Synchronization between the anterior and posterior cortex determines consciousness level in patients with traumatic brain injury (TBI).

Survivors of traumatic brain injury (TBI) often suffer disorders of consciousness as a result of a breakdown in cortical connectivity. However, little is known about the neural discharges and cortical areas working in synchrony to generate consciousness in these patients. In this study, we analyzed cortical connectivity in patients with severe neurocognitive disorder (SND) and in the minimally conscious state (MCS). We found two synchronized networks subserving consciousness; one retrolandic (cognitive network) and the other frontal (executive control network). The synchrony between these networks is severely disrupted in patients in the MCS as compared to those with better levels of consciousness and a preserved state of alertness (SND). The executive control network could facilitate the synchronization and coherence of large populations of distant cortical neurons using high frequency oscillations on a precise temporal scale. Consciousness is altered or disappears after losing synchrony and coherence. We suggest that the synchrony between anterior and retrolandic regions is essential to awareness, and that a functioning frontal lobe is a surrogate marker for preserved consciousness. This article is part of a Special Issue entitled: Brain Integration.

Laterality of temporoparietal causal connectivity during the prestimulus period correlates with phonological decoding task performance in dyslexic and typical readers.

We examined how effective connectivity into and out of the left and right temporoparietal areas (TPAs) to/from other key cortical areas affected phonological decoding in 7 dyslexic readers (DRs) and 10 typical readers (TRs) who were young adults. Granger causality was used to compute the effective connectivity of the preparatory network 500 ms prior to presentation of nonwords that required phonological decoding. Neuromagnetic activity was analyzed within the low, medium, and high beta and gamma subbands. A mixed-model analysis determined whether connectivity to or from the left and right TPAs differed across connectivity direction (in vs. out), brain areas (right and left inferior frontal and ventral occipital-temporal and the contralateral TPA), reading group (DR vs. TR), and/or task performance. Within the low beta subband, better performance was associated with increased influence of the left TPA on other brain areas across both reading groups and poorer performance was associated with increased influence of the right TPA on other brain areas for DRs only. DRs were also found to have an increase in high gamma connectivity between the left TPA and other brain areas. This study suggests that hierarchal network structure rather than connectivity per se is important in determining phonological decoding performance.

A comparison of multivariate causality based measures of effective connectivity.

During the past several years a variety of methods have been developed to estimate the effective connectivity of neural networks from measurements of brain activity in an attempt to study causal interactions among distinct brain areas. Understanding the relative strengths and weaknesses of these methods, the assumptions they rely on, the accuracy they provide, and the computation time they require is of paramount importance in selecting the optimal method for a particular experimental task and for interpreting the results obtained. In this paper, the accuracy of the six most commonly used techniques for calculating effective connectivity are compared, namely directed transfer function, partial directed coherence, squared partial directed coherence, full frequency directed transfer function, direct directed transfer function, and Granger causality. These measures are derived from the coefficients and error terms of autoregressive models calculated using the dynamic autoregressive neuromagnetic causal imaging (DANCI) algorithm. These techniques were evaluated using magnetoencephalography recordings as well as several synthetic datasets that simulate neurophysiological signals, which varied on several parameters, including network size, signal-to-noise ratio, and network complexity, etc. The results show that Granger causality is the most accurate method across all experimental conditions explored and suggest that large multisensor data sets can be accurately analyzed using Granger causality with the DANCI algorithm.

Multichannel least-squares linear regression provides a fast, accurate, unbiased and robust estimation of Granger causality for neurophysiological data.

The most common method for calculating Granger causality requires the fitting of a system of autoregressive equations to multiple interrelated signals. Historically, the Levinson, Wiggins, Robinson (LWR) algorithm and the least-squares linear regression (LSLR) approach are the most widely used methods for fitting these autoregressive equations. In this manuscript we compare these algorithms head-to-head. LSLR, as implemented using the Dynamic Autoregressive Neuromagnetic Causal Imaging (DANCI) method, was faster, and produced better residual error, normality, independence, and autocorrelation functions when analyzing real magnetoencephalography signals. Simulations demonstrated that the accuracy of LSLR was much higher than the LWR method and that the LSLR method, at least as implemented by DANCI, could accurately resolve the causal connectivity of 50 interrelated signals. We conclude that the multichannel LSLR method, as implemented by DANCI, can accurately calculate the interdependencies among multiple signals and has the potential to accurately calculate Granger causality for large-scale neurophysiological networks.

Greater Pre-Stimulus Effective Connectivity from the Left Inferior Frontal Area to other Areas is Associated with Better Phonological Decoding in Dyslexic Readers.

Functional neuroimaging studies suggest that neural networks that subserve reading are organized differently in dyslexic readers (DRs) and typical readers (TRs), yet the hierarchical structure of these networks has not been well studied. We used Granger causality to examine the effective connectivity of the preparatory network that occurs prior to viewing a non-word stimulus that requires phonological decoding in 7 DRs and 10 TRs who were young adults. The neuromagnetic activity that occurred 500 ms prior to each rhyme trial was analyzed from sensors overlying the left and right inferior frontal areas (IFA), temporoparietal areas, and ventral occipital-temporal areas within the low, medium, and high beta and gamma sub-bands. A mixed-model analysis determined whether connectivity to or from the left and right IFAs differed across connectivity direction (into vs. out of the IFAs), brain areas, reading group, and/or performance. Results indicated that greater connectivity in the low beta sub-band from the left IFA to other cortical areas was significantly related to better non-word rhyme discrimination in DRs but not TRs. This suggests that the left IFA is an important cortical area involved in compensating for poor phonological function in DRs. We suggest that the left IFA activates a wider-than usual network prior to each trial in the service of supporting otherwise effortful phonological decoding in DRs. The fact that the left IFA provides top-down activation to both posterior left hemispheres areas used by TRs for phonological decoding and homologous right hemisphere areas is discussed. In contrast, within the high gamma sub-band, better performance was associated with decreased connectivity between the left IFA and other brain areas, in both reading groups. Overly strong gamma connectivity during the pre-stimulus period may interfere with subsequent transient activation and deactivation of sub-networks once the non-word appears.

Information communication networks in severe traumatic brain injury.

In this study we explored the use of coherence and Granger causality (GC) to separate patients in minimally conscious state (MCS) from patients with severe neurocognitive disorders (SND) that show signs of awareness. We studied 16 patients, 7 MCS and 9 SND with age between 18 and 49 years. Three minutes of ongoing electroencephalographic (EEG) activity was obtained at rest from 19 standard scalp locations, while subjects were alert but kept their eyes closed. GC was formulated in terms of linear autoregressive models that predict the evolution of several EEG time series, each representing the activity of one channel. The entire network of causally connected brain areas can be summarized as a graph of incompletely connected nodes. The 19 channels were grouped into five gross anatomical regions, frontal, left and right temporal, central, and parieto-occipital, while data analysis was performed separately in each of the five classical EEG frequency bands, namely delta, theta, alpha, beta, and gamma. Our results showed that the SND group consistently formed a larger number of connections compared to the MCS group in all frequency bands. Additionally, the number of connections in the delta band (0.1-4 Hz) between the left temporal and parieto-occipital areas was significantly different (P < 0.1%) in the two groups. Furthermore, in the beta band (12-18 Hz), the input to the frontal areas from all other cortical areas was also significantly different (P < 0.1%) in the two groups. Finally, classification of the subjects into distinct groups using as features the number of connections within and between regions in all frequency bands resulted in 100% classification accuracy of all subjects. The results of this study suggest that analysis of brain connectivity networks based on GC can be a highly accurate approach for classifying subjects affected by severe traumatic brain injury.