MEG and EEG dipole clusters from extended cortical sources.

Data from magnetoencephalography (MEG) and electroencephalography (EEG) suffer from a rather limited signal-to-noise-ratio (SNR) due to cortical background activities and other artifacts. In order to study the effect of the SNR on the size and distribution of dipole clusters reconstructed from interictal epileptic spikes, we performed simulations using realistically shaped volume conductor models and extended cortical sources with different sensor configurations. Head models and cortical surfaces were derived from an averaged magnetic resonance image dataset (Montreal Neurological Institute). Extended sources were simulated by spherical patches with Gaussian current distributions on the folded cortical surface. Different patch sizes were used to investigate cancellation effects from opposing walls of sulcal foldings and to estimate corresponding changes in MEG and EEG sensitivity distributions. Finally, white noise was added to the simulated fields and equivalent current dipole reconstructions were performed to determine size and shape of the resulting dipole clusters. Neuronal currents are oriented perpendicular to the local cortical surface and show cancellation effects of source components on opposing sulcal walls. Since these mostly tangential aspects from large cortical patches cancel out, large extended sources exhibit more radial components in the head geometry. This effect has a larger impact on MEG data as compared to EEG, because in a spherical head model radial currents do not yield any magnetic field. Confidence volumes of single reconstructed dipoles from simulated data at different SNRs show a good correlation with the extension of clusters from repeated dipole reconstructions. Size and shape of dipole clusters reconstructed from extended cortical sources do not only depend on spike and timepoint selection, but also strongly on the SNR of the measured interictal MEG or EEG data. In a linear approximation the size of the clusters is proportional to the inverse SNR.

Statistical non-parametric mapping in sensor space.

Establishing the significance of observed effects is a preliminary requirement for any meaningful interpretation of clinical and experimental Electroencephalography or Magnetoencephalography (MEG) data. We propose a method to evaluate significance on the level of sensors whilst retaining full temporal or spectral resolution. Input data are multiple realizations of sensor data. In this context, multiple realizations may be the individual epochs obtained in an evoked-response experiment, or group study data, possibly averaged within subject and event type, or spontaneous events such as spikes of different types. In this contribution, we apply Statistical non-Parametric Mapping (SnPM) to MEG sensor data. SnPM is a non-parametric permutation or randomization test that is assumption-free regarding distributional properties of the underlying data. The method, referred to as Maps SnPM, is demonstrated using MEG data from an auditory mismatch negativity paradigm with one frequent and two rare stimuli and validated by comparison with Topographic Analysis of Variance (TANOVA). The result is a time- or frequency-resolved breakdown of sensors that show consistent activity within and/or differ significantly between event or spike types. TANOVA and Maps SnPM were applied to the individual epochs obtained in an evoked-response experiment. The TANOVA analysis established data plausibility and identified latencies-of-interest for further analysis. Maps SnPM, in addition to the above, identified sensors of significantly different activity between stimulus types.

Transcranial slow oscillation stimulation during sleep enhances memory consolidation in rats.

BACKGROUND: The importance of slow-wave sleep (SWS), hallmarked by the occurrence of sleep slow oscillations (SO), for the consolidation of hippocampus-dependent memories has been shown in numerous studies. Previously, the application of transcranial direct current stimulation, oscillating at the frequency of endogenous slow oscillations, during SWS enhanced memory consolidation for a hippocampus dependent task in humans suggesting a causal role of slowly oscillating electric fields for sleep dependent memory consolidation. OBJECTIVE: Here, we aimed to replicate and extend these findings to a rodent model. METHODS: Slow oscillatory direct transcranial current stimulation (SO-tDCS) was applied over the frontal cortex of rats during non-rapid eye movement (NREM) sleep and its effects on memory consolidation in the one-trial object-place recognition task were examined. A retention interval of 24 h was used to investigate the effects of SO-tDCS on long-term memory. RESULTS: Animals' preference for the displaced object was significantly greater than chance only when animals received SO-tDCS. EEG spectral power indicated a trend toward a transient enhancement of endogenous SO activity in the SO-tDCS condition. CONCLUSIONS: These results support the hypothesis that slowly oscillating electric fields causal affect sleep dependent memory consolidation, and demonstrate that oscillatory tDCS can be a valuable tool to investigate the function of endogenous cortical network activity.

Cellular effects of acute direct current stimulation: somatic and synaptic terminal effects.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique to modulate cortical excitability. Although increased/decreased excitability under the anode/cathode electrode is nominally associated with membrane depolarization/hyperpolarization, which cellular compartments (somas, dendrites, axons and their terminals) mediate changes in cortical excitability remains unaddressed. Here we consider the acute effects of DCS on excitatory synaptic efficacy. Using multi-scale computational models and rat cortical brain slices, we show the following. (1) Typical tDCS montages produce predominantly tangential (relative to the cortical surface) direction currents (4-12 times radial direction currents), even directly under electrodes. (2) Radial current flow (parallel to the somatodendritic axis) modulates synaptic efficacy consistent with somatic polarization, with depolarization facilitating synaptic efficacy. (3) Tangential current flow (perpendicular to the somatodendritic axis) modulates synaptic efficacy acutely (during stimulation) in an afferent pathway-specific manner that is consistent with terminal polarization, with hyperpolarization facilitating synaptic efficacy. (4) Maximal polarization during uniform DCS is expected at distal (the branch length is more than three times the membrane length constant) synaptic terminals, independent of and two-three times more susceptible than pyramidal neuron somas. We conclude that during acute DCS the cellular targets responsible for modulation of synaptic efficacy are concurrently somata and axon terminals, with the direction of cortical current flow determining the relative influence.

