Deep Learning and Machine Learning Models for Neural Imaging Decoding: A Review

This literature review provides an overview of the advancements, applications, and challenges associated with deep learning and machine learning models for decoding neuroimaging data. It discusses the various deep learning architectures used in neuroimaging analysis and their strengths and limitations. The review highlights the potential of these models in tasks such as brain tumor segmentation, functional connectivity analysis, and brain disorder classification. It also addresses critiques related to sample bias, reproducibility, and interpretability challenges. Recommendations for future research include the development of hybrid models, improved interpretability techniques, and integration of diverse datasets. The review emphasizes the importance of these models in advancing our understanding of the human brain and improving diagnosis and treatment of neurological disorders.

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Prabhakar S. Deep learning and machine learning models for neural imaging decoding: a review. Open Clinical Annals. 2024:1(1):14-21. DOI 10.17605/OSF.IO/935DE https://openannals.wixsite.com/openclinicalannals/deep-learning-and-machine-learning-models-for-neural-imaging-decoding-a-review