Background Neuropsychological assessment is considered a valid tool in the diagnosis of neurodegenerative disorders. However, there is an important overlap in cognitive profiles between Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD), and the usefulness in diagnosis is uncertain. We aimed to develop machine learning‐based models for the diagnosis using cognitive tests. Methods Three hundred and twenty‐nine participants (170 AD, 72 bvFTD, 87 healthy control [HC]) were enrolled. Evolutionary algorithms, inspired by the process of natural selection, were applied for both mono‐objective and multi‐objective classification and feature selection. Classical algorithms (NativeBayes, Support Vector Machines, among others) were also used, and a meta‐model strategy. Results Accuracies for the diagnosis of AD, bvFTD and the differential diagnosis between them were higher than 84%. Algorithms were able to significantly reduce the number of tests and scores needed. Free and Cued Selective Reminding Test, verbal fluency and Addenbrooke's Cognitive Examination were amongst the most meaningful tests. Conclusions Our study found high levels of accuracy for diagnosis using exclusively neuropsychological tests, which supports the usefulness of cognitive assessment in diagnosis. Machine learning may have a role in improving the interpretation and test selection.

Artificial Intelligence aids early diagnosis and development of new treatments, which is key to slow down the progress of the diseases, which to date have no cure. The patients’ evaluation is carried out through diagnostic techniques such as clinical assessments neuroimaging techniques, which provide high-dimensionality data. In this work, a computational tool is presented that deals with the data provided by the clinical diagnostic techniques. This is a Python-based framework implemented with a modular design and fully extendable. It integrates (i) data processing and management of missing values and outliers; (ii) implementation of an evolutionary feature engineering approach, developed as a Python package, called PyWinEA using Mono-objective and Multi-objetive Genetic Algorithms (NSGAII); (iii) a module for designing predictive models based on a wide range of machine learning algorithms; (iv) a multiclass decision stage based on evolutionary grammars and Bayesian networks. Developed under the eXplainable Artificial Intelligence and open science perspective, this framework provides promising advances and opens the door to the understanding of neurodegenerative diseases from a data-centric point of view. In this work, we have successfully evaluated the potential of the framework for early and automated diagnosis with neuroimages and neurocognitive assessments from patients with Alzheimer’s disease (AD) and frontotemporal dementia (FTD). Graphical abstract

Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer’s disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism.

Few studies have addressed the impact of the association between Alzheimer´s disease (AD) biomarkers and NPSs in the conversion to dementia in patients with mild cognitive impairment (MCI), and no studies have been conducted on the interaction effect of these two risk factors. AT(N) profiles were created using AD-core biomarkers quantified in cerebrospinal fluid (CSF) (normal, brain amyloidosis, suspected non-Alzheimer pathology (SNAP) and prodromal AD). NPSs were assessed using the Neuropsychiatric Inventory Questionnaire (NPI-Q). A total of 500 individuals with MCI were followed-up yearly in a memory unit. Cox regression analysis was used to determine risk of conversion, considering additive and multiplicative interactions between AT(N) profile and NPSs on the conversion to dementia. A total of 224 participants (44.8%) converted to dementia during the 2-year follow-up study. Pathologic AT(N) groups (brain amyloidosis, prodromal AD and SNAP) and the presence of depression and apathy were associated with a higher risk of conversion to dementia. The additive combination of the AT(N) profile with depression exacerbates the risk of conversion to dementia. A synergic effect of prodromal AD profile with depressive symptoms is evidenced, identifying the most exposed individuals to conversion among MCI patients.

Background: Genetic algorithms are methods used in machine learning, which have a proven capability to explore a large space of solutions, deal with very large numbers of input features, and avoid local minima. Diagnosis of Alzheimer's Disease (AD) and Frontotemporal dementia (FTD) is often challenging, and thorough costly assessments are often needed. We hypothesised that the application of machine learning, and specifically genetic algorithms, to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis by selecting the most meaningful features and automating diagnosis. In this study, we aimed to develop algorithms for three common situations: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioural FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants.Method: Eighty-one patients with bvFTD, 88 patients with AD, 68 patients with PPA, and 39 HC were enrolled. Patients underwent a comprehensive clinical and neuropsychological protocol, and FDG-PET imaging. Genetic algorithms, customised with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed applied to the PET imaging and compared with Principal Component Analysis. The highest accuracy rate and most relevant features were identified. K-fold cross validation within the same sample and external validation with ADNI samples were performed.Result: Discrimination accuracy of FDG-PET was 92-95% for AD vs HCs, 95-96% for bvFTD vs HCs, 89-90% for AD vs bvFTD, and 90-91% for classification of PPA subtypes. A reduced number of features was achieved, with cutting rates from 62% to 95.69% regarding the total number of variables, and several key brain regions were selected. External validation with ADNI obtained an accuracy of 82.93% for BayesNet Naives algorithm for the differentiation between AD and HC. Conclusion:Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimised diagnosis of neurodegenerative disorders using brain metabolism.

