I.P. Pavlov Journal of Higher Nervous ActivityI.P. Pavlov Journal of Higher Nervous ActivityЖурнал высшей нервной деятельности им. И.П. Павлова0044-46773034-5316The Russian Academy of Sciences65207810.31857/S0044467724040035ФИЗИОЛОГИЯ ВЫСШЕЙ НЕРВНОЙ (КОГНИТИВНОЙ) ДЕЯТЕЛЬНОСТИ ЧЕЛОВЕКАФИЗИОЛОГИЯ ВЫСШЕЙ НЕРВНОЙ (КОГНИТИВНОЙ) ДЕЯТЕЛЬНОСТИ ЧЕЛОВЕКАResearch ArticleOn most informative regions for binary classification of schizophrenia based on resting state fMRI data done by selection of functionally homogeneous regions methodПоиск наиболее информативных регионов для бинарной классификации шизофрении по данным фМРТ состояния покоя на основе метода выделения функционально однородных регионовZhemchuzhnikovA. D.ЖемчужниковА. Д.
Russian Federation
Poyda_AA@nrcki.ruKartashovS. I.КарташовС. И.
Russian Federation
Poyda_AA@nrcki.ruKozlovS. O.КозловС. О.
Russian Federation
Poyda_AA@nrcki.ruOrlovV. A.ОрловВ. А.
Russian Federation
Poyda_AA@nrcki.ruPoydaA. A.ПойдаА. А.
Russian Federation
Poyda_AA@nrcki.ruZakharovaN. V.ЗахароваН. В.
Russian Federation
Poyda_AA@nrcki.ruBravveL. V.БраввеЛ. В.
Russian Federation
Poyda_AA@nrcki.ruMamedovaG. Sh.МамедоваГ. Ш.
Russian Federation
Poyda_AA@nrcki.ruKaydanM. A.КайданМ. А.
Russian Federation
Poyda_AA@nrcki.ruKurchatov InstituteНациональный исследовательский центр «Курчатовский институт»Samara State Medical UniversityСамарский государственный медицинский университетV.M. Bekhterev National Research Medical Center for Psychiatry and NeurologyНациональный медицинский исследовательский центр психиатрии и неврологии им. В.М. БехтереваPsychiatric Hospital No. 1 named after N.A. Alexeev of the Department of Health of MoscowПсихиатрическая клиническая больница № 1 им. Н.А. Алексеева Департамента здравоохранения города Москвы0911202474441242502022025Copyright ©; 2024, Russian Academy of SciencesCopyright ©; 2024, Российская академия наук2024Russian Academy of SciencesРоссийская академия наукhttps://innoscience.ru/0044-4677/article/view/652078

In this work we solve the problem of automatic binary classification of subjects with a diagnosis of schizophrenia and control groups on a data set obtained on a Siemens 3T tomograph. The data set included 36 subjects undergoing treatment at Psychiatric Hospital no. 1 Named after N.A. Alexeev of the Department of Health of Moscow (GBUZ PKB No. 1 DZM) and 36 subjects from the control group. Machine learning methods were used to solve this problem. As a result, an accuracy of 76% was achieved, which corresponds to the results obtained in other scientific studies. The highest accuracy was obtained for the local homogeneity parameter (regional homogeneity – ReHo), already known in the literature. At the same time, the set of features developed by the authors based on the method for identifying functionally homogeneous regions (FHR) gave a classification accuracy of 74%. But at the same time, the set of FHR features provides higher classification accuracy when using a small number of brain regions. For example, already in 8 regions, the FHR set provided an almost maximum classification accuracy of 72.5% (versus 65% for the ReHo set), which suggests that it is the selected 8 regions that give the highest level of separation.

В работе решается задача автоматической бинарной классификации участников эксперимента с диагнозом шизофрения и группы контроля на наборе данных, полученном на томографе Siemens Magnetom Verio 3Тл. Набор включал данные 36 участников эксперимента, проходящих лечение в «ГБУЗ ПКБ №1 ДЗМ», и 36 участников эксперимента из группы контроля. Для решения поставленной задачи были применены методы машинного обучения. В результате была достигнута точность разделения 76%, что соответствует результатам, полученным в других научных исследованиях. Наиболее высокая точность получена для уже известного в литературе параметра локальной однородности (regional homogeneity – ReHo). Разработанный авторами набор признаков на основе метода выделения функционально однородных регионов (Functionally Homogeneous Regions – FHR) обеспечил достижение максимальной точности классификации 74%. Но при этом набор признаков FHR обеспечивает более высокую точность классификации при использовании малого числа регио нов мозга. Так, например, уже на 8 регионах набор FHR обеспечил почти максимальную точность классификации – 72.5% (против 65% у набора ReHo), что позволяет предположить, что именно выделенные 8 регионов дают наиболее высокий уровень разделения.

