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      Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

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          Abstract

          Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis.

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          NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease

          In 2011, the National Institute on Aging and Alzheimer’s Association created separate diagnostic recommendations for the preclinical, mild cognitive impairment, and dementia stages of Alzheimer’s disease. Scientific progress in the interim led to an initiative by the National Institute on Aging and Alzheimer’s Association to update and unify the 2011 guidelines. This unifying update is labeled a “research framework” because its intended use is for observational and interventional research, not routine clinical care. In the National Institute on Aging and Alzheimer’s Association Research Framework, Alzheimer’s disease (AD) is defined by its underlying pathologic processes that can be documented by postmortem examination or in vivo by biomarkers. The diagnosis is not based on the clinical consequences of the disease (i.e., symptoms/signs) in this research framework, which shifts the definition of AD in living people from a syndromal to a biological construct. The research framework focuses on the diagnosis of AD with biomarkers in living persons. Biomarkers are grouped into those of β amyloid deposition, pathologic tau, and neurodegeneration [AT(N)]. This ATN classification system groups different biomarkers (imaging and biofluids) by the pathologic process each measures. The AT(N) system is flexible in that new biomarkers can be added to the three existing AT(N) groups, and new biomarker groups beyond AT(N) can be added when they become available. We focus on AD as a continuum, and cognitive staging may be accomplished using continuous measures. However, we also outline two different categorical cognitive schemes for staging the severity of cognitive impairment: a scheme using three traditional syndromal categories and a six-stage numeric scheme. It is important to stress that this framework seeks to create a common language with which investigators can generate and test hypotheses about the interactions among different pathologic processes (denoted by biomarkers) and cognitive symptoms. We appreciate the concern that this biomarker-based research framework has the potential to be misused. Therefore, we emphasize, first, it is premature and inappropriate to use this research framework in general medical practice. Second, this research framework should not be used to restrict alternative approaches to hypothesis testing that do not use biomarkers. There will be situations where biomarkers are not available or requiring them would be counterproductive to the specific research goals (discussed in more detail later in the document). Thus, biomarker-based research should not be considered a template for all research into age-related cognitive impairment and dementia; rather, it should be applied when it is fit for the purpose of the specific research goals of a study. Importantly, this framework should be examined in diverse populations. Although it is possible that β-amyloid plaques and neurofibrillary tau deposits are not causal in AD pathogenesis, it is these abnormal protein deposits that define AD as a unique neurodegenerative disease among different disorders that can lead to dementia. We envision that defining AD as a biological construct will enable a more accurate characterization and understanding of the sequence of events that lead to cognitive impairment that is associated with AD, as well as the multifactorial etiology of dementia. This approach also will enable a more precise approach to interventional trials where specific pathways can be targeted in the disease process and in the appropriate people.
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            Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

            Summary Background Neurological disorders are increasingly recognised as major causes of death and disability worldwide. The aim of this analysis from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 is to provide the most comprehensive and up-to-date estimates of the global, regional, and national burden from neurological disorders. Methods We estimated prevalence, incidence, deaths, and disability-adjusted life-years (DALYs; the sum of years of life lost [YLLs] and years lived with disability [YLDs]) by age and sex for 15 neurological disorder categories (tetanus, meningitis, encephalitis, stroke, brain and other CNS cancers, traumatic brain injury, spinal cord injury, Alzheimer's disease and other dementias, Parkinson's disease, multiple sclerosis, motor neuron diseases, idiopathic epilepsy, migraine, tension-type headache, and a residual category for other less common neurological disorders) in 195 countries from 1990 to 2016. DisMod-MR 2.1, a Bayesian meta-regression tool, was the main method of estimation of prevalence and incidence, and the Cause of Death Ensemble model (CODEm) was used for mortality estimation. We quantified the contribution of 84 risks and combinations of risk to the disease estimates for the 15 neurological disorder categories using the GBD comparative risk assessment approach. Findings Globally, in 2016, neurological disorders were the leading cause of DALYs (276 million [95% UI 247–308]) and second leading cause of deaths (9·0 million [8·8–9·4]). The absolute number of deaths and DALYs from all neurological disorders combined increased (deaths by 39% [34–44] and DALYs by 15% [9–21]) whereas their age-standardised rates decreased (deaths by 28% [26–30] and DALYs by 27% [24–31]) between 1990 and 2016. The only neurological disorders that had a decrease in rates and absolute numbers of deaths and DALYs were tetanus, meningitis, and encephalitis. The four largest contributors of neurological DALYs were stroke (42·2% [38·6–46·1]), migraine (16·3% [11·7–20·8]), Alzheimer's and other dementias (10·4% [9·0–12·1]), and meningitis (7·9% [6·6–10·4]). For the combined neurological disorders, age-standardised DALY rates were significantly higher in males than in females (male-to-female ratio 1·12 [1·05–1·20]), but migraine, multiple sclerosis, and tension-type headache were more common and caused more burden in females, with male-to-female ratios of less than 0·7. The 84 risks quantified in GBD explain less than 10% of neurological disorder DALY burdens, except stroke, for which 88·8% (86·5–90·9) of DALYs are attributable to risk factors, and to a lesser extent Alzheimer's disease and other dementias (22·3% [11·8–35·1] of DALYs are risk attributable) and idiopathic epilepsy (14·1% [10·8–17·5] of DALYs are risk attributable). Interpretation Globally, the burden of neurological disorders, as measured by the absolute number of DALYs, continues to increase. As populations are growing and ageing, and the prevalence of major disabling neurological disorders steeply increases with age, governments will face increasing demand for treatment, rehabilitation, and support services for neurological disorders. The scarcity of established modifiable risks for most of the neurological burden demonstrates that new knowledge is required to develop effective prevention and treatment strategies. Funding Bill & Melinda Gates Foundation.
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              N4ITK: improved N3 bias correction.

              A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as "N4ITK," available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized (3)He lung image data, and 9.4T postmortem hippocampus data.
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                Author and article information

