Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Region segmentation for sparse decompositions: better brain
  parcellations from rest fMRI   Functional Magnetic Resonance Images acquired during resting-state provide
information about the functional organization of the brain through measuring
correlations between brain areas. Independent components analysis is the
reference approach to estimate spatial components from weakly structured data
such as brain signal time courses; each of these components may be referred to
as a brain network and the whole set of components can be conceptualized as a
brain functional atlas. Recently, new methods using a sparsity prior have
emerged to deal with low signal-to-noise ratio data. However, even when using
sophisticated priors, the results may not be very sparse and most often do not
separate the spatial components into brain regions. This work presents
post-processing techniques that automatically sparsify brain maps and separate
regions properly using geometric operations, and compares these techniques
according to faithfulness to data and stability metrics. In particular, among
threshold-based approaches, hysteresis thresholding and random walker
segmentation, the latter improves significantly the stability of both dense and
sparse models.

22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess