Deep learning classification of reading disability with regional brain volume features
•Deformation-based deep learning was used to classify reading disability.•Autoencoder pretraining optimized neural network classification accuracy.•Reading disability classification precision (0.75) and recall (0.78) was observed.•Brain regions were differentially predictive of cases and controls.•C...
Saved in:
| Published in: | NeuroImage (Orlando, Fla.) Vol. 273; p. 120075 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
Elsevier Inc
01.06.2023
Elsevier Limited Elsevier |
| Subjects: | |
| ISSN: | 1053-8119, 1095-9572, 1095-9572 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | •Deformation-based deep learning was used to classify reading disability.•Autoencoder pretraining optimized neural network classification accuracy.•Reading disability classification precision (0.75) and recall (0.78) was observed.•Brain regions were differentially predictive of cases and controls.•Classification probability related to non-word and real word reading abilities.
Developmental reading disability is a prevalent and often enduring problem with varied mechanisms that contribute to its phenotypic heterogeneity. This mechanistic and phenotypic variation, as well as relatively modest sample sizes, may have limited the development of accurate neuroimaging-based classifiers for reading disability, including because of the large feature space of neuroimaging datasets. An unsupervised learning model was used to reduce deformation-based data to a lower-dimensional manifold and then supervised learning models were used to classify these latent representations in a dataset of 96 reading disability cases and 96 controls (mean age: 9.86 ± 1.56 years). A combined unsupervised autoencoder and supervised convolutional neural network approach provided an effective classification of cases and controls (accuracy: 77%; precision: 0.75; recall: 0.78). Brain regions that contributed to this classification accuracy were identified by adding noise to the voxel-level image data, which showed that reading disability classification accuracy was most influenced by the superior temporal sulcus, dorsal cingulate, and lateral occipital cortex. Regions that were most important for the accurate classification of controls included the supramarginal gyrus, orbitofrontal, and medial occipital cortex. The contribution of these regions reflected individual differences in reading-related abilities, such as non-word decoding or verbal comprehension. Together, the results demonstrate an optimal deep learning solution for classification using neuroimaging data. In contrast with standard mass-univariate test results, results from the deep learning model also provided evidence for regions that may be specifically affected in reading disability cases. |
|---|---|
| AbstractList | Developmental reading disability is a prevalent and often enduring problem with varied mechanisms that contribute to its phenotypic heterogeneity. This mechanistic and phenotypic variation, as well as relatively modest sample sizes, may have limited the development of accurate neuroimaging-based classifiers for reading disability, including because of the large feature space of neuroimaging datasets. An unsupervised learning model was used to reduce deformation-based data to a lower-dimensional manifold and then supervised learning models were used to classify these latent representations in a dataset of 96 reading disability cases and 96 controls (mean age: 9.86 ± 1.56 years). A combined unsupervised autoencoder and supervised convolutional neural network approach provided an effective classification of cases and controls (accuracy: 77%; precision: 0.75; recall: 0.78) Brain regions that contributed to this classification accuracy were identified by adding noise to the voxel-leve image data, which showed that reading disability classification accuracy was most influenced by the superior temporal sulcus, dorsal cingulate, and lateral occipital cortex. Regions that were most important for the accurate classification of controls included the supramarginal gyrus, orbitofrontal, and medial occipital cortex. The contribution of these regions reflected individual differences in reading-related abilities, such as non-word decoding or verbal comprehension. Together, the results demonstrate an optimal deep learning solution for classification using neuroimaging data. In contrast with standard mass-univariate test results, results from the deep learning model also provided evidence for regions that may be specifically affected in reading disability cases. Developmental reading disability is a prevalent and often enduring problem with varied mechanisms that contribute to its phenotypic heterogeneity. This mechanistic and phenotypic variation, as well as relatively modest sample sizes, may have limited the development of accurate neuroimaging-based classifiers for reading disability, including because of the large feature space of neuroimaging datasets. An unsupervised learning model was used to reduce deformation-based data to a lower-dimensional manifold and then supervised learning models were used to classify these latent representations in a dataset of 96 reading disability cases and 96 controls (mean age: 9.86 ± 1.56 years). A combined unsupervised autoencoder and supervised convolutional neural network approach provided an effective classification of cases and controls (accuracy: 77%; precision: 0.75; recall: 0.78). Brain regions that contributed to this classification accuracy were identified by adding noise to the voxel-level image data, which showed that reading disability classification accuracy was most influenced by the superior temporal sulcus, dorsal cingulate, and lateral occipital cortex. Regions that were most important for the accurate classification of controls included the supramarginal gyrus, orbitofrontal, and medial occipital cortex. The contribution of these regions reflected individual differences in reading-related abilities, such as non-word decoding or verbal comprehension. Together, the results demonstrate an optimal deep learning solution for classification using neuroimaging data. In contrast with standard mass-univariate test results, results from the deep learning model also provided evidence for regions that may be specifically affected in reading disability cases. •Deformation-based deep learning was used to classify reading disability.•Autoencoder pretraining optimized neural network classification accuracy.•Reading disability classification precision (0.75) and recall (0.78) was observed.•Brain regions were differentially predictive of cases and controls.•Classification probability related to non-word and real word reading abilities. Developmental reading disability is a prevalent and often enduring problem with varied mechanisms that contribute to its phenotypic heterogeneity. This mechanistic and phenotypic variation, as well as relatively modest sample sizes, may have limited the development of accurate neuroimaging-based classifiers for reading disability, including because of the large feature space of neuroimaging datasets. An unsupervised learning model was used to reduce deformation-based data to a lower-dimensional manifold and then supervised learning models were used to classify these latent representations in a dataset of 96 reading disability cases and 96 controls (mean age: 9.86 ± 1.56 years). A combined unsupervised autoencoder and supervised convolutional neural network approach provided an effective classification of cases and controls (accuracy: 77%; precision: 0.75; recall: 0.78). Brain regions that contributed to this classification accuracy were identified by adding noise to the voxel-level image data, which showed that reading disability classification accuracy was most influenced by the superior temporal sulcus, dorsal cingulate, and lateral occipital cortex. Regions that were most important for the accurate classification of controls included the supramarginal gyrus, orbitofrontal, and medial occipital cortex. The contribution of these regions reflected individual differences in reading-related abilities, such as non-word decoding or verbal comprehension. Together, the results demonstrate an optimal deep learning solution for classification using neuroimaging data. In contrast with standard mass-univariate test results, results from the deep learning model also provided evidence for regions that may be specifically affected in reading disability cases. Developmental reading disability is a prevalent and often enduring problem with varied mechanisms that contribute to its phenotypic heterogeneity. This mechanistic and