Grey matter changes can improve the prediction of schizophrenia in subjects at high risk

Dopamine-induced pruning in monocyte-derived-neuronal-like cells (MDNCs) from patients with schizophrenia

The long lapse between the presumptive origin of schizophrenia (SCZ) during early development and its diagnosis in late adolescence has hindered the study of crucial neurodevelopmental processes directly in living patients. Dopamine, a neurotransmitter consistently associated with the pathophysiology of SCZ, participates in several aspects of brain development including pruning of neuronal extensions. Excessive pruning is considered the cause of the most consistent finding in SCZ, namely decreased brain volume. It is therefore possible that patients with SCZ carry an increased susceptibility to dopamine's pruning effects and that this susceptibility would be more obvious in the early stages of neuronal development when dopamine pruning effects appear to be more prominent. Obtaining developing neurons from living patients is not feasible. Instead, we used Monocyte-Derived-Neuronal-like Cells (MDNCs) as these cells can be generated in only 20 days and deliver reproducible results. In this study, we expanded the number of individuals in whom we tested the reproducibility of MDNCs. We also deepened the characterization of MDNCs by comparing its neurostructure to that of human developing neurons. Moreover, we studied MDNCs from 12 controls and 13 patients with SCZ. Patients' cells differentiate more efficiently, extend longer secondary neurites and grow more primary neurites. In addition, MDNCs from medicated patients expresses less D1R and prune more primary neurites when exposed to dopamine. Haloperidol did not influence our results but the role of other antipsychotics was not examined and thus, needs to be considered as a confounder.

Interventions for prodromal stage of psychosis

BACKGROUND

Psychosis is a serious mental condition characterised by a loss of contact with reality. There may be a prodromal period or stage of psychosis, where early signs of symptoms indicating onset of first episode psychosis (FEP) occur. A number of services, incorporating multimodal treatment approaches (pharmacotherapy, psychotherapy and psychosocial interventions), developed worldwide, now focus on this prodromal period with the aim of preventing psychosis in people at risk of developing FEP.

OBJECTIVES

The primary objective is to assess the safety and efficacy of early interventions for people in the prodromal stage of psychosis. The secondary objective is, if possible, to compare the effectiveness of the various different interventions.

SEARCH METHODS

We searched Cochrane Schizophrenia's study-based Register of studies (including trials registers) on 8 June 2016 and 4 August 2017.

SELECTION CRITERIA

All randomised controlled trials (RCTs) evaluating interventions for participants older than 12 years, who had developed a prodromal stage of psychosis.

DATA COLLECTION AND ANALYSIS

Review authors independently inspected citations, selected studies, extracted data, and assessed study quality.

MAIN RESULTS

We included 20 studies with 2151 participants. The studies analysed 13 different comparisons. Group A comparisons explored the absolute effects of the experimental intervention. Group B were comparisons within which we could not be clear whether differential interactive effects were also ongoing. Group C comparisons explored differential effects between clearly distinct treatments. A key outcome for this review was 'transition to psychosis'. For details of other main outcomes please see 'Summary of findings' tables. In Group A (comparisons of absolute effects) we found no clear difference between amino acids and placebo (risk ratio (RR) 0.48 95% confidence interval (CI) 0.08 to 2.98; 2 RCTs, 52 participants; very low-quality evidence). When omega-3 fatty acids were compared to placebo, fewer participants given the omega-3 (10%) transitioned to psychosis compared to the placebo group (33%) during long-term follow-up of seven years (RR 0.24 95% CI 0.09 to 0.67; 1 RCT, 81 participants; low-quality evidence). In Group B (comparisons where complex interactions are probable) and in the subgroup focusing on antipsychotic drugs added to specific care packages, the amisulpiride + needs-focused intervention (NFI) compared to NFI comparison (no reporting of transition to psychosis; 1 RCT, 102 participants; very low-quality evidence) and the olanzapine + supportive intervention compared to supportive intervention alone comparison (RR 0.58 95% CI 0.28 to 1.18; 1 RCT, 60 participants; very low-quality evidence) showed no clear differences between groups. In the second Group B subgroup (cognitive behavioural therapies (CBT)), when CBT + supportive therapy was compared with supportive therapy alone around 8% of participants allocated to the combination of CBT and supportive therapy group transitioned to psychosis during follow-up by 18 months, compared with double that percentage in the supportive therapy alone group (RR 0.45 95% CI 0.23 to 0.89; 2 RCTs, 252 participants; very low-quality evidence). The CBT + risperidone versus CBT + placebo comparison identified no clear difference between treatments (RR 1.02 95% CI 0.39 to 2.67; 1 RCT, 87 participants; very low-quality evidence) and this also applies to the CBT + needs-based intervention (NBI) + risperidone versus NBI comparison (RR 0.75 95% CI 0.39 to 1.46; 1 RCT, 59 participants; very low-quality evidence). Group C (differential effects) also involved six comparisons. The first compared CBT with supportive therapy. No clear difference was found for the 'transition to psychosis' outcome (RR 0.74 95% CI 0.28 to 1.98; 1 RCT, 72 participants; very low-quality evidence). The second subgroup compared CBT + supportive intervention was compared with a NBI + supportive intervention, again, data were equivocal, few and of very low quality (RR 6.32 95% CI 0.34 to 117.09; 1 RCT, 57 participants). In the CBT + risperidone versus supportive therapy comparison, again there was no clear difference between groups (RR 0.76 95% CI 0.28 to 2.03; 1 RCT, 71 participants; very low-quality evidence). The three other comparisons in Group C demonstrated no clear differences between treatment groups. When cognitive training was compared to active control (tablet games) (no reporting of transition to psychosis; 1 RCT, 62 participants; very low quality data), family treatment compared with enhanced care comparison (RR 0.54 95% CI 0.18 to 1.59; 2 RCTs, 229 participants; very low-quality evidence) and integrated treatment compared to standard treatment comparison (RR 0.57 95% CI 0.28 to 1.15; 1 RCT, 79 participants; very low-quality evidence) no effects of any of these approaches was evident.

AUTHORS' CONCLUSIONS

There has been considerable research effort in this area and several interventions have been trialled. The evidence available suggests that omega-3 fatty acids may prevent transition to psychosis but this evidence is low quality and more research is needed to confirm this finding. Other comparisons did not show any clear differences in effect for preventing transition to psychosis but again, the quality of this evidence is very low or low and not strong enough to make firm conclusions.

Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients

Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be identified at an individual level. In this study, we used a multivariate pattern classification method to learn informative large-scale functional networks (FNs) and build classifiers to distinguish 32 patients from 30 healthy controls and to classify 34 FDRs as with or without FNs similar to patients. Four informative FNs-the cerebellum, default mode network (DMN), ventral frontotemporal network, and posterior DMN with parahippocampal gyrus-were identified based on a training cohort and pattern classifiers built upon these FNs achieved a correct classification rate of 83.9% (sensitivity 87.5%, specificity 80.0%, and area under the receiver operating characteristic curve [AUC] 0.914) estimated based on leave-one-out cross-validation for the training cohort and a correct classification rate of 77.5% (sensitivity 72.5%, specificity 82.5%, and AUC 0.811) for an independent validation cohort. The classification scores of the FDRs and patients were negatively correlated with their measures of cognitive function. FDRs identified by the classifiers as having SCZ patterns were similar to the patients, but significantly different from the controls and FDRs with normal patterns in terms of their cognitive measures. These results demonstrate that the pattern classifiers built upon the informative FNs can serve as biomarkers for quantifying brain alterations in SCZ and help to identify FDRs with FN patterns and cognitive impairment similar to those of SCZ patients.

Clinical psychopathology in youth at familial high risk for psychosis

AIM

While the course of psychopathology has been explored from an index mental health diagnosis onwards, there are few detailed, prospective studies of the occurrence of clinical psychopathology in youth with familial risk for severe mental illnesses such as psychosis. We sought to describe the appearance of Axis I psychopathology in a unique sample of adolescents with a family history of schizophrenia (FHR).

METHODS

One hundred and sixty two first- and second-degree relatives (mean age 15.7 ± 3.6; range 8-25) of probands with schizophrenia or schizoaffective disorder were assessed at baseline and annual intervals for up to 3 years, focusing on Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) Axis I psychopathology.

RESULTS

Fourteen individuals (8.6%) developed a psychotic disorder. One hundred and five subjects (65%) met criteria for an Axis I disorder over the course of the study, the most common of which was a depressive episode (40 subjects; 25%). Of the 148 individuals who did not develop psychosis, 91 (61%) had one or more Axis I disorders compared with 10/14 converters who had a comorbid Axis I disorder (71%). Ordered by increasing age of onset, diagnoses included cognitive and externalizing disorders, anxiety disorders, affective disorders, substance use disorders and psychotic disorders.

CONCLUSIONS

In addition to an elevated risk of psychosis, young FHR relatives manifest a broad range of non-psychotic Axis I psychopathology in childhood and adolescence. This breadth of diagnoses has implications for the structure and function of mental health services for young people.

Inflammation in Schizophrenia: Pathogenetic Aspects and Therapeutic Considerations

This paper discusses the current evidence from animal and human studies for a central role of inflammation in schizophrenia. In animal models, pre- or perinatal elicitation of the immune response may increase immune reactivity throughout life, and similar findings have been described in humans. Levels of pro-inflammatory markers, such as cytokines, have been found to be increased in the blood and cerebrospinal fluid of patients with schizophrenia. Numerous epidemiological and clinical studies have provided evidence that various infectious agents are risk factors for schizophrenia and other psychoses. For example, a large-scale epidemiological study performed in Denmark clearly showed that severe infections and autoimmune disorders are such risk factors. The vulnerability-stress-inflammation model may help to explain the role of inflammation in schizophrenia because stress can increase pro-inflammatory cytokines and may even contribute to a chronic pro-inflammatory state. Schizophrenia is characterized by risk genes that promote inflammation and by environmental stress factors and alterations of the immune system. Typical alterations of dopaminergic, serotonergic, noradrenergic, and glutamatergic neurotransmission described in schizophrenia have also been found in low-level neuroinflammation and consequently may be key factors in the generation of schizophrenia symptoms. Further support for the relevance of a low-level neuroinflammatory process in schizophrenia is provided by the loss of central nervous system volume and microglial activation demonstrated in neuroimaging studies. Last but not least, the benefit of anti-inflammatory medications found in some studies and the intrinsic anti-inflammatory and immunomodulatory effects of antipsychotics provide further support for the role of inflammation in this debilitating disease.

Imaging and Genetic Biomarkers Predicting Transition to Psychosis

The search for diagnostic and prognostic biomarkers in schizophrenia care and treatment is the focus of many within the research community. Longitudinal cohorts of patients presenting at elevated genetic and clinical risk have provided a wealth of data that has informed our understanding of the development of schizophrenia and related psychotic disorders.Imaging follow-up of high-risk cohorts has demonstrated changes in cerebral grey matter of those that eventually transition to schizophrenia that predate the onset of symptoms and evolve over the course of illness. Longitudinal follow-up studies demonstrate that observed grey matter changes can be employed to differentiate those who will transition to schizophrenia from those who will not prior to the onset of the disorder.In recent years our understanding of the genetic makeup of schizophrenia has advanced significantly. The development of modern analysis techniques offers researchers the ability to objectively quantify genetic risk; these have been successfully applied within a high-risk paradigm to assist in differentiating between high-risk individuals who will subsequently become unwell and those who will not.This chapter will discuss the application of imaging and genetic biomarkers within high-risk groups to predict future transition to schizophrenia and related psychotic disorders. We aim to provide an overview of current approaches focussing on grey matter changes that are predictive of future transition to illness, the developing field of genetic risk scores and other methods being developed to aid clinicians in diagnosis and prognosis.

Morphological brain changes associated with negative symptoms in patients with 22q11.2 Deletion Syndrome

Approximately 30% of individuals with 22q11.2 Deletion Syndrome (22q11DS) develop schizophrenia during adolescence/early adulthood, making this syndrome a model for the disorder. Furthermore, negative symptoms exist in up to 80% of patients diagnosed with 22q11DS. The present study aims to uncover morphological brain alterations associated with negative symptoms in a cohort of patients with 22q11DS who are at-risk for developing schizophrenia. A total of 71 patients with 22q11DS aged 12 to 35 (54% females) with no past or present diagnosis of a schizophrenia were included in the study. Psychotic symptom scores were used to divide patients into subgroups by means of a cluster analysis. Three major subgroups were evident: patients with low negative and positive symptoms; patients with high negative symptoms and low positive symptoms; and patients with high negative and positive symptoms. Cortical volume, thickness and gyrification were compared between subgroups using FreeSurfer software. Results showed that patients with high negative symptoms, compared to those with low negative symptoms, have decreased gyrification in the medial occipito-temporal (MOT) and lateral temporo-parietal (LTP) cortices of the left hemisphere, and in the medial temporal (MT)/posterior cingulate (PCC) cortices of the right hemisphere. These findings suggest that high negative symptoms are associated with gyrification reductions predominantly in medial occipital and temporal regions, which are areas implicated in social cognition and early visual processing. Furthermore, as cortical folding develops in utero and during the first years of life, reduced gyrification may represent an early biomarker predicting the development of negative symptoms.

Improved individualized prediction of schizophrenia in subjects at familial high risk, based on neuroanatomical data, schizotypal and neurocognitive features

To date, there are no reliable markers for predicting onset of schizophrenia in individuals at high risk (HR). Substantial promise is, however, shown by a variety of pattern classification approaches to neuroimaging data. Here, we examined the predictive accuracy of support vector machine (SVM) in later diagnosing schizophrenia, at a single-subject level, using a cohort of HR individuals drawn from multiply affected families and a combination of neuroanatomical, schizotypal and neurocognitive variables. Baseline structural magnetic resonance imaging (MRI), schizotypal and neurocognitive data from 17 HR subjects, who subsequently developed schizophrenia and a matched group of 17 HR subjects who did not make the transition, yet had psychotic symptoms, were included in the analysis. We employed recursive feature elimination (RFE), in a nested cross-validation scheme to identify the most significant predictors of disease transition and enhance diagnostic performance. Classification accuracy was 94% when a self-completed measure of schizotypy, a declarative memory test and structural MRI data were combined into a single learning algorithm; higher than when either quantitative measure was used alone. The discriminative neuroanatomical pattern involved gray matter volume differences in frontal, orbito-frontal and occipital lobe regions bilaterally as well as parts of the superior, medial temporal lobe and cerebellar regions. Our findings suggest that an early SVM-based prediction of schizophrenia is possible and can be improved by combining schizotypal and neurocognitive features with neuroanatomical variables. However, our predictive model needs to be tested by classifying a new, independent HR cohort in order to estimate its validity.

Clinical studies of neuroinflammatory mechanisms in schizophrenia

Schizophrenia is a pervasive neurodevelopmental disorder that appears to result from genetic and environmental factors. Although the dopamine hypothesis is the driving theory behind the majority of translation research in schizophrenia, emerging evidence suggests that aberrant immune mechanisms in the peripheral and central nervous system influence the etiology of schizophrenia and the pathophysiology of psychotic symptoms that define the illness. The initial interest in inflammatory processes comes from epidemiological data and historical observations, dating back several decades. A growing body of research on developmental exposure to infection, stress-induced inflammatory response, glial cell signaling, structural and functional brain changes and therapeutic trials demonstrates the impact that inflammation has on the onset and progression of schizophrenia. Research in animal models of psychosis has helped to advance clinical and basic science investigations of the immune mechanisms disrupted in schizophrenia. However, they are limited by the inability to recapitulate the human experience of hallucinations, delusions and thought disorder that define psychosis. To date, translational studies of inflammatory mechanisms in human subjects have not been reviewed in great detail. Here, we critically review clinical studies that focus on inflammatory mechanisms in schizophrenia. Understanding the neuroinflammatory mechanisms involved in schizophrenia may be essential in identifying potential therapeutic targets to minimize the morbidity and mortality of schizophrenia by interrupting disease development.

