Flexible energy landscape analysis of functional connectivity through region bundling
| dc.contributor.author | Allen, Janerra | |
| dc.contributor.author | Varanasi, Sravani | |
| dc.contributor.author | Hong, Elliot | |
| dc.contributor.author | Choa, Fow-Sen | |
| dc.date.accessioned | 2022-07-12T21:05:18Z | |
| dc.date.available | 2022-07-12T21:05:18Z | |
| dc.date.issued | 2022-06-08 | |
| dc.description | SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States | en_US |
| dc.description.abstract | Segmenting the human brain into networks has been a useful approach in analyzing functional connectivity. Brain network bundling can determine which regions are engaged and if they are working together. The thalamus (THL) and basal ganglia (BSL) regions in the subcortical network are linked to multiple cortical areas due to their roles in neural circuitry outlined in the cortico-basal ganglia-thalamo cortical map. Here we explore their coupling with the default mode network (DMN), frontoparietal network (FPN), salience network (SAN), attention network (ATN), sensorimotor network (SSM), visual network (VIS), and auditory network (AUD) using the energy landscape technique. Energy landscape analysis helps identify the statistical differences in functional behaviors between the healthy control and patient groups, which are obtained from the fMRI activity time courses of the 9 internetworks. In this work, we focused on studying 107 schizophrenic patients and 86 healthy controls and obtained the constructed activity patterns and disconnectivity graphs of each subject. The differences between two groups are compared. The results from bundling THL and BSL with the DMN, FPN, SAN, ATN, SSM, VIS, and AUD shows that these regions are more strongly coupled in controls than in patients. After performing energy calculations and heat map generations, we observed several lower energy band states that are common among all control and patient subjects. The potential implications of these common band states are discussed. | en_US |
| dc.description.sponsorship | This research was supported by the University of Maryland, Baltimore County and University of Maryland, Baltimore. I am thankful to my colleagues Sravani Varanasi, Dr. Elliot Hong and Dr. Fow-Sen Choa who provided expertise that greatly assisted the research. | en_US |
| dc.description.uri | https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12122/1212215/Flexible-energy-landscape-analysis-of-functional-connectivity-through-region-bundling/10.1117/12.2619126.short | en_US |
| dc.format.extent | 8 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.identifier | doi:10.13016/m22kck-rj5c | |
| dc.identifier.citation | Janerra D. Allen, Sravani Varanasi, Elliot Hong, and Fow-Sen Choa "Flexible energy landscape analysis of functional connectivity through region bundling", Proc. SPIE 12122, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI, 1212215 (8 June 2022); https://doi.org/10.1117/12.2619126 | en_US |
| dc.identifier.uri | https://doi.org/10.1117/12.2619126 | |
| dc.identifier.uri | http://hdl.handle.net/11603/25136 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | SPIE | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | ©2022 Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. | en_US |
| dc.title | Flexible energy landscape analysis of functional connectivity through region bundling | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0001-9613-6110 | en_US |