Prof. Rong Chen Profile

Prof. Rong Chen

at Univ of Maryland School of Medicine

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Proceedings Article | 8 June 2022 Presentation + Paper

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

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

Proceedings Volume 12122, 1212212 (2022) https://doi.org/10.1117/12.2618951

KEYWORDS: Brain, Functional magnetic resonance imaging, Visualization, Microelectromechanical systems, Sensors, Control systems, Thalamus, Statistical analysis, Sensor networks, Electrical engineering

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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.

Proceedings Article | 12 April 2021 Presentation + Paper

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

Sravani Varanasi, Janerra Allen, Rong Chen, Fow-Sen Choa

Proceedings Volume 11756, 117560Z (2021) https://doi.org/10.1117/12.2588048

KEYWORDS: Brain, Visualization, Statistical analysis, Sensors, Visual process modeling, Sensor networks, Functional magnetic resonance imaging, Computational neuroscience, Cognitive modeling

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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.

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