Magnetoencephalographic Source Localization of the Eye Area of the Motor Homunculus.

A patient with intractable epilepsy, previous right frontal resection, and active vagus nerve stimulation (VNS) developed new onset quasi-continuous twitching around the left eye. Electroencephalography showed no correlate to the orbicularis oculi twitches apart from myographic potentials at the left supraorbital and anterior frontal electrodes. Magnetoencephalography was performed using spatiotemporal signal space separation to suppress magnetic artifacts associated with the VNS apparatus. Magnetoencephalographic source imaging performed on the data back-averaged from the left supraorbital myographic potentials revealed an intrasulcal cortical generator situated in the posterior wall of the right precentral gyrus representing the eye area of the motor homunculus.

A combined TMS-EEG study of short-latency afferent inhibition in the motor and dorsolateral prefrontal cortex.

Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) enables noninvasive neurophysiological investigation of the human cortex. A TMS paradigm of short-latency afferent inhibition (SAI) is characterized by attenuation of the motor-evoked potential (MEP) and modulation of N100 of the TMS-evoked potential (TEP) when TMS is delivered to motor cortex (M1) following median nerve stimulation. SAI is a marker of cholinergic activity in the motor cortex; however, the SAI has not been tested from the prefrontal cortex. We aimed to explore the effect of SAI in dorsolateral prefrontal cortex (DLPFC). SAI was examined in 12 healthy subjects with median nerve stimulation and TMS delivered to M1 and DLPFC at interstimulus intervals (ISIs) relative to the individual N20 latency. SAI in M1 was tested at the optimal ISI of N20 + 2 ms. SAI in DLPFC was investigated at a range of ISI from N20 + 2 to N20 + 20 ms to explore its temporal profile. For SAI in M1, the attenuation of MEP amplitude was correlated with an increase of TEP N100 from the left central area. A similar spatiotemporal neural signature of SAI in DLPFC was observed with a marked increase of N100 amplitude. SAI in DLPFC was maximal at ISI N20 + 4 ms at the left frontal area. These findings establish the neural signature of SAI in DLPFC. Future studies could explore whether DLPFC-SAI is neurophysiological marker of cholinergic dysfunction in cognitive disorders.

Functional brain activity constrained by structural connectivity reveals cohort-specific features for serum neurofilament light chain.

Background: Neuro-axonal brain damage releases neurofilament light chain (NfL) proteins, which enter the blood. Serum NfL has recently emerged as a promising biomarker for grading axonal damage, monitoring treatment responses, and prognosis in neurological diseases. Importantly, serum NfL levels also increase with aging, and the interpretation of serum NfL levels in neurological diseases is incomplete due to lack of a reliable model for age-related variation in serum NfL levels in healthy subjects. Methods: Graph signal processing (GSP) provides analytical tools, such as graph Fourier transform (GFT), to produce measures from functional dynamics of brain activity constrained by white matter anatomy. Here, we leveraged a set of features using GFT that quantified the coupling between blood oxygen level dependent signals and structural connectome to investigate their associations with serum NfL levels collected from healthy subjects and former athletes with history of concussions. Results: Here we show that GSP feature from isthmus cingulate in the right hemisphere (r-iCg) is strongly linked with serum NfL in healthy controls. In contrast, GSP features from temporal lobe and lingual areas in the left hemisphere and posterior cingulate in the right hemisphere are the most associated with serum NfL in former athletes. Additional analysis reveals that the GSP feature from r-iCg is associated with behavioral and structural measures that predict aggressive behavior in healthy controls and former athletes. Conclusions: Our results suggest that GSP-derived brain features may be included in models of baseline variance when evaluating NfL as a biomarker of neurological diseases and studying their impact on personality traits.

Experimental observation of increased fluctuations in an order parameter before epochs of extended brain synchronization.

The identification of epileptic seizure precursors has potential clinical relevance. It is conjectured that seizures may be represented by dynamical bifurcations and that an adequate order parameter to characterize brain dynamics is the phase difference in the oscillatory activity of neural systems. In this study, the critical point hypothesis that seizures, or more generally periods of widespread high synchronization, represent bifurcations is empirically tested by monitoring the growth of fluctuations in the putative order parameter of phase differences between magnetoencephalographic and electroencephalographic signals in nearby brain regions in patients with epilepsy and normal subjects during hyperventilation. Implications of the results with regard to epileptic phenomena are discussed.

Fluctuations in cortical synchronization in pediatric traumatic brain injury.

