Vadim V. Nikulin

Robustly estimating the flow direction of information in complex physical systems

We propose a new measure (phase-slope index) to estimate the direction of information flux in multivariate time series. This measure (a) is insensitive to mixtures of independent sources, (b) gives meaningful results even if the phase spectrum is not linear, and (c) properly weights contributions from different frequencies. These properties are shown in extended simulations and contrasted to Granger causality which yields highly significant false detections for mixtures of independent sources. An application to electroencephalography data (eyes-closed condition) reveals a clear front-to-back information flow.

A critical assessment of connectivity measures for EEG data: a simulation study.

Information flow between brain areas is difficult to estimate from EEG measurements due to the presence of noise as well as due to volume conduction. We here test the ability of popular measures of effective connectivity to detect an underlying neuronal interaction from simulated EEG data, as well as the ability of commonly used inverse source reconstruction techniques to improve the connectivity estimation. We find that volume conduction severely limits the neurophysiological interpretability of sensor-space connectivity analyses. Moreover, it may generally lead to conflicting results depending on the connectivity measure and statistical testing approach used. In particular, we note that the application of Granger-causal (GC) measures combined with standard significance testing leads to the detection of spurious connectivity regardless of whether the analysis is performed on sensor-space data or on sources estimated using three different established inverse methods. This empirical result follows from the definition of GC. The phase-slope index (PSI) does not suffer from this theoretical limitation and therefore performs well on our simulated data. We develop a theoretical framework to characterize artifacts of volume conduction, which may still be present even in reconstructed source time series as zero-lag correlations, and to distinguish their time-delayed brain interaction. Based on this theory we derive a procedure which suppresses the influence of volume conduction, but preserves effects related to time-lagged brain interaction in connectivity estimates. This is achieved by using time-reversed data as surrogates for statistical testing. We demonstrate that this robustification makes Granger-causal connectivity measures applicable to EEG data, achieving similar results as PSI. Integrating the insights of our study, we provide a guidance for measuring brain interaction from EEG data. Software for generating benchmark data is made available.

Detrended fluctuation analysis: A scale-free view on neuronal oscillations

Recent years of research have shown that the complex temporal structure of ongoing oscillations is scale-free and characterized by long-range temporal correlations. Detrended fluctuation analysis (DFA) has proven particularly useful, revealing that genetic variation, normal development, or disease can lead to differences in the scale-free amplitude modulation of oscillations. Furthermore, amplitude dynamics is remarkably independent of the time-averaged oscillation power, indicating that the DFA provides unique insights into the functional organization of neuronal systems. To facilitate understanding and encourage wider use of scaling analysis of neuronal oscillations, we provide a pedagogical explanation of the DFA algorithm and its underlying theory. Practical advice on applying DFA to oscillations is supported by MATLAB scripts from the Neurophysiological Biomarker Toolbox (NBT) and links to the NBT tutorial website http://www.nbtwiki.net/. Finally, we provide a brief overview of insights derived from the application of DFA to ongoing oscillations in health and disease, and discuss the putative relevance of criticality for understanding the mechanism underlying scale-free modulation of oscillations.

A novel mechanism for evoked responses in the human brain

Magnetoencephalographic and electroencephalographic evoked responses are primary real‐time objective measures of cognitive and perceptual processes in the human brain. Two mechanisms (additive activity and phase reset) have been debated and considered as the only possible explanations for evoked responses. Here we present theoretical and empirical evidence of a third mechanism contributing to the generation of evoked responses. Interestingly, this mechanism can be deduced entirely from the characteristics of spontaneous oscillations in the absence of stimuli. We show that the amplitude fluctuations of neuronal α oscillations at rest are associated with changes in the mean value of ongoing activity in magnetoencephalography, a phenomenon that we term baseline shifts associated with α oscillations. When stimuli modulate the amplitude of α oscillations, baseline shifts become the basis of a novel mechanism for the generation of evoked responses; the averaging of several trials leads to a cancellation of the oscillatory component but the baseline shift remains, which gives rise to an evoked response. We propose that the presence of baseline shifts associated with α oscillations can be explained by the asymmetric flow of inward and outward neuronal currents related to the generation of α oscillations. Our findings are relevant to the vast majority of electroencephalographic and magnetoencephalographic studies involving perceptual, cognitive and motor activity.

Stimulus-induced change in long-range temporal correlations and scaling behaviour of sensorimotor oscillations

The human brain spontaneously generates large‐scale network oscillations at around 10 and 20 Hz. The amplitude envelope of these oscillations fluctuates intermittently and was recently reported to exhibit power‐law decay of the autocorrelation for hundreds of seconds. This indicates that the underlying networks are in a dynamic state resembling the self‐organized critical state known to exist in many complex systems. Based on the mechanism of how correlations emerge in these systems, we hypothesized that the physiological basis of long‐range power‐law correlations is the buildup of a memory of past activity by a continuous modification of the networks functional connectivity by the ongoing oscillations. In this framework, exogenous perturbations of ongoing oscillations would degrade or abolish this dynamic network memory. We investigated the sensitivity of the temporal correlations in sensorimotor 10‐ and 20‐Hz oscillations to median nerve stimulation that is known to have immediate effects on ongoing oscillations. Our results show that the amplitude fluctuations of these oscillations were effectively modulated by the somatosensory stimuli but still exhibited long‐range temporal correlations and power‐law scaling behaviour. The magnitude of the temporal correlations was, however, attenuated and the power‐law exponents were decreased. This implies that the stimuli indeed degraded the networks memory of its past.

