Wavelet-based clustering of resting state MRI data in the rat.
While functional connectivity has typically been calculated over the entire length of the scan (5-10min), interest has been growing in dynamic analysis methods that can detect changes in connectivity on the order of cognitive processes (seconds). Previous work with sliding window correlation has shown that changes in functional connectivity can be observed on these time scales in the awake human and in anesthetized animals. This exciting advance creates a need for improved approaches to characterize dynamic functional networks in the brain. Previous studies were performed using sliding window analysis on regions of interest defined based on anatomy or obtained from traditional steady-state analysis methods. The parcellation of the brain may therefore be suboptimal, and the characteristics of the time-varying connectivity between regions are dependent upon the length of the sliding window chosen. This manuscript describes an algorithm based on wavelet decomposition that allows data-driven clustering of voxels into functional regions based on temporal and spectral properties. Previous work has shown that different networks have characteristic frequency fingerprints, and the use of wavelets ensures that both the frequency and the timing of the BOLD fluctuations are considered during the clustering process. The method was applied to resting state data acquired from anesthetized rats, and the resulting clusters agreed well with known anatomical areas. Clusters were highly reproducible across subjects. Wavelet cross-correlation values between clusters from a single scan were significantly higher than the values from randomly matched clusters that shared no temporal information, indicating that wavelet-based analysis is sensitive to the relationship between areas.
Georgia Tech Research Institute, 250 14th Street NW, Atlanta, GA, 30332, USA.
Alessio
A
Medda
Neuroscience Program, Emory University, Atlanta, GA, 30322, USA.
Lukas
L
Hoffmann
Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, USA.
Matthew
M
Magnuson
Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, USA.
Garth
G
Thompson
Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, USA.
Wen-Ju
WJ
Pan
Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, USA. Electronic address: shella.keilholz@bme.gatech.edu.
Shella
S
Keilholz
D000465:Algorithms; D000818:Animals; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009415:Nerve Net / Q000033:anatomy & histology / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D051381:Rats; D017207:Rats, Sprague-Dawley; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D062211:Spatio-Temporal Analysis; D058067:Wavelet Analysis
functional connectivity; network dynamics; resting state MRI; wavelet analysis