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A Novel Model-Free Data Analysis Technique Based on Clustering in a Mutual Information Space: Application to Resting-State fMRI
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
Department of Clinical Neuroscience, Karolinska Institute.
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.ORCID-id: 0000-0002-2358-7815
2010 (engelsk)Inngår i: Frontiers in Systems Neuroscience, ISSN 1662-5137, E-ISSN 1662-5137, Vol. 4, s. 34:1-34:8Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Non-parametric data-driven analysis techniques can be used to study datasets with few assumptions about the data and underlying experiment. Variations of independent component analysis (ICA) have been the methods mostly used on fMRI data, e.g., in finding resting-state networks thought to reflect the connectivity of the brain. Here we present a novel data analysis technique and demonstrate it on resting-state fMRI data. It is a generic method with few underlying assumptions about the data. The results are built from the statistical relations between all input voxels, resulting in a whole-brain analysis on a voxel level. It has good scalability properties and the parallel implementation is capable of handling large datasets and databases. From the mutual information between the activities of the voxels over time, a distance matrix is created for all voxels in the input space. Multidimensional scaling is used to put the voxels in a lower-dimensional space reflecting the dependency relations based on the distance matrix. By performing clustering in this space we can find the strong statistical regularities in the data, which for the resting-state data turns out to be the resting-state networks. The decomposition is performed in the last step of the algorithm and is computationally simple. This opens up for rapid analysis and visualization of the data on different spatial levels, as well as automatically finding a suitable number of decomposition components.

sted, utgiver, år, opplag, sider
2010. Vol. 4, s. 34:1-34:8
Emneord [en]
Clustering, Data analysis, Functional magnetic resonance imaging, Mutual information, Parallel algorithm, Resting-state
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-52478DOI: 10.3389/fnsys.2010.00034Scopus ID: 2-s2.0-79957813632OAI: oai:DiVA.org:kth-52478DiVA, id: diva2:467055
Merknad
QC 20111221Tilgjengelig fra: 2011-12-21 Laget: 2011-12-18 Sist oppdatert: 2018-01-12bibliografisk kontrollert
Inngår i avhandling
1. On large-scale neural simulations and applications in neuroinformatics
Åpne denne publikasjonen i ny fane eller vindu >>On large-scale neural simulations and applications in neuroinformatics
2013 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

This thesis consists of three parts related to the in silico study of the brain: technologies for large-scale neural simulations, neural algorithms and models and applications in large-scale data analysis in neuroinformatics. All parts rely on the use of supercomputers.

A large-scale neural simulator is developed where techniques are explored for the simulation, analysis and visualization of neural systems on a high biological abstraction level. The performance of the simulator is investigated on some of the largest supercomputers available.

Neural algorithms and models on a high biological abstraction level are presented and simulated. Firstly, an algorithm for structural plasticity is suggested which can set up connectivity and response properties of neural units from the statistics of the incoming sensory data. This can be used to construct biologically inspired hierarchical sensory pathways. Secondly, a model of the mammalian olfactory system is presented where we suggest a mechanism for mixture segmentation based on adaptation in the olfactory cortex. Thirdly, a hierarchical model is presented which uses top-down activity to shape sensory representations and which can encode temporal history in the spatial representations of populations.

Brain-inspired algorithms and methods are applied to two neuroinformatics applications involving large-scale data analysis. In the first application, we present a way to extract resting-state networks from functional magnetic resonance imaging (fMRI) resting-state data where the final extraction step is computationally inexpensive, allowing for rapid exploration of the statistics in large datasets and their visualization on different spatial scales. In the second application, a method to estimate the radioactivity level in arterial plasma from segmented blood vessels from positron emission tomography (PET) images is presented. The method outperforms previously reported methods to a degree where it can partly remove the need for invasive arterial cannulation and continuous sampling of arterial blood during PET imaging.

In conclusion, this thesis provides insights into technologies for the simulation of large-scale neural models on supercomputers, their use to study mechanisms for the formation of neural representations and functions in hierarchical sensory pathways using models on a high biological abstraction level and the use of large-scale, fine-grained data analysis in neuroinformatics applications.

sted, utgiver, år, opplag, sider
Stockholm: KTH Royal Institute of Technology, 2013. s. vi, 66
Serie
TRITA-CSC-A, ISSN 1653-5723 ; 2013:06
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-122190 (URN)978-91-7501-776-1 (ISBN)
Disputas
2013-06-03, F3, Lindstedtsvägen 26, KTH, Stockholm, 13:00 (engelsk)
Opponent
Veileder
Merknad

QC 20130515

Tilgjengelig fra: 2013-05-15 Laget: 2013-05-14 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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