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Investigation of the effect of “Nicotiana rustica/Maraş Otu” use on gray matter using image processing techniques from brain MRI images

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Abstract

In this study, it was investigated on the brain gray matter whether the effect of Nicotiana rustica, which is widely used in Kahramanmaraş and its environs and user age can be lower than secondary school, that harms are not clearly revealed, which can be bought quite cheaply and contains more nicotine than cigarettes. Segmentation of parts of the brain such as gray matter, white matter, and cerebrospinal fluid (CSF) with SPM and calculation of their volume were performed with the Voxel-Based Morphometry (VBM) interface. Segmentation of images in the study was carried out using the Gaussian Mixture Model. With these processes, gray matter volumes of users and non-users of Nicotiana rustica were calculated and classified using classical machine learning and deep learning techniques. A data set was created for those user of Nicotiana rustica and non users, with the volumes of gray and white matter, CSF volume and their ratios to each other obtained with VBM. This data set has been classified using classical machine learning methods (Naive Bayes, J48, kNN, Support Vector Machines, Random Forest), as well as deep learning methods (1D-CNN, CNN + LSTM). Methods classification success for classical methods, respectively: 70%, 85%, 90%, 75%, 95%; for deep learning methods: 85% and 90%, respectively. According to our knowledge, there is no study in the literature that makes classification using various information of people who use Nicotiana rustica. This is the original aspect of the study and contributes to the literature.

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References

  1. Bach, P., Koopmann, A., Bumb, J.M., Vollstädt-Klein, S., Reinhard, I., Rietschel, M., Witt, S.H., Wiedemann, K., Kiefer, F.: Leptin predicts cortical and subcortical gray matter volume recovery in alcohol dependent patients: a longitudinal structural magnetic resonance imaging study. Horm. Behav. 124, 104749 (2020). https://doi.org/10.1016/j.yhbeh.2020.104749

    Article  Google Scholar 

  2. Casanova, R., Srikanth, R., Baer, A., Laurienti, P.J., Burdette, J.H., Hayasaka, S., Flowers, L., Wood, F., Maldjian, J.A.: Biological parametric mapping: a statistical toolbox for multimodality brain image analysis. Neuroimage 34(1), 137–143 (2007). https://doi.org/10.1016/j.neuroimage.2006.09.011

    Article  Google Scholar 

  3. Chen, Y., Chaudhary, S., Wang, W., Li, C.S.R.: Gray matter volumes of the insula and anterior cingulate cortex and their dysfunctional roles in cigarette smoking. Addict. Neurosci. 1, 100003 (2022). https://doi.org/10.1016/j.addicn.2021.100003

    Article  Google Scholar 

  4. Chiao, C.C., Lin, C.I., Lee, M.J.: Multiple approaches for enhancing neural activity to promote neurite outgrowth of retinal explants. Methods Mol. Biol. 2092, 65–75 (2020)

    Article  Google Scholar 

  5. Chou, M.C., Li, J.Y., Lai, P.H.: Longitudinal gray matter changes of the pain matrix in patients with carbon monoxide intoxication: a voxel-based morphometry study. Eur. J. Radiol. 126, 108968 (2020). https://doi.org/10.1016/j.ejrad.2020.108968

    Article  Google Scholar 

  6. Choubey, R.N., Amar, L., Khare, S.: Internet traffic classifier using artificial neural network and 1D-CNN. In: International Conference on Information Technology (ICIT), 2019, Bhubaneswar, India, pp. 291–296.

  7. Cuadra, M.B., Cammoun, L., Butz, T., Cuisenaire, O., Thiran, J.P.: Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images. IEEE Trans. Med. Imaging 24(12), 1548–1565 (2005). https://doi.org/10.1109/TMI.2005.857652

    Article  Google Scholar 

  8. Daş, B., Türkoğlu, İ.: DNA Dizilimlerinin Sınıflandırılmasında Karar Ağacı Algoritmalarının Karşılaştırılması. Eleco 2014 Elektrik – Elektronik – Bilgisayar ve Biyomedikal Mühendisliği Sempozyumu. https://www.emo.org.tr/ekler/91ac323bf2a22ad_ek.pdf.(2014).

  9. Doğan, A., Bayar Muluk, N., İnanç, Y.: Peripheral and central smell regions in migraine patients using maraş powder smokeless tobacco a magnetic resonance imaging evaluation. J. Neurol. Surg. B Skull Base 83, 461–469 (2021)

    Google Scholar 

  10. Dölek, İ.: LSTM. Deep Learning Turkey, https://medium.com/@ishakdolek/lstm-d2c281b92aac (2018). Accessed 10 June 2018.

