11 research outputs found
Construction of embedded fMRI resting state functional connectivity networks using manifold learning
We construct embedded functional connectivity networks (FCN) from benchmark
resting-state functional magnetic resonance imaging (rsfMRI) data acquired from
patients with schizophrenia and healthy controls based on linear and nonlinear
manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric
Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global
graph-theoretical properties of the embedded FCN, we compare their
classification potential using machine learning techniques. We also assess the
performance of two metrics that are widely used for the construction of FCN
from fMRI, namely the Euclidean distance and the lagged cross-correlation
metric. We show that the FCN constructed with Diffusion Maps and the lagged
cross-correlation metric outperform the other combinations
Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation
Common measures of brain functional connectivity (FC) including covariance
and correlation matrices are semi-positive definite (SPD) matrices residing on
a cone-shape Riemannian manifold. Despite its remarkable success for
Euclidean-valued data generation, use of standard generative adversarial
networks (GANs) to generate manifold-valued FC data neglects its inherent SPD
structure and hence the inter-relatedness of edges in real FC. We propose a
novel graph-regularized manifold-aware conditional Wasserstein GAN (GR-SPD-GAN)
for FC data generation on the SPD manifold that can preserve the global FC
structure. Specifically, we optimize a generalized Wasserstein distance between
the real and generated SPD data under an adversarial training, conditioned on
the class labels. The resulting generator can synthesize new SPD-valued FC
matrices associated with different classes of brain networks, e.g., brain
disorder or healthy control. Furthermore, we introduce additional population
graph-based regularization terms on both the SPD manifold and its tangent space
to encourage the generator to respect the inter-subject similarity of FC
patterns in the real data. This also helps in avoiding mode collapse and
produces more stable GAN training. Evaluated on resting-state functional
magnetic resonance imaging (fMRI) data of major depressive disorder (MDD),
qualitative and quantitative results show that the proposed GR-SPD-GAN clearly
outperforms several state-of-the-art GANs in generating more realistic
fMRI-based FC samples. When applied to FC data augmentation for MDD
identification, classification models trained on augmented data generated by
our approach achieved the largest margin of improvement in classification
accuracy among the competing GANs over baselines without data augmentation.Comment: 10 pages, 4 figure
Tangent functional connectomes uncover more unique phenotypic traits
Functional connectomes (FCs) contain pairwise estimations of functional
couplings based on pairs of brain regions activity. FCs are commonly
represented as correlation matrices that are symmetric positive definite (SPD)
lying on or inside the SPD manifold. Since the geometry on the SPD manifold is
non-Euclidean, the inter-related entries of FCs undermine the use of
Euclidean-based distances. By projecting FCs into a tangent space, we can
obtain tangent functional connectomes (tangent-FCs). Tangent-FCs have shown a
higher predictive power of behavior and cognition, but no studies have
evaluated the effect of such projections with respect to fingerprinting. We
hypothesize that tangent-FCs have a higher fingerprint than regular FCs.
Fingerprinting was measured by identification rates (ID rates) on test-retest
FCs as well as on monozygotic and dizygotic twins. Our results showed that
identification rates are systematically higher when using tangent-FCs.
Specifically, we found: (i) Riemann and log-Euclidean matrix references
systematically led to higher ID rates. (ii) In tangent-FCs, Main-diagonal
regularization prior to tangent space projection was critical for ID rate when
using Euclidean distance, whereas barely affected ID rates when using
correlation distance. (iii) ID rates were dependent on condition and fMRI scan
length. (iv) Parcellation granularity was key for ID rates in FCs, as well as
in tangent-FCs with fixed regularization, whereas optimal regularization of
tangent-FCs mostly removed this effect. (v) Correlation distance in tangent-FCs
outperformed any other configuration of distance on FCs or on tangent-FCs
across the fingerprint gradient (here sampled by assessing test-retest,
Monozygotic and Dizygotic twins). (vi)ID rates tended to be higher in task
scans compared to resting-state scans when accounting for fMRI scan length.Comment: 29 pages, 10 figures, 2 table