The role of inflammation in schizophrenia

High levels of pro-inflammatory substances such as cytokines have been described in the blood and cerebrospinal fluid of schizophrenia patients. Animal models of schizophrenia show that under certain conditions an immune disturbance during early life, such as an infection-triggered immune activation, might trigger lifelong increased immune reactivity. A large epidemiological study clearly demonstrated that severe infections and autoimmune disorders are risk factors for schizophrenia. Genetic studies have shown a strong signal for schizophrenia on chromosome 6p22.1, in a region related to the human leucocyte antigen (HLA) system and other immune functions. Another line of evidence demonstrates that chronic (dis)stress is associated with immune activation. The vulnerability-stress-inflammation model of schizophrenia includes the contribution of stress on the basis of increased genetic vulnerability for the pathogenesis of schizophrenia, because stress may increase pro-inflammatory cytokines and even contribute to a lasting pro-inflammatory state. Immune alterations influence the dopaminergic, serotonergic, noradrenergic, and glutamatergic neurotransmission. The activated immune system in turn activates the enzyme indoleamine 2,3-dioxygenase (IDO) of the tryptophan/kynurenine metabolism which influences the serotonergic and glutamatergic neurotransmission via neuroactive metabolites such as kynurenic acid. The described loss of central nervous system volume and the activation of microglia, both of which have been clearly demonstrated in neuroimaging studies of schizophrenia patients, match the assumption of a (low level) inflammatory neurotoxic process. Further support for the inflammatory hypothesis comes from the therapeutic benefit of anti-inflammatory medication. Metaanalyses have shown an advantageous effect of cyclo-oxygenase-2 inhibitors in early stages of schizophrenia. Moreover, intrinsic anti-inflammatory, and immunomodulatory effects of antipsychotic drugs are known since a long time. Anti-inflammatory effects of antipsychotics, therapeutic effects of anti-inflammtory compounds, genetic, biochemical, and immunological findings point to a major role of inflammation in schizophrenia.

Extensive brain structural network abnormality in first-episode treatment-naive patients with schizophrenia: morphometrical and covariation study

BACKGROUND

Alterations in gray matter (GM) are commonly observed in schizophrenia. Accumulating studies suggest that the brain changes associated with schizophrenia are distributed rather than focal, involving interconnected networks of areas as opposed to single regions. In the current study we aimed to explore GM volume (GMV) changes in a relatively large sample of treatment-naive first-episode schizophrenia (FES) patients using optimized voxel-based morphometry (VBM) and covariation analysis.

METHOD

High-resolution T1-weighted images were obtained using 3.0-T magnetic resonance imaging (MRI) from 86 first-episode drug-naive patients with schizophrenia and 86 age- and gender-matched healthy volunteers. Symptom severity was evaluated using the Positive and Negative Syndrome Scale (PANSS). GMV was assessed using optimized VBM and in 16 regions of interest (ROIs), selected on the basis of a previous meta-analysis. The relationships between GMVs in the ROIs were examined using an analysis of covariance (ANCOVA).

RESULTS

The VBM analysis revealed that first-episode patients showed reduced GMV in the hippocampus bilaterally. The ROI analysis identified reductions in GMV in the left inferior frontal gyrus, bilateral hippocampus and right thalamus. The ANCOVA revealed different patterns of regional GMV correlations in patients and controls, including of inter- and intra-insula, inter-amygdala and insula-postcentral gyrus connections.

CONCLUSIONS

Schizophrenia involves regional reductions in GMV and changes in GMV covariance in the insula, amygdala and postcentral gyrus. These findings were evident at the onset of the disorder, before treatment, and therefore cannot be attributable to the effects of chronic illness progression or medication.

Self-disturbances as a possible premorbid indicator of schizophrenia risk: a neurodevelopmental perspective

Self-disturbances (SDs) are increasingly identified in schizophrenia and are theorized to confer vulnerability to psychosis. Neuroimaging research has shed some light on the neural correlates of SDs in schizophrenia. But, the onset and trajectory of the neural alterations underlying SDs in schizophrenia remain incompletely understood. We hypothesize that the aberrant structure and function of brain areas (e.g., prefrontal, lateral temporal, and parietal cortical structures) comprising the "neural circuitry of self" may represent an early, premorbid (i.e., pre-prodromal) indicator of schizophrenia risk. Consistent with neurodevelopmental models, we argue that "early" (i.e., perinatal) dysmaturational processes (e.g., abnormal cortical neural cell migration and mini-columnar formation) affecting key prefrontal (e.g., medial prefrontal cortex), lateral temporal cortical (e.g., superior temporal sulcus), and parietal (e.g., inferior parietal lobule) structures involved in self-processing may lead to subtle disruptions of "self" during childhood in persons at risk for schizophrenia. During adolescence, progressive neurodevelopmental alterations (e.g., aberrant synaptic pruning) affecting the neural circuitry of self may contribute to worsening of SDs. This could result in the emergence of prodromal symptoms and, eventually, full-blown psychosis. To highlight why adolescence may be a period of heightened risk for SDs, we first summarize the literature regarding the neural correlates of self in typically developing children. Next, we present evidence from neuroimaging studies in genetic high-risk youth suggesting that fronto-temporal-parietal structures mediating self-reflection may be abnormal in the premorbid period. Our goal is that the ideas presented here might provide future directions for research into the neurobiology of SDs during the pre-psychosis development of youth at risk for schizophrenia.

Immunology of schizophrenia

Increased proinflammatory markers like cytokines have been described in the blood and cerebrospinal fluid of patients suffering from schizophrenia. Animal models have shown that a hit in early life to the immune system might trigger a lifelong increased immune reactivity. Many epidemiological and clinical studies show the role of various infectious agents as risk factors for schizophrenia with overlap to other psychoses. The first large-scale epidemiological study in psychiatry from Denmark clearly demonstrates severe infections and autoimmune disorders during lifetime to be risk factors for schizophrenia. Genetic studies have shown the strongest signal for schizophrenia on chromosome 6p22.1, in a region related to the major histocompatibility complex and other immune functions. The vulnerability-stress-inflammation model is important as stress may increase proinflammatory cytokines and even contribute to a lasting proinflammatory state. The immune system itself is considered an important further piece in the puzzle, as in autoimmune disorders in general, which are always linked to three factors: genes, the environment and the immune system. Alterations of dopaminergic, serotonergic, noradrenergic and glutamatergic neurotransmission have been shown with low-level neuroinflammation and may directly be involved in the generation of schizophrenic symptoms. Loss of central nervous system volume and microglial activation has been demonstrated in schizophrenia in neuroimaging studies, which supports the assumption of a low-level neuroinflammatory process. Further support comes from the therapeutic benefit of anti-inflammatory medications in specific studies and the anti-inflammatory and immunomodulatory intrinsic effects of antipsychotics.

Psychosis prediction and clinical utility in familial high-risk studies: selective review, synthesis, and implications for early detection and intervention

AIM

Accurate prediction of which high-risk individuals will go on to develop psychosis would assist early intervention and prevention paradigms. We sought to review investigations of prospective psychosis prediction based on markers and variables examined in longitudinal familial high-risk (FHR) studies.

