Dependency criterion based brain pathological age estimation of Alzheimer’s disease patients with MR scans
Objectives Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to the accelerated brain aging. Methods This paper considers this deviation and o...
Saved in:
| Published in | Biomedical engineering online Vol. 16; no. 1; pp. 50 - 20 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
24.04.2017
Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1475-925X 1475-925X |
| DOI | 10.1186/s12938-017-0342-y |
Cover
| Abstract | Objectives
Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to the accelerated brain aging.
Methods
This paper considers this deviation and obtains it by maximizing the correlation between the estimated brain age and the class label rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to the prior knowledge. Secondly, use the support vector regression as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the correlation criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age.
Results
The experimental results showed that the separability of the samples was apparently improved. For normal control- Alzheimer’s disease (NC-AD), normal control- mild cognition impairment (NC-MCI), and mild cognition impairment—Alzheimer’s disease (MCI-AD), the average improvements were 0.164 (31.66%), 0.1284 (34.29%), and 0.0206 (7.1%), respectively. For NC-MCI-AD, the average improvement was 0.2002 (50.39%). The estimated brain pathological age could be not only more helpful for the classification of AD but also more precisely reflect the accelerated brain aging.
Conclusion
In conclusion, this paper proposes a new kind of brain age—brain pathological age and offers an estimation method for it that can distinguish different states of AD, thereby better reflecting accelerated brain aging. Besides, the brain pathological age is most helpful for feature reduction, thereby simplifying the relevant classification algorithm. |
|---|---|
| AbstractList | Abstract Objectives Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to the accelerated brain aging. Methods This paper considers this deviation and obtains it by maximizing the correlation between the estimated brain age and the class label rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to the prior knowledge. Secondly, use the support vector regression as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the correlation criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. Results The experimental results showed that the separability of the samples was apparently improved. For normal control- Alzheimer’s disease (NC-AD), normal control- mild cognition impairment (NC-MCI), and mild cognition impairment—Alzheimer’s disease (MCI-AD), the average improvements were 0.164 (31.66%), 0.1284 (34.29%), and 0.0206 (7.1%), respectively. For NC-MCI-AD, the average improvement was 0.2002 (50.39%). The estimated brain pathological age could be not only more helpful for the classification of AD but also more precisely reflect the accelerated brain aging. Conclusion In conclusion, this paper proposes a new kind of brain age—brain pathological age and offers an estimation method for it that can distinguish different states of AD, thereby better reflecting accelerated brain aging. Besides, the brain pathological age is most helpful for feature reduction, thereby simplifying the relevant classification algorithm. Objectives Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to the accelerated brain aging. Methods This paper considers this deviation and obtains it by maximizing the correlation between the estimated brain age and the class label rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to the prior knowledge. Secondly, use the support vector regression as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the correlation criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. Results The experimental results showed that the separability of the samples was apparently improved. For normal control- Alzheimer’s disease (NC-AD), normal control- mild cognition impairment (NC-MCI), and mild cognition impairment—Alzheimer’s disease (MCI-AD), the average improvements were 0.164 (31.66%), 0.1284 (34.29%), and 0.0206 (7.1%), respectively. For NC-MCI-AD, the average improvement was 0.2002 (50.39%). The estimated brain pathological age could be not only more helpful for the classification of AD but also more precisely reflect the accelerated brain aging. Conclusion In conclusion, this paper proposes a new kind of brain age—brain pathological age and offers an estimation method for it that can distinguish different states of AD, thereby better reflecting accelerated brain aging. Besides, the brain pathological age is most helpful for feature reduction, thereby simplifying the relevant classification algorithm. Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to the accelerated brain aging. This paper considers this deviation and obtains it by maximizing the correlation between the estimated brain age and the class label rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to the prior knowledge. Secondly, use the support vector regression as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the correlation criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. The experimental results showed that the separability of the samples was apparently improved. For normal control- Alzheimer's disease (NC-AD), normal control- mild cognition impairment (NC-MCI), and mild cognition impairment-Alzheimer's disease (MCI-AD), the average improvements were 0.164 (31.66%), 0.1284 (34.29%), and 0.0206 (7.1%), respectively. For NC-MCI-AD, the average improvement was 0.2002 (50.39%). The estimated brain pathological age could be not only more helpful for the classification of AD but also more precisely reflect the accelerated brain aging. In conclusion, this paper proposes a new kind of brain age-brain pathological age and offers an estimation method for it that can distinguish different states of AD, thereby better reflecting accelerated brain aging. Besides, the brain pathological age is most helpful for feature reduction, thereby simplifying the relevant classification algorithm. Objectives Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to the accelerated brain aging. Methods This paper considers this deviation and obtains it by maximizing the correlation between the estimated brain age and the class label rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to the prior knowledge. Secondly, use the support vector regression as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the correlation criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. Results The experimental results showed that the separability of the samples was apparently improved. For normal control- Alzheimer’s disease (NC-AD), normal control- mild cognition impairment (NC-MCI), and mild cognition impairment-Alzheimer’s disease (MCI-AD), the average improvements were 0.164 (31.66%), 0.1284 (34.29%), and 0.0206 (7.1%), respectively. For NC-MCI-AD, the average improvement was 0.2002 (50.39%). The estimated brain pathological age could be not only more helpful for the classification of AD but also more precisely reflect the accelerated brain aging. Conclusion In conclusion, this paper proposes a new kind of brain age-brain pathological age and offers an estimation method for it that can distinguish different states of AD, thereby better reflecting accelerated brain aging. Besides, the brain pathological age is most helpful for feature reduction, thereby simplifying the relevant classification algorithm. Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to the accelerated brain aging.OBJECTIVESTraditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to the accelerated brain aging.This paper considers this deviation and obtains it by maximizing the correlation between the estimated brain age and the class label rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to the prior knowledge. Secondly, use the support vector regression as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the correlation criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age.METHODSThis paper considers this deviation and obtains it by maximizing the correlation between the estimated brain age and the class label rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to the prior knowledge. Secondly, use the support vector regression as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the correlation criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age.The experimental results showed that the separability of the samples was apparently improved. For normal control- Alzheimer's disease (NC-AD), normal control- mild cognition impairment (NC-MCI), and mild cognition impairment-Alzheimer's disease (MCI-AD), the average improvements were 0.164 (31.66%), 0.1284 (34.29%), and 0.0206 (7.1%), respectively. For NC-MCI-AD, the average improvement was 0.2002 (50.39%). The estimated brain pathological age could be not only more helpful for the classification of AD but also more precisely reflect the accelerated brain aging.RESULTSThe experimental results showed that the separability of the samples was apparently improved. For normal control- Alzheimer's disease (NC-AD), normal control- mild cognition impairment (NC-MCI), and mild cognition impairment-Alzheimer's disease (MCI-AD), the average improvements were 0.164 (31.66%), 0.1284 (34.29%), and 0.0206 (7.1%), respectively. For NC-MCI-AD, the average improvement was 0.2002 (50.39%). The estimated brain pathological age could be not only more helpful for the classification of AD but also more precisely reflect the accelerated brain aging.In conclusion, this paper proposes a new kind of brain age-brain pathological age and offers an estimation method for it that can distinguish different states of AD, thereby better reflecting accelerated brain aging. Besides, the brain pathological age is most helpful for feature reduction, thereby simplifying the relevant classification algorithm.CONCLUSIONIn conclusion, this paper proposes a new kind of brain age-brain pathological age and offers an estimation method for it that can distinguish different states of AD, thereby better reflecting accelerated brain aging. Besides, the brain pathological age is most helpful for feature reduction, thereby simplifying the relevant classification algorithm. |
| ArticleNumber | 50 |
| Author | Li, Yongming Liu, Yuchuan Xu, Sha Qiu, Mingguo Wang, Pin Wang, Jie |
