Abstract
Unveiling pathological brain changes associated with Alzheimer’s disease (AD) and its earlier stages including mild cognitive impairment (MCI) is a challenging task especially that patients do not show symptoms of dementia until it is late. Over the past years, neuroimaging techniques paved the way for computer-based diagnosis and prognosis to facilitate the automation of medical decision support and help clinicians identify cognitively intact subjects that are at high-risk of developing AD. As a progressive neurodegenerative disorder, researchers investigated how AD affects the brain using different approaches: (1) image-based methods where mainly neuroimaging modalities are used to provide early AD biomarkers, and (2) network-based methods which focus on functional and structural brain connectivities to give insights into how AD alters brain wiring. In this exceptional review paper, we screened MICCAI proceedings published between 2010 and 2016 and IPMI proceedings published between 2011 and 2017, where ‘seed’ technical ideas generally get published, to identify neuroimaging-based technical methods developed for AD and MCI classification and prediction tasks. We included papers that fit into image-based or network-based categories. We found out that the majority of papers focused on classifying MCI vs. AD brain states, which has enabled the discovery of discriminative or altered brain regions and connections. However, very few works aimed to predict MCI progression from early observations. Despite the high importance of reliably identifying which early MCI patient will convert to AD, remain stable or reverse to normal over months/years, predictive models that foresee MCI evolution are still lagging behind.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Buckner, R.L.: Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron 44, 195–208 (2004)
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)
Bron, E.E., et al.: Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. NeuroImage 111, 562–579 (2015)
Jack Jr., C.R., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging: Official J. Int. Soc. Magn. Reson. Med. 27, 685–691 (2008)
Coupé, P., Eskildsen, S.F., Manjón, J.V., Fonov, V., Collins, D.L.: Simultaneous segmentation and grading of hippocampus for patient classification with Alzheimer’s disease. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 149–157. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23626-6_19
Liu, F., Wee, C.-Y., Chen, H., Shen, D.: Inter-modality relationship constrained multi-task feature selection for AD/MCI classification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 308–315. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40811-3_39
Suk, H.-I., Shen, D.: Deep learning-based feature representation for AD/MCI classification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 583–590. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_72
Jie, B., Zhang, D., Cheng, B., Shen, D.: Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer’s disease. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 275–283. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40811-3_35
Suk, H.-I., Shen, D.: Clustering-induced multi-task learning for AD/MCI classification. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 393–400. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10443-0_50
Min, R., Cheng, J., Price, T., Wu, G., Shen, D.: Maximum-margin based representation learning from multiple atlases for Alzheimer’s disease classification. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 212–219. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10470-6_27
An, L., Adeli, E., Liu, M., Zhang, J., Shen, D.: Semi-supervised hierarchical multimodal feature and sample selection for Alzheimer’s disease diagnosis. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 79–87. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_10
Peng, J., An, L., Zhu, X., Jin, Y., Shen, D.: Structured sparse kernel learning for imaging genetics based Alzheimer’s disease diagnosis. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 70–78. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_9
Liu, F., Suk, H.-I., Wee, C.-Y., Chen, H., Shen, D.: High-order graph matching based feature selection for Alzheimer’s disease identification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 311–318. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_39
Liu, M., Zhang, J., Yap, P.-T., Shen, D.: Diagnosis of Alzheimer’s disease using view-aligned hypergraph learning with incomplete multi-modality data. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 308–316. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46720-7_36
Liu, M., Du, J., Jie, B., Zhang, D.: Ordinal patterns for connectivity networks in brain disease diagnosis. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 1–9. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46720-7_1