METHODS

We performed literature searches in MedLine, PubMed and PsycINFO for articles assessing performance characteristics of predictive clinical tests in FHR studies of psychosis. Studies were included if they reported on one or more predictive variables in subjects at FHR for psychosis. We complemented this search strategy with references drawn from articles, reviews, book chapters and monographs.

RESULTS

Across generations of FHR projects, predictive studies have investigated behavioural, cognitive, psychometric, clinical, neuroimaging and other markers. Recent analyses have incorporated multivariate and multi-domain approaches to risk ascertainment, with generally modest results.

CONCLUSIONS

Although a broad range of risk factors has been identified, no individual marker or combination of markers can at this time enable accurate prospective prediction of emerging psychosis for individuals at FHR. We outline the complex and multi-level nature of psychotic illness, the myriad of factors influencing its development, and methodological hurdles to accurate and reliable prediction. Prospects and challenges for future generations of FHR studies are discussed in the context of early detection and intervention strategies.

A review of neuroimaging studies of young relatives of individuals with schizophrenia: a developmental perspective from schizotaxia to schizophrenia

In an effort to identify the developing abnormalities preceding psychosis, Dr. Ming T. Tsuang and colleagues at Harvard expanded Meehl's concept of "schizotaxia," and examined brain structure and function in families affected by schizophrenia (SZ). Here, we systematically review genetic (familial) high-risk (HR) studies of SZ using magnetic resonance imaging (MRI), examine how findings inform models of SZ etiology, and suggest directions for future research. Neuroimaging studies of youth at HR for SZ through the age of 30 were identified through a MEDLINE (PubMed) search. There is substantial evidence of gray matter volume abnormalities in youth at HR compared to controls, with an accelerated volume reduction over time in association with symptoms and cognitive deficits. In structural neuroimaging studies, prefrontal cortex (PFC) alterations were the most consistently reported finding in HR. There was also consistent evidence of smaller hippocampal volume. In functional studies, hyperactivity of the right PFC during performance of diverse tasks with common executive demands was consistently reported. The only longitudinal fMRI study to date revealed increasing left middle temporal activity in association with the emergence of psychotic symptoms. There was preliminary evidence of cerebellar and default mode network alterations in association with symptoms. Brain abnormalities in structure, function and neurochemistry are observed in the premorbid period in youth at HR for SZ. Future research should focus on the genetic and environmental contributions to these alterations, determine how early they emerge, and determine whether they can be partially or fully remediated by innovative treatments.

Gray Matter Alterations in Schizophrenia High-Risk Youth and Early-Onset Schizophrenia: A Review of Structural MRI Findings

This article reviews the literature on structural magnetic resonance imaging findings in pediatric and young adult populations at clinical or genetic high-risk for schizophrenia and early-onset schizophrenia. The implications of this research are discussed for understanding the pathophysiology of schizophrenia and for early intervention strategies. The evidence linking brain structural changes in prepsychosis development and early-onset schizophrenia with disruptions of normal neurodevelopmental processes during childhood or adolescence is described. Future directions are outlined for research to address knowledge gaps regarding the neurobiological basis of brain structural abnormalities in schizophrenia and to improve the usefulness of these abnormalities for preventative interventions.

Towards clinically useful neuroimaging in psychiatric practice

When psychiatrists see a patient, they consider a diagnosis, estimate a prognosis and treat accordingly, but very few of these decisions are informed by objective tests. Recent advances in neuroimaging data analysis have shown that brain scans can make powerful diagnostic and prognostic predictions in patients with psychosis and depression.

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

BACKGROUND

Existing studies of brain structural changes before the onset of schizophrenia have considered individuals with either familial risk factors or prodromal symptomatology. We aimed to determine whether findings from these studies are also applicable to those at enhanced risk of developing schizophrenia for another reason-intellectual impairment.

METHODS

Participants with intellectual impairment (mean IQ: 78.2) received magnetic resonance imaging of the brain at baseline (mean age: 16 years old) and again 6 years later. The Positive and Negative Syndrome Scale was used to assess psychotic symptoms. Participants were dichotomized using their Positive and Negative Syndrome Scale scores at follow-up and gray matter changes were compared between the groups using tensor based morphometry and semiautomated region of interest analysis.

RESULTS

Forty-six individuals had scans of sufficient quality to be included in the study. The tensor based morphometry analyses revealed that those with psychotic symptoms at follow-up showed significantly greater gray matter reductions over 6 years in the medial temporal lobes bilaterally. Region of interest analyses revealed that those individuals with psychotic symptoms at follow-up showed a reduced right hippocampal volume at age 16 and reduced bilateral hippocampal volumes at follow-up.

CONCLUSIONS

This unique study of individuals vulnerable to schizophrenia due to intellectual impairment highlights aberrant development in the medial temporal lobe associated with the occurrence of psychotic symptoms. These developmental changes are also evident in populations at enhanced risk of schizophrenia for familial and symptomatic reasons, suggesting they are central to the development of the disorder regardless of the nature of the vulnerability state.

Speed of facial affect intensity recognition as an endophenotype of first-episode psychosis and associated limbic-cortical grey matter systems

BACKGROUND

Psychotic disorders are highly heritable such that the unaffected relatives of patients may manifest characteristics, or endophenotypes, that are more closely related to risk genes than the overt clinical condition. Facial affect processing is dependent on a distributed cortico-limbic network that is disrupted in psychosis. This study assessed facial affect processing and related brain structure as a candidate endophenotype of first-episode psychosis (FEP).

METHOD

Three samples comprising 30 FEP patients, 30 of their first-degree relatives and 31 unrelated healthy controls underwent assessment of facial affect processing and structural magnetic resonance imaging (sMRI) data. Multivariate analysis (partial least squares, PLS) was used to identify a grey matter (GM) system in which anatomical variation was associated with variation in facial affect processing speed.

RESULTS

The groups did not differ in their accuracy of facial affect intensity rating but differed significantly in speed of response, with controls responding faster than relatives, who responded faster than patients. Within the control group, variation in speed of affect processing was significantly associated with variation of GM density in amygdala, lateral temporal cortex, frontal cortex and cerebellum. However, this association between cortico-limbic GM density and speed of facial affect processing was absent in patients and their relatives.

CONCLUSIONS

Speed of facial affect processing presents as a candidate endophenotype of FEP. The normal association between speed of facial affect processing and cortico-limbic GM variation was disrupted in FEP patients and their relatives.

Neuroimaging schizophrenia: a picture is worth a thousand words, but is it saying anything important?

Schizophrenia is characterized by neurostructural and neurofunctional aberrations that have now been demonstrated through neuroimaging research. The article reviews recent studies that have attempted to use neuroimaging to understand the relation between neurological abnormalities and aspects of the phenomenology of schizophrenia. Neuroimaging studies show that neurostructural and neurofunctional abnormalities are present in people with schizophrenia and their close relatives and may represent putative endophenotypes. Neuroimaging phenotypes predict the emergence of psychosis in individuals classified as high-risk. Neuroimaging studies have linked structural and functional abnormalities to symptoms; and progressive structural changes to clinical course and functional outcome. Neuroimaging has successfully indexed the neurotoxic and neuroprotective effects of schizophrenia treatments. Pictures can inform about aspects of the phenomenology of schizophrenia including etiology, onset, symptoms, clinical course, and treatment effects but this assertion is tempered by the scientific and practical limitations of neuroimaging.

Predicting first episode psychosis in those at high risk for genetic or cognitive reasons

Structural and functional magnetic resonance imaging (MRI) of patients with psychosis has advanced to the point where there are clear abnormalities at a group level between patients and groups of healthy controls, and suggestions of different patterns of abnormalities between groups of patients. A major area of research endeavour is being able to translate these group differences into clinically relevant predictions at an individual patient level. Here, we briefly summarize our main findings in cohorts at high risk of psychosis because they come from families in which several members have schizophrenia or bipolar disorder, or have educational impairments. We highlight consistent predictors of psychosis in those at high risk of schizophrenia for genetic or cognitive reasons, as compared with quite distinct profiles between those at genetic high risk of schizophrenia v. bipolar disorder on functional MRI during an executive language task. We also consider future research directions and ethical issues in the early diagnostic testing of people at high risk of psychosis.

Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study

BACKGROUND

Reliable prognostic biomarkers are needed for the early recognition of psychosis. Recently, multivariate machine learning methods have demonstrated the feasibility to predict illness onset in clinically defined at-risk individuals using structural magnetic resonance imaging (MRI) data. However, it remains unclear whether these findings could be replicated in independent populations.

METHODS

We evaluated the performance of an MRI-based classification system in predicting disease conversion in at-risk individuals recruited within the prospective FePsy (Früherkennung von Psychosen) study at the University of Basel, Switzerland. Pairwise and multigroup biomarkers were constructed using the MRI data of 22 healthy volunteers, 16/21 at-risk subjects with/without a subsequent disease conversion. Diagnostic performance was measured in unseen test cases using repeated nested cross-validation.

RESULTS

The classification accuracies in the "healthy controls (HCs) vs converters," "HCs vs nonconverters," and "converters vs nonconverters" analyses were 92.3%, 66.9%, and 84.2%, respectively. A positive likelihood ratio of 6.5 in the converters vs nonconverters analysis indicated a 40% increase in diagnostic certainty by applying the biomarker to an at-risk population with a transition rate of 43%. The neuroanatomical decision functions underlying these results particularly involved the prefrontal perisylvian and subcortical brain structures.

CONCLUSIONS

Our findings suggest that the early prediction of psychosis may be reliably enhanced using neuroanatomical pattern recognition operating at the single-subject level. These MRI-based biomarkers may have the potential to identify individuals at the highest risk of developing psychosis, and thus may promote informed clinical strategies aiming at preventing the full manifestation of the disease.

Chances and risks of predicting psychosis

Prevention is currently regarded a promising strategy for fighting the unfavorable consequences of psychosis. Yet, for the error probability inherent in any predictive approach, benefits and costs must be carefully weighed against each other. False attribution of risk may unnecessarily provoke stress and anxiety, and lead to unwarranted intervention exposure. However, clinical risk samples already exhibit psychopathological symptoms, cognitive and functional impairments, and help-seeking for mental problems. Thus, the risk of futile interventions is low as long as preventive measures also provide treatment for current complaints. Differentiation between still normal and clinically relevant mental states is another challenge as psychotic-like phenomena occur frequently in the general population, especially in younger adolescents. Reported prevalence rates vary with age, and if severe in terms of frequency and persistence, these phenomena considerably increase risk of psychosis in clinical as well as general population samples. Stigmatization is another concern, though insufficiently studied. Yet, at least more severe states of risk, which are accompanied by changes in thinking, feeling, and behavior, might lead to unfavorable, (self-) stigmatizing effects already by themselves, independent of any diagnostic "label," and to stress and confusion for the lack of understanding of what is going on. To further improve validity of risk criteria, advanced risk algorithms combining multi-step detection and risk stratification procedures should be developed. However, all prediction models possess a certain error probability. Thus, whether a risk model justifies preventive measures can only be decided by weighing the costs of unnecessary intervention and the benefits of avoiding a potentially devastating outcome.

Predicting the risk of psychosis onset: advances and prospects

AIM

To conduct a systematic review of the methods and performance characteristics of models developed for predicting the onset of psychosis.

METHODS

We performed a comprehensive literature search restricted to English articles and identified using PubMed, Medline and PsychINFO, as well as the reference lists of published studies and reviews. Inclusion criteria included the selection of more than one variable to predict psychosis or schizophrenia onset, and selection of individuals at familial risk or clinical high risk. Eighteen studies met these criteria, and we compared these studies based on the subjects selected, predictor variables used and the choice of statistical or machine learning methods.

RESULTS

Quality of life and life functioning as well as structural brain imaging emerged as the most promising predictors of psychosis onset, particularly when they were coupled with appropriate dimensionality reduction methods and predictive model algorithms like the support vector machine (SVM). Balanced accuracy ranged from 100% to 78% in four studies using the SVM, and 67% to 81% in 14 studies using general linear models.

CONCLUSIONS

Performance of the predictive models improves with quality of life measures, life functioning measures, structural brain imaging data, as well as with the use of methods like SVM. Despite these advances, the overall performance of psychosis predictive models is still modest. In the future, performance can potentially be improved by including genetic variant and new functional imaging data in addition to the predictors that are used currently.

A novel approach to probabilistic biomarker-based classification using functional near-infrared spectroscopy

Pattern recognition approaches to the analysis of neuroimaging data have brought new applications such as the classification of patients and healthy controls within reach. In our view, the reliance on expensive neuroimaging techniques which are not well tolerated by many patient groups and the inability of most current biomarker algorithms to accommodate information about prior class frequencies (such as a disorder's prevalence in the general population) are key factors limiting practical application. To overcome both limitations, we propose a probabilistic pattern recognition approach based on cheap and easy‐to‐use multi‐channel near‐infrared spectroscopy (fNIRS) measurements. We show the validity of our method by applying it to data from healthy controls (n = 14) enabling differentiation between the conditions of a visual checkerboard task. Second, we show that high‐accuracy single subject classification of patients with schizophrenia (n = 40) and healthy controls (n = 40) is possible based on temporal patterns of fNIRS data measured during a working memory task. For classification, we integrate spatial and temporal information at each channel to estimate overall classification accuracy. This yields an overall accuracy of 76% which is comparable to the highest ever achieved in biomarker‐based classification of patients with schizophrenia. In summary, the proposed algorithm in combination with fNIRS measurements enables the analysis of sub‐second, multivariate temporal patterns of BOLD responses and high‐accuracy predictions based on low‐cost, easy‐to‐use fNIRS patterns. In addition, our approach can easily compensate for variable class priors, which is highly advantageous in making predictions in a wide range of clinical neuroimaging applications. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.

The impact of substance use on brain structure in people at high risk of developing schizophrenia

Ventricular enlargement and reduced prefrontal volume are consistent findings in schizophrenia. Both are present in first episode subjects and may be detectable before the onset of clinical disorder. Substance misuse is more common in people with schizophrenia and is associated with similar brain abnormalities. We employ a prospective cohort study with nested case control comparison design to investigate the association between substance misuse, brain abnormality, and subsequent schizophrenia. Substance misuse history, imaging data, and clinical information were collected on 147 subjects at high risk of schizophrenia and 36 controls. Regions exhibiting a significant relationship between level of use of alcohol, cannabis or tobacco, and structure volume were identified. Multivariate regression then elucidated the relationship between level of substance use and structure volumes while accounting for correlations between these variables and correcting for potential confounders. Finally, we established whether substance misuse was associated with later risk of schizophrenia. Increased ventricular volume was associated with alcohol and cannabis use in a dose-dependent manner. Alcohol consumption was associated with reduced frontal lobe volume. Multiple regression analyses found both alcohol and cannabis were significant predictors of these abnormalities when simultaneously entered into the statistical model. Alcohol and cannabis misuse were associated with an increased subsequent risk of schizophrenia. We provide prospective evidence that use of cannabis or alcohol by people at high genetic risk of schizophrenia is associated with brain abnormalities and later risk of psychosis. A family history of schizophrenia may render the brain particularly sensitive to the risk-modifying effects of these substances.