phenotypic variation, as well as relatively modest sample sizes, may have limited the development of accurate neuroimaging-based classifiers for reading disability, including because of the large feature space of neuroimaging datasets. An unsupervised learning model was used to reduce deformation-based data to a lower-dimensional manifold and then supervised learning models were used to classify these latent representations in a dataset of 96 reading disability cases and 96 controls (mean age: 9.86 ± 1.56 years). A combined unsupervised autoencoder and supervised convolutional neural network approach provided an effective classification of cases and controls (accuracy: 77%; precision: 0.75; recall: 0.78). Brain regions that contributed to this classification accuracy were identified by adding noise to the voxel-level image data, which showed that reading disability classification accuracy was most influenced by the superior temporal sulcus, dorsal cingulate, and lateral occipital cortex. Regions that were most important for the accurate classification of controls included the supramarginal gyrus, orbitofrontal, and medial occipital cortex. The contribution of these regions reflected individual differences in reading-related abilities, such as non-word decoding or verbal comprehension. Together, the results demonstrate an optimal deep learning solution for classification using neuroimaging data. In contrast with standard mass-univariate test results, results from the deep learning model also provided evidence for regions that may be specifically affected in reading disability cases. Developmental reading disability is a prevalent and often enduring problem with varied mechanisms that contribute to its phenotypic heterogeneity. This mechanistic and phenotypic variation, as well as relatively modest sample sizes, may have limited the development of accurate neuroimaging-based classifiers for reading disability, including because of the large feature space of neuroimaging datasets. An unsupervised learning model was used to reduce deformation-based data to a lower-dimensional manifold and then supervised learning models were used to classify these latent representations in a dataset of 96 reading disability cases and 96 controls (mean age: 9.86 ± 1.56 years). A combined unsupervised autoencoder and supervised convolutional neural network approach provided an effective classification of cases and controls (accuracy: 77%; precision: 0.75; recall: 0.78). Brain regions that contributed to this classification accuracy were identified by adding noise to the voxel-level image data, which showed that reading disability classification accuracy was most influenced by the superior temporal sulcus, dorsal cingulate, and lateral occipital cortex. Regions that were most important for the accurate classification of controls included the supramarginal gyrus, orbitofrontal, and medial occipital cortex. The contribution of these regions reflected individual differences in reading-related abilities, such as non-word decoding or verbal comprehension. Together, the results demonstrate an optimal deep learning solution for classification using neuroimaging data. In contrast with standard mass-univariate test results, results from the deep learning model also provided evidence for regions that may be specifically affected in reading disability cases.Developmental reading disability is a prevalent and often enduring problem with varied mechanisms that contribute to its phenotypic heterogeneity. This mechanistic and phenotypic variation, as well as relatively modest sample sizes, may have limited the development of accurate neuroimaging-based classifiers for reading disability, including because of the large feature space of neuroimaging datasets. An unsupervised learning model was used to reduce deformation-based data to a lower-dimensional manifold and then supervised learning models were used to classify these latent representations in a dataset of 96 reading disability cases and 96 controls (mean age: 9.86 ± 1.56 years). A combined unsupervised autoencoder and supervised convolutional neural network approach provided an effective classification of cases and controls (accuracy: 77%; precision: 0.75; recall: 0.78). Brain regions that contributed to this classification accuracy were identified by adding noise to the voxel-level image data, which showed that reading disability classification accuracy was most influenced by the superior temporal sulcus, dorsal cingulate, and lateral occipital cortex. Regions that were most important for the accurate classification of controls included the supramarginal gyrus, orbitofrontal, and medial occipital cortex. The contribution of these regions reflected individual differences in reading-related abilities, such as non-word decoding or verbal comprehension. Together, the results demonstrate an optimal deep learning solution for classification using neuroimaging data. In contrast with standard mass-univariate test results, results from the deep learning model also provided evidence for regions that may be specifically affected in reading disability cases. |
| ArticleNumber | 120075 |
| Author | Vaden, Kenneth I. Wang, James Z. Eckert, Mark A. Joshi, Foram |
| AuthorAffiliation | b Department of Otolaryngology - Head and Neck Surgery, Medical University of South Carolina, Charleston, S.C, U.S.A a School of Computing, Clemson University, Clemson, S.C, U.S.A |
| AuthorAffiliation_xml | – name: b Department of Otolaryngology - Head and Neck Surgery, Medical University of South Carolina, Charleston, S.C, U.S.A – name: a School of Computing, Clemson University, Clemson, S.C, U.S.A |
| Author_xml | – sequence: 1 givenname: Foram surname: Joshi fullname: Joshi, Foram organization: School of Computing, Clemson University, Clemson, S.C, U.S.A – sequence: 2 givenname: James Z. surname: Wang fullname: Wang, James Z. organization: School of Computing, Clemson University, Clemson, S.C, U.S.A – sequence: 3 givenname: Kenneth I. surname: Vaden fullname: Vaden, Kenneth I. organization: Department of Otolaryngology - Head and Neck Surgery, Medical University of South Carolina, Charleston, S.C, U.S.A – sequence: 4 givenname: Mark A. orcidid: 0000-0001-9286-7717 surname: Eckert fullname: Eckert, Mark A. email: eckert@musc.edu organization: Department of Otolaryngology - Head and Neck Surgery, Medical University of South Carolina, Charleston, S.C, U.S.A |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37054828$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkktvEzEUhUeoiD7gL6CR2LBJsD3jGXvDq7wqVWIDbC0_7qQ3OHaxZ4Ly73GSUmhWkRe27HM_Hd9zz6uTEANUVU3JnBLavVrOA0wp4kovYM4Ia-aUEdLzR9UZJZLPJO_ZyfbMm5mgVJ5W5zkvCSGStuJJddr0hLeCibPqxweA29qDTgHDorZe54wDWj1iDHUc6gTabV8cZm3Q47ipf-N4U-4XRaF9bZLGUK-jn1ZQD6DHKUF-Wj0etM_w7G6_qL5_-vjt8svs-uvnq8t31zPbNXScNbxvO0q7RgrbU9mRzjhHDZdAuGmFY0aDGIpR6KBtBuGEG4w2VnYNGO5Ic1Fd7bku6qW6TaUjaaOiRrW7iGmhdBrRelCkeNOybw1h0DrHtGiZ4aKlTkjWDqyw3uxZt5NZgbMQxqT9A-jDl4A3ahHXahtJX1YhvLwjpPhrgjyqFWYL3usAccqKCUKloLKRRfriQLqMUyr93Kk6KamQvKie_2_p3svf_IpA7AU2xZwTDPcSSna-1FL9GxW1HRW1H5VS-vqg1OK4i718Dv0xgPd7AJSA1whJZYsQLDhMYMeSAB4DeXsAsR5DmT__EzbHIf4AFC_3_Q |
| CitedBy_id | crossref_primary_10_1016_j_cortex_2024_08_012 crossref_primary_10_1016_j_dcn_2024_101470 crossref_primary_10_1162_imag_a_00219 |
| Cites_doi | 10.1177/0022219409355476 10.1016/j.neuroimage.2011.02.040 10.1111/desc.12422 10.7554/eLife.69523 10.1371/journal.pone.0103537 10.1037/dev0001340 10.1002/hbm.24995 10.1093/cercor/bhq045 10.1016/B978-0-12-407794-2.00065-1 10.1038/nprot.2015.014 10.1111/j.1460-9568.2007.05701.x 10.1002/mrm.10606 10.1016/j.cub.2018.07.018 10.1089/brain.2012.0116 10.1007/s00429-013-0687-3 10.1177/0022219411410042 10.1196/annals.1416.024 10.1007/s11881-003-0001-9 10.1146/annurev-clinpsy-032814-112842 10.3390/sym12111809 10.1002/hbm.23426 10.1016/S0010-9452(08)70268-5 10.1037/amp0000452 10.1523/ENEURO.0103-15.2015 10.1109/ACCESS.2021.3062709 10.1523/JNEUROSCI.2092-13.2013 10.1093/cercor/bhy037 10.1002/jmri.22003 10.1523/JNEUROSCI.2131-07.2007 10.1162/jocn_a_00721 10.1016/j.neuroimage.2009.07.063 10.1016/j.brainres.2018.01.014 10.1016/j.neubiorev.2017.08.001 10.3389/fnhum.2014.00830 10.1044/2015_JSLHR-L-13-0310 10.1016/j.neuroimage.2007.07.007 10.1017/S1355617709090900 10.1037/a0019319 10.1037/a0025323 10.1016/j.neuroimage.2008.05.057 10.1037/0033-2909.130.6.858 10.1016/j.cobeha.2016.06.007 10.1371/journal.pone.0043122 10.1016/j.neuropsychologia.2012.07.017 10.1002/hbm.22127 10.1016/j.jneumeth.2019.04.007 10.1207/s1532799xssr0803_5 10.1002/hbm.23112 10.1001/jama.1990.03450080084036 10.1016/j.tics.2016.06.012 10.1016/j.nicl.2016.03.014 10.1002/hbm.20752 |
| ContentType | Journal Article |
