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A semi-mechanism approach based on MRI and proteomics for prediction of conversion from mild cognitive impairment to Alzheimer’s disease

Scientific Reports volume 6, Article number: 26712 (2016) | Download Citation



Mild cognitive impairment (MCI) is a precursor phase of Alzheimer’s disease (AD). As current treatments may be effective only at the early stages of AD, it is important to track MCI patients who will convert to AD. The aim of this study is to develop a high performance semi-mechanism based approach to predict the conversion from MCI to AD and improve our understanding of MCI-to-AD conversion mechanism. First, analysis of variance (ANOVA) test and lasso regression are employed to identify the markers related to the conversion. Then the Bayesian network based on selected markers is established to predict MCI-to-AD conversion. The structure of Bayesian network suggests that the conversion may start with fibrin clot formation, verbal memory impairment, eating pattern changing and hyperinsulinemia. The Bayesian network achieves a high 10-fold cross-validated prediction performance with 96% accuracy, 95% sensitivity, 65% specificity, area under the receiver operating characteristic curve of 0.82 on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The semi-mechanism based approach provides not only high prediction performance but also clues of mechanism for MCI-to-AD conversion.


Alzheimer’s disease (AD), the most common form of dementia, is characterized by progressive neurodegenerative disorder1. 36 million people worldwide are affected by AD and the number is expected to almost triple by 20502. Many evidences indicate that AD has a years to decade preclinical period followed by a precursor phase termed as mild cognitive impairment (MCI)3. As new treatments are likely to be most effective at the early stages of AD, it is greatly urgent to track patients with MCI who will develop AD4,5.

Several sensitive imaging modalities such as structural magnetic resonance imaging (MRI) and positron emission tomography (PET) have been developed5. A number of previous researches have reported that MRI biomarkers can be used to predict the probability of conversion6,7,8. However, because some of structural changes may not be detected at visual inspection until MCI patients have converted to AD, predictions using MRI biomarkers only may not be accurate enough for application in the routine clinical setting or clinical drug trials3,5. Previous researches show that combined markers such as MRI and cerebrospinal fluid (CSF) biomarkers can improve the prediction accuracy5,9. But CSF sample collection requires lumbar puncture which is too invasive to be used as a routine clinical examination. As damage to the blood-brain barrier may occur in AD, this may increase movement of proteins between the brain and the blood10. It is therefore possible that AD and its precursor, MCI, may be associated with the variation of biomarkers detectable in plasma11. Recent work has demonstrated the possibility of predicting MCI-to-AD conversion based on plasma markers12. In addition, blood sample is more accessible and suitable for repeated collecting. These make plasma-based biomarkers promising for prediction of conversion from MCI to AD.

While the highly sensitive markers are beneficial on the conversion prediction, advanced machine learning methods can further improve the reliability of approaches. Machine learning is the study of algorithms and computational techniques that use previous examples in the form of multivariate datasets to help make future predictions13. A number of machine learning methods such as support vector machines (SVM) and logistic regression (LR) have been used to predict the conversion from MCI to AD5,8. Compared with the traditional data-driven machine learning methods, Bayesian network has unique advantages that it can quantify the causal relationships between the markers, visualize these relationships by the structure of network, and conduct the prediction task based on the causal relationships14. These attractive characteristics make Bayesian network a semi-mechanism method. On one hand, the semi-mechanism nature of Bayesian network can improve our understanding of conversion mechanism. On the other hand, because of the complex etiology and multiple pathogenesis of AD, the conversion from MCI to AD is affected by many uncertain factors which makes its prediction a complicated issue15. Bayesian network is especially well-suited to handle the intricacies of the prediction because it is designed for representing stochastic events and conducting prediction tasks under uncertainty16,17.

