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1. Augmented reality versus standard tests to assess cognition and function in early Alzheimer’s disease

Author

Marijn Muurling, Casper de Boer, Srinivasan Vairavan, Robbert L. Harms, Antonella Santuccione Chadha, Ioannis Tarnanas, Estefania Vilarino Luis, Dorota Religa, Martha Therese Gjestsen, Samantha Galluzzi, Marta Ibarria Sala, Ivan Koychev, Lucrezia Hausner, Mara Gkioka, Dag Aarsland, Pieter Jelle Visser, and Anna-Katharine Brem

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Augmented reality (AR) apps, in which the virtual and real world are combined, can recreate instrumental activities of daily living (IADL) and are therefore promising to measure cognition needed for IADL in early Alzheimer’s disease (AD) both in the clinic and in the home settings. The primary aim of this study was to distinguish and classify healthy controls (HC) from participants with AD pathology in an early AD stage using an AR app. The secondary aims were to test the association of the app with clinical cognitive and functional tests and investigate the feasibility of at-home testing using AR. We furthermore investigated the test-retest reliability and potential learning effects of the task. The digital score from the AR app could significantly distinguish HC from preclinical AD (preAD) and prodromal AD (proAD), and preAD from proAD, both with in-clinic and at-home tests. For the classification of the proAD group, the digital score (AUCclinic_visit = 0.84 [0.75–0.93], AUCat_home = 0.77 [0.61–0.93]) was as good as the cognitive score (AUC = 0.85 [0.78–0.93]), while for classifying the preAD group, the digital score (AUCclinic_visit = 0.66 [0.53–0.78], AUCat_home = 0.76 [0.61–0.91]) was superior to the cognitive score (AUC = 0.55 [0.42–0.68]). In-clinic and at-home tests moderately correlated (rho = 0.57, p

Published
2023
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2. Using a Digital Neuro Signature to measure longitudinal individual-level change in Alzheimer’s disease: the Altoida large cohort study

Author

Irene B. Meier, Max Buegler, Robbert Harms, Azizi Seixas, Arzu Çöltekin, and Ioannis Tarnanas

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Conventional neuropsychological assessments for Alzheimer’s disease are burdensome and inaccurate at detecting mild cognitive impairment and predicting Alzheimer’s disease risk. Altoida’s Digital Neuro Signature (DNS), a longitudinal cognitive test consisting of two active digital biomarker metrics, alleviates these limitations. By comparison to conventional neuropsychological assessments, DNS results in faster evaluations (10 min vs 45–120 min), and generates higher test-retest in intraindividual assessment, as well as higher accuracy at detecting abnormal cognition. This study comparatively evaluates the performance of Altoida’s DNS and conventional neuropsychological assessments in intraindividual assessments of cognition and function by means of two semi-naturalistic observational experiments with 525 participants in laboratory and clinical settings. The results show that DNS is consistently more sensitive than conventional neuropsychological assessments at capturing longitudinal individual-level change, both with respect to intraindividual variability and dispersion (intraindividual variability across multiple tests), across three participant groups: healthy controls, mild cognitive impairment, and Alzheimer’s disease. Dispersion differences between DNS and conventional neuropsychological assessments were more pronounced with more advanced disease stages, and DNS-intraindividual variability was able to predict conversion from mild cognitive impairment to Alzheimer’s disease. These findings are instrumental for patient monitoring and management, remote clinical trial assessment, and timely interventions, and will hopefully contribute to a better understanding of Alzheimer’s disease.

Published
2021
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3. Making pre-screening for Alzheimer's disease (AD) and Postoperative delirium among post-acute COVID-19 syndrome - (PACS) a national priority: The Deep Neuro Study [version 1; peer review: 2 approved, 1 approved with reservations]

Author

Ioannis Tarnanas and Magda Tsolaki

Subjects
prediagnostic Alzheimer's disease (AD), digital neuro signatures (DNS), prodromal Alzheimer's disease (AD), postoperative delirium, post-acute COVID-19 syndrome (PASC), eng, Science, Social Sciences
Abstract

SARS-CoV-2 effects on cognition is a vibrant area of active research. Many researchers suggest that COVID-19 patients with severe symptoms leading to hospitalization, sustain significant neurodegenerative injury, such as encephalopathy and poor discharge disposition. However, despite some post-acute COVID-19 syndrome (PACS) case series that have described elevated neurodegenerative biomarkers, no studies have been identified that directly compared levels to those in mild cognitive impairment, non-PACS postoperative delirium patients after major non-emergent surgery or preclinical Alzheimer’s Disease (AD) patients, that have clinical evidence of Alzheimer's without symptoms. According to recent estimates, there may be 416 million people globally on the AD continuum, which include approximately 315 million people with preclinical AD. In light of all the above, a more effective application of digital biomarker and explainable artificial intelligence methodologies that explored amyloid beta, neuronal, axonal, and glial markers in relation to neurological complications in-hospital or later outcomes could significantly assist progress in the field. Easy and scalable subjects’ risk stratification is of utmost importance, yet current international collaboration initiatives are still challenging due to the limited explainability and accuracy to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials. In this open letter, we propose the administration of selected digital biomarkers previously discovered and validated in other EU funded studies to become a routine assessment for non-PACS preoperative cognitive impairment, PACS neurological complications in-hospital or later PACS and non-PACS improvement in cognition after surgery. The open letter also includes an economic analysis of the implications for such national level initiatives. Similar collaboration initiatives could have existing prediagnostic detection and progression prediction solutions pre-screen the stage before and around diagnosis, enabling new disease manifestation mapping and pushing the field into unchartered territory.

Published
2022
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4. Description of the Method for Evaluating Digital Endpoints in Alzheimer Disease Study: Protocol for an Exploratory, Cross-sectional Study

Author

Jelena Curcic, Vanessa Vallejo, Jennifer Sorinas, Oleksandr Sverdlov, Jens Praestgaard, Mateusz Piksa, Mark Deurinck, Gul Erdemli, Maximilian Bügler, Ioannis Tarnanas, Nick Taptiklis, Francesca Cormack, Rebekka Anker, Fabien Massé, William Souillard-Mandar, Nathan Intrator, Lior Molcho, Erica Madero, Nicholas Bott, Mieko Chambers, Josef Tamory, Matias Shulz, Gerardo Fernandez, William Simpson, Jessica Robin, Jón G Snædal, Jang-Ho Cha, and Kristin Hannesdottir

Subjects
Medicine, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundMore sensitive and less burdensome efficacy end points are urgently needed to improve the effectiveness of clinical drug development for Alzheimer disease (AD). Although conventional end points lack sensitivity, digital technologies hold promise for amplifying the detection of treatment signals and capturing cognitive anomalies at earlier disease stages. Using digital technologies and combining several test modalities allow for the collection of richer information about cognitive and functional status, which is not ascertainable via conventional paper-and-pencil tests. ObjectiveThis study aimed to assess the psychometric properties, operational feasibility, and patient acceptance of 10 promising technologies that are to be used as efficacy end points to measure cognition in future clinical drug trials. MethodsThe Method for Evaluating Digital Endpoints in Alzheimer Disease study is an exploratory, cross-sectional, noninterventional study that will evaluate 10 digital technologies’ ability to accurately classify participants into 4 cohorts according to the severity of cognitive impairment and dementia. Moreover, this study will assess the psychometric properties of each of the tested digital technologies, including the acceptable range to assess ceiling and floor effects, concurrent validity to correlate digital outcome measures to traditional paper-and-pencil tests in AD, reliability to compare test and retest, and responsiveness to evaluate the sensitivity to change in a mild cognitive challenge model. This study included 50 eligible male and female participants (aged between 60 and 80 years), of whom 13 (26%) were amyloid-negative, cognitively healthy participants (controls); 12 (24%) were amyloid-positive, cognitively healthy participants (presymptomatic); 13 (26%) had mild cognitive impairment (predementia); and 12 (24%) had mild AD (mild dementia). This study involved 4 in-clinic visits. During the initial visit, all participants completed all conventional paper-and-pencil assessments. During the following 3 visits, the participants underwent a series of novel digital assessments. ResultsParticipant recruitment and data collection began in June 2020 and continued until June 2021. Hence, the data collection occurred during the COVID-19 pandemic (SARS-CoV-2 virus pandemic). Data were successfully collected from all digital technologies to evaluate statistical and operational performance and patient acceptance. This paper reports the baseline demographics and characteristics of the population studied as well as the study's progress during the pandemic. ConclusionsThis study was designed to generate feasibility insights and validation data to help advance novel digital technologies in clinical drug development. The learnings from this study will help guide future methods for assessing novel digital technologies and inform clinical drug trials in early AD, aiming to enhance clinical end point strategies with digital technologies. International Registered Report Identifier (IRRID)DERR1-10.2196/35442

Published
2022
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5. Associations of digital neuro-signatures with molecular and neuroimaging measures of brain resilience: The altoida large cohort study

Author

Azizi A. Seixas, Farid Rajabli, Margaret A. Pericak-Vance, Girardin Jean-Louis, Robbert L. Harms, and Ioannis Tarnanas

Subjects
digital biomarker, dementia, cognitive impairment, Alzheimer's disease, cognitive resilience, Psychiatry, RC435-571
Abstract

