Summary: Researchers have identified a new method that may help identify neural processes associated with optimal response to stimuli. They report the method, which utilizes EEG data, could eventually have practical applications in sports, education and Parkinson’s research.
Source: Higher School of Economics.
Neuroscientists from Higher School of Economics and Charité University Clinic in Berlin have come up with a new multivariate method for predicting behavioural response to a stimulus using information about the phase of preceding neuronal oscillations recorded with EEG. The method may eventually find practical application in fields such as competitive sports, education and patient treatment.
The study’s findings are published in the paper ‘On optimal spatial filtering for the detection of phase coupling in multivariate neural recordings’.
Even a resting human brain continuously generates complex patterns of neuronal oscillations which can be detected with EEG.
These oscillations occur at different frequencies — e.g. alpha at 10 Hz, beta at 20 Hz and theta at 7 Hz — and are registered by instruments as changes in the electric field generated by neurons in the brain. According to a popular hypothesis, one’s ability to respond to various stimuli and process information depends on the amplitude and phase of neuronal oscillations at the moment a stimulus occurs.
“Imagine that someone needs to remember words presented to them. Interestingly, how well they can remember a particular word depends on the characteristics of the neuronal signal in their brain immediately preceding the moment they hear the word. As another example, Olympic 100-metre runners’ time of response to the starting pistol can vary by tens of milliseconds, which is a fairly large spread given the importance of these milliseconds at the finishing line. Even the same athlete’s reaction time can vary significantly, depending on the current state of their brain.
We tend to respond faster when our brain is in the optimal state for processing information and slower when it is not. In turn, the optimal state leading to fast responses is associated with specific parameters of neuronal oscillations,” explains Vadim Nikulin, study co-author and leading research fellow at the HSE Centre for Cognition and Decision Making.
Scientists already know that a person’s response to a stimulus depends on a variety of factors, including the phase of low-frequency oscillations in the brain at the time of the stimulus. But this time, the researchers have designed a new multi-dimensional method for maximising the relationship between the phase of neuronal oscillations and the subsequent behavioural response (e.g. reaction time to stimulus, memorisation of a sensory stimulus, etc.) They recorded human brain activity using 90 electrodes, and unlike earlier studies, analysed it taking into account the multidimensional distribution of neuronal oscillation parameters for a more accurate prediction of the reaction time.
The subjects were asked to respond to a tactile stimulus as soon as possible. A sensor attached to the index finger of their dominant hand recorded the muscular activity in response to a somatosensory stimulus applied to the index finger of the other hand. At the same time, an EEG was used to record their brain’s neuronal oscillations which are always present but show wide variability over time. The authors showed that the reaction speed depended on the phase of the low-frequency (<1 Hz) neuronal oscillations preceding the stimulus.
According to the researchers, this new method can be useful for identifying the neuronal processes associated with optimal response to stimuli. In the practical aspect, this can be relevant to competitive sports and for clinical practice, e.g. by allowing medics to understand the pathological neuronal processes associated with Parkinson’s disease causing patients to have problems with starting a movement. “Using more sensitive methods of neuronal signal extraction makes it possible to identify the phases of neuronal oscillations associated with maximum use of brain resources,” according to Nikulin. “We can also imagine a not-so-distant future where students wearing comfortable caps fitted with EEG electrodes will be learning new words of a foreign language presented to them precisely at the moments of maximum susceptibility to such information.”
Having tested the model on a motor paradigm, the researchers have recently entered a new phase of their study focusing on the perception of visual signals.
About this neuroscience research article
Funding: The Bernstein Center for Computational Neuroscience, Berlin, Russian Academic Excellence Project ‘5-100’ funded this study.
Source: Liudmila Mezentseva – Higher School of Economics Image Source: NeuroscienceNews.com image is adapted from the Higher School of Economics news release. Original Research:Abstract for “On optimal spatial filtering for the detection of phase coupling in multivariate neural recordings” by G. Waterstraat, G. Curio, and V.V. Nikulin in NeuroImag. Published online August 15 2017 doi:10.1016/j.neuroimage.2017.06.025
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[cbtabs][cbtab title=”MLA”]Higher School of Economics “Maximizing Brain Use.” NeuroscienceNews. NeuroscienceNews, 18 September 2017. <https://neurosciencenews.com/brain-use-maxed-7510/>.[/cbtab][cbtab title=”APA”]Higher School of Economics (2017, September 18). Maximizing Brain Use. NeuroscienceNews. Retrieved September 18, 2017 from https://neurosciencenews.com/brain-use-maxed-7510/[/cbtab][cbtab title=”Chicago”]Higher School of Economics “Maximizing Brain Use.” https://neurosciencenews.com/brain-use-maxed-7510/ (accessed September 18, 2017).[/cbtab][/cbtabs]
On optimal spatial filtering for the detection of phase coupling in multivariate neural recordings
Neuronal oscillations synchronize processing in the brain over large spatiotemporal scales and thereby facilitate integration of individual functional modules. Up to now, the relation between the phases of neuronal oscillations and behavior or perception has mainly been analyzed in sensor space of multivariate EEG/MEG recordings. However, sensor-space analysis distorts the topographies of the underlying neuronal sources and suffers from low signal-to-noise ratio. Instead, we propose an optimized source reconstruction approach (Phase Coupling Optimization, PCO).
PCO maximizes the ‘mean vector length’, calculated from the phases of recovered neuronal sources and a target variable of interest (e.g., experimental performance). As pre-processing, the signal-to-noise ratio in the search-space is maximized by spatio-spectral decomposition. PCO was benchmarked against several competing algorithms and sensor-space analysis using realistic forward model simulations. As a practical example, thirteen 96-channel EEG measurements during a simple reaction time task were analyzed. After time-frequency decomposition, PCO was applied to the EEG to examine the relation between the phases of pre-stimulus EEG activity and reaction times.
In simulations, PCO outperformed other spatial optimization approaches and sensor-space analysis. Scalp topographies of the underlying source patterns and the relation between the phases of the source activity and the target variable could be reconstructed accurately even for very low SNRs (−10 dB). In a simple reaction time experiment, the phases of pre-stimulus delta waves (<0.1 Hz) with widely distributed fronto-parietal source topographies were found predictive of the reaction times. Discussion and conclusions From multivariate recordings, PCO can reconstruct neuronal sources that are phase-coupled to a target variable using a data-driven optimization approach. Its superiority has been shown in simulations and in the analysis of a simple reaction time experiment. From this data, we hypothesize that the phase entrainment of slow delta waves (<1 Hz) facilitates sensorimotor integration in the brain and that this mechanism underlies the faster processing of anticipated stimuli. We further propose that the examined slow delta waves, observed to be phase-coupled to reaction times, correspond to the compound potentials typically observed in paradigms of stimulus anticipation and motor preparation. “On optimal spatial filtering for the detection of phase coupling in multivariate neural recordings” by G. Waterstraat, G. Curio, and V.V. Nikulin in NeuroImag. Published online August 15 2017 doi:10.1016/j.neuroimage.2017.06.025