| Copyright | 2023 The Author(s) Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved. 2023. The Author(s) |
| Copyright_xml | – notice: 2023 The Author(s) – notice: Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved. – notice: 2023. The Author(s) |
| DBID | 6I. AAFTH AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7T9 7TK 7X7 7XB 88E 88G 8AO 8FD 8FE 8FH 8FI 8FJ 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2M M7P P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PSYQQ Q9U RC3 7X8 5PM DOA |
| DOI | 10.1016/j.neuroimage.2023.120075 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Linguistics and Language Behavior Abstracts (LLBA) Neurosciences Abstracts Health & Medical Collection (Proquest) ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Psychology Database (Alumni) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central Korea Engineering Research Database ProQuest Health & Medical Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection ProQuest Health & Medical Collection Medical Database Psychology Database (ProQuest) Biological Science Database Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) 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 (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Psychology ProQuest Central Basic Genetics Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest One Psychology ProQuest Central Student 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 Pharma Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Genetics Abstracts Health Research Premium Collection 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) ProQuest Biological Science Collection ProQuest Central Basic ProQuest One Academic Eastern Edition Linguistics and Language Behavior Abstracts (LLBA) ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Psychology Journals (Alumni) Biological Science Database ProQuest SciTech Collection Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest Psychology Journals ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | ProQuest One Psychology MEDLINE - Academic MEDLINE |
| 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: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1095-9572 |
| EndPage | 120075 |
| ExternalDocumentID | oai_doaj_org_article_0feaa974b02e4dd2a842b5841d8924f2 PMC10167676 37054828 10_1016_j_neuroimage_2023_120075 S1053811923002215 |
| Genre | Research Support, U.S. Gov't, Non-P.H.S Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: NICHD NIH HHS grantid: R01 HD069374 – fundername: NCRR NIH HHS grantid: C06 RR014516 |
| GroupedDBID | --- --K --M .1- .FO .~1 0R~ 123 1B1 1RT 1~. 1~5 29N 4.4 457 4G. 53G 5RE 5VS 7-5 71M 7X7 88E 8AO 8FE 8FH 8FI 8FJ 8P~ 9JM AABNK AAEDT AAEDW AAFWJ AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXLA AAXUO AAYWO ABBQC ABCQJ ABFNM ABFRF ABIVO ABJNI ABMAC ABMZM ABUWG ABXDB ACDAQ ACGFO ACGFS ACIEU ACLOT ACPRK ACRLP ACRPL ACVFH ADBBV ADCNI ADEZE ADFGL ADFRT ADMUD ADNMO ADVLN ADXHL AEBSH AEFWE AEIPS AEKER AENEX AEUPX AFJKZ AFKRA AFPKN AFPUW AFRHN AFTJW AFXIZ AGHFR AGQPQ AGUBO AGWIK AGYEJ AHHHB AHMBA AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRLJ AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX APXCP ASPBG AVWKF AXJTR AZFZN AZQEC BBNVY BENPR BHPHI BKOJK BLXMC BNPGV BPHCQ BVXVI CAG CCPQU COF CS3 DM4 DU5 DWQXO EBS EFBJH EFKBS EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN FYUFA G-2 G-Q GBLVA GNUQQ GROUPED_DOAJ HCIFZ HDW HEI HMCUK HMK HMO HMQ HVGLF HZ~ IHE J1W KOM LG5 LK8 LX8 M1P M29 M2M M2V M41 M7P MO0 MOBAO N9A O-L O9- OAUVE OK1 OVD OZT P-8 P-9 P2P PC. PHGZM PHGZT PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PSYQQ Q38 R2- ROL RPZ SAE SCC SDF SDG SDP SES SEW SNS SSH SSN SSZ T5K TEORI UKHRP UV1 WUQ XPP YK3 Z5R ZMT ZU3 ~G- ~HD 3V. 6I. AACTN AADPK AAFTH AAIAV ABLVK ABYKQ AFKWA AJBFU AJOXV AMFUW C45 LCYCR NCXOZ RIG ZA5 9DU AAYXX AFFHD CITATION AGCQF AGRNS ALIPV CGR CUY CVF ECM EIF NPM 7T9 7TK 7XB 8FD 8FK FR3 K9. P64 PKEHL PQEST PQUKI PRINS Q9U RC3 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c631t-35746116398c719606bdd1b59e05b48d2bae8f828e6e43f8d8dfbabc963eb5d03 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000984006700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1053-8119 1095-9572 |
| IngestDate | Fri Oct 03 12:51:05 EDT 2025 Tue Nov 04 02:06:35 EST 2025 Thu Oct 02 11:24:35 EDT 2025 Sat Nov 08 05:58:53 EST 2025 Mon Jul 21 05:58:47 EDT 2025 Sat Nov 29 06:56:33 EST 2025 Tue Nov 18 22:11:52 EST 2025 Fri Feb 23 02:38:00 EST 2024 Tue Oct 14 19:35:51 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Specific learning disorder in reading Reading disability Dyslexia Brain morphology Convolutional neural network |
| Language | English |
| License | This is an open access article under the CC BY license. Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c631t-35746116398c719606bdd1b59e05b48d2bae8f828e6e43f8d8dfbabc963eb5d03 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-9286-7717 |
| OpenAccessLink | https://doaj.org/article/0feaa974b02e4dd2a842b5841d8924f2 |
| PMID | 37054828 |
| PQID | 2806991895 |
| PQPubID | 2031077 |
| PageCount | 1 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_0feaa974b02e4dd2a842b5841d8924f2 pubmedcentral_primary_oai_pubmedcentral_nih_gov_10167676 proquest_miscellaneous_2801981939 proquest_journals_2806991895 pubmed_primary_37054828 crossref_primary_10_1016_j_neuroimage_2023_120075 crossref_citationtrail_10_1016_j_neuroimage_2023_120075 elsevier_sciencedirect_doi_10_1016_j_neuroimage_2023_120075 elsevier_clinicalkey_doi_10_1016_j_neuroimage_2023_120075 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-06-01 |
| PublicationDateYYYYMMDD | 2023-06-01 |
| PublicationDate_xml | – month: 06 year: 2023 text: 2023-06-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Amsterdam |
| PublicationTitle | NeuroImage (Orlando, Fla.) |
| PublicationTitleAlternate | Neuroimage |
| PublicationYear | 2023 |
| Publisher | Elsevier Inc Elsevier Limited Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier Limited – name: Elsevier |
| References | Ashburner (bib0003) 2007; 38 Kusner, Paige, Hernández-Lobato (bib0031) 2017 Kinga, Adam (bib0026) 2015 O'Hare, Lu, Houston, Bookheimer, Sowell (bib0043) 2008; 42 Moll, Kunze, Neuhoff, Bruder, Schulte-Körne (bib0039) 2014; 9 Zhang, Peng (bib0077) 2022; 58 Eden, Olulade, Evans, Krafnick, Alkire (bib0018) 2016 Usman, Muniyandi, Omar, Mohamad (bib0063) 2021 Costanzo, Menghini, Caltagirone, Oliveri, Vicari (bib0009) 2012; 50 Wagner, Torgesen, Rashotte (bib0064) 1999 Woodcock, Mather, McGrew, Shrank (bib0069) 2001 Wechsler (bib0065) 1999 Cain, Oakhill (bib0007) 2011; 44 Gosztolya, Pintér, Tóth, Grósz, Markó, Csapó (bib0021) 2019 Nair, Hinton (bib0041) 2010 Ramus, Altarelli, Jednoróg, Zhao, Di Covella (bib0050) 2018; 84 Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel, Blondel, Prettenhofer, Weiss, Dubourg (bib0045) 2011; 12 Yan, Jiang, Li, Wang, Perkins, Cao (bib0075) 2021; 10 Hadsell, Chopra, LeCun (bib0024) 2006 Kurth, Gaser, Luders (bib0030) 2015; 10 Vandermosten, Hoeft, Norton (bib0071) 2016; 10 He, Zhang, Ren, Sun (bib0025) 2016 Krishnan, Watkins, Bishop (bib0028) 2016; 20 Richlan, Kronbichler, Wimmer (bib0052) 2009; 30 Wechsler (bib0066) 2004 Peterson, Pennington (bib0046) 2015; 11 Sliwinska, James, Devlin (bib0059) 2015; 27 Bishop, Snowling (bib0006) 2004; 130 Cutting, Clements-Stephens, Pugh, Burns, Cao, Pekar, Davis, Rimrodt (bib0011) 2013; 3 Eckert, Vaden, Maxwell, Cute, Gebregziabher, Berninger (bib0015) 2017; 7 Ballard (bib0004) 1987 Fletcher (bib0019) 2009; 15 Mohajer, Abbasi, Mohammadi, Khazaie, Osorio, Rosenzweig, Eickhoff, Zarei, Tahmasian, Eickhoff (bib0038) 2020; 41 Locascio, Mahone, Eason, Cutting (bib0034) 2010; 43 Vincent, Larochelle, Lajoie, Bengio, Manzagol, Bottou (bib0072) 2010; 11 Yue, Martin, Hamilton, Rose (bib0076) 2019; 29 Wolf, Denckla (bib0067) 2005 Krafnick, Flowers, Luetje, Napoliello, Eden (bib0027) 2014; 34 Petersson, Silva, Castro-Caldas, Ingvar, Reis (bib0047) 2007; 26 Ha, D., Schmidhuber, J., 2018. Recurrent world models facilitate policy evolution. arXiv preprint arXiv:1809.01999. Lyon, Shaywitz, Shaywitz (bib0035) 2003; 53 Rumelhart, Hinton, Williams (bib0056) 1985 Liebenthal, Desai, Ellingson, Ramachandran, Desai, Binder (bib0032) 2010; 20 Srivastava, Hinton, Krizhevsky, Sutskever, Salakhutdinov (bib0060) 2014; 15 Catts, Compton, Tomblin, Bridges (bib0008) 2012; 104 Tamboer, Vorst, Ghebreab, Scholte (bib0062) 2016; 11 Eckert, Leonard, Wilke, Eckert, Richards, Richards, Berninger (bib0013) 2005; 41 Berninger, Abbott (bib0005) 2010; 102 Linkersdörfer, Lonnemann, Lindberg, Hasselhorn, Fiebach (bib0033) 2012 Newell, Deng (bib0042) 2020 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., 2016. Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467. Grigorenko, Compton, Fuchs, Wagner, Willcutt, Fletcher (bib0022) 2020; 75 Vaden, Muftuler, Hickok (bib0070) 2010; 49 Ribeiro, Singh, Guestrin (bib0051) 2016 Płoński, Gradkowski, Altarelli, Monzalvo, van Ermingen-Marbach, Grande, Heim, Marchewka, Bogorodzki, Ramus (bib0049) 2017; 38 Moreau, Stonyer, McKay, Waldie (bib0040) 2018; 1683 Eckert, Berninger, Vaden, Gebregziabher, Tsu (bib0014) 2016; 3 Goodfellow, Bengio, Courville (bib0020) 2016 Suk, Lee, Shen (bib0061) 2015; 220 Wang, Guan, Ma, Luo, Chu, Hu, Zhao, Men, Tan, Gao, Qin (bib0073) 2022 Wilke, Schmithorst, Holland (bib0074) 2003; 50 Richlan, Kronbichler, Wimmer (bib0053) 2011; 56 Rudebeck, Rich (bib0055) 2018; 28 Maisog, Einbinder, Flowers, Turkeltaub, Eden (bib0036) 2008; 1145 Richlan, Kronbichler, Wimmer (bib0054) 2013; 34 Salmelin, Helenius (bib0057) 2004; 8 Woodcock (bib0068) 1987 Manjon, Coupe, Marti-Bonmati, Collins, Robles (bib0037) 2010; 31 Shaywitz, Shaywitz, Fletcher, Escobar (bib0058) 1990; 264 Aboud, Bailey, Petrill, Cutting (bib0002) 2016; 19 Eckert, Iuricich, Vaden, Glaze, Consortium (bib0017) 2020; 12 Plassmann, O'doherty, Rangel (bib0048) 2007; 27 Paulesu, Danelli, Berlingeri (bib0044) 2014; 8 Krizhevsky, Sutskever, Hinton (bib0029) 2012; 25 Cui, Xia, Su, Shu, Gong (bib0010) 2016; 37 Eckert, Vaden (bib0016) 2019; 322 Duff, Tomblin, Catts (bib0012) 2015; 58 Zeiler, Krishnan, Taylor, Fergus (bib0078) 2010 Duff (10.1016/j.neuroimage.2023.120075_bib0012) 2015; 58 Richlan (10.1016/j.neuroimage.2023.120075_bib0054) 2013; 34 Richlan (10.1016/j.neuroimage.2023.120075_bib0053) 2011; 56 Srivastava (10.1016/j.neuroimage.2023.120075_bib0060) 2014; 15 He (10.1016/j.neuroimage.2023.120075_bib0025) 2016 Suk (10.1016/j.neuroimage.2023.120075_bib0061) 2015; 220 Liebenthal (10.1016/j.neuroimage.2023.120075_bib0032) 2010; 20 Mohajer (10.1016/j.neuroimage.2023.120075_bib0038) 2020; 41 Richlan (10.1016/j.neuroimage.2023.120075_bib0052) 2009; 30 Eckert (10.1016/j.neuroimage.2023.120075_bib0016) 2019; 322 Cutting (10.1016/j.neuroimage.2023.120075_bib0011) 2013; 3 Locascio (10.1016/j.neuroimage.2023.120075_bib0034) 2010; 43 Gosztolya (10.1016/j.neuroimage.2023.120075_bib0021) 2019 Moll (10.1016/j.neuroimage.2023.120075_bib0039) 2014; 9 Moreau (10.1016/j.neuroimage.2023.120075_bib0040) 2018; 1683 Ramus (10.1016/j.neuroimage.2023.120075_bib0050) 2018; 84 10.1016/j.neuroimage.2023.120075_bib0001 Costanzo (10.1016/j.neuroimage.2023.120075_bib0009) 2012; 50 Catts (10.1016/j.neuroimage.2023.120075_bib0008) 2012; 104 Lyon (10.1016/j.neuroimage.2023.120075_bib0035) 2003; 53 Wolf (10.1016/j.neuroimage.2023.120075_bib0067) 2005 Rudebeck (10.1016/j.neuroimage.2023.120075_bib0055) 2018; 28 Krishnan (10.1016/j.neuroimage.2023.120075_bib0028) 2016; 20 Berninger (10.1016/j.neuroimage.2023.120075_bib0005) 2010; 102 Yue (10.1016/j.neuroimage.2023.120075_bib0076) 2019; 29 Petersson (10.1016/j.neuroimage.2023.120075_bib0047) 2007; 26 Cain (10.1016/j.neuroimage.2023.120075_bib0007) 2011; 44 Wilke (10.1016/j.neuroimage.2023.120075_bib0074) 2003; 50 Eckert (10.1016/j.neuroimage.2023.120075_bib0014) 2016; 3 Eckert (10.1016/j.neuroimage.2023.120075_bib0017) 2020; 12 Ribeiro (10.1016/j.neuroimage.2023.120075_bib0051) 2016 Plassmann (10.1016/j.neuroimage.2023.120075_bib0048) 2007; 27 Płoński (10.1016/j.neuroimage.2023.120075_bib0049) 2017; 38 Wechsler (10.1016/j.neuroimage.2023.120075_bib0065) 1999 Kinga (10.1016/j.neuroimage.2023.120075_bib0026) 2015 Maisog (10.1016/j.neuroimage.2023.120075_bib0036) 2008; 1145 Zhang (10.1016/j.neuroimage.2023.120075_bib0077) 2022; 58 Woodcock (10.1016/j.neuroimage.2023.120075_bib0068) 1987 Manjon (10.1016/j.neuroimage.2023.120075_bib0037) 2010; 31 Bishop (10.1016/j.neuroimage.2023.120075_bib0006) 2004; 130 Shaywitz (10.1016/j.neuroimage.2023.120075_bib0058) 1990; 264 Krizhevsky (10.1016/j.neuroimage.2023.120075_bib0029) 2012; 25 Krafnick (10.1016/j.neuroimage.2023.120075_bib0027) 2014; 34 O'Hare (10.1016/j.neuroimage.2023.120075_bib0043) 2008; 42 Hadsell (10.1016/j.neuroimage.2023.120075_bib0024) 2006 Linkersdörfer (10.1016/j.neuroimage.2023.120075_bib0033) 2012 Pedregosa (10.1016/j.neuroimage.2023.120075_bib0045) 2011; 12 Usman (10.1016/j.neuroimage.2023.120075_bib0063) 2021 Peterson (10.1016/j.neuroimage.2023.120075_bib0046) 2015; 11 Yan (10.1016/j.neuroimage.2023.120075_bib0075) 2021; 10 Grigorenko (10.1016/j.neuroimage.2023.120075_bib0022) 2020; 75 Eckert (10.1016/j.neuroimage.2023.120075_bib0013) 2005; 41 Tamboer (10.1016/j.neuroimage.2023.120075_bib0062) 2016; 11 Kurth (10.1016/j.neuroimage.2023.120075_bib0030) 2015; 10 Newell (10.1016/j.neuroimage.2023.120075_bib0042) 2020 Salmelin (10.1016/j.neuroimage.2023.120075_bib0057) 2004; 8 Wechsler (10.1016/j.neuroimage.2023.120075_bib0066) 2004 Ashburner (10.1016/j.neuroimage.2023.120075_bib0003) 2007; 38 Fletcher (10.1016/j.neuroimage.2023.120075_bib0019) 2009; 15 Wang (10.1016/j.neuroimage.2023.120075_bib0073) 2022 Paulesu (10.1016/j.neuroimage.2023.120075_bib0044) 2014; 8 10.1016/j.neuroimage.2023.120075_bib0023 Nair (10.1016/j.neuroimage.2023.120075_bib0041) 2010 Kusner (10.1016/j.neuroimage.2023.120075_bib0031) 2017 Ballard (10.1016/j.neuroimage.2023.120075_bib0004) 1987 Eckert (10.1016/j.neuroimage.2023.120075_bib0015) 2017; 7 Vaden (10.1016/j.neuroimage.2023.120075_bib0070) 2010; 49 Vandermosten (10.1016/j.neuroimage.2023.120075_bib0071) 2016; 10 Rumelhart (10.1016/j.neuroimage.2023.120075_bib0056) 1985 Wagner (10.1016/j.neuroimage.2023.120075_bib0064) 1999 Vincent (10.1016/j.neuroimage.2023.120075_bib0072) 2010; 11 Woodcock (10.1016/j.neuroimage.2023.120075_bib0069) 2001 Zeiler (10.1016/j.neuroimage.2023.120075_bib0078) 2010 Sliwinska (10.1016/j.neuroimage.2023.120075_bib0059) 2015; 27 Cui (10.1016/j.neuroimage.2023.120075_bib0010) 2016; 37 Aboud (10.1016/j.neuroimage.2023.120075_bib0002) 2016; 19 Goodfellow (10.1016/j.neuroimage.2023.120075_bib0020) 2016 Eden (10.1016/j.neuroimage.2023.120075_bib0018) 2016 |
| References_xml | – start-page: 1135 year: 2016 end-page: 1144 ident: bib0051 article-title: "Why should I trust you?" Explaining the predictions of any classifier publication-title: Proc 22nd ACM SIGKDD Int Conf Knowl Discovery Data Mining – volume: 30 start-page: 3299 year: 2009 end-page: 3308 ident: bib0052 article-title: Functional abnormalities in the dyslexic brain: a quantitative meta-analysis of neuroimaging studies publication-title: Hum. Brain Mapp. – volume: 27 start-page: 593 year: 2015 end-page: 604 ident: bib0059 article-title: Inferior parietal lobule contributions to visual word recognition publication-title: J. Cog. Neurosci. – volume: 58 start-page: 853 year: 2015 end-page: 864 ident: bib0012 article-title: The influence of reading on vocabulary growth: a case for a Matthew effect publication-title: Jl Speech Lang. Hear. Res. – volume: 1683 start-page: 36 year: 2018 end-page: 47 ident: bib0040 article-title: No evidence for systematic white matter correlates of dyslexia: an activation likelihood estimation meta-analysis publication-title: Brain Res. – volume: 84 start-page: 434 year: 2018 end-page: 452 ident: bib0050 article-title: Neuroanatomy of developmental dyslexia: pitfalls and promise publication-title: Neurosci. Biobehav. Rev. – volume: 49 start-page: 1018 year: 2010 end-page: 1023 ident: bib0070 article-title: Phonological repetition-suppression in bilateral superior temporal sulci publication-title: Neuroimage – volume: 20 start-page: 701 year: 2016 end-page: 714 ident: bib0028 article-title: Neurobiological basis of language learning difficulties publication-title: Trend. Cogn. Sci. – volume: 130 start-page: 858 year: 2004 ident: bib0006 article-title: Developmental dyslexia and specific language impairment: same or different? publication-title: Psychol. Bull. – volume: 34 start-page: 3055 year: 2013 end-page: 3065 ident: bib0054 article-title: Structural abnormalities in the dyslexic brain: a meta-analysis of voxel-based morphometry studies publication-title: Hum. Brain Mapp. – volume: 264 start-page: 998 year: 1990 end-page: 1002 ident: bib0058 article-title: Prevalence of reading disability in boys and girls: results of the Connecticut Longitudinal Study publication-title: JAMA – volume: 104 start-page: 166 year: 2012 ident: bib0008 article-title: Prevalence and nature of late-emerging poor readers publication-title: J.l Educ Psychol – start-page: e43122 year: 2012 ident: bib0033 article-title: Grey matter alterations co-localize with functional abnormalities in developmental dyslexia: an ALE meta-analysis publication-title: PLoS One – volume: 44 start-page: 431 year: 2011 end-page: 443 ident: bib0007 article-title: Matthew effects in young readers: reading comprehension