A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis.

Area of Science:

• Neuroimaging and Artificial Intelligence
• Computational Neuroscience
• Medical Image Analysis

Background:

• Traditional machine learning methods have limitations in analyzing complex neuroimaging data for brain disorder diagnosis.
• Deep learning (DL) has emerged as a powerful tool, demonstrating superior performance in computer-aided diagnosis of neurological and psychiatric conditions.
• Neuroimaging modalities like MRI and PET are crucial for understanding brain structure and function.

Purpose of the Study:

• To provide a comprehensive review of deep learning applications in neuroimaging for brain disorder analysis.
• To summarize recent advancements in deep learning techniques and network architectures relevant to neuroimaging.
• To critically evaluate the current state of DL-based diagnosis for Alzheimer's disease, Parkinson's disease, Autism Spectrum Disorder, and Schizophrenia.

Main Methods:

• Systematic review of deep learning methodologies applied to neuroimaging datasets.
• Analysis of various deep neural network architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
• Categorization of studies based on specific brain disorders (neurodegenerative, neurodevelopmental, psychiatric).

Main Results:

• Deep learning methods show significant performance improvements over traditional machine learning in classifying and diagnosing brain disorders from neuroimages.
• Specific DL architectures have demonstrated high accuracy in identifying biomarkers for Alzheimer's disease, Parkinson's disease, Autism Spectrum Disorder, and Schizophrenia.
• The review highlights the growing trend and effectiveness of DL in advancing neuroimaging-based brain disorder analysis.

Conclusions:

• Deep learning offers a promising avenue for enhancing the accuracy and efficiency of computer-aided diagnosis in neurology and psychiatry.
• Further research is needed to address the limitations of current DL models, including data heterogeneity and interpretability.
• Future directions include developing more robust and generalizable DL frameworks for diverse neuroimaging data and clinical applications.