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      A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer’s Disease

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          Abstract

          In clinical practice, several standardized neuropsychological tests have been designed to assess and monitor the neurocognitive status of patients with neurodegenerative diseases such as Alzheimer’s disease. Important research efforts have been devoted so far to the development of multivariate machine learning models that combine the different test indexes to predict the diagnosis and prognosis of cognitive decline with remarkable results. However, less attention has been devoted to the explainability of these models. In this work, we present a robust framework to (i) perform a threefold classification between healthy control subjects, individuals with cognitive impairment, and subjects with dementia using different cognitive indexes and (ii) analyze the variability of the explainability SHAP values associated with the decisions taken by the predictive models. We demonstrate that the SHAP values can accurately characterize how each index affects a patient’s cognitive status. Furthermore, we show that a longitudinal analysis of SHAP values can provide effective information on Alzheimer’s disease progression.

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          The online version contains supplementary material available at 10.1186/s40708-022-00165-5.

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            The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.

            To develop a 10-minute cognitive screening tool (Montreal Cognitive Assessment, MoCA) to assist first-line physicians in detection of mild cognitive impairment (MCI), a clinical state that often progresses to dementia. Validation study. A community clinic and an academic center. Ninety-four patients meeting MCI clinical criteria supported by psychometric measures, 93 patients with mild Alzheimer's disease (AD) (Mini-Mental State Examination (MMSE) score > or =17), and 90 healthy elderly controls (NC). The MoCA and MMSE were administered to all participants, and sensitivity and specificity of both measures were assessed for detection of MCI and mild AD. Using a cutoff score 26, the MMSE had a sensitivity of 18% to detect MCI, whereas the MoCA detected 90% of MCI subjects. In the mild AD group, the MMSE had a sensitivity of 78%, whereas the MoCA detected 100%. Specificity was excellent for both MMSE and MoCA (100% and 87%, respectively). MCI as an entity is evolving and somewhat controversial. The MoCA is a brief cognitive screening tool with high sensitivity and specificity for detecting MCI as currently conceptualized in patients performing in the normal range on the MMSE.
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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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                Author and article information

                Contributors
                angela.lombardi@uniba.it
                domenico.diacono@ba.infn.it
                nicola.amoroso@uniba.it
                przemyslaw.biecek@pw.edu.pl
                alfonso.monaco@ba.infn.it
                loredana.bellantuono@uniba.it
                ester.pantaleo@uniba.it
                giancarlo.logroscino@uniba.it
                rdeblasi@piafondazionepanico.it
                sabina.tangaro@uniba.it
                roberto.bellotti@uniba.it
                Journal
                Brain Inform
                Brain Inform
                Brain Informatics
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                2198-4018
                2198-4026
                26 July 2022
                26 July 2022
                December 2022
                : 9
                : 1
                : 17
                Affiliations
                [1 ]GRID grid.7644.1, ISNI 0000 0001 0120 3326, Dipartimento di Fisica, , Università degli Studi di Bari Aldo Moro, ; Bari, Italy
                [2 ]GRID grid.470190.b, Istituto Nazionale di Fisica Nucleare, , Sezione di Bari, ; Bari, Italy
                [3 ]GRID grid.7644.1, ISNI 0000 0001 0120 3326, Dipartimento di Farmacia - Scienze del Farmaco, , Università degli Studi di Bari Aldo Moro, ; Bari, Italy
                [4 ]GRID grid.1035.7, ISNI 0000000099214842, Faculty of Mathematics and Information Science, , Warsaw University of Technology, ; Warsaw, Poland
                [5 ]GRID grid.12847.38, ISNI 0000 0004 1937 1290, Faculty of Mathematics, Informatics and Mechanics, , University of Warsaw, ; Warsaw, Poland
                [6 ]GRID grid.7644.1, ISNI 0000 0001 0120 3326, Dipartimento di Scienze mediche di base, Neuroscienze e Organi di senso, , Università degli Studi di Bari Aldo Moro, ; Bari, Italy
                [7 ]Pia Fondazione “Card. G. Panico”, Tricase, Italy
                [8 ]GRID grid.7644.1, ISNI 0000 0001 0120 3326, Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, , Università degli Studi di Bari Aldo Moro, ; Bari, Italy
                Article
                165
                10.1186/s40708-022-00165-5
                9325942
                35882684
                fcb8e4ce-1b7b-43c0-a6b9-4b000e8c358d
                © The Author(s) 2022

                Open AccessThis 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
                : 12 April 2022
                : 3 July 2022
                Funding
                Funded by: Research for Innovation - REFIN funded by Regione Puglia (Italy) in the framework of the POR Puglia FESR FSE 2014-2020 - Asse X - Azione 10.4.
                Award ID: project code 928A7C98
                Award Recipient :
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                Research
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                © The Author(s) 2022

                alzheimer’s disease,cognitive spectrum,explainable artificial intelligence,mild cognitive impairment,xai

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