Patterns of deficits in brain function in bipolar disorder and schizophrenia: a cluster analytic study

Schizophrenia: A Narrative Review of Etiopathogenetic, Diagnostic and Treatment Aspects

Although schizophrenia is currently conceptualized as being characterized as a syndrome that includes a collection of signs and symptoms, there is strong evidence of heterogeneous and complex underpinned etiological, etiopathogenetic, and psychopathological mechanisms, which are still under investigation. Therefore, the present viewpoint review is aimed at providing some insights into the recently investigated schizophrenia research fields in order to discuss the potential future research directions in schizophrenia research. The traditional schizophrenia construct and diagnosis were progressively revised and revisited, based on the recently emerging neurobiological, genetic, and epidemiological research. Moreover, innovative diagnostic and therapeutic approaches are pointed to build a new construct, allowing the development of better clinical and treatment outcomes and characterization for schizophrenic individuals, considering a more patient-centered, personalized, and tailored-based dimensional approach. Further translational studies are needed in order to integrate neurobiological, genetic, and environmental studies into clinical practice and to help clinicians and researchers to understand how to redesign a new schizophrenia construct.

Psychosis Biotypes: Replication and Validation from the B-SNIP Consortium

Current clinical phenomenological diagnosis in psychiatry neither captures biologically homologous disease entities nor allows for individualized treatment prescriptions based on neurobiology. In this report, we studied two large samples of cases with schizophrenia, schizoaffective, and bipolar I disorder with psychosis, presentations with clinical features of hallucinations, delusions, thought disorder, affective, or negative symptoms. A biomarker approach to subtyping psychosis cases (called psychosis Biotypes) captured neurobiological homology that was missed by conventional clinical diagnoses. Two samples (called "B-SNIP1" with 711 psychosis and 274 healthy persons, and the "replication sample" with 717 psychosis and 198 healthy persons) showed that 44 individual biomarkers, drawn from general cognition (BACS), motor inhibitory (stop signal), saccadic system (pro- and anti-saccades), and auditory EEG/ERP (paired-stimuli and oddball) tasks of psychosis-relevant brain functions were replicable (r's from .96-.99) and temporally stable (r's from .76-.95). Using numerical taxonomy (k-means clustering) with nine groups of integrated biomarker characteristics (called bio-factors) yielded three Biotypes that were virtually identical between the two samples and showed highly similar case assignments to subgroups based on cross-validations (88.5%-89%). Biotypes-1 and -2 shared poor cognition. Biotype-1 was further characterized by low neural response magnitudes, while Biotype-2 was further characterized by overactive neural responses and poor sensory motor inhibition. Biotype-3 was nearly normal on all bio-factors. Construct validation of Biotype EEG/ERP neurophysiology using measures of intrinsic neural activity and auditory steady state stimulation highlighted the robustness of these outcomes. Psychosis Biotypes may yield meaningful neurobiological targets for treatments and etiological investigations.

Interactive effects of polygenic risk and cognitive subtype on brain morphology in schizophrenia spectrum and bipolar disorders

Grey matter volume (GMV) may be associated with polygenic risk for schizophrenia (PRS-SZ) and severe cognitive deficits in people with schizophrenia, schizoaffective disorder (collectively SSD), and bipolar disorder (BD). This study examined the interactive effects of PRS-SZ and cognitive subtypes of SSD and BD in relation to GMV. Two-step cluster analysis was performed on 146 clinical cases (69 SSD and 77 BD) assessed on eight cognitive domains (verbal and visual memory, executive function, processing speed, visual processing, language ability, working memory, and planning). Among them, 55 BD, 51 SSD, and 58 healthy controls (HC), contributed to focal analyses of the relationships between cognitive subtypes, PRS-SZ and their interaction on GMV. Two distinct cognitive subtypes were evident among the combined sample of cases: a 'cognitive deficit' group (CD; N = 31, 20SSD/11BD) showed severe impairment across all cognitive indices, and a 'cognitively spared' (CS; N = 75; 31SSD/44BD) group showed intermediate cognitive performance that was significantly worse than the HC group but better than the CD subgroup. A cognitive subgroup-by-PRS-SZ interaction was significantly associated with GMV in the left precentral gyrus. Moderation analyses revealed a significant negative relationship between PRS-SZ and GMV in the CD group only. At low and average (but not high) PRS-SZ, larger precentral GMV was evident in the CD group compared to both CS and HC groups, and in the CS group compared to HCs. This study provides evidence for a relationship between regional GMV changes and PRS-SZ in psychosis spectrum cases with cognitive deficits, but not in cases cognitively spared.

