Energy landscape analysis based sliding window studies of brain dynamics in young and old subjects

Sravani Varanasi; Janerra Allen; Rong Chen; Karuna P. Sahoo; Amit Patra; Fow-Sen Choa

doi:10.1117/12.2618951

8 June 2022 Energy landscape analysis based sliding window studies of brain dynamics in young and old subjects

Sravani Varanasi, Janerra Allen, Rong Chen, Karuna P. Sahoo, Amit Patra, Fow-Sen Choa

Proceedings Volume 12122, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI; 1212212 (2022) https://doi.org/10.1117/12.2618951
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

The study of brain activity changes caused by physiological or other conditions like aging is crucial not only to understand the brain dynamics but also to identify those changes and distinguish the subject groups. In this work, we are performing a sliding window technique on the Energy Landscape analysis to explore temporal signatures of the seven major restingstate networks, namely, default mode (DMN), frontal-parietal (FPN), salience (SAN), attention (ATN), sensory-motor (SMN), visual (VIS) and auditory (AUD) networks. The dataset used for this study consists of 23 young adult and 47 old adult subjects with normal cognitive function. To study the dynamic behavior of the brain, we have applied the sliding window technique on the time courses of the obtained fMRI data. With 90-second windows and 4-second shifts from a total of 180 second time course, we obtain 24 windows of temporal energy landscape information, which is presented as a matrix with the energies of all possible connectivity states vs the sequence of sliding windows. A heat map was displayed using this matrix to examine the energy transition of these states. We found that a few bands of connectivity states are consistently low energies among the different groups of subjects. One observation was that the states in these bands are only one or two hamming distances away from each other, which means these connectivity states with consistently low energy values are close in terms of the region of interest (ROIs) connectivity. Also, SAN and ATN were working synchronously for both young and old subjects in all these bands. In summary, we are using the sliding window technique with the Energy landscape analysis to find out the brain state dynamics for the old and young subjects.

Conference Presentation

Citation Download Citation

Sravani Varanasi, Janerra Allen, Rong Chen, Karuna P. Sahoo, Amit Patra, and Fow-Sen Choa "Energy landscape analysis based sliding window studies of brain dynamics in young and old subjects", Proc. SPIE 12122, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI, 1212212 (8 June 2022); https://doi.org/10.1117/12.2618951

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available