Informing DSM-5: biological boundaries between bipolar I disorder, schizoaffective disorder, and schizophrenia

The overlap across psychotic disorders: A functional network connectivity analysis

Functional network connectivity (FNC) has previously been shown to distinguish patient groups from healthy controls (HC). However, the overlap across psychiatric disorders such as schizophrenia (SZ), bipolar (BP), and schizoaffective disorder (SAD) is not evident yet. This study focuses on studying the overlap across these three psychotic disorders in both dynamic and static FNC (dFNC/sFNC). We used resting-state fMRI, demographics, and clinical information from the Bipolar-Schizophrenia Network on Intermediate Phenotypes cohort (BSNIP). The data includes three groups of patients with schizophrenia (SZ, N = 181), bipolar (BP, N = 163), and schizoaffective (SAD, N = 130) and HC (N = 238) groups. After estimating each individual's dFNC, we group them into three distinct states. We evaluated two dFNC features, including occupancy rate (OCR) and distance travelled over time. Finally, the extracted features, including both sFNC and dFNC, are tested statistically across patients and HC groups. In addition, we explored the link between the clinical scores and the extracted features. We evaluated the connectivity patterns and their overlap among SZ, BP, and SAD disorders (false discovery rate or FDR corrected p < 0.05). Results showed dFNC captured unique information about overlap across disorders where all disorder groups showed similar pattern of activity in state 2. Moreover, the results showed similar patterns between SZ and SAD in state 1 which was different than BP. Finally, the distance travelled feature of SZ (average R = 0.245, p < 0.01) and combined distance travelled from all disorders was predictive of the PANSS symptoms scores (average R = 0.147, p < 0.01).

Clinical characterization of patients with bipolar disorder and a history of asthma: An exploratory study

INTRODUCTION

Bipolar disorder (BD) and asthma are leading causes of morbidity in the US and frequently co-occur.

OBJECTIVES

We evaluated the clinical features and comorbidities of patients with BD and a history of asthma.

METHODS

In a cross-sectional analysis from the Mayo Clinic Bipolar Biobank, we explored the clinical characteristics of the BD and an asthma phenotype and fitted a multivariable regression model to identify risk factors for asthma.

RESULTS

A total of 721 individuals with BD were included. From these, 140 (19%) had a history of asthma. In a multivariable model only sex and evening chronotype were significant predictors of asthma with the odds ratios and 95% confidence intervals being 1.65 (1.00, 2.72; p=0.05) and 1.99 (1.25, 3.17; p < 0.01), respectively. Individuals with asthma had higher odds of having other medical comorbidities after adjusting for age, sex, and site including hypertension (OR = 2.29 (95% CI 1.42, 3.71); p < 0.01), fibromyalgia (2.29 (1.16, 4.51); p=0.02), obstructive sleep apnea (2.03 (1.18, 3.50); p=0.01), migraine (1.98 (1.31, 3.00); p < 0.01), osteoarthritis (2.08 (1.20, 3.61); p < 0.01), and COPD (2.80 (1.14, 6.84); p=0.02). Finally, individuals currently on lithium were less likely to have a history of asthma (0.48 (0.32, 0.71); p < 0.01).

CONCLUSION

A history of asthma is common among patients with BD and is associated with being female and having an evening chronotype, as well as with increased odds of having other medical comorbidities. A lower likelihood of a history of asthma among those currently on lithium is an intriguing finding with potential clinical implications that warrants further study.

Schizophrenia or schizoaffective disorder? A 50-year assessment of diagnostic stability based on a national case registry

BACKGROUND

Schizoaffective disorder (SAD) remains a controversial diagnosis in terms of necessity and reliability.

OBJECTIVES

We assessed diagnostic patterns of SAD and schizophrenia (SZ) among hospitalized psychiatric patients over a fifty-year period.

METHOD

Data from the Israeli National Psychiatric Registry on 16,341 adults diagnosed with SZ or SAD, hospitalized at least twice in 1963-2017, were analyzed. Stability between most-frequent, first and last diagnosis, and diagnostic-constancy (the same diagnosis in >75 % of a person's hospitalizations) were calculated. Three groups were compared: People with both SAD and SZ diagnoses over the years (SZ-SAD), and people with only one of these diagnoses (SZ-only; SAD-only). The incidence of SAD and SZ before and after DSM-5 publication was compared.

RESULTS

Reliability between last and first diagnosis was 60 % for SAD and 94 % for SZ. Agreement between first and most-frequent diagnosis was 86 % for SAD and 92 % for SZ. Diagnostic shifts differ between persons with SAD and with SZ. Diagnostic-constancy was observed for 50 % of SAD-only patients. In the SZ-SAD group, 9 % had a constant SAD diagnosis. Compared to the other groups, the SZ-SAD group exhibited a higher substance use prevalence, younger age at first-hospitalization, and more hospitalizations/person (p < 0.0001). The incidence of a first-hospitalization SAD diagnosis increased by 2.2 % in the 4-years after vs. prior to DSM-5.

CONCLUSIONS

A SAD diagnosis is less stable than SZ. The incidence of a SAD diagnosis increased after DSM-5, despite stricter diagnostic criteria. The SZ-SAD group exhibited the poorest outcomes. SAD may evolve over time necessitating periodic re-evaluation.

