AIMC Topic: Schizophrenia
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Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.
Schizophrenia bulletin
Dec 20, 2024
BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (...
A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data.
Human brain mapping
Dec 1, 2024
Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagnose specific brain disorders, especially when deep learning-based AI algorithms are applied. The successful combination of different brain imaging mod...
Making the most of errors: Utilizing erroneous classifications generated by machine-learning models of neuroimaging data to capture disorder heterogeneity.
Journal of psychopathology and clinical science
Nov 1, 2024
Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disorders conceptualizations. The present study explored the patterns of diagnostic classification errors...
GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console.
Bioinformatics (Oxford, England)
Oct 1, 2024
SUMMARY: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation ...
Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data.
Briefings in functional genomics
Sep 27, 2024
Schizophrenia genome-wide association studies (GWAS) have reported many genomic risk loci, but it is unclear how they affect schizophrenia susceptibility through interactions of multiple SNPs. We propose a stepwise deep learning technique with multi-...
Unveiling Functional Biomarkers in Schizophrenia: Insights from Region of Interest Analysis Using Machine Learning.
Journal of integrative neuroscience
Sep 24, 2024
BACKGROUND: Schizophrenia is a complex and disabling mental disorder that represents one of the most important challenges for neuroimaging research. There were many attempts to understand these basic mechanisms behind the disorder, yet we know very l...
Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders.
Schizophrenia bulletin
Jul 27, 2024
BACKGROUND AND HYPOTHESIS: The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PAD in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), an...
Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
The diagnosis of schizophrenia (SZ) can be challenging due to its diverse symptom presentation. As such, many studies have sought to identify diagnostic biomarkers of SZ using explainable machine learning methods. However, the generalizability of ide...
Classification of Schizophrenia using Intrinsic Connectivity Networks and Incremental Boosting Convolution Neural Networks.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
One of the key challenges in the use of resting brain functional magnetic resonance imaging (fMRI) network analysis for predicting mental illnesses such as schizophrenia (SZ) is the high noise levels variability among individuals including age, sex, ...
Machine learning in small sample neuroimaging studies: Novel measures for schizophrenia analysis.
Human brain mapping
Apr 1, 2024
Novel features derived from imaging and artificial intelligence systems are commonly coupled to construct computer-aided diagnosis (CAD) systems that are intended as clinical support tools or for investigation of complex biological patterns. This stu...
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