Morphological imaging biomarkers in the hippocampus for the detection of mild cognitive impairment
Keywords:
discrete compactness, tortuosity, mild cognitive impairment, random forest algorithm.Abstract
Introduction: The hippocampus is one of the most studied brain structures in the development of Alzheimer's disease because its morphology is affected from the stage of mild cognitive impairment. Quantitative metrics assess morphological changes of different brain structures by neuroimaging analysis. Discrete compactness and tortuosity have been reported as possible biomarkers of significant changes between populations of healthy control subjects and subjects with mild cognitive impairment.
Purpose: To evaluate the performance of discrete compactness and tortuosity in the hippocampi (right and left) as possible magnetic resonance neuroimaging biomarkers to discriminate between populations of controls and subjects with mild cognitive impairment.
Methods: 98 subjects were analyzed, including 49 healthy and 49 with mild cognitive impairment. Magnetic resonance images and other patient data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The structure of the hippocampi (right and left) was segmented using image processing techniques and volume, normalized volume, discrete compactness and tortuosity metrics were calculated. Statistical analysis of the data was performed in order to differentiate the classes. The metrics were incorporated into an automatic random forest classifier to discriminate between study populations.
Results: The statistical analysis showed statistically significant differences for the calculated metrics with a p-value < 0.01 between the two study classes. The automatic random forest classification algorithm, based on the indicators of volume, normalized volume, discrete compactness and tortuosity, achieved an accuracy in the training stage of 89.83 % ± 0.052 %. For the test stage with reserved data, the final accuracy was 85 %.
Conclusions: Discrete compactness and tortuosity are sensitive to morphological changes in the right and left hippocampus, and characterize the stage of mild cognitive impairment; therefore, they can be considered imaging biomarkers, useful in the detection of mild cognitive impairment when the first symptoms of Alzheimer's disease begin to appear.
Downloads
References
1. Ruan Q, D'Onofrio G, Sancarlo D, Bao Z, Greco A, Yu Z. Potential neuroimaging biomarkers of pathologic brain changes in Mild Cognitive Impairment and Alzheimer's disease: a systematic review. BMC Geriatrics. 2016;16:104. DOI: https://doi.org/10.1186/s12877-016-0281-7
2. Salvatore C, Castiglioni I. A wrapped multi-label classifier for the automatic diagnosis and prognosis of Alzheimer's disease. J Neurosci Methods. 2018;302:58-65. DOI: https://doi.org/10.1016/j.jneumeth.2017.12.016
3. Moon SW, Lee B, Choi YC. Changes in the hippocampal volume and shape in early-onset mild cognitive impairment. Psychiatry Investig. 2018;15(5):531-7.DOI: https://doi.org/10.30773/pi.2018.02.12
4. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment. Arch Neurol. 1999;56(3):303-8. DOI: https://doi.org/10.1001/archneur.56.3.303
5. Henneman WJP, Sluimer JD, Barnes J, Flier WM, Sluimer IC, Fox NC, et al. Hippocampal atrophy rates in Alzheimer disease: Added value over whole brain volume measures. Neurology. 2009;72(11):999-1007. DOI: https://doi.org/10.1212/01.wnl.0000344568.09360.31
6. Li YD, Dong HB, Xie GM, Zhang Lj. Discriminative analysis of mild Alzheimer's disease and normal aging using volume of hippocampal subfields and hippocampal mean diffusivity. Am J Alzheimer's Dis Other Demen. 2013;28(6):627-33. DOI: https://doi.org/10.1177/1533317513494452
7. Barnes J, Whitwell JL, Frost C, Josephs KA, Rossor M, Fox NC. Measurements of the amygdala and hippocampus in pathologically confirmed Alzheimer disease and frontotemporal lobar degeneration. Arch Neurol. 2006;63(10):1434-9. DOI: https://doi.org/10.1001/archneur.63.10.1434
8. Karas GB, Scheltens P, Rombouts SARB, Visser PJ, Schijndel RA, Fox NC, et al. Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease. Neuroimage. 2004;23(2):708-16. DOI: https://doi.org/10.1016/j.neuroimage.2004.07.006
9. Achterberg HC, Sorensen L, Wolters FJ, Niessen WJ, Vernooij MW, Ikram MA, et al. The value of hippocampal volume, shape, and texture for 11-year prediction of dementia: a population-based study. Neurobiol Aging. 2019;81:58-66. DOI: https://doi.org/10.1016/j.neurobiolaging.2019.05.007