Associated Genetics and Connectomic Circuitry in Schizophrenia and Bipolar Disorder

BACKGROUND

Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric conditions that can involve symptoms of psychosis and cognitive dysfunction. The 2 conditions share symptomatology and genetic etiology and are regularly hypothesized to share underlying neuropathology. Here, we examined how genetic liability to SCZ and BD shapes normative variations in brain connectivity.

METHODS

We examined the effect of the combined genetic liability for SCZ and BD on brain connectivity from two perspectives. First, we examined the association between polygenic scores for SCZ and BD for 19,778 healthy subjects from the UK Biobank and individual variation in brain structural connectivity reconstructed by means of diffusion weighted imaging data. Second, we conducted genome-wide association studies using genotypic and imaging data from the UK Biobank, taking SCZ-/BD-involved brain circuits as phenotypes of interest.

RESULTS

Our findings showed brain circuits of superior parietal and posterior cingulate regions to be associated with polygenic liability for SCZ and BD, circuitry that overlaps with brain networks involved in disease conditions (r = 0.239, p < .001). Genome-wide association study analysis showed 9 significant genomic loci associated with SCZ-involved circuits and 14 loci associated with BD-involved circuits. Genes related to SCZ-/BD-involved circuits were significantly enriched in gene sets previously reported in genome-wide association studies for SCZ and BD.

CONCLUSIONS

Our findings suggest that polygenic liability of SCZ and BD is associated with normative individual variation in brain circuitry.

Alternative Diagnostic Models of the Psychotic Disorders: Evidence-Based Choices

Standard diagnostic systems, the predominantly categorical DSM-5 and ICD-11, have limitations in validity, utility, and predictive and descriptive power. For psychotic disorders, these issues were partly addressed in current versions, but additional modifications are thought to be needed. Changes should be evidence based. We reviewed categorical, modified-categorical, and continuum-based models versus factor-based models of psychosis. Factors are clusters of symptoms or single prominent aspects of illness. Consistent evidence from studies of the genetics, pathobiology, and clinical presentation of psychotic disorders all support an underlying structure of factors, not categories, as best characterizing psychoses. Factors are not only the best fit but also comprehensive, as they can encompass any key feature of illness, including symptoms and course, as well as determinants of risk or response. Factors are inherently dimensional, even multidimensional, as are the psychoses themselves, and they provide the detail needed for either grouping or distinguishing patients for treatment decisions. The tools for making factor-based diagnoses are available, reliable, and concordant with actual practices used for clinical assessments. If needed, factors can be employed to create categories similar to those in current use. In addition, they can be used to define unique groupings of patients relevant to specific treatments or studies of the psychoses. Lastly, factor-based classifications are concordant with other comprehensive approaches to psychiatric nosology, including personalized (precision treatment) models and hierarchical models, both of which are currently being explored. Factors might be considered as the right primary structural choice for future versions of standard diagnostic systems, both DSM and ICD.

Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping

Background: Psychiatric disorders have been historically classified using symptom information alone. Recently, there has been a dramatic increase in research interest not only in identifying the mechanisms underlying defined pathologies but also in redefining their etiology. This is particularly relevant for the field of personalized medicine, which searches for data-driven approaches to improve diagnosis, prognosis, and treatment selection for individual patients. Methods: This review aims to provide a high-level overview of the rapidly growing field of functional magnetic resonance imaging (fMRI) from the perspective of unsupervised machine learning applications for disease subtyping. Following the PRISMA guidelines for protocol reproducibility, we searched the PubMed database for articles describing functional MRI applications used to obtain, interpret, or validate psychiatric disease subtypes. We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way. Results: From the 20 studies that met the inclusion criteria, five used functional MRI data to interpret symptom-derived disease clusters, four used it to interpret clusters derived from biomarker data other than fMRI itself, and 11 applied clustering techniques involving fMRI directly. Major depression disorder and schizophrenia were the two most frequently studied pathologies (35% and 30% of the retrieved studies, respectively), followed by ADHD (15%), psychosis as a whole (10%), autism disorder (5%), and the consequences of early exposure to violence (5%). Conclusions: The increased interest in personalized medicine and data-driven disease subtyping also extends to psychiatric disorders. However, to date, this subfield is at an incipient exploratory stage, and all retrieved studies were mostly proofs of principle where further validation and increased sample sizes are craved for. Whereas results for all explored diseases are inconsistent, we believe this reflects the need for concerted, multisite data collection efforts with a strong focus on measuring the generalizability of results. Finally, whereas functional MRI is the best way of measuring brain function available to date, its low signal-to-noise ratio and elevated monetary cost make it a poor clinical alternative. Even with technology progressing and costs decreasing, this might incentivize the search for more accessible, clinically ready functional proxies in the future.