| Author_xml | – sequence: 1 givenname: Yongming orcidid: 0000-0002-7542-4356 surname: Li fullname: Li, Yongming email: yongmingli@cqu.edu.cn organization: College of Communication Engineering, Chongqing University, Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Collaborative Innovation Center for Brain Science, Chongqing University – sequence: 2 givenname: Yuchuan surname: Liu fullname: Liu, Yuchuan organization: College of Communication Engineering, Chongqing University – sequence: 3 givenname: Pin surname: Wang fullname: Wang, Pin organization: College of Communication Engineering, Chongqing University – sequence: 4 givenname: Jie surname: Wang fullname: Wang, Jie organization: College of Communication Engineering, Chongqing University – sequence: 5 givenname: Sha surname: Xu fullname: Xu, Sha organization: College of Communication Engineering, Chongqing University – sequence: 6 givenname: Mingguo surname: Qiu fullname: Qiu, Mingguo organization: Department of Medical Image, College of Biomedical Engineering, Third Military Medical University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28438167$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNUsuKFDEULWTEeegHuJECN25K86xKbYRhfA2MCKLgLtxKbndnSCdtUu3QrvwNf88vMTXdDj0DiquE5JyTc8_JcXUQYsCqekzJc0pV-yJT1nPVENo1hAvWbO5VR1R0sumZ_HKwtz-sjnO-JIQR0vYPqkOmBFe07Y4q_wpXGCwGs6lNciMmF0M9QEZbDwlcqFcwLqKPc2fA1zDHGvPoljBOuDirT_33Bbolpl8_fubauoyFO5EchjHXV25c1O8_1tlAyA-r-zPwGR_t1pPq85vXn87eNRcf3p6fnV40RvJ2bCgK7CUbOIV-xi0RHVHUMkOgg15aDtgJS2lrLZeKSwmES9tZy3plhh4sP6nOt7o2wqVepWI3bXQEp68PYpprSKMzHnWvBNhZa6QBJsQw6zuQyFQniGRiwLZosa3WOqxgcwXe3whSoqca9LYGXWrQUw16U0gvt6TVeliiNSWKBP6Wk9s3wS30PH7TUhDBqSwCz3YCKX5dl8T10mWD3kPAuM6aqp4q1bd8Mvj0DvQyrlMo-U4oIVrBGCuoJ_uObqz8-QkFQLcAk2LOCWf_NWZ3h2PceP0zylDO_5O5SzWXV8Ic057pv5J-A4Tn6lM |
| CitedBy_id | crossref_primary_10_1109_JBHI_2019_2897020 crossref_primary_10_3389_fnagi_2022_832195 crossref_primary_10_1016_j_bbe_2021_02_006 crossref_primary_10_1038_s41598_024_63998_6 crossref_primary_10_1016_j_tins_2017_10_001 crossref_primary_10_1186_s12938_018_0489_1 |
| Cites_doi | 10.1016/j.neurobiolaging.2011.08.007 10.1016/j.exger.2015.07.004 10.1007/s00401-009-0485-4 10.1016/j.nbd.2012.03.005 10.1016/j.neurobiolaging.2009.02.008 10.1006/nimg.2001.0786 10.1016/j.forsciint.2014.05.008 10.1016/j.neuroscience.2012.11.038 10.1016/j.neuroimage.2015.04.036 10.1016/j.neurobiolaging.2011.05.018 10.1016/j.neuroimage.2010.04.033 10.1016/j.forsciint.2015.06.001 10.1371/journal.pone.0157514 10.1109/EMBC.2015.7318450 10.1109/EMBC.2015.7319340 10.1016/j.arr.2016.01.002 10.1016/j.jsbmb.2016.03.012 10.1016/j.neurobiolaging.2016.03.016 10.1212/WNL.41.12.1886 10.1016/j.jalz.2016.06.180 10.1126/science.1228541 10.1109/TNNLS.2014.2377245 10.1016/j.ejrad.2016.05.014 10.1016/j.bbadis.2015.11.009 10.1016/j.neuroimage.2016.04.007 10.1016/j.neuropsychologia.2008.09.016 10.1007/978-3-8348-2589-6 10.1016/j.neurobiolaging.2011.06.026 10.1016/j.nicl.2013.12.012 10.1088/0967-3334/36/11/2369 10.1371/journal.pone.0140945 10.1016/j.neurobiolaging.2014.07.046 10.1016/j.neuroimage.2014.06.029 10.1007/s10773-015-2849-y 10.1016/j.neurobiolaging.2006.11.010 10.1109/SMC.2015.397 10.1007/s11682-014-9321-0 10.1016/j.neuroimage.2014.10.002 10.1016/j.jns.2016.03.031 10.1001/archneur.1994.00540210046012 10.1016/j.bbr.2016.05.008 10.1016/j.patrec.2013.04.014 10.1016/j.neuroimage.2010.01.005 10.1016/j.jalz.2012.05.1429 10.1002/ana.24367 10.1016/j.forsciint.2015.12.006 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2017 Copyright BioMed Central 2017 |
| Copyright_xml | – notice: The Author(s) 2017 – notice: Copyright BioMed Central 2017 |
| CorporateAuthor | For the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Alzheimer’s Disease Neuroimaging Initiative (ADNI) |
| CorporateAuthor_xml | – name: For the Alzheimer’s Disease Neuroimaging Initiative (ADNI) – name: Alzheimer’s Disease Neuroimaging Initiative (ADNI) |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QO 7X7 7XB 88E 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AFKRA AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. L6V LK8 M0S M1P M7P M7S P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 7X8 5PM ADTOC UNPAY DOA |
| DOI | 10.1186/s12938-017-0342-y |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Biotechnology Research Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Journals Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Technology Collection (via ProQuest SciTech Premium Collection) Natural Science Collection ProQuest One Community College ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Engineering Collection Biological Sciences Health & Medical Collection (Alumni Edition) Medical Database Biological Science Database Engineering Database (ProQuest) Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection ProQuest Engineering Collection Health Research Premium Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection Engineering Database ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE Publicly Available Content Database MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 5 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 6 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Engineering |
| EISSN | 1475-925X |
| EndPage | 20 |
| ExternalDocumentID | oai_doaj_org_article_984adf6c5ca244bf97a5e28740524be6 10.1186/s12938-017-0342-y PMC5404315 28438167 10_1186_s12938_017_0342_y |
| Genre | Evaluation Studies Journal Article |
