1,413 research outputs found
Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets
The scale of functional magnetic resonance image data is rapidly increasing
as large multi-subject datasets are becoming widely available and
high-resolution scanners are adopted. The inherent low-dimensionality of the
information in this data has led neuroscientists to consider factor analysis
methods to extract and analyze the underlying brain activity. In this work, we
consider two recent multi-subject factor analysis methods: the Shared Response
Model and Hierarchical Topographic Factor Analysis. We perform analytical,
algorithmic, and code optimization to enable multi-node parallel
implementations to scale. Single-node improvements result in 99x and 1812x
speedups on these two methods, and enables the processing of larger datasets.
Our distributed implementations show strong scaling of 3.3x and 5.5x
respectively with 20 nodes on real datasets. We also demonstrate weak scaling
on a synthetic dataset with 1024 subjects, on up to 1024 nodes and 32,768
cores
Tensor Analysis and Fusion of Multimodal Brain Images
Current high-throughput data acquisition technologies probe dynamical systems
with different imaging modalities, generating massive data sets at different
spatial and temporal resolutions posing challenging problems in multimodal data
fusion. A case in point is the attempt to parse out the brain structures and
networks that underpin human cognitive processes by analysis of different
neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the
multimodal, multi-scale nature of neuroimaging data is well reflected by a
multi-way (tensor) structure where the underlying processes can be summarized
by a relatively small number of components or "atoms". We introduce
Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network
notation in order to analyze these models. These diagrams not only clarify
matrix and tensor EEG and fMRI time/frequency analysis and inverse problems,
but also help understand multimodal fusion via Multiway Partial Least Squares
and Coupled Matrix-Tensor Factorization. We show here, for the first time, that
Granger causal analysis of brain networks is a tensor regression problem, thus
allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI
recordings shows the potential of the methods and suggests their use in other
scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE
A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Imaging for Modeling Brain Activities
Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits.
In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the advantages of each one. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events and capture the brain response under specific experimental conditions.
Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results.
EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from each one is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented in this work. A HMM modeling approach is used in order to derive a novel feature-based representation of the EEG signal that characterizes the topographic information of the EEG. We use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI is studied using CPLS. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of mental tasks. Extensions on the proposed models are examined along with future research directions and applications
Nonparametric variational inference
Variational methods are widely used for approximate posterior inference.
However, their use is typically limited to families of distributions that enjoy
particular conjugacy properties. To circumvent this limitation, we propose a
family of variational approximations inspired by nonparametric kernel density
estimation. The locations of these kernels and their bandwidth are treated as
variational parameters and optimized to improve an approximate lower bound on
the marginal likelihood of the data. Using multiple kernels allows the
approximation to capture multiple modes of the posterior, unlike most other
variational approximations. We demonstrate the efficacy of the nonparametric
approximation with a hierarchical logistic regression model and a nonlinear
matrix factorization model. We obtain predictive performance as good as or
better than more specialized variational methods and sample-based
approximations. The method is easy to apply to more general graphical models
for which standard variational methods are difficult to derive.Comment: ICML201
Early error detection predicted by reduced pre-response control process: an ERP topographic mapping study
Advanced ERP topographic mapping techniques were used to study error monitoring functions in human adult participants, and test whether proactive attentional effects during the pre-response time period could later influence early error detection mechanisms (as measured by the ERN component) or not. Participants performed a speeded go/nogo task, and made a substantial number of false alarms that did not differ from correct hits as a function of behavioral speed or actual motor response. While errors clearly elicited an ERN component generated within the dACC following the onset of these incorrect responses, I also found that correct hits were associated with a different sequence of topographic events during the pre-response baseline time-period, relative to errors. A main topographic transition from occipital to posterior parietal regions (including primarily the precuneus) was evidenced for correct hits similar to 170-150 ms before the response, whereas this topographic change was markedly reduced for errors. The same topographic transition was found for correct hits that were eventually performed slower than either errors or fast (correct) hits, confirming the involvement of this distinctive posterior parietal activity in top-down attentional control rather than motor preparation. Control analyses further ensured that this pre-response topographic effect was not related to differences in stimulus processing. Furthermore, I found a reliable association between the magnitude of the ERN following errors and the duration of this differential precuneus activity during the pre-response baseline, suggesting a functional link between an anticipatory attentional control component subserved by the precuneus and early error detection mechanisms within the dACC. These results suggest reciprocal links between proactive attention control and decision making processes during error monitoring
