Luis Garcia Dominguez

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.

Enhanced Synchrony in Epileptiform Activity? Local versus Distant Phase Synchronization in Generalized Seizures

Synchronization is a fundamental characteristic of complex systems and a basic mechanism of self-organization. A traditional, accepted perspective on epileptiform activity holds that hypersynchrony covering large brain regions is a hallmark of generalized seizures. However, a few recent reports have described substantial fluctuations in synchrony before and during ictal events, thus raising questions as to the widespread synchronization notion. In this study, we used magnetoencephalographic recordings from epileptic patients with generalized seizures and normal control subjects to address the extent of the phase synchronization (phase locking) in local (neighboring) and distant cortical areas and to explore the ongoing temporal dynamics for particular ranges of frequencies at which synchrony occurs, during interictal and ictal activity. Synchronization patterns were found to differ somewhat depending on the epileptic syndrome, with primary generalized absence seizures displaying more long-range synchrony in all frequency bands studied (3–55 Hz) than generalized tonic motor seizures of secondary (symptomatic) generalized epilepsy or frontal lobe epilepsy. However, all seizures were characterized by enhanced local synchrony compared with distant synchrony. There were fluctuations in the synchrony between specific cortical areas that varied from seizure to seizure in the same patient, but in most of the seizures studied, regardless of semiology, there was a constant pattern in the dynamics of synchronization, indicating that seizures proceed by a recruitment of neighboring neuronal networks. Together, these data indicate that the concept of widespread “hypersynchronous” activity during generalized seizures may be misleading and valid only for very specific neuronal ensembles and circumstances.

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.

Evidence for inhibitory deficits in the prefrontal cortex in schizophrenia

Abnormal gamma-aminobutyric acid inhibitory neurotransmission is a key pathophysiological mechanism underlying schizophrenia. Transcranial magnetic stimulation can be combined with electroencephalography to index long-interval cortical inhibition, a measure of GABAergic receptor-mediated inhibitory neurotransmission from the frontal and motor cortex. In previous studies we have reported that schizophrenia is associated with inhibitory deficits in the dorsolateral prefrontal cortex compared to healthy subjects and patients with bipolar disorder. The main objective of the current study was to replicate and extend these initial findings by evaluating long-interval cortical inhibition from the dorsolateral prefrontal cortex in patients with schizophrenia compared to patients with obsessive-compulsive disorder. A total of 111 participants were assessed: 38 patients with schizophrenia (average age: 35.71 years, 25 males, 13 females), 27 patients with obsessive-compulsive disorder (average age: 36.15 years, 11 males, 16 females) and 46 healthy subjects (average age: 33.63 years, 23 females, 23 males). Long-interval cortical inhibition was measured from the dorsolateral prefrontal cortex and motor cortex through combined transcranial magnetic stimulation and electroencephalography. In the dorsolateral prefrontal cortex, long-interval cortical inhibition was significantly reduced in patients with schizophrenia compared to healthy subjects (P = 0.004) and not significantly different between patients with obsessive-compulsive disorder and healthy subjects (P = 0.5445). Long-interval cortical inhibition deficits in the dorsolateral prefrontal cortex were also significantly greater in patients with schizophrenia compared to patients with obsessive-compulsive disorder (P = 0.0465). There were no significant differences in long-interval cortical inhibition across all three groups in the motor cortex. These results demonstrate that long-interval cortical inhibition deficits in the dorsolateral prefrontal cortex are specific to patients with schizophrenia and are not a generalized deficit that is shared by disorders of severe psychopathology.

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.

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.