BackgroundMixed results in the predictive ability of traditional biomarkers to determine cognitive functioning and changes in older adults have led to misdiagnosis and inappropriate treatment plans to address mild cognitive impairment and dementia among older adults. To address this critical gap, the primary goal of the current study is to investigate whether a digital neuro signature (DNS-br) biomarker predicted global cognitive functioning and change over time relative among cognitively impaired and cognitive healthy older adults. The secondary goal is to compare the effect size of the DNS-br biomarker on global cognitive functioning compared to traditional imaging and genomic biomarkers. The tertiary goal is to investigate which demographic and clinical factors predicted DNS-br in cognitively impaired and cognitively healthy older adults.MethodsWe conducted two experiments (Study A and Study B) to assess DNS for brain resilience (DNS-br) against the established FDG-PET brain imaging signature for brain resilience, based on a 10 min digital cognitive assessment tool. Study A was a semi-naturalistic observational study that included 29 participants, age 65+, with mild to moderate mild cognitive impairment and AD diagnosis. Study B was also a semi-naturalistic observational multicenter study which included 496 participants (213 mild cognitive impairment (MCI) and 283 cognitively healthy controls (HC), a total of 525 participants—cognitively healthy (n = 283) or diagnosed with MCI (n = 213) or AD (n = 29).ResultsDNS-br total score and majority of the 11 DNS-br neurocognitive subdomain scores were significantly associated with FDG-PET resilience signature, PIB ratio, cerebral gray matter and white matter volume after adjusting for multiple testing. DNS-br total score predicts cognitive impairment for the 80+ individuals in the Altoida large cohort study. We identified a significant interaction between the DNS-br total score and time, indicating that participants with higher DNS-br total score or FDG-PET in the resilience signature would show less cognitive decline over time.ConclusionOur findings highlight that a digital biomarker predicted cognitive functioning and change, which established biomarkers are unable to reliably do. Our findings also offer possible etiologies of MCI and AD, where education did not protect against cognitive decline.

Published
2022
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6. Can detection and prediction models for Alzheimer’s Disease be applied to Prodromal Parkinson’s Disease using explainable artificial intelligence? A brief report on Digital Neuro Signatures. [version 2; peer review: 2 approved]

Author

Ioannis TARNANAS, Panagiotis Vlamos, and Dr Robbert Harms

Subjects
prediagnostic Parkinson's disease, digital neuro signatures, prodromal Parkinson's disease, eng, Science, Social Sciences
Abstract

Parkinson's disease (PD) is the fastest growing neurodegeneration and has a prediagnostic phase with a lot of challenges to identify clinical and laboratory biomarkers for those in the earliest stages or those 'at risk'. Despite the current research effort, further progress in this field hinges on the more effective application of digital biomarker and artificial intelligence applications at the prediagnostic stages of PD. It is of the highest importance to stratify such prediagnostic subjects that seem to have the most neuroprotective benefit from drugs. However, current initiatives to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials are still challenging due to the limited accuracy and explainability of existing prediagnostic detection and progression prediction solutions. In this brief paper, we report on a novel digital neuro signature (DNS) for prodromal-PD based on selected digital biomarkers previously discovered on preclinical Alzheimer's disease. (AD). Our preliminary results demonstrated a standard DNS signature for both preclinical AD and prodromal PD, containing a ranked selection of features. This novel DNS signature was rapidly repurposed out of 793 digital biomarker features and selected the top 20 digital biomarkers that are predictive and could detect both the biological signature of preclinical AD and the biological mechanism of a-synucleinopathy in prodromal PD. The resulting model can provide physicians with a pool of patients potentially eligible for therapy and comes along with information about the importance of the digital biomarkers that are predictive, based on SHapley Additive exPlanations (SHAP). Similar initiatives could clarify the stage before and around diagnosis, enabling the field to push into unchartered territory at the earliest stages of the disease.

Published
2022
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7. Can detection and prediction models for Alzheimer’s Disease be applied to Prodromal Parkinson’s Disease using explainable artificial intelligence? A brief report on Digital Neuro Signatures. [version 1; peer review: 2 approved]

Author

Ioannis TARNANAS and Panagiotis Vlamos

Subjects
prediagnostic Parkinson's disease, digital neuro signatures, prodromal Parkinson's disease, eng, Science, Social Sciences
Abstract

Parkinson's disease (PD) is the fastest growing neurodegeneration and has a prediagnostic phase with a lot of challenges to identify clinical and laboratory biomarkers for those in the earliest stages or those 'at risk'. Despite the current research effort, further progress in this field hinges on the more effective application of digital biomarker and artificial intelligence applications at the prediagnostic stages of PD. It is of the highest importance to stratify such prediagnostic subjects that seem to have the most neuroprotective benefit from drugs. However, current initiatives to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials are still challenging due to the limited accuracy and explainability of existing prediagnostic detection and progression prediction solutions. In this brief paper, we report on a novel digital neuro signature (DNS) for prodromal-PD based on selected digital biomarkers previously discovered on preclinical Alzheimer's disease. (AD). Our preliminary results demonstrated a standard DNS signature for both preclinical AD and prodromal PD, containing a ranked selection of features. This novel DNS signature was rapidly repurposed out of 793 digital biomarker features and selected the top 20 digital biomarkers that are predictive and could detect both the biological signature of preclinical AD and the biological mechanism of a-synucleinopathy in prodromal PD. The resulting model can provide physicians with a pool of patients potentially eligible for therapy and comes along with information about the importance of the digital biomarkers that are predictive, based on SHapley Additive exPlanations (SHAP). Similar initiatives could clarify the stage before and around diagnosis, enabling the field to push into unchartered territory at the earliest stages of the disease.

Published
2021
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8. The impact of SARS‐CoV‐2 in dementia across Latin America: A call for an urgent regional plan and coordinated response

Author

Agustin Ibanez, Hernando Santamaria‐Garcia, Alejandra Guerrero Barragan, Alexander Kornhuber, Alyne Mendonca Marques Ton, Andrea Slachevsky, Antonio Lucio Teixeira, Beatriz Marcela Mar Meza, Cecilia M. Serrano, Carlos Cano, Carolina Arias Gonzalez, Christian Gonzalez‐Billault, Christopher Butler, Julian Bustin, Claudia Duran‐Aniotz, Daisy Acosta, Diana L. Matallana, Diego Acosta‐Alvear, Dominic Trépel, Elisa De Paula França Resende, Fabricio Ferreira deOliveira, Francisco Ibanez, Fernanda G. De Felice, Gorka Navarrete, Ioannis Tarnanas, Irene B. Meier, Jerusa Smid, Jorge Llibre‐Guerra, Juan J. Llibre‐Rodriguez, Laís Fajersztajn, Leonel Tadao Takada, Lissette Duque, Maira Okada de Oliveira, Maria Aparecida Camargos Bicalho, María Isabel Behrens, Maritza Pintado‐Caipa, Mario Parra, Maxwell Z. Wilson, Myriam De La Cruz Puebla, Nilton Custodio, Rodrigo Santibanez, Rodrigo Bernardo Serafim, Ronnielly Melo Tavares, Stefanie Danielle Piña Escudero, Tomas Leon Rodriguez, Walter Dawson, Bruce L. Miller, and Kenneth S. Kosik

Subjects
coronavirus, dementia, health system, Latin American and Caribbean countries, SARS‐CoV‐2, Neurology. Diseases of the nervous system, RC346-429, Geriatrics, RC952-954.6
Abstract

Abstract The SARS‐CoV‐2 global pandemic will disproportionately impact countries with weak economies and vulnerable populations including people with dementia. Latin American and Caribbean countries (LACs) are burdened with unstable economic development, fragile health systems, massive economic disparities, and a high prevalence of dementia. Here, we underscore the selective impact of SARS‐CoV‐2 on dementia among LACs, the specific strain on health systems devoted to dementia, and the subsequent effect of increasing inequalities among those with dementia in the region. Implementation of best practices for mitigation and containment faces particularly steep challenges in LACs. Based upon our consideration of these issues, we urgently call for a coordinated action plan, including the development of inexpensive mass testing and multilevel regional coordination for dementia care and related actions. Brain health diplomacy should lead to a shared and escalated response across the region, coordinating leadership, and triangulation between governments and international multilateral networks.

Published
2020
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9. Digital biomarker‐based individualized prognosis for people at risk of dementia

Author

Maximilian Buegler, Robbert L. Harms, Mircea Balasa, Irene B. Meier, Themis Exarchos, Laura Rai, Rory Boyle, Adria Tort, Maha Kozori, Eutuxia Lazarou, Michaela Rampini, Carlo Cavaliere, Panagiotis Vlamos, Magda Tsolaki, Claudio Babiloni, Andrea Soricelli, Giovanni Frisoni, Raquel Sanchez‐Valle, Robert Whelan, Emilio Merlo‐Pich, and Ioannis Tarnanas

Subjects
Altoida Neuro Motor Index, Alzheimer's disease, artificial intelligence, augmented reality, cognitive aging, digital biomarker, Neurology. Diseases of the nervous system, RC346-429, Geriatrics, RC952-954.6
Abstract

Abstract Background Research investigating treatments and interventions for cognitive decline fail due to difficulties in accurately recognizing behavioral signatures in the presymptomatic stages of the disease. For this validation study, we took our previously constructed digital biomarker‐based prognostic models and focused on generalizability and robustness of the models. Method We validated prognostic models characterizing subjects using digital biomarkers in a longitudinal, multi‐site, 40‐month prospective study collecting data in memory clinics, general practitioner offices, and home environments. Results Our models were able to accurately discriminate between healthy subjects and individuals at risk to progress to dementia within 3 years. The model was also able to differentiate between people with or without amyloid neuropathology and classify fast and slow cognitive decliners with a very good diagnostic performance. Conclusion Digital biomarker prognostic models can be a useful tool to assist large‐scale population screening for the early detection of cognitive impairment and patient monitoring over time.

