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Analyzing global features of magnetic resonance images in widespread neurodegenerative diseases: new hope to understand brain mechanism and robust neurodegenerative disease diagnosis

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Abstract

Neurodegenerative diseases are caused by progressive degeneration of the central nervous system (CNS)’s neuronal structure. Well-known diseases in this category include Alzheimer’s disease (AD), Parkinson’s disease (PD), and multiple sclerosis (MS), which are also addressed in this study. The CNS appears to be a complex dynamic system, whose parameters change during the disease due to neuronal damage, resulting in various symptoms. Since the change in dynamic behavior is due to the neurons’ death and change in neurons’ connectivity, brain images of the affected areas appear to provide a good understanding of this change. This work attempts to focus on brain magnetic resonance images (MRI) and examine the effect of neuronal loss on the images. To this end, the complex features of these images, including 2D and Higuchi’s fractal dimensions (HFD), correlation dimension (CD), largest Lyapunov exponent (LLE), and approximate entropy (ApEn) were calculated. Despite small differences in numerical values (0.01–0.35), these values differ significantly. This shows that the brain dynamic system behaves and functions differently in the disease state, which is clear in the behavior of global features. These three diseases have the same functional pattern, and this study seems to have captured the roots of these seemingly variant diseases.

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References

  1. Brown RC, Lockwood AH, Sonawane BR (2005) Neurodegenerative diseases: an overview of environmental risk factors. Environ Health Perspect 113(9):1250–1256. https://doi.org/10.1289/ehp.7567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Alves G et al (2005) Progression of motor impairment and disability in Parkinson disease: a population-based study. Neurology 65(9):1436–1441. https://doi.org/10.1212/01.wnl.0000183359.50822.f2

    Article  PubMed  Google Scholar 

  3. Farlow MR, Miller ML, Pejovic V (2008) Treatment options in Alzheimer’s disease: maximizing benefit, managing expectations. Dement Geriatr Cogn Disord 25(5):408–422. https://doi.org/10.1159/000122962

    Article  CAS  PubMed  Google Scholar 

  4. Armstrong MJ, Okun MS (2020) Diagnosis and treatment of Parkinson disease: a review. JAMA 323(6):548–560. https://doi.org/10.1001/jama.2019.22360

    Article  PubMed  Google Scholar 

  5. Savitt JM, Dawson VL, Dawson TM (2006) Diagnosis and treatment of Parkinson disease: molecules to medicine. J Clin Investig 116(7):1744–1754. https://doi.org/10.1172/JCI29178

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Stangel M, Hartung H-P (2002) Remyelinating strategies for the treatment of multiple sclerosis. Prog Neurobiol 68(5):361–376. https://doi.org/10.1016/S0301-0082(02)00105-3

    Article  CAS  PubMed  Google Scholar 

  7. Jenner P, Olanow CW (1998) Understanding cell death in Parkinsons disease. Ann Neurol 44(S11):S72–S84. https://doi.org/10.1002/ana.410440712

    Article  CAS  PubMed  Google Scholar 

  8. Eckert GP et al (2012) Mitochondrial dysfunction—a pharmacological target in Alzheimers disease. Mol Neurobiol 46(1):136–150. https://doi.org/10.1007/s12035-012-8271-z

    Article  CAS  PubMed  Google Scholar 

  9. Dachraoui C et al. (2020) Predictive Approach of multiple sclerosis MR-Lesions evolution based on chaotic attributes. in 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE. https://doi.org/10.1109/CoDIT49905.2020.9263901

  10. Beuter A, Vasilakos K (1995) Tremor: Is Parkinson’s disease a dynamical disease? Chaos 5(1):35–42. https://doi.org/10.1063/1.166082

    Article  PubMed  Google Scholar 

  11. Titcombe MS et al (2001) Dynamics of Parkinsonian tremor during deep brain stimulation. Chaos 11(4):766–773. https://doi.org/10.1063/1.1408257

    Article  PubMed  Google Scholar 

  12. Shabestari PS et al (2019) Modeling of neurodegenerative diseases using discrete chaotic systems. Commun Theor Phys 71(10):1241. https://doi.org/10.1088/0253-6102/71/10/1241

    Article  Google Scholar 

  13. Zhang L et al (2007) Quantifying degeneration of white matter in normal aging using fractal dimension. Neurobiol Aging 28(10):1543–1555. https://doi.org/10.1016/j.neurobiolaging.2006.06.020

    Article  CAS  PubMed  Google Scholar 

  14. Esteban FJ et al (2007) Fractal dimension and white matter changes in multiple sclerosis. Neuroimage 36(3):543–549. https://doi.org/10.1016/j.neuroimage.2007.03.057

    Article  PubMed  Google Scholar 

  15. Esteban FJ et al (2009) Fractal dimension analysis of grey matter in multiple sclerosis. J Neurol Sci 282(1–2):67–71. https://doi.org/10.1016/j.jns.2008.12.023

    Article  PubMed  Google Scholar 

  16. Abe T, Chen Y, and Pham TD (2013) Chaos analysis of brain MRI for studying mental disorders. in International Conference on Biomedical Informatics and Technology. Springer. https://doi.org/10.1007/978-3-642-54121-6_22

  17. Roura E et al. (2021) Cortical fractal dimension predicts disability worsening in Multiple Sclerosis patients. 30: 102653. https://doi.org/10.1016/j.nicl.2021.102653

  18. Acharya UR et al (2012) Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control 7(4):401–408. https://doi.org/10.1016/j.bspc.2011.07.007

