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Multi-View Ensemble Classification of Brain Connectivity Images for Neurodegeneration Type Discrimination

Michele Fratello, Giuseppina Caiazzo, Francesca Trojsi, Antonio Russo, Gioacchino Tedeschi, Roberto Tagliaferri, Fabrizio Esposito
2017 Neuroinformatics  
Structural and functional connectivity features were extracted from multi-modal MRI images with a clustering technique, and used for the multi-view classification of different phenotypes of neurodegeneration  ...  These results highlight the potentials of mining complementary information from the integration of multiple data views in the classification of connectivity patterns from multi-modal brain images in the  ...  Luciano for their help in collecting data and running experiments.  ... 
doi:10.1007/s12021-017-9324-2 pmid:28210983 pmcid:PMC5443864 fatcat:5bvxad4ktbg7datb5e2ufq7myu

From Brain Imaging to Graph Analysis: a study on ADNI's patient cohort [article]

Rui Zhang, Luca Giancardo, Danilo A. Pena, Yejin Kim, Hanghang Tong, Xiaoqian Jiang
2019 arXiv   pre-print
We then obtained substructures of interest using a graph decomposition algorithm in order to extract pivotal nodes via multi-view feature selection.  ...  Intensive experiments using robust classification frameworks were conducted to evaluate the performance of using the brain substructures obtained under different thresholds.  ...  ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following:  ... 
arXiv:1905.05861v1 fatcat:hn3pr4wpdbcyzpo76aye26d6oe

Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states

Ines Mahjoub, Mohamed Ali Mahjoub, Islem Rekik
2018 Scientific Reports  
ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.  ...  Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense  ...  Then, we propose two different architectures to explore the relationship between multiple brain connectivity morphological views: (1) a deep multi-level similarity network that aggregates different morphological  ... 
doi:10.1038/s41598-018-21568-7 pmid:29515158 pmcid:PMC5841319 fatcat:2yvfxpeqybhz3ghv3476oumdau

A Method for Automated Classification of Parkinson's Disease Diagnosis Using an Ensemble Average Propagator Template Brain Map Estimated from Diffusion MRI

Monami Banerjee, Michael S. Okun, David E. Vaillancourt, Baba C. Vemuri, Jan Kassubek
2016 PLoS ONE  
Magnetic resonance imaging (MRI) is currently one of the most widely used diagnostic imaging techniques utilized for detection of neurologic diseases.  ...  In this paper, we derived structural biomarkers from diffusion MRI (dMRI), a structural modality that allows for non-invasive inference of neuronal fiber connectivity patterns.  ...  The method however will be applicable to any other type of images in general.  ... 
doi:10.1371/journal.pone.0155764 pmid:27280486 pmcid:PMC4900548 fatcat:xibpvvzjtfe3be7bd55ixj7gja

Multi-Link Analysis: Brain Network Comparison via Sparse Connectivity Analysis [article]

Alessandro Crimi, Luca Giancardo, Fabio Sambataro, Alessandro Gozzi, Vittorio Murino, Diego Sona
2018 bioRxiv   pre-print
The analysis of the brain from a connectivity perspective is unveiling novel insights into brain structure and function.  ...  in classification since this does not guarantee reliable interpretation of specific differences between groups.  ...  Parameter Tuning While the number of discriminative connections selected by our type of model is tuned by the choice of η, we noticed that the algorithm was satisfactorily discriminating the two classes  ... 
doi:10.1101/277046 fatcat:vvgtewdkzjcihpqdhooztn4thi

MultiLink Analysis: Brain Network Comparison via Sparse Connectivity Analysis

Alessandro Crimi, Luca Giancardo, Fabio Sambataro, Alessandro Gozzi, Vittorio Murino, Diego Sona
2019 Scientific Reports  
The analysis of the brain from a connectivity perspective is revealing novel insights into brain structure and function.  ...  Nevertheless, those connections do not necessarily maximize the accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups.  ...  Acknowledgements Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department  ... 
doi:10.1038/s41598-018-37300-4 pmid:30635604 pmcid:PMC6329758 fatcat:x2gszvddcbajjkc4wu7i6pzcgy

Alteration of brain structural connectivity in progression of Parkinson's disease: a connectome-wide network analysis

Yanwu Yang, Chenfei Ye, Junyan Sun, Li Liang, Haiyan Lv, Linlin Gao, Jiliang Fang, Ting Ma, Tao Wu
2021 NeuroImage: Clinical  
Applying machine learning on the key connectivity related to these seed regions demonstrated better classification accuracy compared to conventional network-based statistic.  ...  Recent neuroimage investigations reported disruptive brain white matter connectivity in both iRBD and PD, respectively.  ...  Acknowledgments This study is supported by the National Key Research and Development Program of China  ... 
doi:10.1016/j.nicl.2021.102715 pmid:34130192 pmcid:PMC8209844 fatcat:wgl3z6wgd5aedi2n5xbueedjme

Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients

Chong-Yaw Wee, Pew-Thian Yap, Kevin Denny, Jeffrey N. Browndyke, Guy G. Potter, Kathleen A. Welsh-Bohmer, Lihong Wang, Dinggang Shen, Yong He
2012 PLoS ONE  
Citation: Wee C-Y, Yap P-T, Denny K, Browndyke JN, Potter GG, et al. (2012) Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients. PLoS ONE 7(5): e37828.  ...  Five connectivity networks are constructed, one from each frequency sub-band. Clustering coefficient of each ROI in relation to the other ROIs are extracted as features for classification.  ...  At the stage where symptoms can be observed, significant neurodegeneration of the human brain has already occurred.  ... 
doi:10.1371/journal.pone.0037828 pmid:22666397 pmcid:PMC3364275 fatcat:vuceuzzte5ca5cvfffugvh2zmm

Machine Learning Techniques for AD/MCI Diagnosis and Prognosis [chapter]

Dinggang Shen, Chong-Yaw Wee, Daoqiang Zhang, Luping Zhou, Pew-Thian Yap
2013 Intelligent Systems Reference Library  
magnetic resonance imaging (fMRI), for effective diagnosis and prognosis.  ...  We will discuss how various biomarkers as well as connectivity networks can be extracted from the individual modalities, i.e., structural T1-weighted imaging, diffusion-tensor imaging (DTI) and functional  ...  imaging) is extracted using connectivity networks to provide a comprehensive representation of brain alterations for improved classification performance.  ... 
doi:10.1007/978-3-642-40017-9_8 fatcat:cay26o75sfbi3aa444cuswyhh4

Machine Learning for the Classification of Alzheimer's Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Lucia Billeci, Asia Badolato, Lorenzo Bachi, Alessandro Tonacci
2020 Processes  
The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image  ...  A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal  ...  In this way, the classification accuracy was adopted as the metric to evaluate different types of brain connectivity features, and to understand which ones may have an advantage in predicting MCI or AD  ... 
doi:10.3390/pr8091071 fatcat:p22wqxbulvf3xpv6jognlj3hxu

Medical imaging diagnosis of early Alzheimer rsquo s disease

Ayman El-Baz
2018 Frontiers in Bioscience  
Brain Region WM connectivity networks Note: The most discriminant regions that were selected for classification included the rectus gyrus which is located on the orbital portion of the frontal lobe and  ...  multi-modal SVM for classification/regression.  ...  For example, the movement of patients during the image acquisition process introduces a source of the  ... 
doi:10.2741/4612 pmid:28930568 fatcat:6f5gzcdyireuro3ylaswz2p734

Efficient Morphometric Techniques in Alzheimer's Disease Detection: Survey and Tools

Vinutha N., P. Deepa Shenoy, P. Deepa Shenoy, K.R. Venugopal
2016 Neuroscience International  
The development of advance techniques in the multiple fields such as image processing, data mining and machine learning are required for the early detection of Alzheimer's Disease (AD) and to prevent the  ...  The registration techniques like Mutual Information Registration, Fluid registration, Rigid registration, Spatial Transformation algorithm for registration, Elastic Registration are selected based on type  ...  The corresponding author confirms that all of the other authors have read and approved the manuscript and there are no ethical issues involved.  ... 
doi:10.3844/amjnsp.2016.19.44 fatcat:3zeb2s5pjzfv7mptqi7cy2a3au

Analysis of Features of Alzheimer's Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network

Modupe Odusami, Rytis Maskeliūnas, Robertas Damaševičius, Tomas Krilavičius
2021 Diagnostics  
Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI)  ...  One of the first signs of Alzheimer's disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages.  ...  For each of the subjects, there is a T1-weighted fMRI image with an axial view in a DICOMM file format.  ... 
doi:10.3390/diagnostics11061071 fatcat:ucrm3bajxngsxisjdcixrk7wbi

Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence

Andrea Termine, Carlo Fabrizio, Claudia Strafella, Valerio Caputo, Laura Petrosini, Carlo Caltagirone, Emiliano Giardina, Raffaella Cascella
2021 Journal of Personalized Medicine  
Deep learning allows researchers to find multi-modal biomarkers to develop more effective and personalized treatments, early diagnosis tools, as well as useful information for drug discovering and repurposing  ...  In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible  ...  Acknowledgments: We thank Project MUSA CNR (FOE 2019) for supporting the making of this review article. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jpm11040280 pmid:33917161 pmcid:PMC8067806 fatcat:tbnwt53hj5a6dcboyen3pu5p7m

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Michael W. Weiner, Dallas P. Veitch, Paul S. Aisen, Laurel A. Beckett, Nigel J. Cairns, Jesse Cedarbaum, Robert C. Green, Danielle Harvey, Clifford R. Jack, William Jagust, Johan Luthman, John C. Morris (+7 others)
2015 Alzheimer's & Dementia  
The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal  ...  CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas  ...  funding for  ... 
doi:10.1016/j.jalz.2014.11.001 pmid:26073027 pmcid:PMC5469297 fatcat:2k7ag6astffy5gphqxf5lodkdq
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