T114. Predicting coma outcome using resting-state fMRI and machine learning

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Abstracts / Clinical Neurophysiology 129 (2018) e1–e65

carpal tunnel area after stimulation of the digital nerves at the index or middle finger. Current sources were estimated using spatial filter techniques and were superimposed on X-ray images of the hand. We set a virtual electrode in the carpal tunnel along the median nerve. We reconstructed the current waveform from the magnetic field and calculated the nerve conduction velocity from the peak latency. We also measured sensory nerve action potential just above the carpal tunnel, 3 cm distal and 3 cm proximal to the carpal tunnel in response to stimulation of the digital nerves of the index finger. The conduction velocity was calculated from the peak latency. Results: Neuromagnetic fields propagating from the finger to the wrist were successfully measured in all subjects. Distribution of action currents calculated from MNG showed the axonal activity pattern showing the intra-axonal current and the depolarization/ repolarization current. Nerve conduction velocity estimated from the MNG was 55.3 m/s and corresponded with the sensory nerve conduction velocity. Conclusion: We could visualize action current at any point in carpal tunnel with high resolution using MNG. Moreover, MNG is not affected by the surrounding bone and soft tissues and can be fused with morphological images such as X-rays. MNG is expected to contribute to the clinical diagnosis and treatment of carpal tunnel syndrome. doi:10.1016/j.clinph.2018.04.113

T113. The changes in motor cortex localisation and organisation in motor eloquent tumorous brain lesions detected by navigated transcranial magnetic stimulation—Irena Holeckova *, Jiri Vales, Jan Mracek, Petr Rihanek, Roman Moucek, Pavel Mautner, Vladimir Priban (Czech Republic) ⇑

Presenting author.

Introduction: Navigated transcranial magnetic stimulation (nTMS) is non invasive method to map the motor cortex including primary motor cortex (PrG - precentral gyrus) and premotor areas (PMa). This study aimed to investigate whether tumorous brain lesion induce a change in motor cortex localization or organisation investigated by nTMS. Methods: We enrolled 10 patients with intraaxial motor tumor (gliomas). All patients underwent preoperative navigational MRI folowed by nTMS. Both lesional and non lesional hemispheres were stimulated. MEPs were recorded by EMG. The measured muscle was APB. MEPs latency for each positive stimulation point was measured. The surface of positive stimulation areas on the cortex were calculated for both hemispheres and results were compared. Results: The positive MEPs responses were registered from PrG as well as from PMa with different latencies. There were mosaic distribution of short and long latencies of MEPs responses without dependence on the location in the PrG or PMa in both hemispheres. The positive motor area distributions were significantly larger from lesional than for non lesional hemisphere for both PrG and PMa areas (PrG lesional vs. non lesional surface = 947 mm2 vs. 393 mm2, PMa lesional vs. non lesional surface = 620 mm2 vs. 545 mm2). Conclusion: The intraaxial motor eloquent tumors induce changes in motor cortex. The motor areas spread widely in the anteriorposterior direction in lesional hemisphere. The localisation short latencies found in PMa suggesting for detection of primary motor areas outside the PrG. This study was supported by the Charles University Research Fund Progress Q 39. doi:10.1016/j.clinph.2018.04.114

T114. Predicting coma outcome using resting-state fMRI and machine learning—Deborah Pugin *, Jeremy Hofmeister, Yvan Gasche, Dimitri Van De Ville, Serge Vulliémoz, Sven Haller (Switzerland) ⇑

Presenting author.

Introduction: Early prediction of neurological outcome of postanoxic comatose patients after cardiac arrest(CA) is challenging. Prognosis of comatose patient relies on multimodal testing: clinical examination, electrophysiological testing and structural neuroimaging (mainly diffusion MRI DWI). This multimodal prognostication is accurate for predicting poor outcome(i.e.death) but not sensitive for identifying patients with good outcome (i.e. consciousness recovery). Resting state functional MRI (rs-fMRI) is a powerful tool for mapping functional connectivity, especially in patients with low collaboration. Several studies showed that rsfMRI can differentiate states of consciousness in chronically brain damaged patients. A recent study also showed that fMRI can detect early signs of consciousness in patient with acute traumatic brain injury. However, rs-fMRI has not been systematically assessed for the early prognositcation of post-anoxic comatose patient. Methods: We assessed whole brain functional connectivity (FC) of 17 post-anoxic comatose patients early after CA using rs-fMRI. Nine patients recovered consciousness(good outcome) while eight died (poor outcome). We estimated FC for each patient following a standard procedure described by Leonardi and Richiardi et al. We statistically compared whole brain FC between good and poor outcome group, to assess which brain connections differed between them. Then, we trained a machine learning classifier(a Support Vector Machine classifier, SVM) using a Leave-One-Out Cross-Validation method, to automatically predict coma outcome (good/poor) based on whole-brain FC of comatose patients. Finally, we compared this outcome-prognostication based on fMRI to those using standard structural DWI. Results: Good and poor coma outcome groups were similar in terms of demographics, except for time to ROSC. Good outcome group showed significant increase in whole-brain FC between most cortical brain regions, with the strongest changes occuring within and between occipital and parietal, temporal and frontal regions. Using whole-brain FC and a SVM classifier to predict coma outcome yielded to an overall prediction accuracy of 94.4% (AUC 0.94). Interestingly, automatic outcome prognostication using functional neuroimaging achieved better results that state-of the-art structural neuroimaging methods like DWI (accuracy 70.6%). Conclusion: We used rs-fMRI to predict coma outcome in a cohort of post-anoxic comatose patients early after CA. We deliberately chose to include only patients with indeterminate prognosis after standard multimodal testing, in order to assess the contribution of rs-fMRI in the early prognostication of coma outcome. We found that automatic prediction based on fMRI yielded much better results than current diffusion neuroimaging methods, notably for identifying patients who recovered consciousness. doi:10.1016/j.clinph.2018.04.115