automatic classification of schizophreniafMRIresting statemachine learning methodsавтоматическая классификация шизофрениифМРТсостояние покояметоды машинного обученияДепартамент Здравоохранения г. МосквыDepartment of Health of Moscow123031600072-3Zakharova N.V., Mamedova G., Bravve L.V., Kaydan A., Kartashov S., Orlov V.A., Ushakov V. Differential diagnosis of delusional symptoms in schizophrenia: Brain tractography data. Cognitive Systems Research. 2023. 77. 217–225. https://doi.org/ 10.1016/j.cogsys.2022.10.008Antonucci L.A., Pergola G., Pigoni A., Dwyer D., Kambeitz-Ilankovic L., Penzel N., Romano R., Gelao B., Torretta S., Rampino A., Trojano M., Caforio G., Falkai P., Blasi G., Koutsouleris N., Bertolino A. A Pattern of Cognitive Deficits Stratified for Genetic and Environmental Risk Reliably Classifies Patients With Schizophrenia From Healthy Control Subjects. Biol. Psychiatry. 2020. 87, 8. 697–707.Arlot S., Celisse A. A survey of cross-validation procedures for model selection. Statistics Surveys, 2010, P: 40-79.Bassett D.S., Sporns O. Network neuroscience. Nature Neuroscience. 2017, 20(3), 353–364.Biswal B., Zerrin Yetkin F., Haughton V.M., Hyde J.S. Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magnetic Resonance in Medicine. 1995. 34 (4): 537–541.Calhoun V.D., Liu J., Adalı T. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. NeuroImage. 2009. 45(1), S163–S172, https://doi.org/10.1016/j.neuroimage.2008.10.057Cao B., Cho R.Y., Chen D., Xiu M., Wang L., Soares J.C., Zhang X.Y. Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity. Mol. Psychiatry. 2020. 25(4): 906–913.Chang Y.-W., Tsai S.J., Wu Y.-F., Yang A.C. Development of an Al-Based Web Diagnostic System for Phenotyping Psychiatric Disorders. Front. Psychiatry. 2020. 11. 542394.Corponi F., Zorkina Y., Stahl D., Murru A., Vieta E., Serretti A., Morozova А., Reznik A., Kostyuk G.P., Chekhonin V. Disfunción de los lóbulos frontales en grupos clínicos de esquizofrenia aguda. Revista de Psiquiatría y Salud Mental, 2021, https://doi.org/ 10.1016/j.rpsm.2021.12.002Cui Y., Li C., Liu B., Sui J., Song M., Chen J., Chen Y., Guo H., Li P., Lu L., Lv L., Ning Y., Wan P., Wang H., Wang H., Wu H., Yan H., Yan J., Yang Y., Zhang H., Zhang D., Jiang T. Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks. Br J Psychiatry. 2022. 221 (6): 732–739. https://doi.org/10.1192/bjp.2022.22Freund Y., Schapire R.E. A Short Introduction to Boosting. 1999.Fusar-Poli P., Radua J., McGuire P., Borgwardt S. Neuroanatomical maps of psychosis onset: voxel-wise meta-analysis of antipsychotic-naive VBM studies. Schizophr Bull. 2012 Nov; 38 (6): 1297–307. Epub 2011 Nov 10. PMID: 22080494; PMCID: PMC3494061. https://doi.org/10.1093/schbul/sbr134Gilbert A.R., Rosenberg D.R., Harenski K., Spencer S., Sweeney J.A., Keshavan M.S. Thalamic volumes in patients with first-episode schizophrenia. Am J Psychiatry. 2001 Apr; 158 (4): 618-24. PMID: 11282698. https://doi.org/10.1176/appi.ajp.158.4.618Gutiérrez-Gómez L., Vohryzek J., Chiêm B., Baumann P.S., Conus P., Cuenod K.D., Hagmann P., Delvenne J.C. Stable biomarker identification for predicting schizophrenia in the human connectome. Neuroimage Clin. 2020. Vol. 27. P. 102316.Hu K., Wang M., Liu Y., Yan H., Song M., Chen J., Chen Y., Wang H., Guo H., Wan P., Lv L., Yang Y., Li P., Lu L., Yan J., Wang H., Zhang H., Zhang D., Wu H., Ning Y., Jiang T., Liu B. Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score. Neuroimage Clin. 2021. 32. 102860.Jo Jun-Mo. Effectiveness of Normalization Pre-Processing of Big Data to the Machine Learning Performance. The Journal of the Korea institute of electronic communication sciences. 2019. 14. 547–552. https://doi.org/ 10.13067/JKIECS.2019.14.3.547Kalmady S.V., Greiner R., Agrawal R., Shivakumar V., Narayanaswamy J.C., Brown M.R.G., Greenshaw A.J., Dursun S.M., Venkatasubramanian G. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. NPJ Schizophr. 2019. 5 (1): 2.Kartashov S., Vartanov A., Захарова Н., Arkhipov A., Strelets V., Maslennikova A., Ushakov V. Tractographic studies in the human brain at the norm and the paranoid schizophrenia. Procedia Computer Science. 2021. 190. 