In neuroscience, functional magnetic resonance imaging (fMRI) has become a powerful tool in human brain mapping. Typically, fMRI is used with a rather simple stimulus sequence, aim- ing at improving signal-to-noise ratio for statistical hypothesis testing. When natural stimuli are used, the simple designs are no longer appropriate. The aim of this thesis is in developing data-driven approaches for reliable inference of brain correlates to natural stimuli. Since the beginning of the nineteenth century, neuroscience has focused on the idea that dis- tinct regions of the brain support particular mental processes. However, modern research rec- ognizes that many functions rely on distributed networks, and that a single brain region may participate in more than one function. These rapid paradigm changes in neuroscience raise important methodological challenges. Purely hypothesis-driven methods have been used ex- tensively in functional imaging studies. As the focus in brain research is shifting away from functional specialization towards interaction-based functional networks, those approaches are no longer appropriate. In contrast to the classic statistical hypothesis testing approaches, mod- ern machine learning methods allow for a purely data-driven way to describe the data. They do not use the stimuli, and make no assumptions about whether the brain processes are stimulus related or not. The recordings for each brain region may contain a complicated mixture of act- ivity, which is produced by many spatially distributed processes, and artifacts. Each process can be described as a component having a separate time series and spatial extent, and produc- ing simultaneous changes in the fMRI signals of many regions. The main contribution of the thesis is a reliable independent component analysis (ICA) ap- proach, which is available in the Arabica toolbox. The usefulness of the approach was tested extensively with fMRI data, showing that the method is capable of providing insights into the data that would not be attainable otherwise. The new method was also theoretically analyzed and its asymptotic convergence was proven. The theory offers a thorough explanation of how the method works and justifies its use in practice. Then, the new method is further developed for analyzing networks of distributed brain activity, by combining it with canonical correlation analysis (CCA). The extension was shown to be particularly useful with fMRI studies that use natural stimuli. The approach is further extended to be applicable in cases where independent subspaces emerge, which often happens when using real measurement data that is not guaran- teed to fit all the assumptions made in the development of the methods.

@phdthesis{
    JYlipaavalniemi-2013-PHDTHESIS_whyj,
    author = "Ylipaavalniemi, Jarkko",
    juforank = "NA",
    school = "Aalto University School of Science",
    isbn = "978-952-60-5136-9",
    language = "eng",
    title = "Data-driven Analysis for Natural Studies in Functional Brain Imaging",
    url = "http://urn.fi/URN:ISBN:978-952-60-5136-6",
    abstract = "In neuroscience, functional magnetic resonance imaging (fMRI) has become a powerful tool in human brain mapping. Typically, fMRI is used with a rather simple stimulus sequence, aim- ing at improving signal-to-noise ratio for statistical hypothesis testing. When natural stimuli are used, the simple designs are no longer appropriate. The aim of this thesis is in developing data-driven approaches for reliable inference of brain correlates to natural stimuli. Since the beginning of the nineteenth century, neuroscience has focused on the idea that dis- tinct regions of the brain support particular mental processes. However, modern research rec- ognizes that many functions rely on distributed networks, and that a single brain region may participate in more than one function. These rapid paradigm changes in neuroscience raise important methodological challenges. Purely hypothesis-driven methods have been used ex- tensively in functional imaging studies. As the focus in brain research is shifting away from functional specialization towards interaction-based functional networks, those approaches are no longer appropriate. In contrast to the classic statistical hypothesis testing approaches, mod- ern machine learning methods allow for a purely data-driven way to describe the data. They do not use the stimuli, and make no assumptions about whether the brain processes are stimulus related or not. The recordings for each brain region may contain a complicated mixture of act- ivity, which is produced by many spatially distributed processes, and artifacts. Each process can be described as a component having a separate time series and spatial extent, and produc- ing simultaneous changes in the fMRI signals of many regions. The main contribution of the thesis is a reliable independent component analysis (ICA) ap- proach, which is available in the Arabica toolbox. The usefulness of the approach was tested extensively with fMRI data, showing that the method is capable of providing insights into the data that would not be attainable otherwise. The new method was also theoretically analyzed and its asymptotic convergence was proven. The theory offers a thorough explanation of how the method works and justifies its use in practice. Then, the new method is further developed for analyzing networks of distributed brain activity, by combining it with canonical correlation analysis (CCA). The extension was shown to be particularly useful with fMRI studies that use natural stimuli. The approach is further extended to be applicable in cases where independent subspaces emerge, which often happens when using real measurement data that is not guaran- teed to fit all the assumptions made in the development of the methods.",
    issn = "1799-4934",
    year = "2013",
    month = "May",
    responsibleauthor = "Ylipaavalniemi, Jarkko",
    flags = "copy, public",
    il = "no",
    address = "Espoo, Finland",
    keywords = "canonical correlation analysis, independent component analysis, functional elements, functional networks, functional magnetic resonance imaging, natural stimulation, subspaces, reliability",
    unitcode = "T306-100",
    kay = "NA",
    impactfactor = "G4",
    type = "Doctoral Dissertation",
    pages = "173"
}

AUTHORS:

ABSTRACT:

URL:
http://urn.fi/URN:ISBN:978-952-60-5136-6

AUTHORS:

ABSTRACT:

URL: http://urn.fi/URN:ISBN:978-952-60-5136-6

URL:
http://urn.fi/URN:ISBN:978-952-60-5136-6