Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging
Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18 F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer’s disea...
Uloženo v:
| Vydáno v: | Frontiers in aging neuroscience Ročník 13; s. 708932 |
|---|---|
| Hlavní autoři: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Switzerland
Frontiers Media S.A
03.02.2022
|
| Témata: | |
| ISSN: | 1663-4365, 1663-4365 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to
18
F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer’s disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with
K-Nearest Neighbor
and
BayesNet Naives
as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism. |
|---|---|
| AbstractList | Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer’s disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism. Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer's disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism.Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer's disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism. Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18 F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer’s disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism. Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer's disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with and as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism. |
| Author | Carreras, José Luis Díaz-Álvarez, Josefa Matias-Guiu, Jordi A. Matias-Guiu, Jorge Cabrera-Martín, María Nieves García-Gutiérrez, Fernando Pytel, Vanesa Ayala, José L. Segovia-Ríos, Ignacio Hernández-Lorenzo, Laura |
| AuthorAffiliation | 4 Department of Computer Architecture and Automation, Universidad Complutense , Madrid , Spain 2 Department of Neurology, Hospital Clinico San Carlos, San Carlos Research Health Institute (IdISSC), Universidad Complutense , Madrid , Spain Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; 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.; Lumosity; Lundbeck; Merck Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics 3 Department of Nuclear Medicine, Hospital Clinico San |
| AuthorAffiliation_xml | – name: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; 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.; Lumosity; Lundbeck; Merck Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics – name: 1 Department of Computer Architecture and Communications, Centro Universitario de Mérida, Universidad de Extremadura , Badajoz , Spain – name: 2 Department of Neurology, Hospital Clinico San Carlos, San Carlos Research Health Institute (IdISSC), Universidad Complutense , Madrid , Spain – name: 4 Department of Computer Architecture and Automation, Universidad Complutense , Madrid , Spain – name: 3 Department of Nuclear Medicine, Hospital Clinico San Carlos, San Carlos Research Health Institute (IdISSC), Universidad Complutense , Madrid , Spain |
| Author_xml | – sequence: 1 givenname: Josefa surname: Díaz-Álvarez fullname: Díaz-Álvarez, Josefa – sequence: 2 givenname: Jordi A. surname: Matias-Guiu fullname: Matias-Guiu, Jordi A. – sequence: 3 givenname: María Nieves surname: Cabrera-Martín fullname: Cabrera-Martín, María Nieves – sequence: 4 givenname: Vanesa surname: Pytel fullname: Pytel, Vanesa – sequence: 5 givenname: Ignacio surname: Segovia-Ríos fullname: Segovia-Ríos, Ignacio – sequence: 6 givenname: Fernando surname: García-Gutiérrez fullname: García-Gutiérrez, Fernando – sequence: 7 givenname: Laura surname: Hernández-Lorenzo fullname: Hernández-Lorenzo, Laura – sequence: 8 givenname: Jorge surname: Matias-Guiu fullname: Matias-Guiu, Jorge – sequence: 9 givenname: José Luis surname: Carreras fullname: Carreras, José Luis – sequence: 10 givenname: José L. surname: Ayala fullname: Ayala, José L. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35185510$$D View this record in MEDLINE/PubMed |