Lots of lectures based on data-driven methods, such as neural network with self-organizing maps (SOM), are focused on improving the classification performance and they have showed good performance in the diagnosis task. However, the contribution of these methods on improving our understanding of MCI-to-AD conversion mechanism is limited. As the semi-mechanism nature of Bayesian network can provide causal relationships of markers, this paper proposes a semi-mechanism method based on the combination of Bayesian network and lasso regression for not only the high performance of MCI-to-AD conversion prediction but also improving our understanding the mechanism of the conversion. The data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) is used to develop the model. However, ADNI contains more than 500 biomarkers (including MRI markers and plasma markers), many of which may not relate to MCI-to-AD conversion. Irrelevant biomarkers may interfere the causal relationships identification and reduce the performance of prediction method. Therefore, biomarkers selection should be performed before the conversion prediction. In this study, lasso regression is proposed to conduct the markers selection, which combines variable selection with an efficient computational procedure18. Previous works have shown that lasso regression can enhance the prediction performance of models based on high dimension data sets19,20,21. As such, the combination of Bayesian network and lasso regression is proposed not only to conduct the prediction task but also to improve understanding of the AD-to-MCI conversion mechanism. Moreover, after the conversion probability is calculated, a subgroup analysis is performed for comparing the network disruption of high-risk patients and low-risk patients.


Biomarkers selection

In this section, the process of biomarkers selection is described. The dataset used in this study contains 518 biomarkers (328 MRI markers and 190 plasma markers). 45 biomarkers (1 MRI marker and 44 plasma markers) are deleted during data checking due to too many missing entries. 75 biomarkers (57 MRI markers and 18 plasma markers) with significant difference between converters and non-converters are identified by ANOVA test. 34 biomarkers (25 MRI markers and 9 plasma markers) related to Alzheimer’s disease assessment scale (ADAS-cog) are selected by lasso regression. 7 biomarkers (5 MRI markers and 2 plasma markers) are eliminated during Bayesian network structure learning because they fail to connect to the Bayesian network. In addition, as 2 MRI markers are labeled as “unknown”, they are also eliminated. Finally, 25 biomarkers (18 MRI markers and 7 plasma markers) are selected for conversion prediction. The process of biomarkers identification is summarized in Fig. 1. The list of selected biomarkers is shown in Table 1.

Figure 1: The process of markers selection.
Figure 1
Table 1: List of selected markers.

Structure and performance of Bayesian network

In this section, we present the results of Bayesian structure learning and the performance of conversion prediction. The Bayesian network structure obtained by max-min hill-climbing (MMHC) is given in Fig. 2. It contains 26 nodes and 43 arcs.

Figure 2: The structure of Bayesian network.
Figure 2

It contains 26 nodes and 43 arcs. The nodes in order are: ST109TS, ST111CV, ST114TA, ST11SV, ST121TA, ST30SV, ST31TA, ST40CV, ST49TA, ST52CV, ST56CV, ST70SV, ST72CV, ST83CV, ST83TA, ST88SV, ST91CV, ST99CV, AGRP, C-peptide, CRP, FGF-4, Fibrinogen, Insulin, MMP-10, and “Whether patients converts to AD or not”.

In order to evaluate the performance of Bayesian network, a 10-fold cross-validation is performed to estimate its accuracy, sensitivity and specificity. Furthermore, the performance of Bayesian network is compared to the performances of linear discriminant analysis (LDA) and SOM. The performances of all these methods are evaluated by 10-fold cross-validation. The results are given in Fig. 3. The Fig. 3A shows that the accuracy and sensitivity of Bayesian network are higher than those of LDA and SOM with markers selection. In Fig. 3B, the area under receiver operating characteristic curve (AUC-ROC) of Bayesian network is much higher than that of LDA and SOM with marker selection. Moreover, to evaluate the performance of markers selection, we apply SOM and Bayesian network with or without markers selection and compare their performances. With markers selection, the classification performances of both SOM and Bayesian network are improved.

Figure 3: The performance of five different conversion prediction models.
Figure 3

(A) The receiver operating characteristic (ROC) curve of Linear discriminant analysis (LDA), self-organizing map (SOM) (with or without markers selection) and Bayesian network (with or without markers selection). (B) The performance of LDA, SOM (with or without markers selection) and Bayesian network (with or without selection) measured by three parameters: accuracy, sensitivity, specificity. All these parameters are evaluated by 10-fold cross-validation.

Network disruption profile

According to the result of Bayesian network, a group of highest conversion probability patients (high-risk group, n = 11) and a group of lowest conversion probability patients (low-risk group, n = 48) are drawn from the dataset. 11 biomarkers have significant difference (P < 0.05, ANOVA test) between high-risk group and low risk group. The mini network balance map Fig. 4A) shows that the high-risk group may suffer from more severe network disruption than the low risk group. The network disruption parameters coincide with the mini network balance map. Parameters U and increase significantly in high risk group (P < 0.01, ANOVA test, shown in Fig. 4B) which may suggest that patients with greater U and may have higher conversion risk.

