Abstracts and studies

This abstracts and studies hub offers an opportunity to review some shortlisted studies by condition, and a more comprehensive list in the Book of Abstracts  

Huang et al. (2016). High-resolution MEG source imaging approach to acurately localize Broca’s area in patients with brain tumor or epilepsy. Clinical Neurophysiology, Vol.127, No.5.

MEG in Oncology/Radiation Oncology

Belgers, V., Numan, T., Kulik, S. D., Hillebrand, A., de Witt Hamer, P. C., Geurts, J. J. G., Reijneveld, J. C., Wesseling, P., Klein, M., Derks, J., & Douw, L. (2020). Postoperative oscillatory brain activity as an add-on prognostic marker in diffuse glioma. Journal of Neuro-Oncology, 147(1), 49–58.

Numan, T., Kulik, S. D., Moraal, B., Reijneveld, J. C., Stam, C. J., de Witt Hamer, P. C., Derks, J., Bruynzeel, A. M. E., van Linde, M. E., Wesseling, P., Kouwenhoven, M. C. M., Klein, M., Würdinger, T., Barkhof, F., Geurts, J. J. G., Hillebrand, A., & Douw, L. (2021). Non-invasively measured brain activity and radiological progression in diffuse glioma. Scientific Reports, 11, Article 18990.

Lee, A. T., Faltermeier, C., Morshed, R. A., Young, J. S., Kakaizada, S., Valdivia, C., Findlay, A. M., Tarapore, P. E., Nagarajan, S. S., Hervey-Jumper, S. L., & Berger, M. S. (2020). The impact of high functional connectivity network hub resection on language task performance in adult low- and high-grade glioma. Journal of Neurosurgery, 134(4), 1102–1112. 

Samuel, N., Harmsen, I. E., Ding, M. Y. R., Sarica, C., Vetkas, A., Wong, C., Lawton, V., Yang, A., Rowland, N. C., Kalia, S. K., Valiante, T., Wennberg, R., Zadeh, G., Kongkham, P., Kalyvas, A., & Lozano, A. M. (2023). Investigation of neurophysiologic and functional connectivity changes following glioma resection using magnetoencephalography. Neuro-Oncology Advances, 5(1), vdad091. 

Bulubas, L., Sardesh, N., Traut, T., Findlay, A., Mizuiri, D., Honma, S. M., Krieg, S. M., Berger, M. S., Nagarajan, S. S., & Tarapore, P. E. (2020). Motor cortical network plasticity in patients with recurrent brain tumors. Frontiers in Human Neuroscience, 14, 118. 

MEG in Alzheimers

Gouw, A. A., Hillebrand, A., Schoonhoven, D. N., Demuru, M., Ris, P., Scheltens, P., & Stam, C. J. (2021). Routine magnetoencephalography in memory clinic patients: A machine learning approach. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring. 13(1), e12227.

Cope, T. E., Rittman, T., Borchert, R. J., Jones, P. S., Vatansever, D., Allinson, K. S. J., Passamonti, L., & Rowe, J. B. (2022). Causal evidence for the multiple demand network in change detection: A study of patients with focal brain lesions. The Journal of Neuroscience, 42 (15) 3197-3215

Vaghari, D., Hughes, L. E., Bruna, R., Koelewijn, L., Woolrich, M. W., Luo, Q., & Henson, R. N. (2022). A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: The BioFIND dataset. NeuroImage, Volume 258, 119344.

Giovannetti, A., Susi, G., Casti, P., Mencattini, A., Pusil, S., Eugenia Lopez, M., Di Natale, C., Martinelli, E. (2021) Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography. Neural Comput & Applic 33, 14651–14667. 

Vossel K., Ranasinghe KG., Beagle AJ., et al. Effect of Levetiracetam on Cognition in Patients With Alzheimer Disease With and Without Epileptiform Activity: A Randomized Clinical Trial. JAMA Neurol. 2021;78(11):1345–1354. doi:10.1001/jamaneurol.2021.3310 

MEG in Epilepsy

Corona, L., Tamilia, E., Perry, M.S., Madsen, J.R., Bolton, J., Stone, S.S.D., Stufflebeam, S.M., Pearl, P.L., Papadelis, C. (2023). Non-invasive mapping of epileptogenic networks predicts surgical outcome. Brain, Volume 146, Issue 5, Pages 1916–1931.

Tripathi M., Kaur K., Ramanujam B., Viswanathan, V., Bharti, K., Singh, G., Singh, V., Garg, A., Sekhar Bal, C., Tripathi, M., Chand Sharma, M., Pandey, R., Dash, D., Mandal, P., Sarat Chandra, P. (2021) Diagnostic added value of interictal magnetic source imaging in presurgical evaluation of persons with epilepsy: A prospective blinded study. Eur J Neurol. 28: 2940-2951. 

Tamilia, E., Matarrese, M.A.G., Ntolkeras, G., Grant, P.E., Madsen, J.R., Stufflebeam, S.M., Pearl, P.L. and Papadelis, C. (2021). Noninvasive Mapping of Ripple Onset Predicts Outcome in Epilepsy Surgery. Ann Neurol, 89: 911-925. 

Yin, C., Zhang, X., Xiang, J., Chen, Z., Li, X., Wu, S., Lv, P., Wang, Y. (2020). Altered effective connectivity network in patients with insular epilepsy: A high-frequency oscillations magnetoencephalography study. Clinical Neurophysiology, Volume 131, Issue 2, Pages 377-384.  

Cao, M., Galvis, D., Vogrin, S.J., Woods, W.P., Vogrin, S., Wang, F., Woldman, W., Terry, J.R., Peterson, A., Plummer, C., Cook, M.J. (2022) Virtual intracranial EEG signals reconstructed from MEG with potential for epilepsy surgery. Nat Commun 13, 994.