Electrophysiological correlates of functional network dynamics Shella Keilholz Emory University/Georgia Tech September 12, 2014 Resting State Functional Connectivity Workshop Boston, MA BOLD dynamics Time-varying connectivity • Chang and Glover1: awake humans, DMN • Handwerker et al.2: awake humans, DMN • Hutchison et al.3: anesthetized macaques, OCM • Us: anesthetized rats6 Quasi-periodic patterns • Majeed et al. 4,5 first observed in anesthetized rats • Also can be seen in awake humans5, during REM9 • Involves DMN and TPN in humans • Reproducible and quasiperiodic Individual events Petridou et al., awake humans, DMN, etc7 Liu and Duyn, awake humans, DMN8 NeuroImage 2010; 2NeuroImage 2012; 3Hum Brain Mapp 2012; 4JMRI 2009; 5NeuroImage 2011; 6Brain Connect 2013; 7Hum Brain Mapp 2013; 8PNAS 2014; 9PNAS 2013 1 A CENTRAL QUESTION Steady state connectivity, QPPs, sliding windows, individual events… Are these linked to different neural/metabolic/hemodynamic processes and can we separate them? Neural basis of functional connectivity • Simultaneous EEG/MRI studies in humans: networks linked to multiple frequencies (Laufs et al., Mantini et al.) • LFP/MRI studies in animals: gamma power fluctuations most like BOLD fluctuations (Scholvinck et al., Shmuel and Leopold); delta and theta power correlation linked to BOLD correlation (Lu et al., Pan et al.) Optical imaging Cerebral Blood Volume MRI: 9.4 T, dexmedetomidine or isoflurane anesthesia, single slice gradient echo EPI, TR 500 ms, TE 15 ms, 64 x64, FOV 2.56, 1000 images Postprocessing: global signal correction, detrending, FIR band pass filter 0.01-0.3 Hz Gradient artifacts Exponential decay LFP recording 0-100 Hz Sliding window correlation Sliding window correlation • Range of window lengths used to calculate pairwise correlation, stepped by one image each time • Calculated for simultaneously-recorded BOLD and BLP from ROIs in left and right SI Thompson et al., NeuroImage 2013 Sliding window time courses Sliding Window Series (r) 1 0.8 0.6 BOLD 0.4 Delta 0.2 Theta 0 Alpha Low Beta -0.2 High Beta -0.4 Gamma -0.6 -0.8 -1 0 50 100 150 200 Time (s) 250 300 Thompson et al, NeuroImage 2013 cor(BOLD sliding window series, BLP sliding window series) (z) Correlation between BOLD and LFP sliding windows Global Regression 14 14 12 10 8 * * * No Global Regression 12 10 * * * 8 6 6 4 4 2 2 0 0 -2 -2 -4 -4 actual mismatch ed Thompson et al, NeuroImage 2013 SLIDING WINDOW CORRELATION … gamma BLP correlation (also theta, beta) is linked to BOLD correlation, like Shmuel and Leopold, Scholvinck et al. for single sites. Previous work by Lu et al., Pan et al. found delta, theta best predicted BOLD correlation. Evidence of sensitivity to coordination across areas rather than large scale changes? Quasiperiodic patterns Quasi-periodic patterns (QPPs) • “Template” generated from rsMRI data • Has spatial and temporal components a b c d Majeed et al, NeuroImage 2011 Linked to infraslow LFPs • Comparable in frequency to spontaneous BOLD fluctuations • Seldom recorded due to concerns about drift • May involve astrocytic activity and/or subcortical input • BBB permeability??? See Kiviniemi… • Recorded in 13 rats under isoflurane or dexmedetomidine with simultaneous imaging • Coherence between SI BOLD/LFPs calculated • Cross-correlation with BOLD calculated for range of time lags Time-lagged BOLD/DC correlation Pearson correlation at different time shifts between one LFP electrode and fMRI data at every location Spatial correlation Spatial correlation Spatial correlation Spatial correlation Spatial correlation 15 QPPS …linked to infraslow, sliding window to higher frequencies. So, are these independent processes? Isoflurane Dexmedetomidine Dead rat Bench data 5.5 Not significant Significant LFP vs. fMRI Amplitude Frequency (Hz) Phase amplitude coupling t -1 scores Thompson et al, Front Integ Neurosci 2014 Correlation vs. partial correlation Dexmedetomidine 0.010.17Hz amplitude s ★ fMRI 0.090.29Hz amplitude s ★ fMRI δ/θ power (1-9Hz) ★ fMRI fMRI vs. fast-band correlation ******* β power (15-25Hz) ★ fMRI fMRI vs. Infraslow correlation Mean Correlation (z) Isoflurane Time shift (s) ********* Standard correlation Significantly different partial vs. standard correlation Partial correlation Thompson et al, Front Integ Neurosci 2014 Other contributions? Vasomotion and vascular effects • Pulsation from physiological cycles • Vasomotion – 0.1 Hz – Present in isolated arteries – Signal from periphery correlates with brain in fingernail humans brain ** Mayhew, J.E.W. et al., Neuroimage 1996 Vascular contributions • Simultaneous optical recording of hemodynamic signals in brain and periphery (5 rats under isofluane, 25 runs) • ~0.1 Hz hemodynamic signal correlation within bilateral hind paws as well as correlation between bilateral cortex (S1). Correlations (r) 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 Lpaw vs. Rpaw Lpaw vs. LS1 Lpaw vs. RS1 Lpaw vs. LV1 Rpaw vs. LS1 Rpaw vs. RS1 Rpaw vs. LV1 LS1 vs. RS1 LS1 vs. LV1 RS1 vs. LV1 Hemodynamic peak in BOLD data? Magnuson et al, NMR in Biomed 2013 HEMODYNAMIC OSCILLATIONS …may or may not be independent of neural activity but probably contribute to functional connectivity Glover, Bandettini BOLD/nonBOLD Coherent neural activity Buxton CMRO2/CBF Infraslow, BBB permeability Vascular oscillations Summary • Quasi-periodic BOLD fluctuations appear linked to infraslow electrical activity, may be tied to glial cells and/or BBB permeability • Transient changes in correlation may reflect changes in coordination in higher frequency neural activity • Different types of functional contrast may have increased/decreased sensitivity to these processes • May be able to separate contributions to increase sensitivity to disease, cognition, etc Acknowledgments 44C 48B Other Contributors: Dieter Jaeger Chip Epstein Eric Schumacher Alessio Medda Waqas Majeed Martha Willis Lukas Hoffmann Mac Merritt Matt Magnuson Biomedical Imaging Technology Center Center for Advanced Brain Imaging 38C 3B 24A Funding: NIH 1 R21NS057718-01, NIH 1 R21NS07281001, NIH 1 R01NS078095-01 Air Force Center of Excellence on Bio-nano-enabled Inorganic/Organic Nanostructures and Improved Cognition (BIONIC) NIH Training Grant (Matthew) DHS Fellowship (Garth)
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