A systematic review and narrative synthesis of data-driven studies in schizophrenia symptoms and cognitive deficits

To tackle the phenotypic heterogeneity of schizophrenia, data-driven methods are often applied to identify subtypes of its symptoms and cognitive deficits. However, a systematic review on this topic is lacking. The objective of this review was to summarize the evidence obtained from longitudinal and cross-sectional data-driven studies in positive and negative symptoms and cognitive deficits in patients with schizophrenia spectrum disorders, their unaffected siblings and healthy controls or individuals from general population. Additionally, we aimed to highlight methodological gaps across studies and point out future directions to optimize the translatability of evidence from data-driven studies. A systematic review was performed through searching PsycINFO, PubMed, PsycTESTS, PsycARTICLES, SCOPUS, EMBASE and Web of Science electronic databases. Both longitudinal and cross-sectional studies published from 2008 to 2019, which reported at least two statistically derived clusters or trajectories were included. Two reviewers independently screened and extracted the data. In this review, 53 studies (19 longitudinal and 34 cross-sectional) that conducted among 17,822 patients, 8729 unaffected siblings and 5520 controls or general population were included. Most longitudinal studies found four trajectories that characterized by stability, progressive deterioration, relapsing and progressive amelioration of symptoms and cognitive function. Cross-sectional studies commonly identified three clusters with low, intermediate (mixed) and high psychotic symptoms and cognitive profiles. Moreover, identified subgroups were predicted by numerous genetic, sociodemographic and clinical factors. Our findings indicate that schizophrenia symptoms and cognitive deficits are heterogeneous, although methodological limitations across studies are observed. Identified clusters and trajectories along with their predictors may be used to base the implementation of personalized treatment and develop a risk prediction model for high-risk individuals with prodromal symptoms.

Differential benefits of olanzapine on executive function in schizophrenia patients: Preliminary findings

OBJECTIVE

Schizophrenia patients show executive function (EF) impairments in voluntary orienting as measured by eye-movements. We tested 14 inpatients to investigate the effects of the antipsychotic olanzapine on EF, as measured by antisaccade eye-movement performance.

METHODS

Patients were tested at baseline (before olanzapine), 3-5 days post-medication, and 12-14 days post-medication. Patients were also assessed on the Positive and Negative Syndrome Scale (PANSS) to measure the severity of schizophrenia-related symptoms, and administered the Stroop task, a test of EF. Nine matched controls were also tested on the antisaccade and Stroop.

RESULTS

Both groups showed improvement on Stroop and antisaccade; however, the schizophrenia group improved significantly more on antisaccade, indicating an additional benefit of olanzapine on EF performance. Patients with poorer baseline antisaccade performance (High-Deficit) showed significantly greater improvement on the antisaccade task than patients with better baseline performance (Low-Deficit), suggesting that baseline EF impairment predicts the magnitude of cognitive improvement with olanzapine. These subgroups showed significant and equivalent improvement on PANSS scores, indicating that improvement on the antisaccade task with olanzapine was not a result of differences in magnitude of clinical improvement.

CONCLUSIONS

This preliminary study provides evidence that olanzapine may be most advantageous for patients with greater baseline EF deficits.

Identifying Clinically and Functionally Distinct Groups Among Healthy Controls and First Episode Psychosis Patients by Clustering on EEG Patterns

OBJECTIVE

The mismatch negativity (MMN) is considered as a promising biomarker that can inform future therapeutic studies. However, there is a large variability among patients with first episode psychosis (FEP). Also, most studies report a single electrode site and on comparing case-control group differences. Few have taken advantage of the full wealth of multi-channel EEG signals to examine observable patterns. None, to our knowledge, have used machine learning (ML) approaches to investigate neurophysiological derived subgroups with distinct cognitive and functional outcome characteristics. In this study, we applied ML to empirically stratify individuals into homogeneous subgroups based on multi-channel MMN data. We then characterized the functional, cognitive, and clinical profiles of these neurobiologically derived subgroups. We also explored the underlying low frequency range responses (delta, theta, alpha) during MMN.

