AIMC Topic: Schizophrenia
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Improved patient identification by incorporating symptom severity in deep learning using neuroanatomic images in first episode schizophrenia.
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
Nov 6, 2024
Brain alterations associated with illness severity in schizophrenia remain poorly understood. Establishing linkages between imaging biomarkers and symptom expression may enhance mechanistic understanding of acute psychotic illness. Constructing model...
Multi-Loss Disentangled Generative-Discriminative Learning for Multimodal Representation in Schizophrenia.
IEEE journal of biomedical and health informatics
Nov 6, 2024
Schizophrenia (SCZ) is a multifactorial mental illness, thus it will be beneficial for exploring this disease using multimodal data, including functional magnetic resonance imaging (fMRI), genes, and the gut microbiome. Previous studies reported comb...
Characterization of Brain Abnormalities in Lactational Neurodevelopmental Poly I:C Rat Model of Schizophrenia and Depression Using Machine-Learning and Quantitative MRI.
Journal of magnetic resonance imaging : JMRI
Oct 28, 2024
BACKGROUND: A recent neurodevelopmental rat model, utilizing lactational exposure to polyriboinosinic-polyribocytidilic acid (Poly I:C) leads to mimics of behavioral phenotypes resembling schizophrenia-like symptoms in male offspring and depression-l...
Subcortical and insula functional connectivity aberrations and clinical implications in first-episode schizophrenia.
Asian journal of psychiatry
Oct 28, 2024
INTRODUCTION: Schizophrenia is a complex mental disorder whose pathophysiology remains elusive, particularly in the roles of subcortex. This study aims to explore the role of subcortex and insula and their relationship with symptom changes in first-e...
Identification of Bipolar Disorder and Schizophrenia Based on Brain CT and Deep Learning Methods.
Journal of imaging informatics in medicine
Sep 26, 2024
With the increasing prevalence of mental illness, accurate clinical diagnosis of mental illness is crucial. Compared with MRI, CT has the advantages of wide application, low price, short scanning time, and high patient cooperation. This study aims to...
Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.
Psychiatry and clinical neurosciences
Sep 18, 2024
BACKGROUND: Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in br...
Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary algorithms.
Computer methods and programs in biomedicine
Sep 11, 2024
BACKGROUND AND OBJECTIVE: The accurate diagnosis of schizophrenia spectrum disorder plays an important role in improving patient outcomes, enabling timely interventions, and optimizing treatment plans. Functional connectivity analysis, utilizing func...
Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms.
Molecular neurobiology
Sep 7, 2024
Schizophrenia is a disastrous mental disorder. Identification of diagnostic biomarkers and therapeutic targets is of significant importance. In this study, five datasets of schizophrenia post-mortem prefrontal cortex samples were downloaded from the ...
Prediction of anhedonia in patients with first-episode schizophrenia using a Wavelet-ALFF-based Support vector regression model.
Neuroscience
Sep 5, 2024
Anhedonia is one of the core features of the negative symptoms of schizophrenia and can be extremely burdensome. Our study applied resting-state functional magnetic resonance imaging (fMRI)-based support vector regression (SVR) to predict anhedonia i...
Forecasting the incidence frequencies of schizophrenia using deep learning.
Asian journal of psychiatry
Aug 30, 2024
Mental disorders are becoming increasingly prevalent worldwide, and accurate incidence forecasting is crucial for effective mental health strategies. This study developed a long short-term memory (LSTM)-based recurrent neural network model to predict...
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