Combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG) constitutes a powerful tool to directly assess human cortical excitability and connectivity. TMS of the primary motor cortex elicits a sequence of TMS-evoked EEG potentials (TEPs). It is thought that inhibitory neurotransmission through GABA-A receptors (GABAAR) modulates early TEPs (Ͻ50 ms after TMS), whereas GABA-B receptors (GABABR) play a role for later TEPs (at ϳ100 ms after TMS). However, the physiological underpinnings of TEPs have not been clearly elucidated yet. Here, we studied the role of GABAA/B-ergic neurotransmission for TEPs in healthy subjects using a pharmaco-TMS-EEG approach. In Experiment 1, we tested the effects of a single oral dose of alprazolam (a classical benzodiazepine acting as allosteric-positive modulator at ␣1, ␣2, ␣3, and ␣5 subunit-containing GABAARs) and zolpidem (a positive modulator mainly at the ␣1 GABAAR) in a double-blind, placebo-controlled, crossover study. In Experiment 2, we tested the influence of baclofen (a GABABR agonist) and diazepam (a classical benzodiazepine) versus placebo on TEPs. Alprazolam and diazepam increased the amplitude of the negative potential at 45 ms after stimulation (N45) and decreased the negative component at 100 ms (N100), whereas zolpidem increased the N45 only. In contrast, baclofen specifically increased the N100 amplitude. These results provide strong evidence that the N45 represents activity of ␣1-subunit-containing GABAARs, whereas the N100 represents activity of GABABRs. Findings open a novel window of opportunity to study alteration of GABAA-/GABAB-related inhibition in disorders, such as epilepsy or schizophrenia.

Objective: In the cognitive and clinical neurosciences, the past decade has been marked by dramatic growth in a literature examining brain "connectivity" using noninvasive methods. We offer a critical review of the blood oxygen level dependent functional MRI (BOLD fMRI) literature examining neural connectivity changes in neurological disorders with focus on brain injury and dementia. The goal is to demonstrate that there are identifiable shifts in local and large-scale network connectivity that can be predicted by the degree of pathology. We anticipate that the most common network response to neurological insult is hyperconnectivity but that this response depends upon demand and resource availability. Method: To examine this hypothesis, we initially reviewed the results from 1,426 studies examining functional brain connectivity in individuals diagnosed with multiple sclerosis, traumatic brain injury, mild cognitive impairment, and Alzheimer's disease. Based upon inclusionary criteria, 126 studies were included for detailed analysis. Results: Results from 126 studies examining local and whole brain connectivity demonstrated increased connectivity in traumatic brain injury and multiple sclerosis. This finding is juxtaposed with findings in mild cognitive impairment and Alzheimer's disease where there is a shift to diminished connectivity as degeneration progresses. Conclusion: This summary of the functional imaging literature using fMRI methods reveals that hyperconnectivity is a common response to neurological disruption and that it may be differentially observable across brain regions. We discuss the factors contributing to both hyper-and hypoconnectivity results after neurological disruption and the implications these findings have for network plasticity.

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the 'traditional' set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality.This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified, easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox.Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab ® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

Whether the balance between integration and segregation of information in the brain is damaged in Mild Cognitive Impairment (MCI) subjects is still a matter of debate. Here we characterize the functional network architecture of MCI subjects by means of complex networks analysis. Magnetoencephalograms (MEG) time series obtained during a memory task were evaluated by synchronization likelihood (SL), to quantify the statistical dependence between MEG signals and to obtain the functional networks. Graphs from MCI subjects show an enhancement of the strength of connections, together with an increase in the outreach parameter, suggesting that memory processing in MCI subjects is associated with higher energy expenditure and a tendency toward random structure, which breaks the balance between integration and segregation. All features are reproduced by an evolutionary network model that simulates the degenerative process of a healthy functional network to that associated with MCI. Due to the high rate of conversion from MCI to Alzheimer Disease (AD), these results show that the analysis of functional networks could be an appropriate tool for the early detection of both MCI and AD.

