publication . Article . 2015

Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer's Disease

Laura L. Boles Ponto; Adrian Preda; Robert Perneczky;
Open Access English
  • Published: 28 Apr 2015
  • Publisher: eScholarship, University of California
Abstract
We present a method to discover discriminative brain metabolism patterns in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, facilitating the clinical diagnosis of Alzheimer's disease. In the work, the term "pattern" stands for a certain brain region that characterizes a target group of patients and can be used for a classification as well as interpretation purposes. Thus, it can be understood as a so-called "region of interest (ROI)". In the literature, an ROI is often found by a given brain atlas that defines a number of brain regions, which corresponds to an anatomical approach. The present work introduces a semi-data-driven approach that is...
Subjects
free text keywords: Alzheimer’s Disease Neuroimaging Initiative, Brain, Humans, Alzheimer Disease, Fluorodeoxyglucose F18, Radiopharmaceuticals, Positron-Emission Tomography, Sensitivity and Specificity, Normal Distribution, Models, Theoretical, Aged, Aged, 80 and over, Female, Male, Models, Theoretical, 80 and over, and over, Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, MILD COGNITIVE IMPAIRMENT, DIAGNOSIS, PET, PREDICTION, General Science & Technology, MD Multidisciplinary, Medicine, R, Science, Q, Research Article, General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Medicine, ddc:
Funded by
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
,
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
Communities
Neuroinformatics
42 references, page 1 of 3

1 ALZ. The prevalence of dementia worldwide, Alzheimer’s Disease International. 2008; http://www.alz.co.uk/adi/pdf/prevalence.pdf

2 Drzezga A. Diagnosis of Alzheimer's disease with [18F] PET in mild and asymptomatic stages. Behavioural Neurology. 2009; 21: 101–15. 10.3233/BEN-2009-0228 19847049 [OpenAIRE] [PubMed] [DOI]

3 Dubois B, Feldman HH, Jacova C, Dekosky ST, Barberger-Gateau P, et al Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria. Lancet Neurology. 2007; 6: 734–746. 17616482 [PubMed]

4 Górriz JM, Lassl A, Ramírez J, Salas-Gonzalez D, Puntonet CG, Lang EW. Automatic selection of ROIs infunctional imaging using Gaussian mixture models. Neuroscience Letters. 2009; 460: 108–111. 10.1016/j.neulet.2009.05.039 19454303 [OpenAIRE] [PubMed] [DOI]

5 Metz CE. Evaluation of CAD methods In Computer-Aided Diagnosis in Medical Imaging. In: Doi K.; MacMaho n H.; Giger ML.; Hoffmann KL. 1999; 543–554.

6 de Leon MJ, Mosconi L, Li J, De Santi S, Yao Y, et al Longitudinal CSF isoprostane and MRI atrophy in the progression to AD. Journal of Neurology. 2007; 254: 1666–1675. 17994313 [OpenAIRE] [PubMed]

7 Fjell AM, Walhovd KB, Fennema-Notestine C, McEvoy LK, Hagler DJ, et al CSF biomarkers in prediction of cerebral and clinical change in mild cognitive impairment and Alzheimer's disease. Journal of Neuroscience. 2010; 30: 2088–2101. 10.1523/JNEUROSCI.3785-09.2010 20147537 [OpenAIRE] [PubMed] [DOI]

8 McEvoy LK, Fennema-Notestine C, Roddey JC, Hagler DJ Jr, Holland D, et al Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. Radiology. 2009; 251: 195–205. 10.1148/radiol.2511080924 19201945 [OpenAIRE] [PubMed] [DOI]

9 Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehericy S, et al Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. NeuroImage. 2011; 56: 766–781. 10.1016/j.neuroimage.2010.06.013 20542124 [OpenAIRE] [PubMed] [DOI]

10 Liu MH, Zhang DQ, Shen D.G.Ensemble sparse classification of Alzheimer's disease. NeuroImage. 2012; 60: 1106–1116. 10.1016/j.neuroimage.2012.01.055 22270352 [OpenAIRE] [PubMed] [DOI]

11 Gray KR, Aljabar P, Heckemann RA, Hammers A, Rueckert D. Rand forest-based similarity measures for multi-modal classification ofAlzheimer's disease. NeuroImage. 2013; 65: 167–175. 10.1016/j.neuroimage.2012.09.065 23041336 [OpenAIRE] [PubMed] [DOI]

12 Li R, Hapfelmeier A, Schmidt J, Perneczky R, Drzezga A, Kurz A, Kramer S. A Case Study of Stacked Multi-view Learning in Dementia Research Proceedings of the 13th Conference on Artificial Intelligence in Medicine. 2010; 60–69, Berlin, Heidelberg, Springer LNCS.

13 Zhang DQ, Wang YP, Zhou LP, Yuan H, Shen DG. Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage. 2011; 55: 856–867. 10.1016/j.neuroimage.2011.01.008 21236349 [OpenAIRE] [PubMed] [DOI]

14 Sharif MS, Abbod M, Amira A, Zaidi H. Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification Advances in Fuzzy Systems—Special issue on Hybrid Biomedical Intelligent Systems. 2012; Hindawi.