Wee, C.-Y., Yap, P.-T., Zhang, D., Denny, K., Wang, L., Shen, D.: Identification of individuals with MCI via multimodality connectivity networks. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6892, pp. 277–284. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23629-7_34
Wee, C.-Y., Yap, P.-T., Zhang, D., Wang, L., Shen, D.: Constrained sparse functional connectivity networks for MCI classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7511, pp. 212–219. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33418-4_27
Wee, C.-Y., Li, Y., Jie, B., Peng, Z.-W., Shen, D.: Identification of MCI using optimal sparse MAR modeled effective connectivity networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 319–327. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_40
Jie, B., Shen, D., Zhang, D.: Brain connectivity hyper-network for MCI classification. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 724–732. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10470-6_90
Suk, H.-I., Lee, S.-W., Shen, D.: A hybrid of deep network and hidden Markov model for MCI identification with resting-state fMRI. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 573–580. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_70
Chen, X., et al.: High-order resting-state functional connectivity network for MCI classification. Hum. Brain Mapp. 37, 3282–3296 (2016)
Yu, R., Zhang, H., An, L., Chen, X., Wei, Z., Shen, D.: Correlation-weighted sparse group representation for brain network construction in MCI classification. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 37–45. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46720-7_5
Leung, K.K., et al.: Increasing power to predict mild cognitive impairment conversion to Alzheimer’s disease using hippocampal atrophy rate and statistical shape models. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 125–132. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15745-5_16
Schmidt-Richberg, A., et al.: Multi-stage biomarker models for progression estimation in Alzheimer’s disease. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 387–398. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19992-4_30
Guo, R., Ahn, M., Hongtu Zhu, H.Z.: Spatially weighted principal component analysis for imaging classification. J. Comput. Graph. Stat. 24, 274–296 (2015)
Batmanghelich, N.K., Dalca, A.V., Sabuncu, M.R., Golland, P.: Joint modeling of imaging and genetics. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 766–777. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38868-2_64
Nichols, T.E., et al.: Best practices in data analysis and sharing in neuroimaging using MRI. Nat. Neurosci. 20, 299 (2017)
Sabuncu, M.R., Konukoglu, E., Initiative, A.N., et al.: Clinical prediction from structural brain MRI scans: a large-scale empirical study. Neuroinformatics 13, 31–46 (2015)
Brown, C., Hamarneh, G.: Machine learning on human connectome data from MRI. arXiv:1611.08699v1 (2016)
Karas, G., et al.: A comprehensive study of gray matter loss in patients with Alzheimer’s disease using optimized voxel-based morphometry. Neuroimage 18, 895–907 (2003)
Du, A., et al.: Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 71, 441–447 (2001)
Iglesias, J.E., Jiang, J., Liu, C.-Y., Tu, Z.: Classification of Alzheimer’s disease using a self-smoothing operator. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 58–65. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23626-6_8
Zhu, X., Suk, H.-I., Shen, D.: Multi-modality canonical feature selection for Alzheimer’s disease diagnosis. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 162–169. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10470-6_21
Liu, S., et al.: Multifold Bayesian kernelization in Alzheimer’s diagnosis. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 303–310. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_38
Venkatraghavan, V., Bron, E.E., Niessen, W.J., Klein, S.: A discriminative event based model for Alzheimer’s disease progression modeling. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 121–133. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_10
Thung, K.-H., Yap, P.-T., Adeli-M, E., Shen, D.: Joint diagnosis and conversion time prediction of progressive mild cognitive impairment (pMCI) using low-rank subspace clustering and matrix completion. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 527–534. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_63
Zhu, X., Suk, H.I., Lee, S.W., Shen, D.: Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis. Brain Imaging Behav. 10, 818–828 (2016)
Zhang, J., Li, Q., Caselli, R.J., Thompson, P.M., Ye, J., Wang, Y.: Multi-source multi-target dictionary learning for prediction of cognitive decline. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 184–197. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_15
