ABSTRACT
Magnetoencephalography (MEG) is an imaging technique that enables the assessment of cortical activity via direct measures of neurophysiology. It is a non-invasive and passive technique that is completely painless. MEG has gained increasing prominence in the field of pediatric neuroimaging. This dedicated review article for the pediatric population summarizes the fundamental technical and clinical aspects of MEG for the clinician. We discuss methods tailored for children to improve data quality, including child-friendly MEG facility environments and strategies to mitigate motion artifacts. We provide an in-depth overview on accurate localization of neural sources and different analysis methods, as well as data interpretation. The contemporary platforms and approaches of two quaternary pediatric referral centers are illustrated, shedding light on practical implementations in clinical settings. Finally, we describe the expanding clinical applications of MEG, including its pivotal role in presurgical evaluation of epilepsy patients, presurgical mapping of eloquent cortices (somatosensory and motor cortices, visual and auditory cortices, lateralization of language), its emerging relevance in autism spectrum disorder research and potential future clinical applications, and its utility in assessing mild traumatic brain injury. In conclusion, this review serves as a comprehensive resource of clinicians as well as researchers, offering insights into the evolving landscape of pediatric MEG. It discusses the importance of technical advancements, data acquisition strategies, and expanding clinical applications in harnessing the full potential of MEG to study neurological conditions in the pediatric population.
ABSTRACT
The increased fragmentation caused by harsher ionization methods used during mass spectrometry such as electron ionization can make interpreting the mass spectra of peptides difficult. Therefore, the development of tools to aid in this spectral analysis is important in utilizing these harsher ionization methods to study peptides, as these tools may be more accessible to some researchers. We have compiled fragmentation mechanisms described in the literature, confirmed them experimentally, and used them to create a Python-based fragment prediction model for peptides analyzed under direct exposure probe electron ionization mass spectrometry. This initial model has been tested using single amino acids as well as targeted libraries of short peptides. It was found that the model does well in predicting fragments of peptides composed of amino acids for which the model is well-defined, but several cases where additional mechanistic information needs to be incorporated have been identified.
