and applied.

Dysconnectivity in the frontoparietal attention network in schizophrenia

Cognitive impairment is common in patients with schizophrenia, and even those with relatively preserved function perform worse than healthy volunteers (HVs) on attentional tasks. This is consistent with the hypothesis that connectivity - in the frontoparietal network (FPN) activated during attention - is disrupted in schizophrenia. We examined attentional effects on connectivity in the FPN, in schizophrenia, using magnetoencephalography (MEG). Twenty-three HVs and 19 first-episode schizophrenia patients were scanned during a simple visual change test, known to activate the FPN, in which attention was monitored and directed with an orthogonal flicker-detection task. Dynamic causal modeling (DCM) of evoked responses was used to assess effective connectivity - and its modulation by changes in the attended stimulus dimension - in the following network: higher visual area; temporoparietal junction (TPJ); intraparietal sulcus (IPS); dorsal anterior cingulate cortex; and ventrolateral prefrontal cortex. The final MEG analysis included 18 HVs and 14 schizophrenia patients. While all participants were able to maintain attention, HVs responded slightly, but non-significantly, more accurately than schizophrenia patients. HVs, but not schizophrenia patients, exhibited greater cortical responses to attended visual changes. Bayesian model comparison revealed that a DCM with attention dependent changes in both top-down and bottom-up connections best explained responses by patients with schizophrenia, while in HVs the best model required only bottom-up changes. Quantitative comparison of connectivity estimates revealed a significant group difference in changes in the right IPS-TPJ connection: schizophrenia patients showed relative reductions in connectivity during attended stimulus changes. Crucially, this reduction predicted lower intelligence. These data are consistent with the hypothesis that functional dysconnections in the FPN contribute to cognitive impairment in schizophrenia.

Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level

Standard univariate analyses of brain imaging data have revealed a host of structural and functional brain alterations in schizophrenia. However, these analyses typically involve examining each voxel separately and making inferences at group-level, thus limiting clinical translation of their findings. Taking into account the fact that brain alterations in schizophrenia expand over a widely distributed network of brain regions, univariate analysis methods may not be the most suited choice for imaging data analysis. To address these limitations, the neuroimaging community has turned to machine learning methods both because of their ability to examine voxels jointly and their potential for making inferences at a single-subject level. This article provides a critical overview of the current and foreseeable applications of machine learning, in identifying imaging-based biomarkers that could be used for the diagnosis, early detection and treatment response of schizophrenia, and could, thus, be of high clinical relevance. We discuss promising future research directions and the main difficulties facing machine learning researchers as far as their potential translation into clinical practice is concerned.