| BookMark | eNp1UstOHDEQHEVEgRA-IJfIx1x248fYM3OJhIAlKyGRA5wtr92eNZqxN7YHZTnlN3LIz-VLYliIIFJ8cctdVa0u19tqzwcPVfWe4DljbffJetW7OcWUzBvcdoy-qg6IEGxWM8H3ntX71VFKN7gcxjDm7Ztqn3HSck7wQfXrHDxkp9Hx0Ifo8npMyIaILjfZje4ODDp1qvchuYSCLai7NbgR4u8fP1NpJVAJkPIGLWLwOWQYNyGqAZ3CCD47ha6T8z1aDFOIwUD4vu2HSYdC-lo0cyGhs9Gl5EpxFcbQR7VZb9FyLMv5_l312qohwdHjfVhdL86uTr7MLi7PlyfHFzNdC55nTNcNbyhmdYcpF3hFTGeNMEYIbTC3rTBKlOWpAQW8NU0NjepYwzh0jSCKHVbLna4J6kZuohtV3MqgnHx4CLGXKhaXBpANNIToleVMqNpS2q1MJ7jVwuoVJ7UuWp93WptpNYLRxYZiyAvRlx3v1rIPt7JtOaE1KQIfHwVi-DZByrIYpGEYlIcwJUkFI4LWWNAC_fB81t8hT_9bAM0OoGNIKYKV2mWVi9tltBskwfI-TPIhTPI-THIXpsIk_zCfxP_P-QP0TNMQ |
| CitedBy_id | crossref_primary_10_1016_j_neuroimage_2024_120695 crossref_primary_10_3389_fnagi_2025_1547727 crossref_primary_10_2147_NDT_S496307 crossref_primary_10_3233_JAD_220907 crossref_primary_10_1016_j_procs_2025_04_423 crossref_primary_10_3389_fnagi_2022_1005731 crossref_primary_10_3389_fnagi_2022_961718 crossref_primary_10_3389_fnhum_2025_1526554 crossref_primary_10_3390_sym17071108 crossref_primary_10_1177_19714009251313511 |
| Cites_doi | 10.1016/j.biopsych.2020.01.016 10.1007/s00415-014-7608-0 10.1093/brain/awp232 10.1007/s12021-014-9235-4 10.1016/j.jalz.2011.03.005 10.1016/j.dadm.2019.06.002 10.3389/fncom.2019.00072 10.7326/0003-4819-122-6-199503150-00004 10.1016/j.nrl.2013.03.001 10.1016/j.jalz.2013.05.1769 10.1016/j.neuropsychologia.2017.05.018 10.1212/wnl.43.11.2412-a 10.1212/WNL.0b013e31823b9c5e 10.3390/app7070651 10.1007/s00259-015-2994-9 10.1136/jnnp-2019-320774 10.1038/s41598-017-06624-y 10.1007/s00415-018-8762-6 10.1007/s00259-018-4034-z 10.1186/s12859-019-3027-7 10.1111/jon.12214 10.1007/s00259-009-1264-0 10.1016/j.nicl.2019.101811 10.1016/j.neuroimage.2019.116456 10.1016/j.trci.2019.10.006 10.1093/brain/awm319 10.2214/AJR.18.19822 10.1016/j.jalz.2016.10.006 10.1007/s00259-018-4035-y 10.1007/s00259-013-2458-z 10.1093/brain/awr179 10.1212/WNL.0b013e3182574f79 10.3389/fnagi.2018.00230 10.1186/s12883-018-1060-1 10.1212/WNL.0b013e31821103e6 10.3233/JAD-160874 10.1016/S0140-6736(15)00461-4 10.1016/j.jneumeth.2017.11.002 10.1007/BFb0029438 10.1016/j.neuroimage.2018.10.003 10.1016/j.neuroimage.2003.09.027 10.1016/j.cortex.2019.05.007 10.1093/arclin/acp027 10.3174/ajnr.A0620 |
| ContentType | Journal Article |
| Copyright | Copyright © 2022 Díaz-Álvarez, Matias-Guiu, Cabrera-Martín, Pytel, Segovia-Ríos, García-Gutiérrez, Hernández-Lorenzo, Matias-Guiu, Carreras, Ayala and Alzheimer’s Disease Neuroimaging Initiative. Copyright © 2022 Díaz-Álvarez, Matias-Guiu, Cabrera-Martín, Pytel, Segovia-Ríos, García-Gutiérrez, Hernández-Lorenzo, Matias-Guiu, Carreras, Ayala and Alzheimer’s Disease Neuroimaging Initiative. 2022 Díaz-Álvarez, Matias-Guiu, Cabrera-Martín, Pytel, Segovia-Ríos, García-Gutiérrez, Hernández-Lorenzo, Matias-Guiu, Carreras and Ayala |