    Article  Google Scholar 

  19. Dlask M and Kukal J (2017) Correlation dimension estimation from eeg time series for alzheimer disease diagnostics. in Proceedings of the International Conference on Bioinformatics Research and Applications 2017. https://doi.org/10.1145/3175587.3175591

  20. Saikia A et al. (2019) Significance of Lyapunov exponent in Parkinsons disease using electroencephalography. in 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE. https://doi.org/10.1109/SPIN.2019.8711669

  21. Kutepov IE et al (2020) EEG analysis in patients with schizophrenia based on Lyapunov exponents. Informa Med Unlocked 18:100289. https://doi.org/10.1016/j.imu.2020.100289

    Article  Google Scholar 

  22. Jack CR Jr et al (2008) The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging 27(4):685–691. https://doi.org/10.1002/jmri.21049

    Article  PubMed  PubMed Central  Google Scholar 

  23. Tomassini S et al. (2022) Brain-on-Cloud for automatic diagnosis of Alzheimer’s disease from 3D structural magnetic resonance whole-brain scans. 227: 107191. https://doi.org/10.1016/j.cmpb.2022.107191

  24. Marek K et al (2011) The parkinson progression marker initiative (PPMI). Prog Neurobiol 95(4):629–635. https://doi.org/10.1016/j.pneurobio.2011.09.005

    Article  PubMed Central  Google Scholar 

  25. Wang J-J et al. (2011) Parkinson disease: diagnostic utility of diffusion kurtosis imaging. 261(1): 210-217

  26. NeuroImaging Tools and Resources Collaboratory (NITRC) (RRID:SCR_003430). https://www.nitrc.org/

  27. Galler S et al. (2016) Improved lesion detection by using axial T2-weighted MRI with full spinal cord coverage in multiple sclerosis. 37(5): 963–969. https://doi.org/10.3174/ajnr.A4638

  28. Marr D and Hildreth E (1980) Theory of edge detection. 207(1167): 187–217. https://doi.org/10.1098/rspb.1980.0020

  29. Soille P (2004) Erosion and dilation, in Morphological Image Analysis. Springer. p. 63–103. ISBN: 978–3–662–05088–0

  30. Shattuck DW, Leahy RM (2002) BrainSuite: an automated cortical surface identification tool. 6(2):129–142. https://doi.org/10.1016/S1361-8415(02)00054-3

  31. Mandelbrot BB and Mandelbrot BB (1982) The fractal geometry of nature. 1: WH freeman New York. ISBN: 978–1648370403

  32. Blanton RE et al (2001) Mapping cortical asymmetry and complexity patterns in normal children. Psychiatr Res: Neuroimaging 107(1):29–43. https://doi.org/10.1016/S0925-4927(01)00091-9

    Article  CAS  Google Scholar 

  33. Kesić S, Spasić SZ (2016) Application of Higuchi’s fractal dimension from basic to clinical neurophysiology: a review. Comput Methods Programs Biomed 133:55–70. https://doi.org/10.1016/j.cmpb.2016.05.014

    Article  PubMed  Google Scholar 

  34. Theiler J (1987) Efficient algorithm for estimating the correlation dimension from a set of discrete points. Phys Rev A 36(9):4456. https://doi.org/10.1103/PhysRevA.36.4456

    Article  CAS  Google Scholar 

  35. Rhodes C, Morari M (1997) False-nearest-neighbors algorithm and noise-corrupted time series. Phys Rev E 55(5):6162. https://doi.org/10.1103/PhysRevE.55.6162

    Article  CAS  Google Scholar 

  36. Fraser AM, Swinney HL (1986) Independent coordinates for strange attractors from mutual information. Phys Rev A 33(2):1134. https://doi.org/10.1103/PhysRevA.33.1134

    Article  CAS  Google Scholar 

  37. Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci 88(6):2297–2301. https://doi.org/10.1073/pnas.88.6.2297

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Rosenstein MT, Collins JJ, De Luca CJ (1993) A practical method for calculating largest Lyapunov exponents from small data sets. Physica D 65(1–2):117–134. https://doi.org/10.1016/0167-2789(93)90009-P

    Article  Google Scholar 

  39. Massey FJ Jr (1951) The Kolmogorov-Smirnov test for goodness of fit. J Am Stat Assoc 46(253):68–78. https://doi.org/10.1080/01621459.1951.10500769

    Article  Google Scholar 

  40. McKight PE and Najab J (2010) Kruskal‐wallis test. The corsini encyclopedia of psychology, 1–1. https://doi.org/10.1002/9780470479216.corpsy0491

  41. Hilborn RC (2000) Chaos and nonlinear dynamics: an introduction for scientists and engineers: Oxford University Press on Demand. https://doi.org/10.1093/acprof:oso/9780198507239.001.0001

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Correspondence to Yashar Sarbaz.

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This is an observational study.

The data was obtained from publically available datasets. The data that support the findings of this study are available from [22, 24, 26].

As per ADNI, PPMI, and NITRC protocols, all procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. More details can be found at adni.loni.usc.edu, https://www.ppmi-info.org, https://www.nitrc.org. (This article does not contain any studies with human participants performed by any of the authors).

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Garehdaghi, F., Sarbaz, Y. Analyzing global features of magnetic resonance images in widespread neurodegenerative diseases: new hope to understand brain mechanism and robust neurodegenerative disease diagnosis. Med Biol Eng Comput 61, 773–784 (2023). https://doi.org/10.1007/s11517-022-02748-0

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