Introduction to Deep Learning in Clinical Neuroscience

Area of Science:

• Neuroscience
• Artificial Intelligence
• Medical Imaging

Background:

• Deep learning (DL) models, inspired by brain neural networks, are increasingly used in clinical neuroscience.
• These models utilize multiple sequential learning layers for complex data analysis.

Purpose of the Study:

• To provide examples of DL applications in analyzing MRI data within clinical neuroscience.
• To discuss potential applications, methodological considerations, and the utility of DL in this field.

Main Methods:

• Data pre-processing and volumetric segmentation are crucial steps.
• Convolutional Neural Networks (CNNs) and Autoencoders (AEs) are key DL methods.
• Generative Adversarial Network (GAN)-expansion and domain mapping were used for data augmentation and integration.

Main Results:

• DL-based segmentation achieved Dice scores between 0.77 and 0.89.
• Prediction of IDH mutation in glioma cohorts showed high accuracy (sensitivity 0.98, specificity 0.97).
• GAN-expansion and domain mapping improved accuracy by 5-7% in test cohorts.

Conclusions:

• DL shows promise in analyzing neuroimaging data, particularly for data augmentation and handling variability.
• While not yet standard clinical practice, DL models offer potential as decision support systems for data-intensive tasks.
• Further research, development, and validation are essential for widespread clinical adoption of DL in neuroscience.