Putative structural neuroimaging endophenotypes in schizophrenia: a comprehensive review of the current evidence

The genetic contribution to schizophrenia etiopathogenesis is underscored by the fact that the best predictor of developing schizophrenia is having an affected first-degree relative, which increases lifetime risk by tenfold, as well as the observation that when both parents are affected, the risk of schizophrenia increases to approximately 50%, compared with 1% in the general population. The search to elucidate the complex genetic architecture of schizophrenia has employed various approaches, including twin and family studies to examine co-aggregation of brain abnormalities, studies on genetic linkage and studies using genome-wide association to identify genetic variations associated with schizophrenia. ‘Endophenotypes’, or ‘intermediate phenotypes’, are potentially narrower constructs of genetic risk. Hypothetically, they are intermediate in the pathway between genetic variation and clinical phenotypes and can supposedly be implemented to assist in the identification of genetic diathesis for schizophrenia and, possibly, in redefining clinical phenomenology.

Abnormalities in connectivity of white-matter tracts in patients with familial and non-familial schizophrenia

BACKGROUND

Abnormalities in the connectivity of white-matter (WM) tracts in schizophrenia are supported by evidence from post-mortem investigations, functional and structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). The aims of this study were to explore the microstructural changes in first-episode schizophrenia in a Han Chinese population and to investigate whether a family history of psychiatric disorder is related to the severity of WM tract integrity abnormalities in these patients.

METHOD

T1-weighted MR and DT images were collected in 68 patients with first-episode schizophrenia [22 with a positive family history (PFH) and 46 with a negative family history (NFH)] and 100 healthy controls. Voxel-based analysis was performed and WM integrity was quantified by fractional anisotropy (FA). Cluster- and voxel-level analyses were performed by using two-sample t tests between patients and controls and/or using a full factorial model with one factor and three levels among the three sample groups (patients with PFH or NFH, and controls), as appropriate.

RESULTS

FA deficits were observed in the patient group, especially in the left temporal lobe and right corpus callosum. This effect was more severe in the non-familial schizophrenia than in the familial schizophrenia subgroup.

CONCLUSIONS

Overall, these findings support the hypothesis that loss of WM integrity may be an important pathophysiological feature of schizophrenia, with particular implications for brain dysmaturation in non-familial and familial schizophrenia.

Prediction of psychosis by mismatch negativity

BACKGROUND

To develop risk-adapted prevention of psychosis, an accurate estimation of the individual risk of psychosis at a given time is needed. Inclusion of biological parameters into multilevel prediction models is thought to improve predictive accuracy of models on the basis of clinical variables. To this aim, mismatch negativity (MMN) was investigated in a sample clinically at high risk, comparing individuals with and without subsequent conversion to psychosis.

METHODS

At baseline, an auditory oddball paradigm was used in 62 subjects meeting criteria of a late risk at-state who remained antipsychotic-naive throughout the study. Median follow-up period was 32 months (minimum of 24 months in nonconverters, n = 37). Repeated-measures analysis of covariance was employed to analyze the MMN recorded at frontocentral electrodes; additional comparisons with healthy controls (HC, n = 67) and first-episode schizophrenia patients (FES, n = 33) were performed. Predictive value was evaluated by a Cox regression model.

RESULTS

Compared with nonconverters, duration MMN in converters (n = 25) showed significantly reduced amplitudes across the six frontocentral electrodes; the same applied in comparison with HC, but not FES, whereas the duration MMN in in nonconverters was comparable to HC and larger than in FES. A prognostic score was calculated based on a Cox regression model and stratified into two risk classes, which showed significantly different survival curves.

CONCLUSIONS

Our findings demonstrate the duration MMN is significantly reduced in at-risk subjects converting to first-episode psychosis compared with nonconverters and may contribute not only to the prediction of conversion but also to a more individualized risk estimation and thus risk-adapted prevention.

Do we have any solid evidence of clinical utility about the pathophysiology of schizophrenia?

A diagnosis of schizophrenia, as in most of psychiatric practice, is made largely by eliciting symptoms with reference to subjective, albeit operationalized, criteria. This diagnosis then provides some rationale for management. Objective diagnostic and therapeutic tests are much more desirable, provided they are reliably measured and interpreted. Definite advances have been made in our understanding of schizophrenia in recent decades, but there has been little consideration of how this information could be used in clinical practice. We review here the potential utility of the strongest and best replicated risk factors for and manifestations of schizophrenia within clinical, epidemiological, cognitive, blood biomarker and neuroimaging domains. We place particular emphasis on the sensitivity, specificity and predictive power of pathophysiological indices for making a diagnosis, establishing an early diagnosis or predicting treatment response in schizophrenia. We conclude that a number of measures currently available have the potential to increase the rigour of clinical assessments in schizophrenia. We propose that the time has come to more fully evaluate these and other well replicated abnormalities as objective potential diagnostic and prognostic guides, and to steer future clinical, therapeutic and nosological research in this direction.

The 2nd Schizophrenia International Research Society Conference, 10-14 April 2010, Florence, Italy: summaries of oral sessions

The 2nd Schizophrenia International Research Society Conference, was held in Florence, Italy, April 10-15, 2010. Student travel awardees served as rapporteurs of each oral session and focused their summaries on the most significant findings that emerged from each session and the discussions that followed. The following report is a composite of these reviews. It is hoped that it will provide an overview for those who were present, but could not participate in all sessions, and those who did not have the opportunity to attend, but who would be interested in an update on current investigations ongoing in the field of schizophrenia research.

Between- and within-scanner variability in the CaliBrain study n-back cognitive task

Psychiatric neuroimaging techniques are likely to improve understanding of the brain in health and disease, but studies tend to be small, based in one imaging centre and of unclear generalisability. Multicentre studies have great appeal but face problems if functional magnetic resonance imaging (fMRI) data from different centres are to be combined. Fourteen healthy volunteers had two brain scans on different days at three scanners. Considerable effort was first made to use similar scanning sequences and standardise task implementation across centres. The n-back cognitive task was used to investigate between- and within-scanner reproducibility and reliability. Both the functional imaging and behavioural results were in good accord with the existing literature. We found no significant differences in the activation/deactivation maps between scanners, or between repeat visits to the same scanners. Between- and within-scanner reproducibility and reliability was very similar. However, the smoothness of images from the scanners differed, suggesting that smoothness equalization might further reduce inter-scanner variability. Our results for the n-back task suggest it is possible to acquire fMRI data from different scanners which allows pooling across centres, when the same field strength scanners are used and scanning sequences and paradigm implementations are standardised.

Use of neuroanatomical pattern regression to predict the structural brain dynamics of vulnerability and transition to psychosis

BACKGROUND

The at-risk mental state for psychosis (ARMS) has been associated with abnormal structural brain dynamics underlying disease transition or non-transition. To date, it is unknown whether these dynamic brain changes can be predicted at the single-subject level prior to disease transition using MRI-based machine-learning techniques.

METHODS

First, deformation-based morphometry and partial-least-squares (PLS) was used to investigate patterns of volumetric changes over time in 25 ARMS individuals versus 28 healthy controls (HC) (1) irrespective of the clinical outcome and (2) according to illness transition or non-transition. Then, the baseline MRI data were employed to predict the expression of these volumetric changes at the individual level using support-vector regression (SVR).

RESULTS

PLS revealed a pattern of pronounced morphometric changes in ARMS versus HC that affected predominantly the right prefrontal, as well as the perisylvian, parietal and periventricular structures (p<0.011), and that was more pronounced in the converters versus the non-converters (p<0.010). The SVR analysis facilitated a reliable prediction of these longitudinal brain changes in individual out-of training cases (HC vs ARMS: r=0.83, p<0.001; HC vs converters vs non-converters: r=0.83, p<0.001) by relying on baseline patterns that involved ventricular enlargements, as well as prefrontal, perisylvian, limbic, parietal and subcortical volume reductions.

CONCLUSIONS

Abnormal brain changes over time may underlie an elevated vulnerability for psychosis and may be most pronounced in subsequent converters to psychosis. Pattern regression techniques may facilitate an accurate prediction of these structural brain dynamics, potentially allowing for an early recognition of individuals at risk of developing psychosis-associated neuroanatomical changes over time.