Diacylglycerol Kinase η Activity in Cells Using Protein Myristoylation and Cellular Phosphatidic Acid Sensor

Diacylglycerol kinase (DGK) phosphorylates diacylglycerol to produce phosphatidic acid (PtdOH) and regulates the balance between two lipid second messengers: diacylglycerol and PtdOH. Several lines of evidence suggest that the η isozyme of DGK is involved in the pathogenesis of bipolar disorder. However, the detailed molecular mechanisms regulating the pathophysiological functions remain unclear. One reason is that it is difficult to detect the cellular activity of DGKη. To overcome this difficulty, we utilized protein myristoylation and a cellular PtdOH sensor, the N-terminal region of α-synuclein (α-Syn-N). Although DGKη expressed in COS-7 cells was broadly distributed in the cytoplasm, myristoylated (Myr)-AcGFP-DGKη and Myr-AcGFP-DGKη-KD (inactive (kinase-dead) mutant) were substantially localized in the plasma membrane. Moreover, DsRed monomer-α-Syn-N significantly colocalized with Myr-AcGFP-DGKη but not Myr-AcGFP-DGKη-KD at the plasma membrane. When COS-7 cells were osmotically shocked, all DGKη constructs were exclusively translocated to osmotic shock-responsive granules (OSRG). DsRed monomer-α-Syn-N markedly colocalized with only Myr-AcGFP-DGKη at OSRG and exhibited a higher signal/background ratio (3.4) than Myr-AcGFP-DGKη at the plasma membrane in unstimulated COS-7 cells (2.5), indicating that α-Syn-N more effectively detects Myr-AcGFP-DGKη activity in OSRG. Therefore, these results demonstrated that the combination of myristoylation and the PtdOH sensor effectively detects DGKη activity in cells and that this method is convenient to examine the molecular functions of DGKη. Moreover, this method will be useful for the development of drugs targeting DGKη. Furthermore, the combination of myristoylation (intensive accumulation in membranes) and α-Syn-N can be applicable to assays for various cytosolic PtdOH-generating enzymes.

Towards a brain-based predictome of mental illness

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.

NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders

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Propose a new pipeline to link brain changes among different datasets, studies, and disorders.

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Identify reproducible biomarkers in schizophrenia using independent data.

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Find both common and unique brain impairments in schizophrenia and autism.

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Reveal gradual changes from healthy controls to mild cognitive impairment to Alzheimer’s disease.

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Obtain high classification accuracy (~90%) between bipolar disorder and major depressive disorder.

Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer’s disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder.

Using Two-Step Cluster Analysis and Latent Class Cluster Analysis to Classify the Cognitive Heterogeneity of Cross-Diagnostic Psychiatric Inpatients

The heterogeneity of cognitive profiles among psychiatric patients has been reported to carry significant clinical information. However, how to best characterize such cognitive heterogeneity is still a matter of debate. Despite being well suited for clinical data, cluster analysis techniques, like the Two-Step and the Latent Class, received little to no attention in the literature. The present study aimed to test the validity of the cluster solutions obtained with Two-Step and Latent Class cluster analysis on the cognitive profile of a cross-diagnostic sample of 387 psychiatric inpatients. Two-Step and Latent Class cluster analysis produced similar and reliable solutions. The overall results reported that it is possible to group all psychiatric inpatients into Low and High Cognitive Profiles, with a higher degree of cognitive heterogeneity in schizophrenia and bipolar disorder patients than in depressive disorders and personality disorder patients.

Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis

It is difficult to distinguish schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychosis (BPP) as their clinical diagnoses rely on symptoms that overlap. In this paper, we investigate if there is biological evidence to support the symptom-based clinical categories by looking across the three disorders using dynamic connectivity measures, and provide meaningful characteristics on which brain functional connectivity measures are commonly or uniquely impaired. Large-sample functional magnetic resonance image (fMRI) datasets from 623 subjects including 238 healthy controls (HCs), 113 SZ patients, 132 SAD patients, and 140 BPP patients were analyzed. First, we computed whole-brain dynamic functional connectivity (DFC) using a sliding-window technique, and then extracted the individual connectivity states by applying our previously proposed decomposition-based DFC analysis method. Next, with the features from the dominant connectivity state, we assessed the clinical categories by performing both four-group (SZ, SAD, BPP and healthy control groups) and pair-wise classification using a support vector machine within cross-validation. Furthermore, we comprehensively summarized the shared and unique connectivity alterations among the disorders. In terms of the classification performance, our method achieved 69% in the four-group classification and >80% in the between-group classifications for the mean overall accuracy; and yielded 66% in the four-group classification and >80% in the between-group classifications for the mean balanced accuracy. Through summarizing the features that were automatically selected in the classifications, we found that among the three symptom-related disorders, their disorder-common impairments primarily included the decreased connectivity strength between thalamus and cerebellum and the increased strength between postcentral gyrus and thalamus. The disorder-unique changes included more various brain regions, mainly in the temporal and frontal gyrus. Our work demonstrates that dynamic functional connectivity provides biological evidence that both common and unique impairments exist in psychosis sub-groups.