Published
2020
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10. Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Author

Tobias Nef, Prabitha Urwyler, Marcel Büchler, Ioannis Tarnanas, Reto Stucki, Dario Cazzoli, René Müri, and Urs Mosimann

Subjects
healthcare technology, smart homes, smart cities, ambient assisted living, activities of daily living, data classification, data mining, Chemical technology, TP1-1185
Abstract

Smart homes for the aging population have recently started attracting the attention of the research community. The “health state” of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Published
2015
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11. Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs)

Author

Stavros I. Dimitriadis, Christos Salis, Ioannis Tarnanas, and David E. Linden

Subjects
EEG, fMRI, resting state, graph theory, phase locking value, dynamic functional connectivity, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

The human brain is a large-scale system of functionally connected brain regions. This system can be modeled as a network, or graph, by dividing the brain into a set of regions, or “nodes,” and quantifying the strength of the connections between nodes, or “edges,” as the temporal correlation in their patterns of activity. Network analysis, a part of graph theory, provides a set of summary statistics that can be used to describe complex brain networks in a meaningful way. The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. The adaptation of both bivariate (mutual information) and multivariate (Granger causality) connectivity estimators to quantify the synchronization between multichannel recordings yields a fully connected, weighted, (a)symmetric functional connectivity graph (FCG), representing the associations among all brain areas. The aforementioned procedure leads to an extremely dense network of tens up to a few hundreds of weights. Therefore, this FCG must be filtered out so that the “true” connectivity pattern can emerge. Here, we compared a large number of well-known topological thresholding techniques with the novel proposed data-driven scheme based on orthogonal minimal spanning trees (OMSTs). OMSTs filter brain connectivity networks based on the optimization between the global efficiency of the network and the cost preserving its wiring. We demonstrated the proposed method in a large EEG database (N = 101 subjects) with eyes-open (EO) and eyes-closed (EC) tasks by adopting a time-varying approach with the main goal to extract features that can totally distinguish each subject from the rest of the set. Additionally, the reliability of the proposed scheme was estimated in a second case study of fMRI resting-state activity with multiple scans. Our results demonstrated clearly that the proposed thresholding scheme outperformed a large list of thresholding schemes based on the recognition accuracy of each subject compared to the rest of the cohort (EEG). Additionally, the reliability of the network metrics based on the fMRI static networks was improved based on the proposed topological filtering scheme. Overall, the proposed algorithm could be used across neuroimaging and multimodal studies as a common computationally efficient standardized tool for a great number of neuroscientists and physicists working on numerous of projects.

Published
2017
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12. Digital biomarkers and sex impacts in Alzheimer’s disease management — potential utility for innovative 3P medicine approach

Author

Robbert L. Harms, Alberto Ferrari, Irene B. Meier, Julie Martinkova, Enrico Santus, Nicola Marino, Davide Cirillo, Simona Mellino, Silvina Catuara Solarz, Ioannis Tarnanas, Cassandra Szoeke, Jakub Hort, Alfonso Valencia, Maria Teresa Ferretti, Azizi Seixas, Antonella Santuccione Chadha, and Barcelona Supercomputing Center

Subjects
Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC], Sex bias, Predictive preventive personalized medicine (PPPM), Health Policy, Biochemistry (medical), Mild cognitive impairment, Classifier, Personalized medicine, Alzheimer, Malaltia d', Drug Discovery, Sex, Digital biomarkers, Alzheimer’s disease, Biomarkers
Abstract

Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) to dementia due to AD in individuals aged 55 + . Digital biomarkers show promise to transform clinical practice. Nevertheless, their use may be affected by variables such as demographics, genetics, and phenotype. Among these factors, sex is particularly important in Alzheimer’s, where men and women present with different symptoms and progression patterns that impact diagnosis. In this study, we explore sex differences in Altoida’s digital medical application in a sample of 568 subjects consisting of a clinical dataset (MCI and dementia due to AD) and a healthy population. We found that a biological sex-classifier, built on digital biomarker features captured using Altoida’s application, achieved a 75% ROC-AUC (receiver operating characteristic — area under curve) performance in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females. The performance dropped when we applied this classifier to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. Our results indicate that neurocognitive performance signatures built on data from digital biomarker features are different between men and women. These results stress the need to integrate traditional approaches to dementia research with digital biomarker technologies and personalized medicine perspectives to achieve more precise predictive diagnostics, targeted prevention, and customized treatment of cognitive decline. RLH and IT were supported by Altoida Inc. JM was supported in this work by the Charles University Grant Agency (GA UK) project no. 436119 at Charles University, Second Faculty of Medicine, Prague, Czech Republic. Peer Reviewed "Article signat per 16 autors/es: Robbert L. Harms, Alberto Ferrari, Irene B. Meier, Julie Martinkova, Enrico Santus, Nicola Marino, Davide Cirillo, Simona Mellino, Silvina Catuara Solarz, Ioannis Tarnanas, Cassandra Szoeke, Jakub Hort, Alfonso Valencia, Maria Teresa Ferretti, Azizi Seixas & Antonella Santuccione Chadha "

Published
2022

13. Dementia in Latin America: Paving the way toward a regional action plan

Author

Raffaele Ferrari, Oscar L. Lopez, Leonel T. Takada, José Alberto Avila-Funes, Katherine L. Possin, Marcio Luiz Figueredo Balthazar, Carlos Cano-Gutierrez, Fábio Henrique de Gobbi Porto, David Orozco, Letícia Lessa Mansur, Jeronimo Martin Ramirez, André Luiz Rodrigues Palmeira, Adolfo M. García, Juan de Jesús Llibre, Henry Engler, Elisa de Paula França Resende, Silvia Kochen, Sebastian Moguilner, Lingani Mbakile-Mahlanza, Renata Kochhann, Antônio Lúcio Teixeira, Dominic Trépel, Maritza Pintado Caipa, Patricia Cogram, Yakeel T. Quiroz, Agustín Ibáñez, Ronnielly Melo Tavares, Maira Tonidandel Barbosa, Andrea Slachevsky, Roberta Diehl Rodriguez, Gonzalo A. Farías, Laís Fajersztajn, Bárbara Costa Beber, Maira Okada de Oliveira, Norberto Anizio Ferreira Frota, David Lira, Patricio Fuentes, Laura Morelli, Patricia Lillo, Paulo Caramelli, Renata Eloah de Lucena Ferretti-Rebustini, Stefanie Danielle Piña Escudero, Cecilia Serrano, Sonia Maria Dozzi Brucki, Heather M. Snyder, Sandra Baez, Ana Luisa Sosa, Alejandra Guerrero Barragan, Adelina Comas, Rosa Montesinos, Paulo Henrique Ferreira Bertolucci, Juan F. Cardona, Ioannis Tarnanas, Jerusa Smid, Lea T. Grinberg, Fernanda G. De Felice, Mario A. Parra, Christian Gonzalez-Billault, Cecilia Gonzalez Campo, Eliane Correa Miotto, David Huepe, Jose Manuel Santacruz-Escudero, Maria Aparecida Camargos Bicalho, Bruce L. Miller, P. Solis, Claudia K. Suemoto, Lissette Duque Peñailillo, Patricia J. Garcia, Marcia Lorena Fagundes Chaves, Lucas Porcello Schilling, Rodrigo B. Serafim, Claudia Ramos, Hernando Santamaría-García, Fabricio Joao Pio, Alyne Mendonca Marques Ton, Ignacio F. Mata, Irene Maier, Leonardo Cruz de Souza, Felipe A. Court, Lucas Sedeño, Myriam de la Cruz, Mônica Sanches Yassuda, Jenifer Yokoyama, Jorge J. Llibre Guerra, Fabricio Ferreira de Oliveira, Serggio Lanata, Nilton Custodio, Ricardo Nitrini, Andres Damian, Kenneth S. Kosik, Julián Bustin, Graciela Muniz-Terrera, Ceres Ferretti, Ivan Aprahamian, Diana Matallana, Olivier Piguet, Marcio Soto-Añari, Fiona Kumfor, Sergio T. Ferreira, Francisco Lopera, Mirna Lie Hosogi, and Luis Arnoldo Muñoz-Nevárez

Subjects
0301 basic medicine, MEDICINA BASEADA EM EVIDÊNCIAS, Latin Americans, purl.org/pe-repo/ocde/ford#3.02.25 [https], NONPHARMACOLOGICAL INTERVENTIONS, 0302 clinical medicine, purl.org/becyt/ford/3.2 [https], EPIDEMIOLOGY, genetics, NETWORKING AND TRANSLATIONAL RESEARCH, media_common, KNOWLEDGE TO ACTION FRAMEWORK, Health Policy, DEMENTIA, Public relations, networking and translational research, Psychiatry and Mental health, Action plan, Evidence-Based Practice, Perspective, purl.org/becyt/ford/3 [https], epidemiology, CLINICAL TRIALS, evidence‐based recommendations, GENETICS, media_common.quotation_subject, BIOMARKERS, Translational research, LATIN AMERICA, 03 medical and health sciences, Cellular and Molecular Neuroscience, Developmental Neuroscience, knowledge to action framework, Political science, nonpharmacological interventions, medicine, Dementia, Humans, EVIDENCE-BASED RECOMMENDATIONS, Socioeconomic status, clinical trials, business.industry, biomarkers, Global strategy, medicine.disease, 030104 developmental biology, Transformative learning, Latin America, Socioeconomic Factors, RC0321, Neurology (clinical), Geriatrics and Gerontology, business, 030217 neurology & neurosurgery, Diversity (politics)
Abstract