15 Salaun PY, Campion L, Ansquer C, Frampas E, Mathieu C, et al 18F-FDG PET predicts survival after pretargeted radioimmunotherapy in patients with progressive metastatic medullary thyroid carcinoma. European Journal of Nuclear Medicine and Molecular Imaging. 2014; 41: 1501–1510. 10.1007/s00259-014-2772-0 24806110 [OpenAIRE] [PubMed] [DOI]

42 references, page 1 of 3
Abstract
We present a method to discover discriminative brain metabolism patterns in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, facilitating the clinical diagnosis of Alzheimer's disease. In the work, the term "pattern" stands for a certain brain region that characterizes a target group of patients and can be used for a classification as well as interpretation purposes. Thus, it can be understood as a so-called "region of interest (ROI)". In the literature, an ROI is often found by a given brain atlas that defines a number of brain regions, which corresponds to an anatomical approach. The present work introduces a semi-data-driven approach that is...
Subjects
free text keywords: Alzheimer’s Disease Neuroimaging Initiative, Brain, Humans, Alzheimer Disease, Fluorodeoxyglucose F18, Radiopharmaceuticals, Positron-Emission Tomography, Sensitivity and Specificity, Normal Distribution, Models, Theoretical, Aged, Aged, 80 and over, Female, Male, Models, Theoretical, 80 and over, and over, Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, MILD COGNITIVE IMPAIRMENT, DIAGNOSIS, PET, PREDICTION, General Science & Technology, MD Multidisciplinary, Medicine, R, Science, Q, Research Article, General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Medicine, ddc:
Funded by
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
,
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
Communities
Neuroinformatics
42 references, page 1 of 3

1 ALZ. The prevalence of dementia worldwide, Alzheimer’s Disease International. 2008; http://www.alz.co.uk/adi/pdf/prevalence.pdf

2 Drzezga A. Diagnosis of Alzheimer's disease with [18F] PET in mild and asymptomatic stages. Behavioural Neurology. 2009; 21: 101–15. 10.3233/BEN-2009-0228 19847049 [OpenAIRE] [PubMed] [DOI]

3 Dubois B, Feldman HH, Jacova C, Dekosky ST, Barberger-Gateau P, et al Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria. Lancet Neurology. 2007; 6: 734–746. 17616482 [PubMed]

4 Górriz JM, Lassl A, Ramírez J, Salas-Gonzalez D, Puntonet CG, Lang EW. Automatic selection of ROIs infunctional imaging using Gaussian mixture models. Neuroscience Letters. 2009; 460: 108–111. 10.1016/j.neulet.2009.05.039 19454303 [OpenAIRE] [PubMed] [DOI]

5 Metz CE. Evaluation of CAD methods In Computer-Aided Diagnosis in Medical Imaging. In: Doi K.; MacMaho n H.; Giger ML.; Hoffmann KL. 1999; 543–554.

6 de Leon MJ, Mosconi L, Li J, De Santi S, Yao Y, et al Longitudinal CSF isoprostane and MRI atrophy in the progression to AD. Journal of Neurology. 2007; 254: 1666–1675. 17994313 [OpenAIRE] [PubMed]

7 Fjell AM, Walhovd KB, Fennema-Notestine C, McEvoy LK, Hagler DJ, et al CSF biomarkers in prediction of cerebral and clinical change in mild cognitive impairment and Alzheimer's disease. Journal of Neuroscience. 2010; 30: 2088–2101. 10.1523/JNEUROSCI.3785-09.2010 20147537 [OpenAIRE] [PubMed] [DOI]

8 McEvoy LK, Fennema-Notestine C, Roddey JC, Hagler DJ Jr, Holland D, et al Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. Radiology. 2009; 251: 195–205. 10.1148/radiol.2511080924 19201945 [OpenAIRE] [PubMed] [DOI]

9 Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehericy S, et al Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. NeuroImage. 2011; 56: 766–781. 10.1016/j.neuroimage.2010.06.013 20542124 [OpenAIRE] [PubMed] [DOI]

10 Liu MH, Zhang DQ, Shen D.G.Ensemble sparse classification of Alzheimer's disease. NeuroImage. 2012; 60: 1106–1116. 10.1016/j.neuroimage.2012.01.055 22270352 [OpenAIRE] [PubMed] [DOI]

11 Gray KR, Aljabar P, Heckemann RA, Hammers A, Rueckert D. Rand forest-based similarity measures for multi-modal classification ofAlzheimer's disease. NeuroImage. 2013; 65: 167–175. 10.1016/j.neuroimage.2012.09.065 23041336 [OpenAIRE] [PubMed] [DOI]

12 Li R, Hapfelmeier A, Schmidt J, Perneczky R, Drzezga A, Kurz A, Kramer S. A Case Study of Stacked Multi-view Learning in Dementia Research Proceedings of the 13th Conference on Artificial Intelligence in Medicine. 2010; 60–69, Berlin, Heidelberg, Springer LNCS.

13 Zhang DQ, Wang YP, Zhou LP, Yuan H, Shen DG. Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage. 2011; 55: 856–867. 10.1016/j.neuroimage.2011.01.008 21236349 [OpenAIRE] [PubMed] [DOI]

14 Sharif MS, Abbod M, Amira A, Zaidi H. Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification Advances in Fuzzy Systems—Special issue on Hybrid Biomedical Intelligent Systems. 2012; Hindawi.

15 Salaun PY, Campion L, Ansquer C, Frampas E, Mathieu C, et al 18F-FDG PET predicts survival after pretargeted radioimmunotherapy in patients with progressive metastatic medullary thyroid carcinoma. European Journal of Nuclear Medicine and Molecular Imaging. 2014; 41: 1501–1510. 10.1007/s00259-014-2772-0 24806110 [OpenAIRE] [PubMed] [DOI]

42 references, page 1 of 3
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue