Multiplex Networks for Early Diagnosis of Alzheimer's Disease

Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Several studies have suggested that brain topological organization can reveal early signs of...

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Published in	Frontiers in aging neuroscience Vol. 10; p. 365
Main Authors	Amoroso, Nicola, La Rocca, Marianna, Bruno, Stefania, Maggipinto, Tommaso, Monaco, Alfonso, Bellotti, Roberto, Tangaro, Sabina
Format	Journal Article
Language	English
Published	Switzerland Frontiers Research Foundation 14.11.2018 Frontiers Media S.A
Subjects	Aging Alzheimer's disease Atrophy Brain architecture Clinical trials Cognitive ability Dementia diagnosis support system Image processing machine learning Magnetic resonance imaging magnetic resonance imaging (MRI) Medical imaging mild cognitive impairment Morphometry multiplex networks Neurodegeneration Neurodegenerative diseases Neuroimaging Neuroscience NMR Nuclear magnetic resonance Segmentation Italy magnetic resonance imaging (MRI) diagnosis support system mild cognitive impairment brain Connectivity machine learning multiplex networks Alzheimer's disease
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ISSN	1663-4365 1663-4365
DOI	10.3389/fnagi.2018.00365

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Abstract	Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra- and inter-subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex-based score segregates (i) normal controls (NC) from AD patients with a 0.86±0.01 accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of 0.84±0.01. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of 3, 000 mm ("patches"), without any segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel-based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies.
AbstractList	Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra- and inter-subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex-based score segregates (i) normal controls (NC) from AD patients with a 0.86±0.01 accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of 0.84±0.01. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of 3, 000 mm ("patches"), without any segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel-based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies. Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra- and inter-subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex-based score segregates (i) normal controls (NC) from AD patients with a 0.86±0.01 accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of 0.84±0.01. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of 3, 000 mm3 (“patches”), without any a priori segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel-based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies. Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra- and inter-subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex-based score segregates (i) normal controls (NC) from AD patients with a 0.86±0.01 accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of 0.84±0.01. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of 3, 000 mm3 ("patches"), without any a priori segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel-based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies.Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra- and inter-subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex-based score segregates (i) normal controls (NC) from AD patients with a 0.86±0.01 accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of 0.84±0.01. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of 3, 000 mm3 ("patches"), without any a priori segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel-based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies. Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra- and inter-subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex-based score segregates (i) normal controls (NC) from AD patients with a $0.86 \pm 0.01$ accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of $0.84 \pm 0.01$. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of $3000$ mm\textsuperscript{3} ("patches''), without any \textit{a priori} segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel-based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies.
Author	Maggipinto, Tommaso Amoroso, Nicola Bellotti, Roberto Monaco, Alfonso La Rocca, Marianna Bruno, Stefania Tangaro, Sabina
AuthorAffiliation	3 Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California , Los Angeles, CA , United States 2 Dipartimento Interateneo di Fisica “M. Merlin”, Istituto Nazionale di Fisica Nucleare, Sezione di Bari , Bari , Italy 4 Blackheath Brain Injury Rehabilitation Centre , London , United Kingdom 1 Dipartimento Interateneo di Fisica “M. Merlin”, Università degli studi di Bari “A. Moro” , Bari , Italy
AuthorAffiliation_xml	– name: 3 Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California , Los Angeles, CA , United States – name: 2 Dipartimento Interateneo di Fisica “M. Merlin”, Istituto Nazionale di Fisica Nucleare, Sezione di Bari , Bari , Italy – name: 4 Blackheath Brain Injury Rehabilitation Centre , London , United Kingdom – name: 1 Dipartimento Interateneo di Fisica “M. Merlin”, Università degli studi di Bari “A. Moro” , Bari , Italy
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Copyright	2018. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © Amoroso, La Rocca, Bruno, Maggipinto, Monaco, Bellotti, and Tangaro. 2018 Amoroso, La Rocca, Bruno, Maggipinto, Monaco, Bellotti, and Tangaro
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Keywords	magnetic resonance imaging (MRI) diagnosis support system mild cognitive impairment brain Connectivity machine learning multiplex networks Alzheimer's disease
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Reviewed by: Patrizia Giannoni, University of Nîmes, France; Ghulam Md Ashraf, King Abdulaziz University, Saudi Arabia 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. More details are given in the Acknowledgments Edited by: Fernanda Laezza, The University of Texas Medical Branch at Galveston, United States These authors have contributed equally to this work and last authorship
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SubjectTerms	Aging Alzheimer's disease Atrophy Brain architecture Clinical trials Cognitive ability Dementia diagnosis support system Image processing machine learning Magnetic resonance imaging magnetic resonance imaging (MRI) Medical imaging mild cognitive impairment Morphometry multiplex networks Neurodegeneration Neurodegenerative diseases Neuroimaging Neuroscience NMR Nuclear magnetic resonance Segmentation
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Title	Multiplex Networks for Early Diagnosis of Alzheimer's Disease
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