مشارکت سه بافت مغزی در تشخیص بیماری آلزایمر از MRI ساختاری
محورهای موضوعی : عمومى
شیما تاج الدینی
1
(Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran)
حبیب اله دانیالی
2
(Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran)
محمدصادق هل فروش
3
(Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran)
یعقوب فاطمی
4
(Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran)
کلید واژه: بیماری آلزایمر, نشانگر زیستی, طبقه بندی, استخراج ویژگی, تصویربرداری رزونانس مغناطیسی,
چکیده مقاله :
بیماری آلزایمر (AD) یک بیماری پیشرونده و برگشت ناپذیر است که به تدریج باعث می شود بیماران نتوانند کارهای روزمره خود را انجام دهند. اگرچه روش های درمانی فعلی نمی توانند بیماری را به طور کامل درمان کنند ، اما تشخیص به موقع آن می تواند علائم را کاهش داده و کیفیت زندگی بیماران را افزایش دهد. در ادبیات فعلی ، استفاده از بافت ماده خاکستری (GM) که به عنوان نشانگر زیستی مناسب شناخته می شود ، در تشخیص AD بسیار رایج است. با این حال ، به نظر می رسد دو بافت مغز دیگر معروف به مایع مغزی نخاعی (CSF) و ماده سفید (WM) اطلاعات مفیدی را درباره تغییرات مغزی بیماران نشان می دهند. هدف از مطالعه حاضر ایجاد یک سیستم اتوماتیک برای تشخیص زود هنگام بیماری آلزایمر از MRI ساختاری با در نظر گرفتن همزمان ویژگی های مناسب از تمام بافت های GM ، CSF و WM است. یک طبقه بندی SVM-RBF بر روی پایگاه داده OASIS آموزش داده شده و مورد ارزیابی قرار می گیرد تا AD از افراد سالم کنترل شود. نتایج به دست آمده نشان دهنده دقت و حساسیت بالاتر الگوریتم پیشنهادی در مقایسه با روش مشابه است
Alzheimer’s disease (AD) is a progressive and irreversible disease which gradually makes patients unable to do their daily routines. Although the present treatments can not cure the disease completely, its early detection can reduce symptoms and enhance the patients’ life quality. In the current literature, using the grey matter (GM) tissue which is known as an appropriate biomarker is highly common in AD diagnosis. However, two other brain tissues known as cerebrospinal fluid (CSF) and white matter (WM) seem to reveal beneficial information about the patients’ brain changes. The aim of the present study is to develop an automatic system for the early diagnosis of Alzheimer’s disease from structural MRI by simultaneously considering suitable features of all GM, CSF and WM tissues. A SVM-RBF classifier is trained and evaluated on the OASIS database to separate AD from healthy control (HC) subjects. The obtained results represent higher accuracy and sensitivity of the proposed algorithm in comparison with similar method.
[1] Alzheimer’s disease and Dementia Alzheimer’s Association, What is Alzheimer’s disease, http://alz.org/alzheimers_disease_what_is_alzheimers.asp (accessed 25.06.16).
[2] F. Falahati, E. Westman, and A. Simmons, ‘’Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging,’’ Journal of Alzheimer's disease: JAD, vol. 41, pp. 685-708, 2012.
[3] M. Atiya, B. Hyman, M. Albert, and R. Killiany, ‘’Structural magnetic resonance imaging in established and prodromal Alzheimer disease: a review,’’ Alzheimer Disease & Associated Disorders, Vol. 17, pp. 177-195, 2003.
[4] S. Kazemifar, J. Drozd, N. Rajakumar, M. Borrie, and R. Bartha, "Automated algorithm to measure changes in medial temporal lobe volume in Alzheimer disease," Journal of neuroscience methods, vol. 227, pp. 35-46, 2014.
[5] R. Wolz, V. Julkunen, J. Koikkalainen, E. Niskanen, D. Zhang, D. Rueckert, H. Soininen, and J. Lötjönen, ‘’Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease,’’ PloS one, vol. 6, p.e25446, 2011.
[6] S. Costafreda, I. Dinov, Z. Tu, Y. Shi, C. Liu, I. Kloszewska, P. Mecocci, H. Soininen, M. Tsolaki, B. Vellas, and L. Wahlund, ‘’Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment,’’ Neuroimage, Vol. 56, pp. 212-219, 2011.
[7] K. Shen, J. Fripp, F. Mériaudeau, G. Chételat, O. Salvado, and P. Bourgeat, ‘’Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models,’’ Neuroimage, vol. 59, pp. 2155-2166, 2012.
