Attention-driven auditory cortex short-term plasticity helps segregate relevant sounds from noise.

How can we concentrate on relevant sounds in noisy environments? A "gain model" suggests that auditory attention simply amplifies relevant and suppresses irrelevant afferent inputs. However, it is unclear whether this suffices when attended and ignored features overlap to stimulate the same neuronal receptive fields. A "tuning model" suggests that, in addition to gain, attention modulates feature selectivity of auditory neurons. We recorded magnetoencephalography, EEG, and functional MRI (fMRI) while subjects attended to tones delivered to one ear and ignored opposite-ear inputs. The attended ear was switched every 30 s to quantify how quickly the effects evolve. To produce overlapping inputs, the tones were presented alone vs. during white-noise masking notch-filtered ±1/6 octaves around the tone center frequencies. Amplitude modulation (39 vs. 41 Hz in opposite ears) was applied for "frequency tagging" of attention effects on maskers. Noise masking reduced early (50-150 ms; N1) auditory responses to unattended tones. In support of the tuning model, selective attention canceled out this attenuating effect but did not modulate the gain of 50-150 ms activity to nonmasked tones or steady-state responses to the maskers themselves. These tuning effects originated at nonprimary auditory cortices, purportedly occupied by neurons that, without attention, have wider frequency tuning than ±1/6 octaves. The attentional tuning evolved rapidly, during the first few seconds after attention switching, and correlated with behavioral discrimination performance. In conclusion, a simple gain model alone cannot explain auditory selective attention. In nonprimary auditory cortices, attention-driven short-term plasticity retunes neurons to segregate relevant sounds from noise.

On the effect of resistive EEG electrodes and leads during 7 T MRI: simulation and temperature measurement studies.

The purpose of the study was to assess the effects of electrodes and leads on electromagnetic field and specific absorption rate (SAR) distributions during simultaneous electroencephalography (EEG) and 7-T MRI. Two different approaches were evaluated and compared to the case without electrodes: (a) the use of different EEG lead resistivity and (b) the use of a radiofrequency (RF) resistor on the lead near the EEG electrode. These configurations are commonly used in research and clinical settings. Electromagnetic field and SAR distributions generated by the transmit RF coil were evaluated using finite difference time domain simulations on an anatomically accurate head model. The spatiotemporal changes of temperature were estimated with the heat equation. Temperature changes during turbo spin echo sequences were also measured using a custom-made phantom: the conductive head mannequin anthropomorphic (CHEMA). The results of this study showed that the SAR and temperature distributions in CHEMA (a) increased when using low resistive leads, with respect to the no-electrode case; (b) were affected by the resistivity of the EEG leads, with carbon fiber leads performing better than standard copper leads; and (c) were not affected by the use of an RF resistor between the EEG electrode and the lead.

Telematics enabled virtual simulation system for radiation treatment planning.

In this paper, GALENOS, a Telematics Enabled Virtual Simulation System for Radiation Treatment Planning (RTP) is described. The design architecture of GALENOS is in accordance with the dual aim of virtual simulation of RTP, i.e. to allow (a) delineation of target volume and critical organs, and (b) placement of irradiation fields. An important feature of GALENOS is the possibility for on-line tele-collaboration between health care professionals under a secure framework. The advantages of GALENOS include elimination of patient transfers between departments and health care institutions as well as availability of patient data at sites different than those of his/her physical presence.

Classification of event-related potentials associated with response errors in actors and observers based on autoregressive modeling.

Event-Related Potentials (ERPs) provide non-invasive measurements of the electrical activity on the scalp related to the processing of stimuli and preparation of responses by the brain. In this paper an ERP-signal classification method is proposed for discriminating between ERPs of correct and incorrect responses of actors and of observers seeing an actor making such responses. The classification method targeted signals containing error-related negativity (ERN) and error positivity (Pe) components, which are typically associated with error processing in the human brain. Feature extraction consisted of Multivariate Autoregressive modeling combined with the Simulated Annealing technique. The resulting information was subsequently classified by means of an Artificial Neural Network (ANN) using back-propagation algorithm under the "leave-one-out cross-validation" scenario and the Fuzzy C-Means (FCM) algorithm. The ANN consisted of a multi-layer perceptron (MLP). The approach yielded classification rates of up to 85%, both for the actors' correct and incorrect responses and the corresponding ERPs of the observers. The electrodes needed for such classifications were situated mainly at central and frontal areas. Results provide indications that the classification of the ERN is achievable. Furthermore, the availability of the Pe signals, in addition to the ERN, improves the classification, and this is more pronounced for observers' signals. The proposed ERP-signal classification method provides a promising tool to study error detection and observational-learning mechanisms in performance monitoring and joint-action research, in both healthy and patient populations.

Location-specific cortical activation changes during sleep after training for perceptual learning.

Visual perceptual learning is defined as performance enhancement on a sensory task and is distinguished from other types of learning and memory in that it is highly specific for location of the trained stimulus. The location specificity has been shown to be paralleled by enhancement in functional magnetic resonance imaging (fMRI) signal in the trained region of V1 after visual training. Although recently the role of sleep in strengthening visual perceptual learning has attracted much attention, its underlying neural mechanism has yet to be clarified. Here, for the first time, fMRI measurement of human V1 activation was conducted concurrently with a polysomnogram during sleep with and without preceding training for visual perceptual learning. As a result of predetermined region-of-interest analysis of V1, activation enhancement during non-rapid-eye-movement sleep after training was observed specifically in the trained region of V1. Furthermore, improvement of task performance measured subsequently to the post-training sleep session was significantly correlated with the amount of the trained-region-specific fMRI activation in V1 during sleep. These results suggest that as far as V1 is concerned, only the trained region is involved in improving task performance after sleep.

EEG/(f)MRI measurements at 7 Tesla using a new EEG cap ("InkCap").

We aimed at improving the signal-to-noise ratio (SNR) of electroencephalography (EEG) during magnetic resonance imaging (MRI) by introducing a new EEG cap ("InkCap") based on conductive ink technology. The InkCap was tested with temperature measurements on an electrically conductive phantom head and during structural and functional MRI (fMRI) recordings in 11 healthy human volunteers at 7 T. Combined EEG/fMRI measurements were conducted to study the interaction between the two modalities. The EEG recordings with the InkCap demonstrated up to a five-fold average decrease in signal variance during echo-planar imaging, with respect to a cap made of standard carbon fiber leads. During concurrent EEG/fMRI measurements in human volunteers, alpha oscillations were clearly detected at 7 T. Minimal artifacts were present in the T2* and high-resolution structural MR images of the brain parenchyma. Our results show that the InkCap technology considerably improves the quality of both EEG and (f)MRI during concurrent measurements even at 7 T.