10. Perez JL, Yanez O, Bribiesca E, Cosío FA, Jiménez JR, Medina V. Description and classification of normal and pathological aging processes based on brain magnetic resonance imaging morphology measures. J Medical Imaging. 2014;1(3):034002. DOI: https://doi.org/10.1117/1.jmi.1.3.034002
11. Barbará E, Pérez J, Rojas KC, Medina V. Evaluation of brain tortuosity measurement for the automatic multimodal classification of subjects with Alzheimer's disease. Comput Intell Neurosci. 2020;2020:4041832. DOI: https://doi.org/10.1155/2020/4041832
12. Lebedev AV, Westman E, Westen GJPV, Kramberger MG, Lundervold A, Aarsland D, et al. Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness. Neuroimage Clin. 2014;6:115-25. DOI: https://doi.org/10.1016/j.nicl.2014.08.023
13. Dimitriadis SI, Liparas D, Tsolaki MN. Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database. J Neurosci Methods. 2018;302:14-23. DOI: https://doi.org/10.1016/j.jneumeth.2017.12.010
14. Ramírez J, Górriz JM, Ortiz A, Martínez FJ, Segovia F, Salas D, et al. Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares. J Neurosci Methods. 2018;302:47-57. DOI: https://doi.org/10.1016/j.jneumeth.2017.12.005
15. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation. Neuron. 2002;33(3):341-55. DOI: https://doi.org/10.1016/s0896-6273(02)00569-x
16. Fischl B, Kouwe A, Destrieux C, Halgren E, Ségonne F, Salat D, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14(1):11-22. DOI: https://doi.org/10.1093/cercor/bhg087
17. Bribiesca E. An easy measure of compactness for 2D and 3D shapes. Pattern Recogn. 2008;41(2):543-54. DOI: https://doi.org/10.1016/j.patcog.2007.06.029
18. Bullitt E, Gerig G, Pizer SM, Lin W, Aylward SR. Measuring tortuosity of the intracerebral vasculature from MRA images. IEEE Trans Med Imaging. 2003;22(9):1163-71. DOI: https://doi.org/10.1109/tmi.2003.816964
19. Ostertagová E, Ostertag O, Kovác J. Methodology and application of the Kruskal-Wallis Test. Applied mechanics and materials. 2014;611:115-20. DOI: https://doi.org/10.4028/www.scientific.net/AMM.611.115
20. Sarica A, Cerasa A, Quattrone A. Random Forest algorithm for the classification of neuroimaging data in Alzheimers disease: a systematic review. Front Aging Neurosci. 2017;9:329. DOI: https://doi.org/10.3389/fnagi.2017.00329
21. Sorensen L, Igel C, Pai A, Balas I, Anker C, Lillholm M, et al. Differential diagnosis of mild cognitive impairment and Alzheimers disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry. Neuroimage Clin. 2017;13:470-82. DOI: https://doi.org/10.1016/j.nicl.2016.11.025
22. Gonzalo C, Ladrón de Guevara D, Rodrigo F, Brunetti E, Evelyng L, Marcelo M. Neuroimágenes en demencia. Rev Méd Clín Las Condes. 2016;27(3):338-56. DOI: https://doi.org/10.1016/j.rmclc.2016.06.008
Downloads
Published
How to Cite
Issue
Section
License
Aquellos autores/as que tengan publicaciones con esta revista, aceptan los términos siguientes: Los autores/as conservarán sus derechos de autor y garantizarán a la revista el derecho de primera publicación de su obra, el cuál estará simultáneamente sujeto a la Licencia de reconocimiento de Creative Commons (CC-BY-NC 4.0) que permite a terceros compartir la obra siempre que se indique su autor y su primera publicación esta revista. Los autores/as podrán adoptar otros acuerdos de licencia no exclusiva de distribución de la versión de la obra publicada (p. ej.: depositarla en un archivo telemático institucional o publicarla en un volumen monográfico) siempre que se indique la publicación inicial en esta revista. Se permite y recomienda a los autores/as difundir su obra a través de Internet (p. ej.: en archivos telemáticos institucionales o en su página web) antes y durante el proceso de envío, lo cual puede producir intercambios interesantes y aumentar las citas de la obra publicada. (Véase El efecto del acceso abierto).
Como Revista Cubana de Investigaciones Biomédicas forma parte de la red SciELO, una vez los artículos sean aceptados para entrar al proceso editorial (revisión), estos pueden ser depositados por parte de los autores, si estan de acuerdo, en SciELO preprints, siendo actualizados por los autores al concluir el proceso de revisión y las pruebas de maquetación.