| Copyright_xml | – notice: Copyright © 2022 Díaz-Álvarez, Matias-Guiu, Cabrera-Martín, Pytel, Segovia-Ríos, García-Gutiérrez, Hernández-Lorenzo, Matias-Guiu, Carreras, Ayala and Alzheimer’s Disease Neuroimaging Initiative. – notice: Copyright © 2022 Díaz-Álvarez, Matias-Guiu, Cabrera-Martín, Pytel, Segovia-Ríos, García-Gutiérrez, Hernández-Lorenzo, Matias-Guiu, Carreras, Ayala and Alzheimer’s Disease Neuroimaging Initiative. 2022 Díaz-Álvarez, Matias-Guiu, Cabrera-Martín, Pytel, Segovia-Ríos, García-Gutiérrez, Hernández-Lorenzo, Matias-Guiu, Carreras and Ayala |
| CorporateAuthor | Alzheimer’s Disease Neuroimaging Initiative |
| CorporateAuthor_xml | – name: Alzheimer’s Disease Neuroimaging Initiative |
| DBID | AAYXX CITATION NPM 7X8 5PM DOA |
| DOI | 10.3389/fnagi.2021.708932 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic CrossRef PubMed |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1663-4365 |
| ExternalDocumentID | oai_doaj_org_article_7e711cbf536a4f229bd965fc6fcb514c PMC8851241 35185510 10_3389_fnagi_2021_708932 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NIA NIH HHS grantid: U01 AG024904 – fundername: ; |
| GroupedDBID | --- 53G 5VS 7X7 8FE 8FH 8FI 9T4 AAFWJ AAYXX ABIVO ABUWG ACGFO ACGFS ADBBV ADRAZ AEGXH AENEX AFKRA AFPKN AIAGR ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI CITATION DIK E3Z EIHBH F5P FYUFA GROUPED_DOAJ GX1 HCIFZ HYE KQ8 LK8 M2P M48 M7P M~E O5R O5S OK1 PGMZT PIMPY PQQKQ PROAC RNS RPM TR2 UKHRP 88I 8FJ ACXDI ALIPV CCPQU DWQXO GNUQQ HMCUK IPNFZ NPM PHGZM PHGZT PQGLB RIG 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c465t-3c47572034902560b1d9fd6dd66cd05f86da63302deae58d74e7a93735e9761a3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 9 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000759928400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1663-4365 |
| IngestDate | Fri Oct 03 12:26:42 EDT 2025 Tue Sep 30 16:52:51 EDT 2025 Wed Oct 01 15:01:59 EDT 2025 Mon Jul 21 05:55:49 EDT 2025 Sat Nov 29 06:11:40 EST 2025 Tue Nov 18 20:51:03 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | genetic algorithm Alzheimer’s disease frontotemporal dementia unsupervised algorithm machine learning positron emission tomography evolutionary algorithm primary progressive aphasia |
| Language | English |
| License | Copyright © 2022 Díaz-Álvarez, Matias-Guiu, Cabrera-Martín, Pytel, Segovia-Ríos, García-Gutiérrez, Hernández-Lorenzo, Matias-Guiu, Carreras, Ayala and Alzheimer’s Disease Neuroimaging Initiative. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c465t-3c47572034902560b1d9fd6dd66cd05f86da63302deae58d74e7a93735e9761a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 This article was submitted to Alzheimer’s Disease and Related Dementias, a section of the journal Frontiers in Aging Neuroscience Edited by: Fermín Segovia, University of Granada, Spain Reviewed by: Akira Masuda, Doshisha University, Japan; Elena Rodriguez-Vieitez, Karolinska Institutet (KI), Sweden Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). 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. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf |
| OpenAccessLink | https://doaj.org/article/7e711cbf536a4f229bd965fc6fcb514c |
| PMID | 35185510 |
| PQID | 2631624062 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_7e711cbf536a4f229bd965fc6fcb514c pubmedcentral_primary_oai_pubmedcentral_nih_gov_8851241 proquest_miscellaneous_2631624062 pubmed_primary_35185510 crossref_citationtrail_10_3389_fnagi_2021_708932 crossref_primary_10_3389_fnagi_2021_708932 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-02-03 |
| PublicationDateYYYYMMDD | 2022-02-03 |