Figure 4
Figure 4

(A) Network disruption analysis of markers with significant difference between high-risk group and low-risk group. In normal state, the shape of radar graph is a regular polygon. With the shape deformation, the difference from normal state gets greater. (B) Box plot of parameters U, K, and . If the value of disruption parameters U and is beyond the horizontal lines in figures, the patient may have more conversion risk. *P < 0.05, **P < 0.01 vs low risk group.


In this study, we propose a semi-mechanism based Bayesian network to predict the conversion from MCI to AD. The proposed method has two contributions. Firstly, the proposed approach achieves relative high prediction performance. Secondly, as the Bayesian network can learn the causal relationships among biomarkers from the database, these causal relationships can provide some more insight into the mechanism of MCI-to-AD conversion.

The proposed model is compared to previous researches based on data-driven methods (Table 2). Comparing with LDA and SOM, Bayesian network has higher accuracy and sensitivity with markers selection. The high sensitivity of Bayesian network may lie in two points. On one hand, the semi-mechanism nature of Bayesian network may provide higher performance because it can learn causal relationships from data and combine these knowledge and data to conduct the prediction task22. On the other hand, plasma markers may be highly sensitive in conversion prediction12. Though the data-driven methods also achieved high performance, the Bayesian network still has its unique advantage. The structure of Bayesian network may contain the causal relationships of markers which makes it a semi-mechanism method and provide more information beyond the performance of classification.

Table 2: Comparisons to other methods.

In addition, with markers selection, the classification performance of Bayesian network is improved. It suggests that Bayesian network should work with an appropriate marker selection strategy. In another words, without markers selection, Bayesian network may produce false positive causal relationships which may not only decrease the performance but also mislead the MCI-to-AD conversion mechanism investigation. Therefore, combining Bayesian network and lasso marker selection strategy is very helpful in improving understanding the conversion mechanism and classification performance.

The semi-mechanism nature of Bayesian network is beneficial on investigating the mechanism of conversion. Structure of Bayesian network shows that 6 markers including volume of left middle temporal, cortical thickness average of right entorhinal, volume of right inferior temporal, AGRP, c-peptide, and fibrinogen may be related to the conversion directly. Our result, that destruction of entorhinal is associated with MCI-to-AD conversion, is consistent with previous research23. Previous researches had also reported the variations of temporal, c-peptide level, and fibrinogen level in AD patients19,24,25. However, our results suggest that these changes may have happened at MCI stage. It indicates that the conversion from MCI to AD may start with destruction of temporal, entorhnal, increased level of AGRP, c-peptide, and fibrin25.

Bayesian network identifies the variations of above six markers caused by MCI-to-AD conversion directly. But some of these changings may not be the key factors in the conversion. Therefore a reanalysis based on the results of Bayesian network is performed to identify the major factors. The subgroups network disruption profile suggests that the progress MCI patient may suffer from more severe network disruption than stable MCI patients. Network disruption may be related to the marker panel including 11 markers. The six markers identified by Bayesian network and marker panel related network disruption share three markers: Cortical Thickness of Entorhinal, Volume of Temporal and AGRP. These three markers may be the key factors in the conversion. In other word, they might be attributed to the warming signals of conversion. Previous researches showed that the destruction of entorhinal and temporal is associated with verbal memory impairment and verbal memory impairment might be the warming indicator of MCI-to-AD conversion26. In addition, clinical researches have reported that AD patients have greater preference for high-fat and sweet food than normal groups. However, our results suggested that such change in eating pattern may have happened at MCI stage27,28, as the elevated level of AGRP, an orexigenic peptide, in high-risk patients may increase the preference for a high fat diet29.

The crosstalk between cerebral destruction and plasma markers alteration revealed by Bayesian network can provide more clues for the mechanism of conversion. The crosstalk between C-peptide and cerebral destruction may play a vital role in the MCI-to-AD conversion. C-peptide is a measure of insulin secretion. Elevated C-peptide level represents high peripheral insulin secretion. It is reported that high peripheral insulin secretion can increase the risk of AD. Because high level peripheral insulin secretion impairs amyloid clearance by inhibiting brain insulin production which is a beneficial effect on amyloid clearance30. Bayesian network suggests that C-peptide may be related to the destruction of middle temporal, entorhinal, and inferior temporal. It suggests that amyloid may mainly aggregate in the above three regions at the MCI stage which may aggravate their damage. As all above three regions are involved in verbal memory, high level of C-peptide may impair to verbal memory which was confirmed by previous works26,31,32,33.