State-space solutions to the dynamic magnetoencephalography inverse problem using high performance computing
Determining the magnitude and location of neural sources within the brain
that are responsible for generating magnetoencephalography (MEG) signals
measured on the surface of the head is a challenging problem in functional
neuroimaging. The number of potential sources within the brain exceeds by an
order of magnitude the number of recording sites. As a consequence, the
estimates for the magnitude and location of the neural sources will be
ill-conditioned because of the underdetermined nature of the problem. One
well-known technique designed to address this imbalance is the minimum norm
estimator (MNE). This approach imposes an regularization constraint that
serves to stabilize and condition the source parameter estimates. However,
these classes of regularizer are static in time and do not consider the
temporal constraints inherent to the biophysics of the MEG experiment. In this
paper we propose a dynamic state-space model that accounts for both spatial and
temporal correlations within and across candidate intracortical sources. In our
model, the observation model is derived from the steady-state solution to
Maxwell's equations while the latent model representing neural dynamics is
given by a random walk process.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS483 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
The Reference Ability Neural Network Study: Motivation, Design, and Initial Feasibility Analyses
We introduce and describe the Reference Ability Neural Network Study and provide initial feasibility data. Based on analyses of large test batteries administered to individuals ranging from young to old, four latent variables, or reference abilities (RAs) that capture the majority of the variance in age-related cognitive change have been identified: episodic memory, fluid reasoning, perceptual speed, and vocabulary. We aim to determine whether spatial fMRI networks can be derived that are uniquely associated with the performance of each reference ability. We plan to image 375 healthy adults (50 per decade from age 20 to 50; 75 per decade from age 50 to 80) while performing a set of 12 cognitive tasks. Data on 174 participants are reported here. Three tasks were grouped a priori into each of the four reference ability domains. We first assessed to what extent both cognitive task scores and activation patterns readily show convergent and discriminant validity, i.e. increased similarity between tasks within the same domain and decreased similarity between tasks between domains, respectively. Block-based time-series analysis of each individual task was conducted for each participant via general linear modeling. We partialled activation common to all tasks out of the imaging data. For both test scores and activation topographies, we then calculated correlations for each of 66 possible pairings of tasks, and compared the magnitude of correlation of tasks within reference ability domains to that of tasks between domains. For the behavioral data, globally there were significantly stronger inter-task correlations within than between domains. When examining individual abilities, 3 of the domains also met these criteria but memory reached only borderline significance. Overall there was greater topographic similarity within reference abilities than between them (p<0.0001), but when examined individually, statistical significance was reached only for episodic memory and perceptual speed. We then turned to a multivariate technique, linear indicator regression analysis, to derive four unique linear combinations of Principal Components (PC) of imaging data that were associated with each RA. We investigated the ability of the identified PCs to predict the reference domain associated with the activation of individual subjects for individual tasks. Median accuracy rates for associating component task activation with a particular reference ability were quite good: memory: 82%; reasoning: 87%; speed: 84%; vocabulary: 77%. These results demonstrate that even using basic GLM analysis, the topography of activation of tasks within a domain is more similar than tasks between domains. The follow-up regression analyses suggest that all tasks with each RA rely on a common network, unique to that RA. Our ultimate goal is to better characterize these RA neural networks and then study how their expression changes across the age span. Our hope is that by focusing on these networks associated with key features of cognitive aging, as opposed to task-related activation associated with individual tasks, we will be able to advance our knowledge regarding the key brain changes that underlie cognitive aging
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