Multimodal approaches are of growing interest in the study of neural processes. To this end much attention has been paid to the integration of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data because of their complementary properties. However, the simultaneous acquisition of both types of data causes serious artifacts in the EEG, with amplitudes that may be much larger than those of EEG signals themselves. The most challenging of these artifacts is the ballistocardiogram (BCG) artifact, caused by pulse-related electrode movements inside the magnetic field. Despite numerous efforts to find a suitable approach to remove this artifact, still a considerable discrepancy exists between current EEG-fMRI studies. This paper attempts to clarify several methodological issues regarding the different approaches with an extensive validation based on event-related potentials (ERPs). More specifically, Optimal Basis Set (OBS) and Independent Component Analysis (ICA) based methods were investigated. Their validation was not only performed with measures known from previous studies on the average ERPs, but most attention was focused on task-related measures, including their use on trial-to-trial information. These more detailed validation criteria enabled us to find a clearer distinction between the most widely used cleaning methods. Both OBS and ICA proved to be able to yield equally good results. However, ICA methods needed more parameter tuning, thereby making OBS more robust and easy to use. Moreover, applying OBS prior to ICA can optimize the data quality even more, but caution is recommended since the effect of the additional ICA step may be strongly subject-dependent.

Research on the neural basis of language processing has often avoided investigating spoken language production by fear of the electromyographic (EMG) artifacts that articulation induces on the electro-encephalogram (EEG) signal. Indeed, such articulation artifacts are typically much larger than the brain signal of interest. Recently, a Blind Source Separation technique based on Canonical Correlation Analysis was proposed to separate tonic muscle artifacts from continuous EEG recordings in epilepsy. In this paper, we show how the same algorithm can be adapted to remove the short EMG bursts due to articulation on every trial. Several analyses indicate that this method accurately attenuates the muscle contamination on the EEG recordings, providing to the neurolinguistic community a powerful tool to investigate the brain processes at play during overt language production.

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Microbubbles have shown potential as intralymphatic ultrasound contrast agents while nanoparticle-loaded microbubbles are increasingly investigated for ultrasound-triggered drug and gene delivery. To explore whether mRNA-nanoparticle loaded microbubbles could serve as theranostics for detection of and mRNA transfer to the lymph nodes, we investigate the behavior of unloaded and mRNA-loaded microbubbles using contrast-enhanced ultrasound imaging after subcutaneous injection in dogs. Our results indicate that both types of microbubbles are equally capable of rapidly entering the lymph vessels and nodes upon injection, and novel, valuable and detailed information on the lymphatic structure in the animals could be obtained. Furthermore, additional observations were made regarding the dynamics of microbubble lymph node uptake. Importantly, neither the microbubble migration distance within the lymphatics, nor the observed contrast signal intensity was influenced by mRNA-loading. Although further optimization of acoustic parameters will be needed, this could represent a first step towards ultrasound-guided, ultrasound-triggered intranodal mRNA delivery using these theranostic microbubbles.

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