and reading experience aid vocabulary development publication-title: J. Learn. Disabil. – year: 2001 ident: bib0069 article-title: Woodcock-Johnson III Tests of Cognitive Abilities – volume: 58 start-page: 1035 year: 2022 end-page: 1050 ident: bib0077 article-title: Reading real words versus pseudowords: a meta-analysis of research in developmental dyslexia publication-title: Devel. Psychol. – year: 1985 ident: bib0056 article-title: Learning Internal Representations By Error Propagation – volume: 56 start-page: 1735 year: 2011 end-page: 1742 ident: bib0053 article-title: Meta-analyzing brain dysfunctions in dyslexic children and adults publication-title: Neuroimage – volume: 15 start-page: 501 year: 2009 end-page: 508 ident: bib0019 article-title: Dyslexia: the evolution of a scientific concept publication-title: J. Int. Neuropsychol. Soc. – volume: 31 start-page: 192 year: 2010 end-page: 203 ident: bib0037 article-title: Adaptive non-local means denoising of MR images with spatially varying noise levels publication-title: JMRI – volume: 75 start-page: 37 year: 2020 end-page: 51 ident: bib0022 article-title: Understanding, educating, and supporting children with specific learning disabilities: 50 years of science and practice publication-title: Am. Psychol. – year: 2021 ident: bib0063 article-title: Advance machine learning methods for dyslexia biomarker detection: a review of implementation details and challenges publication-title: IEEE Access – start-page: 1735 year: 2006 end-page: 1742 ident: bib0024 article-title: Dimensionality reduction by learning an invariant mapping publication-title: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit – volume: 28 start-page: R1083 year: 2018 end-page: R1088 ident: bib0055 article-title: Orbitofrontal cortex publication-title: Curr. Biol. – volume: 50 start-page: 749 year: 2003 end-page: 757 ident: bib0074 article-title: Normative pediatric brain data for spatial normalization and segmentation differs from standard adult data publication-title: Magn. Reson. Med. – volume: 12 start-page: 1809 year: 2020 ident: bib0017 article-title: The topology of pediatric structural asymmetries in language-related cortex publication-title: Symmetry (Basel) – reference: Ha, D., Schmidhuber, J., 2018. Recurrent world models facilitate policy evolution. arXiv preprint arXiv:1809.01999. – volume: 9 year: 2014 ident: bib0039 article-title: Specific learning disorder: prevalence and gender differences publication-title: PLoS One – volume: 102 start-page: 635 year: 2010 end-page: 651 ident: bib0005 article-title: Listening comprehension, oral expression, reading comprehension, and written expression: related yet unique language systems in grades 1, 3, 5, and 7 publication-title: J. Ed. Psychol. – start-page: 1945 year: 2017 end-page: 1954 ident: bib0031 article-title: Grammar variational autoencoder publication-title: Int. Conf. Mach. Learn. – volume: 29 start-page: 1398 year: 2019 end-page: 1413 ident: bib0076 article-title: Non-perceptual regions in the left inferior parietal lobe support phonological short-term memory: evidence for a buffer account? publication-title: Cereb. Cortex – volume: 34 start-page: 901 year: 2014 end-page: 908 ident: bib0027 article-title: An investigation into the origin of anatomical differences in dyslexia publication-title: J. Neurosci. – volume: 25 start-page: 1097 year: 2012 end-page: 1105 ident: bib0029 article-title: Imagenet classification with deep convolutional neural networks publication-title: Adv. Neural. Inf. Process. Syst. – year: 1999 ident: bib0064 article-title: Comprehensive Test of Phonological Processing – volume: 26 start-page: 791 year: 2007 end-page: 799 ident: bib0047 article-title: Literacy: a cultural influence on functional left–right differences in the inferior parietal cortex publication-title: Eur. J. Neurosci. – start-page: 770 year: 2016 end-page: 778 ident: bib0025 article-title: Deep residual learning for image recognition publication-title: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit – year: 2015 ident: bib0026 article-title: Adam: a method for stochastic optimization publication-title: Int Conf Learning Representations – start-page: 2528 year: 2010 end-page: 2535 ident: bib0078 article-title: Deconvolutional networks publication-title: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit – volume: 41 start-page: 3034 year: 2020 end-page: 3044 ident: bib0038 article-title: Gray matter volume and estimated brain age gap are not linked with sleep-disordered breathing publication-title: Hum. Brain Mapp. – year: 2005 ident: bib0067 article-title: Rapid Automatized Naming and Rapid Alternating Stimulus Tests (RAN/RAS) – volume: 10 start-page: 155 year: 2016 end-page: 161 ident: bib0071 article-title: Integrating MRI brain imaging studies of pre-reading children with current theories of developmental dyslexia: a review and quantitative meta-analysis publication-title: Curr Opin Behav Sci – year: 2016 ident: bib0020 article-title: Deep Learning – volume: 41 start-page: 304 year: 2005 end-page: 315 ident: bib0013 article-title: Anatomical signatures of dyslexia in children: unique information from manual and voxel based morphometry brain measures publication-title: Cortex – reference: Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., 2016. Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467. – volume: 3 start-page: 199 year: 2013 end-page: 211 ident: bib0011 article-title: Not all reading disabilities are dyslexia: distinct neurobiology of specific comprehension deficits publication-title: Brain Connect. – volume: 10 start-page: 293 year: 2015 end-page: 304 ident: bib0030 article-title: A 12-step user guide for analyzing voxel-wise gray matter asymmetries in statistical parametric mapping (SPM) publication-title: Nat. Prot. – start-page: 7345 year: 2020 end-page: 7354 ident: bib0042 article-title: How useful is self-supervised pretraining for visual tasks? publication-title: Proc IEEE/CVF Conf Comput Vis Pattern Recognit – volume: 37 start-page: 1443 year: 2016 end-page: 1458 ident: bib0010 article-title: Disrupted white matter connectivity underlying developmental dyslexia: a machine learning approach publication-title: Human Brain Mapp. – year: 1999 ident: bib0065 article-title: Wechsler Abbreviated Scale of Intelligence – volume: 12 start-page: 2825 year: 2011 end-page: 2830 ident: bib0045 article-title: Scikit-learn: machine learning in python publication-title: J. Mach. Learn. Res. – volume: 3 year: 2016 ident: bib0014 article-title: Gray matter features of reading disability: a combined meta-analytic and direct analysis approach publication-title: eNeuro – volume: 43 start-page: 441 year: 2010 end-page: 454 ident: bib0034 article-title: Executive dysfunction among children with reading comprehension deficits publication-title: J. Learm. Disabil. – volume: 27 start-page: 9984 year: 2007 end-page: 9988 ident: bib0048 article-title: Orbitofrontal cortex encodes willingness to pay in everyday economic transactions publication-title: J. Neurosci. – volume: 38 start-page: 95 year: 2007 end-page: 113 ident: bib0003 article-title: A fast diffeomorphic image registration algorithm publication-title: Neuroimage – year: 2010 ident: bib0041 article-title: Rectified linear units improve restricted boltzmann machines publication-title: Icml – volume: 11 start-page: 283 year: 2015 end-page: 307 ident: bib0046 article-title: Developmental dyslexia publication-title: Annu. Rev. Clinl. Psychol. – volume: 11 start-page: 3371 year: 2010 end-page: 3408 ident: bib0072 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – start-page: 1 year: 2019 end-page: 8 ident: bib0021 article-title: Autoencoder-based articulatory-to-acoustic mapping for ultrasound silent speech interfaces publication-title: 2019 Int J Conf Neural Networks – volume: 8 start-page: 830 year: 2014 ident: bib0044 article-title: Reading the dyslexic brain: multiple dysfunctional routes revealed by a new meta-analysis of PET and fMRI activation studies publication-title: Front. Hum. Neurosci. – volume: 38 start-page: 900 year: 2017 end-page: 908 ident: bib0049 article-title: Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia publication-title: Hum. Brain Mapp. – volume: 7 start-page: 1 year: 2017 end-page: 10 ident: bib0015 article-title: Common brain structure findings across children with varied reading disability profiles publication-title: SciRep – volume: 8 start-page: 257 year: 2004 end-page: 272 ident: bib0057 article-title: Functional neuroanatomy of impaired reading in dyslexia publication-title: Sci. Stud. Read. – volume: 15 start-page: 1929 year: 2014 end-page: 1958 ident: bib0060 