Using psychosis biotypes and the Framingham model for parsing psychosis biology

CNN AND LSTM MODELS FOR FMRI-BASED SCHIZOPHRENIA CLASSIFICATION USING C-ICA OF DFNC

Resting-state fMRI (rs-fMRI) captures brain activity at rest, it demonstrates information on how different regions interact without explicity task-based influences. This provides insights into both healthy and disordered brain states. However, clinical application of rs-fMRI remains challenging due to the wide variability in functional connectivity across individuals. Traditional data-driven methods like independent component analysis (ICA) struggle to balance these individual differences with broader patterns. Constrained methods, such as constrained ICA (cICA), have been introduced to address this by integrating templates from multiple external datasets to enhance accuracy and consistency. In our study, we analyzed rs-fMRI data from 100,517 individuals from diverse datasets, processed through a robust quality-control dynamic connectivity pipeline established in previous work. Using the resulting brain state templates as cICA priors, we examined the effectiveness of cICA for schizophrenia classification using a combined CNN and LSTM architecture. Results showed stable classification accuracy (87.6% to 86.43%) for the CNN model, while the LSTM model performed less optimally, likely due to sequence processing, yet still yielded comparable results. These findings underscore the potential of group-informed methods and prior data templates in constrained dynamic ICA, offering improved reliability and clinical relevance in rs-fMRI analysis and advancing our understanding of brain function.

A narrative review of nosology and the concept of schizophrenia: criticism and proposal

Schizophrenia diagnostics have evolved to adapt to clinical needs and scientific advances, and the current denominations emerged at the beginning of the twentieth century. Most problems arise while integrating clinical experiences, based on historical psychopathological descriptions, with emerging translational neuroscience research. This study aimed to evaluate the state-of-the-art critics of the current schizophrenia concept and their recommendations for new concepts. We performed a narrative review of the literature and searched for studies published in English in PubMed in the last 2 years which discussed the diagnosis of schizophrenia. Two authors independently selected the studies after analyzing the abstracts. Subsequently, studies were selected for this review by consensus. Twenty-six studies were selected, and all authors, except two, had restrictions on the current categorical model for the diagnosis of schizophrenia owing to the heterogeneity of symptomatology and high frequency of comorbidity. Eight studies proposed changes to the concept of schizophrenia. The central proposition was to adopt psychotic syndrome as a core feature instead of the current concept of schizophrenia. We synthesize these proposals using psychosis as a spectrum that includes schizophrenia as a more severe case at the end of the spectrum.

The schizophrenia syndrome, circa 2024: What we know and how that informs its nature

With new data about different aspects of schizophrenia being continually generated, it becomes necessary to periodically revisit exactly what we know. Along with a need to review what we currently know about schizophrenia, there is an equal imperative to evaluate the construct itself. With these objectives, we undertook an iterative, multi-phase process involving fifty international experts in the field, with each step building on learnings from the prior one. This review assembles currently established findings about schizophrenia (construct, etiology, pathophysiology, clinical expression, treatment) and posits what they reveal about its nature. Schizophrenia is a heritable, complex, multi-dimensional syndrome with varying degrees of psychotic, negative, cognitive, mood, and motor manifestations. The illness exhibits a remitting and relapsing course, with varying degrees of recovery among affected individuals with most experiencing significant social and functional impairment. Genetic risk factors likely include thousands of common genetic variants that each have a small impact on an individual's risk and a plethora of rare gene variants that have a larger individual impact on risk. Their biological effects are concentrated in the brain and many of the same variants also increase the risk of other psychiatric disorders such as bipolar disorder, autism, and other neurodevelopmental conditions. Environmental risk factors include but are not limited to urban residence in childhood, migration, older paternal age at birth, cannabis use, childhood trauma, antenatal maternal infection, and perinatal hypoxia. Structural, functional, and neurochemical brain alterations implicate multiple regions and functional circuits. Dopamine D-2 receptor antagonists and partial agonists improve psychotic symptoms and reduce risk of relapse. Certain psychological and psychosocial interventions are beneficial. Early intervention can reduce treatment delay and improve outcomes. Schizophrenia is increasingly considered to be a heterogeneous syndrome and not a singular disease entity. There is no necessary or sufficient etiology, pathology, set of clinical features, or treatment that fully circumscribes this syndrome. A single, common pathophysiological pathway appears unlikely. The boundaries of schizophrenia remain fuzzy, suggesting the absence of a categorical fit and need to reconceptualize it as a broader, multi-dimensional and/or spectrum construct.

Categorical and Dimensional Approaches for Psychiatric Classification and Treatment Targeting: Considerations from Psychosis Biotypes

Categorical diagnosis, a pillar of the medical model, has not worked well in psychiatry where most diagnoses are still exclusively symptom based. Uncertainty continues about whether categories or dimensions work better for the assessment and treatment of idiopathic psychoses. The Bipolar Schizophrenia Network for Intermediate Phenotypes (B-SNIP) examined multiple cognitive and electrophysiological biomarkers across a large transdiagnostic psychosis data set. None of the variables supported neurobiological distinctiveness for conventional clinical psychosis diagnoses but showed a continuum of severity. Using numerical taxonomy of these data, B-SNIP identified three biological subtypes (Biotypes) agnostic to DSM diagnoses. Biotype-1 is characterized by reduced physiological response to salient stimuli, while Biotype-2 showed accentuated intrinsic (background or ongoing) neural activity and the worst inhibition. Biotype-3 cases are like healthy persons on many laboratory measures. These Biotypes differed in imaging and other electrophysiological measures not included in subgroup creation, illustrating external validation. The Biotypes solution also replicated in an independent sample of psychosis cases. Biotypes are differentiable by clinical characteristics, leading to a feasible algorithm for Biotype estimates. Identifying Biotypes may aid treatment selection and outcome prediction. As an example, preliminary cross-sectional B-SNIP data suggest that Biotype-1 cases may have physiological features that predict a more favorable response to clozapine. While psychosis Biotypes reveal physiological heterogeneity across cases with similar clinical characteristics, data also suggest a dimensional vulnerability for serious psychopathology that cuts across diagnostic boundaries. Both categorical and dimensional diagnostic approaches should be considered within idiopathic psychosis for optimum diagnosis, care, and research.