High dimensional classification of structural MRI Alzheimer’s disease data based on large scale regularization
In this work we use a large scale regularization approach based on penalized logistic regression to automatically classify structural MRI images (sMRI) according to cognitive status. Its performance is illustrated using sMRI data from the Alzheimer Disease Neuroimaging Initiative (ADNI) clinical dat...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2011-10-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fninf.2011.00022/full |
| _version_ | 1818137486536212480 |
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| author | Ramon eCasanova Benjamin eWagner Christopher T. Whitlow Jeff D. Williamson Sally A. Shumaker Joseph A. Maldjian Mark A Espeland |
| author_facet | Ramon eCasanova Benjamin eWagner Christopher T. Whitlow Jeff D. Williamson Sally A. Shumaker Joseph A. Maldjian Mark A Espeland |
| author_sort | Ramon eCasanova |
| collection | DOAJ |
| description | In this work we use a large scale regularization approach based on penalized logistic regression to automatically classify structural MRI images (sMRI) according to cognitive status. Its performance is illustrated using sMRI data from the Alzheimer Disease Neuroimaging Initiative (ADNI) clinical database. We downloaded sMRI data from 98 subjects (49 cognitive normal and 49 patients) matched by age and sex from the ADNI website. Images were segmented and normalized using SPM8 and ANTS software packages. Classification was performed using GLMNET library implementation of penalized logistic regression based on coordinate-wise descent optimization techniques. To avoid optimistic estimates classification accuracy, sensitivity and specificity were determined based on a combination of three-way split of the data with nested 10-fold cross-validations. One of the main features of this approach is that classification is performed based on large scale regularization. The methodology presented here was highly accurate, sensitive and specific when automatically classifying sMRI images of cognitive normal subjects and Alzheimer disease patients. Higher levels of accuracy, sensitivity, and specificity were achieved for gray matter volume maps (85.7%, 82.9% and 90%, respectively) compared to white matter volume maps (81.1%, 80.6%, and 82.5%, respectively). We found that gray matter and white matter tissues carry useful information for discriminating patients from cognitive normal subjects using sMRI brain data. Although we have demonstrated the efficacy of this voxel-wise classification method in discriminating cognitive normal subjects from Alzheimer disease patients, in principle it could be applied to any clinical population. |
| first_indexed | 2024-12-11T09:57:04Z |
| format | Article |
| id | doaj.art-80434e7babf846e89026767ad575b99f |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-11T09:57:04Z |
| publishDate | 2011-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-80434e7babf846e89026767ad575b99f2022-12-22T01:12:14ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962011-10-01510.3389/fninf.2011.000229841High dimensional classification of structural MRI Alzheimer’s disease data based on large scale regularizationRamon eCasanova0Benjamin eWagner1Christopher T. Whitlow2Jeff D. Williamson3Sally A. Shumaker4Joseph A. Maldjian5Mark A Espeland6Wake Forest University School of MedicineWake Forest University School of MedicineWake Forest University School of MedicineWake Forest University School of MedicineWake Forest University School of MedicineWake Forest University School of MedicineWake Forest University School of MedicineIn this work we use a large scale regularization approach based on penalized logistic regression to automatically classify structural MRI images (sMRI) according to cognitive status. Its performance is illustrated using sMRI data from the Alzheimer Disease Neuroimaging Initiative (ADNI) clinical database. We downloaded sMRI data from 98 subjects (49 cognitive normal and 49 patients) matched by age and sex from the ADNI website. Images were segmented and normalized using SPM8 and ANTS software packages. Classification was performed using GLMNET library implementation of penalized logistic regression based on coordinate-wise descent optimization techniques. To avoid optimistic estimates classification accuracy, sensitivity and specificity were determined based on a combination of three-way split of the data with nested 10-fold cross-validations. One of the main features of this approach is that classification is performed based on large scale regularization. The methodology presented here was highly accurate, sensitive and specific when automatically classifying sMRI images of cognitive normal subjects and Alzheimer disease patients. Higher levels of accuracy, sensitivity, and specificity were achieved for gray matter volume maps (85.7%, 82.9% and 90%, respectively) compared to white matter volume maps (81.1%, 80.6%, and 82.5%, respectively). We found that gray matter and white matter tissues carry useful information for discriminating patients from cognitive normal subjects using sMRI brain data. Although we have demonstrated the efficacy of this voxel-wise classification method in discriminating cognitive normal subjects from Alzheimer disease patients, in principle it could be applied to any clinical population.http://journal.frontiersin.org/Journal/10.3389/fninf.2011.00022/fullmachine learningLogistic regressionADNIcurse of dimensionalityelastic netGLMNET |
| spellingShingle | Ramon eCasanova Benjamin eWagner Christopher T. Whitlow Jeff D. Williamson Sally A. Shumaker Joseph A. Maldjian Mark A Espeland High dimensional classification of structural MRI Alzheimer’s disease data based on large scale regularization Frontiers in Neuroinformatics machine learning Logistic regression ADNI curse of dimensionality elastic net GLMNET |
| title | High dimensional classification of structural MRI Alzheimer’s disease data based on large scale regularization |
| title_full | High dimensional classification of structural MRI Alzheimer’s disease data based on large scale regularization |
| title_fullStr | High dimensional classification of structural MRI Alzheimer’s disease data based on large scale regularization |
| title_full_unstemmed | High dimensional classification of structural MRI Alzheimer’s disease data based on large scale regularization |
| title_short | High dimensional classification of structural MRI Alzheimer’s disease data based on large scale regularization |
| title_sort | high dimensional classification of structural mri alzheimer s disease data based on large scale regularization |
| topic | machine learning Logistic regression ADNI curse of dimensionality elastic net GLMNET |
| url | http://journal.frontiersin.org/Journal/10.3389/fninf.2011.00022/full |
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