Deep learning in neuroimaging data analysis: Applications, challenges, and solutions.

Area of Science:

• Neuroscience
• Machine Learning
• Data Science

Background:

• Neuroimaging data analysis methods have advanced significantly.
• Traditional statistical procedures often assume linearity in neural processes, limiting their scope.
• Deep learning presents a powerful alternative to overcome these limitations.

Purpose of the Study:

• To review deep learning concepts and their applications in neuroimaging.
• To discuss challenges and potential solutions for deep learning in neuroimaging.
• To explore the current and future potential of deep learning in neuroscience.

Main Methods:

• Explanation of deep learning concepts, structures, and computational operations.
• Review of common deep learning applications: outcome prediction, representation interpretation, synthetic data generation, and segmentation.
• Discussion of challenges: multidimensionality, multimodality, overfitting, and computational cost, with proposed solutions.

Main Results:

• Deep learning models can analyze complex multivariate patterns in neuroimaging data.
• Applications include prediction, interpretation, synthetic data generation, and segmentation.
• Identified research gaps include limited criterion variables and undefined strategies for architecture/hyperparameter selection.

Conclusions:

• Deep learning holds significant potential to advance neuroimaging data analysis beyond linear models.
• Addressing challenges like data dimensionality and computational cost is crucial for wider adoption.
• Future research should explore transfer learning, synthetic data generation, and frameworks like RDoC.