Theta burst transcranial magnetic stimulation is associated with increased EEG synchronization in the stimulated relative to unstimulated cerebral hemisphere

Theta burst transcranial magnetic stimulation (TBS) may induce behavioural changes that outlast the stimulation period. The neurophysiological basis of these behavioural changes are currently under investigation. Given the evidence that cortical information processing relies on transient synchronization and desynchronization of neuronal assemblies, we set out to test whether TBS is associated with changes of neuronal synchronization as assessed by surface EEG. In four healthy subjects one TBS train of 600 pulses (200 bursts, each burst consisting of 3 pulses at 30 Hz, repeated at intervals of 100 ms) was applied over the right frontal eye field and EEG synchronization was assessed in a time-resolved manner over 60 min by using a non-overlapping moving window. For each time step the linear cross-correlation matrix for six EEG channels of the right and for the six homotopic EEG channels of the left hemisphere were computed and their largest eigenvalues used to assess changes of synchronization. Synchronization was computed for broadband EEG and for the delta, theta, alpha, beta and gamma frequency bands. In all subjects EEG synchronization of the stimulated hemisphere was significantly and persistently increased relative to EEG synchronization of the unstimulated hemisphere. This effect occurred immediately after TBS for the theta, alpha, beta and gamma frequency bands and 10-20 min after TBS for broadband and delta frequency band EEG. Our results demonstrate that TBS is associated with increased neuronal synchronization of the cerebral hemisphere ipsilateral to the stimulation site relative to the unstimulated hemisphere. We speculate that enhanced synchronization interferes with cortical information processing and thus may be a neurophysiological correlate of the impaired behavioural performance detected previously.

Improving the performance of GIS/spatial analysts though novel applications of the Emotiv EPOC EEG headset

Geospatial information systems are used to analyze spatial data to provide decision makers with relevant, up-to-date, information. The processing time required for this information is a critical component to response time. Despite advances in algorithms and processing power, we still have many “human-in-the-loop” factors. Given the limited number of geospatial professionals, analysts using their time effectively is very important. The automation and faster humancomputer interactions of common tasks that will not disrupt their workflow or attention is something that is very desirable. The following research describes a novel approach to increase productivity with a wireless, wearable, electroencephalograph (EEG) headset within the geospatial workflow.

Acute disseminated encephalomyelitis in adults: a reappraisal of clinical, CSF, EEG, and MRI findings

OBJECTIVES: To establish an adequate definition of acute disseminated encephalomyelitis (ADEM) in adults, based on our clinical observations of a case-series. METHODS: Over a period of three years 10 adult patients with a para- or postinfectious disseminated (diffuse or multifocal) syndrome of the CNS fulfilling predefined strict criteria for the diagnosis of ADEM were encountered and systematically followed. RESULTS: The age ranged from 21 to 62 years, two were men. MRI was normal in 5 patients and only mildly abnormal in the remaining patients. CSF was normal in 5 patients and mildly abnormal in the remainder, EEG was abnormal in 7/8 patients. All patients survived and were followed over a period of 30 months (range: 8 to 48 months). Nine patients were left with some residual defects, consisting most often of a mild cognitive impairment. CONCLUSIONS: The EEG as an investigation of brain function can be crucial in establishing the organic nature of disease. MRI is important to exclude other diffuse or multifocal encephalopathies. However, in contrast to previous reports in the literature abnormal MRI should not be considered mandatory in adult ADEM. Difficulties in the diagnosis of ADEM are discussed and the importance of clinical and paraclinical findings for establishing the diagnosis is outlined.

Evolution of sleep and sleep EEG after hemispheric stroke

The evolution of subjective sleep and sleep electroencephalogram (EEG) after hemispheric stroke have been rarely studied and the relationship of sleep variables to stroke outcome is essentially unknown. We studied 27 patients with first hemispheric ischaemic stroke and no sleep apnoea in the acute (1-8 days), subacute (9-35 days), and chronic phase (5-24 months) after stroke. Clinical assessment included estimated sleep time per 24 h (EST) and Epworth sleepiness score (ESS) before stroke, as well as EST, ESS and clinical outcome after stroke. Sleep EEG data from stroke patients were compared with data from 11 hospitalized controls and published norms. Changes in EST (>2 h, 38% of patients) and ESS (>3 points, 26%) were frequent but correlated poorly with sleep EEG changes. In the chronic phase no significant differences in sleep EEG between controls and patients were found. High sleep efficiency and low wakefulness after sleep onset in the acute phase were associated with a good long-term outcome. These two sleep EEG variables improved significantly from the acute to the subacute and chronic phase. In conclusion, hemispheric strokes can cause insomnia, hypersomnia or changes in sleep needs but only rarely persisting sleep EEG abnormalities. High sleep EEG continuity in the acute phase of stroke heralds a good clinical outcome.

