Ezequiel Gleichgerrcht1, Simon S Keller2,3, Daniel L Drane4,5,6, Brent C Munsell7, Kathryn A Davis8, Erik Kaestner9, Bernd Weber10, Samantha Krantz1, William A Vandergrift11, Jonathan C Edwards1, Carrie R McDonald12, Ruben Kuzniecky12, Leonardo Bonilha1. 1. Department of Neurology, Medical University of South Carolina, Charleston, SC, USA. 2. Department of Pharmacology and Therapeutics, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK. 3. Department of Neuroradiology, Walton Centre NHS Foundation Trust, Liverpool, UK. 4. Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA. 5. Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA. 6. Department of Neurology, University of Washington School of Medicine, Seattle, WA, USA. 7. Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA. 8. Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA. 9. Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA. 10. Institute of Experimental Epileptology and Cognition Research, University of Bonn, Bonn, Germany. 11. Department of Neurosurgery, Medical University of South Carolina, Charleston, SC, USA. 12. Department of Neurology, Hofstra University/Northwell, New York, NY, USA.
Abstract
OBJECTIVE: Medial temporal lobe epilepsy (TLE) is the most common form of medication-resistant focal epilepsy in adults. Despite removal of medial temporal structures, more than one-third of patients continue to have disabling seizures postoperatively. Seizure refractoriness implies that extramedial regions are capable of influencing the brain network and generating seizures. We tested whether abnormalities of structural network integration could be associated with surgical outcomes. METHODS: Presurgical magnetic resonance images from 121 patients with drug-resistant TLE across 3 independent epilepsy centers were used to train feed-forward neural network models based on tissue volume or graph-theory measures from whole-brain diffusion tensor imaging structural connectomes. An independent dataset of 47 patients with TLE from 3 other epilepsy centers was used to assess the predictive values of each model and regional anatomical contributions toward surgical treatment results. RESULTS: The receiver operating characteristic area under the curve based on regional betweenness centrality was 0.88, significantly higher than a random model or models based on gray matter volumes, degree, strength, and clustering coefficient. Nodes most strongly contributing to the predictive models involved the bilateral parahippocampal gyri, as well as the superior temporal gyri. INTERPRETATION: Network integration in the medial and lateral temporal regions was related to surgical outcomes. Patients with abnormally integrated structural network nodes were less likely to achieve seizure freedom. These findings are in line with previous observations related to network abnormalities in TLE and expand on the notion of underlying aberrant plasticity. Our findings provide additional information on the mechanisms of surgical refractoriness. ANN NEUROL 2020;88:970-983.
OBJECTIVE: Medial temporal lobe epilepsy (TLE) is the most common form of medication-resistant focal epilepsy in adults. Despite removal of medial temporal structures, more than one-third of patients continue to have disabling seizures postoperatively. Seizure refractoriness implies that extramedial regions are capable of influencing the brain network and generating seizures. We tested whether abnormalities of structural network integration could be associated with surgical outcomes. METHODS: Presurgical magnetic resonance images from 121 patients with drug-resistant TLE across 3 independent epilepsy centers were used to train feed-forward neural network models based on tissue volume or graph-theory measures from whole-brain diffusion tensor imaging structural connectomes. An independent dataset of 47 patients with TLE from 3 other epilepsy centers was used to assess the predictive values of each model and regional anatomical contributions toward surgical treatment results. RESULTS: The receiver operating characteristic area under the curve based on regional betweenness centrality was 0.88, significantly higher than a random model or models based on gray matter volumes, degree, strength, and clustering coefficient. Nodes most strongly contributing to the predictive models involved the bilateral parahippocampal gyri, as well as the superior temporal gyri. INTERPRETATION: Network integration in the medial and lateral temporal regions was related to surgical outcomes. Patients with abnormally integrated structural network nodes were less likely to achieve seizure freedom. These findings are in line with previous observations related to network abnormalities in TLE and expand on the notion of underlying aberrant plasticity. Our findings provide additional information on the mechanisms of surgical refractoriness. ANN NEUROL 2020;88:970-983.