METHODS

Clinical, neurocognitive, functioning data of 33 healthy controls and 20 FEP patients were collected. 90% of the patients had 6-month follow-up data. Neurocognition, social cognition, and functioning measures were assessed using the NCCB Cognitive Battery, the Awareness of Social Inference Test, UCSD Performance-Based Skills Assessment, and Multnomah Community Ability Scale. Symptom severity was collected using the PANSS. MMN amplitude and single-trial derived low frequency activity across 24 frontocentral channels were used as main variables in the ML k-means clustering analyses.

RESULTS

We found a consistent pattern of two distinctive subgroups. We labeled them as "better functioning" and "poorer functioning" clusters, respectively. Each subgroup can be mapped onto either better or poorer clinical, cognitive, and functioning profiles. Also, we identified two subgroups of patients: one showed improved MMN and one showed worsening of MMN over time. Patients with improved MMN had better follow-up clinical, cognitive, and functioning profile than those with worsening MMN. Among the low frequency bands, delta frequency appeared to be the most relevant to the observed MMN responses in all individuals. However, higher delta responses were not necessarily associated with a better functioning profile, suggesting that delta frequency alone may not be useful in clinical characterization.

CONCLUSIONS

The ML approach could be a robust tool to explore heterogeneity and facilitate the identification of neurobiological homogeneous subgroups in FEP.

A Systematic Review of Studies Reporting Data-Driven Cognitive Subtypes across the Psychosis Spectrum

The delineation of cognitive subtypes of schizophrenia and bipolar disorder may offer a means of determining shared genetic markers and neuropathology among individuals with these conditions. We systematically reviewed the evidence from published studies reporting the use of data-driven (i.e., unsupervised) clustering methods to delineate cognitive subtypes among adults diagnosed with schizophrenia, schizoaffective disorder, or bipolar disorder. We reviewed 24 studies in total, contributing data to 13 analyses of schizophrenia spectrum patients, 8 analyses of bipolar disorder, and 5 analyses of mixed samples of schizophrenia and bipolar disorder participants. Studies of bipolar disorder most consistently revealed a 3-cluster solution, comprising a subgroup with 'near-normal' (cognitively spared) cognition and two other subgroups demonstrating graded deficits across cognitive domains. In contrast, there was no clear consensus regarding the number of cognitive subtypes among studies of cognitive subtypes in schizophrenia, while four of the five studies of mixed diagnostic groups reported a 4-cluster solution. Common to all cluster solutions was a severe cognitive deficit subtype with cognitive impairments of moderate to large effect size relative to healthy controls. Our review highlights several key factors (e.g., symptom profile, sample size, statistical procedures, and cognitive domains examined) that may influence the results of data-driven clustering methods, and which were largely inconsistent across the studies reviewed. This synthesis of findings suggests caution should be exercised when interpreting the utility of particular cognitive subtypes for biological investigation, and demonstrates much heterogeneity among studies using unsupervised clustering approaches to cognitive subtyping within and across the psychosis spectrum.

Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force

OBJECTIVES

The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD.

METHOD

A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD.

RESULTS

The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding.

CONCLUSION

Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.

Longitudinal trajectory of early functional recovery in patients with first episode psychosis

BACKGROUND

There is a large variability in the recovery trajectory and outcome of first episode of psychosis [FEP] patients. To date, individuals' outcome trajectories at early stage of illness and potential risk factors associated with a poor outcome trajectory are largely unknown. This study aims to apply three separate predictors (positive symptoms, negative symptoms, and soft neurological signs) to identify homogeneous function outcome trajectories in patients with FEP using objective data-driven methods, and to explore the potential risk /protective factors associated with each trajectory.

METHODS

A total of 369 first episode patients (93% antipsychotic naive) were included in the baseline assessments and followed-up at 4-8 weeks, 6 months, and 1 year. K means cluster modeling for longitudinal data (kml3d) was used to identify distinct, homogeneous clusters of functional outcome trajectories. Patients with at least 3 assessments were included in the trajectory analyses (N = 129). The Scale for the Assessment of Negative Symptoms (SANS), Scale for the Assessment of Positive Symptoms (SAPS), and Neurological examination abnormalities (NEA) were used as predictors against Global Assessment of Functioning Scale (GAF).

RESULTS

In each of the three predictor models, four distinct functional outcome trajectories emerged: "Poor", "Intermediate", High" and "Catch-up". Individuals with male gender; ethnic minority status; low premorbid adjustment; low executive function/IQ, low SES, personality disorder, substance use history may be risk factors for poor recovery.