                Contributors
                sdefrancesco@fatebenefratelli.eu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                13 October 2023
                13 October 2023
                2023
                : 13
                : 17355
                Affiliations
                [1 ]GRID grid.419422.8, Laboratory of Neuroinformatics, , IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, ; Brescia, Italy
                [2 ]ASST Bergamo Ovest, Bergamo, Italy
                [3 ]GRID grid.417894.7, ISNI 0000 0001 0707 5492, Division of Neurology V/Neuropathology, , Fondazione IRCCS Istituto Neurologico Carlo Besta, ; Milan, Italy
                [4 ]GRID grid.66875.3a, ISNI 0000 0004 0459 167X, Department of Information Technology, , Mayo Clinic and Foundation, ; Rochester, Minnesota USA
                [5 ]GRID grid.417894.7, ISNI 0000 0001 0707 5492, Department of Neuroradiology, , Fondazione IRCCS Istituto Neurologico Carlo Besta, ; Milan, Italy
                [6 ]GRID grid.417894.7, ISNI 0000 0001 0707 5492, Scientific Directorate, , Fondazione IRCCS Istituto Neurologico Carlo Besta, ; Milan, Italy
                [7 ]Department of Biomedical and Neuromotor Sciences, University of Bologna, ( https://ror.org/01111rn36) Bologna, Italy
                [8 ]IRCCS Istituto delle Scienze Neurologiche di Bologna, ( https://ror.org/02mgzgr95) Bologna, Italy
                [9 ]Department of Brain and Behavioral Sciences, University of Pavia, ( https://ror.org/00s6t1f81) Pavia, Italy
                [10 ]GRID grid.419416.f, ISNI 0000 0004 1760 3107, IRCCS Mondino Foundation, ; Pavia, Italy
                [11 ]Department of Neurology, Mayo Clinic, ( https://ror.org/03zzw1w08) Rochester, Minnesota USA
                [12 ]Department of Radiology, Mayo Clinic, ( https://ror.org/02qp3tb03) Rochester, Minnesota USA
                [13 ]Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, ( https://ror.org/01kj2bm70) Newcastle Upon Tyne, UK
                [14 ]Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, ( https://ror.org/048b34d51) London, UK
                [15 ]University of Pavia, ( https://ror.org/00s6t1f81) Pavia, Italy
                [16 ]IRCCS Stella Maris, Pisa, Italy
                [17 ]Istituto di Ricerche Farmacologiche Mario Negri IRCCS, ( https://ror.org/05aspc753) Milan, Italy
                [18 ]IRCCS SYNLAB SDN, Naples, Italy
                [19 ]IRCCS Istituti Clinici Scientifici Maugeri, ( https://ror.org/00mc77d93) Pavia, Italy
                [20 ]GRID grid.419843.3, ISNI 0000 0001 1250 7659, Oasi Research Institute-IRCCS, ; Troina, Italy
                [21 ]GRID grid.420417.4, ISNI 0000 0004 1757 9792, Scientific Institute, IRCCS E. Medea, ; Milan, Italy
                [22 ]GRID grid.417894.7, ISNI 0000 0001 0707 5492, Fondazione IRCCS Istituto Neurologico Carlo Besta, ; Milan, Italy
                [23 ]GRID grid.418563.d, ISNI 0000 0001 1090 9021, IRCCS Fondazione Don Carlo Gnocchi ONLUS, ; Milan, Italy
                [24 ]IRCCS Centro Neurolesi Bonino Pulejo, ( https://ror.org/05tzq2c96) Messina, Italy
                [25 ]GRID grid.414125.7, ISNI 0000 0001 0727 6809, IRCCS Istituto Ospedale Pediatrico Bambino Gesù, ; Rome, Italy
                [26 ]GRID grid.417778.a, ISNI 0000 0001 0692 3437, Fondazione IRCCS Santa Lucia, ; Rome, Italy
                [27 ]IRCCS Neuromed, ( https://ror.org/00cpb6264) Pozzilli, Italy
                [28 ]IRCCS Ospedale San Raffaele, ( https://ror.org/039zxt351) Milan, Italy
                [29 ]IRCCS Istituto Auxologico Italiano, ( https://ror.org/033qpss18) Milan, Italy
                [30 ]Fondazione IRCCS Ca’ Granda Osp. Maggiore Policlinico, ( https://ror.org/016zn0y21) Milan, Italy
                [31 ]University of Genoa, ( https://ror.org/0107c5v14) Genoa, Italy
                [32 ]Philips Healthcare, Milan, Italy
                [33 ]Fondazione Policlinico Universitario Agostino Gemelli IRCCS, ( https://ror.org/00rg70c39) Rome, Italy
                [34 ]IRCCS Humanitas Research Hospital, ( https://ror.org/05d538656) Rozzano, Italy
                [35 ]IRCCS Ospedale Policlinico San Martino, ( https://ror.org/04d7es448) Genoa, Italy
                [36 ]GRID grid.419504.d, ISNI 0000 0004 1760 0109, IRCCS Istituto Giannina Gaslini, ; Genoa, Italy
                [37 ]University of Turin, ( https://ror.org/048tbm396) Turin, Italy
                Article
                43706
                10.1038/s41598-023-43706-6
                10575864
                37833302
                b6774dab-55da-41db-b338-9da7be331ab3
                © Springer Nature Limited 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 January 2023
                : 27 September 2023
                Funding
                Funded by: Italian Ministry of Economy and Finance (MEF)
                Award ID: CCR-2017-23669078
                Funded by: Italian Ministry of Health (MoH)
                Award ID: “RETE IRCCS DI NEUROSCIENZE E NEURORIABILITAZIONE” (Imaging Project - RRC-2016-2361095; RRC-2017-2364915; RRC-2018-2365796; RRC-2019-23669119_001; RCR-2022-23682285)
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                © Springer Nature Limited 2023

                Uncategorized
                information technology,software,neurology,dementia,neurodegenerative diseases
                Uncategorized
                information technology, software, neurology, dementia, neurodegenerative diseases

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