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J. Mach. Learn. Res. – volume: 50 start-page: 2645 year: 2012 end-page: 2651 ident: bib0009 article-title: High frequency rTMS over the left parietal lobule increases non-word reading accuracy publication-title: J. Neuropsychologia – volume: 1145 start-page: 237 year: 2008 end-page: 259 ident: bib0036 article-title: A meta-analysis of functional neuroimaging studies of dyslexia publication-title: Ann. N.Y. Acad. Sci. – year: 2004 ident: bib0066 article-title: The Wechsler Intelligence Scale for Children—fourth edition – volume: 19 start-page: 632 year: 2016 end-page: 656 ident: bib0002 article-title: Comprehending text versus reading words in young readers with varying reading ability: distinct patterns of functional connectivity from common processing hubs publication-title: Dev. Sci. – volume: 322 start-page: 1 year: 2019 end-page: 9 ident: bib0016 article-title: A deformation-based approach for characterizing brain asymmetries at different spatial scales of resolution publication-title: J. Neurosci. Meth. – volume: 53 start-page: 1 year: 2003 end-page: 14 ident: bib0035 article-title: A definition of dyslexia publication-title: Ann. Dyslexia – start-page: bhac206 year: 2022 ident: bib0073 article-title: Learning to read may help promote attention by increasing the volume of the left middle frontal gyrus and enhancing its connectivity to the ventral attention network publication-title: Cereb. Cortex – volume: 10 start-page: e69523 year: 2021 ident: bib0075 article-title: Convergent and divergent brain structural and functional abnormalities associated with developmental dyslexia publication-title: Elife – start-page: 279 year: 1987 end-page: 284 ident: bib0004 article-title: Modular learning in neural networks publication-title: Proceedings of the Sixth National Conference on Artificial Intelligence - Volume 1 AAAI – start-page: 815 year: 2016 end-page: 826 ident: bib0018 article-title: Developmental dyslexia publication-title: Neurobiol. Lang. – year: 1987 ident: bib0068 article-title: Woodcock Reading Mastery Test: Revised – volume: 20 start-page: 2958 year: 2010 end-page: 2970 ident: bib0032 article-title: Specialization along the left superior temporal sulcus for auditory categorization publication-title: Cereb. Cortex – volume: 220 start-page: 841 year: 2015 end-page: 859 ident: bib0061 article-title: Latent feature representation with stacked auto-encoder for AD/MCI diagnosis publication-title: Brain Struct. Funct. – volume: 11 start-page: 508 year: 2016 end-page: 514 ident: bib0062 article-title: Machine learning and dyslexia: classification of individual structural neuro-imaging scans of students with and without dyslexia publication-title: NeuroImage Clin. – volume: 42 start-page: 1678 year: 2008 end-page: 1685 ident: bib0043 article-title: Neurodevelopmental changes in verbal working memory load-dependency: an fMRI investigation publication-title: Neuroimage – year: 2005 ident: 10.1016/j.neuroimage.2023.120075_bib0067 – volume: 43 start-page: 441 year: 2010 ident: 10.1016/j.neuroimage.2023.120075_bib0034 article-title: Executive dysfunction among children with reading comprehension deficits publication-title: J. Learm. Disabil. doi: 10.1177/0022219409355476 – volume: 56 start-page: 1735 year: 2011 ident: 10.1016/j.neuroimage.2023.120075_bib0053 article-title: Meta-analyzing brain dysfunctions in dyslexic children and adults publication-title: Neuroimage doi: 10.1016/j.neuroimage.2011.02.040 – volume: 19 start-page: 632 year: 2016 ident: 10.1016/j.neuroimage.2023.120075_bib0002 article-title: Comprehending text versus reading words in young readers with varying reading ability: distinct patterns of functional connectivity from common processing hubs publication-title: Dev. Sci. doi: 10.1111/desc.12422 – year: 1999 ident: 10.1016/j.neuroimage.2023.120075_bib0064 – volume: 10 start-page: e69523 year: 2021 ident: 10.1016/j.neuroimage.2023.120075_bib0075 article-title: Convergent and divergent brain structural and functional abnormalities associated with developmental dyslexia publication-title: Elife doi: 10.7554/eLife.69523 – volume: 9 year: 2014 ident: 10.1016/j.neuroimage.2023.120075_bib0039 article-title: Specific learning disorder: prevalence and gender differences publication-title: PLoS One doi: 10.1371/journal.pone.0103537 – volume: 58 start-page: 1035 issue: 6 year: 2022 ident: 10.1016/j.neuroimage.2023.120075_bib0077 article-title: Reading real words versus pseudowords: a meta-analysis of research in developmental dyslexia publication-title: Devel. Psychol. doi: 10.1037/dev0001340 – volume: 25 start-page: 1097 year: 2012 ident: 10.1016/j.neuroimage.2023.120075_bib0029 article-title: Imagenet classification with deep convolutional neural networks publication-title: Adv. Neural. Inf. Process. Syst. – volume: 41 start-page: 3034 year: 2020 ident: 10.1016/j.neuroimage.2023.120075_bib0038 article-title: Gray matter volume and estimated brain age gap are not linked with sleep-disordered breathing publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.24995 – start-page: 2528 year: 2010 ident: 10.1016/j.neuroimage.2023.120075_bib0078 article-title: Deconvolutional networks – volume: 20 start-page: 2958 year: 2010 ident: 10.1016/j.neuroimage.2023.120075_bib0032 article-title: Specialization along the left superior temporal sulcus for auditory categorization publication-title: Cereb. Cortex doi: 10.1093/cercor/bhq045 – start-page: 815 year: 2016 ident: 10.1016/j.neuroimage.2023.120075_bib0018 article-title: Developmental dyslexia publication-title: Neurobiol. Lang. doi: 10.1016/B978-0-12-407794-2.00065-1 – year: 2015 ident: 10.1016/j.neuroimage.2023.120075_bib0026 article-title: Adam: a method for stochastic optimization – volume: 10 start-page: 293 year: 2015 ident: 10.1016/j.neuroimage.2023.120075_bib0030 article-title: A 12-step user guide for analyzing voxel-wise gray matter asymmetries in statistical parametric mapping (SPM) publication-title: Nat. Prot. doi: 10.1038/nprot.2015.014 – volume: 26 start-page: 791 year: 2007 ident: 10.1016/j.neuroimage.2023.120075_bib0047 article-title: Literacy: a cultural influence on functional left–right differences in the inferior parietal cortex publication-title: Eur. J. Neurosci. doi: 10.1111/j.1460-9568.2007.05701.x – volume: 50 start-page: 749 issue: 4 year: 2003 ident: 10.1016/j.neuroimage.2023.120075_bib0074 article-title: Normative pediatric brain data for spatial normalization and segmentation differs from standard adult data publication-title: Magn. Reson. Med. doi: 10.1002/mrm.10606 – volume: 28 start-page: R1083 year: 2018 ident: 10.1016/j.neuroimage.2023.120075_bib0055 article-title: Orbitofrontal cortex publication-title: Curr. Biol. doi: 10.1016/j.cub.2018.07.018 – year: 2010 ident: 10.1016/j.neuroimage.2023.120075_bib0041 article-title: Rectified linear units improve restricted boltzmann machines – start-page: 1735 year: 2006 ident: 10.1016/j.neuroimage.2023.120075_bib0024 article-title: Dimensionality reduction by learning an invariant mapping – volume: 3 start-page: 199 year: 2013 ident: 10.1016/j.neuroimage.2023.120075_bib0011 article-title: Not all reading disabilities are dyslexia: distinct neurobiology of specific comprehension deficits publication-title: Brain Connect. doi: 10.1089/brain.2012.0116 – volume: 220 start-page: 841 year: 2015 ident: 10.1016/j.neuroimage.2023.120075_bib0061 article-title: Latent feature representation with stacked auto-encoder for AD/MCI diagnosis publication-title: Brain Struct. Funct. doi: 10.1007/s00429-013-0687-3 – volume: 44 start-page: 431 year: 2011 ident: 10.1016/j.neuroimage.2023.120075_bib0007 article-title: Matthew effects in young readers: reading comprehension and reading experience aid vocabulary development publication-title: J. Learn. Disabil. doi: 10.1177/0022219411410042 – volume: 12 start-page: 2825 year: 2011 ident: 10.1016/j.neuroimage.2023.120075_bib0045 article-title: Scikit-learn: machine learning in python publication-title: J. Mach. Learn. Res. – start-page: 1945 year: 2017 ident: 10.1016/j.neuroimage.2023.120075_bib0031 article-title: Grammar variational autoencoder publication-title: Int. Conf. Mach. Learn. – year: 2016 ident: 10.1016/j.neuroimage.2023.120075_bib0020 – volume: 1145 start-page: 237 year: 2008 ident: 10.1016/j.neuroimage.2023.120075_bib0036 article-title: A meta-analysis of functional neuroimaging studies of dyslexia publication-title: Ann. N.Y. Acad. Sci. doi: 10.1196/annals.1416.024 – volume: 53 start-page: 1 year: 2003 ident: 10.1016/j.neuroimage.2023.120075_bib0035 article-title: A definition of dyslexia publication-title: Ann. Dyslexia doi: 10.1007/s11881-003-0001-9 – start-page: 1135 year: 2016 ident: 