Analysis and Inference of Brain Networks from Magnetoencephalographic Signals
- Members: David M. Halliday, Luis R. Peraza, Aziz Asghar
- Website: http://www-users.york.ac.uk/~dh20/research1.html
- Dates: October 2008 - October 2011
Analysis and Inference of Brain Networks from Magnetoencephalographic Signals
The human brain is maybe the most fascinating and unknown organ in our body, it is composed of approximately 100 billion nerve cells, known as neurons, and each one is connected to other neurons through 10,000 synapses, forming a massive and efficient network capable of performing all cognitive functions and movement control.
During its evolution, the nervous system developed specialized neural circuits at different areas to optimize resources and data processing. Hence, different brain regions are in charge of one or several functions. Although this statement can be argued due to the complexity, plasticity, interaction and continuous change of the brain network, it is well established that the brain works using regions with specialized neural architecture, which are in constant interaction and perform multitasking/parallel processing.
The understanding and study of the brain and its neural interactions have important applications apart from the desire of knowing how our brain works, some of these are:
- Diagnosis and prediction of brain related diseases such as Alzheimer's, Parkinson's or Dementia.
- Design of robotic prosthesis to recover a lost limb functionality.
- Design of brain machine interfaces.
This project focuses on studying and researching signal processing techniques to infer networks in the brain, using different recordings of electrical signals generated by the nervous system like motor unit discharges , Electromyograms (EMG), Electroencephalograms (EEG) and Magnetoencephalograms (MEG).
By the study of these biological signals it is possible to infer the behaviour, communication, interaction and even physical connectivity of our nervous system. Some current well known techniques are correlation, partial correlation, coherence, partial coherence, direct transfer function (DTF) and partial directed coherence (PDC).
A nice introduction to these techniques and their applications can be found in videolectures.net/mda07_schloegl_eegc/.
Magnetoencephalography
Magnetoencephalography or MEG acquires magnetic fields generated mainly by the neural axons in our cerebral cortex. The electric currents produced by every neuron in our brain creates a magnetic field that flows outside the skull, and due to the very small current in the neural axon and hence the very small magnetic field generated, it is needed the synchronous firing of 10,000 axons in a small region of the cortex to increase their power to measurable levels.
The University of York has one of the finest equipments for magnetic brain mapping, a 4D Neuroimaging, whole head helmet and 248 sensors/channels MEG. We use the signals measured by the MEG to infer networks in the human brain cortex in a macro scale, studying the interactions among brain cortex regions. For more information about MEG, visit the York Neuroimaging Centre (YNiC) webpages www.ynic.york.ac.uk/information/meg.
Project Partners
National Council on Science and Technology (CONACyT).
York Neuroimaging Centre (YNiC).