  11. Franklin, T.R., Wetherill, R.R., Jagannathan, K., Johnson, B., Mumma, J., Hager, N., et al.: The effects of chronic cigarette smoking on gray matter volume: influence of sex. PLoS ONE 9(8), e104102 (2014). https://doi.org/10.1371/journal.pone.0104102

    Article  Google Scholar 

  12. Hanlon, C.A., Owens, M.M., Joseph, J.E., Zhu, X., George, M.S., Brady, K.T., Hartwell, K.J.: Lower subcortical gray matter volume in both younger smokers and established smokers relative to non-smokers. Addict. Biol. 21, 185–195 (2016). https://doi.org/10.1111/adb.12171

    Article  Google Scholar 

  13. Gülcan, O.: Doğruluk (Accuracy) Kesinlik (Precision) Duyarlılık (Recall) F1 Score, https://medium.com/@gulcanogundur/do%C4%9Fruluk-accuracy-kesinlik-precisionduyarl% C4%B1l%C4%B1k-recall-ya-da-f1-score-300c925feb38, erişim tarihi: 24 Haziran 2020.

  14. Güner, Z. B.: Veri Madenciliğinde Cart ve Lojistik Regresyon Analizinin Yeri: İlaç Provizyon Sistemi Verileri Üzerinde Örnek Bir Uygulama. Sosyal Güvence(6), 53–99. http://dergipark.gov.tr/sosyalguvence/issue/16499/172290. (2015).

  15. Hochreiter, S., Schmidhuber, J.: Long-short term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  16. Ide, J.S., Zhang, S., Hu, S., Sinha, R., Mazure, C.M., Li, C.R.: Cerebral gray matter volumes and low-frequency fluctuation of BOLD signals in cocaine dependence: duration of use and gender difference. Drug Alcohol Depend. 134, 51–62 (2014)

    Article  Google Scholar 

  17. Kaag, A.M., Schulte, M.H.J., Jansen, J.M., Wingen, G., Van, H.J., van den Brink, W., Wiers, R.W., Schmaalh, L., Goudriaan, A.E., Reneman, L.: The relation between gray matter volume and the use of alcohol, tobacco, cocaine and cannabis in male polysubstance users. Drug Alcohol Depend. 187, 186–194 (2018). https://doi.org/10.1016/j.drugalcdep.2018.03.010

    Article  Google Scholar 

  18. Kurth, F., Zilles, K., Fox, P.T., et al.: A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis. Brain Struct. Funct. 214, 519–534 (2010). https://doi.org/10.1007/s00429-010-0255-z

    Article  Google Scholar 

  19. Maldjian, J.A., Laurienti, P.J., Kraft, R.A., Burdette, J.H.: An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 19(3), 1233–1239 (2003). https://doi.org/10.1016/S1053-8119(03)00169-1

    Article  Google Scholar 

  20. Powers, W., Ailab, A.: Evaluation: from precision, recall and F-measure to ROC informedness, markedness and correlation. J. Mach. Learn. Technol. 2, 2229–3981 (2008)

    Google Scholar 

  21. Saitoğlu, Y. S.: Sınıflama ve Regresyon Ağaçları. Yayınlanmamış Doktora Tezi. İstanbul: Marmara Üniversitesi, SBE (2015)

  22. Vapnik, V.: The nature of statistical learning theory, p. 187. SpringerVerlag, NewYork (1995)

    Book  MATH  Google Scholar 

  23. Yarğı, V., Postalcıoğlu, S.: EEG İşareti Kullanılarak Bağımlılığa Yatkınlığın Makine Öğrenmesi Teknikleri ile Analizi. El-Cezerî J. Sci. Eng. 8(1), 142–154 (2021). https://doi.org/10.31202/ecjse.787726

    Article  Google Scholar 

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Funding

This study was supported by TÜBİTAK with the project number 121E053, entitled "Examination of the Effect of Maras Otu (Nicotiana rustica) Use on Gray Ore Using Image Processing Techniques from Brain MRI Images." We thank TÜBİTAK for its support.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Kahramanmaras Sutcu Imam University, 46050, Kahramanmaraş, Turkey

    Ahmet Alkan, Sinan Altun & İbrahim Efeoğlu

  2. Department of Radiology, Kahramanmaras Sutcu Imam University, 46050, Kahramanmaraş, Turkey

    Adil Doğan

  3. Department of Neurosurgery, Kahramanmaras Sutcu Imam University, 46050, Kahramanmaraş, Turkey

    İdiris Altun

Authors
  1. Ahmet Alkan
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  2. Sinan Altun
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  3. Adil Doğan
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  4. İbrahim Efeoğlu
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  5. İdiris Altun
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Contributions

All authors write and review paper. İdiris Altun and Adil Doğan collected data.

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Correspondence to Sinan Altun.

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Conflict of interest

The authors declare no conflict of interests.

Ethical approval

Ethics Committee Permission for the used data set in the study was obtained from Kahramanmaras Sutcu Imam University Faculty of Medicine (decision no. 7 dated 15.06.2021).

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Alkan, A., Altun, S., Doğan, A. et al. Investigation of the effect of “Nicotiana rustica/Maraş Otu” use on gray matter using image processing techniques from brain MRI images. SIViP 17, 3485–3493 (2023). https://doi.org/10.1007/s11760-023-02572-5

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  • DOI: https://doi.org/10.1007/s11760-023-02572-5

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