Sliding window study of brain connectivity dynamics based on Energy Landscape analysis.

Thumbnail Image

Files

117560Z.pdf (915.5 KB)

Links to Files

https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11756/117560Z/Sliding-window-study-of-brain-connectivity-dynamics-based-on-Energy/10.1117/12.2588048.short

Permanent Link

https://doi.org/10.1117/12.2588048
 http://hdl.handle.net/11603/21537

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

Author/Creator

Author/Creator ORCID

Date

2021-04-12

Type of Work

conference papers and proceedings
Text

Department

Program

Citation of Original Publication

Sravani Varanasi, Janerra Allen, Rong Chen, Fow-Sen Choa, "Sliding window study of brain connectivity dynamics based on Energy Landscape analysis.," Proc. SPIE 11756, Signal Processing, Sensor/Information Fusion, and Target Recognition XXX, 117560Z (12 April 2021); https://doi.org/10.1117/12.2588048

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

Subjects

Abstract

Computational neuroscience models can be used to understand neural dynamics in the brain and these dynamics change as the physiological and other conditions like aging. One such approach we have used in this work is Energy Landscape analysis based on resting-state fMRI data. The dataset consists of 70 subjects with normal cognitive function, of which 23 are young adults and 47 are old adults. In this analysis, disconnectivity graphs and activity patterns are generated and using connectivity statistics among seven prominent brain networks. To study brain dynamic behaviors, we perform sliding window studies on the dataset and observe local minima of each window evolving in time. By varying the window shift from multiple seconds to 1 second, we can obtain statistics and evaluate the speed and activity pattern holding time of individual and group subjects. We found that older subjects can hold the brain states for a longer time but then jump to other dominated brain state local minima with a large hamming distance, whereas young subjects change dominated local minima more frequently but with a small hamming distance of 1 or 2. In fact, when averaged over the full time course, old subjects have more stable brain states local minima compared to young subjects. For both young and old subjects, the default mode network (DMN) and visual network (VIS) are coupled but for young subjects the two networks are on and off together and strongly correlated. For old subjects, there is an extra dominated brain state local minimum that the DMN and attention network (ATN) are correlated and anti-correlated with (VIS) and sensory-motor networks (SMN). This state may suggest old subjects are more capable of focusing on brain internal models and not getting influenced by external visual and sensory factors than young subjects.