Skoltech researchers proposed a method of interpreting brain activity data that was found to be up to five times more accurate than the technique conventionally used in cases where the MRI data contained artifacts or where only a Low resolution head model was available. Reported in IEEE Transactions on Medical Imaging, the findings are useful for treating drug-resistant epilepsy and understanding cognitive processes in the healthy brain, including how it responds to visual stimuli and records new words.
Brain activity mapping is the standard way to determine which parts of the brain are involved in a specific cognitive task -; for example, receiving sensory information by pricking a cat with a finger -; or involved in pathological processes, such as epileptic seizures or sleep disturbances. Brain activity is usually recorded by electro- or magnetoencephalography, abbreviated EEG and MEG, respectively. The first technique involves placing an array of electrodes on the surface of the scalp to measure local electrical potentials. The second uses sensors to record the magnetic field rather than potentials, but both measurements are proxies for detecting and locating electrical currents in the brain.
EEG has been around for about 100 years, and certain types of neural activity are very well studied. For example, it is quite easy for an experienced doctor to study a sleep disorder by reading raw EEG data. Other cases are more difficult. To identify the precise hot spots in a patient’s brain that are responsible for epileptic seizures, EEG or MEG data is combined with high-resolution MRIs, which models the patient’s head, and processed with advanced computer algorithms. Provided that the troublesome region is precisely located, it can then be operated on without damaging surrounding tissue to help a patient with epilepsy when drugs do not work. “
Nikolay Yavich, Principal Study Author, Principal Scientist, Skoltech
However, MRI scans used in conjunction with brain activity maps are not always perfect. They are often corrupted by noise and other image artifacts. This leads to inaccuracies in the segmentation of the images. According to Skoltech researchers involved in the study, their technique is much less sensitive to such data imperfections.
“We found that when modeling neural activity on low-resolution head models, our method was up to five times more accurate than the conventional approach. Although it also requires a higher computational load , the benefits seem to justify its application, “commented Yavich.
This means that the method can help cognitive scientists, neurologists and brain surgeons working with less than perfect data understand the neurological basis underlying diseases such as epilepsy, attention deficit disorder and autism, as well as healthy cognitive processes involved in memory, sensual perception, locomotion, and more.
Yavitch, N., et al. (2021) Conservative finite element modeling of EEG and MEG on unstructured grids. IEEE Transactions on Medical Imaging. doi.org/10.1109/TMI.2021.3119851.