Beyond the first episode: candidate factors for a risk prediction model of schizophrenia

Many early psychosis services are financially compromised and cannot offer a full tenure of care to all patients. To maintain viability of services it is important that those with schizophrenia are identified early to maximize long-term outcomes, as are those with better prognoses who can be discharged early. The duration of untreated psychosis remains the mainstay in determining those who will benefit from extended care, yet its ability to inform on prognosis is modest in both the short and medium term. There are a number of known or putative genetic and environmental risk factors that have the potential to improve prognostication, though a multivariate risk prediction model combining them with clinical characteristics has yet to be developed. Candidate risk factors for such a model are presented, with an emphasis on environmental risk factors. More work is needed to corroborate many putative factors and to determine which of the established factors are salient and which are merely proxy measures. Future research should help clarify how gene-environment and environment-environment interactions occur and whether risk factors are dose-dependent, or if they act additively or synergistically, or are redundant in the presence (or absence) of other factors.

Immune System and Schizophrenia

Although an immune dysfunction and the involvement of infectious agents in the pathophysiology of schizophrenia are discussed since decades, the field never came into the mainstream of research. In schizophrenia a blunted type-1 immune response seems to be associated with a dysbalance in the activation of the enzyme indoleamine 2,3-dioxygenase (IDO) and in the tryptophan - kynurenine metabolism resulting in increased production of kynurenic acid in schizophrenia. This is associated with an imbalance in the glutamatergic neurotransmission, leading to an NMDA antagonism in schizophrenia. The immunological effects of antipsychotics rebalance partly the immune imbalance and the overweight of the production of the kynurenic acid. This immunological imbalance results in an inflammatory state combined with increased prostaglandin E(2) (PGE(2)) production and increased cyclo-oxygenase-2 (COX-2) expression. COX-2 inhibitors have been tested in clinical trials, pointing to favourable effects in schizophrenia.

Gamma oscillation deficits and the onset and early progression of schizophrenia

A fascinating convergence of evidence in recent years has implicated the disturbances of neural synchrony in the gamma frequency band (30-100 Hz) as a major pathophysiologic feature of schizophrenia. Evidence suggests that reduced glutamatergic neurotransmission via the N-methyl-D-aspartate (NMDA) receptors that are localized to inhibitory interneurons, perhaps especially the fast-spiking cells that contain the calcium-binding protein parvalbumin (PV), may contribute to gamma band synchrony deficits. These deficits may underlie the brain's failure to integrate information and hence the manifestations of many symptoms and deficits of schizophrenia. Furthermore, because gamma oscillations are thought to provide the temporal structure that is necessary for synaptic plasticity, gamma oscillation deficits may disturb the developmental synaptic reorganization process that is occurring during the period of late adolescence and early adulthood. This disturbance may contribute to the onset of schizophrenia and the functional deterioration that is characteristic of the early stage of the illness. Finally, reduced NMDA neurotransmission on inhibitory interneurons, including the PV-containing cells, may inflict excitotoxic or oxidative injury to downstream pyramidal neurons, leading to further loss of synapses and dendritic branchings. Hence, a key element in the conceptualization of rational early-intervention and prevention strategies for schizophrenia may involve correcting the abnormal NMDA neurotransmission on inhibitory interneurons-possibly that on the PV-containing neurons, in particular-thereby normalizing gamma oscillation deficits and attenuating downstream neuronal pathology.

Grey matter and cognitive deficits in young relatives of schizophrenia patients

Grey-matter volumetric and cognitive deficits in young, high-risk relatives of schizophrenia patients may be vulnerability markers of the illness. Although these markers may be correlated, it is unclear if their distributions in relatives overlap. We examined convergence of these markers in 94 young first and second-degree relatives (HR) and 81 healthy controls. Subjects were assessed using WCST, CPT-IP and Benton-Hamscher tests and on grey-matter volumes of brain regions related to language, attention and executive function using FreeSurfer to process T1-MR-images. K-means clustering using cognitive performance scores split relatives into sub-samples with better (HR+C, n=35) and worse (HR-C, n=59) cognition after controlling for age and gender. All regional volumes and language related regional laterality-indices were compared between HR-C, HR+C and control subjects, controlling for age, gender and intra-cranial volume. Volumes of caudate nuclei, thalami, hippocampi, inferior frontal gyri, Heschl's gyri, superior parietal cortices, supramarginal gyri, right angular gyrus, right middle frontal gyrus and right superior frontal gyrus, leftward laterality of supramarginal and inferior frontal gyri and rightward laterality of the angular gyrus were reduced in HR-C compared to controls. Volumes of Heschl's gyri, left supramarginal gyrus, inferior frontal gyri, hippocampi and caudate nuclei HR-C were smaller in HR-C compared to HR+C. HR+C showed deficits compared to controls only for the superior parietal and right angular volumes. Premorbid neuroanatomical and laterality alterations in schizophrenia may selectively manifest in cognitively compromised relatives. Overlapping structural and cognitive deficits may define a hyper vulnerable sub-sample among individuals at familial predisposition to schizophrenia.

Neuroimaging predictors of transition to psychosis--a systematic review and meta-analysis

OBJECTIVES

In early stage psychosis research the identification of neurobiological correlates of vulnerability to schizophrenia is an important hurdle.

METHODS

We systematically reviewed the neuroimaging publications on high-risk subjects with subsequent transition to psychosis (HR-T) and conducted a meta-analysis calculating the effect size Cohen's d.

RESULTS

Out of 30 identified studies 25 met the inclusion criteria. Structural (s)MRI studies showed small to medium effect sizes of decreased prefrontal, cingulate, insular and cerebellar gray matter volume in HR-T compared to high-risk subjects without transition (HR-NT). Meta-analysis revealed relatively larger whole brain volumes in HR-T compared to HR-NT subjects (mean Cohen's d 0.36, 95% CI 0.27-0. 46). Compared to HR-NT, HR-T subjects showed in functional imaging studies reduced brain activation in prefrontal cortex, reduced neuronal density, and increased membrane turnover in frontal and cingulate cortex with medium to large effect sizes.

CONCLUSIONS

Despite methodological differences between studies, structural and neurochemical abnormalities in prefrontal, anterior cingulate, medial temporal and cerebellar cortex might be predictive for development of psychosis within HR subjects.

Functional Magnetic Resonance Imaging - Implications for Detection of Schizophrenia

Functional magnetic resonance imaging (fMRI) is an invaluable non-invasive instrument that has been used to investigate physiological disturbances that lead to manifest psychiatric illnesses. It is hoped that efficient application of fMRI can be utilised to characterise and diagnose mental illnesses such as schizophrenia. Although there are various fMRI research studies presenting very promising diagnosis results for schizophrenia, we believe that there is much to be done to develop effective diagnostic tools for clinical purposes. We present specific examples based mostly on our past and recent work together with various examples from the recent literature. We discuss where we currently stand in the efforts of fMRI being used for diagnosis of schizophrenia, examine common possible biases and offer some solutions with the hope that fMRI can be more efficiently used in diagnostic research.

Progressive temporal lobe grey matter loss in adolescents with schizotypal traits and mild intellectual impairment

Adolescents with mild intellectual impairment are known to have an increased risk of schizophrenia compared to the general population. However, little is known regarding the association between potential risk markers for later schizophrenia within this population. We therefore set out to examine the association between schizotypal traits and progressive grey matter loss in adolescents with mild intellectual impairment. Ninety-eight adolescents receiving educational assistance were divided into two groups based on their degree of schizotypal features, measured using the Structured Interview for Schizotypy (SIS). Each participant received two structural magnetic resonance imaging scans approximately 16 months apart. Changes over time in the voxel-wise presentation of tissue were evaluated using tensor based morphometry. Those with marked schizotypal features exhibited significantly greater grey matter losses in the left medial temporal lobe than those without. Three focal locations were identified, two within the left amygdala and one in the left parahippocampal gyrus. Thus, adolescents with cognitive impairment and schizotypal features show changes in brain structure over time, changes that are consistent with those identified in other high risk populations. Medial temporal grey matter loss may therefore represent a common neuroanatomical substrate of risk for schizophrenia, common to familial, prodromal and cognitive high risk groups.

