Prediction of Postoperative Speech Dysfunctions in Neurosurgery Based on Cortico-Cortical Evoked Potentials and Machine Learning Technology

被引:1
作者
Ishankulov, T. A. [1 ]
Danilov, G., V [1 ]
Pitskhelauri, D., I [2 ]
Titov, O. Yu [2 ]
Ogurtsova, A. A. [3 ]
Buklina, S. B. [3 ]
Gulaev, E., V [3 ,5 ]
Konakova, T. A. [4 ]
Bykanov, A. E. [3 ]
机构
[1] Minist Hlth Russian Federat, NN Burdenko Natl Med Res Ctr Neurosurg, Lab Biomed Informat & Artificial Intelligence, 16,4th Tverskaya Yamskaya St, Moscow 125047, Russia
[2] Minist Hlth Russian Federat, NN Burdenko Natl Med Res Ctr Neurosurg, Dept Neurosurg 7, 16,4th Tverskaya Yamskaya St, Moscow 125047, Russia
[3] Minist Hlth Russian Federat, NN Burdenko Natl Med Res Ctr Neurosurg, 16,4th Tverskaya Yamskaya St, Moscow 125047, Russia
[4] Minist Hlth Russian Federat, NN Burdenko Natl Med Res Ctr Neurosurg, Dept Radiol, 16,4th Tverskaya Yamskaya St, Moscow 125047, Russia
[5] Minist Hlth Russian Federat, Natl Med Res Ctr Traumatol & Orthoped, 10 Priorova St, Moscow 127299, Russia
基金
俄罗斯基础研究基金会;
关键词
cortico-cortical evoked potentials; machine learning; artificial intelligence; neuro-oncology; glial tumors; speech function; connectome; FUNCTIONAL CONNECTIVITY; LANGUAGE SYSTEM; RESPONSES; CORTEX;
D O I
10.17691/stm2022.14.1.03
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
Intraoperative recording of cortico-cortical evoked potentials (CCEPs) enables studying effective connections between various functional areas of the cerebral cortex. The fundamental possibility of postoperative speech dysfunction prediction in neurosurgery based on CCEP signal variations could serve as a basis to develop the criteria for the physiological permissibility of intracerebral tumors removal for maximum preservation of the patients' quality of life. The aim of the study was to test the possibility of predicting postoperative speech disorders in patients with glial brain tumors by using the CCEP data recorded intraoperatively before the stage of tumor resection. Materials and Methods. CCEP data were reported for 26 patients. To predict the deterioration of speech functions in the postoperative period, we used four options for presenting CCEP data and several machine learning models' a random forest of decision trees, logistic regression, and support vector machine method with different types of kernels: linear, radial, and polynomial. Twenty variants of models were trained: each in 300 experiments with resampling. A total of 6000 tests were performed in the study. Results. The prediction quality metrics for each model trained in 300 tests with resampling were averaged to eliminate the influence of "successful" and "unsuccessful" data grouping. The best result with F1-score = 0.638 was obtained by the support vector machine with a polynomial kernel. In most tests, a high sensitivity score was observed, and in the best model, it reached a value of 0.993; the specificity of the best model was 0.370. Conclusion. This pilot study demonstrated the possibility of predicting speech dysfunctions based on CCEP data taken before the main stage of glial tumors resection; the data were processed using traditional machine learning methods. The best model with high sensitivity turned out to be insufficiently specific. Further studies will be aimed at assessing the changes in CCEP during the operation and their relationship with the development of postoperative speech deficit.
引用
收藏
页码:25 / 32
页数:8
相关论文
共 27 条
  • [11] New Approach for Exploring Cerebral Functional Connectivity: Review of Cortico-cortical Evoked Potential
    Kunieda, Takeharu
    Yamao, Yukihiro
    Kikuchi, Takayuki
    Matsumoto, Riki
    [J]. NEUROLOGIA MEDICO-CHIRURGICA, 2015, 55 (05) : 374 - 382
  • [12] Thinking, Walking, Talking: Integratory Motor and Cognitive Brain Function
    Leisman, Gerry
    Moustafa, Ahmed A.
    Shafir, Tal
    [J]. FRONTIERS IN PUBLIC HEALTH, 2016, 4
  • [13] Functional connectivity in the human language system:: a cortico-cortical evoked potential study
    Matsumoto, R
    Nair, DR
    LaPresto, E
    Najm, I
    Bingaman, W
    Shibasaki, H
    Lüders, HO
    [J]. BRAIN, 2004, 127 : 2316 - 2330
  • [14] Functional connectivity in human cortical motor system:: a cortico-cortical evoked potential study
    Matsumoto, Riki
    Nair, Dileep R.
    LaPresto, Eric
    Bingaman, William
    Shibasaki, Hiroshi
    Luders, Hans O.
    [J]. BRAIN, 2007, 130 : 181 - 197
  • [15] Mercier M., 2015, EPILEPSY CURR, V15, P469
  • [16] Comparing connectivity metrics in cortico-cortical evoked potentials using synthetic cortical response patterns
    Prime, David
    Woolfe, Matthew
    Rowlands, David
    O'Keefe, Steven
    Dionisio, Sasha
    [J]. JOURNAL OF NEUROSCIENCE METHODS, 2020, 334
  • [17] ELECTROPHYSIOLOGICAL CONNECTIONS BETWEEN THE HIPPOCAMPUS AND ENTORHINAL CORTEX IN PATIENTS WITH COMPLEX PARTIAL SEIZURES
    RUTECKI, PA
    GROSSMAN, RG
    ARMSTRONG, D
    IRISHLOEWEN, S
    [J]. JOURNAL OF NEUROSURGERY, 1989, 70 (05) : 667 - 675
  • [18] Intraoperative cortico-cortical evoked potentials for the evaluation of language function during brain tumor resection: initial experience with 13 cases
    Saito, Taiichi
    Tamura, Manabu
    Muragaki, Yoshihiro
    Maruyama, Takashi
    Kubota, Yuichi
    Fukuchi, Satoko
    Nitta, Masayuki
    Chernov, Mikhail
    Okamoto, Saori
    Sugiyama, Kazuhiko
    Kurisu, Kaoru
    Sakai, Kuniyoshi L.
    Okada, Yoshikazu
    Iseki, Hiroshi
    [J]. JOURNAL OF NEUROSURGERY, 2014, 121 (04) : 827 - 838
  • [19] Dynamic tractography: Integrating cortico-cortical evoked potentials and diffusion imaging
    Silverstein, Brian H.
    Asano, Eishi
    Sugiura, Ayaka
    Sonoda, Masaki
    Lee, Min-Hee
    Jeong, Jeong-Won
    [J]. NEUROIMAGE, 2020, 215
  • [20] The human connectome: a complex network
    Sporns, Olaf
    [J]. YEAR IN COGNITIVE NEUROSCIENCE, 2011, 1224 : 109 - 125