| PublicationDate_xml | – month: 02 year: 2022 text: 2022-02-03 day: 03 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland |
| PublicationTitle | Frontiers in aging neuroscience |
| PublicationTitleAlternate | Front Aging Neurosci |
| PublicationYear | 2022 |
| Publisher | Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Media S.A |
| References | Bachli (B2) 2020; 208 Nanni (B32) 2018; 302 Bouwman (B4) 2018; 45 Gamberger (B16) 2017; 7 Peña-Casanova (B35) 2009; 24 Li (B23) 2007; 28 Arbizu (B1) 2013; 40 Rabinovici (B36) 2011; 77 Shankle (B40) 1997 Ranganathan (B37) 2018 Habes (B19) 2020; 88 Weiner (B43) 2013; 9 Morris (B31) 1993; 43 Bang (B3) 2015; 386 Matias-Guiu (B26); 42 Yang (B46) 2014 Feis (B13) 2019; 90 Díaz-Álvarez (B10) 2019; 20 Gupta (B18) 2019; 13 Matias-Guiu (B28) 2018; 10 Nori (B34) 2019; 5 Weiner (B44) 2017; 13 Lao (B22) 2004; 21 Fernández-Matarrubia (B15) 2014; 29 Whitwell (B45) 2009; 132 McKhann (B30) 2011; 7 Devenney (B9) 2018; 18 Rascovsky (B38) 2011; 134 Callahan (B5) 1995; 122 Nestor (B33) 2018; 45 Gorno-Tempini (B17) 2011; 76 Dyrba (B12) 2015; 25 Kim (B20) 2019; 23 So (B41) 2017; 7 Della Rosa (B8) 2014; 12 Matias-Guiu (B27) 2017; 101 Matias-Guiu (B25); 262 Davies (B7) 1994 Fernández-Matarrubia (B14) 2017; 57 Zukotynski (B47) 2018; 211 Sajjadi (B39) 2012; 78 Davatzikos (B6) 2019; 197 Matias-Guiu (B29) 2019; 119 Donnelly-Kehoe (B11) 2019; 11 Klippel (B21) 2008; 131 Varrone (B42) 2009; 36 Marshall (B24) 2018; 265 |
| References_xml | – volume: 88 start-page: 70 year: 2020 ident: B19 article-title: Disentangling heterogeneity in Alzheimer’s disease and related dementias using data - driven methods. publication-title: Biol. Psychiatry doi: 10.1016/j.biopsych.2020.01.016 – volume: 262 start-page: 570 ident: B25 article-title: Clinical course of primary progressive aphasia: clinical and FDG-PET patterns. publication-title: J. Neurol. doi: 10.1007/s00415-014-7608-0 – volume: 132 start-page: 2932 year: 2009 ident: B45 article-title: Distinct anatomical subtypes of the behavioural variant of frontotemporal dementia: a cluster analysis study. publication-title: Brain doi: 10.1093/brain/awp232 – volume: 12 start-page: 575 year: 2014 ident: B8 article-title: A standardized [18F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. publication-title: Neuroinformatics doi: 10.1007/s12021-014-9235-4 – volume: 7 start-page: 263 year: 2011 ident: B30 article-title: The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. publication-title: Alzheimers Dement. doi: 10.1016/j.jalz.2011.03.005 – start-page: 37 year: 1994 ident: B7 article-title: Np-completeness of searches for smallest possible feature sets publication-title: Proceedings of the 1994 AAAI Symposium on Intelligent Relevance – volume: 11 start-page: 588 year: 2019 ident: B11 article-title: Robust automated computational approach for classifying frontotemporal neurodegeneration: multimodal/multicenter neuroimaging. publication-title: Alzheimers Dement. doi: 10.1016/j.dadm.2019.06.002 – volume: 13 start-page: 72 year: 2019 ident: B18 article-title: Prediction and classification of Alzheimer’s disease based on combined features from Apolipoprotein-E genotype, cerebrospinal fluid, MR, and FDG-PET imaging biomarkers. publication-title: Front. Comput. Neurosci. doi: 10.3389/fncom.2019.00072 – volume: 122 start-page: 422 year: 1995 ident: B5 article-title: Documentation and evaluation of cognitive impairment in elderly primary care patients. publication-title: Ann. Intern. Med. doi: 10.7326/0003-4819-122-6-199503150-00004 – volume: 29 start-page: 464 year: 2014 ident: B15 article-title: Behavioural variant frontotemporal dementia: clinical and therapeutic approaches. publication-title: Neurologia doi: 10.1016/j.nrl.2013.03.001 – volume: 9 start-page: e111 year: 2013 ident: B43 article-title: The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. publication-title: Alzheimers Dement. doi: 10.1016/j.jalz.2013.05.1769 – volume: 101 start-page: 132 year: 2017 ident: B27 article-title: Reading difficulties in primary progressive aphasia in a regular language-speaking cohort of patients. publication-title: Neuropsychologia doi: 10.1016/j.neuropsychologia.2017.05.018 – volume: 43 start-page: 2412 year: 1993 ident: B31 article-title: The Clinical Dementia Rating (CDR): current version and scoring rules. publication-title: Neurology doi: 10.1212/wnl.43.11.2412-a – volume: 77 start-page: 2034 year: 2011 ident: B36 article-title: Amyloid vs FDG-PET in the differential diagnosis of AD and FTLD. publication-title: Neurology doi: 10.1212/WNL.0b013e31823b9c5e – volume: 7 start-page: 651 year: 2017 ident: B41 article-title: Early diagnosis of dementia from clinical data by machine learning techniques. publication-title: Appl. Sci. doi: 10.3390/app7070651 – volume: 42 start-page: 916 ident: B26 article-title: Visual and statistical analysis of 18F-FDG-PET in primary progressive aphasia. publication-title: Eur. J. Nucl. Med. Mol. Imaging doi: 10.1007/s00259-015-2994-9 – year: 2014 ident: B46 publication-title: Nature-Inspired Optimization Algorithms – volume: 90 start-page: 1207 year: 2019 ident: B13 article-title: A multimodal MRI-based classification signature emerges just prior to symptom onset in frontotemporal dementia mutation carriers. publication-title: J. Neurol. Neurosurg. Psychiatry doi: 10.1136/jnnp-2019-320774 – year: 2018 ident: B37 publication-title: Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics – volume: 7 start-page: 6763 year: 2017 ident: B16 article-title: Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer’s disease. publication-title: Sci. Rep. doi: 10.1038/s41598-017-06624-y – volume: 265 start-page: 1474 year: 2018 ident: B24 article-title: Primary progressive aphasia: a clinical approach. publication-title: J. Neurol. doi: 10.1007/s00415-018-8762-6 – volume: 45 start-page: 1526 year: 2018 ident: B4 article-title: Diagnostic utility of fdg-pet in the differential diagnosis between different forms of primary progressive aphasia. publication-title: Eur. J. Nucl. Med. Mol. Imaging doi: 10.1007/s00259-018-4034-z – volume: 20 start-page: 491 year: 2019 ident: B10 article-title: An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders. publication-title: BMC Bioinformatics doi: 10.1186/s12859-019-3027-7 – volume: 25 start-page: 738 year: 2015 ident: B12 article-title: Predicting Prodromal Alzheimer’s Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data. publication-title: J. Neuroimaging doi: 10.1111/jon.12214 – volume: 36 start-page: 2103 year: 2009 ident: B42 article-title: EANM procedure guidelines for PET brain imaging using [18F]FDG, version 2. publication-title: Eur. J. Nucl. Med. Mol. Imaging doi: 10.1007/s00259-009-1264-0 – volume: 23 start-page: 101811 year: 2019 ident: B20 article-title: Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer’s disease. publication-title: Neuroimage Clin. doi: 10.1016/j.nicl.2019.101811 – volume: 208 start-page: 116456 year: 2020 ident: B2 