The Amygdala in Schizophrenia and Bipolar Disorder: A Synthesis of Structural MRI, Diffusion Tensor Imaging, and Resting-State Functional Connectivity Findings

Frequently implicated in psychotic spectrum disorders, the amygdala serves as an important hub for elucidating the convergent and divergent neural substrates in schizophrenia and bipolar disorder, the two most studied groups of psychotic spectrum conditions. A systematic search of electronic databases through December 2017 was conducted to identify neuroimaging studies of the amygdala in schizophrenia and bipolar disorder, focusing on structural MRI, diffusion tensor imaging (DTI), and resting-state functional connectivity studies, with an emphasis on cross-diagnostic studies. Ninety-four independent studies were selected for the present review (49 structural MRI, 27 DTI, and 18 resting-state functional MRI studies). Also selected, and analyzed in a separate meta-analysis, were 33 volumetric studies with the amygdala as the region-of-interest. Reduced left, right, and total amygdala volumes were found in schizophrenia, relative to both healthy controls and bipolar subjects, even when restricted to cohorts in the early stages of illness. No volume abnormalities were observed in bipolar subjects relative to healthy controls. Shape morphometry studies showed either amygdala deformity or no differences in schizophrenia, and no abnormalities in bipolar disorder. In contrast to the volumetric findings, DTI studies of the uncinate fasciculus tract (connecting the amygdala with the medial- and orbitofrontal cortices) largely showed reduced fractional anisotropy (a marker of white matter microstructure abnormality) in both schizophrenia and bipolar patients, with no cross-diagnostic differences. While decreased amygdalar-orbitofrontal functional connectivity was generally observed in schizophrenia, varying patterns of amygdalar-orbitofrontal connectivity in bipolar disorder were found. Future studies can consider adopting longitudinal approaches with multimodal imaging and more extensive clinical subtyping to probe amygdalar subregional changes and their relationship to the sequelae of psychotic disorders.

Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging

Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided.

Recent Genetics and Epigenetics Approaches to PTSD

PURPOSE OF REVIEW

Following a life-threatening traumatic exposure, about 10% of those exposed are at considerable risk for developing posttraumatic stress disorder (PTSD), a severe and disabling syndrome characterized by uncontrollable intrusive memories, nightmares, avoidance behaviors, and hyperarousal in addition to impaired cognition and negative emotion symptoms. This review will explore recent genetic and epigenetic approaches to PTSD that explain some of the differential risk following trauma exposure.

RECENT FINDINGS

A substantial portion of the variance explaining differential risk responses to trauma exposure may be explained by differential inherited and acquired genetic and epigenetic risk. This biological risk is complemented by alterations in the functional regulation of genes via environmentally induced epigenetic changes, including prior childhood and adult trauma exposure. This review will cover recent findings from large-scale genome-wide association studies as well as newer epigenome-wide studies. We will also discuss future "phenome-wide" studies utilizing electronic medical records as well as targeted genetic studies focusing on mechanistic ways in which specific genetic or epigenetic alterations regulate the biological risk for PTSD.

Similar white matter changes in schizophrenia and bipolar disorder: A tract-based spatial statistics study

Several strands of evidence reported a significant overlapping, in terms of clinical symptoms, epidemiology and treatment response, between the two major psychotic disorders—Schizophrenia (SCZ) and Bipolar Disorder (BD). Nevertheless, the shared neurobiological correlates of these two disorders are far from conclusive. This study aims toward a better understanding of possible common microstructural brain alterations in SCZ and BD. Magnetic Resonance Diffusion data of 33 patients with BD, 19 with SCZ and 35 healthy controls were acquired. Diffusion indexes were calculated, then analyzed using Tract-Based Spatial Statistics (TBSS). We tested correlations with clinical and psychological variables. In both patient groups mean diffusion (MD), volume ratio (VR) and radial diffusivity (RD) showed a significant increase, while fractional anisotropy (FA) and mode (MO) decreased compared to the healthy group. Changes in diffusion were located, for both diseases, in the fronto-temporal and callosal networks. Finally, no significant differences were identified between patient groups, and a significant correlations between length of disease and FA and VR within the corpus callosum, corona radiata and thalamic radiation were observed in bipolar disorder. To our knowledge, this is the first study applying TBSS on all the DTI indexes at the same time in both patient groups showing that they share similar impairments in microstructural connectivity, with particular regards to fronto-temporal and callosal communication, which are likely to worsen over time. Such features may represent neural common underpinnings characterizing major psychoses and confirm the central role of white matter pathology in schizophrenia and bipolar disorder.

Identifying dynamic functional connectivity biomarkers using GIG-ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder

Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here, we aim to explore DFC across a spectrum of symptomatically-related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ). We introduce a group information guided independent component analysis procedure to estimate both group-level and subject-specific connectivity states from DFC. Using resting-state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD, and 113 SZ patients, we identified measures differentiating groups from the whole-brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis-related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post-central, frontal, and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD-unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis. Hum Brain Mapp 38:2683-2708, 2017. © 2017 Wiley Periodicals, Inc.

Advances in Psychiatric Diagnosis: Past, Present, and Future

This editorial examines controversies identified by the articles in this special issue, which explore psychopathology in the broad history of the classification of selected psychiatric disorders and syndromes over time through current American criteria. Psychiatric diagnosis has a long history of scientific investigation and application, with periods of rapid change, instability, and heated controversy associated with it. The articles in this issue examine the history of psychiatric nomenclature and explore current and future directions in psychiatric diagnosis through the various versions of accepted diagnostic criteria and accompanying research literature addressing the criteria. The articles seek to guide readers in appreciating the complexities of psychiatric diagnosis as the field of psychiatry pushes forward toward future advancements in diagnosis. Despite efforts of many scientists to advance a diagnostic classification system that incorporates neuroscience and genetics, it has been argued that it may be premature to attempt to move to a biologically-based classification system, because psychiatric disorders cannot yet be fully distinguished by any specific biological markers. For now, the symptom-based criteria that the field has been using continue to serve many essential purposes, including selection of the most effective treatment, communication about disease with colleagues, education about psychiatric illness, and support for ongoing research.