Combining sparsity and rotational invariance in EEG/MEG source reconstruction.

We introduce Focal Vector Field Reconstruction (FVR), a novel technique for the inverse imaging of vector fields. The method was designed to simultaneously achieve two goals: a) invariance with respect to the orientation of the coordinate system, and b) a preference for sparsity of the solutions and their spatial derivatives. This was achieved by defining the regulating penalty function, which renders the solutions unique, as a global l(1)-norm of local l(2)-norms. We show that the method can be successfully used for solving the EEG inverse problem. In the joint localization of 2-3 simulated dipoles, FVR always reliably recovers the true sources. The competing methods have limitations in distinguishing close sources because their estimates are either too smooth (LORETA, Minimum l(1)-norm) or too scattered (Minimum l(2)-norm). In both noiseless and noisy simulations, FVR has the smallest localization error according to the Earth Movers Distance (EMD), which is introduced here as a meaningful measure to compare arbitrary source distributions. We also apply the method to the simultaneous localization of left and right somatosensory N20 generators from real EEG recordings. Compared to its peers FVR was the only method that delivered correct location of the source in the somatosensory area of each hemisphere in accordance with neurophysiological prior knowledge.

Task-related differential dynamics of EEG alpha- and beta-band synchronization in cortico-basal motor structures

Movement‐related processing results in the modulation of neuronal synchronization over several electroencephalography (EEG) frequency ranges, including alpha‐ (8–12 Hz) and beta‐band (14–30 Hz). Whether modulation patterns differ across sites within the motor system remains unclear, but could denote how information is conveyed across the cortico‐basal network. We therefore compared the event‐related synchronization/desynchronization (ERS/ERD) in recordings from the scalp, basal ganglia and thalamic structures during a motor task.

Alcohol Reduces Prefrontal Cortical Excitability in Humans: A Combined TMS and EEG Study

The effects of alcohol (0.8 g/kg) on the prefrontal cortex were studied in nine healthy subjects using the technique of transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG). A total of 120 magnetic pulses were delivered with a figure-of-eight coil to the left prefrontal cortex at the rate of 0.4–0.7 Hz. The EEG was recorded simultaneously with 60 scalp electrodes (41 electrodes were used for analysis); the TMS-evoked activation was estimated by the area under the global mean field amplitude (GMFA) time curve. TMS caused changes in EEG activity lasting up to 270 ms poststimulus. Alcohol decreased GMFA at 30–270 ms poststimulus (713±303 vs 478±142 μV ms; p=0.007). Alcohol-induced differences were most pronounced at anterior electrodes. These results suggest that alcohol reduces the excitability in the prefrontal cortex.

Attenuation of long-range temporal correlations in the amplitude dynamics of alpha and beta neuronal oscillations in patients with schizophrenia

Although schizophrenia was previously associated with affected spatial neuronal synchronization, surprisingly little is known about the temporal dynamics of neuronal oscillations in this disease. However, given that the coordination of neuronal processes in time represents an essential aspect of practically all cognitive operations, it might be strongly affected in patients with schizophrenia. In the present study we aimed at quantifying long-range temporal correlations (LRTC) in patients (18 with schizophrenia; 3 with schizoaffective disorder) and 28 healthy control subjects matched for age and gender. Ongoing neuronal oscillations were recorded with multi-channel EEG at rest condition. LRTC in the range 5-50s were analyzed with Detrended Fluctuation Analysis. The amplitude of neuronal oscillations in alpha and beta frequency ranges did not differ between patients and control subjects. However, LRTC were strongly attenuated in patients with schizophrenia in both alpha and beta frequency ranges. Moreover, the cross-frequency correlation between LRTC belonging to alpha and beta oscillations was stronger for patients than healthy controls, indicating that similar neurophysiological processes affect neuronal dynamics in both frequency ranges. We believe that the attenuation of LRTC is most likely due to the increased variability in neuronal activity, which was previously hypothesized to underlie an excessive switching between the neuronal states in patients with schizophrenia. Attenuated LRTC might allow for more random associations between neuronal activations, which in turn might relate to the occurrence of thought disorders in schizophrenia.

Rapid Cortical Plasticity Underlying Novel Word Learning

Humans are unique in developing large lexicons as their communication tool. To achieve this, they are able to learn new words rapidly. However, neural bases of this rapid learning, which may be an expression of a more general cognitive mechanism, are not yet understood. To address this, we exposed our subjects to familiar words and novel spoken stimuli in a short passive perceptual learning session and compared automatic brain responses to these items throughout the learning exposure. Initially, we found enhanced activity for known words, indexing the ignition of their underlying memory traces. However, just after 14 min of learning exposure, the novel items exhibited a significant increase in response magnitude matching in size with that to real words. This activation increase, as we would like to propose, reflects rapid mapping of new word forms onto neural representations. Similar to familiar words, the neural activity subserving rapid learning of new word forms was generated in the left-perisylvian language cortex, especially anterior superior-temporal areas. This first report of a neural correlate of rapid learning suggests that our brain may effectively form new neuronal circuits online as it gets exposed to novel patterns in the sensory input. Understanding such fast learning is key to the neurobiological explanation of the human language faculty and learning mechanisms in general.