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Giehl, J.

Publications and source records attributed to Giehl, J..

3 recordsLinked to original sources

Electrophysiological lag threads reveal a temporal hierarchy of the human cortex

The human brain is spatially organized along functional gradients, but the temporal organization of these gradients remains poorly understood. In EEG and MEG, this question has been difficult to address because volume conduction obscures temporal precedence. Here, we introduce lag-specific orthogonalization, an extension of pairwise orthogonalization that allows us to estimate non-zero-lag amplitude-envelope correlations. Applying this approach to large-scale resting-state MEG, we identify electrophysiological lag threads: reproducible maps of temporal ordering that are most coherent in the alpha and beta bands and organize activity from occipital and sensorimotor regions toward frontal and temporal association cortex. These fast electrophysiological lag threads share subject-specific spatial structure with much slower fMRI lag threads, supporting a neural rather than vascular origin of fMRI lag structure. Finally, we show that cortical lag threads are not fixed anatomical constraints. They covary with age and handedness and are selectively reconfigured during cognitive task performance. Together, these findings reveal a frequency-specific electrophysiological temporal hierarchy of the human cortex that links fast neural dynamics to slower hemodynamic propagation structure.

neuroscience↗

Spectral waveform analysis dissociates human cortical alpha rhythms

The non-sinusoidal waveform of neuronal oscillations reflects the physiological properties of underlying circuit interactions and may serve as an informative biomarker of healthy and diseased human brain function. However, little is known about which brain rhythms can be dissociated based on their waveform and methods to comprehensively characterize waveforms are missing. Here, we introduce a novel spectral waveform analysis (SWA) that provides a complete waveform description, is noise-resistant, and allows to reconstruct time-domain waveforms. We applied this framework to human magnetoencephalography (MEG) recordings during rest and identified several distinct and previously unknown cortical alpha waveforms that were temporally stable and specific for individual subjects. Our findings suggest at least four distinct alpha rhythms in human sensorimotor, occipital, temporal, and parietal cortex. SWA provides a powerful new framework to characterize the waveform of neural oscillations in the healthy and diseased human brain.

neuroscience↗

Dissociating harmonic and non-harmonic phase-amplitude coupling in the human brain

Phase-amplitude coupling (PAC) has been hypothesized to coordinate cross-frequency interactions of neuronal activity in the brain. However, little is known about the distribution of PAC across the human brain and the frequencies involved. Furthermore, it remains unclear to what extend PAC may reflect spurious cross-frequency coupling induced by physiological artifacts or rhythmic non-sinusoidal signals with higher harmonics. Here, we combined MEG, source-reconstruction and different measures of cross-frequency coupling to systematically characterize PAC across the resting human brain. We show that cross-frequency measures of phase-amplitude, phase-phase, and amplitude-amplitude coupling are all sensitive to signals with higher harmonics. In conjunction, these measures allow to distinguish harmonic and non-harmonic PAC. Based on these insights, we found no evidence for non-harmonic PAC in resting-state MEG. Instead, we found cortically and spectrally wide-spread PAC driven by harmonic signals. Furthermore, we show how physiological artifacts and spectral leakage cause spurious PAC across wide frequency ranges. Our result clarify how different measures of cross-frequency interactions can be combined to characterize PAC and cast doubt on the presence of prominent non-harmonic phase-amplitude coupling in human resting-state MEG.

neuroscience↗