10.1016/j.neuroimage.2023.120075_bib0051 article-title: "Why should I trust you?" Explaining the predictions of any classifier – year: 2004 ident: 10.1016/j.neuroimage.2023.120075_bib0066 – ident: 10.1016/j.neuroimage.2023.120075_bib0001 – volume: 11 start-page: 283 year: 2015 ident: 10.1016/j.neuroimage.2023.120075_bib0046 article-title: Developmental dyslexia publication-title: Annu. Rev. Clinl. Psychol. doi: 10.1146/annurev-clinpsy-032814-112842 – volume: 12 start-page: 1809 year: 2020 ident: 10.1016/j.neuroimage.2023.120075_bib0017 article-title: The topology of pediatric structural asymmetries in language-related cortex publication-title: Symmetry (Basel) doi: 10.3390/sym12111809 – volume: 38 start-page: 900 year: 2017 ident: 10.1016/j.neuroimage.2023.120075_bib0049 article-title: Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.23426 – year: 1987 ident: 10.1016/j.neuroimage.2023.120075_bib0068 – volume: 41 start-page: 304 issue: 3 year: 2005 ident: 10.1016/j.neuroimage.2023.120075_bib0013 article-title: Anatomical signatures of dyslexia in children: unique information from manual and voxel based morphometry brain measures publication-title: Cortex doi: 10.1016/S0010-9452(08)70268-5 – volume: 75 start-page: 37 issue: 1 year: 2020 ident: 10.1016/j.neuroimage.2023.120075_bib0022 article-title: Understanding, educating, and supporting children with specific learning disabilities: 50 years of science and practice publication-title: Am. Psychol. doi: 10.1037/amp0000452 – volume: 3 year: 2016 ident: 10.1016/j.neuroimage.2023.120075_bib0014 article-title: Gray matter features of reading disability: a combined meta-analytic and direct analysis approach publication-title: eNeuro doi: 10.1523/ENEURO.0103-15.2015 – year: 2021 ident: 10.1016/j.neuroimage.2023.120075_bib0063 article-title: Advance machine learning methods for dyslexia biomarker detection: a review of implementation details and challenges publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3062709 – year: 1999 ident: 10.1016/j.neuroimage.2023.120075_bib0065 – volume: 34 start-page: 901 issue: 3 year: 2014 ident: 10.1016/j.neuroimage.2023.120075_bib0027 article-title: An investigation into the origin of anatomical differences in dyslexia publication-title: J. Neurosci. doi: 10.1523/JNEUROSCI.2092-13.2013 – volume: 29 start-page: 1398 year: 2019 ident: 10.1016/j.neuroimage.2023.120075_bib0076 article-title: Non-perceptual regions in the left inferior parietal lobe support phonological short-term memory: evidence for a buffer account? publication-title: Cereb. Cortex doi: 10.1093/cercor/bhy037 – start-page: 7345 year: 2020 ident: 10.1016/j.neuroimage.2023.120075_bib0042 article-title: How useful is self-supervised pretraining for visual tasks? – volume: 31 start-page: 192 year: 2010 ident: 10.1016/j.neuroimage.2023.120075_bib0037 article-title: Adaptive non-local means denoising of MR images with spatially varying noise levels publication-title: JMRI doi: 10.1002/jmri.22003 – volume: 27 start-page: 9984 year: 2007 ident: 10.1016/j.neuroimage.2023.120075_bib0048 article-title: Orbitofrontal cortex encodes willingness to pay in everyday economic transactions publication-title: J. Neurosci. doi: 10.1523/JNEUROSCI.2131-07.2007 – volume: 27 start-page: 593 year: 2015 ident: 10.1016/j.neuroimage.2023.120075_bib0059 article-title: Inferior parietal lobule contributions to visual word recognition publication-title: J. Cog. Neurosci. doi: 10.1162/jocn_a_00721 – ident: 10.1016/j.neuroimage.2023.120075_bib0023 – year: 2001 ident: 10.1016/j.neuroimage.2023.120075_bib0069 – volume: 49 start-page: 1018 year: 2010 ident: 10.1016/j.neuroimage.2023.120075_bib0070 article-title: Phonological repetition-suppression in bilateral superior temporal sulci publication-title: Neuroimage doi: 10.1016/j.neuroimage.2009.07.063 – volume: 1683 start-page: 36 year: 2018 ident: 10.1016/j.neuroimage.2023.120075_bib0040 article-title: No evidence for systematic white matter correlates of dyslexia: an activation likelihood estimation meta-analysis publication-title: Brain Res. doi: 10.1016/j.brainres.2018.01.014 – volume: 84 start-page: 434 year: 2018 ident: 10.1016/j.neuroimage.2023.120075_bib0050 article-title: Neuroanatomy of developmental dyslexia: pitfalls and promise publication-title: Neurosci. Biobehav. Rev. doi: 10.1016/j.neubiorev.2017.08.001 – volume: 8 start-page: 830 year: 2014 ident: 10.1016/j.neuroimage.2023.120075_bib0044 article-title: Reading the dyslexic brain: multiple dysfunctional routes revealed by a new meta-analysis of PET and fMRI activation studies publication-title: Front. Hum. Neurosci. doi: 10.3389/fnhum.2014.00830 – volume: 11 start-page: 3371 year: 2010 ident: 10.1016/j.neuroimage.2023.120075_bib0072 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – volume: 58 start-page: 853 year: 2015 ident: 10.1016/j.neuroimage.2023.120075_bib0012 article-title: The influence of reading on vocabulary growth: a case for a Matthew effect publication-title: Jl Speech Lang. Hear. Res. doi: 10.1044/2015_JSLHR-L-13-0310 – volume: 38 start-page: 95 year: 2007 ident: 10.1016/j.neuroimage.2023.120075_bib0003 article-title: A fast diffeomorphic image registration algorithm publication-title: Neuroimage doi: 10.1016/j.neuroimage.2007.07.007 – year: 1985 ident: 10.1016/j.neuroimage.2023.120075_bib0056 – volume: 15 start-page: 501 year: 2009 ident: 10.1016/j.neuroimage.2023.120075_bib0019 article-title: Dyslexia: the evolution of a scientific concept publication-title: J. Int. Neuropsychol. Soc. doi: 10.1017/S1355617709090900 – start-page: 1 year: 2019 ident: 10.1016/j.neuroimage.2023.120075_bib0021 article-title: Autoencoder-based articulatory-to-acoustic mapping for ultrasound silent speech interfaces – volume: 102 start-page: 635 year: 2010 ident: 10.1016/j.neuroimage.2023.120075_bib0005 article-title: Listening comprehension, oral expression, reading comprehension, and written expression: related yet unique language systems in grades 1, 3, 5, and 7 publication-title: J. Ed. Psychol. doi: 10.1037/a0019319 – start-page: 770 year: 2016 ident: 10.1016/j.neuroimage.2023.120075_bib0025 article-title: Deep residual learning for image recognition – volume: 104 start-page: 166 year: 2012 ident: 10.1016/j.neuroimage.2023.120075_bib0008 article-title: Prevalence and nature of late-emerging poor readers publication-title: J.l Educ Psychol doi: 10.1037/a0025323 – volume: 42 start-page: 1678 year: 2008 ident: 10.1016/j.neuroimage.2023.120075_bib0043 article-title: Neurodevelopmental changes in verbal working memory load-dependency: an fMRI investigation publication-title: Neuroimage doi: 10.1016/j.neuroimage.2008.05.057 – volume: 130 start-page: 858 year: 2004 ident: 10.1016/j.neuroimage.2023.120075_bib0006 article-title: Developmental dyslexia and specific language impairment: same or different? publication-title: Psychol. Bull. doi: 10.1037/0033-2909.130.6.858 – volume: 7 start-page: 1 year: 2017 ident: 10.1016/j.neuroimage.2023.120075_bib0015 article-title: Common brain structure findings across children with varied reading disability profiles publication-title: SciRep – volume: 10 start-page: 155 year: 2016 ident: 10.1016/j.neuroimage.2023.120075_bib0071 article-title: Integrating MRI brain imaging studies of pre-reading children with current theories of developmental dyslexia: a review and quantitative meta-analysis publication-title: Curr Opin Behav Sci doi: 10.1016/j.cobeha.2016.06.007 – start-page: 279 year: 1987 ident: 10.1016/j.neuroimage.2023.120075_bib0004 article-title: Modular learning in neural networks – start-page: e43122 year: 2012 ident: 10.1016/j.neuroimage.2023.120075_bib0033 article-title: Grey matter alterations co-localize with functional abnormalities in developmental dyslexia: an ALE meta-analysis publication-title: PLoS One doi: 10.1371/journal.pone.0043122 – volume: 50 start-page: 2645 year: 2012 ident: 10.1016/j.neuroimage.2023.120075_bib0009 article-title: High frequency rTMS over the left parietal lobule increases non-word reading accuracy publication-title: J. Neuropsychologia doi: 10.1016/j.neuropsychologia.2012.07.017 – volume: 34 start-page: 3055 year: 2013 ident: 10.1016/j.neuroimage.2023.120075_bib0054 article-title: Structural abnormalities in the dyslexic brain: a meta-analysis of voxel-based morphometry studies publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.22127 – volume: 322 start-page: 1 year: 2019 ident: 10.1016/j.neuroimage.2023.120075_bib0016 article-title: A deformation-based approach for characterizing brain asymmetries at different spatial scales of resolution publication-title: J. Neurosci. Meth. doi: 10.1016/j.jneumeth.2019.04.007 – start-page: bhac206 year: 2022 ident: 10.1016/j.neuroimage.2023.120075_bib0073 