| GrantInformation_xml | – fundername: China Postdoctoral Science Foundation grantid: 2013M532153 funderid: http://dx.doi.org/10.13039/501100002858 – fundername: ; grantid: 2013M532153 |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5GY 5VS 6J9 6PF 7X7 88E 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AASML AAWTL ABDBF ABJCF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADRAZ ADUKV AEAQA AENEX AFKRA AFPKN AFRAH AHBYD AHMBA AHSBF AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK E3Z EAD EAP EAS EBD EBLON EBS EJD EMB EMK EMOBN ESX F5P FRP FYUFA GROUPED_DOAJ GX1 H13 HCIFZ HMCUK HYE I-F IAO IGS IHR INH INR ISR ITC KQ8 L6V LK8 M1P M48 M7P M7S MK~ ML~ M~E O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PTHSS PUEGO RBZ RNS ROL RPM RSV SEG SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XSB AAYXX CITATION -A0 3V. ACRMQ ADINQ ALIPV C24 CGR CUY CVF ECM EIF NPM 7QO 7XB 8FD 8FK AZQEC DWQXO FR3 GNUQQ K9. P64 PKEHL PQEST PQUKI PRINS 7X8 5PM 2VQ 4.4 ADTOC C1A IPNFZ RIG UNPAY |
| ID | FETCH-LOGICAL-c536t-1e4e952b31a9f3d047081d2c0a7a95d3ae74d116dd358355a035d7dd298cb9ad3 |
| IEDL.DBID | M48 |
| ISSN | 1475-925X |
| IngestDate | Fri Oct 03 12:22:03 EDT 2025 Sun Oct 26 02:42:24 EDT 2025 Tue Sep 30 16:56:19 EDT 2025 Thu Sep 04 19:11:36 EDT 2025 Mon Oct 06 18:21:08 EDT 2025 Wed Feb 19 02:43:51 EST 2025 Thu Apr 24 23:01:37 EDT 2025 Wed Oct 01 00:48:13 EDT 2025 Sat Sep 06 07:30:07 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Alzheimer’s disease Magnetic resonance imaging Classification Correlation criterion Brain age estimation Support vector regression Brain pathological age |
| Language | English |
| License | Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c536t-1e4e952b31a9f3d047081d2c0a7a95d3ae74d116dd358355a035d7dd298cb9ad3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23 |
| ORCID | 0000-0002-7542-4356 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1186/s12938-017-0342-y |
| PMID | 28438167 |
| PQID | 1894464222 |
| PQPubID | 42562 |
| PageCount | 20 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_984adf6c5ca244bf97a5e28740524be6 unpaywall_primary_10_1186_s12938_017_0342_y pubmedcentral_primary_oai_pubmedcentral_nih_gov_5404315 proquest_miscellaneous_1891889636 proquest_journals_1894464222 pubmed_primary_28438167 crossref_primary_10_1186_s12938_017_0342_y crossref_citationtrail_10_1186_s12938_017_0342_y springer_journals_10_1186_s12938_017_0342_y |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2017-04-24 |
| PublicationDateYYYYMMDD | 2017-04-24 |
| PublicationDate_xml | – month: 04 year: 2017 text: 2017-04-24 day: 24 |
| PublicationDecade | 2010 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Biomedical engineering online |
| PublicationTitleAbbrev | BioMed Eng OnLine |
| PublicationTitleAlternate | Biomed Eng Online |
| PublicationYear | 2017 |
| Publisher | BioMed Central Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: Springer Nature B.V – name: BMC |
| References | Monika Bekiesińska-Figatowska (342_CR11) 2016; 85 R Hullinger (342_CR26) 2017; 322 Geert Jan Biessels (342_CR2) 2016; 1862 K Franke (342_CR38) 2013; 5 A Pepe (342_CR9) 2014; 100 D Hoyer (342_CR18) 2015; 36 AM Coppus (342_CR12) 2012; 33 BC Riedel (342_CR17) 2016; 160 LA Teverovskiy (342_CR39) 2008; 5 Q Zhang (342_CR47) 2015; 26 DJ Selkoe (342_CR1) 2012; 337 L Pini (342_CR27) 2016; 30 K Franke (342_CR36) 2010; 50 M Lorenzi (342_CR28) 2015; 36 E Capitani (342_CR15) 2009; 47 E Luders (342_CR44) 2016; 134 H Takao (342_CR10) 2013; 231 CD Good (342_CR20) 2001; 14 LC Loewe (342_CR43) 2016; 11 C Davatzikos (342_CR33) 2008; 29 JH Kim (342_CR14) 2012; 33 Andres Ortiz (342_CR6) 2013; 34 LG Apostolova (342_CR7) 2014; 24 Thomas A Runkler (342_CR49) 2012 D Terribilli (342_CR21) 2011; 32 Elaheh Moradi (342_CR46) 2015; 104 L Fratiglioni (342_CR25) 1991; 41 R Scherzer-Attali (342_CR13) 2012; 46 A Pfefferbaum (342_CR19) 1994; 51 R Tokuchi (342_CR16) 2016; 365 Duygu Tosun (342_CR4) 2010; 52 342_CR5 S Duchesne (342_CR24) 2016; 12 JH Cole (342_CR22) 2015; 77 P Rzezak (342_CR23) 2015; 10 S Hirano (342_CR35) 2012; 8 K Franke (342_CR37) 2015; 15 I Alafuzoff (342_CR8) 2009; 117 W Li (342_CR48) 2016; 55 M Tondelli (342_CR3) 2012; 33 A Rieckmann (342_CR29) 2016; 42 Pia Baumann (342_CR30) 2015; 253 LC Löwe (342_CR45) 2016; 11 A Irimia (342_CR40) 2015; 9 342_CR41 342_CR42 O Ekizoglu (342_CR32) 2016; 260 Catherine Bortolon (342_CR34) 2015; 70 Volker Vieth (342_CR31) 2014; 241 8080387 - Arch Neurol. 1994 Sep;51(9):874-87 27143434 - Neurobiol Aging. 2016 Jun;42:177-88 26176647 - Exp Gerontol. 2015 Oct;70:46-53 24381557 - Front Aging Neurosci. 2013 Dec 17;5:90 25376330 - Brain Imaging Behav. 2015 Dec;9(4):678-89 21782287 - Neurobiol Aging. 2012 Apr;33(4):825.e25-36 24634832 - Neuroimage Clin. 2014 Jan 04;4:461-72 26093127 - Forensic Sci Int. 2015 Aug;253:76-80 26969397 - J Steroid Biochem Mol Biol. 2016 Jun;160:134-47 27206864 - J Neurol Sci. 2016 Jun 15;365:3-8 20070949 - Neuroimage. 2010 Apr 15;50(3):883-92 25913700 - Neuroimage. 2015 Jul 15;115:1-6 27079530 - Neuroimage. 2016 Jul 1;134:508-13 21958962 - Neurobiol Aging. 2012 Sep;33(9):1988-94 24952229 - Neuroimage. 2014 Oct 15;100:444-59 26736350 - Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015 :666-9 27163751 - Behav Brain Res. 2017 Mar 30;322(Pt B):191-205 22449754 - Neurobiol Dis. 2012 Jun;46(3):663-72 26797254 - Forensic Sci Int. 2016 Mar;260:102.e1-7 26489779 - Physiol Meas. 2015 Nov;36(11):2369-78 20406691 - Neuroimage. 