Towards magnetoencephalography based on ultrasensitive laser pumped non-zero field magnetic sensor
The principal possibility of creating optically pumped compact magnetic sensor for MEG operating in a wide magnetic field range is experimentally proved.
Phase synchronization among neuronal oscillations within the same frequency band has been hypothesized to be a major mechanism for communication between different brain areas. On the other hand, cross-frequency communications are more flexible allowing interactions between oscillations with different frequencies. Among such cross-frequency interactions amplitude-to-amplitude interactions are of a special interest as they show how the strength of spatial synchronization in different neuronal populations relates to each other during a given task. While, previously, amplitude-to-amplitude correlations were studied primarily on the sensor level, we present a source separation approach using spatial filters which maximize the correlation between the envelopes of brain oscillations recorded with electro-/magnetoencephalography (EEG/MEG) or intracranial multichannel recordings. Our approach, which is called canonical source power correlation analysis (cSPoC), is thereby capable of extracting genuine brain oscillations solely based on their assumed coupling behavior even when the signal-to-noise ratio of the signals is low. In addition to using cSPoC for the analysis of cross-frequency interactions in the same subject, we show that it can also be utilized for studying amplitude dynamics of neuronal oscillations across subjects. We assess the performance of cSPoC in simulations as well as in three distinctively different analysis scenarios of real EEG data, each involving several subjects. In the simulations, cSPoC outperforms unsupervised state-of-the-art approaches. In the analysis of real EEG recordings, we demonstrate excellent unsupervised discovery of meaningful power-to-power couplings, within as well as across subjects and frequency bands.
The neurobiological basis and temporal dynamics of communicative language processing pose important yet unresolved questions. It has previously been suggested that comprehension of the communicative function of an utterance, i.e. the so-called speech act, is supported by an ensemble of neural networks, comprising lexico-semantic, action and mirror neuron as well as theory of mind circuits, all activated in concert. It has also been demonstrated that recognition of the speech act type occurs extremely rapidly. These findings however, were obtained in experiments with insufficient spatio-temporal resolution, thus possibly concealing important facets of the neural dynamics of the speech act comprehension process. Here, we used magnetoencephalography to investigate the comprehension of Naming and Request actions performed with utterances controlled for physical features, psycholinguistic properties and the probability of occurrence in variable contexts. The results show that different communicative actions are underpinned by a dynamic neural network, which differentiates between speech act types very early after the speech act onset. Within 50-90 ms, Requests engaged mirror-neuron action-comprehension systems in sensorimotor cortex, possibly for processing action knowledge and intentions. Still, within the first 200 ms of stimulus onset (100-150 ms), Naming activated brain areas involved in referential semantic retrieval. Subsequently (200-300 ms), theory of mind and mentalising circuits were activated in medial prefrontal and temporo-parietal areas, possibly indexing processing of intentions and assumptions of both communication partners. This cascade of stages of processing information about actions and intentions, referential semantics, and theory of mind may underlie dynamic and interactive speech act comprehension.
The spatiotemporal coupling of brainwaves is commonly quantified using the amplitude or phase of signals measured by electro- or magnetoencephalography (EEG/MEG). To enhance the temporal resolution for coupling delays down to millisecond level, a new power correlation (PC) method is proposed and tested.
The cross-correlations of any two brainwave powers at two locations are calculated sequentially through a measurement using the convolution theorem. For noise suppression, the cross-correlation series is moving-average filtered, preserving the millisecond resolution in the cross-correlations, but with reduced noise. The coupling delays are determined from the delays of the cross-correlation peaks.
Simulations showed that the new method detects reliably power cross-correlations with millisecond accuracy. Moreover, in MEG measurements on three healthy volunteers, the method showed average alpha–alpha coupling delays of around 0–20 ms between the occipital areas of two hemispheres. Lower-frequency brainwaves vs. alpha waves tended to have a larger lag; higher-frequency waves vs. alpha waves showed delays with large deviations.
Comparison with existing methods
The use of signal power instead of its square root (amplitude) in the cross-correlations improves noise cancellation. Compared to signal phase, the signal power analysis time delays do not have periodic ambiguity. In addition, the novel method allows fast calculation of cross-correlations.
The PC method conveys novel information about brainwave dynamics. The method may be extended from sensor-space to source-space analysis, and can be applied also for electroencephalography (EEG) and local field potentials (LFP).