"Apples and pears are similar, but still different things." Bipolar disorder and schizophrenia- discrete disorders or just dimensions ?

A unified pathogenic hypothesis for mental disorders based on schismogenesis

Major Depressive Disorder, Bipolar Disorder, and Schizophrenia, share significant genetic, epigenetic, and phenotypic overlap, manifesting as dimensional psychopathology and convergent neuroimaging findings. These shared features have led to various models exploring common underlying pathophysiological mechanisms, including excitatory-inhibitory imbalance, the triple network model, network analysis, and social disconnection. While these models offer valuable insights, a unifying framework remains elusive. Schismogenesis, a transdisciplinary construct, is proposed to reconcile divergent perspectives on mental health conditions. Characterized by positive feedback loops leading to functional dissociation due to insufficient inhibitory control, complementary schismogenesis results in rigid hyperactivation and hypoactivation within neural, cognitive, and social networks, compromising system flexibility. This pathological process underlies the core features of Major Depressive Disorder, Bipolar Disorder, and Schizophrenia, depending on its location within networks. The schismogenesis hypothesis suggests that when individuals are overwhelmed by excessive stress or tension, they may experience a breakdown or disconnection to prevent irreversible damage, reflecting evolutionary adaptations. Importantly, the potential reversibility of schismogenesis, particularly through interventions that facilitate system reintegration, suggests promising therapeutic avenues for further exploration.

Distinguished Frontal White Matter Abnormalities Between Psychotic and Nonpsychotic Bipolar Disorders in a Pilot Study

BACKGROUND/OBJECTIVES

Recent studies indicate extensive shared white matter (WM) abnormalities between bipolar disorder (BD) and schizophrenia (SZ). However, the heterogeneity of WM in BD in terms of the presence of psychosis remains a critical issue for exploring the boundaries between BD and SZ. Previous studies comparing WM microstructures in psychotic and nonpsychotic BDs (PBD and NPBD) have resulted in limited findings, probably due to subtle changes, emphasizing the need for further investigation.

METHODS

Diffusion tensor imaging measures were obtained from 8 individuals with PBD, 8 with NPBD, and 22 healthy controls (HC), matched for age, gender, handedness, and educational years. Group comparisons were conducted using tract-based spatial statistics (TBSS). The most significant voxels showing differences between PBD and HC in the TBSS analyses were defined as a TBSS-ROI and subsequently analyzed.

RESULTS

Increased radial diffusivity (RD) in PBD compared to NPBD (p < 0.006; d = 1.706) was observed in TBSS-ROI, distributed in the confined regions of some WM tracts, including the body of the corpus callosum (bCC), the left genu of the CC (gCC), and the anterior and superior corona radiata (ACR and SCR). Additionally, NPBD exhibited significant age-associated RD increases (R2 = 0.822, p < 0.001), whereas the greater RD observed in PBD compared to NPBD remained consistent across middle age.

CONCLUSIONS

Preliminary findings from this small sample suggest severe frontal WM disconnection in the anterior interhemispheric communication, left fronto-limbic circuits, and cortico-striatal-thalamic loop in PBD compared to NPBD. While these results require replication and validation in larger and controlled samples, they provide insights into the pathophysiology of PBD, which is diagnostically located at the boundary between BD and SZ.

Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging

After more than 30 years since its inception, the utility of brain imaging for understanding and diagnosing mental illnesses is in doubt, receiving well-grounded criticisms from clinical practitioners. Symptom-based correlational approaches have struggled to provide psychiatry with reliable brain-imaging metrics. However, the emergence of computational psychiatry has paved a new path not only for understanding the psychopathology of mental illness but also to provide practical tools for clinical practice in terms of computational metrics, specifically computational phenotypes. However, these phenotypes still lack sufficient test-retest reliability. In this review, we describe recent works revealing that mind and brain-related computational phenotypes show structural (not random) variation over time, longitudinal changes. Furthermore, we show that these findings suggest that understanding the causes of these changes will improve the construct validity of the phenotypes with an ensuing increase in test-retest reliability. We propose that the active inference framework offers a general-purpose approach for causally understanding these longitudinal changes by incorporating brain imaging as observations within partially observable Markov decision processes.

Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis

Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis. Balanced accuracies of 64% for the prediction of psychosis status are in line with recent results from other large heterogenous psychiatric samples. They are confirmed by external validation in independent large samples including probands with (1) psychosis (N = 727) versus healthy controls (N = 292), (2) psychotic (N = 49) and non-psychotic bipolar disorder (N = 36), and (3) non-psychotic affective disorders (N = 119) and psychosis (N = 51) yielding accuracies of 65%, 66% and 58%, respectively, albeit slightly different psychosis syndromes. Our findings make a significant contribution to the identification of biologically defined profiles of heterogeneous psychosis syndromes on an individual level underlining the impact of sensorimotor dysfunction in psychosis.