2010 Aug 1;52(1):186-97 25623048 - Ann Neurol. 2015 Apr;77(4):571-81 1745343 - Neurology. 1991 Dec;41(12):1886-92 26827786 - Ageing Res Rev. 2016 Sep;30:25-48 26737240 - Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015 :4278-81 22997326 - Science. 2012 Sep 21;337(6101):1488-92 17174012 - Neurobiol Aging. 2008 Apr;29(4):514-23 21907459 - Neurobiol Aging. 2012 Sep;33(9):1959-66 24908196 - Forensic Sci Int. 2014 Aug;241:118-22 25311276 - Neurobiol Aging. 2015 Jan;36 Suppl 1:S42-52 27410431 - PLoS One. 2016 Jul 13;11(7):e0157514 25532195 - IEEE Trans Neural Netw Learn Syst. 2015 Aug;26(8):1828-33 25312773 - Neuroimage. 2015 Jan 1;104:398-412 18929584 - Neuropsychologia. 2009 Jan;47(2):423-9 11525331 - Neuroimage. 2001 Jul;14(1 Pt 1):21-36 26612719 - Biochim Biophys Acta. 2016 May;1862(5):869-77 19282066 - Neurobiol Aging. 2011 Feb;32(2):354-68 23219841 - Neuroscience. 2013 Feb 12;231:1-12 26474472 - PLoS One. 2015 Oct 16;10(10):e0140945 19184666 - Acta Neuropathol. 2009 Mar;117(3):309-20 27423683 - Eur J Radiol. 2016 Aug;85(8):1427-31 |
| References_xml | – volume: 33 start-page: 1988 issue: 9 year: 2012 ident: 342_CR12 publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2011.08.007 – volume: 70 start-page: 46 year: 2015 ident: 342_CR34 publication-title: Exp Gerontol doi: 10.1016/j.exger.2015.07.004 – volume: 117 start-page: 309 year: 2009 ident: 342_CR8 publication-title: Acta Neuropathol doi: 10.1007/s00401-009-0485-4 – volume: 46 start-page: 663 issue: 3 year: 2012 ident: 342_CR13 publication-title: Neurobiol Dis doi: 10.1016/j.nbd.2012.03.005 – volume: 32 start-page: 354 issue: 2 year: 2011 ident: 342_CR21 publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2009.02.008 – volume: 14 start-page: 21 issue: 1 year: 2001 ident: 342_CR20 publication-title: NeuroImage doi: 10.1006/nimg.2001.0786 – volume: 241 start-page: 118 year: 2014 ident: 342_CR31 publication-title: Forensic Sci Int doi: 10.1016/j.forsciint.2014.05.008 – volume: 231 start-page: 1 year: 2013 ident: 342_CR10 publication-title: Neuroscience doi: 10.1016/j.neuroscience.2012.11.038 – volume: 5 start-page: 1509 issue: 1 year: 2008 ident: 342_CR39 publication-title: IEEE Int SympBiomed Imaging – volume: 15 start-page: 1 issue: 115 year: 2015 ident: 342_CR37 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2015.04.036 – volume: 33 start-page: 825 issue: 4 year: 2012 ident: 342_CR3 publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2011.05.018 – volume: 52 start-page: 186 issue: 1 year: 2010 ident: 342_CR4 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2010.04.033 – volume: 253 start-page: 76 year: 2015 ident: 342_CR30 publication-title: Forensic Sci Int doi: 10.1016/j.forsciint.2015.06.001 – volume: 5 start-page: 90 issue: 1 year: 2013 ident: 342_CR38 publication-title: Front Aging Neurosci – volume: 11 start-page: 1 issue: 7 year: 2016 ident: 342_CR43 publication-title: PLoS ONE doi: 10.1371/journal.pone.0157514 – ident: 342_CR41 doi: 10.1109/EMBC.2015.7318450 – ident: 342_CR5 doi: 10.1109/EMBC.2015.7319340 – volume: 30 start-page: 25 year: 2016 ident: 342_CR27 publication-title: Ageing Res Rev doi: 10.1016/j.arr.2016.01.002 – volume: 160 start-page: 134 year: 2016 ident: 342_CR17 publication-title: J Steroid Biochem Mol Biol doi: 10.1016/j.jsbmb.2016.03.012 – volume: 42 start-page: 177 year: 2016 ident: 342_CR29 publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2016.03.016 – volume: 41 start-page: 1886 issue: 12 year: 1991 ident: 342_CR25 publication-title: Neurology doi: 10.1212/WNL.41.12.1886 – volume: 12 start-page: 111 issue: 7 year: 2016 ident: 342_CR24 publication-title: Alzheimers Dementia doi: 10.1016/j.jalz.2016.06.180 – volume: 337 start-page: 1488 issue: 6101 year: 2012 ident: 342_CR1 publication-title: Science doi: 10.1126/science.1228541 – volume: 26 start-page: 1828 issue: 8 year: 2015 ident: 342_CR47 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2014.2377245 – volume: 85 start-page: 1427 issue: 8 year: 2016 ident: 342_CR11 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2016.05.014 – volume: 1862 start-page: 869 issue: 5 year: 2016 ident: 342_CR2 publication-title: Biochimica Biophysica Acta doi: 10.1016/j.bbadis.2015.11.009 – volume: 134 start-page: 508 year: 2016 ident: 342_CR44 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2016.04.007 – volume: 47 start-page: 423 issue: 2 year: 2009 ident: 342_CR15 publication-title: Neuropsychologia doi: 10.1016/j.neuropsychologia.2008.09.016 – volume-title: Data analytics: models and algorithms for intelligent data analysis year: 2012 ident: 342_CR49 doi: 10.1007/978-3-8348-2589-6 – volume: 33 start-page: 1959 issue: 9 year: 2012 ident: 342_CR14 publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2011.06.026 – volume: 24 start-page: 461 issue: 4 year: 2014 ident: 342_CR7 publication-title: NeuroImage Clin doi: 10.1016/j.nicl.2013.12.012 – volume: 36 start-page: 2369 issue: 11 year: 2015 ident: 342_CR18 publication-title: Physiol Measurement doi: 10.1088/0967-3334/36/11/2369 – volume: 10 start-page: e0140945 issue: 10 year: 2015 ident: 342_CR23 publication-title: PLoS ONE doi: 10.1371/journal.pone.0140945 – volume: 36 start-page: S42 issue: Suppl 1 year: 2015 ident: 342_CR28 publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2014.07.046 – volume: 100 start-page: 444 issue: 15 year: 2014 ident: 342_CR9 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2014.06.029 – volume: 55 start-page: 2097 issue: 4 year: 2016 ident: 342_CR48 publication-title: Int J Theor Phys doi: 10.1007/s10773-015-2849-y – volume: 29 start-page: 514 issue: 4 year: 2008 ident: 342_CR33 publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2006.11.010 – ident: 342_CR42 doi: 10.1109/SMC.2015.397 – volume: 9 start-page: 678 issue: 4 year: 2015 ident: 342_CR40 publication-title: Brain Imaging Behav doi: 10.1007/s11682-014-9321-0 – volume: 11 start-page: e0157514 issue: 7 year: 2016 ident: 342_CR45 publication-title: PLoS ONE doi: 10.1371/journal.pone.0157514 – volume: 104 start-page: 398 year: 2015 ident: 342_CR46 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2014.10.002 – volume: 365 start-page: 3 year: 2016 ident: 342_CR16 publication-title: J Neurol Sci doi: 10.1016/j.jns.2016.03.031 – volume: 51 start-page: 874 issue: 9 year: 1994 ident: 342_CR19 publication-title: Arch Neurol doi: 10.1001/archneur.1994.00540210046012 – volume: 322 start-page: 191 issue: Part B year: 2017 ident: 342_CR26 publication-title: Behav Brain Res doi: 10.1016/j.bbr.2016.05.008 – volume: 34 start-page: 1725 issue: 14 year: 2013 ident: 342_CR6 publication-title: Pattern Recognit Lett doi: 10.1016/j.patrec.2013.04.014 – volume: 50 start-page: 883 issue: 3 year: 2010 ident: 342_CR36 publication-title: Neuroimage. doi: 10.1016/j.neuroimage.2010.01.005 – volume: 8 start-page: 531 issue: 4 year: 2012 ident: 342_CR35 publication-title: Alzheimers Dementia doi: 10.1016/j.jalz.2012.05.1429 – volume: 77 start-page: 571 issue: 4 year: 2015 ident: 342_CR22 publication-title: Ann Neurol doi: 10.1002/ana.24367 – volume: 260 start-page: 102.e1 year: 2016 ident: 342_CR32 publication-title: Forensic Sci Int doi: 10.1016/j.forsciint.2015.12.006 – reference: 26612719 - Biochim Biophys Acta. 2016 May;1862(5):869-77 – reference: 27206864 - J Neurol Sci. 2016 Jun 15;365:3-8 – reference: 8080387 - Arch Neurol. 1994 Sep;51(9):874-87 – reference: 20070949 - Neuroimage. 2010 Apr 15;50(3):883-92 – reference: 24381557 - Front Aging Neurosci. 2013 Dec 17;5:90 – reference: 26827786 - Ageing Res Rev. 2016 Sep;30:25-48 – reference: 22449754 - Neurobiol Dis. 2012 Jun;46(3):663-72 – reference: 1745343 - Neurology. 1991 Dec;41(12):1886-92 – reference: 25623048 - Ann Neurol. 2015 Apr;77(4):571-81 – reference: 27410431 - PLoS One. 2016 Jul 13;11(7):e0157514 – reference: 26093127 - Forensic Sci Int. 2015 Aug;253:76-80 – reference: 25376330 - Brain Imaging Behav. 2015 Dec;9(4):678-89 – reference: 22997326 - Science. 2012 Sep 21;337(6101):1488-92 – reference: 19282066 - Neurobiol Aging. 2011 Feb;32(2):354-68 – reference: 24952229 - Neuroimage. 2014 Oct 15;100:444-59 – reference: 24908196 - Forensic Sci Int. 2014 Aug;241:118-22 – reference: 23219841 - Neuroscience. 2013 Feb 12;231:1-12 – reference: 11525331 - Neuroimage. 2001 Jul;14(1 Pt 1):21-36 – reference: 21907459 - Neurobiol Aging. 2012 Sep;33(9):1959-66 – reference: 21958962 - Neurobiol Aging. 2012 Sep;33(9):1988-94 – reference: 26489779 - Physiol Meas. 2015 Nov;36(11):2369-78 – reference: 17174012 - Neurobiol Aging. 2008 Apr;29(4):514-23 – reference: 25311276 - Neurobiol Aging. 2015 Jan;36 Suppl 1:S42-52 – reference: 26736350 - Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015 :666-9 – reference: 24634832 - Neuroimage Clin. 2014 Jan 04;4:461-72 – reference: 27079530 - Neuroimage. 2016 Jul 1;134:508-13 – reference: 25312773 - Neuroimage. 2015 Jan 1;104:398-412 – reference: 26176647 - Exp Gerontol. 2015 Oct;70:46-53 – reference: 20406691 - Neuroimage. 2010 Aug 1;52(1):186-97 – reference: 27163751 - Behav Brain Res. 2017 Mar 30;322(Pt B):191-205 – reference: 27143434 - Neurobiol Aging. 2016 Jun;42:177-88 – reference: 27423683 - Eur J Radiol. 2016 Aug;85(8):1427-31 – reference: 21782287 - Neurobiol Aging. 2012 Apr;33(4):825.e25-36 – reference: 25532195 - IEEE Trans Neural Netw Learn Syst. 2015 Aug;26(8):1828-33 – reference: 26474472 - PLoS One. 2015 Oct 16;10(10):e0140945 – reference: 26797254 - Forensic Sci Int. 2016 Mar;260:102.e1-7 – reference: 19184666 - Acta Neuropathol. 2009 Mar;117(3):309-20 – reference: 26969397 - J Steroid Biochem Mol Biol. 2016 Jun;160:134-47 – reference: 25913700 - Neuroimage. 2015 Jul 15;115:1-6 – reference: 18929584 - Neuropsychologia. 2009 Jan;47(2):423-9 – reference: 26737240 - Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015 :4278-81 |
| SSID | ssj0020069 |
| Score | 2.1904078 |
| Snippet | Objectives
Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that... Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that there is a... Objectives Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that... Abstract Objectives Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore... |
| SourceID | doaj unpaywall pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 50 |
