Abstract
The clinical therapy of schizophrenia (SCZ) replies on the corresponding accurate and reliable recognition. Although efforts have been paid, the diagnosis of SCZ is still roughly subjective, it is thus urgent to search for related objective physiological parameters. Motivated by the great potential of resting-state networks in underling the brain deficits among different SCZ groups, in this study, we then developed a multi-class feature extraction approach that could effectively extract the spatial network topology and facilitate the recognition of the SCZ, by combining a network structure based supervised learning with an ensemble co-decision strategy. The results demonstrated that the multi-class spatial pattern of the network (MSPN) features outperformed the other conventional electrophysiological features, such as relative power spectrums and network properties, and achieved the highest classification accuracy of 71.58% in the alpha band. These findings did validate that the resting-state MSPN is a promising tool for the clinical assessment of the SCZ.
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References
Alonso-Solis A et al (2015) Resting-state functional connectivity alterations in the default network of schizophrenia patients with persistent auditory verbal hallucinations. Schizophr Res 161:261–268. https://doi.org/10.1016/j.schres.2014.10.047
Bassett DS, Sporns O (2017) Network neuroscience. Nat Neurosci 20:353–364. https://doi.org/10.1038/nn.4502
Blankertz B, Dornhege G, Krauledat M, Muller KR, Curio G (2007) The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects. Neuroimage 37:539–550. https://doi.org/10.1016/j.neuroimage.2007.01.051
Blankertz B, Tomioka R, Lemm S, Kawanabe M, Muller K-R (2008) Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Proc Mag 25:41–56. https://doi.org/10.1109/Msp.2008.4408441
Bose T, Sivakumar SD, Kesavamurthy B (2016) Identification of schizophrenia using EEG alpha band power during hyperventilation and post-hyperventilation. J Med Biol Eng 36:901–911. https://doi.org/10.1007/s40846-016-0192-2
Brazo P et al (2002) Cognitive patterns in subtypes of schizophrenia. Eur Psychiatry 17:155–162. https://doi.org/10.1016/s0924-9338(02)00648-x
Chun J et al (2013) Can P300 distinguish among schizophrenia, schizoaffective and bipolar I disorders? An ERP study of response inhibition. Schizophr Res 151:175–184. https://doi.org/10.1016/j.schres.2013.10.020
da Cruz JR et al (2020) EEG microstates are a candidate endophenotype for schizophrenia. Nat Commun. https://doi.org/10.1038/s41467-020-16914-1
de Bock R, Mackintosh AJ, Maier F, Borgwardt S, Riecher-Rossler A, Andreou C (2020) EEG microstates as biomarker for psychosis in ultra-high-risk patients. Transl Psychiatry. https://doi.org/10.1038/s41398-020-00963-7
Dosenbach NUF et al (2010) Prediction of individual brain maturity using fMRI. Science 329:1358–1361. https://doi.org/10.1126/science.1194144
Dubey R, Zhou J, Wang Y, Thompson PM, Ye J (2014) Analysis of sampling techniques for imbalanced data: an n = 648 ADNI study. Neuroimage 87:220–241. https://doi.org/10.1016/j.neuroimage.2013.10.005
Ehrlich S et al (2014) Associations of white matter integrity and cortical thickness in patients with schizophrenia and healthy controls. Schizophr Bull 40:665–674. https://doi.org/10.1093/schbul/sbt056
Friston KJ, Frith CD (1995) Schizophrenia: a disconnection syndrome? Clin Neurosci 3:89–97
Goldstein G, Shemansky WJ, Allen DN (2005) Cognitive function in schizoaffective disorder and clinical subtypes of schizophrenia. Arch Clin Neuropsychol 20:153–159. https://doi.org/10.1016/j.acn.2004.03.008
Gomez-Pilar J et al. (2016) Novel measure of the weigh distribution balance on the brain network: graph complexity applied to schizophrenia. Conf Proc IEEE Eng Med Biol Soc 700–703
Harris A, Melkonian D, Williams L, Gordon E (2006) Dynamic spectral analysis findings in first episode and chronic schizophrenia. Int J Neurosci 116:223–246. https://doi.org/10.1080/00207450500402977
Hill SK, Ragland JD, Gur RC, Gur RE (2001) Neuropsychological differences among empirically derived clinical subtypes of schizophrenia. Neuropsychology 15:492–501. https://doi.org/10.1037/0894-4105.15.4.492
Houmani N, Vialatte F, Gallego-Jutgla E, Dreyfus G, Nguyen-Michel VH, Mariani J, Kinugawa K (2018) Diagnosis of Alzheimer’s disease with electroencephalography in a differential framework. PLoS ONE 13:e0193607. https://doi.org/10.1371/journal.pone.0193607
John JP, Rangaswamy M, Thennarasu K, Khanna S, Nagaraj RB, Mukundan CR, Pradhan N (2009) EEG power spectra differentiate positive and negative subgroups in neuroleptic-naive schizophrenia patients. J Neuropsychiatr Clin Neurosci 21:160–172. https://doi.org/10.1176/appi.neuropsych.21.2.160
Kam JW, Bolbecker AR, O’Donnell BF, Hetrick WP, Brenner CA (2013) Resting state EEG power and coherence abnormalities in bipolar disorder and schizophrenia. J Psychiatr Res 47:1893–1901. https://doi.org/10.1016/j.jpsychires.2013.09.009
Lehmann D, Ozaki H, Pal I (1987) EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalogr Clin Neurophysiol 67:271–288. https://doi.org/10.1016/0013-4694(87)90025-3
Li F et al (2015) Relationships between the resting-state network and the P3: evidence from a scalp EEG study. Sci Rep 5:15129. https://doi.org/10.1038/Srep15129
