Multi-scale graph-based grading for Alzheimer’s disease prediction

•This work introduces a new approach combining patch-based grading and graph-based model.•Combination of inter-subject similarity and intra-subject variability helps to predict Alzheimer’s disease.•Analysis of whole brain structures and hippocampal subfields jointly enables improvement of prediction...

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Veröffentlicht in:	Medical image analysis Jg. 67; S. 101850
Hauptverfasser:	Hett, Kilian, Ta, Vinh-Thong, Oguz, Ipek, Manjón, José V., Coupé, Pierrick
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.01.2021 Elsevier BV Elsevier
Schlagworte:	Alzheimer Disease - diagnostic imaging Alzheimer's disease Alzheimer’s disease classification Bioengineering Biomarkers Brain Brain - diagnostic imaging Cognitive ability Cognitive Dysfunction - diagnostic imaging Cognitive science Computer science Computer Vision and Pattern Recognition Conversion Datasets Graph-based method Hippocampal subfields Hippocampus Humans Imaging Inter-subject similarity Intra-subject variability Life Sciences Magnetic Resonance Imaging Mild cognitive impairment Multiscale analysis Neurodegenerative diseases Neuroscience Patch-based grading State-of-the-art reviews Whole brain analysis Hippocampal subfields Inter-subject similarity Graph-based method Intra-subject variability Alzheimer’s disease classification Mild cognitive impairment Patch-based grading Whole brain analysis
ISSN:	1361-8415, 1361-8423, 1361-8423
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Abstract	•This work introduces a new approach combining patch-based grading and graph-based model.•Combination of inter-subject similarity and intra-subject variability helps to predict Alzheimer’s disease.•Analysis of whole brain structures and hippocampal subfields jointly enables improvement of prediction performances.•Image-based biomarkers and cognitive data are highly complementary for Alzheimer’s disease prediction. [Display omitted] The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer’s disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.
AbstractList	The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer's disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset. •This work introduces a new approach combining patch-based grading and graph-based model.•Combination of inter-subject similarity and intra-subject variability helps to predict Alzheimer’s disease.•Analysis of whole brain structures and hippocampal subfields jointly enables improvement of prediction performances.•Image-based biomarkers and cognitive data are highly complementary for Alzheimer’s disease prediction. [Display omitted] The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer’s disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset. The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer's disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer's disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.
ArticleNumber	101850
Author	Manjón, José V. Oguz, Ipek Ta, Vinh-Thong Hett, Kilian Coupé, Pierrick
AuthorAffiliation	a CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, PICTURA, F-33400 Talence, France b Universitat Politècnia de València, ITACA, 46022 Valencia, Spain c Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, TN, USA
AuthorAffiliation_xml	– name: b Universitat Politècnia de València, ITACA, 46022 Valencia, Spain – name: c Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, TN, USA – name: a CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, PICTURA, F-33400 Talence, France
Author_xml	– sequence: 1 givenname: Kilian surname: Hett fullname: Hett, Kilian email: kilian.hett@vanderbilt.edu organization: CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, PICTURA, Talence F-33400, France – sequence: 2 givenname: Vinh-Thong surname: Ta fullname: Ta, Vinh-Thong organization: CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, PICTURA, Talence F-33400, France – sequence: 3 givenname: Ipek surname: Oguz fullname: Oguz, Ipek organization: Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, TN, USA – sequence: 4 givenname: José V. surname: Manjón fullname: Manjón, José V. organization: Universitat Politècnica de Valèncica, ITACA, Valencia 46022, Spain – sequence: 5 givenname: Pierrick surname: Coupé fullname: Coupé, Pierrick organization: CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, PICTURA, Talence F-33400, France
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Keywords	Hippocampal subfields Inter-subject similarity Graph-based method Intra-subject variability Alzheimer’s disease classification Mild cognitive impairment Patch-based grading Whole brain analysis
Language	English
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 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. Authorship contributions K.H., J.V.M., V.-T.T. and P.C. carried out the experiment and wrote the manuscript with support from I.O. All authors reviewed the manuscript. The data used in this manuscript is obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI). 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.
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Snippet	•This work introduces a new approach combining patch-based grading and graph-based model.•Combination of inter-subject similarity and intra-subject variability... The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer's disease (AD) is clinically relevant, and may above all have a... The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer’s disease (AD) is clinically relevant, and may above all have a...
SourceID	pubmedcentral hal proquest pubmed crossref elsevier
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	101850
SubjectTerms	Alzheimer Disease - diagnostic imaging Alzheimer's disease Alzheimer’s disease classification Bioengineering Biomarkers Brain Brain - diagnostic imaging Cognitive ability Cognitive Dysfunction - diagnostic imaging Cognitive science Computer science Computer Vision and Pattern Recognition Conversion Datasets Graph-based method Hippocampal subfields Hippocampus Humans Imaging Inter-subject similarity Intra-subject variability Life Sciences Magnetic Resonance Imaging Mild cognitive impairment Multiscale analysis Neurodegenerative diseases Neuroscience Patch-based grading State-of-the-art reviews Whole brain analysis
Title	Multi-scale graph-based grading for Alzheimer’s disease prediction
URI	https://dx.doi.org/10.1016/j.media.2020.101850 https://www.ncbi.nlm.nih.gov/pubmed/33075641 https://www.proquest.com/docview/2508914062 https://www.proquest.com/docview/2452495988 https://hal.science/hal-02967401 https://pubmed.ncbi.nlm.nih.gov/PMC7725970
Volume	67
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