Transcranial electrical stimulation accelerates human sleep homeostasis.

The sleeping brain exhibits characteristic slow-wave activity which decays over the course of the night. This decay is thought to result from homeostatic synaptic downscaling. Transcranial electrical stimulation can entrain slow-wave oscillations (SWO) in the human electro-encephalogram (EEG). A computational model of the underlying mechanism predicts that firing rates are predominantly increased during stimulation. Assuming that synaptic homeostasis is driven by average firing rates, we expected an acceleration of synaptic downscaling during stimulation, which is compensated by a reduced drive after stimulation. We show that 25 minutes of transcranial electrical stimulation, as predicted, reduced the decay of SWO in the remainder of the night. Anatomically accurate simulations of the field intensities on human cortex precisely matched the effect size in different EEG electrodes. Together these results suggest a mechanistic link between electrical stimulation and accelerated synaptic homeostasis in human sleep.

The effect of repetitive transcranial magnetic stimulation on monoamine outflow in the nucleus accumbens shell in freely moving rats.

Evidence exists that modulation of neuronal activity in nucleus accumbens shell region may re-establish normal function in various neuropsychiatric conditions such as drug-withdrawal, obsessive-compulsive disorder, depression and chronic pain. Here, we study the effects of acute repetitive transcranial magnetic stimulation on monoamine outflow in the nucleus accumbens shell in awake and freely moving rats using in vivo microdialysis. To scale the biochemical results to the induced electric field in the rat brain, we obtained a realistic simulation of the stimulation scenario using a finite element model. Applying 20 Hz repetitive transcranial magnetic stimulation in 6 trains of 50 stimuli with 280 µs pulse width at a magnetic field strength of 130% of the individual motor threshold, dopamine as well as serotonin outflow in the nucleus accumbens shell significantly increased compared to sham stimulation. Since the electric field decays rapidly with depth in the rat brain, we can conclude that the modulation in neurotransmitter outflow from the nucleus accumbens shell is presumably a remote effect of cortical stimulation.

Simulation of a conductive shield plate for the focalization of transcranial magnetic stimulation in the rat.

Transcranial Magnetic Stimulation (TMS) in the rat is a powerful tool for investigating brain function. However, the state-of-the-art experiments are considerably limited because the stimulation usually affects undesired anatomical structures. A simulation of a conductive shield plate placed between the coil stimulator and the rat brain during TMS is presented. The Finite Element (FE) method is used to obtain the 3D electric field distribution on a four-layer rat head model. The simulations show that the shield plate with a circular window can improve the focalization of stimulation, as quantitatively seen by computing the three-dimensional half power region (HPR). Focalization with the shield plate showed a clear compromise with the attenuation of the induced field. The results suggest that the shield plate can work as a helpful tool for conducting TMS rat experiments on specific targets.

Automated segmentation of tissue structures in optical coherence tomography data.

Segmentation of optical coherence tomography (OCT) images provides useful information, especially in medical imaging applications. Because OCT images are subject to speckle noise, the identification of structures is complicated. Addressing this issue, two methods for the automated segmentation of arbitrary structures in OCT images are proposed. The methods perform a seeded region growing, applying a model-based analysis of OCT A-scans for the seed's acquisition. The segmentation therefore avoids any user-intervention dependency. The first region-growing algorithm uses an adaptive neighborhood homogeneity criterion based on a model of an OCT intensity course in tissue and a model of speckle noise corruption. It can be applied to an unfiltered OCT image. The second performs region growing on a filtered OCT image applying the local median as a measure for homogeneity in the region. Performance is compared through the quantitative evaluation of artificial data, showing the capabilities of both in terms of structures detected and leakage. The proposed methods were tested on real OCT data in different scenarios and showed promising results for their application in OCT imaging.

Model-based detection of white matter in optical coherence tomography data.

A method for white matter detection in Optical Coherence Tomography A-Scans is presented. The Kalman filter is used to obtain a slope change estimate of the intensity signal. The estimate is subsequently analyzed by a spike detection algorithm and then evaluated by a neural network binary classifier (Perceptron). The capability of the proposed method is shown through the quantitative evaluation of simulated A-Scans. The method was also applied to data obtained from a rat's brain in vitro. Results show that the developed algorithm identifies less false positives than other two spike detection methods, thus, enhancing the robustness and quality of detection.