| SubjectTerms | Acetylcholinesterase Adults Age Age determination Aged Aged, 80 and over Aging Aging - pathology Algorithms Alzheimer Disease - diagnostic imaging Alzheimer Disease - pathology Alzheimer's disease Analytics Anatomy Apolipoprotein E Asymmetry Atrophy Biomarkers Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Biotechnology Brain Brain - diagnostic imaging Brain - pathology Brain age estimation Brain pathological age Brain research Cerebrospinal fluid Chronology Classification Cognitive ability Conversion Correlation criterion Cortex Cybernetics Dementia Dementia disorders Diabetes mellitus Diagnosis Disease Progression Engineering Estimation Feasibility studies Female High resolution Humans Image Interpretation, Computer-Assisted - methods International conferences Learning algorithms Life span Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Mathematical models Menstrual cycle Mental disorders Neurodegenerative diseases Neuroimaging NMR Nuclear magnetic resonance Pattern recognition Physiology Positron emission tomography Principal components analysis Radiology Regression analysis Reproducibility of Results Resonance Senescence Sensitivity and Specificity Severity of Illness Index Sexually transmitted diseases Statistical analysis STD Studies Tissues Training Traumatic brain injury |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrZ1Lb9QwEIAt1EOhB1TKK1CQkThRRXX8iJ1jW6gqpOWAqNSb5VfUSmm2anaFlhN_g7_HL8GTONGuePTCNXEiZ2bsGXucbxB6WxaWMy9JHmMhgGozmltnWB5dEw1KKMcc_I08-1SenfOPF-JirdQXnAkb8MCD4A4rxY2vSyeciZ7I1pU0IgCknQjKbehh20RV42IqLbUAwJtymIUqDzvwanBoS-aAvMtXG16oh_X_KcL8_aDklC3dQfeX7Y1ZfTVNs-aQTnfRwxRJ4qPhCx6he6HdQztrfME9tD1LmfPHqHmfqt26FY4TBRCa5y0GH-axhTIRGGoTjzMhjrMMBv7G8GMjntf4qPl2Ga6uw-3P7z86nPI6OGFZOwz7uXj2GXdRVd0TdH764cvJWZ4qLeROsHKRF4GHSlDLClPVzBMuY6TgqSNGmkp4ZoLkvihK75mIIZswhAkvvaeVcrYynj1FW-28Dc8RNp5Chkwp4NYbS5SrC2FrEjytHS1lhsgoee0ShhyqYTS6X46oUg_K0lFZGpSlVxl6Nz1yMzA4_tX4GNQ5NQR8dn8hGpVORqXvMqoM7Y_GoNOY7nShqrh2hi2zDL2ZbsfRCCkW04b5sm9TKBUntfiKZ4PtTD2JgQBkaaME5IZVbXR18057ddkTv0XPQBIZOhjtb61bf5fEwWSid8vtxf-Q20v0gPaDjOeU76Otxe0yvIpB28K-7sfnL8UcPno priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV3ta9QwGH-YN1D3QXS-VadE8JOjrE2aNv0gsunGEO6Q4WDfQt7qDo723N0h519vnjatd6jza5OWNM9r8iS_H8DbPNUZs0US-1wIQbUZjbVRLPahiTrBhWEGbyOPJ_n5Zfb5il_twKS_C4PHKnuf2Dpq2xjcIz9KRelXLrhh8WH-PUbWKKyu9hQaKlAr2PctxNgd2KWIjDWC3ZPTyZeLYQmGwLyhtpmK_GiB0Q4PcxUxQuHF663o1IL4_y3z_PMA5VBF3YN7q3qu1j_UbLYRqM4ewoOQYZLjTiUewY6r92FvA3dwH-6OQ0X9MUw_BRZcsybegSByc1MTjG2WaKSPIMhZ3HtI4r0PQVyO7sIjaSpyPPt57aa4-01CsYcErNYFwU1eMr4gCy-_xRO4PDv9-vE8DvQLseEsX8apy1zJqWapKitmk6zw6YOlJlGFKrllyhWZTdPcWsZ9HsdVwrgtrKWlMLpUlj2FUd3U7jkQZSmWzYRAMHulE2GqlOsqcZZWhuZFBEk_7dIEbHKkyJjJdo0ictlJSnpJSZSUXEfwbnhl3gFz3Nb5BGU5dERM7fZBc_NNBhOVpciUrXLDjfI5j67KQnGHdAAJp5l2eQQHvSbIYOgL-VstI3gzNHsTxbqLql2zavukQnhP5z_xrFOcYSQ-O8DSrZ-BYkultoa63VJPr1sYcN4CI_EIDnvl2xjWv2ficNDP_8_bi9t_-SXcp63tZDHNDmC0vFm5Vz5HW-rXwfB-AYSYOlE priority: 102 providerName: ProQuest – databaseName: Springer Nature OA Free Journals dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3LbtQwFL2CIgFdICiPphRkJFZUEYkdO86yLVQV0rBAVOrO8itqpTRTNTNC01V_o7_Hl-CbeKIZUUBs44cs36dz7HMB3ovcFMyVWRpyISTVZjQ1VrM0hCbqJZeWWXyNPPkqjk-KL6f8NJJF41uYVfw-l-Jjh_EIr1uVKZLVpYv78CDEKNHjsuJwPFsh424ELe8cthZ2enb-u1LK329GjvDoJjyat5d68UM3zUoEOnoKT2LqSPYHWT-De77dgs0VQsEteDiJUPlzaD7F8rZ2QYJnQErmaUswaDlisC4EwWLES9dHglshSLgxvGQk05rsN9dn_vzCX_28ue1IBHJI5GHtCP7AJZNvpAuy6V7AydHn74fHaSytkFrOxCzNfeErTg3LdVUzlxVlSA0ctZkudcUd074sXJ4L5xgPORrXGeOudI5W0ppKO_YSNtpp67eBaEcREpMSieq1yaStc27qzDtaWyrKBLLlzisbecex_EWj-vOHFGoQlgrCUigstUjgwzjkciDd-FvnAxTn2BH5svsPQY1UND9VyUK7WlhudchnTF2Vmnuk-s84LYwXCewulUFFI-5ULqtwWMZ_ZAm8G5uD-SGmols_nfd9cimDFwtTvBp0Z1xJiPwIy4YdKNe0am2p6y3t-VlP8c170iOewN5S_1aW9eed2BtV9N_7tvNfc7-Gx7S3piKlxS5szK7m_k1Ix2bmbW-IvwBmxi7F priority: 102 providerName: Springer Nature – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEF6VVAJ64FFegYIWiRPVpvau114fw6OqkFIhRKQgDta-TKsaJ4oToVQc-Bv8PX4JO_baJFBAHLhZ9tpaj8fffPbsfIPQkzhUETNJQBwXAlFtRonSkhEXmqgVXGimoRp5dBwfjaNXEz7ZQu_bWpim6BzmR-wPQT7SyEYM1kvSixrH3YY-O5iZvHn9RXxQQQSDBVoJAXk7srqEtmPuiHoPbY-PXw_f1fVGCScp5ROf57zwvI1IVQv6X8RCf11M2WVUd9CVZTmTq0-yKNaC1uF19Lm93WatytlguVADff6TEuR_sscNdM2TXTxsvPMm2rLlLtpZk0DcRZdHPrl_CxUvfENevcIOy0BEelpiCLMGK-hkgaF9cgvW2AEhBomQpvYST3M8LM5P7OlHO__25WuFfeoJe-XYCsMvZzx6gyvnTdVtND58-fb5EfHNIIjmLF6Q0EY25VSxUKY5M0GUODJjqA5kIlNumLRJZMIwNoZxxyq5DBg3iTE0FVql0rA7qFdOS3sPYWkoJPGEAGl9qQKh85CrPLCG5prGSR8F7YPPtFdKh4YdRVZ_MYk4a2yaOZtmYNNs1UdPu1NmjUzInwY_A2_qBoLCd71jOv-QecDIUhFJk8eaa-kYmMrTRHILzQkCTiNl4z7aa30x87BTZaFI3ec9_NXro8fdYQcYkAWSpZ0u6zGhEA533SXuNq7bzcRxFUgkOwskG069MdXNI-XpSS1KzmuZJt5H-637r03r95bY796Qv9vt_j-NfoCu0trpI0KjPdRbzJf2oSOQC_XIA8F35ylumg priority: 102 providerName: Unpaywall |
| Title | Dependency criterion based brain pathological age estimation of Alzheimer’s disease patients with MR scans |
| URI | https://link.springer.com/article/10.1186/s12938-017-0342-y https://www.ncbi.nlm.nih.gov/pubmed/28438167 https://www.proquest.com/docview/1894464222 https://www.proquest.com/docview/1891889636 https://pubmed.ncbi.nlm.nih.gov/PMC5404315 https://biomedical-engineering-online.biomedcentral.com/track/pdf/10.1186/s12938-017-0342-y https://doaj.org/article/984adf6c5ca244bf97a5e28740524be6 |
| UnpaywallVersion | publishedVersion |
