Connectionists: CfP: Graph Learning for Brain Imaging in Frontiers of Neuroscience (deadline in 10 days)

[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*:
Full paper deadline: 31-Jan-2022
(submission of abstract is not required by highly preferred)

*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

Thank you
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