Depletion of Highly Abundant Proteins of the Human Blood Plasma: Applications in Proteomics Studies of Psychiatric Disorders

Psychiatric disorders are complex diseases involving exogenous and endogenous factors. Biomarkers for diagnosis or prediction of successful treatment are not existent. In addition, the molecular basis of these diseases is still poorly understood. Blood plasma represents the most complex proteome as it contains subproteomes from several body tissues. However, the high abundance of some little proteins can obscure the analysis of hundreds of low abundance proteins, which are potential biomarkers. Therefore, removal of these high abundance proteins is pivotal in any proteomic study of plasma. Here, we present a method of depleting these proteins using immunoaffinity liquid chromatography.

Surface-based brain morphometry and diffusion tensor imaging in schizoaffective disorder

BACKGROUND

The profile of grey matter abnormalities and related white-matter pathology in schizoaffective disorder has only been studied to a limited extent. The aim of this study was to identify grey- and white-matter abnormalities in patients with schizoaffective disorder using complementary structural imaging techniques.

METHODS

Forty-five patients meeting Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition criteria and Research Diagnostic Criteria for schizoaffective disorder and 45 matched healthy controls underwent structural-T1 and diffusion magnetic resonance imaging to enable surface-based brain morphometry and diffusion tensor imaging analyses. Analyses were conducted to determine group differences in cortical volume, cortical thickness and surface area, as well as in fractional anisotropy and mean diffusivity.

RESULTS

At a threshold of p = 0.05 corrected, all measures revealed significant differences between patients and controls at the group level. Spatial overlap of abnormalities was observed across the various structural neuroimaging measures. In grey matter, patients with schizoaffective disorder showed abnormalities in the frontal and temporal lobes, striatum, fusiform, cuneus, precuneus, lingual and limbic regions. White-matter abnormalities were identified in tracts connecting these areas, including the corpus callosum, superior and inferior longitudinal fasciculi, anterior thalamic radiation, uncinate fasciculus and cingulum bundle.

CONCLUSION

The spatial overlap of abnormalities across the different imaging techniques suggests widespread and consistent brain pathology in schizoaffective disorder. The abnormalities were mainly detected in areas that have commonly been reported to be abnormal in schizophrenia, and to some extent in bipolar disorder, which may explain the clinical and aetiological overlap in these disorders.

Liability indicators aggregate many years before transition to illness in offspring descending from kindreds affected by schizophrenia or bipolar disorder

OBJECTIVES

Offspring born to patients with affective and non-affective psychoses display indicators of brain dysfunctions that affected parents carry. Such indicators may help understand the risk trajectory.

METHODS

We followed up the clinical/developmental trajectories of 84 young offspring born to affected parents descending from the Quebec kindreds affected by schizophrenia or bipolar disorder. We longitudinally characterized childhood trajectories using 5 established risk indicators: cognitive impairments, psychotic-like experiences, non-psychotic DSM diagnosis and episodes of poor functioning, trauma and drug use.

RESULTS

Overall, offspring individually presented a high rate of risk indicators with 39% having 3 or more indicators. Thirty-three offspring progressed to an axis 1 DSM-IV disorder, 15 of whom transitioned to a major affective or non-affective disorder. The relative risks for each risk indicator were low in these vulnerable offspring (RR = 1.92 to 2.99). Remarkably, transitioners accumulated more risk indicators in childhood-adolescence than non-transitioners (Wilcoxon rank test; Z = 2.64, p = 0.008). Heterogeneity in the risk trajectories was observed. Outcome was not specific to parent's diagnosis.

CONCLUSION

Young offspring descending from kindreds affected by major psychoses would accumulate risk indicators many years before transition. A clustering of risk factors has also been observed in children at risk of metabolic-cardiovascular disorders and influences practice guidelines in this field. Our findings may be significant for the primary care surveillance of millions of children born to affected parents in the G7 nations. Future longitudinal risk research of children at genetic risk should explore concurrently several intrinsic and environmental risk modalities to increase predictivity.

Neuropsychological and neuroimaging underpinnings of schizoaffective disorder: a systematic review

OBJECTIVES

The neurobiological basis and nosological status of schizoaffective disorder remains elusive and controversial. This study provides a systematic review of neurocognitive and neuroimaging findings in the disorder.

METHODS

A comprehensive literature search was conducted via PubMed, ScienceDirect, Scopus and Web of Knowledge (from 1949 to 31st March 2015) using the keyword 'schizoaffective disorder' and any of the following terms: 'neuropsychology', 'cognition', 'structural neuroimaging', 'functional neuroimaging', 'multimodal', 'DTI' and 'VBM'. Only studies that explicitly examined a well defined sample, or subsample, of patients with schizoaffective disorder were included.

RESULTS

Twenty-two of 43 neuropsychological and 19 of 51 neuroimaging articles fulfilled inclusion criteria. We found a general trend towards schizophrenia and schizoaffective disorder being related to worse cognitive performance than bipolar disorder. Grey matter volume loss in schizoaffective disorder is also more comparable to schizophrenia than to bipolar disorder which seems consistent across further neuroimaging techniques.