| Volume | 16 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central_OA刊 customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: RBZ dateStart: 20020101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAFT databaseName: Colorado Digital library customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: KQ8 dateStart: 20020501 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAFT databaseName: Colorado Digital library customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: KQ8 dateStart: 20020101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: DOA dateStart: 20020101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: ABDBF dateStart: 20020101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: ADMLS dateStart: 20020101 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: DIK dateStart: 20020101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: GX1 dateStart: 20020101 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: M~E dateStart: 20020101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: RPM dateStart: 20020101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: 8FG dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest – providerCode: PRVFZP databaseName: Scholars Portal Journals: Open Access customDbUrl: eissn: 1475-925X dateEnd: 20250131 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: M48 dateStart: 20020501 isFulltext: true titleUrlDefault: http://journals.scholarsportal.info providerName: Scholars Portal – providerCode: PRVAVX databaseName: Springer Nature HAS Fully OA customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: AAJSJ dateStart: 20021201 isFulltext: true titleUrlDefault: https://www.springernature.com providerName: Springer Nature – providerCode: PRVAVX databaseName: Springer Nature OA Free Journals customDbUrl: eissn: 1475-925X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0020069 issn: 1475-925X databaseCode: C6C dateStart: 20020112 isFulltext: true titleUrlDefault: http://www.springeropen.com/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bb9MwFD7aRQL2gGDcCqMyEk9MYYkdx84DQl1ZmZBaTROVxlPk2A6bFNLRtILyxN_g7_FL8EmTqBUFxEukxE5knYvPsY_zfQDPoyANmRG-53IhBNVm1Eu1Yp4LTdRKLjXT-DfycBSdjsN3F_xiCxp6q1qA5calHfJJjaf5y6-fF6-dw7-qHF5GRyXGLDySJTwEtPMW27DrAlWMTA7DsC0q4OI5rgubG19bC00Vgv-mtPP305NtCXUPbs6La7X4ovJ8JUoN7sDtOr0kvaU93IUtW-zD3gro4D7cGNbl9HuQv6kpcPWCuNkDYZsnBcHAZkiK3BEECYub6ZG4qYcgKMfyb0cyyUgv_3Zprz7Z6c_vP0pSF3tIjdVaEtzkJcNzUjr9lfdhPDh53z_1avoFT3MWzbzAhjbmNGWBijNm_FC49MFQ7SuhYm6YsiI0QRAZw7jL47jyGTfCGBpLncbKsAewU0wK-wiIMhTLZlIimL1KfamzgKeZbw3NNI1EB_xG8omuscmRIiNPqjWKjJKlshKnrASVlSw68KJ95XoJzPG3zseozrYjYmpXDybTj0ntokksQ2WySHOtXM6TZrFQ3CIdgM9pmNqoAweNMSSNnSaBjN2CGvfROvCsbXYuinUXVdjJvOoTSOlmOveJh0vbaUfisgMs3ToJiDWrWhvqektxdVnBgPMKGIl34LCxv5Vh_VkSh62J_ltuj__r20_gFq28KfRoeAA7s-ncPnUp2yztwra4EO4qB2-7sHt8Mjo7d3f9qN-tNkG6laO6lvHorPfhF9KiRDk |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Nb9QwEB2VIlF6QFC-FgoYCS5UURM7TpwDQoVSbWm3B9RKezOO7dCVVsnS7KoKP4rfiCdf3RVQTr1unMjrGb8Ze-z3AN5EQRoyE_uey4WQVJtRL9WKeS40USu40EzjbeTRSTQ8C7-M-XgNfnV3YfBYZYeJNVCbQuMe-W4gErdywQ2LD7MfHqpGYXW1k9Bo3OLIVpduyVa-P9x39n1L6cHn009Dr1UV8DRn0dwLbGgTTlMWqCRjxg9jFxUN1b6KVcINUzYOTRBExjDu0hOufMZNbAxNhE4TZZj77i24HTKHJW7-xOOrBR7S_raV00BEuyXGUjwqFntItOdVK7Gvlgj4W1775_HMvka7CRuLfKaqSzWdLoXBg_twr81fyV7jcA9gzeZbsLnEargFd0Ztvf4hTPZbjV1dEQdPyAtd5AQjpyEpilMQVETu8Jc4bCPI-tFcpyRFRvamP8_tBPfWSVtKIi0TbElwC5mMvpLSeUf5CM5uxAyPYT0vcvsUiDIUi3JCIFW-Sn2hs4CnmW8NzTSN4gH43bBL3TKfowDHVNYrIBHJxlLSWUqipWQ1gHf9K7OG9uO6xh_Rln1DZOyufyguvssWAGQiQmWySHOtXEaVZkmsuEWxAZ_TMLXRALY7T5AtjJTyyukH8Lp_7AAAqzoqt8WibhMI4XDUfeJJ4zh9T1zugYVhNwLxikutdHX1ST45r0nGeU27xAew0znfUrf-PRI7vX_-f9yeXf-XX8HG8HR0LI8PT46ew11az6PQo-E2rM8vFvaFywbn6ct6ChL4dtNz_jcflnBW |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3LbtQwFLWgSIUuEBQKAwWMxIoqamLHjrMsU0blMRVCVOrO8iu0UkhGkxmhYcVv8Ht8Cb6JE82IAmIbP2Tdh-91jn0uQi94olNqszjyuRCQalMSaaNo5EMTcYIJQw28Rp6e8pOz9O05Ow91Tpv-tnsPSXZvGoClqVoczmzRubjghw1EKbiElUVAYRetrqMbqQ9uUMJgzMfDiQt4eAOUeeWwjWDUcvZflWj-fl9yAE130M1lNVOrr6os1-LS5A66HRJKfNRZwF10zVW7aGeNZnAXbU8DgH4Plceh6K1ZYb9fAFFzXWEIZRZrqBaBoURxvyFiv9lgoOHo3jfiusBH5bcLd_nFzX9-_9HgAO_gwM7aYPiti6cfceM11txHZ5PXn8YnUSi4EBlG-SJKXOpyRjRNVF5QG6eZl6klJlaZypmlymWpTRJuLWU-c2Mqpsxm1pJcGJ0rS_fQVlVX7iHCyhIAyoQA-nqlY2GKhOkidpYUhvBshOJe8tIENnIoilHK9lQiuOyUJb2yJChLrkbo5TBk1lFx_K3zK1Dn0BFYtNsP9fyzDE4pc5EqW3DDjPJZji7yTDEHBQBiRlLt-Ajt98Ygg2s3MhG5P0LDn7MRej40e6cEpEVVrl62fRIh_N7mp3jQ2c6wEp8PAFjrJZBtWNXGUjdbqsuLlvibtVRIbIQOevtbW9afJXEwmOi_5fbov-Z-hrY_HE_k-zen7x6jW6R1rDQi6T7aWsyX7onP1xb6aeuTvwDFlzn7 |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEF6VVAJ64FFegYIWiRPVpvau114fw6OqkFIhRKQgDta-TKsaJ4oToVQc-Bv8PX4JO_baJFBAHLhZ9tpaj8fffPbsfIPQkzhUETNJQBwXAlFtRonSkhEXmqgVXGimoRp5dBwfjaNXEz7ZQu_bWpim6BzmR-wPQT7SyEYM1kvSixrH3YY-O5iZvHn9RXxQQQSDBVoJAXk7srqEtmPuiHoPbY-PXw_f1fVGCScp5ROf57zwvI1IVQv6X8RCf11M2WVUd9CVZTmTq0-yKNaC1uF19Lm93WatytlguVADff6TEuR_sscNdM2TXTxsvPMm2rLlLtpZk0DcRZdHPrl_CxUvfENevcIOy0BEelpiCLMGK-hkgaF9cgvW2AEhBomQpvYST3M8LM5P7OlHO__25WuFfeoJe-XYCsMvZzx6gyvnTdVtND58-fb5EfHNIIjmLF6Q0EY25VSxUKY5M0GUODJjqA5kIlNumLRJZMIwNoZxxyq5DBg3iTE0FVql0rA7qFdOS3sPYWkoJPGEAGl9qQKh85CrPLCG5prGSR8F7YPPtFdKh4YdRVZ_MYk4a2yaOZtmYNNs1UdPu1NmjUzInwY_A2_qBoLCd71jOv-QecDIUhFJk8eaa-kYmMrTRHILzQkCTiNl4z7aa30x87BTZaFI3ec9_NXro8fdYQcYkAWSpZ0u6zGhEA533SXuNq7bzcRxFUgkOwskG069MdXNI-XpSS1KzmuZJt5H-637r03r95bY796Qv9vt_j-NfoCu0trpI0KjPdRbzJf2oSOQC_XIA8F35ylumg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Dependency+criterion+based+brain+pathological+age+estimation+of+Alzheimer%E2%80%99s+disease+patients+with+MR+scans&rft.jtitle=Biomedical+engineering+online&rft.au=Li%2C+Yongming&rft.au=Liu%2C+Yuchuan&rft.au=Wang%2C+Pin&rft.au=Wang%2C+Jie&rft.date=2017-04-24&rft.pub=BioMed+Central&rft.eissn=1475-925X&rft.volume=16&rft.issue=1&rft_id=info:doi/10.1186%2Fs12938-017-0342-y&rft.externalDocID=10_1186_s12938_017_0342_y |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1475-925X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1475-925X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1475-925X&client=summon |