This paper presents a theoretical elaboration aimed to explain the correlation found between a new rs-fMRI modality and electrophysiology and nuclear medicine neuroimaging, performed to localize epileptogenic brain areas. We present in detail the clinical history, and electrophysiological and neuroimaging results of one child with intractable epilepsy, who was submitted for Phase-1 work-up as candidate for epilepsy surgery. The patient underwent a thorough workup including video-telemetry, ictal and interictal nuclear medicine imaging, resting-state fMRI, EEG-fMRI, intracranial electroencephalography (ECoG), deep electrode implantation, and resective surgery. Electrophysiology and neuroimaging findings were concordant with findings provided by the resting-state mean signal. The patient became seizure-free after the resection of the target area. A theoretical discussion is provided that considers the presence of a stable BOLD effect explaining the findings of the observed resting-state mean signal. This stable BOLD is linked to low regional metabolism usually present in the epileptogenic area during interictal periods, coupled with low oxygen extraction. Low oxygen extraction leaves more oxygen for the draining venule and consequently increases the BOLD signal.
References
[1]
Ogawa, S., Tank, D.W., Menon, R., Ellermann, J.M., Kim, S.G., Merkle, H., et al. (1992) Intrinsic Signal Changes Accompanying Sensory Stimulation: Functional Brain Mapping with Magnetic Resonance Imaging. Proceedings of the National Academy of Sciences of the United States of America, 89, 5951-5955. https://doi.org/10.1073/pnas.89.13.5951
[2]
Erecińska, M. and Silver, I.A. (1989) ATP and Brain Function. Journal of Cerebral Blood Flow & Metabolism, 9, 2-19. https://doi.org/10.1038/jcbfm.1989.2
[3]
Logothetis, N.K. and Pfeuffer, J. (2004) On the Nature of the BOLD fMRI Contrast Mechanism. Magnetic Resonance Imaging, 22, 1517-1531. https://doi.org/10.1016/j.mri.2004.10.018
[4]
Wells, J.A., Christie, I.N., Hosford, P.S., Huckstepp, R.T.R., Angelova, P.R., Vihko, P., et al. (2015) A Critical Role for Purinergic Signalling in the Mechanisms Underlying Generation of BOLD fMRI Responses. Journal of Neuroscience, 35, 5284-5292. https://doi.org/10.1523/JNEUROSCI.3787-14.2015
[5]
Buxton, R.B. (2010) Interpreting Oxygenation-Based Neuroimaging Signals: The Importance and the Challenge of Understanding Brain Oxygen Metabolism. Frontiers in Neuroenergetics, 2, 8. https://doi.org/10.3389/fnene.2010.00008
[6]
Chen, H.-H., Chen, C., Hung, S.-C., Liang, S.-Y., Lin, S.-C., Hsu, T.-R., et al. (2014) Cognitive and Epilepsy Outcomes after Epilepsy Surgery Caused by Focal Cortical Dysplasia in Children: Early Intervention Maybe Better. Child’s Nervous System, 30, 1885-1895. https://doi.org/10.1007/s00381-014-2463-y
[7]
Roder, C., Charyasz-Leks, E., Breitkopf, M., Decker, K., Ernemann, U., Klose, U., et al. (2016) Resting-State Functional MRI in an Intraoperative MRI Setting: Proof of Feasibility and Correlation to Clinical Outcome of Patients. Journal of Neurosurgery, 125, 401-409. https://doi.org/10.3171/2015.7.JNS15617
[8]
Vergun, S., Gaggl, W., Nair, V.A., Suhonen, J.I., Birn, R.M., Ahmed, A.S., et al. (2016) Classification and Extraction of Resting State Networks Using Healthy and Epilepsy fMRI Data. Frontiers in Neuroscience, 10, 440. https://doi.org/10.3389/fnins.2016.00440
[9]
Tie, Y., Rigolo, L., Norton, I.H., Huang, R.Y., Wu, W., Orringer, D., et al. (2014) Defining Language Networks from Resting-State fMRI for Surgical Planning—A Feasibility Study. Human Brain Mapping, 35, 1018-1030. https://doi.org/10.1002/hbm.22231
[10]
Lu, J., Zhang, H., Hameed, N.U.F., Zhang, J., Yuan, S., Qiu, T., et al. (2017) An Automated Method for Identifying an Independent Component Analysis-Based Language-Related Resting-State Network in Brain Tumor Subjects for Surgical Planning. Scientific Reports, 7, 13769. https://doi.org/10.1038/s41598-017-14248-5
[11]
Bernal, B., Guillen, M.R., Zamora, K. and Altman, N. (2015) Utilizing rs-fMRI’s Mean to Localize Seizure Focus. Open Journal of Radiology, 5, 92-103. https://doi.org/10.4236/ojrad.2015.52015
[12]
Allen, P.J., Polizzi, G., Krakow, K., Fish, D.R. and Lemieux, L. (1998) Identification of EEG Events in the MR Scanner: The Problem of Pulse Artifact and a Method for Its Subtraction. NeuroImage, 8, 229-239. https://doi.org/10.1006/nimg.1998.0361
[13]
Bell, A.J. and Sejnowski, T.J. (1995) An Information-Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation, 7, 1129-1159. https://doi.org/10.1162/neco.1995.7.6.1129
[14]
Makeig, S., Jung, T.P., Bell, A.J., Ghahremani, D. and Sejnowski, T.J. (1997) Blind Separation of Auditory Event-Related Brain Responses into Independent Components. Proceedings of the National Academy of Sciences of the United States of America, 94, 10979-10984. https://doi.org/10.1073/pnas.94.20.10979
[15]
Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.-P., Frith, C.D. and Frackowiak, R.S.J. (1994) Statistical Parametric Maps in Functional Imaging: A General Linear Approach. Human Brain Mapping, 2, 189-210. https://doi.org/10.1002/hbm.460020402
[16]
Davis, T.L., Kwong, K.K., Weisskoff, R.M. and Rosen, B.R. (1998) Calibrated Functional MRI: Mapping the Dynamics of Oxidative Metabolism. Proceedings of the National Academy of Sciences of the United States of America, 95, 1834-1839. https://doi.org/10.1073/pnas.95.4.1834