Traumatic brain injury (TBI) is the leading cause of death and acquired disability in the pediatric population worldwide. We hypothesized that electroencephalography (EEG) synchrony and its temporal variability, analyzed during the acute phase following TBI, would be altered from that of normal children and as such would offer insights into TBI pathophysiology. Seventeen pediatric patients with mild to severe head injury admitted to a pediatric critical care unit were recruited along with 10 age- and gender-matched controls. Patients had two electroencephalographs performed 3 days apart. Outcome was measured at 1 year post-TBI utilizing the Pediatric Cerebral Performance Category score (PCPC). Maximal synchrony between EEG channels correlated to areas of primary injury as seen on computed tomography (CT) scan. The temporal variability of phase synchronization among EEG electrodes increased as patients recovered and emerged from coma (p < 0.001). This temporal variability correlated with outcome (Pearson coefficient of 0.74) better than the worst Glasgow Coma Scale score, length of coma, or extent of injury on CT scan. This represents a novel approach in the evaluation of TBI in children.

Investigating Cortical Inhibition in First-Degree Relatives and Probands in Schizophrenia.

Deficits in GABAergic inhibitory neurotransmission are a reliable finding in schizophrenia (SCZ) patients. Previous studies have reported that unaffected first-degree relatives of patients with SCZ demonstrate neurophysiological abnormalities that are intermediate between probands and healthy controls. In this study, first-degree relatives of patients with SCZ and their related probands were investigated to assess frontal cortical inhibition. Long-interval cortical inhibition (LICI) was measured from the dorsolateral prefrontal cortex (DLPFC) using combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG). The study presents an extended sample of 129 subjects (66 subjects have been previously reported): 19 patients with SCZ or schizoaffective disorder, 30 unaffected first-degree relatives of these SCZ patients, 13 obsessive-compulsive disorder (OCD) patients, 18 unaffected first-degree relatives of these OCD patients and 49 healthy subjects. In the DLPFC, cortical inhibition was significantly decreased in patients with SCZ compared to healthy subjects. First-degree relatives of patients with SCZ showed significantly more cortical inhibition than their SCZ probands. No differences were demonstrated between first-degree relatives of SCZ patients and healthy subjects. Taken together, these findings show that more studies are needed to establish an objective biological marker for potential diagnostic usage in severe psychiatric disorders.

Unbiased cluster estimation of electrophysiological brain response.

BACKGROUND: Recent increase in the size and complexity of electrophysiological data from multidimensional electroencephalography (EEG) and magnetoencephalography (MEG) studies has prompted the development of sophisticated statistical frameworks for data analysis. One of the main challenges for such frameworks is the multiple comparisons problem, where the large number of statistical tests performed within a high-dimensional dataset lead to an increased risk of Type I errors (false positives). A solution to this problem, cluster analysis, applies the biologically-motivated knowledge of correlation between adjacent voxels in one or more dimensions of the dataset to correct for the multiple comparisons problem and detect true neurophysiological effects. Cluster-based methods provide increased sensitivity towards detecting neurophysiological events compared to conservative methods such as Bonferroni correction, but are limited by their dependency on an initial cluster-forming statistical threshold (e.g. t-score, alpha) obstructing precise comparisons of results across studies. NEW METHOD: Rather than selecting a single threshold value, unbiased cluster estimation (UCE) computes a significance distribution across all possible threshold values to provide an unbiased overall significance value. COMPARISON TO EXISTING METHODS: UCE functions as a novel extension to existing cluster analysis methods. RESULTS: Using data from EEG combined with brain stimulation study, we showed the impact of statistical threshold on outcome measures and introduction of bias. We showed the application of UCE for different study designs (e.g., within-group, between-group comparisons). CONCLUSION: We propose that researchers consider employing UCE for multidimensional EEG/MEG datasets toward an unbiased comparison of results between subjects, groups, and studies.

Phase synchronization measurements using electroencephalographic recordings: what can we really say about neuronal synchrony?

Phase synchrony analysis is a relatively new concept that is being increasingly used on neurophysiological data obtained through different methodologies. It is currently believed that phase synchrony is an important signature of information binding between distant sites of the brain, especially during cognitive tasks. Electroencephalographic (EEG) recordings are the most widely used recording technique for recording brain signals and assessing phase synchrony patterns. In this study, we address the suitability of phase synchrony analysis in EEG recordings. Using geometrical arguments and numerical examples, employing EEG and magnetoencephalographic data, we show that the presence of a common reference signal in the case of EEG recordings results in a distortion of the synchrony values observed, in that the amplitudes of the signals influence the synchrony measured, and in general destroys the intended physical interpretation of phase synchrony.

A model of functional brain connectivity and background noise as a biomarker for cognitive phenotypes: application to autism.

