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Brief Occurrences of Brain Signals Leading to Dynamic Patterns in Resting State FMRI

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N Ball

N Ball1*, N Chen2 , (1) Duke University, Durham, NC, (2) Duke University Medical Center, Durham, North Carolina

Presentations

SU-E-I-66 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall


Purpose: To identify functional connectivity patterns in the resting-state network of the brain using blood-oxygen level dependent (BOLD) signals from functional magnetic resonance imaging (fMRI).

Methods: Extracted fMRI BOLD signals from the posterior cingulate cortex (PCC) were averaged and used to find time points at which the BOLD signal exceeded a threshold. These time points were then compared with signals at all points within the brain to locate the BOLD signals which breached a threshold within a given time period. Next, regions with similar temporal patterns were grouped and analyzed to create networks.

Results: Investigation of the number of correlating time points between PCC region activity, and above threshold signals throughout the brain, demonstrates signal correlation with the seed region’s activity. Time points in the top 5% demonstrated 87% more matches relative to those within the 5% of time points closest to zero activity. When comparing the bottom 12% of the time points in the PCC, this number dropped to 12%, however this is substantially higher than the median BOLD signals.

Conclusion: Conclusion: This work demonstrates that interactions between different regions of the brain can be revealed through spontaneous increases in BOLD activity within the PCC. Future investigation is needed to find out how low fluctuations in the signal from a seed region interact and form patterns with other regions of the brain. Further analysis of temporal patterns and subtle changes in the pathology can be used to potentially investigate possible differences between healthy brains, and those afflicted with abnormalities such as Parkinson’s disease.

Funding Support, Disclosures, and Conflict of Interest: This research is supported by NIH R01-NS074045.


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