CONCLUSIONS

Functioning recovery in individuals with FEP is heterogeneous, although distinct recovery profiles are apparent. Data-driven trajectory analysis can facilitate better characterization of individual longitudinal patterns of functioning recovery.

Polygenic pleiotropy and potential causal relationships between educational attainment, neurobiological profile, and positive psychotic symptoms

Event-related potential (ERP) components have been used to assess cognitive functions in patients with psychotic illness. Evidence suggests that among patients with psychosis there is a distinct heritable neurophysiologic phenotypic subtype captured by impairments across a range of ERP measures. In this study, we investigated the genetic basis of this "globally impaired" ERP cluster and its relationship to psychosis and cognitive abilities. We applied K-means clustering to six ERP measures to re-derive the globally impaired (n = 60) and the non-globally impaired ERP clusters (n = 323) in a sample of cases with schizophrenia (SCZ = 136) or bipolar disorder (BPD = 121) and healthy controls (n = 126). We used genome-wide association study (GWAS) results for SCZ, BPD, college completion, and childhood intelligence as the discovery datasets to derive polygenic risk scores (PRS) in our study sample and tested their associations with globally impaired ERP. We conducted mediation analyses to estimate the proportion of each PRS effect on severity of psychotic symptoms that is mediated through membership in the globally impaired ERP. Individuals with globally impaired ERP had significantly higher PANSS-positive scores (β = 3.95, P = 0.005). The SCZ-PRS was nominally associated with globally impaired ERP (unadjusted P = 0.01; R² = 3.07%). We also found a significant positive association between the college-PRS and globally impaired ERP (FDR-corrected P = 0.004; R² = 6.15%). The effect of college-PRS on PANSS positivity was almost entirely (97.1%) mediated through globally impaired ERP. These results suggest that the globally impaired ERP phenotype may represent some aspects of brain physiology on the path between genetic influences on educational attainment and psychotic symptoms.

Mismatch negativity in bipolar disorder: A neurophysiological biomarker of intermediate effect?

The event-related potential, mismatch negativity (MMN), has been touted as a robust and specific neurophysiological biomarker of schizophrenia. Earlier studies often included bipolar disorder (BD) as a clinical comparator and reported that MMN was significantly impaired only in schizophrenia. However, with the increasing number of MMN studies of BD (with larger sample sizes), the literature is now providing somewhat consistent evidence of this biomarker also being perturbed in BD, albeit to a lesser degree than that observed in schizophrenia. Indeed, two meta-analyses have now shown that the effect sizes in BD samples suggest a moderate impairment in MMN, compared to the large effect sizes shown in schizophrenia. Pharmacologically, MMN is an extremely useful non-invasive probe of glutamatergic (more specifically, N-methyl-d-aspartate [NMDA] receptor) disturbances and this system has been implicated in the pathophysiology of both schizophrenia and BD. Therefore, it may be best to conceptualize/utilize MMN as an index of a psychopathology that is shared across psychotic and related disorders, rather than being a diagnosis-specific biomarker. More research is needed, particularly longitudinal designs including studies that assess MMN over an individual's life course and then examine NMDA receptor expression/binding post-mortem. At this point and despite a disproportionate amount of research, the current evidence suggests that with respect to BD, MMN is a neurophysiological biomarker of intermediate effect. With replication and validation of this effect, MMN may prove to be an important indicator of a common psychopathology shared by a significant proportion of individuals with schizophrenia and bipolar spectrum illnesses.

The impact of machine learning techniques in the study of bipolar disorder: A systematic review

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation.

Sex-Dependent Association of Perigenual Anterior Cingulate Cortex Volume and Migration Background, an Environmental Risk Factor for Schizophrenia

Migration status is one of the best-established risk factors for schizophrenia. An increase in risk is observed in both first- and second-generation immigrants, with a varying magnitude depending on the ethnic background of the individuals. The underlying mechanisms for the increased risk are only recently coming into focus. A causal role for social stress has been widely proposed, and recent work indicated altered neural stress processing in the perigenual anterior cingulate cortex (pACC) in migrants. Since previous work shows that social stress may lead to enduring changes in the gray matter volume of vulnerable brain regions, we investigated the impact of migration background on brain structure. We studied healthy young adults (N = 124), native Germans and second-generation migrants, using whole-brain structural magnetic resonance imaging. Groups were matched for a broad range of sociodemographic characteristics including age, gender, urban exposure, and education. We found a significant group by sex interaction effect in pACC gray matter volume, which was reduced in males with migration background only. This mirrors previous findings in urban upbringing, another risk factor for schizophrenia. Our results provide convergent evidence for an impact of environmental risk factors linked to schizophrenia on gray matter volume and extend prior data by highlighting the possibility that the pACC structure may be particularly sensitive to the convergent risk factors linked to schizophrenia.