434–438. https://doi.org/ 10.1016/j.procs.2021.06.051Kambeitz-Ilankovic L., Meisenzahl E.M., Cabral C., von Saldern S., Kambeitz J., Falkai P., Möller H.J., Reiser M., Koutsouleris N. Prediction of outcome in the psychosis prodrome using neuroanatomical pattern classification. Schizophr. Res. 2016. 173 (3): 159–165.Kottaram A., Johnston L.A., Tian Y., Ganella E.P., Laskaris L., Cocchi L., McGorry P., Pantelis C., Kotagiri R., Cropley V., Zalesky A. Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors. Hum. Brain Mapp. 2020. 41 (12): 3342–3357.Kozlov S., Poyda A., Orlov V., Malakhov D., Ushakov V., Sharaev M. Selection of functionally homogeneous brain regions based on correlation-clustering analysis. Procedia Comput. Sci. Elsevier BV, 2020. 169. 519–526.Kozlov S., Poyda A., Orlov V., Sharaev M., Ushakov V. Selection of functionally homogeneous human brain regions for functional connectomes building based on fMRI data. Advances in Cognitive Research, Artificial Intelligence and Neuroinformatics. Cham: Springer International Publishing. 2021. 709–719.Larkman D.J., Hajnal J.V., Herlihy A.H., Coutts G.A., Young I.R., Ehnholm G. Use of multicoil arrays for separation of signal from multiple slices simultaneously excited. J. Magn. Reson. Imaging. 2001. 13 (2): 313–317.Lauer M., Senitz D., Beckmann H. Increased volume of the nucleus accumbens in schizophrenia. J Neural Transm (Vienna). 2001;108 (6):645–60. PMID: 11478417. https://doi.org/10.1007/s007020170042Luo X., Mao Q., Shi J., Wang X., Li C.R. Putamen Gray Matter Volumes in Neuropsychiatric and Neurodegenerative Disorders. World J Psychiatry Ment Health Res. 2019. Vol. 3(1): 1020. Messaritaki E., Foley S., Barawi K., Ettinger U., Jones D. Increased sensorimotor structural connectivity in high schizotypy. bioRxiv, 2022. https://doi.org/10.1101/2022.05.12.491533Morozova A., Zorkina Y., Abramova O., Pavlova O., Pavlov K., Soloveva K., Volkova M., Alekseeva P., Andryshchenko A., Kostyuk G., Gurina O., Chekhonin V. Neurobiological Highlights of Cognitive Impairment in Psychiatric Disorders. Int J Mol Sci. 2022 Jan 22;23(3):1217. PMID: 35163141; PMCID: PMC8835608. https://doi.org/10.3390/ijms23031217.Nieto-Castanon A., Whitfield-Gabrieli S. CONN functional connectivity toolbox: RRID SCR_009550, release 22. Hilbert Press. 2022. https://doi.org/10.56441/hilbertpress.2246.5840Pedregosa F., Varoquaux Gael, Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D, Brucher M., Perrot M. Duchesnay E., Louppe G. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 2011. 12(Oct), 2825–2830.Pudjihartono N., Fadason T., Kempa-Liehr A.W., O’Sullivan J.M. A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction. Front. Bioinform. 2022.2:927312. https://doi.org/10.3389/fbinf.2022.927312Rosazza C., Minati L. Resting-state brain networks: Literature review and clinical applications. Neurological Sciences. 2011; 32(5),773–785.Sharaff A., Gupta H. Extra-Tree Classifier with Metaheuristics Approach for Email Classification. 2019. https://doi.org/ 10.1007/978-981-13-6861-5_17.Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2001. 546.Todd N., Josephs O., Zeidman P., Flandin G., Moeller S. Weiskopf N. Functional Sensitivity of 2D Simultaneous Multi-Slice Echo-Planar Imaging: Effects of Acceleration on g-factor and Physiological Noise. Front. Neurosci. 2017. 11: 158. https://doi.org/10.3389/fnins.2017.00158Tohid H., Faizan M., Faizan U. Alterations of the occipital lobe in schizophrenia. Neurosciences (Riyadh). 2015 Jul; 20 (3): 213–24. PMID: 26166588; PMCID: PMC4710336. https://doi.org/10.17712/nsj.2015.3.20140757Wolf A. The Machine Learning Simplified: A Gentle Introduction to Supervised Learning. Leanpub, 2022.Xu K., Liu Y., Zhan Y., Ren J., Jiang T. BRANT: A Versatile and Extendable Resting-State fMRI Toolkit. Front Neuroinform. 2018. 12: 52. https://doi.org/10.3389/fninf.2018.00052Zang Y., Jiang T., Lu Y., He Y., Tian L. Regional homogeneity approach to fMRI data analysis. Neuroimage. 2004. 22 (1): 394–400. PMID: 15110032Zou Q.H., Zhu C.Z., Yang Y., Zuo X.N., Long X.Y., Cao Q.J., Wang Y.F., Zang Y.F. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Me thods. 2008 Jul 15; 172 (1): 137–41. Epub 2008 Apr 22. PMID: 18501969; PMCID: PMC3902859. https://doi.org/10.1016/j.jneumeth.2008.04.012