Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-19T12:26:38.904Z Has data issue: false hasContentIssue false

Chapter 9 - A Neuroimaging Network-Level Approach to Drug-Resistant Epilepsy

Published online by Cambridge University Press:  06 January 2023

By
Niels Alexander Foit ,
Fatemeh Fadaie ,
Andrea Bernasconi  and
Neda Bernasconi
Edited by
Rod C. Scott  and
J. Matthew Mahoney
Rod C. Scott
Affiliation:
University of Vermont
J. Matthew Mahoney
Affiliation:
University of Vermont
Get access

Summary

Epilepsy affects approximately 1% of the population [1]. Although generally treatable, up to 30% of patients do not achieve seizure freedom from anticonvulsive medication alone. Due to its relationship with cognitive abilities [2], quality of life [3], and the associated risk of premature death [4], drug-refractory epilepsy should be treated promptly. Temporal lobe epilepsy (TLE) associated with mesiotemporal sclerosis [5] and extra-temporal lobe epilepsy related to focal cortical dysplasia (FCD) [6] constitute the most common refractory epilepsy syndromes. Surgical resection of these lesions remains the treatment of choice [7], with success rates approaching 80% [8]. By allowing the detection of epileptogenic lesions and offering system-level mechanisms of the disease process, MRI has shifted the field from electro-clinical correlations toward a multidisciplinary approach.

