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Found 5 papers, 1 papers with code
Functional connectivity modules in recurrent neural networks: function, origin and dynamics
Understanding the ubiquitous phenomenon of neural synchronization across species and organizational levels is crucial for decoding brain function.
High-Accuracy Machine Learning Techniques for Functional Connectome Fingerprinting and Cognitive State Decoding
More broadly, we explore the accuracy and reliability of five different machine learning techniques on subject fingerprinting and cognitive state decoding objectives, using functional connectivity data from fMRI scans of a high number of subjects (865) across a number of cognitive states (8).
Diurnal variations of resting-state fMRI data: A graph-based analysis
Also, connectivity and hub analyses showed that the somatomotor, ventral attention, and visual networks are the most densely-connected brain areas in both sessions, respectively, with the first being more active in the evening session and the two latter in the morning session.
Modularity maximization as a flexible and generic framework for brain network exploratory analysis
Next, we show that the modularity maximization frame is well-suited for detecting task- and condition-specific modules.
Edges in Brain Networks: Contributions to Models of Structure and Function
Here, we underscore the important contributions made by brain network edges for understanding distributed brain organization.