Visual and spectral analysis of sleep EEG in acute hemispheric stroke

BACKGROUND: Reports on the effects of focal hemispheric damage on sleep EEG are rare and contradictory. PATIENTS AND METHODS: Twenty patients (mean age +/- SD 53 +/- 14 years) with a first acute hemispheric stroke and no sleep apnea were studied. Stroke severity [National Institute of Health Stroke Scale (NIHSS)], volume (diffusion-weighted brain MRI), and short-term outcome (Rankin score) were assessed. Within the first 8 days after stroke onset, 1-3 sleep EEG recordings per patient were performed. Sleep scoring and spectral analysis were based on the central derivation of the healthy hemisphere. Data were compared with those of 10 age-matched and gender-matched hospitalized controls with no brain damage and no sleep apnea. RESULTS: Stroke patients had higher amounts of wakefulness after sleep onset (112 +/- 53 min vs. 60 +/- 38 min, p < 0.05) and a lower sleep efficiency (76 +/- 10% vs. 86 +/- 8%, p < 0.05) than controls. Time spent in slow-wave sleep (SWS) and rapid eye movement (REM) sleep and total sleep time were lower in stroke patients, but differences were not significant. A positive correlation was found between the amount of SWS and stroke volume (r = 0.79). The slow-wave activity (SWA) ratio NREM sleep/wakefulness was lower in patients than in controls (p < 0.05), and correlated with NIHSS (r = -0.47). CONCLUSION: Acute hemispheric stroke is accompanied by alterations of sleep EEG over the healthy hemisphere that correlate with stroke volume and outcome. The increased SWA during wakefulness and SWS over the healthy hemisphere contralaterally to large strokes may reflect neuronal hypometabolism induced transhemispherically (diaschisis).

BOLD correlates of EEG alpha phase-locking and the fMRI default mode network

Phase locking or synchronization of brain areas is a key concept of information processing in the brain. Synchronous oscillations have been observed and investigated extensively in EEG during the past decades. EEG oscillations occur over a wide frequency range. In EEG, a prominent type of oscillations is alpha-band activity, present typically when a subject is awake, but at rest with closed eyes. The spectral power of alpha rhythms has recently been investigated in simultaneous EEG/fMRI recordings, establishing a wide-range cortico-thalamic network. However, spectral power and synchronization are different measures and little is known about the correlations between BOLD effects and EEG synchronization. Interestingly, the fMRI BOLD signal also displays synchronous oscillations across different brain regions. These oscillations delineate so-called resting state networks (RSNs) that resemble the correlation patterns of simultaneous EEG/fMRI recordings. However, the nature of these BOLD oscillations and their relations to EEG activity is still poorly understood. One hypothesis is that the subunits constituting a specific RSN may be coordinated by different EEG rhythms. In this study we report on evidence for this hypothesis. The BOLD correlates of global EEG synchronization (GFS) in the alpha frequency band are located in brain areas involved in specific RSNs, e.g. the 'default mode network'. Furthermore, our results confirm the hypothesis that specific RSNs are organized by long-range synchronization at least in the alpha frequency band. Finally, we could localize specific areas where the GFS BOLD correlates and the associated RSN overlap. Thus, we claim that not only the spectral dynamics of EEG are important, but also their spatio-temporal organization.

Parameter-free extraction of the functional network topology from intracranial EEG recordings: a time-resolved study of graph properties in focal onset seizures