Thought Disorder in Schizophrenia and Bipolar Disorder Probands, Their Relatives, and Nonpsychiatric Controls

Thought disorder (TD) has long been associated with schizophrenia (SZ) and is now widely recognized as a symptom of mania and other psychotic disorders as well. Previous studies have suggested that the TD found in the clinically unaffected relatives of SZ, schizoaffective and bipolar probands is qualitatively similar to that found in the probands themselves. Here, we examine which quantitative measures of TD optimize the distinction between patients with diagnoses of SZ and bipolar disorder with psychotic features (BP) from nonpsychiatric controls (NC) and from each other. In addition, we investigate whether these same TD measures also distinguish their respective clinically unaffected relatives (RelSZ, RelBP) from controls as well as from each other. We find that deviant verbalizations are significantly associated with SZ and are co-familial in clinically unaffected RelSZ, but are dissociated from, and are not co-familial for, BP disorder. In contrast, combinatory thinking was nonspecifically associated with psychosis, but did not aggregate in either group of relatives. These results provide further support for the usefulness of TD for identifying potential non-penetrant carriers of SZ-risk genes, in turn enhancing the power of genetic analyses. These findings also suggest that further refinement of the TD phenotype may be needed in order to be suitable for use in genetic studies of bipolar disorder.

Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology

Deficits in information processing and cognition are among the most robust findings in schizophrenia patients. Previous efforts to translate group-level deficits into clinically relevant and individualized information have, however, been non-successful, which is possibly explained by biologically different disease subgroups. We applied machine learning algorithms on measures of electrophysiology and cognition to identify potential subgroups of schizophrenia. Next, we explored subgroup differences regarding treatment response. Sixty-six antipsychotic-naive first-episode schizophrenia patients and sixty-five healthy controls underwent extensive electrophysiological and neurocognitive test batteries. Patients were assessed on the Positive and Negative Syndrome Scale (PANSS) before and after 6 weeks of monotherapy with the relatively selective D₂ receptor antagonist, amisulpride (280.3±159 mg per day). A reduced principal component space based on 19 electrophysiological variables and 26 cognitive variables was used as input for a Gaussian mixture model to identify subgroups of patients. With support vector machines, we explored the relation between PANSS subscores and the identified subgroups. We identified two statistically distinct subgroups of patients. We found no significant baseline psychopathological differences between these subgroups, but the effect of treatment in the groups was predicted with an accuracy of 74.3% (P=0.003). In conclusion, electrophysiology and cognition data may be used to classify subgroups of schizophrenia patients. The two distinct subgroups, which we identified, were psychopathologically inseparable before treatment, yet their response to dopaminergic blockade was predicted with significant accuracy. This proof of principle encourages further endeavors to apply data-driven, multivariate and multimodal models to facilitate progress from symptom-based psychiatry toward individualized treatment regimens.

Maternal T. gondii, offspring bipolar disorder and neurocognition

Prenatal exposure to maternal Toxoplasma gondii (T. gondii) IgG antibody titer has been associated previously with an increased risk of offspring schizophrenia (SZ) and cognitive impairment. We examined maternal T. gondii, offspring bipolar disorder (BP) and childhood cognition using a population-based birth cohort. Maternal sera, drawn in the third trimester, were analyzed for T. gondii IgG antibody titer, and offspring cognition at ages 5 and 9-11 was measured with the Peabody Picture Vocabulary Test (PPVT) and the Raven Matrices (Raven). Raw scores were standardized and the ages combined. Potential cases with BP from the cohort were identified by database linkages. This protocol identified 85 cases who were matched 1:2 to controls. Maternal T. gondii IgG was not associated with the risk of BP in offspring. Neither moderate [HR=1.43 (CI: 0.49, 4.17)] nor high IgG titer [HR=1.6 [CI: 0.74, 3.48)] were associated with offspring BP. Associations were not observed between maternal T. gondii and BP with psychotic features or BP type 1. In addition, maternal T. gondii was not associated with childhood cognition. Our study suggests that T. gondii may be specific to SZ among major psychotic disorders, though further studies with larger sample sizes are required.