article-title: Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: a machine learning approach. publication-title: Neuroimage doi: 10.1016/j.neuroimage.2019.116456 – volume: 5 start-page: 918 year: 2019 ident: B34 article-title: Machine learning models to predict onset of dementia: a label learning approach. publication-title: Alzheimers Dement. doi: 10.1016/j.trci.2019.10.006 – volume: 131 start-page: 681 year: 2008 ident: B21 article-title: Automatic classification of MR scans in Alzheimer’s disease. publication-title: Brain doi: 10.1093/brain/awm319 – volume: 211 start-page: 246 year: 2018 ident: B47 article-title: PET/CT of Dementia. publication-title: AJR Am. J. Roentgenol. doi: 10.2214/AJR.18.19822 – volume: 13 start-page: 561 year: 2017 ident: B44 article-title: The Alzheimer’s disease neuroimaging initiative 3: continued innovation for clinical trial improvement. publication-title: Alzheimers Dement. doi: 10.1016/j.jalz.2016.10.006 – volume: 45 start-page: 1509 year: 2018 ident: B33 article-title: Clinical utility of FDG-PET for the differential diagnosis among the main forms of dementia. publication-title: Eur. J. Nucl. Med. Mol. Imaging doi: 10.1007/s00259-018-4035-y – volume: 40 start-page: 1394 year: 2013 ident: B1 article-title: Automated analysis of FDG PET as a tool for single-subject probabilistic prediction and detection of Alzheimer’s disease dementia. publication-title: Eur. J. Nucl. Med. Mol. Imaging doi: 10.1007/s00259-013-2458-z – volume: 134 start-page: 2456 year: 2011 ident: B38 article-title: Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. publication-title: Brain doi: 10.1093/brain/awr179 – volume: 78 start-page: 1670 year: 2012 ident: B39 article-title: Primary progressive aphasia: a tale of two syndromes and the rest. publication-title: Neurology doi: 10.1212/WNL.0b013e3182574f79 – volume: 10 start-page: 230 year: 2018 ident: B28 article-title: Clustering analysis of FDG-PET imaging in primary progressive aphasia. publication-title: Front. Aging Neurosci. doi: 10.3389/fnagi.2018.00230 – volume: 18 start-page: 56 year: 2018 ident: B9 article-title: The behavioural variant frontotemporal dementia phenocopy syndrome is a distinct entity - evidence from a longitudinal study. publication-title: BMC Neurol. doi: 10.1186/s12883-018-1060-1 – volume: 76 start-page: 1006 year: 2011 ident: B17 article-title: Classification of primary progressive aphasia and its variants. publication-title: Neurology doi: 10.1212/WNL.0b013e31821103e6 – volume: 57 start-page: 1251 year: 2017 ident: B14 article-title: Episodic memory dysfunction in behavioral variant frontotemporal dementia: a clinical And FDG-PET Study. publication-title: J. Alzheimers Dis. doi: 10.3233/JAD-160874 – volume: 386 start-page: 1672 year: 2015 ident: B3 article-title: Frontotemporal dementia. publication-title: Lancet doi: 10.1016/S0140-6736(15)00461-4 – volume: 302 start-page: 42 year: 2018 ident: B32 article-title: Ensemble based on static classifier selection for automated diagnosis of Mild Cognitive Impairment. publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2017.11.002 – start-page: 71 year: 1997 ident: B40 article-title: Detecting very early stages of dementia from normal aging with machine learning methods publication-title: Artificial Intelligence in Medicine doi: 10.1007/BFb0029438 – volume: 197 start-page: 652 year: 2019 ident: B6 article-title: Machine learning in neuroimaging: progress and challenges. publication-title: Neuroimage doi: 