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Resting state network profiles of Alzheimer disease and frontotemporal dementia: A preliminary examination

Published online by Cambridge University Press:  10 May 2018

Joey Annette Contreras
Affiliation:
Indiana University School of Medicine, Indianapolis, IN, USA
Shannon L. Risacher
Affiliation:
Indiana University School of Medicine, Indianapolis, IN, USA
Mario Dzemidzic
Affiliation:
Indiana University School of Medicine, Indianapolis, IN, USA
John D. West
Affiliation:
Indiana University School of Medicine, Indianapolis, IN, USA
Brenna C. McDonald
Affiliation:
Indiana University School of Medicine, Indianapolis, IN, USA
Martin R. Farlow
Affiliation:
Indiana University School of Medicine, Indianapolis, IN, USA
Brandy R. Matthews
Affiliation:
Indiana University School of Medicine, Indianapolis, IN, USA
Liana G. Apostolova
Affiliation:
Indiana University School of Medicine, Indianapolis, IN, USA
Jared Brosch
Affiliation:
Indiana University School of Medicine, Indianapolis, IN, USA
Bernard Ghetti
Affiliation:
Indiana University School of Medicine, Indianapolis, IN, USA
Joaquin GoÑi
Affiliation:
Indiana University School of Medicine, Indianapolis, IN, USA
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Abstract

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OBJECTIVES/SPECIFIC AIMS: Recent evidence from resting-state fMRI studies have shown that brain network connectivity is altered in patients with neurodegenerative disorders. However, few studies have examined the complete connectivity patterns of these well-reported RSNs using a whole brain approach and how they compare between dementias. Here, we used advanced connectomic approaches to examine the connectivity of RSNs in Alzheimer disease (AD), Frontotemporal dementia (FTD), and age-matched control participants. METHODS/STUDY POPULATION: In total, 44 participants [27 controls (66.4±7.6 years), 13 AD (68.5.63±13.9 years), 4 FTD (59.575±12.2 years)] from an ongoing study at Indiana University School of Medicine were used. Resting-state fMRI data was processed using an in-house pipeline modeled after Power et al. (2014). Images were parcellated into 278 regions of interest (ROI) based on Shen et al. (2013). Connectivity between each ROI pair was described by Pearson correlation coefficient. Brain regions were grouped into 7 canonical RSNs as described by Yeo et al. (2015). Pearson correlation values were then averaged across pairs of ROIs in each network and averaged across individuals in each group. These values were used to determine relative expression of FC in each RSN (intranetwork) and create RSN profiles for each group. RESULTS/ANTICIPATED RESULTS: Our findings support previous literature which shows that limbic networks are disrupted in FTLD participants compared with AD and age-matched controls. In addition, interactions between different RSNs was also examined and a significant difference between controls and AD subjects was found between FP and DMN RSNs. Similarly, previous literature has reported a disruption between executive (frontoparietal) network and default mode network in AD compared with controls. DISCUSSION/SIGNIFICANCE OF IMPACT: Our approach allows us to create profiles that could help compare intranetwork FC in different neurodegenerative diseases. Future work with expanded samples will help us to draw more substantial conclusions regarding differences, if any, in the connectivity patterns between RSNs in various neurodegenerative diseases.

Type
Basic Science/Methodology
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Association for Clinical and Translational Science 2018