Across Latin American and Caribbean countries (LACs), the fight against dementia faces pressing challenges, such as heterogeneity, diversity, political instability, and socioeconomic disparities. These can be addressed more effectively in a collaborative setting that fosters open exchange of knowledge. In this work, the Latin American and Caribbean Consortium on Dementia (LAC-CD) proposes an agenda for integration to deliver a Knowledge to Action Framework (KtAF). First, we summarize evidence-based strategies (epidemiology, genetics, biomarkers, clinical trials, nonpharmacological interventions, networking, and translational research) and align them to current global strategies to translate regional knowledge into transformative actions. Then we characterize key sources of complexity (genetic isolates, admixture in populations, environmental factors, and barriers to effective interventions), map them to the above challenges, and provide the basic mosaics of knowledge toward a KtAF. Finally, we describe strategies supporting the knowledge creation stage that underpins the translational impact of KtAF. Fil: Parra, Mario Alfredo. Universidad Autónoma del Caribe; Colombia Fil: Baez, Sandra. Universidad de los Andes; Colombia Fil: Sedeño, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Gonzalez Campo, Cecilia. Universidad de San Andrés; Argentina Fil: Santamaría García, Hernando. Pontificia Universidad Javeriana; Colombia Fil: Aprahamian, Ivan. No especifíca; Fil: Bertolucci, Paulo. Universidade Federal de Sao Paulo; Brasil Fil: Bustin, Julian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina Fil: Camargos Bicalho, Maria Aparecida. Universidade Federal de Minas Gerais; Brasil Fil: Cano Gutierrez, Carlos. Hospital Universitario San Ignacio; Colombia Fil: Caramelli, Paulo. Universidade Federal de Minas Gerais; Brasil Fil: Chaves, Marcia L. F.. Universidade Federal do Rio Grande do Sul; Brasil Fil: Perez Cogram, Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina Fil: Beber, Bárbara Costa. Universidade Federal do Rio Grande do Sul; Brasil Fil: Court, Felipe A.. Universidad Mayor; Chile Fil: de Souza, Leonardo Cruz. Universidade Federal de Minas Gerais; Brasil Fil: Custodio, Nilton. Peruvian Institute of Neurosciences; Perú Fil: Damian, Andres. Universidad de la República; Uruguay Fil: de la Cruz, Myriam. University of California; Estados Unidos Fil: Diehl Rodriguez, Roberta. Universidade de Sao Paulo; Brasil Fil: Dozzi Brucki, Sonia Maria. Universidade de Sao Paulo; Brasil Fil: Fajersztajn, Lais. Universidade de Sao Paulo; Brasil Fil: Farías, Gonzalo A.. Universidad de Chile; Chile Fil: De Felice, Fernanda G.. Universidade Federal do Rio de Janeiro; Brasil Fil: Ferrari, Raffaele. Colegio Universitario de Londres; Reino Unido Fil: Ferreira de Oliveira, Fabricio. Universidade de Sao Paulo; Brasil Fil: Ferreira, Sergio Teixeira. Universidade Federal do Rio de Janeiro; Brasil Fil: Ferretti, Ceres. Universidade de Sao Paulo; Brasil Fil: Figueredo Balthazar, Marcio Luiz. Universidade Estadual de Campinas; Brasil Fil: Ferreira Frota, Norberto Anizio. Universidade de Fortaleza; Brasil Fil: Fuentes, Patricio. No especifíca; Fil: García, Adolfo Martín. Universidad Nacional de Cuyo; Argentina. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Garcia, Patricia J.. Cayetano Heredia University; Perú Fil: de Gobbi Porto, Fábio Henrique. Universidade de Sao Paulo; Brasil Fil: Duque Peñailillo, Lissette. Ecuadorian Dementia Association; Ecuador Fil: Engler, Henry Willy. Universidad de la República; Uruguay Fil: Maier, Irene. Universidad de Chile; Chile Fil: Mata, Ignacio F.. University of Pittsburgh; Estados Unidos Fil: González Billault, Christian. Universidad de Chile; Chile Fil: Lopez, Oscar L.. University of Pittsburgh; Estados Unidos Fil: Morelli, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina Fil: Nitrini, Ricardo. Universidade de Sao Paulo; Brasil Fil: Quiroz, Yakeel T.. Harvard Medical School; Estados Unidos Fil: Guerrero Barragan, Alejandra. Universidad de la Sabana; Colombia Fil: Huepe, David. Universidad Adolfo Ibañez; Chile Fil: Pio, Fabricio Joao. Hospital Governador Celso Ramos; Brasil Fil: Suemoto, Claudia Kimie. Universidade de Sao Paulo; Brasil Fil: Kochhann, Renata. Pontificia Universidade Católica do Rio Grande do Sul; Brasil Fil: Kochen, Sara Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; Argentina Fil: Kumfor, Fiona. University of Western Sydney; Australia Fil: Lanata, Serggio. University of California; Estados Unidos Fil: Miller, Bruce. University of California; Estados Unidos Fil: Lessa Mansur, Leticia. Universidade de Sao Paulo; Brasil Fil: Lie Hosogi, Mirna. Universidade de Sao Paulo; Brasil Fil: Lillo, Patricia. Universidad de Chile; Chile Fil: Llibre Guerra, Jorge. University of California; Estados Unidos Fil: Lira, David. Peruvian Institute of Neurosciences; Perú Fil: Lopera, Francisco. Universidad de Antioquia; Colombia Fil: Comas, Adelina. No especifíca; Fil: Avila Funes, José Alberto. Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán; México Fil: Sosa, Ana Luisa. Instituto Nacional de Neurología y Neurocirugía; México Fil: Ramos, Claudia. University of California; Estados Unidos Fil: de Paula França Resende, Elisa. University of California; Estados Unidos Fil: Snyder, Heather M.. No especifíca; Fil: Tarnanas, Ioannis. University of California; Estados Unidos Fil: Yokoyama, Jenifer. University of California; Estados Unidos Fil: Llibre, Juan. Centro de Estudios de Alzheimer; Cuba Fil: Cardona Londoño, Juan Felipe. Universidad del Valle; Colombia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Possin, Kate. University of California; Estados Unidos Fil: Kosik, Kenneth. University of California; Estados Unidos Fil: Montesinos, Rosa. Peruvian Institute of Neurosciences; Perú Fil: Moguilner, Sebastian Gabriel. University of California; Estados Unidos Fil: Solis, Patricia Cristina Lourdes. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; Argentina. Universidad Nacional Arturo Jauretche; Argentina. Universidad de Buenos Aires; Argentina Fil: de Lucena Ferretti Rebustini, Renata Eloah. Universidade de Sao Paulo; Brasil Fil: Ramirez, Jeronimo Martin. No especifíca; Fil: Matallana, Diana. Pontificia Universidad Javeriana; Colombia Fil: Mbakile Mahlanza, Lingani. University of California; Estados Unidos Fil: Mendonça Marques Ton, Alyne. Universidade Federal do Rio de Janeiro; Brasil Fil: Melo Tavares, Ronnielly. Centro Universitario de Belo Horizonte.; Brasil Fil: Miotto, Eliane C.. Universidade de Sao Paulo; Brasil Fil: Muniz-Terrera, Graciela. University of Edinburgh; Reino Unido Fil: MuñozN evárez, Luis Arnoldo. University of California; Estados Unidos Fil: Orozco, David. Universidad Nacional del Sur; Argentina Fil: Okada de Oliveira, Maira. University of California; Estados Unidos Fil: Piguet, Olivier. University of Western Sydney; Australia Fil: Pintado Caipa, Maritza. University of California; Estados Unidos Fil: Piña Escudero, Stefanie Danielle. University of California; Estados Unidos Fil: Porcello Schilling, Lucas. Pontificia Universidade Católica do Rio Grande do Sul; Brasil Fil: Rodrigues Palmeira, André Luiz. Hospital Ernesto Dornelles; Brasil Fil: Sanches Yassuda, Mônica. Universidade de Sao Paulo; Brasil Fil: Santacruz Escudero, Jose Manuel. Pontificia Universidad Javeriana; Colombia Fil: Serafim, Rodrigo Bernardo. Universidade Federal do Rio de Janeiro; Brasil Fil: Smid, Jerusa. Universidade de Sao Paulo; Brasil Fil: Slachevsky, Andrea. Universidad de Chile; Chile Fil: Serrano, Cecilia Mariela. No especifíca; Fil: Soto Añari, Marcio. Universidad Católica San Pablo; Perú Fil: Takada, Leonel Tadao. Universidade de Sao Paulo; Brasil Fil: Tenenholz Grinberg, Lea. Universidade de Sao Paulo; Brasil Fil: Teixeira, Antonio Lucio. Universidade Federal de Minas Gerais; Brasil Fil: Tonidandel Barbosa, Maira. Universidade Federal de Minas Gerais; Brasil Fil: Trépel, Dominic. Trinity College; Estados Unidos Fil: Ibañez, Agustin Mariano. Universidad Autónoma del Caribe; Colombia. Universidad Adolfo Ibañez; Chile. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