Li F et al (2016) The time-varying networks in P300: a task-evoked EEG study. IEEE Trans Neural Syst Rehab Eng 24:725–733. https://doi.org/10.1109/Tnsre.2016.2523678
Li F et al (2019a) The dynamic brain networks of motor imagery: time-varying causality analysis of scalp EEG. Int J Neural Syst 29:1850016. https://doi.org/10.1142/s0129065718500168
Li F et al (2019b) Differentiation of Schizophrenia by combining the spatial EEG brain network patterns of rest and task P300. IEEE Trans Neural Syst Rehabil Eng 27:594–602. https://doi.org/10.1109/tnsre.2019.2900725
Li C, et al (2020) Hierarchical emotion-recognition framework based on discriminative brain neural network topology and ensemble co-decision strategy. arXiv:2002.10804
Lin WC, Tsai CF, Hu YH, Jhang JS (2017) Clustering-based undersampling in class-imbalanced data information. Sciences 409:17–26. https://doi.org/10.1016/j.ins.2017.05.008
Michel CM, Koenig T (2018) EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review. Neuroimage 180:577–593. https://doi.org/10.1016/j.neuroimage.2017.11.062
Mouchlianitis E et al (2015) Treatment-resistant schizophrenia patients show elevated anterior cingulate cortex glutamate compared to treatment-responsive. Schizophrenia Bull. https://doi.org/10.1093/schbul/sbv151
Nunez P, Poza J, Bachiller A, Gomez-Pilar J, Lubeiro A, Molina V, Hornero R (2017) Exploring non-stationarity patterns in schizophrenia: neural reorganization abnormalities in the alpha band. J Neural Eng 14:046001. https://doi.org/10.1088/1741-2552/aa6e05
O’Leary DS, Flaum M, Kesler ML, Flashman LA, Arndt S, Andreasen NC (2000) Cognitive correlates of the negative, disorganized, and psychotic symptom dimensions of schizophrenia. J Neuropsychiatr Clin Neurosci 12:4–15. https://doi.org/10.1176/jnp.12.1.4
Ohtani T et al (2014) Abnormalities in white matter connections between orbitofrontal cortex and anterior cingulate cortex and their associations with negative symptoms in schizophrenia: a DTI study. Schizophr Res 157:190–197. https://doi.org/10.1016/j.schres.2014.05.016
Portnova GV, Girzhova IN, Martynova OV (2021) Residual and compensatory changes of resting-state EEG in successful recovery after moderate TBI. Brain Sci Adv 6:364–378. https://doi.org/10.26599/bsa.2020.9050025
K Rieger L Diaz Hernandez A Baenninger T Koenig 2016 15 years of Microstate research in schizophrenia—where are we? A meta-analysis Front Psychiatry https://doi.org/10.3389/fpsyt.2016.00022
Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
Sakkalis V (2011) Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comput Biol Med 41:1110–1117. https://doi.org/10.1016/j.compbiomed.2011.06.020
Si Y et al (2020) Predicting individual decision-making responses based on single-trial EEG. Neuroimage 206:116333. https://doi.org/10.1016/j.neuroimage.2019.116333
Stam CJ, Montez T, Jones BF, Rombouts SARB, van der Made Y, Pijnenburg YAL, Scheltens P (2005) Disturbed fluctuations of resting state EEG synchronization in Alzheimer’s disease. Clin Neurophysiol 116:708–715. https://doi.org/10.1016/j.clinph.2004.09.022
Stephan KE, Friston KJ, Frith CD (2009) Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring. Schizophrenia Bull. https://doi.org/10.1093/schbul/sbn176
Walther S, Horn H, Razavi N, Koschorke P, Muller TJ, Strik W (2009) Quantitative motor activity differentiates schizophrenia subtypes. Neuropsychobiology 60:80–86. https://doi.org/10.1159/000236448
Xie J et al (2021) Rehabilitation of motor function in children with cerebral palsy based on motor imagery. Cogn Neurodyn. https://doi.org/10.1007/s11571-021-09672-3
Xu P et al (2014a) Differentiating between psychogenic nonepileptic seizures and epilepsy based on common spatial pattern of weighted EEG resting networks. IEEE Trans Biomed Eng 61:1747–1755. https://doi.org/10.1109/TBME.2014.2305159
Xu P et al (2014b) Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference. Physiol Meas 35:1279–1298. https://doi.org/10.1088/0967-3334/35/7/1279
Yi C et al (2021) A novel method for constructing EEG large-scale cortical dynamical functional network connectivity (dFNC): WTCS. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2021.3090770
Yin Z, Li J, Zhang Y, Ren A, Von Meneen KM, Huang L (2017) Functional brain network analysis of schizophrenic patients with positive and negative syndrome based on mutual information of EEG time series. Biomed Signal Proces 31:331–338. https://doi.org/10.1016/j.bspc.2016.08.013
Zhang S, Sun J, Gao X (2020) The effect of fatigue on brain connectivity networks. Brain Sci Adv 6:120–131. https://doi.org/10.26599/bsa.2020.9050008
Acknowledgements
This work was supported by the National Natural Science Foundation of China (#61961160705, #U19A2082, #62103085, #62006197), the Science and Technology Development Fund, Macau SAR (File No. 0045/2019/AFJ), and the Sichuan Science and Technology Program (#2021YFSY0040, #2020ZYD013).
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Li, F., Jiang, L., Liao, Y. et al. Recognition of the Multi-class Schizophrenia Based on the Resting-State EEG Network Topology. Brain Topogr 35, 495–506 (2022). https://doi.org/10.1007/s10548-022-00907-y
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DOI: https://doi.org/10.1007/s10548-022-00907-y