The cliff edge model of the evolution of schizophrenia: Mathematical, epidemiological, and genetic evidence

How has schizophrenia, a condition that significantly reduces an individual's evolutionary fitness, remained common across generations and cultures? Numerous theories about the evolution of schizophrenia have been proposed, most of which are not consistent with modern epidemiological and genetic evidence. Here, we briefly review this evidence and explore the cliff edge model of schizophrenia. It suggests that schizophrenia is the extreme manifestation of a polygenic trait or a combination of traits that, within a normal range of variation, confer cognitive, linguistic, and/or social advantages. Only beyond a certain threshold, these traits precipitate the onset of schizophrenia and reduce fitness. We provide the first mathematical model of this qualitative concept and show that it requires only very weak positive selection of the underlying trait(s) to explain today's schizophrenia prevalence. This prediction, along with expectations about the effect size of schizophrenia risk alleles, are surprisingly well matched by empirical evidence. The cliff edge model predicts a dynamic change of selection of risk alleles, which explains the contradictory findings of evolutionary genetic studies.

Bipolar disorders and schizophrenia: discrete disorders?

BACKGROUND

With similarities in heritability, neurobiology and symptomatology, the question has been raised whether schizophrenia and bipolar disorder are truly distinctive disorders or belong to a continuum. This narrative review summarizes common and distinctive findings from genetics, neuroimaging, cognition and clinical course that may help to solve this ethiopathogenetic puzzle.

METHODS

The authors conducted a literature search for papers listed in PubMed and Google Scholar, using the search terms "schizophrenia" and "bipolar disorder" combined with different terms such as "genes", "neuroimaging studies", "phenomenology differences", "cognition", "epidemiology". Articles were considered for inclusion if they were written in English or Spanish, published as full articles, if they compared subjects with schizophrenia and bipolar disorder, or subjects with either disorder with healthy controls, addressing differences between groups.

RESULTS

Several findings support the hypothesis that schizophrenia and bipolar disorder are discrete disorders, yet some overlapping of findings exists. The evidence for heritability of both SZ and BD is obvious, as well as the environmental impact on individual manifestations of both disorders. Neuroimaging studies support subtle differences between disorders, it appears to be rather a pattern of irregularities than an unequivocally unique finding distinguishing schizophrenia from bipolar disorder. The cognitive profile displays differences between disorders in certain domains, such as premorbid intellectual functioning and executive functions. Finally, the timing and trajectory of cognitive impairment in both disorders also differs.

CONCLUSION

The question whether SZ and BD belong to a continuum or are separate disorders remains a challenge for further research. Currently, our research tools may be not precise enough to carve out distinctive, unique and undisputable differences between SZ and BD, but current evidence favors separate disorders. Given that differences are subtle, a way to overcome diagnostic uncertainties in the future could be the application of artificial intelligence based on BigData.

LIMITATIONS

Despite the detailed search, this article is not a full and complete review of all available studies on the topic. The search and selection of papers was also limited to articles in English and Spanish. Selection of papers and conclusions may be biased by the personal view and clinical experience of the authors.

Different Frequency of Heschl’s Gyrus Duplication Patterns in Neuropsychiatric Disorders: An MRI Study in Bipolar and Major Depressive Disorders

An increased prevalence of duplicated Heschl’s gyrus (HG) has been repeatedly demonstrated in various stages of schizophrenia as a potential neurodevelopmental marker, but it remains unknown whether other neuropsychiatric disorders also exhibit this macroscopic brain feature. The present magnetic resonance imaging study aimed to examine the disease specificity of the established finding of altered HG patterns in schizophrenia by examining independent cohorts of bipolar disorder (BD) and major depressive disorder (MDD). Twenty-six BD patients had a significantly higher prevalence of HG duplication bilaterally compared to 24 age- and sex-matched controls, while their clinical characteristics (e.g., onset age, number of episodes, and medication) did not relate to HG patterns. No significant difference was found for the HG patterns between 56 MDD patients and 33 age- and sex-matched controls, but the patients with a single HG were characterized by more severe depressive/anxiety symptoms compared to those with a duplicated HG. Thus, in keeping with previous findings, the present study suggests that neurodevelopmental pathology associated with gyral formation of the HG during the late gestation period partly overlaps between schizophrenia and BD, but that HG patterns may make a somewhat distinct contribution to the phenomenology of MDD.