[8] C. Aguilar, E. Westman, J. Muehlboeck, P. Mecocci, B. Vellas, M. Tsolaki, I. Kloszewska, H. Soininen, S. Lovestone, C. Spenger, and A. Simmons, ‘’Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment,’’ Psychiatry Research: Neuroimaging, vol. 212, pp. 89-98, 2013.
[9] O. Colliot, G. Chételat, M. Chupin, B. Desgranges, B. Magnin, H. Benali, B. Dubois, L. Garnero, F. Eustache, and S. Lehéricy, ‘’Discrimination between Alzheimer disease, Mild Cognitive Impairment, and Normal Aging by Using Automated Segmentation of the Hippocampus 1,’’ Radiology, vol. 248, pp. 194-201, 2008.
[10] T. Stoub and B. Dickerson, ‘’Parahippocampal white matter volume predicts Alzheimer's disease risk in cognitively normal old adults,’’ Neurobiology of aging, vol. 35, pp. 1855-1861, 2014.
[11] C. Wang, G. Stebbins, D. Medina, R. Shah, R. Bammer, M. Moseley, and L. deToledo-Morrell, ‘’Atrophy and dysfunction of parahippocampal white matter in mild Alzheimer’s disease,’’ Neurobiol. Aging, vol. 33, pp. 43e52, 2012.
[12] D. Salat, D. Greve, J. Pacheco, B. Quinn, K. Helmer, R. Buckner, and B. Fischl, ‘’Regional white matter volume differences in nondemented aging and Alzheimer’s disease,’’ Neuroimage, vol. 44, pp. 1247e1258, 2009.
[13] D. Salat, D. Tuch, A. Van der Kouwe, D. Greve, V. Pappu, S. Lee, N. Hevelone, A. Zaleta, J. Growdon, S. Corkin, and B. Fischl, ‘’White matter pathology isolates the hippocampal formation in Alzheimer's disease,’’ Neurobiology of aging, vol. 31, pp.244-256, 2010.
[14] S. Madsen, B. Gutman, S. Joshi, A. Toga, C. Jack, M. Weiner, and P. Thompson, ‘’Mapping ventricular expansion onto cortical gray matter in older adults,’’ Neurobiology of aging, vol. 36, pp. S32-S41, 2015.
[15] S. Nestor, R. Rupsingh, M. Borrie, M. Smith, V. Accomazzi, J. Wells, J. Fogarty, and R. Bartha, ‘’Ventricular enlargement as a possible measure of Alzheimer's disease progression validated using the Alzheimer's disease neuroimaging initiative database,’’ Brain, vol. 131, pp. 2443-2454, 2008.
[16] L. Apostolova, A. Green, S. Babakchanian, K. Hwang, Y. Chou, A. Toga, and P. Thompson, ‘’Hippocampal atrophy and ventricular enlargement in normal aging, mild cognitive impairment and Alzheimer's disease,’’ Alzheimer disease and associated disorders, vol. 26, p. 17, 2012.
[17] L. Clerx, I. A. van Rossum, L. Burns, D. L .Knol, P. Scheltens, F. Verhey, P. Aalten, P. Lapuerta,L. van de Pol, R. van Schijndel, and R. de Jong, ‘’Measurements of medial temporal lobe atrophy for prediction of Alzheimer's disease in subjects with mild cognitive impairment,’’ Neurobiology of aging, vol. 34, pp. 2003-2013, 2013.
[18] D. Marcus, T. Wang, J. Parker, J. Csernansky, J. Morris, and R. Buckner, ‘’Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults,’’ Journal of cognitive neuroscience, Vol. 19, pp.1498-1507, 2007.
[19] G A. Papakostas, A. Savio, M. Graña, V. G. Kaburlasos, ‘’A lattice computing approach to Alzheimer’s disease computer assisted diagnosis based on MRI data,’’ Neurocomputing, vol. 150, pp. 37-42, 2015.
[20] K. Juottonen, M. Laakso, K. Partanen, and H. Soininen, ‘’Comparative MR analysis of the entorhinal cortex and hippocampus in diagnosing Alzheimer disease,’’ American Journal of Neuroradiology, Vol. 20, pp. 139-144, 1999.
[21] N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, and M. Joliot, ‘’Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain,’’ Neuroimage, Vol. 15, pp. 273-289, 2002.
[22] E. Atashpaz-Gargari, and C. Lucas, ‘’Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition,’’ Evolutionary Computation, IEEE Congress on, pp. 4661-4667, 2007.