Neuropsychological and social cognitive function in young people at genetic risk of bipolar disorder

BACKGROUND

Impairments in key neuropsychological domains (e.g. working memory, attention) and social cognitive deficits have been implicated as intermediate (endo) phenotypes for bipolar disorder (BD), and should therefore be evident in unaffected relatives.

METHOD

Neurocognitive and social cognitive ability was examined in 99 young people (age range 16-30 years) with a biological parent or sibling diagnosed with the disorder [thus deemed to be at risk (AR) of developing BD], compared with 78 healthy control (HC) subjects, and 52 people with a confirmed diagnosis of BD.

RESULTS

Only verbal intelligence and affective response inhibition were significantly impaired in AR relative to HC participants; the BD participants showed significant deficits in attention tasks compared with HCs. Neither AR nor BD patients showed impairments in general intellectual ability, working memory, visuospatial or language ability, relative to HC participants. Analysis of BD-I and BD-II cases separately revealed deficits in attention and immediate memory in BD-I patients (only), relative to HCs. Only the BD (but not AR) participants showed impaired emotion recognition, relative to HCs.

CONCLUSIONS

Selective cognitive deficits in the capacity to inhibit negative affective information, and general verbal ability may be intermediate markers of risk for BD; however, the extent and severity of impairment in this sample was less pronounced than has been reported in previous studies of older family members and BD cases. These findings highlight distinctions in the cognitive profiles of AR and BD participants, and provide limited support for progressive cognitive decline in association with illness development in BD.

Electrical mapping in bipolar disorder patients during the oddball paradigm

Bipolar disorder (BD) is characterized by an alternated occurrence between acute mania episodes and depression or remission moments. The objective of this study is to analyze the information processing changes in BP (Bipolar Patients) (euthymia, depression and mania) during the oddball paradigm, focusing on the P300 component, an electric potential of the cerebral cortex generated in response to external sensorial stimuli, which involves more complex neurophysiological processes related to stimulus interpretation. Twenty-eight bipolar disorder patients (BP) (17 women and 11 men with average age of 32.5, SD: 9.5) and eleven healthy controls (HC) (7 women and 4 men with average age of 29.78, SD: 6.89) were enrolled in this study. The bipolar patients were divided into 3 major groups (i.e., euthymic, depressive and maniac) according to the score on the Clinical Global Impression--Bipolar Version (CGI-BP). The subjects performed the oddball paradigm simultaneously to the EEG record. EEG data were also recorded before and after the execution of the task. A one-way ANOVA was applied to compare the P300 component among the groups. After observing P300 and the subcomponents P3a and P3b, a similarity of amplitude and latency between euthymic and depressive patients was observed, as well as small amplitude in the pre-frontal cortex and reduced P3a response. This can be evidence of impaired information processing, cognitive flexibility, working memory, executive functions and ability to shift the attention and processing to the target and away from distracting stimuli in BD. Such neuropsychological impairments are related to different BD symptoms, which should be known and considered, in order to develop effective clinical treatment strategies.

Genomewide association analyses of electrophysiological endophenotypes for schizophrenia and psychotic bipolar disorders: a preliminary report

Several event-related potentials (ERP), including P3, sensory gating (P50), and gamma oscillation, are robustly impaired in patients with schizophrenia (SCZ) and bipolar disorder (BIP). Although these ERPs are known to be heritable, little is known about the specific genetic loci involved and the degree to which they overlap with loci influencing mood and psychotic disorders. In the present study, we conducted GWAS to a) identify common variants associated with ERP endophenotypes, and b) construct polygenic risk scores (PRS) to examine overlap between genetic components of ERPs and mood and psychotic disorders. The sample consisted of 271 patients with SCZ or psychotic BIP diagnosis and 128 controls for whom ERP and genomewide data were available. GWAS were conducted using the full sample. PRS, derived from the Psychiatric Genomics Consortium (PGC) analyses of SCZ, BIP, and major depressive disorder were applied to each ERP phenotype. We identified a region on chromosome 14 that was significantly associated with sensory gating (peak SNP rs10132223, P = 1.27 × 10(-9) ). This locus has not been previously associated with psychotic illness in PGC-GWAS. In the PRS analyses, patients with a higher load of SCZ risk alleles had reduced gamma response whereas patients with a higher load of BIP risk alleles had smaller P3 amplitude. We observed a genomewide significant locus on chromosome 14 for P50. This locus may influence P50 but not psychotic illness. Among patients with psychotic illness, PRS results indicated genetic overlap between SCZ loci and gamma oscillation and between BIP loci and P3 amplitude.

Cognitive variability in psychotic disorders: a cross-diagnostic cluster analysis

BACKGROUND

Cognitive dysfunction is a core feature of psychotic disorders; however, substantial variability exists both within and between subjects in terms of cognitive domains of dysfunction, and a clear 'profile' of cognitive strengths and weaknesses characteristic of any diagnosis or psychosis as a whole has not emerged. Cluster analysis provides an opportunity to group individuals using a data-driven approach rather than predetermined grouping criteria. While several studies have identified meaningful cognitive clusters in schizophrenia, no study to date has examined cognition in a cross-diagnostic sample of patients with psychotic disorders using a cluster approach. We aimed to examine cognitive variables in a sample of 167 patients with psychosis using cluster methods.

METHOD

Subjects with schizophrenia (n = 41), schizo-affective disorder (n = 53) or bipolar disorder with psychosis (n = 73) were assessed using a battery of cognitive and clinical measures. Cognitive data were analysed using Ward's method, followed by a K-means cluster approach. Clusters were then compared on diagnosis and measures of clinical symptoms, demographic variables and community functioning.

RESULTS

A four-cluster solution was selected, including a 'neuropsychologically normal' cluster, a globally and significantly impaired cluster, and two clusters of mixed cognitive profiles. Clusters differed on several clinical variables; diagnoses were distributed amongst all clusters, although not evenly.

CONCLUSIONS

Identification of groups of patients who share similar neurocognitive profiles may help pinpoint relevant neural abnormalities underlying these traits. Such groupings may also hasten the development of individualized treatment approaches, including cognitive remediation tailored to patients' specific cognitive profiles.

Neurophysiologic effect of GWAS derived schizophrenia and bipolar risk variants

Genome-wide association studies (GWAS) have identified multiple single nucleotide polymorphisms (SNPs) as disease associated variants for schizophrenia (SCZ), bipolar disorder (BPD), or both. Although these results are statistically robust, the functional effects of these variants and their role in the pathophysiology of SCZ or BPD remain unclear. Dissecting the effects of risk genes on distinct domains of brain function can provide important biological insights into the mechanisms by which these genes may confer illness risk. This study used quantitative event related potentials to characterize the neurophysiological effects of well-documented GWAS-derived SCZ/BPD susceptibility variants in order to map gene effects onto important domains of brain function. We genotyped 199 patients with DSM-IV diagnoses of SCZ or BPD and 74 healthy control subjects for 19 risk SNPs derived from previous GWAS findings and tested their association with five neurophysiologic traits (P3 amplitude, P3 latency, N1 amplitude, P2 amplitude, and P50 sensory gating responses) known to be abnormal in psychosis. The TCF4 SNP rs17512836 risk allele showed a significant association with reduced auditory P3 amplitude (P = 0.00016) after correction for multiple testing. The same allele was also associated with delayed P3 latency (P = 0.005). Our results suggest that a SCZ risk variant in TCF4 is associated with neurophysiologic traits thought to index attention and working memory abnormalities in psychotic disorders. These findings suggest a mechanism by which TCF4 may contribute to the neurobiological basis of psychotic illness.

Disorders and borders: psychiatric genetics and nosology

Over the past century, the definition and classification of psychiatric disorders has evolved through a combination of historical trends, clinical observations, and empirical research. The current nosology, instantiated in the DSM-5 and ICD-10, rests on descriptive criteria agreed upon by a consensus of experts. While the development of explicit criteria has enhanced the reliability of diagnosis, the validity of the current diagnostic categories has been the subject of debate and controversy. Genetic studies have long been regarded as a key resource for validating the boundaries among diagnostic categories. Genetic epidemiologic studies have documented the familiality and heritability of clinically defined psychiatric disorders and molecular genetic studies have begun to identify specific susceptibility variants. At the same time, there is growing evidence from family, twin and genomic studies that genetic influences on psychiatric disorders transcend clinical boundaries. Here I review this evidence for cross-disorder genetic effects and discuss the implications of these findings for psychiatric nosology. Psychiatric genetic research can inform a bottom-up reappraisal of psychopathology that may help the field move beyond a purely descriptive classification and toward an etiology-based nosology.