Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 376

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images

Open access
Lu, Donghuan, Popuri, Karteek, Ding, Gavin Weiguang, Balachandar, Rakesh, Beg, Mirza Faisal, Alzheimer’s Disease Neuroimaging Initiative, Weiner, Michael, Aisen, Paul, Petersen, Ronald, Jack, Cliford, Jagust, William, Trojanowki, John, Toga, Arthur, Beckett, Laurel, Green, Robert, Saykin, Andrew, Morris, John, Shaw, Leslie, Kaye, Jefrey, Quinn, Joseph, Silbert, Lisa, Lind, Betty, Carter, Raina, Dolen, Sara, Schneider, Lon, Pawluczyk, Sonia, Beccera, Mauricio, Teodoro, Liberty, Spann, Bryan, Brewer, James, Vanderswag, Helen, Fleisher, Adam, Heidebrink, Judith, Lord, Joanne, Mason, Sara, Albers, Colleen, Knopman, David, Johnson, Kris, Doody, Rachelle, Villanueva-Meyer, Javier, Chowdhury, Munir, Rountree, Susan, Dang, Mimi, Stern, Yaakov, Honig, Lawrence, Bell, Karen, Ances, Beau, Carroll, Maria, Creech, Mary, Franklin, Erin, Mintun, Mark, Schneider, Stacy, Oliver, Angela, Marson, Daniel, Grifth, Randall, Clark, David, Geldmacher, David, Brockington, John, Roberson, Erik, Love, Marissa Natelson, Grossman, Hillel, Mitsis, Efe, Shah, Raj, deToledo-Morrell, Leyla, Duara, Ranjan, Varon, Daniel, Greig, Maria, Roberts, Peggy, Albert, Marilyn, Onyike, Chiadi, D’Agostino, Daniel, Kielb, Stephanie, Galvin, James, Cerbone, Brittany, Michel, Christina, Pogorelec, Dana, Rusinek, Henry, de Leon, Mony, Glodzik, Lidia, Santi, Susan De, Doraiswamy, P., Petrella, Jefrey, Borges-Neto, Salvador, Wong, Terence, Coleman, Edward, Smith, Charles, Jicha, Greg, Hardy, Peter, Sinha, Partha, Oates, Elizabeth, Conrad, Gary, Porsteinsson, Anton, Goldstein, Bonnie, Martin, Kim, Makino, Kelly, Ismail, M., Brand, Connie, Mulnard, Ruth, Thai, Gaby, Mc-Adams-Ortiz, Catherine, Womack, Kyle, Mathews, Dana, Quiceno, Mary, Levey, Allan, Lah, James, Cellar, Janet, Burns, Jefrey, Swerdlow, Russell, Brooks, William, Apostolova, Liana, Tingus, Kathleen, Woo, Ellen, Silverman, Daniel, Lu, Po, Bartzokis, George, Graf-Radford, Neill, Parftt, Francine, Kendall, Tracy, Johnson, Heather, Farlow, Martin, Hake, Ann