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

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.