Characterizing Long Interval Cortical Inhibition over the Time-Frequency Domain

Objective Long-interval cortical inhibition (LICI) can be recorded from motor and non-motor regions of the cortex through combined transcranial magnetic stimulation (TMS) with electroencephalography (EEG). This study aimed to evaluate additional dimensions of LICI characteristics over an extended time-frequency and spatial domain. This was done by introducing two alternative measures of LICI signal amplitude: the Discrete Fourier Transform (DFT) and the Hilbert transform (HT). Both approaches estimate signal amplitude not taking into account the phase. In both cases LICI was measured as the difference between the unconditioned and conditioned activity evoked by the test pulse. Finally, we evaluated whether the topographical patterns of single and paired responses differed beyond the expected variations in amplitude. Materials and Methods LICI was delivered as single and paired pulses to the motor cortex (MC) and dorsolateral prefrontal cortex (DLPFC) in 33 healthy subjects with TMS-EEG. Results Significant differences (p<0.0001) between the unconditioned and conditioned evoked activity were found for both the DLPFC and MC using both methods (i.e., DFT and HT) after correcting for multiple comparisons in the time-frequency domain. The influence of inhibition was found to be significantly larger in space and time than previously considered. Single and paired conditions differ in intensity, but also in their topographic pattern (i.e., the specific spatiotemporal configuration of active sources). Conclusion Similar results were found by both DFT and HT. The effect of inhibition across the cortex was also found to be complex and extended. In particular, it was found that LICI may be measured with high sensitivity in areas that were relatively distant from the stimulation site, which may have important practical applications. The analysis presented in this study overcomes some limitations of previous studies and could serve as a key reference for future studies examining TMS-indices of inhibition/excitation in healthy and diseased states.

Prefrontal White Matter Structure Mediates the Influence of GAD1 on Working Memory.

The glutamic acid decarboxylase 1 (GAD1) gene is a major determinant of γ-aminobutyric acid (GABA), the most abundant inhibitory neurotransmitter modulating local neuronal circuitry. GABAergic dysfunction and expression of GAD1 have been implicated in the pathophysiology of schizophrenia, and in working memory impairment. We examined the influence of the functional GAD1 rs3749034 variant on white matter fractional anisotropy (FA), cortical thickness, and working memory performance in schizophrenia patients and healthy controls (N=197). Using transcranial magnetic stimulation with electroencephalography (TMS-EEG), we subsequently examined the effect of rs3749034 on long-interval cortical inhibition (LICI) in the dorsolateral prefrontal cortex (DLPFC) in schizophrenia patients and healthy controls (N=66). We found that the rs3749034 T-allele carrier risk group had lower voxel-wise FA in the prefrontal cortex region (PFWE-corrected<0.05) but not cortical thickness. Mixed-model regression revealed a significant effect on attentional processing and working memory across four performance measures (F1,182=11.5, P=8 × 10−4). FA in the prefrontal cortex was associated with digit-span performance. Voxel-wise mediation analysis revealed that the effect GAD1 on poorer digit-span performance statistically predicted the lower white matter FA (PFWE-corrected<0.05). In exploratory analysis, we found a prominent GAD1 genotype-by-diagnosis interaction on DLPFC LICI (F1,56=14.3, P=4.1 × 10−4). Our findings converge on variation in GAD1 gene predicting a susceptibility mechanism that affects white matter FA, GABAergic inhibitory neurotransmission in the DLPFC, and working memory performance. Furthermore, via voxel mediation of FA and TMS-EEG intervention, we provide evidence for a potentially causal mechanism through which aberrant DLPFC GABA signaling may contribute to working memory dysfunction.

A novel method for removal of deep brain stimulation artifact from electroencephalography

BACKGROUND Deep brain stimulation (DBS) has treatment efficacy in neurological and psychiatric disorders such as Parkinsons disease and major depression. Electroencephalography (EEG) is a versatile neurophysiological tool that can be used to better understand DBS treatment mechanisms. DBS causes artifacts in EEG recordings that preclude meaningful neurophysiological activity from being quantified during stimulation. NEW METHOD In this study, we modeled the DBS stimulation artifact and illustrated a technique for removing the artifact using matched filters. The approach was validated using a synthetically generated DBS artifact superimposed on EEG data. Mean squared error (MSE) between the recovered signal and the artifact-free signal was used to quantify the effectiveness of the approach. RESULTS The DBS artifact was characterized by a series of narrow band components at the harmonic frequencies of DBS stimulation. The filtering approach successfully removed the DBS artifact with MSE value being less than 2% of base signal power for the typical stimulation and recording setups. General guidelines on how to setup DBS EEG studies and configure the subsequent artifact removal process are described. COMPARISON WITH PREVIOUS METHOD To avoid stimulus artifacts, a number of EEG studies with DBS subjects have resorted to turning the stimulator off during recording, while other studies have used low pass filters to remove artifacts and look at frequencies well below 50 Hz. CONCLUSIONS This study establishes a method through which DBS artifact in EEG recordings can be reliably eliminated, thereby preserving a meaningful neurophysiological signal through which to better understand DBS treatment mechanisms.

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.