Predicting brain age with complex networks: From adolescence to adulthood

Bellantuono, Loredana; Marzano, Luca; Rocca, Marianna La; Duncan, Dominique; Lombardi, Angela; Maggipinto, T.; Monaco, A.; Tangaro, Sabina; Amoroso, Nicola; Bellotti, Roberto

doi:10.1016/j.neuroimage.2020.117458

Cited by 40 publications

(32 citation statements)

References 65 publications

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“…The results the DNN models achieved compare favorably with the literature showing the overall performance MAE = 2.7 and R = 0.86 (Ball et al, 2019;Corps and Rekik, 2019,? ;Zhao et al, 2019;Bellantuono et al, 2020). However, as shown in Figure 3, our models exhibit a systematic age under-estimation in the most extreme age-range of the distribution, reporting worse performance (MAE > 4) at sites with individuals with chronological age in that range.…”

Section: Discussionmentioning

confidence: 74%

“…In particular, machine learning (ML) and deep learning (DL) algorithms have been successfully applied to predict age from brain MRI scans. Two main approaches are largely adopted to perform brain age prediction: on one hand, a number of selected features such as morphological descriptors, graph-based or other imaging-related features can be extracted from imaging to train different models (Erus et al, 2015;Amoroso et al, 2018Amoroso et al, , 2019Bellantuono et al, 2020;Han et al, 2020); on the other hand, more complex models such as convolutional neural networks directly exploiting raw image as input have proven to be particularly effective in brain age prediction even in broad age ranges (Cole et al, 2017(Cole et al, , 2019Feng et al, 2020;Levakov et al, 2020;Peng et al, 2021). Although convolutional neural networks offer undoubted advantages such as reduced preprocessing time and high performance (Cole et al, 2017), both ML and DL feature-based learning approaches based on morphological features are still widely adopted by scientific communities as they allow to investigate the morphological age-related brain changes in a great variety of disorders and conditions (Van Rooij et al, 2018;Corps and Rekik, 2019;Boedhoe et al, 2020;Han et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Explainable Deep Learning for Personalized Age Prediction With Brain Morphology

et al. 2021

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Predicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as an effective biomarker for different brain diseases and conditions. A great variety of machine learning (ML) approaches and deep learning (DL) techniques have been proposed to predict age from brain magnetic resonance imaging scans. If on one hand, DL models could improve performance and reduce model bias compared to other less complex ML methods, on the other hand, they are typically black boxes as do not provide an in-depth understanding of the underlying mechanisms. Explainable Artificial Intelligence (XAI) methods have been recently introduced to provide interpretable decisions of ML and DL algorithms both at local and global level. In this work, we present an explainable DL framework to predict the age of a healthy cohort of subjects from ABIDE I database by using the morphological features extracted from their MRI scans. We embed the two local XAI methods SHAP and LIME to explain the outcomes of the DL models, determine the contribution of each brain morphological descriptor to the final predicted age of each subject and investigate the reliability of the two methods. Our findings indicate that the SHAP method can provide more reliable explanations for the morphological aging mechanisms and be exploited to identify personalized age-related imaging biomarker.

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Section: Discussionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Explainable Deep Learning for Personalized Age Prediction With Brain Morphology

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“…ML and DL have been extensively used in various and critical health care applications, such as predicting brain age [ 194 ], diagnosis of liver diseases [ 195 ], and many other diseases [ 196 , 197 ]. In the current pandemic, governments and healthcare organizations are in critical need of support and decision-aid tools, which may help get timely and efficient support to avoid virus spread.…”

Section: The Study Taxonomymentioning

confidence: 99%

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

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Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

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“… 2018 , 2019 ; Bellantuono et al. 2021 ), genetics (Monaco et al. 2019 , 2020 ), natural (Weathers and Strayer 2013 ; Cowen et al.…”

Section: Introductionmentioning

confidence: 99%

Sustainable development goals: conceptualization, communication and achievement synergies in a complex network framework

et al. 2022

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In this work we use a network-based approach to investigate the complex system of interactions among the 17 Sustainable Development Goals (SDGs), that constitute the structure of the United Nations 2030 Agenda for a sustainable future. We construct a three-layer multiplex, in which SDGs represent nodes, and their connections in each layer are determined by similarity definitions based on conceptualization, communication, and achievement, respectively. In each layer of the multiplex, we investigate the presence of nodes with high centrality, corresponding to strategic SDGs. We then compare the networks to establish whether and to which extent similar patterns emerge. Interestingly, we observe a significant relation between the SDG similarity patterns determined by their achievement and their communication and perception, revealed by social network data. The proposed framework represents an instrument to unveil new and nontrivial aspects of sustainability, laying the foundation of a decision support system to define and implement SDG achievement strategies.

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Predicting brain age with complex networks: From adolescence to adulthood

Cited by 40 publications

References 65 publications

Explainable Deep Learning for Personalized Age Prediction With Brain Morphology

Explainable Deep Learning for Personalized Age Prediction With Brain Morphology

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Sustainable development goals: conceptualization, communication and achievement synergies in a complex network framework

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