During the past three decades non-invasive functional brain imaging has developed immensely in terms of measurement technologies, analysis methods, and innovative paradigms to capture information about brain function both in healthy and diseased individuals. Although functional MRI (fMRI) has become very useful, it only provides indirect information about neuronal activity through the neurovascular coupling with a limited temporal resolution. Magnetoencephalography (MEG) and electroencephalography (EEG) remain the only available noninvasive techniques capable of directly measuring the electrophysiological activity with a millisecond resolution. During the past eight years we have developed, with NIH support, the MNE-Python software, which covers multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. To further extend our software to meet the needs of a growing user base and reflect recent developments in the MEG/EEG field we will pursue three specific Aims.
In Aim 1 we will: (i) Create an all-embracing suite of noise cancellation tools incorporating and extending methods present in different MEG systems; (ii) Implement device independent methods for head-movement determination and compensation on the basis of head movement data recorded during a MEG session; (iii) Develop methods for automatic tagging of artifacts using machine learning approaches.
In Aim 2 our focus is to extend the software to make modern distributed computing resources easily usable in processing and to allow for remote visualization without the need to move large amounts of data across the network. Finally, in Aim 3, we will continue to develop MNE-Python using best programming practices ensuring multiplatform compatibility, extensive web-based documentation, training and forums, and hands-on training workshops. As a result of these developments the MNE-Python will be able to effectively process large number of subjects and huge amounts data ensuing and from multi-site studies harmoniously across different MEG/EEG systems.
MEG and EEG can be used to understand and diagnose abnormalities underlying a wide range neurological and psychiatric illnesses including epilepsy, schizophrenia, obsessive-compulsive disorder, autism spectrum disorders, and Alzheimer's disease, as well as cognitive deficits such as delayed acquisition of language. However, widespread use of these methods especially in large populations has been problematic because of the lack of well-established analysis approaches, which map the sensor data into the brain space for detailed temporal, spatial, and connectivity analysis. This research will provide well-documented and tested novel analysis software to promote both basic neuroscience and clinical research applications using MEG and EEG.