CONCLUSIONS

Neurocognitive and neuroimaging abnormalities in schizoaffective disorder resemble more schizophrenia than bipolar disorder. This is suggestive for schizoaffective disorder being a subtype of schizophrenia or being part of the continuum spectrum model of psychosis, with schizoaffective disorder being more skewed towards schizophrenia than bipolar disorder.

Exploring difference and overlap between schizophrenia, schizoaffective and bipolar disorders using resting-state brain functional networks

Schizophrenia, schizoaffective and bipolar disorders share some common symptoms. However, the biomarkers underlying those disorders remain unclear. In fact, there is still controversy about the schizoaffective disorder with respect to its validity of independent category and its relationship with schizophrenia and bipolar disorders. In this paper, based on brain functional networks extracted from resting-state fMRI using a recently proposed group information guided ICA (GIG-ICA) method, we explore the biomarkers for discriminating healthy controls, schizophrenia patients, bipolar patients, and patients with two symptom defined subsets of schizoaffective disorder, and then investigate the relationship between different groups. The results demonstrate that the discriminating regions mainly including frontal, parietal, precuneus, cingulate, supplementary motor, cerebellar, insular and supramarginal cortices perform well in distinguishing the different diagnostic groups. The results also suggest that schizoaffective disorder may be an independent disorder, although its subtype characterized by depressive episodes shares more similarity with schizophrenia.

A critical appraisal of paliperidone long-acting injection in the treatment of schizoaffective disorder

Schizoaffective disorder (SCA) is a chronic and disabling mental illness that presents with mixed symptoms of schizophrenia and affective disorders. SCA is recognized as a discrete disorder, but with greater heterogeneity and symptom overlap, leading to difficulty and delay in diagnosis. Although the overall prognosis is intermediate between schizophrenia and mood disorders, SCA is associated with higher rates of suicide and hospitalization than schizophrenia. No treatment guidelines exist for SCA, and treatment is frequently complex, involving off-label use and polypharmacy (typically combinations of antipsychotics, mood stabilizers, and antidepressants). Oral paliperidone extended-release was the first agent to be approved for the treatment of SCA. As in schizophrenia and bipolar disorder, adherence to oral medications is poor, further contributing to suboptimal outcomes. The use of an antipsychotic in a long-acting injection (LAI) addresses adherence issues, thus potentially reducing relapse. Paliperidone palmitate represents the LAI formulation of paliperidone. In a long-term, double-blind, randomized, controlled trial of adult patients (n=334; intent-to-treat [ITT]) with SCA, paliperidone long-acting injection (PLAI) significantly delayed risk of relapse compared to placebo (hazard ratio 2.49, 95% confidence interval, 1.55–3.99; P<0.001). This study demonstrated the efficacy and safety of PLAI when used as either monotherapy or adjunctive therapy for the maintenance treatment of SCA. The results are consistent with a similarly designed study conducted in patients with schizophrenia, which suggests a benefit in the long-term control of not only psychotic but also affective symptoms. No new safety signals were observed. When used in monotherapy, PLAI simplifies treatment by reducing complex pharmacotherapy and obviating the necessity for daily oral medications. PLAI is the second agent, and the first LAI, to be approved for the treatment of SCA; as an LAI formulation, there is the advantage of improved adherence and simplified treatment in the long-term management of SCA.

Toward a Valid Animal Model of Bipolar Disorder: How the Research Domain Criteria Help Bridge the Clinical-Basic Science Divide

Bipolar disorder is a diagnostically heterogeneous disorder, although mania emerges as a distinct phenotype characterized by elevated mood and increased activity or energy. While bipolar disorder's cyclicity is difficult to represent in animals, models of mania have begun to decode its fundamental underlying neurobiology. When psychostimulants such as amphetamine or cocaine are administered to rodents, a resulting upsurge of motor activity is thought to share face and predictive validity with mania in humans. Studying black Swiss mice, which inherently exhibit proclivity for reward seeking and risk taking, also has yielded some insight. Further, translating the biology of bipolar disorder in humans into animal models has led to greater understanding of roles for candidate biological systems such as the GRIK2 and CLOCK genes, as well as the extracellular signal-related kinase pathway involved in the pathophysiology of the illness. The National Institute of Mental Health Research Domain Criteria initiative seeks to identify building blocks of complex illnesses like bipolar disorder in hopes of uncovering the neurobiology of each, as well as how each fits together to produce syndromes like bipolar disorder or why so many mental illnesses co-occur together. Research Domain Criteria-driven preclinical models of isolated behaviors and domains involved in mania and bipolar disorder will ultimately inform movement toward nosology supported by neurobiology.

Clinical predictors of therapeutic response to antipsychotics in schizophrenia

The search for clinical outcome predictors for schizophrenia is as old as the field of psychiatry. However, despite a wealth of large, longitudinal studies into prognostic factors, only very few clinically useful outcome predictors have been identified. The goal of future treatment is to either affect modifiable risk factors, or use nonmodifiable factors to parse patients into therapeutically meaningful subgroups. Most clinical outcome predictors are nonspecific and/or nonmodifiable. Nonmodifiable predictors for poor odds of remission include male sex, younger age at disease onset, poor premorbid adjustment, and severe baseline psychopathology. Modifiable risk factors for poor therapeutic outcomes that clinicians can act upon include longer duration of untreated illness, nonadherence to antipsychotics, comorbidities (especially substance-use disorders), lack of early antipsychotic response, and lack of improvement with non-clozapine antipsychotics, predicting clozapine response. It is hoped that this limited capacity for prediction will improve as pathophysiological understanding increases and/or new treatments for specific aspects of schizophrenia become available.

