473 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
Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data
Clustering is a fundamental data processing technique. While clustering of static (vector based) data and of fixed window size time series have been well explored, dynamic clustering of spatiotemporal data has been little researched if at all. Especially when patterns of changes (events) in the data across space and time have to be captured and understood. The paper presents novel methods for clustering of spatiotemporal data using the NeuCube spiking neural network (SNN) architecture. Clusters of spatiotemporal data were created and modified on-line in a continuous, incremental way, where spatiotemporal relationships of changes in variables are incrementally learned in a 3D SNN model and the model connectivity and spiking activity are incrementally clustered. Two clustering methods were proposed for SNN, one performed during unsupervised and one—during supervised learning models. Before submitted to the models, the data is encoded as spike trains, a spike representing a change in the variable value (an event). During the unsupervised learning, the cluster centres were predefined by the spatial locations of the input data variables in a 3D SNN model. Then clusters are evolving during the learning, i.e. they are adapted continuously over time reflecting the dynamics of the changes in the data. In the supervised learning, clusters represent the dynamic sequence of neuron spiking activities in a trained SNN model, specific for a particular class of data or for an individual instance. We illustrate the proposed clustering method on a real case study of spatiotemporal EEG data, recorded from three groups of subjects during a cognitive task. The clusters were referred back to the brain data for a better understanding of the data and the processes that generated it. The cluster analysis allowed to discover and understand differences on temporal sequences and spatial involvement of brain regions in response to a cognitive task
Application of the Kalman Filter in Functional Magnetic Resonance Image Data
The Kalman-Bucy filter was applied on the preprocessing of the functional magnetic resonance image-fMRI. Numerical simulations of hemodynamic response added Gaussian noise were performed to evaluate the performance of the filter. After the proceeding was applied in auditory real data. The Kohonen’s self-organized map was employed as tools to compare the performance of the Kalman’s filter with another type of pre-processing. The results of the application of Kalman-Bucy filter for simulated data and real auditory data showed that it can be used as a tool in the temporal filtering step in fMRI data
Contributions in computational intelligence with results in functional neuroimaging
This thesis applies computational intelligence methodologies to study functional brain images. It is a state-of-the-art application relative to unsupervised learning domain to functional neuroimaging. There are also contributions related to computational intelligence on topics relative to clustering validation and spatio-temporal clustering analysis. Speci_cally, there are the presentation of a new separation measure based on fuzzy sets theory to establish the validity of the fuzzy clustering outcomes and the presentation of a framework to approach the parcellation of functional neuroimages taking in account both spatial and temporal patterns. These contributions have been applied to neuroimages obtained with functional Magnetic Resonance Imaging, using both active and passive paradigm and using both in-house data and fMRI repository. The results obtained shown, globally, an improvement on the quality of the neuroimaging analysis using the methodological contributions proposed
Deep Time-Series Clustering: A Review
We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives
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