10.1016/j.neuroimage.2018.10.003 – volume: 21 start-page: 46 year: 2004 ident: B22 article-title: Morphological classification of brains via high-dimensional shape transformations and machine learning methods. publication-title: Neuroimage doi: 10.1016/j.neuroimage.2003.09.027 – volume: 119 start-page: 312 year: 2019 ident: B29 article-title: Machine learning in the clinical and language characterisation of primary progressive aphasia variants. publication-title: Cortex doi: 10.1016/j.cortex.2019.05.007 – volume: 24 start-page: 307 year: 2009 ident: B35 article-title: Spanish Multicenter Normative Studies (NEURONORMA Project): methods and sample characteristics. publication-title: Arch. Clin. Neuropsychol. doi: 10.1093/arclin/acp027 – volume: 28 start-page: 1339 year: 2007 ident: B23 article-title: Hippocampal shape analysis of Alzheimer disease based on machine learning methods. publication-title: AJNR Am. J. Neuroradiol. doi: 10.3174/ajnr.A0620 |
| SSID | ssj0000330058 |
| Score | 2.3513088 |
| Snippet | Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 708932 |
| SubjectTerms | Aging Neuroscience Alzheimer’s disease frontotemporal dementia machine learning positron emission tomography primary progressive aphasia unsupervised algorithm |
| Title | Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/35185510 https://www.proquest.com/docview/2631624062 https://pubmed.ncbi.nlm.nih.gov/PMC8851241 https://doaj.org/article/7e711cbf536a4f229bd965fc6fcb514c |
| Volume | 13 |
| WOSCitedRecordID | wos000759928400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1663-4365 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000330058 issn: 1663-4365 databaseCode: DOA dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1663-4365 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000330058 issn: 1663-4365 databaseCode: M~E dateStart: 20090101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lj9MwELZghRAXxJvyWBmJE1LYJn4lx4VtBRK77GFBvUV-jGmkJkFNi2APiL_BgT_HL2HsZKsWIbhwySG2ZcszGX_jmXxDyFNnVAHGQmLUOE-4SE2iReoTDTJLmRKQxzvd92_UyUk-mxWnW6W-Qk5YTw_cb9yBApWm1njBpOY-ywrjCim8ld4aPOxtsL5jVWw5U9EGs0DDnvdhTPTCigMfqv6gP5ilz3FVBct2DqLI1_8nkPl7ruTW4TO9Qa4PqJEe9qu9SS5Bc4tcPR7i4rfJj8AejW30cPGhRXd_XncU0Sh9iwahrs7B0aM-pa7qaOux1_kcqhqWP79977ApxmiobhydRj6Dga9qQY_i5WGlacwsoNPFukWDC-3nL0OqOz0NSV84iE5QYcLNGz1r64EGm76uYw2kO-TddHL28lUyFF5ILJdilTDLlQjxWV5ESGRSV3gnnZPSurHwuXRa4g5nDjSI3CkOSiPOYQIQ3aSa3SV7TdvAfULzgqfcM-eNt9x4Zhy4MQAYbjOvpB2R8YUUSjuwkofiGIsSvZMguDIKrgyCK3vBjcizzZCPPSXH3zq_CKLddAxs2vEF6lg56Fj5Lx0bkScXilHiZoaQim6gXXdlJlkqAyjCie71irKZignEQmjyRkTtqNDOWnZbmmoeGb5zxMEIrR78j8U_JNey8MtGyDRnj8jearmGx-SK_bSquuU-uaxm-X78ePB5_HXyC6ywJw0 |
| linkProvider | Directory of Open Access Journals |
| 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=Genetic+Algorithms+for+Optimized+Diagnosis+of+Alzheimer%27s+Disease+and+Frontotemporal+Dementia+Using+Fluorodeoxyglucose+Positron+Emission+Tomography+Imaging&rft.jtitle=Frontiers+in+aging+neuroscience&rft.au=D%C3%ADaz-%C3%81lvarez%2C+Josefa&rft.au=Matias-Guiu%2C+Jordi+A&rft.au=Cabrera-Mart%C3%ADn%2C+Mar%C3%ADa+Nieves&rft.au=Pytel%2C+Vanesa&rft.date=2022-02-03&rft.issn=1663-4365&rft.eissn=1663-4365&rft.volume=13&rft.spage=708932&rft_id=info:doi/10.3389%2Ffnagi.2021.708932&rft_id=info%3Apmid%2F35185510&rft.externalDocID=35185510 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1663-4365&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1663-4365&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1663-4365&client=summon |