Longitudinal investigation of the relationship between family history of psychosis and affective disorders and Child Behavior Checklist ratings in clinical high-risk adolescents

This is the first study to investigate whether positive family history (FH) of psychosis and affective disorders moderates the relationship between child diagnostic status and parent-reported social and behavioral problems on the Child Behavior Checklist (CBCL) in clinical high-risk adolescents. This longitudinal investigation assessed 122 participants (mean age=14.25±1.8years) from three groups (at-risk, other personality disorders, non-psychiatric controls) at baseline and one year follow-up. As predicted, there was a main effect of FH for a number of CBCL scales indicating higher scores for adolescents with positive FH. The findings also demonstrate a significant Diagnostic Status×Family History interaction for several behavioral scales providing support for FH as a concurrent and longitudinal moderator of the relationship between diagnostic status and CBCL scales. The moderating effect is present for areas of functioning associated with depression, anxiety, social adjustment, thought problems, attention problems, and aggressive behavior. The findings also indicate that both positive and negative symptoms are related to the genetic vulnerability for developing psychosis in clinical high-risk individuals, particularly those symptoms reflective of emotional, attentional, and interpersonal functioning. The present findings are novel and have significant clinical and research implications. This investigation provides a platform for future studies to clarify further the role of FH in clinical high-risk individuals and contributes to integration of this knowledge in the development of early intervention and prevention approaches in at-risk populations for the emergence of severe mental illness.

A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders

Schizophrenia (SZ), bipolar disorder (BP) and schizoaffective disorder (SAD) share some common symptoms, and there is still a debate about whether SAD is an independent category. To the best of our knowledge, no study has been done to differentiate these three disorders or to investigate the distinction of SAD as an independent category using fMRI data. This study is aimed to explore biomarkers from resting-state fMRI networks for differentiating these disorders and investigate the relationship among these disorders based on fMRI networks with an emphasis on SAD. Firstly, a novel group ICA method, group information guided independent component analysis (GIG-ICA), was applied to extract subject-specific brain networks from fMRI data of 20 healthy controls (HC), 20 SZ patients, 20 BP patients, 20 patients suffering from SAD with manic episodes (SADM), and 13 patients suffering from SAD with depressive episodes exclusively (SADD). Then, five-level one-way analysis of covariance and multiclass support vector machine recursive feature elimination were employed to identify discriminative regions from the networks. Subsequently, the t-distributed stochastic neighbor embedding (t-SNE) projection and the hierarchical clustering were implemented to investigate the relationship among those groups. Finally, to evaluate the generalization ability, 16 new subjects were classified based on the found regions and the trained model using original 93 subjects. Results show that the discriminative regions mainly included frontal, parietal, precuneus, cingulate, supplementary motor, cerebellar, insula and supramarginal cortices, which performed well in distinguishing different groups. SADM and SADD were the most similar to each other, although SADD had greater similarity to SZ compared to other groups, which indicates that SAD may be an independent category. BP was closer to HC compared with other psychotic disorders. In summary, resting-state fMRI brain networks extracted via GIG-ICA provide a promising potential to differentiate SZ, BP, and SAD.

What Have Mass Spectrometry-Based Proteomics and Metabolomics (Not) Taught Us about Psychiatric Disorders?

Understanding the molecular causes and finding appropriate therapies for psychiatric disorders are challenging tasks for research; -omics technologies are used to elucidate the molecular mechanisms underlying brain dysfunction in a hypothesis-free manner. In this review, we will focus on mass spectrometry-based proteomics and metabolomics and address how these approaches have contributed to our understanding of psychiatric disorders. Specifically, we will discuss what we have learned from mass spectrometry-based proteomics and metabolomics studies in rodent models and human cohorts, outline current limitations and discuss the potential of these methods for future applications in psychiatry.

Can proteomics-based diagnostics aid clinical psychiatry?

Despite major advances in infrastructure and instrumentation, proteomics-driven translational applications have not yet yielded the results that the scientific community has envisaged. In this viewpoint, the perspective of proteomics-based diagnostics in the field of clinical psychiatry is explored. The challenges that proteomics faces in the context of translational approaches are outlined and directions toward a successful clinical implementation are provided. Additional challenges that psychiatric disorders pose for clinical proteomics are highlighted and the potential of proteomics-based, blood tests for psychiatric disorders is being assessed. Proteomics offers a valuable toolkit for clinical translation that needs to be handled in a pragmatic manner and with realistic expectations.

