Symptom Profiles in Psychotic Disorder Not Otherwise Specified

Comparison of clinical features and 1-year outcomes between patients with psychotic disorder not otherwise specified and those with schizophrenia

AIM

Research on psychotic disorder not otherwise specified (PNOS) that clearly mentions its subgroups is very rare. This study was conducted to identify the demographic and clinical features, cognitive function, and 1-year outcomes of patients with early stage PNOS compared with those with early stage schizophrenia (SZ).

METHODS

The study subjects were 54 and 321 patients with PNOS and SZ, respectively, who were registered at least more than 1 year ago. Due to drop out, only 37 and 210 patients with PNOS and SZ were evaluated at the 1-year follow-up. We compared clinical variables (duration of untreated psychosis, symptom severity, self-rating scales, and so on), cognitive function, and short-term outcomes (treatment response, remission, compliance, drop out, relapse) between the two groups.

RESULTS

The patients with PNOS were associated with higher diagnostic stability (53.7%) compared with those in previous studies. They had lower symptom severity, better treatment response at 2 months and higher remission rates at 12 months, but poorer compliance at 6 months compared with patients with SZ. Level of cognitive impairment in PNOS was intermediate between those of SZ patients and healthy controls.

CONCLUSIONS

These findings indicate that PNOS has unique clinical features, suggesting that it should be treated as a distinct clinical syndrome. At the same time, however, prevention of its possible progression to other psychotic disorders in some patients with PNOS is also important.

Altered functional connectivity in psychotic disorder not otherwise specified

BACKGROUND

Few studies have investigated functional connectivity (FC) in patients with psychotic disorder not otherwise specified (PNOS). We sought to identify distinct FC differentiating PNOS from schizophrenia (SZ).

METHODS

In total, 49 patients with PNOS, 42 with SZ, and 55 healthy controls (HC) matched for age, sex, and education underwent functional magnetic resonance imaging (fMRI) brain scans and clinical evaluation. Using six functional networks consisting of 40 regions of interest (ROIs), we conducted ROI to ROI and intra- and inter-network FC analyses using resting-state fMRI (rs-fMRI) data. Correlations of altered FC with symptomatology were explored.

RESULTS

We found common brain connectomics in PNOS and SZ including thalamo-cortical (especially superior temporal gyrus) hyperconnectivity, thalamo-cerebellar hypoconnectivity, and reduced within-thalamic connectivity compared to HC. Additionally, features differentiating the two patient groups included hyperconnectivity between the thalamic subregion and anterior cingulate cortex in PNOS compared to SZ and hyperconnectivity of the thalamic subregions with the posterior cingulate cortex and precentral gyrus in SZ compared to PNOS.

CONCLUSIONS

These findings suggest that PNOS and SZ exhibit both common and differentiating changes in neuronal connectivity. Furthermore, they may support the hypothesis that PNOS should be treated as a separate clinical syndrome with distinct neural connectomics.

20-Year Prospective, Sequential Follow-Up Study of Heterogeneity in Associations of Duration of Untreated Psychosis With Symptoms, Functioning, and Quality of Life Following First-Episode Psychosis

OBJECTIVE

Determining the extent to which relationships between duration of untreated psychosis (DUP) and outcome endure longitudinally across the lifetime course of psychotic illness requires prospective, systematic studies of epidemiologically representative incidence cohorts across decades. Transience, persistence, or heterogeneity in associations between DUP and distinct outcome domains are yet to be investigated over such time frames.

METHODS

Prospective, sequential follow-up studies of an epidemiologically representative first-episode psychosis incidence cohort in Ireland were conducted at 6 months and 4, 8, 12, and 20 years (N=171). Linear mixed-model analyses were applied to determine whether prospective associations of DUP with symptoms, functioning, and quality of life were consistent or varied across psychotic illness trajectory over a 20-year period. Evaluations included time, DUP quartile, and DUP quartile-by-time interaction effects.

RESULTS

Prospective, sequential follow-ups showed positive and negative symptoms, function, and quality of life to exhibit distinct trajectories of improvement in relation to shorter DUP. Despite heterogeneity in course and relationship to premorbid features, associations between shorter DUP and greater improvement were still evident 20 years after the first psychotic episode. Across the long-term course of psychotic illness, trajectories of association between shorter DUP and better outcome differed between domains of psychopathology, functionality, and quality of life. Nevertheless, such associations with shorter DUP were sustained for at least 20 years.

CONCLUSIONS

These profiles indicate that while associations between DUP and long-term outcome can vary according to the domain of outcome, they are sustained across decades in a manner that could not be fully accounted for in terms of premorbid features or lead-time bias.

A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data

In this paper, we propose a novel Machine Learning Model based on Bayesian Linear Regression intended to deal with the low sample-to-variable ratio typically found in neuroimaging studies and focusing on mental disorders. The proposed model combines feature selection capabilities with a formulation in the dual space which, in turn, enables efficient work with neuroimaging data. Thus, we have tested the proposed algorithm with real MRI data from an animal model of schizophrenia. The results show that our proposal efficiently predicts the diagnosis and, at the same time, detects regions which clearly match brain areas well-known to be related to schizophrenia.

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.

Characterization of affective lability across subgroups of psychosis spectrum disorders

BACKGROUND

Affective lability is elevated and associated with increased clinical burden in psychosis spectrum disorders. The extent to which the level, structure and dispersion of affective lability varies between the specific disorders included in the psychosis spectrum is however unclear. To have potential value as a treatment target, further characterization of affective lability in these populations is necessary. The main aim of our study was to investigate differences in the architecture of affective lability in different psychosis spectrum disorders, and if putative differences remained when we controlled for current symptom status.

METHODS

Affective lability was measured with The Affective Lability Scale Short Form (ALS-SF) in participants with schizophrenia (SZ, n = 76), bipolar I disorder (BD-I, n = 105), bipolar II disorder (BD-II, n = 68) and a mixed psychosis-affective group (MP, n = 48). Multiple analyses of covariance were conducted to compare the ALS-SF total and subdimension scores of the diagnostic groups, correcting for current psychotic, affective and anxiety symptoms, substance use and sex. Double generalized linear models were performed to compare the dispersion of affective lability in the different groups.

RESULTS

Overall group differences in affective lability remained significant after adjusting for covariates (p = .001). BD-II had higher affective lability compared to SZ and BD-I (p = .004), with no significant differences between SZ and BD-I. There were no significant differences in the contributions of ALS-SF dimensions to the total affective lability or in dispersion of affective lability between the groups.

CONCLUSIONS

This study provides the construct of affective lability in psychosis spectrum disorders with more granular details that may have implications for research and clinical care. It demonstrates that despite overlap in core symptom profiles, BD-I is more similar to SZ than it is to BD-II concerning affective lability and the BD groups should consequently be studied apart. Further, affective lability appears to be characterized by fluctuations between depressive- and other affective states across different psychosis spectrum disorders, indicating that affective lability may be related to internalizing problems in these disorders. Finally, although the level varies between groups, affective lability is evenly spread and not driven by extremes across psychosis spectrum disorders and should be assessed irrespective of diagnosis.