| Autor: |
Alfred Lenin Fred, Subbiahpillai Neelakantapillai Kumar, Ajay Kumar Haridhas, Sayantan Ghosh, Harishita Purushothaman Bhuvana, Wei Khang Jeremy Sim, Vijayaragavan Vimalan, Fredin Arun Sedly Givo, Veikko Jousmäki, Parasuraman Padmanabhan, Balázs Gulyás |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Brain Sciences, Vol 12, Iss 6, p 788 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3425 |
| DOI: |
10.3390/brainsci12060788 |
| Popis: |
Magnetoencephalography (MEG) plays a pivotal role in the diagnosis of brain disorders. In this review, we have investigated potential MEG applications for analysing brain disorders. The signal-to-noise ratio (SNRMEG = 2.2 db, SNREEG < 1 db) and spatial resolution (SRMEG = 2–3 mm, SREEG = 7–10 mm) is higher for MEG than EEG, thus MEG potentially facilitates accurate monitoring of cortical activity. We found that the direct electrophysiological MEG signals reflected the physiological status of neurological disorders and play a vital role in disease diagnosis. Single-channel connectivity, as well as brain network analysis, using MEG data acquired during resting state and a given task has been used for the diagnosis of neurological disorders such as epilepsy, Alzheimer’s, Parkinsonism, autism, and schizophrenia. The workflow of MEG and its potential applications in the diagnosis of disease and therapeutic planning are also discussed. We forecast that computer-aided algorithms will play a prominent role in the diagnosis and prediction of neurological diseases in the future. The outcome of this narrative review will aid researchers to utilise MEG in diagnostics. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
|
Nepřihlášeným uživatelům se plný text nezobrazuje |
K zobrazení výsledku je třeba se přihlásit.
|