Published
2020

14. A Brain Capital Grand Strategy: toward economic reimagination

Author

Marion Leboyer, Charles F. Reynolds, Erin Smith, Ioannis Tarnanas, Ernestine Fu, Julio Licinio, Chee H. Ng, Dilip V. Jeste, Sarah Caddick, Sandra B. Chapman, Michael Berk, Zoe Wainer, Harris A. Eyre, Husseini K. Manji, William Hynes, Ian H. Robertson, Eric A. Storch, Howard Fillit, Rhoda Au, Ryan Abbott, Jair C. Soares, Andrew W. Lo, Agustín Ibáñez, Gowri Aragam, Walter D. Dawson, Diab Ali, Facundo Manes, Patrick Brannelly, Helen Lavretsky, Cara M. Altimus, Jeffrey L. Cummings, Bill Wilkerson, and Kunmi Sobowale

Subjects
Employment, 0301 basic medicine, Aging, Work, 2019-20 coronavirus outbreak, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), media_common.quotation_subject, BRAIN SKILLS, Efficiency, Medical and Health Sciences, Article, 03 medical and health sciences, Cellular and Molecular Neuroscience, Cognition, 0302 clinical medicine, Humans, ECONOMIC RESILIENCE, Investments, Molecular Biology, media_common, Psychiatry, Resilience, Extramural, Psychology and Cognitive Sciences, Neurosciences, Brain, COVID-19, purl.org/becyt/ford/3.1 [https], Art, Resilience, Psychological, Biological Sciences, BRAIN CAPITAL, Capital (architecture), Psychiatry and Mental health, Mental Health, 030104 developmental biology, Neurology, Socioeconomic Factors, Workforce, Psychological, purl.org/becyt/ford/3 [https], Quality-Adjusted Life Years, Humanities, 030217 neurology & neurosurgery
Abstract

The time is now to catalyze a Grand Strategy. Brain Capital can be a key component in shaping economic resilience?linked to our digitalized, globalized, complex, and interconnected yet fragile global economy. Brain Capital resonates with existing efforts and helps build long-term globaleconomic recovery that promotes brain health and brain skills helping address urgent challenges caused and exacerbated by COVID. Current policies, concepts and economic measures neither account for the neuroscientific drivers of productivity nor the increasingly brain skills focused workforce demands of the twenty-first century.Furthermore, current approaches are insufficient to combat the rising complexity of challenges brought about by COVID. The preservation and development of Brain Capital positively impacts educational, social, health, institutional, and economic dimensions at the individual andcollective level. Hence, it should be considered a strategic resource for a country, corporations, and public policies in a brain-based world. Fil: Smith, Erin. University of Stanford; Estados Unidos Fil: Ali, Diab. Ochsner Clinical School; Estados Unidos. University of Queensland; Australia Fil: Wilkerson, Bill. Mental Health International; Canadá Fil: Dawson, Walter D.. Oregon Health and Science University; Estados Unidos. Portland State University; Estados Unidos. Trinity College Dublin; Irlanda. University of California; Estados Unidos Fil: Sobowale, Kunmi. University of California; Estados Unidos Fil: Reynolds, Charles. Univeristy of Pittsburgh. School of Medicine; Estados Unidos Fil: Berk, Michael. Deakin University; Australia. University of Melbourne; Australia Fil: Lavretsky, Helen. University of California; Estados Unidos Fil: Jeste, Dilip. University of California at San Diego; Estados Unidos Fil: Ng, Chee H.. University of Melbourne; Australia Fil: Soares, Jair C.. University of Texas Health Science Center at Houston; Estados Unidos Fil: Aragam, Gowri. University of Stanford; Estados Unidos Fil: Wainer, Zoe. University of Melbourne; Australia Fil: Manji, Husseini K.. Johnson & Johnson; Estados Unidos. University of Duke; Estados Unidos Fil: Licinio, Julio. SUNY Upstate Medical University; Estados Unidos Fil: Lo, Andrew W.. Massachusetts Institute of Technology; Estados Unidos Fil: Storch, Eric. Baylor College of Medicine; Fil: Fu, Ernestine. University of Stanford; Estados Unidos Fil: Leboyer, Marion. University Paris Est Créteil; Francia Fil: Tarnanas, Ioannis. Trinity College Dublin; Irlanda. University of California; Estados Unidos Fil: Ibañez, Agustin Mariano. Trinity College Dublin; Irlanda. Universidad Autónoma del Caribe; Colombia. University of California; Estados Unidos. Universidad Adolfo Ibañez; Chile. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Manes, Facundo Francisco. Universidad Favaloro; Argentina. Instituto de Neurología Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Caddick, Sarah. Thalamic; Reino Unido Fil: Fillit, Howard. The Icahn School of Medicine at Mount Sinai; Estados Unidos Fil: Abbott, Ryan. University of California; Estados Unidos. University of Surrey; Reino Unido Fil: Robertson, Ian H.. University of Texas; Estados Unidos. Trinity College Dublin; Irlanda. University of California; Estados Unidos Fil: Chapman, Sandra B.. University of Texas; Estados Unidos Fil: Au, Rhoda. University of Boston. School of Medicine; Estados Unidos Fil: Altimus, Cara M.. Milken Institute; Estados Unidos Fil: Hynes, William. Organisation for Economic Cooperation and Development. New Approaches to Economic Challenges Unit; Francia Fil: Brannelly, Patrick. Rainwater Charitable Foundation; Estados Unidos Fil: Cummings, Jeffrey. Cleveland Clinic Lou Ruvo Center for Brain Health; Estados Unidos. University Of Nevada, Las Vegas; Estados Unidos Fil: Eyre, Harris A.. Deakin University; Australia. University of Melbourne; Australia. University of Stanford; Estados Unidos. University of Adelaide; Australia

Published
2020

15. Description of the Method for Evaluating Digital Endpoints in Alzheimer Disease Study: Protocol for an Exploratory, Cross-sectional Study

Author

Jelena Curcic, Vanessa Vallejo, Jennifer Sorinas, Oleksandr Sverdlov, Jens Praestgaard, Mateusz Piksa, Mark Deurinck, Gul Erdemli, Maximilian Bügler, Ioannis Tarnanas, Nick Taptiklis, Francesca Cormack, Rebekka Anker, Fabien Massé, William Souillard-Mandar, Nathan Intrator, Lior Molcho, Erica Madero, Nicholas Bott, Mieko Chambers, Josef Tamory, Matias Shulz, Gerardo Fernandez, William Simpson, Jessica Robin, Jón G Snædal, Jang-Ho Cha, and Kristin Hannesdottir

Subjects
General Medicine
Abstract

Background More sensitive and less burdensome efficacy end points are urgently needed to improve the effectiveness of clinical drug development for Alzheimer disease (AD). Although conventional end points lack sensitivity, digital technologies hold promise for amplifying the detection of treatment signals and capturing cognitive anomalies at earlier disease stages. Using digital technologies and combining several test modalities allow for the collection of richer information about cognitive and functional status, which is not ascertainable via conventional paper-and-pencil tests. Objective This study aimed to assess the psychometric properties, operational feasibility, and patient acceptance of 10 promising technologies that are to be used as efficacy end points to measure cognition in future clinical drug trials. Methods The Method for Evaluating Digital Endpoints in Alzheimer Disease study is an exploratory, cross-sectional, noninterventional study that will evaluate 10 digital technologies’ ability to accurately classify participants into 4 cohorts according to the severity of cognitive impairment and dementia. Moreover, this study will assess the psychometric properties of each of the tested digital technologies, including the acceptable range to assess ceiling and floor effects, concurrent validity to correlate digital outcome measures to traditional paper-and-pencil tests in AD, reliability to compare test and retest, and responsiveness to evaluate the sensitivity to change in a mild cognitive challenge model. This study included 50 eligible male and female participants (aged between 60 and 80 years), of whom 13 (26%) were amyloid-negative, cognitively healthy participants (controls); 12 (24%) were amyloid-positive, cognitively healthy participants (presymptomatic); 13 (26%) had mild cognitive impairment (predementia); and 12 (24%) had mild AD (mild dementia). This study involved 4 in-clinic visits. During the initial visit, all participants completed all conventional paper-and-pencil assessments. During the following 3 visits, the participants underwent a series of novel digital assessments. Results Participant recruitment and data collection began in June 2020 and continued until June 2021. Hence, the data collection occurred during the COVID-19 pandemic (SARS-CoV-2 virus pandemic). Data were successfully collected from all digital technologies to evaluate statistical and operational performance and patient acceptance. This paper reports the baseline demographics and characteristics of the population studied as well as the study's progress during the pandemic. Conclusions This study was designed to generate feasibility insights and validation data to help advance novel digital technologies in clinical drug development. The learnings from this study will help guide future methods for assessing novel digital technologies and inform clinical drug trials in early AD, aiming to enhance clinical end point strategies with digital technologies. International Registered Report Identifier (IRRID) DERR1-10.2196/35442

Published
2021

16. ALTOIDA‐iADL for the diagnosis of Mild Cognitive Impairment and early Alzheimer's disease

Author

Adrià Tort‐Merino, Ioannis Tarnanas, Maximilian Bügler, Robbert Harms, Guadalupe Fernandez‐Villullas, Jordi Juncà‐Parella, Beatriz Bosch, Albert Lladó, Raquel Sanchez‐Valle, and Mircea Balasa

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2021

18. Συγκριτική νευροφυσιολογική διερεύνηση στην πρώιμη διάγνωση πιθανής νόσου Alzheimer