In summary, the analysis of Bayesian network shows that the conversion from MCI to AD may start with multiple pathological changes such as verbal memory impairment, vascular abnormalities, hyperinsulinemia and eating pattern change. In this study, a high performance semi-mechanism based approach is developed to predict the conversion from MCI to AD by combining MRI and plasma markers. The semi-mechanism based approach provides not only high performance prediction but also more insight into the mechanism of conversion from MCI to AD.

Subject and Method



In this study, the following criteria are used to select subjects for model developing:

  • Patients with baseline MRI scan records

  • Patients with baseline plasma-based biomarker data

  • Patients with baseline ADAS-cog scores

  • Patients with MCI due to Alzheimer’s disease

  • Patients with diagnosis records which can be used to determine whether they convert from MCI to AD in 18 months

Finally, a data set with complete imaging, plasma-based biomarkers, ADAS data is drawn from ADNI including 316 MCI patients (99 converters and 217 non-converters). The demographic information of subjects is given in Table 3.

Table 3: Subjects demographic information.

Imaging biomarkers

Imaging data in this study is obtained from dataset UCSF—Cross-Sectional FreeSurfer (FreeSurfer Version 4.3). The dataset is available at In this dataset, all scans were acquired on 1.5 T MRI scanners. The imaging data were processed and analyzed with FreeSurfer 4.3 by the UCSF team. The dataset includes 328 MRI biomarkers which can be grouped into 5 categories: average cortical thickness, standard deviation in cortical thickness, the volumes of cortical parcellations (based on regions of interest automatically segmented in the cortex), the volumes of specific white matter parcellations, and the total surface area of the cortex. Details of the analysis procedure are available at

Plasma-based biomarkers

The plasma-based biomarker data is obtained from dataset Biomarkers Consortium Plasma Proteomics Project RBM multiplex data. The data is available at The data was acquired by analyzing a subset of plasma samples from the ADNI cohort in a 190 analyte multiplex immunoassay panel. The panel, referred to as the human discovery map, was developed on the Luminex xMAP platform by Rules-Based Medicine (RBM) to contain proteins previously reported in the literature to be altered as a result of cancer, cardiovascular disease, metabolic disorders and inflammation. Details of the assay technology and validation has been described elsewhere ( Primer.pdf).


Considering that ADNI contains more than 500 biomarkers, it is essential to select the more predictive biomarkers to obtain a parsimonious model and avoid the classifier suffering overfitting. Then the Bayesian network is established based on the causal relationships among selected markers for predicting the AD-to-MCI conversion. Finally, a reanalysis of Bayesian network results is performed to profile the network disruption of the patients with highest probability of converting to AD and those with lowest probability. The framework is summarized in Fig. 5.

Figure 5: The machine learning framework.
Figure 5

Biomarkers selection

Biomarkers selection includes two stages. At the first stage, ANOVA test is employed to screen biomarkers with significant difference (P < 0.05) between converters and non-converters. At the second stage, lasso regression is used to filter biomarkers related to ADAS-cog from the selected biomarkers at the first stage.

Lasso regression is a popular technique for feature selection which can continuously shrinks coefficients34. It drops biomarkers by shrinking some of coefficients to zero. In this study, a Least Angle Regression (LARS) algorithm is used to solve lasso35.

Bayesian network

Considering that the causal relationships among the selected markers may remain unknown, a Bayesian network structure learning algorithm termed as the max-min hill-climbing (MMHC) is employed to learn the causal relationships among the selected markers. MMHC algorithm is a hybrid method, using concepts and techniques from both constraint-based approaches and score-based approaches, which can achieve high quality in structure learning36. After the Bayesian network is learned from data, the most popular Bayesian network inference algorithm named junction tree is employed to acquire the conversion prediction37.

