All ETDs from UAB

Advisory Committee Chair

Jerzy P Szaflarski

Advisory Committee Members

Burel Goodin

Rajesh Kana

Yingying Wang

Jarred Younger

Document Type


Date of Award


Degree Name by School

Doctor of Philosophy (PhD) College of Arts and Sciences


The characterization of brain networks contributing to healthy learning and memory can inform abnormalities and treatment approaches among clinical populations. A recent shift from lesion-based to network-based approaches of studying healthy and atypical brain development highlights the need for a more comprehensive understanding across spatiotemporal domains, particularly in the case of high-level cognitive processes. Both, associative learning and working memory involve distributed and interconnected networks of specialized brain regions. Dynamic communication within- and betweensuch systems are unable to be fully resolved by individual non-invasive imaging techniques such as fMRI or MEG. While fMRI serves as an ideal tool to investigate spatial contributions underlying sustained neural activity related to a task, MEG provides temporal resolution unparalleled by hemodynamic methods. A framework aimed to integrate these complementary methods should be flexible enough to account for inconsistencies between the intrinsic sensitivities of fMRI and MEG and it should encourage a distributed solution that best captures the complex neural activity often spanning fronto-temporal areas. This dissertation aims to develop a co-processing stream that integrates information from fMRI and MEG tasks in a data-driven manner to investigate the effects of a multimodal approach in evaluating high-level cognitive processes. The first aim was to characterize networks underlying associative learning and examine the effects of age, iii sex, and handedness on active and passive learning. We found broad fronto-parietal activity contributed to self-generation with that activity within specific task-related brain areas modulated by sex and age. The second aim was to integrate fMRI and MEG data from the same pairedassociate learning task within a Bayesian framework to improve temporal visualization of nodes within relevant networks. This work provided an implementable framework to merge such datasets, which was tested on an event-related Sternberg memory task within the third aim. Across studies, this work also shed light on what information may be gained by constraining MEG inverse solutions with fMRI spatial priors. Some benefits of incorporating fMRI spatial information to MEG source reconstruction include the ability to detect additional contributing brain areas during learning and memory, and to better characterize information flow between relevant brain areas.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.