Fransson, P.: Spontaneous low-frequency bold signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Hum. Brain Mapp. 26, 15–29 (2005)
Wang, K., et al.: Altered functional connectivity in early Alzheimer’s disease: a resting-state fMRI study. Hum. Brain Mapp. 28, 967–978 (2007)
Wee, C.Y., et al.: Resting-state multi-spectrum functional connectivity networks for identification of MCI patients. PLoS ONE 7, e37828 (2012)
Chen, X., Zhang, H., Shen, D.: Ensemble hierarchical high-order functional connectivity networks for MCI classification. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 18–25. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_3
Li, S., et al.: Analysis of group ICA-based connectivity measures from fMRI: application to Alzheimer’s disease. PLoS ONE 7, e49340 (2012)
Wee, C.Y., Yap, P.T., Zhang, D., Wang, L., Shen, D.: Group-constrained sparse fmri connectivity modeling for mild cognitive impairment identification. Brain Struct. Funct. 219, 641–656 (2014)
Hutchison, R.M., et al.: Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80, 360–378 (2013)
Serrano-Pozo, A., Frosch, M.P., Masliah, E., Hyman, B.T.: Neuropathological alterations in Alzheimer’s disease. Cold Spring Harb. Perspect. Med. 1, a006189 (2011)
Hu, C., Cheng, L., Sepulcre, J., El Fakhri, G., Lu, Y.M., Li, Q.: Matched signal detection on graphs: theory and application to brain network classification. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 1–12. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38868-2_1
Zhu, Y., Zhu, X., Kim, M., Kaufer, D., Wu, G.: A novel dynamic hyper-graph inference framework for computer assisted diagnosis of neuro-diseases. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 158–169. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_13
Gao, Y., Adeli-M., E., Kim, M., Giannakopoulos, P., Haller, S., Shen, D.: Medical image retrieval using multi-graph learning for MCI diagnostic assistance. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 86–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24571-3_11
Gao, Y., et al.: MCI identification by joint learning on multiple MRI data. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 78–85. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24571-3_10
Wang, D., et al.: Structural brain network constrained neuroimaging marker identification for predicting cognitive functions. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 536–547. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38868-2_45
Ithapu, V.K., et al.: Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment. Alzheimer’s Dementia 11, 1489–1499 (2015)
Querbes, O., et al.: The Alzheimer’s Disease Neuroimaging Initiative: early diagnosis of Alzheimer’s disease using cortical thickness: impact of cognitive reserve. Brain 132, 2036 (2009)
Mahjoub, I., Mahjoub, M.A., Rekik, I.: Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Sci. Rep. 8, 4103 (2018)
Lisowska, A., Rekik, I.: Joint pairing and structured mapping of convolutional brain morphological multiplexes for early dementia diagnosis. Brain Connectivity 9, 22–36 (2018)
Cheng, B., Zhang, D., Shen, D.: Domain transfer learning for MCI conversion prediction. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 82–90. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33415-3_11
Singh, N., Wang, A.Y., Sankaranarayanan, P., Fletcher, P.T., Joshi, S.: Genetic, structural and functional imaging biomarkers for early detection of conversion from MCI to AD. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 132–140. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33415-3_17
Zhang, J., et al.: Hyperbolic space sparse coding with its application on prediction of Alzheimer’s disease in mild cognitive impairment. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 326–334. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46720-7_38
Wang, X., et al.: Predicting interrelated Alzheimer’s disease outcomes via new self-learned structured low-rank model. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 198–209. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_16
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Soussia, M., Rekik, I. (2019). 7 Years of Developing Seed Techniques for Alzheimer’s Disease Diagnosis Using Brain Image and Connectivity Data Largely Bypassed Prediction for Prognosis. In: Rekik, I., Adeli, E., Park, S. (eds) Predictive Intelligence in Medicine. PRIME 2019. Lecture Notes in Computer Science(), vol 11843. Springer, Cham. https://doi.org/10.1007/978-3-030-32281-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-32281-6_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32280-9
Online ISBN: 978-3-030-32281-6
eBook Packages: Computer ScienceComputer Science (R0)