Connectionists: CfP: Call for Paper Frontiers in Neuroscience: Special Issue "Graph Learning for Brain Imaging"

[Feng Liu]

Dear Colleagues,

We are writing to let you know that we are organizing a special issue "Graph
 Learning for Brain Imaging" in Frontiers in Neuroscience (impact factor
4.7). We believe this is a timely special issue to showcase the new
developments using graph representation, deep learning on graph-structured
data to address important brain imaging and computational neuroscience
problems.

*Link*: https://www.frontiersin.org/research-topics/23683/graph-learning
-for-brain-imaging


*Keywords*: Brain Networks, Graph Neural Networks, Brain Imaging,
Graph Embedding,
Multi-Modal Imaging.


*Topics*: We are looking for original, high-quality submissions on
innovative research and developments in the analysis of brain imaging using
graph learning techniques. Topics of interest include (but are not limited
to):

• Graph neural networks (GNN) for network neuroscience applications
• Graph neural network for brain mapping and data integration
• Graph convolution network (GCN) for brain disorder classification
• (Dynamic) Functional brain networks
• Brain networks development trajectories
• Graphical model for brain imaging data analysis
• Spatial-temporal brain network modeling
• Graph embedding and graph representation learning
• Information fusion for brain networks from multiple modalities or scales
(fMRI, M/EEG, DTI, PET, genetics)
• Generative graph models in brain imaging
• Brain network inference: scalable, online, and from non-linear
relationships
• Machine learning over graphs: kernel-based techniques, clustering
methods, scalable algorithms for brain imaging
• A few-shot learning for learning from limited brain data
• Graph federated learning for brain imaging

*Important Dates*:
Abstract: 30-Sep-2021
Full paper: 30-Dec-2021

*Background:*

Unprecedented collections of large-scale brain imaging data, such as MRI,
PET, fMRI, M/EEG, DTI, etc. provide a unique opportunity to deepen our
understanding of the brain working mechanisms, improve prognostic
predictions for mental disorders, and tailor personalized treatment plans
for brain diseases. Recent advances in machine learning and large-scale
brain imaging data collection, storage, and sharing lead to a series of
novel interdisciplinary approaches among the fields of computational
neuroscience, signal processing, deep learning, brain imaging, cognitive
science, and computational psychiatry, among which graph learning provides
a valuable means to address important questions in brain imaging.

Graph learning refers to designing effective machine learning and deep
learning methods extracting important information from graphs or exploiting
the graph structure in the data to guide the knowledge discovery. Given the
complex data structure in different imaging modalities as well as the
networked organizational structure of the human brain, novel learning methods
based on graphs inferred from imaging data, graph regularizations for the
data, and graph embedding of the recorded data, have shown great promise in
modeling the interactions of multiple brain regions, information fusion
among networks derived from different brain imaging modalities, latent
space modeling of the high dimensional brain networks, and quantifying
topological neurobiomarkers. The goal of this Research Topic is to
synergize the start-of-the-art discoveries in terms of new computational
brain imaging models and insights of brain mechanisms through the lens of
brain networks and graph learning.


--On Behalf of all the Guest Editors

Feng Liu, Stevens Institute of Technology, Hoboken, NJ, USA

Yu Zhang, Lehigh University, Bethlehem, PA, USA
Jordi Solé-Casals, Universitat de Vic - Universitat Central de
Catalunya, Barcelona, SpainIslem Rekik, Istanbul Technical University,
Istanbul, TurkeyYehia Massoud, King Abdullah University of Science and
Technology, Thuwal, Saudi Arabia

Best regards
Feng Liu
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