Marie, Matthews, Brandy, Brosch, Jared, Herring, Scott, Hunt, Cynthia, Dyck, Christopher, Carson, Richard, MacAvoy, Martha, Varma, Pradeep, Chertkow, Howard, Bergman, Howard, Hosein, Chris, Black, Sandra, Stefanovic, Bojana, Caldwell, Curtis, Hsiung, Ging-Yuek Robin, Feldman, Howard, Mudge, Benita, Assaly, Michele, Finger, Elizabeth, Pasternack, Stephen, Rachisky, Irina, Trost, Dick, Kertesz, Andrew, Bernick, Charles, Munic, Donna, Mesulam, Marek-Marsel, Lipowski, Kristine, Weintraub, Sandra, Bonakdarpour, Borna, Kerwin, Diana, Wu, Chuang-Kuo, Johnson, Nancy, Sadowsky, Carl, Villena, Teresa, Turner, Raymond Scott, Johnson, Kathleen, Reynolds, Brigid, Sperling, Reisa, Johnson, Keith, Marshall, Gad, Yesavage, Jerome, Taylor, Joy, Lane, Barton, Rosen, Allyson, Tinklenberg, Jared, Sabbagh, Marwan, Belden, Christine, Jacobson, Sandra, Sirrel, Sherye, Kowall, Neil, Killiany, Ronald, Budson, Andrew, Norbash, Alexander, Johnson, Patricia Lynn, Obisesan, Thomas, Wolday, Saba, Allard, Joanne, Lerner, Alan, Ogrocki, Paula, Tatsuoka, Curtis, Fatica, Parianne, Fletcher, Evan, Maillard, Pauline, Olichney, John, DeCarli, Charles, Carmichael, Owen, Kittur, Smita, Borrie, Michael, Lee, T.-Y., Bartha, Rob, Johnson, Sterling, Asthana, Sanjay, Carlsson, Cynthia, Potkin, Steven, Preda, Adrian, Nguyen, Dana, Tariot, Pierre, Burke, Anna, Trncic, Nadira, Reeder, Stephanie, Bates, Vernice, Capote, Horacio, Rainka, Michelle, Scharre, Douglas, Kataki, Maria, Adeli, Anahita, Zimmerman, Earl, Celmins, Dzintra, Brown, Alice, Pearlson, Godfrey, Blank, Karen, Anderson, Karen, Flashman, Laura, Seltzer, Marc, Hynes, Mary, Santulli, Robert, Sink, Kaycee, Gordineer, Leslie, Williamson, Jef, Garg, Pradeep, Watkins, Franklin, Ott, Brian, Querfurth, Henry, Tremont, Geofrey, Salloway, Stephen, Malloy, Paul, Correia, Stephen, Rosen, Howard, Miller, Bruce, Perry, David, Mintzer, Jacobo, Spicer, Kenneth, Bachman, David, Pomara, Nunzio, Hernando, Raymundo, Sarrael, Antero, Relkin, Norman, Chaing, Gloria, Lin, Michael, Ravdin, Lisa, Smith, Amanda, Raj, Balebail Ashok, Fargher, Kristin·2018·Scientific Reports
DOI 10.1038/s41598-018-22871-z
Open this paper in CoChat — free account

