Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.
 

Neurometric Encoding and Decoding: Using Multivariate Functional Connectivity Methods to Describe Cognitive States, Traits and Clinical Endophenotypes

Loading...
Thumbnail Image

Persistent link to this item

Statistics
View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Neurometric Encoding and Decoding: Using Multivariate Functional Connectivity Methods to Describe Cognitive States, Traits and Clinical Endophenotypes

Published Date

2014-10

Publisher

Type

Thesis or Dissertation

Abstract

This research was undertaken for the purpose of demonstrating the neurometric utility of functional connectivity methods by combining metrics that utilize information derived from independent component analyses (ICAs) with traditional fMRI and graph theory analyses. The combination of these methodologies was used to establish traits and evaluate cognitive states from a behavioral genetics perspective, as well as to posit connectivity endophenotypes related to psychiatric and neurological diseases. The studies described below demonstrate that the metrics used to study intrinsic connectivity networks (ICNs) are useful tools for studying the in vivo brain in states of normalcy and disease. For instance, by examining ICNs across tasks and monozygotic twins, it was possible to establish these brain networks as traits. The ICNs were stable across cognitive states, while still exhibiting sensitivity to specific demands. In addition, the state- dependent modulation of these ICNs, as well as their other characteristics, was shown to be influenced by genetic factors in two separate twin samples. In the second twin sample, and a study of connectivity phenotypes related to schizophrenia, ICNs were useful for establishing the relationships between ICNs and tasks in both cases. The task-related characteristics and resting state profiles of ICNs were also useful for establishing novel endophenotypes of the disease states of schizophrenia and Parkinson's disease. Overall, this research serves to establish the study of the brain's intrinsic connectivity across the domains of both cognitive and clinical neuroscience and this work serves a contribution to the understanding of the dimensions along which normal and abnormal neurobiological functioning lie, and how intrinsic connectivity networks can be examined in both spheres.

Description

University of Minnesota Ph.D. dissertation. October 2014. Major: Neuroscience. Advisors: Angus MacDonald III, Kelvin Lim. 1 computer file (PDF); viii, 190 pages.

Related to

Replaces

License

Collections

Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Other identifiers

Suggested citation

Moodie, Craig. (2014). Neurometric Encoding and Decoding: Using Multivariate Functional Connectivity Methods to Describe Cognitive States, Traits and Clinical Endophenotypes. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/183347.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.