[This corrects the article DOI: 10.3389/fnagi.2020.00176.]

Alzheimer’s Disease and Neuroimaging Initiative

Kyriacou, P. A.

Lamnisos, D.

Leandrou, S.

Mamais, I.

Pattichis, C. S.

Alzheimer's disease (AD) brain magnetic resonance imaging (MRI) biomarkers based on larger-scale tissue neurodegeneration changes, such as atrophy, are currently widely used. Texture analysis evaluates the statistical properties of the tissue image quantitatively; therefore, it could detect smaller-scale changes of neurodegeneration. Entorhinal cortex is the first region affected, and no study has investigated texture analysis on this region before. This study aims to differentiate AD patients from Normal Control (NC) and Mild Cognitive Impairment (MCI) subjects using entorhinal cortex texture features. Furthermore, it was evaluated whether texture has association to MCI beyond that of volume, to evaluate if atrophy development may precede. Texture features were extracted from 194 NC, 200 MCI, 84 MCI who converted to AD (MCIc), and 130 AD subjects. Receiving operating characteristic curves determined the performance of the various features in discriminating the groups, and a predictive model was used to predict conversion of MCIc subjects to AD. An area under the curve (AUC) of 0.872, 0.710, 0.730, and 0.764 was seen between NC vs. AD, NC vs. MCI, MCI vs. MCIc, and MCI vs. AD subjects, respectively. Including entorhinal cortex volume improved the AUCs to 0.914, 0.740, 0.756, and 0.780, respectively. For the disease prediction, binary logistic regression was applied on five randomly selected test groups and achieved on average AUC's of 0.760 and 0.764 on the training and validation cohorts, respectively. Entorhinal cortex texture features were significantly different between the four groups and in many cases provided better results compared to other methods such as volumetry

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Assessment of Alzheimer's Disease Based on Texture Analysis of the Entorhinal Cortex

              City, University of London Institutional RepositoryCitation: Leandrou, S., Lamnisos, D., Mamais, I., Kyriacou, P. A. ORCID: 0000-0002-2868-485X and Pattichis, C. S. (2020). Corrigendum: Assessment of Alzheimer's Disease Based on Texture Analysis of the Entorhinal Cortex. Frontiers in Aging Neuroscience, 12, 596070.. doi: 10.3389/fnagi.2020.596070 This is the published version of the paper. This version of the publication may differ from the final published version. Permanent repository link:  https://openaccess.city.ac.uk/id/eprint/25292/Link to published version: http://dx.doi.org/10.3389/fnagi.2020.596070Copyright and reuse: City Research Online aims to make research outputs of City, University of London available to a wider audience. Copyright and Moral Rights remain with the author(s) and/or copyright holders. URLs from City Research Online may be freely distributed and linked to.City Research Online:            http://openaccess.city.ac.uk/            publications@city.ac.ukCity Research OnlineCORRECTIONpublished: 22 October 2020doi: 10.3389/fnagi.2020.596070Frontiers in Aging Neuroscience | www.frontiersin.org 1 October 2020 | Volume 12 | Article 596070Edited and reviewed by:Ashok Kumar,University of Florida, United States*Correspondence:Stephanos Leandrous.leandrou@euc.ac.cy†A portion of data used in preparationof this article were obtained from theAlzheimer’s Disease NeuroimagingInitiative (ADNI) database(adni.loni.usc.edu). As such, theinvestigators within the ADNIcontributed to the design andimplementation of ADNI and/orprovided data but did not participatein analysis or writing of this report. Acomplete listing of ADNI investigatorscan be found at:http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdfReceived: 18 August 2020Accepted: 04 September 2020Published: 22 October 2020Citation:Leandrou S, Lamnisos D, Mamais I,Kyriacou PA and Pattichis CS (2020)Corrigendum: Assessment ofAlzheimer’s Disease Based on TextureAnalysis of the Entorhinal Cortex.Front. Aging Neurosci. 12:596070.doi: 10.3389/fnagi.2020.596070Corrigendum: Assessment ofAlzheimer’s Disease Based onTexture Analysis of the EntorhinalCortexStephanos Leandrou 1,2*, Demetris Lamnisos 1, Ioannis Mamais 1,Panicos A. Kyriacou 2 andConstantinos S. Pattichis 3,4 for the Alzheimer’s Disease and Neuroimaging Initiative †1 School of Science, European University Cyprus, Nicosia, Cyprus, 2 School of Mathematical Sciences, Computer Scienceand Engineering, City, University of London, London, United Kingdom, 3Department of Computer Science, University ofCyprus, Nicosia, Cyprus, 4 Research Centre on Interactive Media, Smart Systems and Emerging Technologies (RISE CoE),Nicosia, CyprusKeywords: Alzheimer’s disease, mild cognitive impairment, entorhinal cortex, magnetic resonance imaging,textureA Corrigendum onAssessment of Alzheimer’s Disease Based on Texture Analysis of the Entorhinal Cortexby Leandrou, S., Lamnisos, D., Mamais, I., Kyriacou, P. A., and Pattichis, C. S. (2020). Front. AgingNeurosci. 12:176. doi: 10.3389/fnagi.2020.00176In the original article, there was a mistake in Table 6 as published. The first column title was “NCvs. MCI” and it should be “MCI vs. MCIc.” The corrected Table 6 appears below.The authors apologize for this error and state that this does not change the scientific conclusionsof the article in any way. The original article has been updated.Copyright © 2020 Leandrou, Lamnisos, Mamais, Kyriacou and Pattichis. This is an open-access article distributed under theterms 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 withthese terms.Leandrou et al. Corrigendum: Assessment of AD Using TextureTABLE 6 | Entorhinal cortex texture and volume in classifying MCI vs. MCIc.MCI vs. MCIc ROC analysis AUC 95% CI P-valueEntorhinal cortexTexture featuresASM 0.565 0.494–0.637 0.85Contrast 0.583 0.510–0.657 0.028Corelation 0.580 0.505–0.654 0.038Variance 0.531 0.458–0.604 0.037Sum average 0.591 0.520–0.662 0.036Sum variance 0.527 0.451–0.603 0.475Entropy 0.593 0.522–0.662 0.014Cluster shade 0.696 0.632–0.759 0.032Volume and thicknessErc. volume 0.642 0.573–0.711 <0.001Erc. thickness 0.670 0.603–0.737 <0.001Features combinationTexture (ASM, correlation, variance, sum average, and cluster shade) 0.730 0.665–0.795 <0.001Texture & Erc. volume 0.756 0.692–0.820 <0.001HippocampusHippocampal volume 0.685 0.617–0.753 <0.001MCIc, mild cognitive impairment converter; ROC, receiver operating characteristic; AUC, area under curve; CI, confidence interval; ASM, angular second moment; Erc, entorhinal cortex.Frontiers in Aging Neuroscience | www.frontiersin.org 2 October 2020 | Volume 12 | Article 596070