Abstract

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1–3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-018-22871-z

Cites

14 of 14 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 14 references

Research works printed but not linked: 34

  1. Brookmeyer, R., Johnson, E., Ziegler-Graham, K. & Arrighi, H. M. Forecasting the global burden of Alzheimer’s disease. Alzheimer’s & dementia 3, 186–191 (2007). · DOI · Paper page
  2. Davatzikos, C., Bhatt, P., Shaw, L. M., Batmanghelich, K. N. & Trojanowski, J. Q. Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol. aging 32, 2322–e19 (2011). · DOI · Paper page
  3. Landau, S. M. et al. Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol. aging 32, 1207–1218 (2011). · DOI · Paper page
  4. Farhan, S., Fahiem, M. A. & Tauseef, H. An ensemble-of-classifiers based approach for early diagnosis of Alzheimer’s disease: Classification using structural features of brain images. Comput. and mathematical methods medicine 2014 (2014). · DOI
  5. Payan, A. & Montana, G. Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. arXiv preprint arXiv:1502.02506 (2015). · arXiv
  6. Eskildsen, S. F. et al. Prediction of Alzheimer’s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. Neuroimage 65, 511–521 (2013). · DOI · Paper page
  7. Misra, C., Fan, Y. & Davatzikos, C. Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage 44, 1415–1422 (2009). · DOI · Paper page
  8. Wolz, R. et al. Multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. PLoS One 6, e25446 (2011). · DOI · Paper page
  9. Cuingnet, R. et al. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56, 766–781 (2011). · DOI · Paper page
  10. Cho, Y. et al. Individual subject classification for Alzheimer’s disease based on incremental learning using a spatial frequency representation of cortical thickness data. Neuroimage 59, 2217–2230 (2012). · DOI
  11. Mosconi, L. et al. Pre-clinical detection of Alzheimer’s disease using FDG-PET, with or without amyloid imaging. J. Alzheimer’s Dis. 20, 843–854 (2010). · DOI · Paper page
  12. Gray, K. R. et al. Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer’s disease. NeuroImage 60, 221–229 (2012). · DOI · Paper page
  13. Toussaint, P.-J. et al. Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer’s disease using conjoint univariate and independent component analyses. Neuroimage 63, 936–946 (2012). · DOI
  14. Illán, I. et al. 18 F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Inf. Sci. 181, 903–916 (2011). · DOI
  15. Zhang, D. et al. Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55, 856–867 (2011). · DOI · Paper page
  16. Moradi, E. et al. Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage 104, 398–412 (2015). · DOI · Paper page
  17. Korolev, I. O. et al. Predicting progression from mild cognitive impairment to Alzheimer’s dementia using clinical, MRI, and plasma biomarkers via probabilistic pattern classification. PloS One 11, e0138866 (2016). · DOI · Paper page
  18. Ye, J. et al. Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data. BMC Neurol 12, 46 (2012). · DOI
  19. Gaser, C., Franke, K., Kloppel, S., Koutsouleris, N. & Sauer, H. BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease. PLoS One 8, e67346 (2013). · DOI · Paper page
  20. Zhang, D. et al. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage 59, 895–907 (2012). · DOI · Paper page
  21. Liu, S. et al. Early diagnosis of Alzheimer’s disease with deep learning. In Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on, 1015–1018 (IEEE, 2014). · DOI · Paper page
  22. Zhang, W., Zelinsky, G. & Samaras, D. Real-time accurate object detection using multiple resolutions. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, 1–8 (IEEE, 2007). · DOI
  23. Tang, Y. & Mohamed, A.-R. Multiresolution Deep Belief Networks. In AISTATS, 1203–1211 (2012).
  24. Dale, A. M., S., M. & Fischl, B. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9(2), 195–207 (1999). · DOI · Paper page
  25. Raamana, P. R. et al. Thickness network features for prognostic applications in dementia. Neurobiol. aging 36, S91–S102 (2015). · DOI
  26. Beg, F., Miller, M., Trouvé, A. & Younes, L. Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. journal computer vision 61(2), 139–157 (2005). · DOI · Paper page
  27. Kingma, D. & Ba, J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). · arXiv
  28. Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014).
  29. Liu, K., Chen, K., Yao, L. & Guo, X. Prediction of Mild Cognitive Impairment Conversion Using a Combination of Independent Component Analysis and the Cox Model. Front. human neuroscience 11 (2017). · DOI
  30. Cheng, B., Liu, M., Zhang, D., Munsell, B. C. & Shen, D. Domain transfer learning for MCI conversion prediction. IEEE Transactions on Biomed. Eng. 62, 1805–1817 (2015). · DOI · Paper page
  31. Zhu, X. et al. A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Med. image analysis (2017). · DOI · Paper page
  32. Xu, L., Wu, X., Chen, K. & Yao, L. Multi-modality sparse representation-based classification for Alzheimer’s disease and mild cognitive impairment. Comput. methods programs biomedicine 122, 182–190 (2015). · DOI
  33. Zhang, D. & Shen, D. Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PLoS One 7, e33182 (2012). · DOI · Paper page
  34. An, L. et al. A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer’s Disease Diagnosis. Sci. Reports 7 (2017). · DOI