Rationale: Focal onset epileptic seizures are due to abnormal interactions between distributed brain areas. By estimating the cross-correlation matrix of multi-site intra-cerebral EEG recordings (iEEG), one can quantify these interactions. To assess the topology of the underlying functional network, the binary connectivity matrix has to be derived from the cross-correlation matrix by use of a threshold. Classically, a unique threshold is used that constrains the topology [1]. Our method aims to set the threshold in a data-driven way by separating genuine from random cross-correlation. We compare our approach to the fixed threshold method and study the dynamics of the functional topology. Methods: We investigate the iEEG of patients suffering from focal onset seizures who underwent evaluation for the possibility of surgery. The equal-time cross-correlation matrices are evaluated using a sliding time window. We then compare 3 approaches assessing the corresponding binary networks. For each time window: * Our parameter-free method derives from the cross-correlation strength matrix (CCS)[2]. It aims at disentangling genuine from random correlations (due to finite length and varying frequency content of the signals). In practice, a threshold is evaluated for each pair of channels independently, in a data-driven way. * The fixed mean degree (FMD) uses a unique threshold on the whole connectivity matrix so as to ensure a user defined mean degree. * The varying mean degree (VMD) uses the mean degree of the CCS network to set a unique threshold for the entire connectivity matrix. * Finally, the connectivity (c), connectedness (given by k, the number of disconnected sub-networks), mean global and local efficiencies (Eg, El, resp.) are computed from FMD, CCS, VMD, and their corresponding random and lattice networks. Results: Compared to FMD and VMD, CCS networks present: *topologies that are different in terms of c, k, Eg and El. *from the pre-ictal to the ictal and then post-ictal period, topological features time courses that are more stable within a period, and more contrasted from one period to the next. For CCS, pre-ictal connectivity is low, increases to a high level during the seizure, then decreases at offset. k shows a ‘‘U-curve’’ underlining the synchronization of all electrodes during the seizure. Eg and El time courses fluctuate between the corresponding random and lattice networks values in a reproducible manner. Conclusions: The definition of a data-driven threshold provides new insights into the topology of the epileptic functional networks.

Ragu: a free tool for the analysis of EEG and MEG event-related scalp field data using global randomization statistics

We present a program (Ragu; Randomization Graphical User interface) for statistical analyses of multichannel event-related EEG and MEG experiments. Based on measures of scalp field differences including all sensors, and using powerful, assumption-free randomization statistics, the program yields robust, physiologically meaningful conclusions based on the entire, untransformed, and unbiased set of measurements. Ragu accommodates up to two within-subject factors and one between-subject factor with multiple levels each. Significance is computed as function of time and can be controlled for type II errors with overall analyses. Results are displayed in an intuitive visual interface that allows further exploration of the findings. A sample analysis of an ERP experiment illustrates the different possibilities offered by Ragu. The aim of Ragu is to maximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest) that interact with and bias statistics.

A deviant EEG brain microstate in acute, neuroleptic-naive schizophrenics at rest

Momentary brain electric field configurations are manifestations of momentary global functional states of the brain. Field configurations tend to persist over some time in the sub-second range (“microstates”) and concentrate within few classes of configurations. Accordingly, brain field data can be reduced efficiently into sequences of re-occurring classes of brain microstates, not overlapping in time. Different configurations must have been caused by different active neural ensembles, and thus different microstates assumedly implement different functions. The question arises whether the aberrant schizophrenic mentation is associated with specific changes in the repertory of microstates. Continuous sequences of brain electric field maps (multichannel EEG resting data) from 9 neuroleptic-naive, first-episode, acute schizophrenics and from 18 matched controls were analyzed. The map series were assigned to four individual microstate classes; these were tested for differences between groups. One microstate class displayed significantly different field configurations and shorter durations in patients than controls; degree of shortening correlated with severity of paranoid symptomatology. The three other microstate classes showed no group differences related to psychopathology. Schizophrenic thinking apparently is not a continuous bias in brain functions, but consists of intermittent occurrences of inappropriate brain microstates that open access to inadequate processing strategies and context information

Decreased functional connectivity of EEG theta-frequency activity in first-episode, neuroleptic-naïve patients with schizophrenia: preliminary results

We explored and refined the hypothesis that during a first episode of acute schizophrenia a disorganization of brain functioning is present. A novel EEG measure was introduced, Global Field Synchronization (GFS), that estimates functional connectivity of brain processes in different EEG frequency bands. The measure was applied to EEG's from 11 never-treated, first-episode, young patients with an acute, positive, schizophrenic symptomatology and from 19 controls, residing in Bern, Switzerland. In comparison to age- and sex- matched controls, patients had significantly decreased GFS in the theta EEG frequency band, indicating a loosened functional connectivity of processes in this frequency. The result was confirmed in an independent, comparable patient group from Osaka, Japan (9 patients and 9 controls), thus making a total of 20 analyzed patients. Previous EEG research in healthy, awake subjects indicated a positive correlation of theta activity with memory functions. Thus, our result suggests a loss of mutual interdependence of memory functions in patients with acute schizophrenia, which agrees well with previous reports of working memory dysfunction in schizophrenia.