Type
Chapter
Information
A Complex Systems Approach to Epilepsy
Concept, Practice, and Therapy
 , pp. 127 - 134
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Fiest, K. M. et al. Prevalence and incidence of epilepsy. Neurology, 88, 296–303 (2017).CrossRefGoogle ScholarPubMed
Bernhardt, B. C., Hong, S., Bernasconi, A., and Bernasconi, N. Imaging structural and functional brain networks in temporal lobe epilepsy. Front. Hum. Neurosci., 7, 624 (2013).Google Scholar
Seiam, A.-H. R., Dhaliwal, H., and Wiebe, S. Determinants of quality of life after epilepsy surgery: Systematic review and evidence summary. Epilepsy Behav., 21, 441–5 (2011).CrossRefGoogle ScholarPubMed
Engel, J. What can we do for people with drug-resistant epilepsy? Neurology, 87, 2483–9 (2016).Google Scholar
Blümcke, I., Thom, M., Aronica, E., et al. International consensus classification of hippocampal sclerosis in temporal lobe epilepsy: A Task Force report from the ILAE Commission on Diagnostic Methods. Epilepsia, 54(7), 1315–29 (2013).CrossRefGoogle ScholarPubMed
Blümcke, I., Thom, M., Aronica, E. et al. The clinicopathologic spectrum of focal cortical dysplasias: A consensus classification proposed by an ad hoc Task Force of the ILAE Diagnostic Methods Commission1. Epilepsia, 52 (1), 158–74 (2011).CrossRefGoogle Scholar
West, S., Nevitt, S. J., Cotton, J., et al. Surgery for epilepsy. Cochrane Database Syst. Rev., 6(6), CD010541 (2019).Google ScholarPubMed
Wiebe, S., Blume, W. T., Girvin, J. P., and Eliasziw, M. A randomized, controlled trial of surgery for temporal-lobe epilepsy. N. Engl. J. Med., 345, 311–8 (2001).CrossRefGoogle ScholarPubMed
Richardson, M. P. Large scale brain models of epilepsy: Dynamics meets connectomics. J. Neurol. Neurosurg. Psychiatry, 83, 1238–48 (2012).Google Scholar
Spencer, S. S. Neural networks in human epilepsy: evidence of and implications for treatment. Epilepsia, 43, 219–27 (2002).Google Scholar
Sporns, O., Tononi, G., and Kötter, R. The human connectome: A structural description of the human brain. PLoS Comput. Biol., 1, e42 (2005).Google Scholar
Power, J. D., Fair, D. A., Schlaggar, B. L., and Petersen, S. E. The development of human functional brain networks. Neuron, 67, 735–48 (2010).CrossRefGoogle ScholarPubMed
Bassett, D. S., and Sporns, O. Network neuroscience. Nat. Neurosci., 20, 353–64 (2017).Google Scholar
Jones, D. K., Knösche, T. R., and Turner, R. White matter integrity, fiber count, and other fallacies: The do’s and don’ts of diffusion MRI. Neuroimage, 73, 239–54 (2012).Google ScholarPubMed
Conturo, T. E., Lori, N. F., Cull, T. S., et al. Tracking neuronal fiber pathways in the living human brain. Proc. Natl. Acad. Sci. USA, 96(18), 10422–7 (1999).Google Scholar
Alexander-Bloch, A., Giedd, J. N., and Bullmore, E. Imaging structural co-variance between human brain regions. Nat. Rev. Neurosci., 14, 322–36 (2013).Google Scholar
Power, J. D., Cohen, A. L., Nelson, S. M.,et al. Functional network organization of the human brain. Neuron, 72, 665–78 (2011).Google Scholar
Abbott, D. F., Waites, A. B., Lillywhite, L. M., and Jackson, G. D. fMRI assessment of language lateralization: An objective approach. NeuroImage, 50, 1446–55 (2010).Google Scholar
Biswal, B. B., Mennes, M., Zuo, X. N., et al. Toward discovery science of human brain function. Proc. Natl. Acad. Sci. USA,107, 4734–9 (2010).Google Scholar
van Diessen, E., Zweiphenning, W. J., Jansen, F. E., et al. Brain network organization in focal epilepsy: A systematic review and meta-analysis. PLoS ONE, 9(12), e114606 (2014).CrossRefGoogle ScholarPubMed
Bullmore, E., and Sporns, O. Complex brain networks: Graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci., 10, 186–98 (2009).Google Scholar
Rubinov, M., and Sporns, O. Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52, 1059–69 (2010).Google Scholar
van den Heuvel, M. P., and Sporns, O. A cross-disorder connectome landscape of brain dysconnectivity. Nat. Rev. Neurosci., 20, 435–46 (2019).CrossRefGoogle ScholarPubMed
Colizza, V., Flammini, A., Serrano, M. A., and Vespignani, A. Detecting rich-club ordering in complex networks. Nat. Phys., 2, 110–5 (2006).CrossRefGoogle Scholar
Gu, S., Pasqualetti, F., Cieslak, M., et al. Controllability of structural brain networks. Nat. Comm., 6, 8414 (2015).CrossRefGoogle ScholarPubMed
Chari, A., Seunarine, K. K., He, X., et al. Drug-resistant focal epilepsy in children is associated with increased modal controllability of the whole brain and epileptogenic regions. Commun. Biol., 5, 394 (2022).Google Scholar
Liu, Y.-Y., Slotine, J.-J., and Barabási, A.-L. Controllability of complex networks. Nature, 473, 167–73 (2011).Google Scholar
Tavakol, S. Royer, J., Lowe, A. J., et al. Neuroimaging and connectomics of drug-resistant epilepsy at multiple scales: From focal lesions to macroscale networks. Epilepsia, 60, 593–604 (2019).Google Scholar
Larivière, S., Bernasconi, A., Bernasconi, N., and Bernhardt, B. C. Connectome biomarkers of drug-resistant epilepsy. Epilepsia n/a,.Google Scholar
Gleichgerrcht, E., Kocher, M., and Bonilha, L. Connectomics and graph theory analyses: Novel insights into network abnormalities in epilepsy. Epilepsia, 56, 1660–8 (2015).Google Scholar