Corrigendum: Assessment of Alzheimer's Disease Based on Texture Analysis of the Entorhinal Cortex

Alzheimer’s disease (AD) brain magnetic resonance imaging (MRI) biomarkers based on larger-scale tissue neurodegeneration changes, such as atrophy, are currently widely used. Texture analysis evaluates the statistical properties of the tissue image quantitatively; therefore, it could detect smaller-scale changes of neurodegeneration. Entorhinal cortex is the first region affected, and no study has investigated texture analysis on this region before. This study aims to differentiate AD patients from Normal Control (NC) and Mild Cognitive Impairment (MCI) subjects using entorhinal cortex texture features. Furthermore, it was evaluated whether texture has association to MCI beyond that of volume, to evaluate if atrophy development may precede. Texture features were extracted from 194 NC, 200 MCI, 84 MCI who converted to AD (MCIc), and 130 AD subjects. Receiving operating characteristic curves determined the performance of the various features in discriminating the groups, and a predictive model was used to predict conversion of MCIc subjects to AD. An area under the curve (AUC) of 0.872, 0.710, 0.730, and 0.764 was seen between NC vs. AD, NC vs. MCI, MCI vs. MCIc, and MCI vs. AD subjects, respectively. Including entorhinal cortex volume improved the AUCs to 0.914, 0.740, 0.756, and 0.780, respectively. For the disease prediction, binary logistic regression was applied on five randomly selected test groups and achieved on average AUC’s of 0.760 and 0.764 on the training and validation cohorts, respectively. Entorhinal cortex texture features were significantly different between the four groups and in many cases provided better results compared to other methods such as volumetry.This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development

Stephanos Leandrou

Demetris Lamnisos

Ioannis Mamais

Panicos A. Kyriacou

Constantinos S. Pattichis

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https://openaccess.city.ac.uk/id/eprint/25292/1/fnagi-12-596070%281%29.pdf

Corrigendum: Assessment of Alzheimer's Disease Based on Texture Analysis of the Entorhinal Cortex

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