Model evaluation

In this study, the receiver operating characteristic (ROC) curve is used to evaluate the performance of Bayesian network. The ROC, which has become established as an important tool for classifier evaluation, is a graph of true positive rate (TPR) against false positive rate (FPR) at various operating points as a decision threshold38. The area under the ROC curve (AUC) is a measure of predictive ability39. Moreover, three parameters termed as accuracy (number of correctly classified samples divided by the total number of samples), sensitivity (the number of correctly classified converters divided by the total number of converters) and specificity (the number of correctly classified non-converters divided by the total number of non-converters) are calculated and evaluated by 10-fold cross-validation for a further measurement for the model performance40.

Network disruption analysis

To get more insight into the mechanism of the conversion, a reanalysis of Bayesian network results is performed using a mathematic method for evaluating the disruption of biology network which was proposed in our previous research41. In this study, subjects are divided into two subgroups high risk group and low risk group according to the results of Bayesian network and a mini network balance model is developed to evaluate the network disruption for both high-risk group and low-risk group. The network disruption comparison between these two subgroups may provide more insight into the mechanism of AD-to-MCI conversion.

The mini network balance model contains three parameters U, K, and . U is response to both consistency variation and inconsistency variation comprehensively. K responds to multi-marker consistency variation. is response to the multi-marker inconsistency variation. These three parameters can be calculated as below:

Let be the state vector of patients with conversion risk and be the state vector of normal control group.

Additional Information

How to cite this article: Liu, H. et al. A semi-mechanism approach based on MRI and proteomics for prediction of conversion from mild cognitive impairment to Alzheimer's disease. Sci. Rep. 6, 26712; doi: 10.1038/srep26712 (2016).


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This study was supported by the National Natural Science Foundation of the People’s Republic of China. (Nos 81273588 and 81473274). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Rev December 5, 2013 Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. A complete listing of ADNI investigators can be found at:

Author information


  1. Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, 210009, China

    • Haochen Liu
    • , Xiaoting Zhou
    • , Hao Jiang
    • , Hua He
    •  & Xiaoquan Liu
  2. UC San Francisco, San Francisco, CA 94107, USA.

    • Michael W. Weiner
    • , Norbert Schuff
    • , Howard J. Rosen
    • , Bruce L. Miller
    •  & David Perry
  3. UC San Diego, La Jolla, CA 92093, USA.

    • Paul Aisen
    • , Ronald G. Thomas
    • , Michael Donohue
    • , Sarah Walter
    • , Tamie Sather
    • , Gus Jiminez
    • , Archana B. Balasubramanian
    • , Jennifer Mason
    • , Iris Sim
    • , Lean Thal
    • , James Brewer
    • , Helen Vanderswag
    •  & Adam Fleisher
  4. Mayo Clinic, Rochester, MN USA.

    • Ronald Petersen
    • , Clifford R. Jack Jr.
    • , Matthew Bernstein
    • , Bret Borowski
    • , Jeff Gunter
    • , Matt Senjem
    • , Prashanthi Vemuri
    • , David Jones
    • , Kejal Kantarci
    • , Chad Ward
    • , Sara S. Mason
    • , Colleen S. Albers
    • , David Knopman
    •  & Kris Johnson
  5. UC Berkeley, Berkeley, San Francisco, USA.

    • William Jagust
    • , Susan Landau
    • , Judith L. Heidebrink
    •  & Joanne L. Lord
  6. University of Pennsylvania, Philadelphia, PA 19104, USA.

    • John Q. Trojanowki
    • , Leslie M. Shaw
    • , Steven E. Arnold
    • , Jason H. Karlawish
    • , David Wolk
    •  & Christopher M. Clark
  7. USC, Los Angeles, CA 90032, USA.

    • Arthur W. Toga
    • , Karen Crawford
    • , Scott Neu
    • , Lon S. Schneider
    • , Sonia Pawluczyk
    • , Mauricio Beccera
    • , Liberty Teodoro
    •  & Bryan M. Spann
  8. UC Davis, Sacramento, CA, USA.

    • Laurel Beckett
    • , Danielle Harvey
    • , Charles DeCArli
    • , Evan Fletcher
    • , Pauline Maillard
    • , John Olichney
    •  & Owen Carmichael
  9. Brigham and Women’s Hospital/Harvard Medical School, Boston, MA 02215 USA.

    • Robert C. Green
    • , Reisa A. Sperling
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    •  & Gad Marshall
  10. Indiana University, Bloomington, IN 47405 USA.

    • Andrew J. Saykin
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  11. Washington University St. Louis, MO 63110 USA.