Caciagli, L., Bernhardt, B. C., Hong, S.-J., Bernasconi, A., and Bernasconi, N. Functional network alterations and their structural substrate in drug-resistant epilepsy. Front. Neurosci., 8, (2014).Google Scholar
Balachandra, A. R., Kaestner, E., Bahrami, N., et al. Clinical utility of structural connectomics in predicting memory in temporal lobe epilepsy. Neurology, 94, e2424–35 (2020).CrossRefGoogle ScholarPubMed
Reyes, A., Kaestner, E., Bahrami, N., et al. Cognitive phenotypes in temporal lobe epilepsy are associated with distinct patterns of white matter network abnormalities. Neurology, 92, e1957–68 (2019).Google Scholar
Besson, P., Dinkelacker, V., Valabregue, R., et al. Structural connectivity differences in left and right temporal lobe epilepsy. Neuroimage, 100, 135–144 (2014).CrossRefGoogle ScholarPubMed
Chiang, S., Stern, J. M., Engel, J., and Haneef, Z. Structural-functional coupling changes in temporal lobe epilepsy. Brain Res., 1616, 45–57 (2015).Google Scholar
Caciagli, L., Bernhardt, B. C., Bernasconi, A., and Bernasconi, N. Network Modeling of Epilepsy Using Structural and Functional MRI. In Imaging Biomarkers in Epilepsy, Bernasconi, A., Koepp, M., and Bernasconi, N. (eds.) Cambridge: Cambridge University Press. 2019. p. 77–94.Google Scholar
Bettus, G., Bartolomei, F., Confort-Gouny, S., et al. Role of resting state functional connectivity MRI in presurgical investigation of mesial temporal lobe epilepsy. J. Neurol. Neurosurg. Psychiatry, 81(10), 1147–54 (2010).Google Scholar
Haneef, Z., Chiang, S., Yeh, H. J., Engel, J., and Stern, J. M. Functional connectivity homogeneity correlates with duration of temporal lobe epilepsy. Epilepsy Behav., 46, 227–33 (2015).Google Scholar
Hermann, B., Conant, L. L., Cook, C. J., et al. Network, clinical and sociodemographic features of cognitive phenotypes in temporal lobe epilepsy. NeuroImage Clin., 27, 102341 (2020).Google Scholar
Rodríguez-Cruces, R., Bernhardt, B. C., and Concha, L. Multidimensional associations between cognition and connectome organization in temporal lobe epilepsy. NeuroImage, 213, 116706 (2020).Google Scholar
Kaestner, E., Balachandra, A. R., Bahrami, N., et al. The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy. NeuroImage Clin., 25, 102125 (2019).Google Scholar
Audrain, S., Barnett, A. J., and McAndrews, M. P. Language network measures at rest indicate individual differences in naming decline after anterior temporal lobe resection. Hum. Brain Mapp., 39, 4404–19 (2018).CrossRefGoogle ScholarPubMed
Steiger, B. K., Muller, A. M., Spirig, E., Toller, G., and Jokeit, H. Mesial temporal lobe epilepsy diminishes functional connectivity during emotion perception. Epilepsy Res., 134, 33–40 (2017).Google Scholar
Doucet, G. E., Rider, R., Taylor, N., et al. Presurgery resting-state local graph-theory measures predict neurocognitive outcomes after brain surgery in temporal lobe epilepsy. Epilepsia, 56(4), 517–26 (2015).Google Scholar
Pressl, C., Brandner, P., Schaffelhofer, S., et al. Resting state functional connectivity patterns associated with pharmacological treatment resistance in temporal lobe epilepsy. Epilepsy Res., 149, 37–43 (2019).CrossRefGoogle ScholarPubMed
Bonilha, L., Jensen, J. H., Baker, N., et al. The brain connectome as a personalized biomarker of seizure outcomes after temporal lobectomy. Neurology, 84, 1846–53 (2015).Google Scholar
Morgan, V. L., Rogers, B. P., Anderson, A. W., Landman, B. A., and Englot, D. J. Divergent network properties that predict early surgical failure versus late recurrence in temporal lobe epilepsy. J. Neurosurg., 1, 1–10 (2019).Google Scholar
Xu, Q., Zhang, Z., Liao, W., et al. Time-shift homotopic connectivity in mesial temporal lobe epilepsy. Am. J. Neuroradiol., 35, 1746–52 (2014).Google Scholar
Foit, N. A., Bernasconi, A., and Bernasconi, N. Functional networks in epilepsy presurgical evaluation. Neurosurg Clin N Am., 31(3), 395–405 (2020).Google Scholar
Hong, S.-J., Lee, H.-M., Gill, R., et al. A connectome-based mechanistic model of focal cortical dysplasia. Brain, 142, 688–99 (2019).Google Scholar
Hong, S.-J., Bernhardt, B. C., Gill, R. S., Bernasconi, N., and Bernasconi, A. The spectrum of structural and functional network alterations in malformations of cortical development. Brain, 140, 2133–43 (2017).Google Scholar
Vlooswijk, M. C. G., Jansen, J. F., Jeukens, C. R., et al. Memory processes and prefrontal network dysfunction in cryptogenic epilepsy. Epilepsia, 52(8), 1467–75 (2011).CrossRefGoogle ScholarPubMed
Lhatoo, S. D., Bernasconi, N., Blumcke, I., et al. Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League against Epilepsy. Epilepsia, 61(9), 1869–83 (2020).Google Scholar
Sisodiya, S. M., Whelan, C. D., Hatton, S. N., et al. The ENIGMA-Epilepsy working group: Mapping disease from large data sets. Hum. Brain Mapp., 43(1), 113–128 (2020).Google Scholar
Larivière, S., Rodríguez-Cruces, R., Royer, J., et al. Network-based atrophy modeling in the common epilepsies: A worldwide ENIGMA study. Sci. Adv., 6(47), eabc6457 (2020).Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×