Author

Ioannis Tarnanas

Abstract

Ο σκοπός της παρούσας διατριβής ήταν η σύγκριση και συσχέτιση α) ενός καινοτόμου περιβάλλοντος εικονικής πραγματικότητας, το οποίο σχεδιάστηκε για την αξιολόγηση βασικών και σύνθετων καθημερινών δραστηριοτήτων, π.χ., πλοήγηση στο χώρο κατά τη διάρκεια μιας επίσκεψης σε ένα εικονικό μουσείο ή μιας εικονικής διαδικασίας εκκένωσης λόγω πυρκαγιάς υπό πίεση χρόνου, με β) τυποποιημένα νευροψυχολογικά ερωτηματολόγια, γ) νευροφυσιολογικές μετρήσεις, όπως είναι το ΗΕΓ και τα ακουστικά γνωσιακά προκλητά δυναμικά (ΑΓΠΔ) και δ) νευροαπεικόνιση όπως είναι η μαγνητική τομογραφία. Απώτερος στόχος ήταν η διερεύνηση της χρήσης των εξατομικευμένων αποτελεσμάτων απόδοσης, από τα ανωτέρω περιβάλλοντα εικονικής πραγματικότητας, ως εργαλείο ανίχνευσης για την πρώιμη άνοια. Σύμφωνα με τη βιβλιογραφία, υπήρξε μόνο ένα προηγούμενο έργο με εικονική πραγματικότητα και τη χρήση της ως εργαλείο πρώιμης ανίχνευσης για την ΗΝΔ, αλλά δεν υπάρχει καμία μελέτη που να συγκρίνει τη χρησιμότητά της σε σχέση με σύγχρονους μη παρεμβατικούς βιοδείκτες όπως είναι τα προκλητά δυναμικά, το ΗΕΓ και η μαγνητική τομογραφία. Η παρούσα εργασία είναι μια μονογραφία η οποία αναφέρεται σε τρία στάδια, τμήματα των οποίων δημοσιεύτηκαν, οι οποίες διερεύνησαν τη χρήση εικονικών πραγματικοτήτων ως εργαλείο πρώιμης ανίχνευσης για την πρώιμη άνοια με χρήση διαφόρων μεθοδολογικών προσεγγίσεων. Σύμφωνα με τα αποτελέσματα των διαχρονικών αυτών μελετών σχετικά με την ευαισθησία και εξειδίκευση καθεμίας από τις παραπάνω μεθόδους για την πρώιμη ανίχνευση της ΗΝΔ, οι επιδόσεις στη δοκιμασία εικονικής πραγματικότητας και τα ΑΓΠΔ μπόρεσαν να διακρίνουν την ομάδα ΗΝΔ που προχώρησε σε ΝΑ με 94% ευαισθησία, 82% εξειδίκευση, και 88% ευαισθησία, 78% εξειδίκευση, αντίστοιχα. Η νευροαπεικόνιση και οι νευροψυχολογικές δοκιμασίες, έδειξαν 83% ευαισθησία, 75% εξειδίκευση και 78% ευαισθησία, 63% εξειδίκευση, αντίστοιχα. Συμπερασματικά, το περιβάλλον εικονικής πραγματικότητας έδειξε τη μεγαλύτερη ευαισθησία και εξειδίκευση στην πρώιμη ανίχνευση της ΗΝΔ, με τα ΑΓΠΔ να ακολουθούν με λίγο λιγότερη ευαισθησία και εξειδίκευση. Σε αυτό το πλαίσιο, η παρούσα εργασία επιβεβαίωσε επίσης τα αποτελέσματα προηγούμενων μελετών για τη χρησιμότητα των λανθανόντων χρόνων των προκλητών δυναμικών στη βρεγματική περιοχή ως εργαλείο για την εκτίμηση της εξέλιξης της έκπτωσης αλλά και ως παράγοντας πρόβλεψης της εξέλιξης σε ΝΑ. Τα αποτελέσματα αυτά δείχνουν γενικά ότι οι πρώιμες αλλαγές της βαθμολογίας επίδοσης στην εικονική πραγματικότητα μπορεί να αντικατοπτρίζουν το έλλειμμα της διαδικασίας προσοχής που συνδέεται με τη λειτουργική μνήμη και τη δυσλειτουργία της μετωπιαίας επεξεργασίας σε ασθενείς με ΗΝΔ και για ασθενείς που βρίσκονται στο πρώιμο στάδιο της ΝΑ. Εν κατακλείδι, η συμβολή της παρούσας εργασίας έγκειται στην ανάπτυξη και εμπειρική μελέτη ενός καινοτόμου περιβάλλοντος εικονικής πραγματικότητας, το οποίο ήταν λιγότερο δομημένο και το οποίο έμοιαζε περισσότερο με πραγματικές καθημερινές αποστολές από ό,τι τα υφιστάμενα ερωτηματολόγια ή οι εργαστηριακές νευροφυσιολογικές μετρήσεις. Η παρούσα έρευνα απέδειξε ότι οι δοκιμασίες εικονικής πραγματικότητας των καθημερινών λειτουργιών μπορεί να είναι καλύτεροι παράγοντες πρόβλεψης των συμπεριφορών του πραγματικού κόσμου από ότι πολλές από τις συχνότερα χρησιμοποιούμενες παραδοσιακές δοκιμασίες των επιτελικών λειτουργιών. Το σύστημα εικονικής πραγματικότητας που χρησιμοποιείται εδώ είναι φορητό και μπορεί να χρησιμοποιηθεί για προσομοίωση διαφόρων περιβαλλόντων και διαφόρων απαιτήσεων πλοήγησης (νοητικών, κινητικών, οπτικών), επιτρέποντας εύκολα τη δημιουργία μιας οικολογικά έγκυρης μελέτης και ελέγχου σε ποικίλα κλινικά και ερευνητικά περιβάλλοντα.

Published
2021

19. Neurological and mental health consequences of COVID-19: potential implications for well-being and labour force

Author

Fareed Mirza, Camila Vieira Ligo Teixeira, Irene B. Meier, Lawrence Rajendran, and Ioannis Tarnanas

Subjects
0301 basic medicine, medicine.medical_specialty, Review Article, Disease, post-operative cognitive dysfunction, 03 medical and health sciences, 0302 clinical medicine, medicine, Cognitive decline, Psychiatry, AcademicSubjects/SCI01870, business.industry, General Engineering, COVID-19, alzheimer’s disease, Cognition, cognitive decline, medicine.disease, Mental health, production loss, 030104 developmental biology, Infectious disease (medical specialty), Workforce, Acute disseminated encephalomyelitis, Delirium, AcademicSubjects/MED00310, medicine.symptom, business, 030217 neurology & neurosurgery
Abstract

Recent case studies show that the SARS-CoV-2 infectious disease, COVID-19, is associated with accelerated decline of mental health, in particular, cognition in elderly individuals, but also with neurological and neuropsychiatric illness in young people. Recent studies also show a bidirectional link between COVID-19 and mental health in that people with previous history of psychiatric illness have a higher risk for contracting COVID-19 and that COVID-19 patients display a variety of psychiatric illnesses. Risk factors and the response of the central nervous system to the virus show large overlaps with pathophysiological processes associated with Alzheimer’s disease, delirium, post-operative cognitive dysfunction and acute disseminated encephalomyelitis, all characterized by cognitive impairment. These similarities lead to the hypothesis that the neurological symptoms could arise from neuroinflammation and immune cell dysfunction both in the periphery as well as in the central nervous system and the assumption that long-term consequences of COVID-19 may lead to cognitive impairment in the well-being of the patient and thus in today’s workforce, resulting in large loss of productivity. Therefore, particular attention should be paid to neurological protection during treatment and recovery of COVID-19, while cognitive consequences may require monitoring.

Published
2021
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20. Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel

Author

Marco Onofrj, Jaisalmer de Frutos-Lucas, Gary F. Egan, Fabrizio Stocchi, Alan C. Evans, Lew Lim, Marco Weiergräber, Fernando Maestú, Kerry Kilborn, Laura Bonanni, Karim Bennys, Mitchell Valdés-Sosa, Görsev Yener, Ian G. McKeith, Rebecca M. Edelmayer, Giovanni B. Frisoni, Michael G. Harrington, Petra Ritter, Hamed Azami, John-Paul Taylor, Roberta Lizio, Harald Hampel, Maciej Kaminski, Pedro A. Valdes-Sosa, Alice D. Lam, Susanna Lopez, Keith N. Fargo, Claudio Babiloni, Jaeseung Jeong, Maria C. Carrillo, Stefan J. Teipel, Raffaele Ferri, Vesna Jelic, Xianghong Arakaki, Andrzej Cichocki, Davide V. Moretti, Ricardo S. Osorio, Claudio Del Percio, William J. McGeown, Andrea Soricelli, Bahar Güntekin, Francesca R Farina, Yang Jiang, David Lopez, Stéphane Epelbaum, Voyko Kavcic, Ana Buján, Brendan P. Lucey, Ioannis Tarnanas, Bruno Dubois, Giuseppe Noce, Mario Parra-Rodriguez, Alberto Fernández, John M Olichney, Sanjeev Kumar, Tarek K. Rajji, Javier Escudero, Federico Tucci, Katarzyna J. Blinowska, and Flavio Nobili