article-title: Learning to read may help promote attention by increasing the volume of the left middle frontal gyrus and enhancing its connectivity to the ventral attention network publication-title: Cereb. Cortex – volume: 8 start-page: 257 year: 2004 ident: 10.1016/j.neuroimage.2023.120075_bib0057 article-title: Functional neuroanatomy of impaired reading in dyslexia publication-title: Sci. Stud. Read. doi: 10.1207/s1532799xssr0803_5 – volume: 37 start-page: 1443 year: 2016 ident: 10.1016/j.neuroimage.2023.120075_bib0010 article-title: Disrupted white matter connectivity underlying developmental dyslexia: a machine learning approach publication-title: Human Brain Mapp. doi: 10.1002/hbm.23112 – volume: 264 start-page: 998 year: 1990 ident: 10.1016/j.neuroimage.2023.120075_bib0058 article-title: Prevalence of reading disability in boys and girls: results of the Connecticut Longitudinal Study publication-title: JAMA doi: 10.1001/jama.1990.03450080084036 – volume: 20 start-page: 701 year: 2016 ident: 10.1016/j.neuroimage.2023.120075_bib0028 article-title: Neurobiological basis of language learning difficulties publication-title: Trend. Cogn. Sci. doi: 10.1016/j.tics.2016.06.012 – volume: 11 start-page: 508 year: 2016 ident: 10.1016/j.neuroimage.2023.120075_bib0062 article-title: Machine learning and dyslexia: classification of individual structural neuro-imaging scans of students with and without dyslexia publication-title: NeuroImage Clin. doi: 10.1016/j.nicl.2016.03.014 – volume: 30 start-page: 3299 year: 2009 ident: 10.1016/j.neuroimage.2023.120075_bib0052 article-title: Functional abnormalities in the dyslexic brain: a quantitative meta-analysis of neuroimaging studies publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.20752 – volume: 15 start-page: 1929 year: 2014 ident: 10.1016/j.neuroimage.2023.120075_bib0060 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J. Mach. Learn. Res. |
| SSID | ssj0009148 |
| Score | 2.452116 |
| Snippet | •Deformation-based deep learning was used to classify reading disability.•Autoencoder pretraining optimized neural network classification accuracy.•Reading... Developmental reading disability is a prevalent and often enduring problem with varied mechanisms that contribute to its phenotypic heterogeneity. This... |
| SourceID | doaj pubmedcentral proquest pubmed crossref elsevier |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 120075 |
| SubjectTerms | Accuracy Brain Brain - diagnostic imaging Brain morphology Brain research Child Classification Comprehension Convolutional neural network Cortex Data Data compression Decoding Deep Learning Disability Discussion groups Dyslexia Dyslexia - diagnostic imaging Humans Individual differences Language Learning Learning disabilities Medical imaging Morphology Neural networks Neuroimaging Neuroimaging - methods Occipital lobe People with disabilities Phenotypic variations Phonology Reading comprehension Reading disabilities Reading disability Regions Specific learning disorder in reading Superior temporal sulcus Support vector machines Temporal lobe |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQhRAXVN5pCzIS15Q4iWNbPfGqOEDFAareLL8CW0G22t1W6r9nxnZCAwf2gHKL48iZGdvfeCbfEPKykka4uvGlYzyULccgITjOpeqCEKYJXsW_-E8_ipMTeXamPt8o9YU5YYkeOAnuVdUHYwD02qoOrfe1kW1tYddkXoLr0MfVtxJqdKZGul1A-TlvJ2VzRXbIxU-Yo4dYMPyQ4Rkdn21GkbN_tif9jTn_TJ28sRcd75J7GUTS12nw98mtMDwgdz7lMPlDcvouhAuaC0J8ow4RMqYERS3QZU9XKXWe-sywu7mmeCBLsUwDQnNqsXIETUsXBdlgnGH9iHw9fv_l7YcyF1AoXdewTdlw0XYMEJeSTjD0Vaz3zHIVKm5b6WtrguzB5wpdaJteeul7a6yDSRks91XzmOwMyyE8JdSCE9147vrQ1S1vmXQMQ5zwjtqo3rGCiFGS2mV2cSxy8UOPaWTn-rcONOpAJx0UhE09LxLDxhZ93qCypueRIzveAMvR2XL0vyynIGpUtR5_Q4WFE1602GIAR1PfDFUSBNmy98FoWTovGWuNIW4A61JB84upGSY7RnDMEJaX8RmmAMM1qiBPkiFOMmgEoG_QZUHkzERnQpq3DIvvkVAcByvg2vsfYt0nd_FTUzrdAdnZrC7DM3LbXW0W69XzOE1_Ab0HRNs priority: 102 providerName: Directory of Open Access Journals |
| Title | Deep learning classification of reading disability with regional brain volume features |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S1053811923002215 https://dx.doi.org/10.1016/j.neuroimage.2023.120075 https://www.ncbi.nlm.nih.gov/pubmed/37054828 https://www.proquest.com/docview/2806991895 https://www.proquest.com/docview/2801981939 https://pubmed.ncbi.nlm.nih.gov/PMC10167676 https://doaj.org/article/0feaa974b02e4dd2a842b5841d8924f2 |
| Volume | 273 |
| WOSCitedRecordID | wos000984006700001&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: 1095-9572 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: DOA dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVESC databaseName: ScienceDirect Freedom Collection - Elsevier customDbUrl: eissn: 1095-9572 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: AIEXJ dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVPQU databaseName: Biological Science Database (ProQuest) customDbUrl: eissn: 1095-9572 dateEnd: 20251012 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: M7P dateStart: 19980501 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1095-9572 dateEnd: 20251012 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: 7X7 dateStart: 20020801 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1095-9572 dateEnd: 20251012 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: BENPR dateStart: 19980501 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Psychology Database customDbUrl: eissn: 1095-9572 dateEnd: 20251012 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: M2M dateStart: 20020801 isFulltext: true titleUrlDefault: https://www.proquest.com/psychology providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwED-xDaG98M0IjMpIvGbUSRzb4gEx2MQDrSoEU9-i-CNbESSl7ZD47_E5TkpAQpVQpTw0ceT4zuff-c6_A3gxFiXXSWpiTZmNM4ZBQuc4xzK3nJepNdKf4r_4wKdTMZ_LWdhwW4e0ys4mekNtGo175C8xAuiwjJDs9fJ7jFWjMLoaSmjswQGyJCQ-dW-2Jd2lWXsUjqWxoFSGTJ42v8vzRS6-uVl7giXETyju2rHB8uRZ_Aer1N8o9M9kyt9Wp_M7__tdd-F2wKXkTatI9-CGre_DrUmIvD-Ai3fWLkmoMXFJNIJuzDLygiVNRVZtNj4xgbR385PgHi_Byg-I9onCYhSktYaksp5QdP0QPp-ffXr7Pg41GWKdp3QTp4xnOXUgTgrNKbo_yhiqmLRjpjJhElVaUTk3zuY2SythhKlUqbSb51YxM04fwX7d1PYxEOX88tQwXdk8yVhGhaYYNXXvSEpZaRoB70RR6EBYjnUzvhZdZtqXYivEAoVYtEKMgPYtly1pxw5tTlHa_fNIu-3_aFaXRZjFxdiNTuk8MDVObGZMUoosUQ7CUSOcH1slEchOV4ruZKuzxe5Fix068KpvG9BPi2p2bH3cqVcRrNC62OpWBM_7285-YFCorG1z7Z-h0sHCVEZw1GpyPwYpd4DeyTICMdDxwSAN79SLK89Rjp3l7vfk3_16Cof4EW3u3THsb1bX9hnc1D82i_VqBHt8zv1VjODg9Gw6-zjymybuOkkmIz_bfwHnQVor |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LbxMxEB6VgoAL78dCASPBcUvsXWdtIYSAUrVqUnEoVW_u-rElCJKQpKD-KX4jM-vdhICEcukB5ZZdW1778-cZzwvgWUeVhROZTx2XIc0lGQlRcU51NxRFmQWv6yj-w16xv6-OjvSHNfjZxsKQW2XLiTVR-5GjO_IXZAFEWUZp-Xr8LaWqUWRdbUtoRFjshbMfqLJNX-1u4fo-F2L7_cG7nbSpKpC6bsZnaSaLvMtRDNHKFZwEeOs9t1KHjrS58sKWQVWoiIRuyLNKeeUrW1qHSA1W-k6G_V6AizmFjJKroOgvkvzyPIbeySxVnOvGcyj6k9X5KQdfkSU2qWT5JqdbQrl0HNZVA5ZOxb-l3j-dN387Dbev_2_zeAOuNXI3exM3yk1YC8NbcLnfeBbchsOtEMasqaFxwhwpFeRFVQOXjSo2idEGzDdJiWdnjO6wGVW2IG2GWSq2wSLbsyrUCVOnd-DjuXzVXVgfjobhPjCrkT69dFXoilzmXDlOVmHsQ5S6cjyBol1645qE7FQX5ItpPe8-mwVoDIHGRNAkwOctxzEpyQpt3hK65u9TWvH6j9HkxDQsZTo4OyVqmLYjQu69KFUuLIqo3CvU0yuRgG6xadrIXTxrsKPBCgN4OW_bSHdRalux9UYLZ9Ow7NQssJzA0_lj5EcyepXDMDqt3-Eaxd5MJ3Av7pz5HGQFKiy4lgmopT21NEnLT4aDT3UOdhpsgb8H_x7XE7iyc9Dvmd7u_t5DuEofFP0MN2B9NjkNj-CS-z4bTCePayZhcHzeW-4XZFayQQ |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFD4aHZp44X4JDDASPGark7ixhRBidBXTRlUhmPZm4ktGEbSl7UD7a_w6zkmcloKE-rIHlLckthznOzefG8DTtixym6Qutlz4OBPkJETDOVYdn-dF6p2qsviPj_J-X56cqMEG_GxyYSissuGJFaN2Y0tn5LvkAURdRiqxW4awiEG393LyLaYOUuRpbdpp1BA59Oc_0HybvTjo4r9-liS9_fev38Shw0BsOymfx6nIsw5HlURJm3NS5o1z3Ajl28Jk0iWm8LJEo8R3fJaW0klXmsJYRK03wrVTnPcSbOaoZGQt2Nzb7w_eLUv-8qxOxBNpLDlXIY6oji6rqlUOvyLP2KEG5juczgzFinCsegisyMi_deA_Qzl_k429a__zrl6Hq0EjZ69qEroBG350E7behpiDW3Dc9X7CQneNU2bJ3KD4qgrSbFyyaZ2HwFwoVzw_Z3S6zajnBdk5zFAbDlbLAVb6qpTq7DZ8uJCvugOt0Xjk7wEzChmrE7b0nSQTGZeWk78Y50gKVVoeQd7AQNtQqp06hnzRTUzeZ70EkCYA6RpAEfDFyEldrmSNMXuEtMX7VHC8ujGenurAv3Qbd6dA29O0E585lxQySwwqr9xJxH6ZRKAanOompxelEE40XGMBzxdjg95X63Nrjt5uoK0D_53pJa4jeLJ4jJyT3GHFyI_Pqne4QoU4VRHcralosQdpjqYM_ssI5Ap9rWzS6pPR8FNVnZ0Wm-N1_9_regxbSGn66KB_-ACu0PfUAYjb0JpPz_xDuGy_z4ez6aPAVhh8vGia-wUjhrxb |
| 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=Deep+learning+classification+of+reading+disability+with+regional+brain+volume+features&rft.jtitle=NeuroImage+%28Orlando%2C+Fla.%29&rft.au=Joshi%2C+am&rft.au=Wang%2C+James+Z&rft.au=Vaden%2C+Kenneth+I&rft.au=Eckert%2C+Mark+A&rft.date=2023-06-01&rft.pub=Elsevier+Limited&rft.issn=1053-8119&rft.eissn=1095-9572&rft.volume=273&rft_id=info:doi/10.1016%2Fj.neuroimage.2023.120075&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1053-8119&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1053-8119&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1053-8119&client=summon |