Cognitive processes require a functional interaction between specialized multiple, local and remote brain regions. Although these interactions can be strongly altered by an acquired brain injury, brain plasticity allows network reorganization to be principally responsible for recovery. The present work evaluates the impact of brain injury on functional connectivity patterns. Networks were calculated from resting-state magnetoencephalographic recordings from 15 brain injured patients and 14 healthy controls by means of wavelet coherence in standard frequency bands. We compared the parameters defining the network, such as number and strength of interactions as well as their topology, in controls and patients for two conditions: following a traumatic brain injury and after a rehabilitation treatment. A loss of delta- and theta-based connectivity and conversely an increase in alpha- and beta-band-based connectivity were found. Furthermore, connectivity parameters approached controls in all frequency bands, especially in slow-wave bands. A correlation between network reorganization and cognitive recovery was found: the reduction of delta-band-based connections and the increment of those based on alpha band correlated with Verbal Fluency scores, as well as Perceptual Organization and Working Memory Indexes, respectively. Additionally, changes in connectivity values based on theta and beta bands correlated with the Patient Competency Rating Scale. The current study provides new evidence of the neurophysiological mechanisms underlying neuronal plasticity processes after brain injury, and suggests that these changes are related with observed changes at the behavioural level.

Mild cognitive impairment (MCI) has been considered an intermediate state between healthy aging and dementia. The early damage in anatomical connectivity and progressive loss of synapses that characterize early Alzheimer's disease suggest that MCI could also be a disconnection syndrome. Here, we compare the degree of synchronization of brain signals recorded with magnetoencephalography from patients (22) with MCI with that of healthy controls (19) during a memory task. Synchronization Likelihood, an index based on the theory of nonlinear dynamical systems, was used to measure functional connectivity. During the memory task patients showed higher interhemispheric synchronization than healthy controls between left and right -anterior temporo-frontal regions (in all studied frequency bands) and in posterior regions in the γ band. On the other hand, the connectivity pattern from healthy controls indicated two clusters of higher synchronization, one among left temporal sensors and another one among central channels. Both of them were found in all frequency bands. In the γ band, controls showed higher Synchronization Likelihood values than MCI patients between central-posterior and frontal-posterior channels and a high synchronization in posterior regions. The inter-hemispheric increased synchronization values could reflect a compensatory mechanism for the lack of efficiency of the memory networks in MCI patients. Therefore, these connectivity profiles support only partially the idea of MCI as a disconnection syndrome, as patients showed increased long distance inter-hemispheric connections but a decrease in antero-posterior functional connectivity.

Biomarkers relevant to the pre-dementia stages of Alzheimer’s disease are needed. Using MEG, PET, and MRI, Nakamura et al. disentangle resting state regional spectral patterns in cognitively normal subjects and individuals with mild cognitive impairment into MEG signatures related to Aβ deposition, disease progression, or changes non-specific to Alzheimer’s disease.

People with mild cognitive impairment (MCI) show a high risk to develop Alzheimer's disease (AD; Petersen et al., 2001). Nonetheless, there is a lack of studies about how functional connectivity patterns may distinguish between progressive (pMCI) and stable (sMCI) MCI patients. To examine whether there were differences in functional connectivity between groups, MEG eyes-closed recordings from 30 sMCI and 19 pMCI subjects were compared. The average conversion time of pMCI was 1 year, so they were considered as fast converters. To this end, functional connectivity in different frequency bands was assessed with phase locking value in source space. Then the significant differences between both groups were correlated with neuropsychological scores and entorhinal, parahippocampal, and hippocampal volumes. Both groups did not differ in age, gender, or educational level. pMCI patients obtained lower scores in episodic and semantic memory and also in executive functioning. At the structural level, there were no differences in hippocampal volume, although some were found in left entorhinal volume between both groups. Additionally, pMCI patients exhibit a higher synchronization in the alpha band between the right anterior cingulate and temporo-occipital regions than sMCI subjects. This hypersynchronization was inversely correlated with cognitive performance, both hippocampal volumes, and left entorhinal volume. The increase in phase synchronization between the right anterior cingulate and temporo-occipital areas may be predictive of conversion from MCI to AD.