    • John Morris
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  14. Alzheimer’s Association, Chicago, IL 60631 USA.

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  15. University of Pittsburg, Pittsburgh, PA 15213 USA.

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    •  & Donna M. Simpson
  16. Cornell University, Ithaca, NY 14853 USA.

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  17. Albert Einstein College of Medicine of Yeshiva University, Bronx, NY 10461 USA.

    • Peter Davies
  18. AD Drug Discovery Foundation, New York, NY 10019 USA.

    • Howard Fillit
  19. Acumen Pharmaceuticals, Livermore, CA 94551, USA.

    • Franz Hefti
  20. Northwestern University, Chicago, IL 60611 USA.

    • M. Marcel Mesulam
    • , Kristine Lipowski
    • , MASandra Weintraub
    • , Borna Bonakdarpour
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  21. National Institute of Mental Health, Bethesda, MD 20892 USA.

    • William Potter
  22. Brown University, Providence, RI 02912 USA.

    • Peter Snyder
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    •  & Stephen Correia
  23. University of Washington, Seattle, WA 98195, USA.

    • Tom Montine
  24. University of London, London, UK.

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  25. UCLA, Torrance, CA 90509, USA.

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  26. University of Michigan, Ann Arbor, MI, 48109-2800, USA.

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  27. University of Utah, Salt Lake City, UT, 84112, USA.

    • Norm Foster
  28. Banner Alzheimer’s Institute, Phoenix, AZ 85006, USA.

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  29. UUC Irvine, Orange, CA 92868, USA.

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  30. Johns Hopkins University, Baltimore, MD 21205 USA.

    • Marilyn Albert
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  31. Richard Frank Consulting, USA.

    • Richard Frank
  32. National Institute on Aging, Baltimore, Maryland, USA.

    • John Hsiao
  33. Oregon Health and Science University, Portland, OR 97239 USA.

    • Jeffrey Kaye
    • , Joseph Quinn
    • , Lisa Silbert
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  34. University of Alabama, Birmingham, AL USA.

    • Daniel Marson
    • , Randall Griffith
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    • , David Geldmacher
    • , John Brockington
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    •  & Marissa Natelson Love
  35. Mount Sinai School of Medicine, New York, NY USA.

    • Hillel Grossman
    •  & Effie Mitsis
  36. Rush University Medical Center, Chicago, IL 60612, USA.

    • Raj C. Shah
    •  & Leyla deToledo-Morrell
  37. Baylor College of Medicine, Houston, TX, USA.

    • Rachelle S. Doody
    • , Javier Villanueva-Meyer
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    •  & Mimi Dang
  38. Wien Center, Miami Beach, FL 33140 USA.

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  39. Columbia University Medical Center, New York, NY USA.

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  40. New York University, New York, NY USA.

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  41. University of Texas Southwestern Medical School, Galveston, TX 77555 USA.

    • Kyle Womack
    • , Dana Mathews
    •  & Mary Quiceno
  42. Duke University Medical Center, Durham, NC USA.

    • P. Murali Doraiswamy
    • , Jeffrey R. Petrella
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  43. Emory University, Atlanta, GA, 30307, USA.

    • Allan I. Levey
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  44. University of Kansas Medical Center, Kansas City, Kansas, USA.

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  45. University of Kentucky, Lexington, KY, USA.

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  47. University of Rochester Medical Center, Rochester, NY 14642, USA.

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    • Christopher H. van Dyck
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    •  & Pradeep Varma
  49. McGill Univ. Montreal-Jewish General Hospital, Montreal, PQ H3A 2A7, Canada.

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  58. Howard University, Washington, DC 20059 USA.

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  59. Case Western Reserve University, Cleveland, OH 44106 USA.

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  62. Dent Neurologic Institute, Amherst, NY 14226 USA.

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  63. Ohio State University, Columbus, OH 43210 USA.

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  1. Alzheimer’s Disease Neuroimaging Initiative


  1. Search for Haochen Liu in:

  2. Search for Xiaoting Zhou in:

  3. Search for Hao Jiang in:

  4. Search for Hua He in:

  5. Search for Xiaoquan Liu in:


H.L., X.Z., H.J., H.H. and X.L. developed the model. H.L. do the computational work. H.L. wrote the manuscript. The data used in this manuscript is obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI). Database ( As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Hua He or Xiaoquan Liu.

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