Subjects
physiopathology [Cognitive Dysfunction], Epidemiology, Psychological intervention, Alzheimer’s disease (AD), Disease, Electroencephalography, physiopathology [Brain], 0302 clinical medicine, electroencephalography (EEG), 0303 health sciences, Clinical Trials as Topic, medicine.diagnostic_test, Health Policy, Brain, Alzheimer's disease, 3. Good health, The Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART), Dementia, Electroencephalography (EEG), Eyes-closed Resting State Condition, Clinical Trials, Biomarkers, Psychiatry and Mental health, Disease Progression, medicine.medical_specialty, Alpha (ethology), physiopathology [Alzheimer Disease], Article, 03 medical and health sciences, Cellular and Molecular Neuroscience, Physical medicine and rehabilitation, Rhythm, Developmental Neuroscience, biomarkers, clinical trials, dementia, eyes-closed resting state condition, Alzheimer Disease, medicine, Humans, Cognitive Dysfunction, Clinical Trials, ddc:610, 030304 developmental biology, business.industry, medicine.disease, standards [Electroencephalography], Clinical trial, Neurology (clinical), Geriatrics and Gerontology, business, 030217 neurology & neurosurgery, Biomarkers
Abstract

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and “interrelatedness” at posterior alpha (8-12 Hz) and widespread delta (

Published
2021

21. The necessity of diplomacy in brain health

Author

Timothy Grant Evans, Walter D. Dawson, Adelina Comas-Herrera, Harris A. Eyre, Kristine Yaffe, Jeffrey L. Cummings, Ioannis Tarnanas, Jeffrey Kaye, Kirsten Bobrow, Laura Booi, Bruce L. Miller, Maritza Pintado-Caipa, Stacey Yamamoto, and Agustín Ibáñez

Subjects
2019-20 coronavirus outbreak, Brain Diseases, Organizations, Coronavirus disease 2019 (COVID-19), media_common.quotation_subject, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), MEDLINE, Clinical Neurology, Global Health, Article, Nursing, Political science, Models, Organizational, Global health, Humans, Program development, Neurology (clinical), Program Development, Diplomacy, media_common
Published
2020

22. Using a Digital Neuro Signature to measure longitudinal individual-level change in Alzheimer's disease: the Altoida large cohort study

Author

Azizi Seixas, Max Buegler, Irene B. Meier, Ioannis Tarnanas, Robbert Harms, and Arzu Çöltekin

Subjects
medicine.medical_specialty, Remote patient monitoring, Computer applications to medicine. Medical informatics, R858-859.7, Medicine (miscellaneous), Health Informatics, Disease, 050105 experimental psychology, Article, 03 medical and health sciences, Prognostic markers, 0302 clinical medicine, Physical medicine and rehabilitation, Health Information Management, medicine, 0501 psychology and cognitive sciences, business.industry, 05 social sciences, fungi, Neuropsychology, Cognition, Alzheimer's disease, Computer Science Applications, Cognitive test, Clinical trial, Biomarker (medicine), Observational study, business, 030217 neurology & neurosurgery
Abstract

Conventional neuropsychological assessments for Alzheimer’s disease are burdensome and inaccurate at detecting mild cognitive impairment and predicting Alzheimer’s disease risk. Altoida’s Digital Neuro Signature (DNS), a longitudinal cognitive test consisting of two active digital biomarker metrics, alleviates these limitations. By comparison to conventional neuropsychological assessments, DNS results in faster evaluations (10 min vs 45–120 min), and generates higher test-retest in intraindividual assessment, as well as higher accuracy at detecting abnormal cognition. This study comparatively evaluates the performance of Altoida’s DNS and conventional neuropsychological assessments in intraindividual assessments of cognition and function by means of two semi-naturalistic observational experiments with 525 participants in laboratory and clinical settings. The results show that DNS is consistently more sensitive than conventional neuropsychological assessments at capturing longitudinal individual-level change, both with respect to intraindividual variability and dispersion (intraindividual variability across multiple tests), across three participant groups: healthy controls, mild cognitive impairment, and Alzheimer’s disease. Dispersion differences between DNS and conventional neuropsychological assessments were more pronounced with more advanced disease stages, and DNS-intraindividual variability was able to predict conversion from mild cognitive impairment to Alzheimer’s disease. These findings are instrumental for patient monitoring and management, remote clinical trial assessment, and timely interventions, and will hopefully contribute to a better understanding of Alzheimer’s disease.

Published
2020

23. Digital biomarker‐based individualized prognosis for people at risk of dementia

Author

Panayiotis Vlamos, Adria Tort, Themis Exarchos, Giovanni B. Frisoni, Carlo Cavaliere, Irene B. Meier, Claudio Babiloni, Ioannis Tarnanas, Robert Whelan, Maha Kozori, Robbert Harms, Andrea Soricelli, Rory Boyle, Michaela Rampini, Emilio Merlo-Pich, Raquel Sánchez-Valle, Magda Tsolaki, Mircea Balasa, Laura Rai, Eutuxia Lazarou, and Maximilian Buegler

Subjects
medicine.medical_specialty, Psychological intervention, Neuropathology, Disease, lcsh:Geriatrics, lcsh:RC346-429, 03 medical and health sciences, risk prediction, 0302 clinical medicine, Physical medicine and rehabilitation, medicine, Dementia, Generalizability theory, Cognitive decline, lcsh:Neurology. Diseases of the nervous system, 030304 developmental biology, 0303 health sciences, business.industry, cognitive aging, Cognition, Alzheimer's disease, medicine.disease, artificial intelligence, augmented reality, Psychiatry and Mental health, lcsh:RC952-954.6, machine learning, digital biomarker, Altoida Neuro Motor Index, Biomarker (medicine), Cognitive & Behavioral Assessment, Neurology (clinical), business, 030217 neurology & neurosurgery, Research Article
Abstract

Background Research investigating treatments and interventions for cognitive decline fail due to difficulties in accurately recognizing behavioral signatures in the presymptomatic stages of the disease. For this validation study, we took our previously constructed digital biomarker‐based prognostic models and focused on generalizability and robustness of the models. Method We validated prognostic models characterizing subjects using digital biomarkers in a longitudinal, multi‐site, 40‐month prospective study collecting data in memory clinics, general practitioner offices, and home environments. Results Our models were able to accurately discriminate between healthy subjects and individuals at risk to progress to dementia within 3 years. The model was also able to differentiate between people with or without amyloid neuropathology and classify fast and slow cognitive decliners with a very good diagnostic performance. Conclusion Digital biomarker prognostic models can be a useful tool to assist large‐scale population screening for the early detection of cognitive impairment and patient monitoring over time.

Published
2020

24. The impact of SARS-CoV-2 in dementia across Latin America: A call for an urgent regional plan and coordinated response

Author

Christopher C Butler, Jorge J. Llibre-Guerra, Leonel T. Takada, Ioannis Tarnanas, Maritza Pintado-Caipa, Antônio Lúcio Teixeira, Laís Fajersztajn, Walter D. Dawson, Cecilia Serrano, Irene B. Meier, Fernanda G. De Felice, Carlos Cano, Alejandra Guerrero Barragan, Hernando Santamaría-García, Gorka Navarrete, Mario A. Parra, Francisco S. Ibáñez, Fabricio Ferreira de Oliveira, Elisa de Paula França Resende, Kenneth S. Kosik, Julián Bustin, Dominic Trépel, Juan J. Llibre-Rodriguez, Andrea Slachevsky, Alexander Kornhuber, Lissette Duque, Agustín Ibáñez, Maxwell Z. Wilson, Maira Okada de Oliveira, Nilton Custodio, Tomas Leon Rodriguez, Claudia Duran-Aniotz, Alyne Mendonca Marques Ton, Diana Matallana, Rodrigo B. Serafim, Beatriz Marcela Mar Meza, Jerusa Smid, Daisy Acosta, Stefanie Danielle Piña Escudero, Carolina Arias Gonzalez, Christian Gonzalez-Billault, Maria I. Behrens, Maria Aparecida Camargos Bicalho, Bruce L. Miller, Myriam De la Cruz Puebla, Diego Acosta-Alvear, Rodrigo Santibáñez, and Ronnielly Melo Tavares

Subjects
0301 basic medicine, Latin Americans, Inequality, media_common.quotation_subject, Best practice, Clinical Neurology, coronavirus, Plan (drawing), SARS‐CoV‐2, 03 medical and health sciences, 0302 clinical medicine, Latin American and Caribbean countries, Political science, Development economics, Pandemic, medicine, Acquired Cognitive Impairment, Dementia, health system, RC346-429, Diplomacy, media_common, Policy Forum, SARS-CoV-2, Prevention, RC952-954.6, COVID-19, purl.org/becyt/ford/3.1 [https], medicine.disease, Brain Disorders, Psychiatry and Mental health, 030104 developmental biology, Emerging Infectious Diseases, Geriatrics, Action plan, Neurological, RC0321, purl.org/becyt/ford/3 [https], Neurology. Diseases of the nervous system, Neurology (clinical), Reduced Inequalities, 030217 neurology & neurosurgery, dementia
Abstract

