Indiana University-Purdue University Indianapolis (IUPUI) / A popular non-invasive brain activity measurement method is based on the functional magnetic resonance imaging (fMRI). Such data are frequently used to study functional connectivity (FC) defined as statistical association among two or more anatomically distinct fMRI
signals (Friston, 1994). FC has emerged in recent years as a valuable tool for providing
a deeper understanding of neurodegenerative diseases and neuropsychiatric disorders, such
as Alzheimer's disease and autism. Information about complex association structure in
high-dimensional fMRI data is often discarded by a calculating an average across complex
spatiotemporal processes without providing an uncertainty measure around it.
First, we propose a non-parametric approach to estimate the uncertainty of dynamic
FC (dFC) estimates. Our method is based on three components: an extension of a boot
strapping method for multivariate time series, recently introduced by Jentsch and Politis
(2015); sliding window correlation estimation; and kernel smoothing.
Second, we propose a two-step approach to analyze and summarize dFC estimates from
a task-based fMRI study of social-to-heavy alcohol drinkers during stimulation with
avors.
In the first step, we apply our method from the first paper to estimate dFC for each region
subject combination. In the second step, we use semiparametric additive mixed models to
account for complex correlation structure and model dFC on a population level following
the study's experimental design. Third, we propose to utilize the estimated dFC to study the system's modularity defined
as the mutually exclusive division of brain regions into blocks with intra-connectivity greater
than the one obtained by chance. As a result, we obtain brain partition suggesting the
existence of common functionally-based brain organization.
The main contribution of our work stems from the combination of the methods from
the fields of statistics, machine learning and network theory to provide statistical tools for
studying brain connectivity from a holistic, multi-disciplinary perspective.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/12835 |
| Date | 06 January 2017 |
| Creators | Kudela, Maria Aleksandra |
| Contributors | Harezlak, Jaroslaw, Dzemidzic, Mario, Li, Shanshan, He, Chunyan, Yiannoutsos, Constantin |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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