Cited by

See all 376

Metadata

Authors
Lu, D., Popuri, K., Ding, G. W., Balachandar, R., Beg, M. F., Initiative, A. D. N., Weiner, M., Aisen, P., Petersen, R., Jack, C., Jagust, W., Trojanowki, J., Toga, A., Beckett, L., Green, R., Saykin, A., Morris, J., Shaw, L., Kaye, J., Quinn, J., Silbert, L., Lind, B., Carter, R., Dolen, S., Schneider, L., Pawluczyk, S., Beccera, M., Teodoro, L., Spann, B., Brewer, J., Vanderswag, H., Fleisher, A., Heidebrink, J., Lord, J., Mason, S., Albers, C., Knopman, D., Johnson, K., Doody, R., Villanueva-Meyer, J., Chowdhury, M., Rountree, S., Dang, M., Stern, Y., Honig, L., Bell, K., Ances, B., Carroll, M., Creech, M., Franklin, E., Mintun, M., Schneider, S., Oliver, A., Marson, D., Grifth, R., Clark, D., Geldmacher, D., Brockington, J., Roberson, E., Love, M. N., Grossman, H., Mitsis, E., Shah, R., deToledo-Morrell, L., Duara, R., Varon, D., Greig, M., Roberts, P., Albert, M., Onyike, C., D’Agostino, D., Kielb, S., Galvin, J., Cerbone, B., Michel, C., Pogorelec, D., Rusinek, H., de Leon, M., Glodzik, L., Santi, S. D., Doraiswamy, P., Petrella, J., Borges-Neto, S., Wong, T., Coleman, E., Smith, C., Jicha, G., Hardy, P., Sinha, P., Oates, E., Conrad, G., Porsteinsson, A., Goldstein, B., Martin, K., Makino, K., Ismail, M., Brand, C., Mulnard, R., Thai, G., Mc-Adams-Ortiz, C., Womack, K., Mathews, D., Quiceno, M., Levey, A., Lah, J., Cellar, J., Burns, J., Swerdlow, R., Brooks, W., Apostolova, L., Tingus, K., Woo, E., Silverman, D., Lu, P., Bartzokis, G., Graf-Radford, N., Parftt, F., Kendall, T., Johnson, H., Farlow, M., Hake, A. M., Matthews, B., Brosch, J., Herring, S., Hunt, C., Dyck, C., Carson, R., MacAvoy, M., Varma, P., Chertkow, H., Bergman, H., Hosein, C., Black, S., Stefanovic, B., Caldwell, C., Hsiung, G.-Y. R., Feldman, H., Mudge, B., Assaly, M., Finger, E., Pasternack, S., Rachisky, I., Trost, D., Kertesz, A., Bernick, C., Munic, D., Mesulam, M.-M., Lipowski, K., Weintraub, S., Bonakdarpour, B., Kerwin, D., Wu, C.-K., Johnson, N., Sadowsky, C., Villena, T., Turner, R. S., Johnson, K., Reynolds, B., Sperling, R., Johnson, K., Marshall, G., Yesavage, J., Taylor, J., Lane, B., Rosen, A., Tinklenberg, J., Sabbagh, M., Belden, C., Jacobson, S., Sirrel, S., Kowall, N., Killiany, R., Budson, A., Norbash, A., Johnson, P. L., Obisesan, T., Wolday, S., Allard, J., Lerner, A., Ogrocki, P., Tatsuoka, C., Fatica, P., Fletcher, E., Maillard, P., Olichney, J., DeCarli, C., Carmichael, O., Kittur, S., Borrie, M., Lee, T.-Y., Bartha, R., Johnson, S., Asthana, S., Carlsson, C., Potkin, S., Preda, A., Nguyen, D., Tariot, P., Burke, A., Trncic, N., Reeder, S., Bates, V., Capote, H., Rainka, M., Scharre, D., Kataki, M., Adeli, A., Zimmerman, E., Celmins, D., Brown, A., Pearlson, G., Blank, K., Anderson, K., Flashman, L., Seltzer, M., Hynes, M., Santulli, R., Sink, K., Gordineer, L., Williamson, J., Garg, P., Watkins, F., Ott, B., Querfurth, H., Tremont, G., Salloway, S., Malloy, P., Correia, S., Rosen, H., Miller, B., Perry, D., Mintzer, J., Spicer, K., Bachman, D., Pomara, N., Hernando, R., Sarrael, A., Relkin, N., Chaing, G., Lin, M., Ravdin, L., Smith, A., Raj, B. A., & Fargher, K.
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2018-04-09
Volume
8
Issue
1
Pages
5697-5697
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-018-22871-z
OpenAlex
W2762081760
Cited by
376
References
14
FWCI
25.84
Type
journal article
Language
en
Open access
gold
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images. https://app.cochat.ai/papers/10.1038/s41598-018-22871-z
The paper
Lu, D., Popuri, K., Ding, G. W., Balachandar, R., Beg, M. F., Initiative, A. D. N., Weiner, M., Aisen, P., Petersen, R., Jack, C., Jagust, W., Trojanowki, J., Toga, A., Beckett, L., Green, R., Saykin, A., Morris, J., Shaw, L., Kaye, J., . . . Fargher, K. (2018). Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images. Scientific Reports. https://doi.org/10.1038/s41598-018-22871-z

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.