Exploring predictors of outcome in the psychosis prodrome: implications for early identification and intervention

Functional disability is a key component of many psychiatric illnesses, particularly schizophrenia. Impairments in social and role functioning are linked to cognitive deficits, a core feature of psychosis. Retrospective analyses demonstrate that substantial functional decline precedes the onset of psychosis. Recent investigations reveal that individuals at clinical-high-risk (CHR) for psychosis show impairments in social relationships, work/school functioning and daily living skills. CHR youth also demonstrate a pattern of impairment across a range of cognitive domains, including social cognition, which is qualitatively similar to that of individuals with schizophrenia. While many studies have sought to elucidate predictors of clinical deterioration, specifically the development of schizophrenia, in such CHR samples, few have investigated factors relevant to psychosocial outcome. This review integrates recent findings regarding cognitive and social-cognitive predictors of outcome in CHR individuals, and proposes potential directions for future research that will contribute to targeted interventions and improved outcome for at-risk youth.

Workshop on defining the significance of progressive brain change in schizophrenia: December 12, 2008 American College of Neuropsychopharmacology (ACNP) all-day satellite, Scottsdale, Arizona. The rapporteurs' report

In 1990 a satellite session of the American College of Neuropsychopharmacology (ACNP) Annual Meeting was held that focused on the question of whether progressive changes in brain structure occur in schizophrenia and this session raised considerable controversy. Eighteen years later, on December 12, 2008, after much data have since accumulated on this topic, a group of approximately 45 researchers gathered after the annual ACNP meeting to participate in a similar workshop on several unresolved questions still remaining: (1) How strong and consistent is the evidence? (2) Is there anatomic specificity to changes and is it disease specific or subject specific? (3) What is the time course? (4) What is the underlying pathophysiology (i.e. is it central to the disease process or is it due to neuroleptic treatment or other epiphenomena? (5) What is its clinical significance? and (6) Are there treatment implications? The day was chaired by Lynn E. DeLisi and co-chaired by Stephen J. Wood. Christos Pantelis and Jeffrey A. Lieberman extensively helped with its planning. The ACNP assisted in its organization as an official satellite of its annual meeting and several pharmaceutical companies provided support with unrestricted educational grants. The following is a summary of the sessions as recounted by rapporteurs whose job was to record as closely as possible the outcome of discussions on the above outlined questions.

Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition

CONTEXT

Identification of individuals at high risk of developing psychosis has relied on prodromal symptomatology. Recently, machine learning algorithms have been successfully used for magnetic resonance imaging-based diagnostic classification of neuropsychiatric patient populations.

OBJECTIVE

To determine whether multivariate neuroanatomical pattern classification facilitates identification of individuals in different at-risk mental states (ARMS) of psychosis and enables the prediction of disease transition at the individual level.

DESIGN

Multivariate neuroanatomical pattern classification was performed on the structural magnetic resonance imaging data of individuals in early or late ARMS vs healthy controls (HCs). The predictive power of the method was then evaluated by categorizing the baseline imaging data of individuals with transition to psychosis vs those without transition vs HCs after 4 years of clinical follow-up. Classification generalizability was estimated by cross-validation and by categorizing an independent cohort of 45 new HCs.

SETTING

Departments of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany.

PARTICIPANTS

The first classification analysis included 20 early and 25 late at-risk individuals and 25 matched HCs. The second analysis consisted of 15 individuals with transition, 18 without transition, and 17 matched HCs.

MAIN OUTCOME MEASURES

Specificity, sensitivity, and accuracy of classification.

RESULTS

The 3-group, cross-validated classification accuracies of the first analysis were 86% (HCs vs the rest), 91% (early at-risk individuals vs the rest), and 86% (late at-risk individuals vs the rest). The accuracies in the second analysis were 90% (HCs vs the rest), 88% (individuals with transition vs the rest), and 86% (individuals without transition vs the rest). Independent HCs were correctly classified in 96% (first analysis) and 93% (second analysis) of cases.

CONCLUSIONS

Different ARMSs and their clinical outcomes may be reliably identified on an individual basis by assessing patterns of whole-brain neuroanatomical abnormalities. These patterns may serve as valuable biomarkers for the clinician to guide early detection in the prodromal phase of psychosis.

Prospective multi-centre Voxel Based Morphometry study employing scanner specific segmentations: procedure development using CaliBrain structural MRI data

BACKGROUND

Structural Magnetic Resonance Imaging (sMRI) of the brain is employed in the assessment of a wide range of neuropsychiatric disorders. In order to improve statistical power in such studies it is desirable to pool scanning resources from multiple centres. The CaliBrain project was designed to provide for an assessment of scanner differences at three centres in Scotland, and to assess the practicality of pooling scans from multiple-centres.

METHODS

We scanned healthy subjects twice on each of the 3 scanners in the CaliBrain project with T1-weighted sequences. The tissue classifier supplied within the Statistical Parametric Mapping (SPM5) application was used to map the grey and white tissue for each scan. We were thus able to assess within scanner variability and between scanner differences. We have sought to correct for between scanner differences by adjusting the probability mappings of tissue occupancy (tissue priors) used in SPM5 for tissue classification. The adjustment procedure resulted in separate sets of tissue priors being developed for each scanner and we refer to these as scanner specific priors.

RESULTS

Voxel Based Morphometry (VBM) analyses and metric tests indicated that the use of scanner specific priors reduced tissue classification differences between scanners. However, the metric results also demonstrated that the between scanner differences were not reduced to the level of within scanner variability, the ideal for scanner harmonisation.

CONCLUSION

Our results indicate the development of scanner specific priors for SPM can assist in pooling of scan resources from different research centres. This can facilitate improvements in the statistical power of quantitative brain imaging studies.

Regional gray matter reduction and theory of mind deficit in the early phase of schizophrenia: a voxel-based morphometric study

OBJECTIVE

We tested the association between theory of mind (ToM) performance and structural changes in the brains of patients in the early course of schizophrenia.

METHOD

Voxel-based morphometry (VBM) data of 18 patients with schizophrenia were compared with those of 21 controls. ToM skills were assessed by computerized faux pas (FP) tasks.

RESULTS

Patients with schizophrenia performed significantly worse in FP tasks than healthy subjects. VBM revealed significantly reduced gray matter density in certain frontal, temporal and subcortical regions in patients with schizophrenia. Poor FP performance of schizophrenics correlated with gray matter reduction in the left orbitofrontal cortex and right temporal pole.

CONCLUSION

Our data indicate an association between poor ToM performance and regional gray matter reduction in the left orbitofrontal cortex and right temporal pole shortly after the onset of schizophrenia.

Neurobiology of the early course of schizophrenia

The past three decades have seen a great upsurge in studies focusing on the neurobiology of schizophrenia. Early studies, dating back to the start of the previous century, largely relied either on post-mortem examination of the brains of older patients with chronic schizophrenia or on brain scans in patients with established schizophrenia. It was therefore difficult to appraise the effects of the illness separately from those of aging, illness chronicity and medications. Avoiding such difficulties, studies of individuals in the early phases of schizophrenia have greatly enhanced our understanding of the course and predictive value of the neurobiological changes as well as approaches to optimal early interventions. In this paper, we review what we see as key directions in neurobiology research in early schizophrenia. We first provide an overview of alterations in cognition, structural and functional neuroanatomy, and neurochemistry in the early phases of schizophrenia. We conclude by summarizing the current state of understanding of the role of genetic and environmental factors and their interactions in the etiology of schizophrenia.