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Neuronal & Physiological Correlation to Hemodynamic Resting-State Fluctuations in Health and Diseases

Aggarwal, Sowmya (2016) Neuronal & Physiological Correlation to Hemodynamic Resting-State Fluctuations in Health and Diseases. Master's Thesis, University of Pittsburgh. (Unpublished)

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Low-frequency, spatially coherent fluctuations present in functional magnetic resonance imaging (fMRI) time series have had a tremendous impact on brain connectomics. To interpret the neurophysiological nature of changes in connectivity measured by hemodynamic signals, studies that investigate the dynamic relationship between neuronal and hemodynamic fluctuations are needed. Previously, our group used a transgenic animal model to simultaneously acquire bi-hemispheric images sensitive to neuronal and hemodynamic signals. We showed that hemodynamic connectivity is highly correlated with neuronal-connectivity in scans >5 min. This work used the same animal model and imaging method to calculate and compare the agreement between sliding window (SW) and dynamic conditional correlation (DCC) metrics. Transgenic GCaMP3 mice were used to simultaneously image ongoing changes in neuronal-activity (GCAMP) as well as hemodynamic measurements of blood oxygenation (OIS-BOLD, analogous to fMRI) from the same animals (n=6). Bi-hemispheric GCAMP and OIS-BOLD images were acquired at 10Hz from the exposed superior surface of the mouse brain under light ketamine anesthesia (30mg/kg/hr) for 5 to 20min periods. Pre-processing consisted of temporal band-pass filtering (0.02-0.20Hz). Then, k-means clustering was used on the GCAMP data to obtain 6 regions-of-interest. For each GCAMP and OIS-BOLD ROI time-series, we first examined the SW lengths for which the GCAMP and OIS-BOLD connectivity matrices were significantly correlated (r>0.47 corresponds to p<0.05). Over non-overlapping windows, the average SW correlation and fraction of significant windows are reported. We then examined the temporal sampling resolution for which comparisons between the GCAMP DCC and OIS-BOLD DCC connectivity matrices were significantly correlated. GCAMP (neuronal) and OIS-BOLD (hemodynamic) time-series were used to calculate SW and DCC connectivity matrices. For each non-overlapping SW window, the inter-node connectivity of the GCAMP data was compared to that of the OIS-BOLD data and tested for significance using a correlation analysis. The average correlation shows significant relationships for window lengths over 20 sec, while the average fraction of significantly correlated windows was >80% for windows >40 sec. A similar analysis of the GCAMP and OIS-BOLD DCC connectivity shows that average significant relationships were observed for temporal resolution >1.4sec, and >80% of the comparisons were significant for temporal resolution >2.2sec.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Aggarwal, Sowmyasoa19@pitt.eduSOA19
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorVazquez, Albertoalv15@pitt.eduALV
Committee MemberKang, Kimkangkim@upmc.eduKANGKIM
Committee MemberStetten, Georgestetten@pitt.eduSTETTEN
Date: 14 June 2016
Date Type: Publication
Defense Date: 26 April 2016
Approval Date: 14 June 2016
Submission Date: 14 April 2016
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 74
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Bioengineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Low-frequency, hemodynamic, conditional correlation, dynamic correlation, GCAMP, OIS, neuroimaging, optical imaging, mouse, brain
Date Deposited: 14 Jun 2016 15:18
Last Modified: 15 Nov 2016 14:32


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