ArticleSchizophrenia research2022
Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: A deep classification and clustering framework using fMRI time series.
Article in Schizophrenia research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Concurrent Supervised-Unsupervised Representative Subspace Clustering (CSU-RSC).bioRxiv : the preprint server for biology · 2026Article
- Subtyping schizophrenia via machine learning by using structural neuroimaging.Translational psychiatry · 2025Review
- FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (Food chemistry: X · 2024Article
- Multiclass motor imagery classification with Riemannian geometry and temporal-spectral selection.Medical & biological engineering & computing · 2024Article
- The overlap across psychotic disorders: A functional network connectivity analysis.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2024Article
- Identification of important gene signatures in schizophrenia through feature fusion and genetic algorithm.Mammalian genome : official journal of the International Mammalian Genome Society · 2024Article
- Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2024Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment.Computers in biology and medicine · 2023Article
- Intrinsic neural timescales in autism spectrum disorder and schizophrenia. A replication and direct comparison study.Schizophrenia (Heidelberg, Germany) · 2023Article
- Identification of Homogeneous Subgroups from Resting-State fMRI Data.Sensors (Basel, Switzerland) · 2023Article
- Segregation, integration and balance in resting-state brain functional networks associated with bipolar disorder symptoms.Human brain mapping · 2023Article
- Psychotic Symptom, Mood, and Cognition-associated Multimodal MRI Reveal Shared Links to the Salience Network Within the Psychosis Spectrum Disorders.Schizophrenia bulletin · 2023Article
- Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG.Frontiers in psychiatry · 2023Article
- An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data.Medical image analysis · 2022Article
- Deep learning in neuroimaging data analysis: Applications, challenges, and solutions.Frontiers in neuroimaging · 2022Review
- Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models.Frontiers in neuroinformatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundPsychiatric disorders are categorized using self-report and observational information rather than biological data. There is also considerable symptomatic overlap between different types of psychiatric disorders, which makes diagnostic categorization and multi-class classification challenging.
methodsIn this work, we propose a unified framework for supervised classification and unsupervised clustering of psychotic disorders using brain imaging data. A new multi-scale recurrent neural network (MsRNN) model was developed and applied to fMRI time courses (TCs) for multi-class classification. The high-level representations of the original TCs were then submitted to a tSNE clustering model for visualizing the group differences between disorders. A leave-one-feature-out approach was used for disorder-related biomarker identification.
resultsWhen studying fMRI from schizophrenia, psychotic bipolar disorder, schizoaffective disorder, and healthy individuals, the accuracy of a 4-class classification reached 46%, significantly above chance. The hippocampus, supplementary motor area and paracentral lobule were discovered as the most contributing regional TCs in the multi-class classification. Beyond this, visualization of the tSNE clustering suggested that the disease severity can be captured and schizoaffective disorder (SAD) may be separated into two subtypes. SAD cluster1 has significantly higher Positive And Negative Syndrome Scale (PANSS) scores than SAD cluster2 in PANSS negative2 (emotional withdrawal), general2 (anxiety), general3 (guilt feelings), general4 (tension).
conclusionsThe proposed deep classification and clustering framework is not only able to identify psychiatric disorders with high accuracy, but also interpret the correlation between brain networks and specific psychiatric disorders, and reveal the relationship between them. This work provides a promising way to investigate a spectrum of similar disorders using neuroimaging-based measures.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.