8,357 research outputs found
Group Analysis of Self-organizing Maps based on Functional MRI using Restricted Frechet Means
Studies of functional MRI data are increasingly concerned with the estimation
of differences in spatio-temporal networks across groups of subjects or
experimental conditions. Unsupervised clustering and independent component
analysis (ICA) have been used to identify such spatio-temporal networks. While
these approaches have been useful for estimating these networks at the
subject-level, comparisons over groups or experimental conditions require
further methodological development. In this paper, we tackle this problem by
showing how self-organizing maps (SOMs) can be compared within a Frechean
inferential framework. Here, we summarize the mean SOM in each group as a
Frechet mean with respect to a metric on the space of SOMs. We consider the use
of different metrics, and introduce two extensions of the classical sum of
minimum distance (SMD) between two SOMs, which take into account the
spatio-temporal pattern of the fMRI data. The validity of these methods is
illustrated on synthetic data. Through these simulations, we show that the
three metrics of interest behave as expected, in the sense that the ones
capturing temporal, spatial and spatio-temporal aspects of the SOMs are more
likely to reach significance under simulated scenarios characterized by
temporal, spatial and spatio-temporal differences, respectively. In addition, a
re-analysis of a classical experiment on visually-triggered emotions
demonstrates the usefulness of this methodology. In this study, the
multivariate functional patterns typical of the subjects exposed to pleasant
and unpleasant stimuli are found to be more similar than the ones of the
subjects exposed to emotionally neutral stimuli. Taken together, these results
indicate that our proposed methods can cast new light on existing data by
adopting a global analytical perspective on functional MRI paradigms.Comment: 23 pages, 5 figures, 4 tables. Submitted to Neuroimag
Using a novel source-localized phase regressor technique for evaluation of the vascular contribution to semantic category area localization in BOLD fMRI.
Numerous studies have shown that gradient-echo blood oxygen level dependent (BOLD) fMRI is biased toward large draining veins. However, the impact of this large vein bias on the localization and characterization of semantic category areas has not been examined. Here we address this issue by comparing standard magnitude measures of BOLD activity in the Fusiform Face Area (FFA) and Parahippocampal Place Area (PPA) to those obtained using a novel method that suppresses the contribution of large draining veins: source-localized phase regressor (sPR). Unlike previous suppression methods that utilize the phase component of the BOLD signal, sPR yields robust and unbiased suppression of large draining veins even in voxels with no task-related phase changes. This is confirmed in ideal simulated data as well as in FFA/PPA localization data from four subjects. It was found that approximately 38% of right PPA, 14% of left PPA, 16% of right FFA, and 6% of left FFA voxels predominantly reflect signal from large draining veins. Surprisingly, with the contributions from large veins suppressed, semantic category representation in PPA actually tends to be lateralized to the left rather than the right hemisphere. Furthermore, semantic category areas larger in volume and higher in fSNR were found to have more contributions from large veins. These results suggest that previous studies using gradient-echo BOLD fMRI were biased toward semantic category areas that receive relatively greater contributions from large veins
Neural Mechanisms Underlying Paradoxical Performance for Monetary Incentives Are Driven by Loss Aversion
Employers often make payment contingent on
performance in order to motivate workers. We used
fMRI with a novel incentivized skill task to examine
the neural processes underlying behavioral responses
to performance-based pay. We found that
individuals’ performance increased with increasing
incentives; however, very high incentive levels led
to the paradoxical consequence of worse performance.
Between initial incentive presentation and
task execution, striatal activity rapidly switched
between activation and deactivation in response
to increasing incentives. Critically, decrements in
performance and striatal deactivations were directly
predicted by an independent measure of behavioral
loss aversion. These results suggest that incentives
associated with successful task performance are
initially encoded as a potential gain; however, when
actually performing a task, individuals encode the
potential loss that would arise from failure
Learning to rank from medical imaging data
Medical images can be used to predict a clinical score coding for the
severity of a disease, a pain level or the complexity of a cognitive task. In
all these cases, the predicted variable has a natural order. While a standard
classifier discards this information, we would like to take it into account in
order to improve prediction performance. A standard linear regression does
model such information, however the linearity assumption is likely not be
satisfied when predicting from pixel intensities in an image. In this paper we
address these modeling challenges with a supervised learning procedure where
the model aims to order or rank images. We use a linear model for its
robustness in high dimension and its possible interpretation. We show on
simulations and two fMRI datasets that this approach is able to predict the
correct ordering on pairs of images, yielding higher prediction accuracy than
standard regression and multiclass classification techniques
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
Dynamic functional connectivity, as measured by the time-varying covariance
of neurological signals, is believed to play an important role in many aspects
of cognition. While many methods have been proposed, reliably establishing the
presence and characteristics of brain connectivity is challenging due to the
high dimensionality and noisiness of neuroimaging data. We present a latent
factor Gaussian process model which addresses these challenges by learning a
parsimonious representation of connectivity dynamics. The proposed model
naturally allows for inference and visualization of time-varying connectivity.
As an illustration of the scientific utility of the model, application to a
data set of rat local field potential activity recorded during a complex
non-spatial memory task provides evidence of stimuli differentiation
A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments
Effective connectivity analysis provides an understanding of the functional
organization of the brain by studying how activated regions influence one
other. We propose a nonparametric Bayesian approach to model effective
connectivity assuming a dynamic nonstationary neuronal system. Our approach
uses the Dirichlet process to specify an appropriate (most plausible according
to our prior beliefs) dynamic model as the "expectation" of a set of plausible
models upon which we assign a probability distribution. This addresses model
uncertainty associated with dynamic effective connectivity. We derive a Gibbs
sampling approach to sample from the joint (and marginal) posterior
distributions of the unknowns. Results on simulation experiments demonstrate
our model to be flexible and a better candidate in many situations. We also
used our approach to analyzing functional Magnetic Resonance Imaging (fMRI)
data on a Stroop task: our analysis provided new insight into the mechanism by
which an individual brain distinguishes and learns about shapes of objects.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS470 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
- …