Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning on Human Connectome Data from MRI   Functional MRI (fMRI) and diffusion MRI (dMRI) are non-invasive imaging
modalities that allow in-vivo analysis of a patient's brain network (known as a
connectome). Use of these technologies has enabled faster and better diagnoses
and treatments of neurological disorders and a deeper understanding of the
human brain. Recently, researchers have been exploring the application of
machine learning models to connectome data in order to predict clinical
outcomes and analyze the importance of subnetworks in the brain. Connectome
data has unique properties, which present both special challenges and
opportunities when used for machine learning. The purpose of this work is to
review the literature on the topic of applying machine learning models to
MRI-based connectome data. This field is growing rapidly and now encompasses a
large body of research. To summarize the research done to date, we provide a
comparative, structured summary of 77 relevant works, tabulated according to
different criteria, that represent the majority of the literature on this
topic. (We also published a living version of this table online at
http://connectomelearning.cs.sfu.ca that the community can continue to
contribute to.) After giving an overview of how connectomes are constructed
from dMRI and fMRI data, we discuss the variety of machine learning tasks that
have been explored with connectome data. We then compare the advantages and
drawbacks of different machine learning approaches that have been employed,
discussing different feature selection and feature extraction schemes, as well
as the learning models and regularization penalties themselves. Throughout this
discussion, we focus particularly on how the methods are adapted to the unique
nature of graphical connectome data. Finally, we conclude by summarizing the
current state of the art and by outlining what we believe are strategic
directions for future research.

28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph