ArticleSchizophrenia research2019
A symptom-based continuum of psychosis explains cognitive and real-world functional deficits better than traditional diagnoses.
Article in Schizophrenia research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Revealing differential psychotic symptoms in schizophrenia and bipolar I disorder by manifold learning and network analyses.Translational psychiatry · 2025Article
- Psychosis superspectrum II: neurobiology, treatment, and implications.Molecular psychiatry · 2024Review
- The future of diagnosis in clinical neurosciences: Comparing multiple sclerosis and schizophrenia.European psychiatry : the journal of the Association of European Psychiatrists · 2023Review
- What factors should we modify to promote high functioning and prevent functional decline in people with schizophrenia?Frontiers in psychiatry · 2023Article
- The Relationships Between Childhood Abuse and Neglect, Sub-clinical Symptoms of Psychosis and Self-harm in a Non-clinical Community Sample.Journal of child & adolescent trauma · 2022Article
- Reconceptualizing schizophrenia in the Hierarchical Taxonomy Of Psychopathology (HiTOP).Schizophrenia research · 2022Article
- Predicting Long-Term Outcomes in First-Admission Psychosis: Does the Hierarchical Taxonomy of Psychopathology Aid DSM in Prognostication?Schizophrenia bulletin · 2021Article
- The clinical relevance of gray matter atrophy and microstructural brain changes across the psychosis continuum.Schizophrenia research · 2021Article
- Differing functional mechanisms underlie cognitive control deficits in psychotic spectrum disorders.Journal of psychiatry & neuroscience : JPN · 2020Article
- Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): I. Psychosis superspectrum.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2020Article
- Rethinking the effects of working memory training on executive functions in schizophrenia: A machine learning approach.International journal of clinical and health psychology : IJCHPArticle
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Authors and funding
11 authors.
Funding
Abstract
backgroundPatients with psychotic spectrum disorders share overlapping clinical/biological features, making it often difficult to separate them into a discrete nosology (i.e., Diagnostic and Statistical Manual of Mental Disorders [DSM]).
methodsThe current study investigated whether a continuum classification scheme based on symptom burden would improve conceptualizations for cognitive and real-world dysfunction relative to traditional DSM nosology. Two independent samples (New Mexico [NM] and Bipolar and Schizophrenia Network on Intermediate Phenotypes [B-SNIP]) of patients with schizophrenia (NM: N = 93; B-SNIP: N = 236), bipolar disorder Type I (NM: N = 42; B-SNIP: N = 195) or schizoaffective disorder (NM: N = 15; B-SNIP: N = 148) and matched healthy controls (NM: N = 64; B-SNIP: N = 717) were examined. Linear regressions examined how variance differed as a function of classification scheme (DSM diagnosis, negative and positive symptom burden, or a three-cluster solution based on symptom burden).
resultsSymptom-based classification schemes (continuous and clustered) accounted for a significantly larger portion of captured variance of real-world functioning relative to DSM diagnoses across both samples. The symptom-based classification schemes accounted for large percentages of variance for general cognitive ability and cognitive domains in the NM sample. However, in the B-SNIP sample, symptom-based classification schemes accounted for roughly equivalent variance as DSM diagnoses. A potential mediating variable across samples was the strength of the relationship between negative symptoms and impaired cognition.
conclusionsCurrent results support suggestions that a continuum perspective of psychopathology may be more powerful for explaining real-world functioning than the DSM diagnostic nosology, whereas results for cognitive dysfunction were sample dependent.
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