Background: The FACEmemory® online platform comprises a novel, self-administered memory test with embedded voice recognition technology and a questionnaire with relevant sociodemographic and medical/family history data. This is the first study about a completely self-administered memory test with voice recognition, pre-tested in a memory clinic, offered freely worldwide on a website platform. The aims of this study are to investigate the demographic and clinical variables associated with FACEmemory total score, and to identify differentiable patterns of memory performance among the first 3,000 individuals who completed the FACEmemory. Methods: A marketing campaign was carried out to make FACEmemory accessible worldwide to individuals whose native language was Spanish or Catalan. Data from the first 3,000 subjects over 18 years old who completed the FACEmemory were analysed. Descriptive analyses were applied to demographic, FACEmemory scores, and medical/family history variables reported in a questionnaire; t-test and chi-square analyses were used to compare participants with preserved (>31 points) versus impaired performance (<32) on total FACEmemory; and multiple linear regression was used to identify variables that modulate FACEmemory performance. Finally, Machine Learning techniques were applied to identify differentiable patterns of memory performance. Results: The study sample had a mean age of 50.57 years and 13.65 years of schooling. 64.1% were women and most (82.1%) participants reported memory complaints that worried them. The group with impaired FACEmemory performance (20.4%) was older, had fewer years of formal education and a higher prevalence of hypertension, diabetes mellitus, dyslipidemia, and family history of a neurodegenerative disease compared with the group with preserved FACEmemory performance. Multiple regression analysis showed that age, schooling, sex, country and completion of the questionnaire were statistically associated with FACEmemory total score. Finally, Machine Learning techniques identified 4 patterns of FACEmemory performance: normal, dysexecutive, storage and completely impaired. Conclusions: FACEmemory is a promising tool for the pre-screening of people with subjective memory complaints in the community in order to identify those with objective memory deficits and raise awareness about cognitive decline. The FACEmemory website platform is an opportunity to facilitate a free, online and self-administered episodic memory assessment to Spanish or Catalan speaking individuals worldwide, and potentially extensible to other languages.

Background Diagnosis of Alzheimer’s disease and behavioral variant frontotemporal dementia is often challenging. In spite of comprehensive clinical and cognitive assessments, the use of biomarkers is usually needed. We aimed to develop machine learning based models for the diagnosis of AD and bvFTD using only cognitive testing. These techniques may allow selecting the most relevant tests for an optimized neuropsychological diagnosis. Method We included 329 participants: 171 patients with AD, 72 patients with bvFTD, and 87 healthy controls (HC). All patients met the current diagnostic criteria, had a neuroimaging compatible with FDG‐PET, and had at least two years of follow‐up confirming the diagnosis. A comprehensive neuropsychological protocol was performed. Evolutionary algorithms were developed, including NSGA‐II. F1‐score was calculated, as a measure of accuracy. Algorithms were developed for the following classification problems between AD vs FTD, AD vs HC, FTD vs HC, and AD/FTD vs HC. Results Differentiation between FTD vs HC, AD vs HC and FTD/AD vs HC reached an F1 superior to 90%. Differentiation between FTD vs AD was slightly inferior (F1 80‐85%). Test selected by the NSGAII algorithm for AD vs FTD were as follows: Symbol Digit Modalities Test, Stroop test, Trail Making Test, Corsi blocks, and ACE‐III. For FTD vs HC, the algorithm selected Free and Cued Selective Reminding Test, Trail Making Test, Rey Figure (copy type), Boston Naming Test, and ACE. For AD vs HC, the following test was selected: Rey Figure, Trail Making Test, Corsi test, and verbal fluency. Conclusions We have developed a machine‐learning approach to perform feature selection and modeling of neuropsychological test scores, with a high level of discrimination between groups. Our study suggests the interest of applying machine learning for an optimized use of cognitive tests and to improve the interpretation of neuropsychological assessment. . These algorithms allow maximizing the diagnostic capacity, selecting the tests with best characteristics, and the automation, which may be useful for diagnosis.