References
- Glover G.H. Overview of functional magnetic resonance imaging. Neurosurg. Clin. N. Am. 2011;22:133–139. doi: 10.1016/j.nec.2010.11.001
- Uludağ K, Roebroeck A. General overview on the merits of multimodal neuroimaging data fusion. Neuroimage. 2014:3–10. doi: 10.1016/j.neuroimage.2014.05.018
- Niso G., Rogers C., Moreau J. T., Chen L. Y., Madjar C., Das S., Bock E., Tadel F., Evans A. C., Jolicoeur P. et al. OMEGA: The Open MEG Archive. Neuroimage. 2015;124:1182–1187. doi: 10.1016/j.neuroimage.2015.04.028
- Shafto M.A., Tyler L.K., Dixon M., Taylor J.R., Rowe J.B., Cusak R., Calder A.J., Marsen-Wilson W.D., Duncan J., Dalgleish T. et. al., Cam-CAN. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: A crosssectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurology. 2014;14. Article No. 204. doi: 10.1186/s12883-014-0204-1
- McNabb C.B., Driver I.D., Hyde V., Hughes G., Chandler H.L., Thomas H., Allen C., Messaritaki E., Hodgetts C.J., Hedge C. et al. WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis. Sci. Data. 2025;12. Article No. 220. doi: 10.1038/s41597-024-04154-7
- Schoffelen JM., Oostenveld R., Lam N.H.L., Uddén J., Hultén A., Hagoort P. A 204-subject multimodal neuroimaging dataset to study language processing. Sci. Data. 2019;6. Article No. 17. doi: 10.1038/s41597-019-0020-y
- Nugent A.C., Thomas A.G., Mahoney M., Gibbons A., Smith J.T., Charles A.J., Shaw J.S., Stout J.D., Namyst A.M., Basavaraj A. et al. The NIMH intramural healthy volunteer dataset: A comprehensive MEG, MRI, and behavioral resource. Sci. Data. 2022;9. Article No. 518. doi: 10.1038/s41597-022-01623-9
- Alexander L.M., Escalera J., Ai L., Andreotti C., Febre K., Mangone A., Vega-Potler N., Langer N., Alexander A., Kovacs M. et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci. Data. 2017;4. Article No. 170181. doi: 10.1038/sdata.2017.181
- Tadel F., Baillet S., Mosher J.C., Pantazis D., Leahy R.M., Brainstorm: A User-Friendly Application for MEG/EEG Analysis. Computational Intelligence and Neuroscience. 2011. Article No. 879716. doi: 10.1155/2011/879716
- Fischl B. FreeSurfer. Neuroimage. 2012;62:774–781. doi: 10.1016/j.neuroimage.2012.01.021
- Oostenveld R., Fries P., Maris E., Schoffelen J.-M., FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Computational Intelligence and Neuroscience. 2011;1. Article No. 156869. doi: 10.1155/2011/156869
- Llinás R.R., Rykunov S., Walton K.D., Boyko A., Ustinin M. Splitting of the magnetic encephalogram into «brain» and «non-brain» physiological signals based on the joint analysis of frequency-pattern functional tomograms and magnetic resonance images. Front. Neural Circuits. 2022;16. Article No. 834434. doi: 10.3389/fncir.2022.834434
- Ustinin M.N., Rykunov S.D., Boyko A.I., Maslova O.A., Pankratova N.M. Study of Attention Deficit and Hyperactivity Disorder Using the Method of Functional Tomography Based On Magnetic Encephalography Data. Ìàthematical Biology and Bioinformatics. 2019;14(2):517–532. doi: 10.17537/2019.14.517
- Ustinin M.N., Rykunov S.D., Boyko A.I. Correlation of the Brain Compartments in the Attention Deficit and Hyperactivity Disorder Calculated by the Method of Virtual Electrodes from Magnetic Encephalography Data. Ìàthematical Biology and Bioinformatics. 2020;15(2):471–486. doi: 10.17537/2020.15.471
- Llinás R.R., Ustinin M.N. Frequency-pattern functional tomography of magnetoencephalography data allows new approach to the study of human brain organization. Front. Neural Circuits. 2014;8. Article No. 43. doi: 10.3389/fncir.2014.00043
- Ustinin M, Boyko A, Rykunov S. Healthy aging changes in conventional frequency bands of neuroelectric brain activity reconstructed from resting-state MEG. Geroscience. 2025;47:4093–4108. doi: 10.1007/s11357-025-01522-y
|
|
|