Cross-Frequency Synchrony Correlation between PS and CFS hubs Large-scale synchrony within and across frequencies in resting-state SEEG data Felix Siebenhühner1, Gabriele Arnulfo2, Lino Nobili3, Matias Palva1, Satu Palva1 1: Neuroscience Center, University of Helsinki; 2: Universita degli Studi di Genova; 3: Claudio Munari Epilepsy Surgery Centre, Niguarda Hospital, Milan Phase Synchrony Connection Density Strength 1:2 Connection Density 1:3 Connection Density Significant CFS was observed at 1:2 between q and a and between a and b bands and 1:3 between a and b/g Significant CFS was reduced, but still showed same patterns with very conservative controlling for spurious CFS (right) Connection Density without spurious control with spurious control Strength Cross-Frequency Synchrony In 1:2 CFS, strength and connection density followed similar patterns for all functional subsystems In 1:3 CFS, strength is similar across subsystems 1:2 Strength 1:3 Strength Introduction Long-range within-frequency (1:1) phase synchrony (PS) of neuronal oscillations regulates neuronal communication among human brain areas [Fries 2015]. Large-scale PS networks are crucial for cognitive functions [Palva 2012, Siegel 2012, Petersen 2015]. Neuronal activity during task performance is also characterized by n:m cross-frequency phase synchrony (CFS), which may coordinate processing across distinct frequencies [Palva 2005, Siebenhühner 2016]. Yet, concerns have been raised over the possibility of CFS observations being spurious [Gerber 2016, Scheffer-Tezeira 2016] Observations of long-range CFS in human resting-state data have been sparse [Palva 2005], and it has not been investigated whether it connects PS networks Hypothesis: Long-range CFS connects networks of cortical oscillations in resting state Frequency [Hz] 3 4 5 6 7 10 20 30 40 50 70 100 0 .1 .2 .3 .4 K Frequency [Hz] 3 4 5 6 7 10 20 30 40 50 70 100 0 .02 .04 .06 .08 PLV Frequency [Hz] 3 4 5 6 7 10 20 30 40 50 70 100 0 .04 .08 .12 .16 PLV Frequency [Hz] 3 4 5 6 7 10 20 30 40 50 70 100 .01 .1 K Frequency [Hz] 3 4 5 6 7 10 20 30 40 50 70 100 .01 .1 K 1:1 phase synchrony f1 n:m-phase synchrony (here 1:3) f2 Frequency [Hz] 3 4 5 6 7 10 20 30 40 50 70 100 0 .04 .08 .12 .16 PLV Frequency [Hz] 3 4 5 6 7 10 20 30 40 50 70 100 0 .1 .2 .3 .4 K Frequency [Hz] 3 4 5 6 10 20 30 50 100 0 .1 .2 .3 .4 .5 PLV Frequency [Hz] 3 4 5 6 10 20 30 50 100 .01 .1 K Materials and Methods Stereotactical EEG (SEEG) data were recorded from epileptic patients undergoing pre-surgical clinical assessment Data was recorded from monopolar (with shared white matter reference) local field potentials (LFPs) from brain tissue with platinum–iridium, multi-lead electrodes Closest White Matter (CW) scheme was used for referencing [Arnulfo 2015] 38 subjects with 116±17 channels each, recording time 10 min Only cortico-cortical connections between non-epileptic channels were analyzed Data were filtered into 31 center frequencies from 3.3 – 100 Hz using Morlet wavelets 1:1 phase synchrony (PS) and cross-frequency phase synchrony (CFS, for ratios 1:2 – 1:6) were computed with the phase-locking value (PLV) over the whole time period (time windows with spiky/epileptic activity excluded) Surrogate values were constructed by shifting time series by a random number of samples The connection density K was computed as the fraction of connections above 2.42*mean(surrogates) over the total number of possible connections Electrode locations were assigned to the functional subsystems defined by Yeo et al. [Yeo 2011] Frequency [Hz] 3 4 5 6 10 20 30 50 100 0 .1 .2 .3 .4 .5 PLV Frequency [Hz] 3 4 5 6 10 20 30 50 100 .01 .1 K Both strength and connection density K of phase synchrony peak in a band a peak can be observed in all functional subsystems Subsytems: Vis = Visual, SM = Somatomotor, DA = Dorsal Attention, VA = Ventral Attention, Lim = Limbic, FP = Frontoparietal, DM = Default Mode Significant Cross-Frequency synchrony (CFS) is observed in human SEEG data in resting state Observed CFS can not be explained by possibly spurious CFS Strength and Connection density of both PS and CFS tend to be similar across functional subsystems Hubs of the low-frequency PS networks and 1:2 CFS networks are correlated Conclusions Correlation between PS and CFS hubs Controlling for possible spurious CFS Theoretically, spurious CFS might arise as a result of PS and local CFS and of harmonic or subharmonic components of non-sinusoidal signals Importantly, both sinusoidal and non-sinusoidal signals may be accompanied by true CFS. We developed a conservative approach for detecting true CFS. We posit that spurious CFS must always be accompanied with 1:1 PS. In our model scheme of this approach all full “triangle motifs” of PS, local CFS & long-range CFS are discarded; this rules out all spurious CFS observations and also may discard a number of true CFS connections. Degree Betweenness Centrality References Arnulfo G, et al. (2015): ‘Phase and amplitude correlations in resting-state activity in humanstereotactical EEG recordings’, NeuroImage, 112, pp. 114-127 Fries P (2015): ‘Rhythms for Cognition: Communication through coherence’, Neuron, 88, pp. 220-235 Gerber EM, et al. (2016): ‘Non-Sinusoidal Activity Can Produce Cross-Frequency Coupling in Cortical Signal in Absence of Functional Interaction between Neural Sources’, PLoS One, e0167351 Palva JM, et al. (2005): ‘Phase synchrony among neuronal oscillations in the human cortex’, Journal of Neuroscience, 25/15, pp. 3962-3972 Palva S, Palva JM (2012): ’Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs’, Trends in Cognitive Sciences, 16/4, pp. 219-230 Petersen SE, Sporns O (2015): ‘Brain Networks and Cognitive Architectures’, Neuron, 88, pp. 207-219 Scheffer-Teixerira R, Tort ABL (2016): ‘On cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus’, eLife 5:e20515 Siebenhühner F, et al. (2016): ‘Cross-frequency synchronization connects networks of fast and slow oscillations during visual working memory maintenance’, eLife 5:e13451 Siegel M, et al. (2012): ‘Spectral fingerprints of large-scale neuronal interactions’, Nature Reviews Neuroscience, 13, pp. 121-134 Yeo BT, et al. (2011): ‘The organization of the human cerebral cortex estimated by intrinsic functional connectivity’, Journal of Neurophysiology 106, pp. 1125–1165. This work was supported by: Academy of Finland, Helsinki University Research Funds, Doctoral Program of Brain & Mind of the University of Helsinki. Frequency [Hz] 3 4 5 6 10 20 30 50 100 0 .1 .2 .3 .4 .5 Pearson’s r True CFS Spurious CFS A B A B A B A B A B A B Regular b signal Non-zero-mean, amplitude- modulated b b signal a signal m signal (1) (2) (3) (1) A B A B A B A B A B A B A B (2) Hubs of PS networks, as identified by node degree and betweenness centrality, of q, a and b bands and corresponding 1:2 and 1:3 CFS networks are significantly correlated across subjects (Pearson correlation, p=0.05, Benjamini-Hochberg corrected) Significant correlations were also found in most individual subjects for both metrics A B A B A B A B True CFS/PS Spurious CFS/PS (3) Sinusoidal a Non-sinusoidal a Sinusoidal b Harmonic b Non-sinusoidal b Subharmonic a True positives False negatives True negatives