Authors: Jean-Philippe Fortin; Drew Parker; Birkan Tunç; Takanori Watanabe; Mark A Elliott; Kosha Ruparel; David R Roalf; Theodore D Satterthwaite; Ruben C Gur; Raquel E Gur; Robert T Schultz; Ragini Verma; Russell T Shinohara Journal: Neuroimage Date: 2017-08-18 Impact factor: 6.556
Authors: Jerome Engel; Michael P McDermott; Samuel Wiebe; John T Langfitt; John M Stern; Sandra Dewar; Michael R Sperling; Irenita Gardiner; Giuseppe Erba; Itzhak Fried; Margaret Jacobs; Harry V Vinters; Scott Mintzer; Karl Kieburtz Journal: JAMA Date: 2012-03-07 Impact factor: 56.272
Authors: Christopher D Whelan; Andre Altmann; Juan A Botía; Neda Jahanshad; Derrek P Hibar; Julie Absil; Saud Alhusaini; Marina K M Alvim; Pia Auvinen; Emanuele Bartolini; Felipe P G Bergo; Tauana Bernardes; Karen Blackmon; Barbara Braga; Maria Eugenia Caligiuri; Anna Calvo; Sarah J Carr; Jian Chen; Shuai Chen; Andrea Cherubini; Philippe David; Martin Domin; Sonya Foley; Wendy França; Gerrit Haaker; Dmitry Isaev; Simon S Keller; Raviteja Kotikalapudi; Magdalena A Kowalczyk; Ruben Kuzniecky; Soenke Langner; Matteo Lenge; Kelly M Leyden; Min Liu; Richard Q Loi; Pascal Martin; Mario Mascalchi; Marcia E Morita; Jose C Pariente; Raul Rodríguez-Cruces; Christian Rummel; Taavi Saavalainen; Mira K Semmelroch; Mariasavina Severino; Rhys H Thomas; Manuela Tondelli; Domenico Tortora; Anna Elisabetta Vaudano; Lucy Vivash; Felix von Podewils; Jan Wagner; Bernd Weber; Yi Yao; Clarissa L Yasuda; Guohao Zhang; Nuria Bargalló; Benjamin Bender; Neda Bernasconi; Andrea Bernasconi; Boris C Bernhardt; Ingmar Blümcke; Chad Carlson; Gianpiero L Cavalleri; Fernando Cendes; Luis Concha; Norman Delanty; Chantal Depondt; Orrin Devinsky; Colin P Doherty; Niels K Focke; Antonio Gambardella; Renzo Guerrini; Khalid Hamandi; Graeme D Jackson; Reetta Kälviäinen; Peter Kochunov; Patrick Kwan; Angelo Labate; Carrie R McDonald; Stefano Meletti; Terence J O'Brien; Sebastien Ourselin; Mark P Richardson; Pasquale Striano; Thomas Thesen; Roland Wiest; Junsong Zhang; Annamaria Vezzani; Mina Ryten; Paul M Thompson; Sanjay M Sisodiya Journal: Brain Date: 2018-02-01 Impact factor: 13.501
Authors: Graham W Johnson; Leon Y Cai; Saramati Narasimhan; Hernán F J González; Kristin E Wills; Victoria L Morgan; Dario J Englot Journal: J Neurol Neurosurg Psychiatry Date: 2022-03-28 Impact factor: 13.654
Authors: Taha Gholipour; Xiaozhen You; Steven M Stufflebeam; Murray Loew; Mohamad Z Koubeissi; Victoria L Morgan; William D Gaillard Journal: Epilepsia Date: 2022-01-04 Impact factor: 6.740
Authors: Lohith G Kini; Ashesh A Thaker; Peter N Hadar; Russell T Shinohara; Mesha-Gay Brown; Jacob G Dubroff; Kathryn A Davis Journal: Epilepsy Behav Date: 2021-01-21 Impact factor: 2.937