We present an efficient approach to discriminate between typical and atypical brains from macroscopic neural dynamics recorded as magnetoencephalograms (MEG). Our approach is based on the fact that spontaneous brain activity can be accurately described with stochastic dynamics, as a multivariate Ornstein-Uhlenbeck process (mOUP). By fitting the data to a mOUP we obtain: 1) the functional connectivity matrix, corresponding to the drift operator, and 2) the traces of background stochastic activity (noise) driving the brain. We applied this method to investigate functional connectivity and background noise in juvenile patients (n = 9) with Asperger's syndrome, a form of autism spectrum disorder (ASD), and compared them to age-matched juvenile control subjects (n = 10). Our analysis reveals significant alterations in both functional brain connectivity and background noise in ASD patients. The dominant connectivity change in ASD relative to control shows enhanced functional excitation from occipital to frontal areas along a parasagittal axis. Background noise in ASD patients is spatially correlated over wide areas, as opposed to control, where areas driven by correlated noise form smaller patches. An analysis of the spatial complexity reveals that it is significantly lower in ASD subjects. Although the detailed physiological mechanisms underlying these alterations cannot be determined from macroscopic brain recordings, we speculate that enhanced occipital-frontal excitation may result from changes in white matter density in ASD, as suggested in previous studies. We also venture that long-range spatial correlations in the background noise may result from less specificity (or more promiscuity) of thalamo-cortical projections. All the calculations involved in our analysis are highly efficient and outperform other algorithms to discriminate typical and atypical brains with a comparable level of accuracy. Altogether our results demonstrate a promising potential of our approach as an efficient biomarker for altered brain dynamics associated with a cognitive phenotype.

Single trial classification of magnetoencephalographic recordings using Granger causality.

The use of Granger causality (GC) for studying dependencies in neuroimaging data has recently been gaining popularity. Several frameworks exist for applying GC to neurophysiological questions but many rely heavily on specific statistical assumptions regarding autoregressive (AR) models for hypothesis testing. Since it is often difficult to satisfy these assumptions in practical settings, this study proposes an alternative statistical methodology based on the classification of individual trials of data. Instead of testing for significance using statistics based on estimated AR models or prediction errors, hypotheses were tested by determining whether or not individual magnetoencephalography (MEG) recording segments belonging to either of two experimental conditions can be successfully classified using features derived from AR and GC concepts. Using this novel approach, we show that bivariate temporal GC can be used to distinguish button presses based on whether they were experimentally forced or free. Additionally, the methodology was used to determine useful parameter settings for various steps of the analysis and this revealed surprising insight into several aspects of AR and GC analysis which, previously, could not be obtained in a comparable manner. A final mean accuracy of 79.2% was achieved for classifying forced and free button presses for 6 subjects suggesting that classification using GC features is a viable option for studying MEG signals and useful for evaluating the effectiveness of parameter variations in GC analysis.

On the risk of extracting relevant information from random data.

This comment constitutes a re-assessment of a recent study in which near-infrared spectroscopy (NIRS) was used to decode decision making. In the original study, the process of feature selection was carried out on all of the data, and those features which displayed the greater classification accuracy were selected, but no independent assessment or validation of the result was performed on a separated set of trials. In order to show the risk of this procedure, the same methodology was applied here to a set of random and independent time series instead of actual NIRS signals. This simulation produced statistically similar results to the original experimental study. It is my opinion that, from the reported classification accuracy of the original paper, no relevant or useful information is really obtained.

Fluctuations in neuronal synchronization in brain activity correlate with the subjective experience of visual recognition.

The scientific study of subjective experience is a current major research area in the neurosciences. Coordination patterns of brain activity are being studied to address the question of how brain function relates to behaviour, and particularly methods to estimate neuronal synchronization can unravel the spatio-temporal dynamics of the transient formation of neuronal assemblies. We report here a biophysical correlate of subjective experience. Subjects visualised figures with different levels of noise, while their brain activity was recorded using magnetoencephalography (MEG), and reported the moment in time (corresponding to a noise level) of figure recognition, which varied between individuals, as well as the moment when they saw the figure more clearly, which was mostly common among the participants (thus less subjective). This latter moment is considered to represent psychophysical stochastic resonance (PSR). Fluctuations in neuronal synchronization, quantified using a diffusion coefficient, were lower in occipital cortex when subjects recognised the figure, for a certain noise level, but did not correlate with the moment of PSR. A different pattern was observed in frontal cortex, where lower values of the diffusion coefficient in neuronal synchronization was maintained from the moment of recognition to the moment of PSR. No specific pattern was found analysing signals from temporal or parietal cortical areas. These observations provide support for distinct synchronization patterns in different cortical areas, and represent another demonstration that the subjective, first-person perspective is accessible to scientific methods.