Clinical predictors of therapeutic response to antipsychotics in schizophrenia

The search for clinical outcome predictors for schizophrenia is as old as the field of psychiatry. However, despite a wealth of large, longitudinal studies into prognostic factors, only very few clinically useful outcome predictors have been identified. The goal of future treatment is to either affect modifiable risk factors, or use nonmodifiable factors to parse patients into therapeutically meaningful subgroups. Most clinical outcome predictors are nonspecific and/or nonmodifiable. Nonmodifiable predictors for poor odds of remission include male sex, younger age at disease onset, poor premorbid adjustment, and severe baseline psychopathology. Modifiable risk factors for poor therapeutic outcomes that clinicians can act upon include longer duration of untreated illness, nonadherence to antipsychotics, comorbidities (especially substance-use disorders), lack of early antipsychotic response, and lack of improvement with non-clozapine antipsychotics, predicting clozapine response. It is hoped that this limited capacity for prediction will improve as pathophysiological understanding increases and/or new treatments for specific aspects of schizophrenia become available.

Trait or state? A longitudinal neuropsychological evaluation and fMRI study in schizoaffective disorder

Schizoaffective patients can have neurocognitive deficits and default mode network dysfunction while being acutely ill. It remains unclear to what extent these abnormalities persist when they go into clinical remission. Memory and executive function were tested in 22 acutely ill schizoaffective patients; they also underwent fMRI scanning during performance of the n-back working memory test. The same measures were obtained after they had been in remission for ≥ 2 months. Twenty-two matched healthy individuals were also examined. In clinical remission, schizomanic patients showed an improvement of memory but not of executive function, while schizodepressive patients did not change in either domain. All schizoaffective patients in clinical remission showed memory and executive impairment compared to the controls. On fMRI, acutely ill schizomanic patients had reversible frontal hypo-activation when compared to clinical remission, while activation patterns in ill and remitted schizodepressive patients were similar. The whole group of schizoaffective patients in clinical remission showed a failure of de-activation in the medial frontal gyrus compared to the healthy controls. There was evidence for memory improvement and state dependent changes in activation in schizomanic patients across relapse and remission. Medial frontal failure of de-activation in remitted schizoaffective patients, which probably reflects default mode network dysfunction, appears to be a state independent feature of the illness.

Applications of blood-based protein biomarker strategies in the study of psychiatric disorders

Major psychiatric disorders such as schizophrenia, major depressive and bipolar disorders are severe, chronic and debilitating, and are associated with high disease burden and healthcare costs. Currently, diagnoses of these disorders rely on interview-based assessments of subjective self-reported symptoms. Early diagnosis is difficult, misdiagnosis is a frequent occurrence and there are no objective tests that aid in the prediction of individual responses to treatment. Consequently, validated biomarkers are urgently needed to help address these unmet clinical needs. Historically, psychiatric disorders are viewed as brain disorders and consequently only a few researchers have as yet evaluated systemic changes in psychiatric patients. However, promising research has begun to challenge this concept and there is an increasing awareness that disease-related changes can be traced in the peripheral system which may even be involved in the precipitation of disease onset and course. Converging evidence from molecular profiling analysis of blood serum/plasma have revealed robust molecular changes in psychiatric patients, suggesting that these disorders may be detectable in other systems of the body such as the circulating blood. In this review, we discuss the current clinical needs in psychiatry, highlight the importance of biomarkers in the field, and review a representative selection of biomarker studies to highlight opportunities for the implementation of personalized medicine approaches in the field of psychiatry. It is anticipated that the implementation of validated biomarker tests will not only improve the diagnosis and more effective treatment of psychiatric patients, but also improve prognosis and disease outcome.

Problems in the boundaries of bipolar disorders

Classical concepts of bipolarity (bipolar I and bipolar II) have sometimes been extended into a broader spectrum that includes a wide variety of conditions previously diagnosed as separate forms of psychopathology. Differential diagnosis remains important, particularly in personality disorders characterized by affective instability, and in behavior disorders affecting pre-pubertal children. In the absence of biological markers or other external sources of validity, as well as lack of evidence for response to pharmacological treatment when disorders are defined more broadly, the bipolar spectrum remains an unproven hypothesis.

Specificity of psychosis, mania and major depression in a contemporary family study

There has been increasing attention to the subgroups of mood disorders and their boundaries with other mental disorders, particularly psychoses. The goals of the present paper were (1) to assess the familial aggregation and co-aggregation patterns of the full spectrum of mood disorders (that is, bipolar, schizoaffective (SAF), major depression) based on contemporary diagnostic criteria; and (2) to evaluate the familial specificity of the major subgroups of mood disorders, including psychotic, manic and major depressive episodes (MDEs). The sample included 293 patients with a lifetime diagnosis of SAF disorder, bipolar disorder and major depressive disorder (MDD), 110 orthopedic controls, and 1734 adult first-degree relatives. The diagnostic assignment was based on all available information, including direct diagnostic interviews, family history reports and medical records. Our findings revealed specificity of the familial aggregation of psychosis (odds ratio (OR)=2.9, confidence interval (CI): 1.1-7.7), mania (OR=6.4, CI: 2.2-18.7) and MDEs (OR=2.0, CI: 1.5-2.7) but not hypomania (OR=1.3, CI: 0.5-3.6). There was no evidence for cross-transmission of mania and MDEs (OR=.7, CI:.5-1.1), psychosis and mania (OR=1.0, CI:.4-2.7) or psychosis and MDEs (OR=1.0, CI:.7-1.4). The strong familial specificity of psychotic, manic and MDEs in this largest controlled contemporary family study challenges the growing assertion that the major types of mood disorders are manifestations of a common underlying diathesis.