Lecture 1 (1/12): Welcome and Course Introduction (Zhengwu Zhang and Guorong Wu, UNC)

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Lecture 2 (1/19): Neuroscience perspective of human connectome (Paul Laurienti, Wake Forest)

In this lecture, we will briefly introduce the history of brain networks in neuroscience, basic concepts in graph theory, and complex systems.

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Lecture 3 (1/26): Image processing pipeline for structural neuroimages (Guorong Wu, UNC)

We will cover the basic image processing techniques, including histogram, voxel-based processing, segmentation, and registration. After that, we will introduce the major steps of T1-weighted magnetic resonance images (MRI) and cortical surface construction.

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Lecture 4 (2/2): Diffusion-weighted imaging and diffusion tensor image analysis - I (Martin Styner, UNC)

We will go through the imaging physics of diffusion-weighted imaging and the image processing pipeline for diffusion tensor images.

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Lecture 5 (2/9): Diffusion-weighted imaging and diffusion tensor image analysis - II (Martin Styner, UNC)

We will demonstrate the computational pipeline for DWI/DTI image processing. 

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Lecture 6 (2/16): Optimizing Brain Network Acquisition (Zhengwu Zhang, UNC)

We will introduce acquisition approaches to enhance the network quality. 

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Lecture 7 (2/23):

Hands-on sessions for constructing your first structural and functional brain network (Jiaqi Ding and Tingting Dan, UNC)

This lecture will guide the students in practicing the tissue segmentation and registration pipelines for T1-weighted MRI, using the software developed in-house. Also, the students will practice surface reconstruction and generate structural brain networks based on the pre-calculated tractography results. 

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Lecture 8 (3/1): Machine learning on human connectome data -- Session I (Zhengwu Zhang, UNC)

General introduction to machine learning, including dimension reduction, clustering, classification, evaluation metrics, and state-of-the-art deep learning techniques.

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Lecture 9 (3/8): Guest lecture on Bayesian analysis for human connectomes (Heather Shappell, Wake Forest)

This lecture will talk about the Bayesian approach for modeling the brain state changes underlying functional fluctuations.

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Lecture 10 (3/22): Guest lecture on functional network analysis (Martin Lindquist, John Hopkins University)

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Lecture 10 (4/5): Graph theory for structural/functional network analysis (Guorong Wu, UNC)

We will explain the widely used graph measurement for brain networks. The students will practice using the web-based software to calculate the graph measures in a group comparison study. We will study small-world properties of brain networks and computational methods to characterize network communities and hubs.

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Lecture 12 (4/12): Machine learning on human connectome data -- Session II (Guorong Wu, UNC)

Graph neural networks and graph convolutional networks.

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Lecture 13 (4/19): Guest lecture on connectome-genetics (Hongtu Zhu, UNC)

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Lecture 14 (4/26): Course summary (Zhengwu Zhang and Guorong Wu, UNC)

We will summarize the computational techniques for human connectome analysis.