The SARS-CoV-2 global pandemic will disproportionately impact countries with weak economies and vulnerable populations including people with dementia. Latin American and Caribbean countries (LACs) are burdened with unstable economic development, fragile health systems, massive economic disparities, and a high prevalence of dementia. Here, we underscore the selective impact of SARS-CoV-2 on dementia among LACs, the specific strain on health systems devoted to dementia, and the subsequent effect of increasing inequalities among those with dementia in the region. Implementation of best practices for mitigation and containment faces particularly steep challenges in LACs. Based upon our consideration of these issues, we urgently call for a coordinated action plan, including the development of inexpensive mass testing and multilevel regional coordination for dementia care and related actions. Brain health diplomacy should lead to a shared and escalated response across the region, coordinating leadership, and triangulation between governments and international multilateral networks. Fil: Ibañez, Agustin Mariano. Universidad Adolfo Ibáñez; Chile. Universidad de San Andrés; Argentina. Universidad Autónoma del Caribe; Colombia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Santamaria Garcia, Hernando. Pontificia Universidad Javeriana; Colombia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Guerrero Barragan, Alejandra. University of California; Estados Unidos Fil: Kornhuber, Alexander. University of California; Estados Unidos Fil: Mendonca Marques Ton, Alyne. Universidade Vila Velha; Brasil Fil: Slachevsky, Andrea. Universidad de Chile; Chile Fil: Teixeira, Antonio Lucio. No especifíca; Fil: Mar Meza, Beatriz Marcela. University of California; Estados Unidos Fil: Serrano, Cecilia Mariela. Unidad Asistencial "Dr. César Milstein"; Argentina Fil: Cano, Carlos. Pontificia Universidad Javeriana; Colombia Fil: Arias Gonzalez, Carolina. University of California; Estados Unidos Fil: González Billault, Christian. Universidad de Chile; Chile Fil: Butler, Christopher. University of Oxford; Reino Unido. Pontificia Universidad Católica de Chile; Chile Fil: Bustin, Julian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: Duran Aniotz, Claudia. Universidad Adolfo Ibañez; Chile Fil: Acosta, Daisy. Universidad Nacional Pedro Henríquez Ureña; República Dominicana Fil: Matallana, Diana L.. Pontificia Universidad Javeriana; Colombia Fil: Acosta Alvear, Diego. University of California; Estados Unidos Fil: Trépel, Dominic. No especifíca; Fil: De Paula França Resende, Elisa. University of California; Estados Unidos. Universidade Federal do Rio de Janeiro; Brasil Fil: Ferreira de Oliveira, Fabricio. Universidade Federal de Sao Paulo; Brasil Fil: Ibanez, Francisco. Universidad Nacional de San Juan; Argentina Fil: De Felice, Fernanda G.. Universidade Federal do Rio de Janeiro; Brasil Fil: Navarrete, Gorka. Universidad Adolfo Ibañez; Chile Fil: Tarnanas, Ioannis. No especifíca; Fil: Meier, Irene B.. No especifíca; Fil: Smid, Jerusa. No especifíca; Fil: Llibre-Guerra, Jorge. University of California; Estados Unidos Fil: Llibre Rodriguez, Juan J.. Universidad de Ciencias Médicas; Cuba Fil: Fajersztajn, Laís. University of California; Estados Unidos. Universidade de Sao Paulo; Brasil Fil: Tadao Takada, Leonel. Universidade de Sao Paulo; Brasil Fil: Duque, Lissette. National Commission In Bioethics; Ecuador Fil: Okada de Oliveira, Maira. Universidade de Sao Paulo; Brasil Fil: Aparecida Camargos Bicalho, Maria. Universidade Federal de Minas Gerais; Brasil Fil: Behrens, María Isabel. Universidad Nacional de San Juan; Argentina. Universidad de Chile; Chile Fil: Pintado Caipa, Maritza. University of California; Estados Unidos Fil: Parra, Mario. University of Strathclyde; Reino Unido Fil: Wilson, Maxwell Z.. University of California; Estados Unidos Fil: De La Cruz Puebla, Myriam. Universidad de Barcelona; España. University of California; Estados Unidos Fil: Custodio, Nilton. Peruvian National Institute Of Health; Perú Fil: Santibanez, Rodrigo. Pontificia Universidad Católica de Chile; Chile. Universidad Católica de Chile; Chile Fil: Serafim, Rodrigo Bernardo. No especifíca; Fil: Melo Tavares, Ronnielly. Universidade Federal do Rio de Janeiro; Brasil Fil: Piña Escudero, Stefanie Danielle. University of California; Estados Unidos Fil: Rodriguez, Tomas Leon. Universidad de Chile; Chile Fil: Dawson, Walter. University of California; Estados Unidos Fil: Miller, Bruce L.. University of California; Estados Unidos Fil: Kosik, Kenneth. University of California; Estados Unidos

Published
2020

25. Five-year biomarker progression variability for Alzheimer's disease dementia prediction: Can a complex instrumental activities of daily living marker fill in the gaps?

Author

Ioannis Tarnanas, Mark D. Wiederhold, Brenda K. Wiederhold, Anthoula Tsolaki, and Magda Tsolaki

Subjects
Oncology, medicine.medical_specialty, Activities of daily living, Developmental psychology, Internal medicine, medicine, Dementia, Cognitive decline, Rate of progression, Diagnostics, Proportional hazards model, Computerized cognitive assessment, Hazard ratio, Cognitive declines, Cognition, Early detection, Biomarker, Alzheimer's disease, medicine.disease, Confidence interval, MCI, Psychiatry and Mental health, PET, Biomarker progressions, Biomarker (medicine), Cognitive & Behavioral Assessment, Neurology (clinical), Psychology, MRI
Abstract

Introduction Biomarker progressions explain higher variability in cognitive decline than baseline values alone. This study examines progressions of established biomarkers along with a novel marker in a longitudinal cognitive decline. Methods A total of 215 subjects were used with a diagnosis of normal, mild cognitive impairment (MCI) or Alzheimer's disease (AD) at baseline. We calculated standardized biomarker progression rates and used them as predictors of outcome within 5 years. Results Early cognitive declines were more strongly explained by fluorodeoxyglucose-positron emission tomography, precuneus and medial temporal cortical thickness, and the complex instrumental activities of daily living (iADL) marker progressions. Using Cox proportional hazards model, we found that these progressions were a significant risk factor for conversion from both MCI to AD (adjusted hazard ratio 1.45; 95% confidence interval 1.20–1.93; P = 1.23 × 10 −5 ) and cognitively normal to MCI (adjusted hazard ratio 1.76; 95% confidence interval 1.32–2.34; P = 1.55 × 10 −5 ). Discussion Compared with standard biological biomarkers, complex functional iADL markers could also provide predictive information for cognitive decline during the presymptomatic stage. This has important implications for clinical trials focusing on prevention in asymptomatic individuals.

Published
2015
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26. Digital technologies as biomarkers, clinical outcomes assessment, and recruitment tools in Alzheimer's disease clinical trials

Author

Gabriel Vargas, Julie M. Robillard, Maria B. Tome, Holly Soares, Ioannis Tarnanas, Lisa J. Bain, Michael Gold, James Hendrix, Maria C. Carrillo, Bradley B. Miller, John Rice, Sara J. Czaja, Jesse M. Cedarbaum, and Joan Amatniek

Subjects
Activities of daily living, Exploit, Emerging technologies, Disease, 03 medical and health sciences, 0302 clinical medicine, Cognition, Clinical trials, medicine, Digital technology, Dementia, 030212 general & internal medicine, Function, Wearable technology, business.industry, Alzheimer's disease, medicine.disease, Data science, Clinical trial, Psychiatry and Mental health, Perspective, Neurology (clinical), business, Psychology, 030217 neurology & neurosurgery, Biomarkers
Abstract

Digital technology is transforming the development of drugs for Alzheimer's disease and was the topic of the Alzheimer's Association's Research Roundtable on its May 23-24, 2017 meeting. Research indicates that wearable devices and unobtrusive passive sensors that enable the collection of frequent or continuous, objective, and multidimensional data during daily activities may capture subtle changes in cognition and functional capacity long before the onset of dementia. The potential to exploit these technologies to improve clinical trials as both recruitment and retention tools as well as for potential end points was discussed. The implications for the collection and use of large amounts of data, lessons learned from other related disease areas, ethical concerns raised by these new technologies, and regulatory issues were also covered in the meeting. Finally, the challenges and opportunities of these new technologies for future use were discussed.

Published
2018

27. P4-213: ALZHEIMER'S DISEASE PREDICTION SERVICE (ADPS): STANDARDIZING A DIGITAL BIOMARKER FOR COGNITION ASSESSMENT IN ALZHEIMER'S DISEASE CLINICAL TRIALS

Author

Raquel Sánchez-Valle, Themis Exarhos, Adria Tort, Carlo Cavaliere, Ioannis Tarnanas, Magda Tsolaki, Robert Whelan, Mahi Kozori, Andrea Soricelli, Michela Rampini, Laura Rai, Giovanni B. Frisoni, Panagiotis Vlamos, Ian H. Robertson, Rory Boyle, Claudio Babiloni, and Mircea Balasa

Subjects
Service (business), medicine.medical_specialty, Epidemiology, business.industry, Health Policy, Cognition, Disease, Clinical trial, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, medicine, Biomarker (medicine), Neurology (clinical), Geriatrics and Gerontology, Intensive care medicine, business
Published
2019

28. Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Author

Ioannis Tarnanas, Prabitha Urwyler, Reto Stucki, Tobias Nef, Marcel Büchler, Urs Peter Mosimann, Dario Cazzoli, and René M. Müri

Subjects
Adult, Male, Engineering, Support Vector Machine, smart cities, Active learning (machine learning), Monitoring, Ambulatory, 610 Medicine & health, computer.software_genre, lcsh:Chemical technology, Biochemistry, Article, Pattern Recognition, Automated, Analytical Chemistry, Activity recognition, Naive Bayes classifier, Assisted Living Facilities, Home automation, Smart city, Cluster Analysis, Humans, lcsh:TP1-1185, data classification, Electrical and Electronic Engineering, Cluster analysis, Instrumentation, Aged, ambient assisted living, business.industry, technology, industry, and agriculture, data mining, Middle Aged, 620 Engineering, Atomic and Molecular Physics, and Optics, humanities, Support vector machine, Statistical classification, smart homes, Female, Data mining, business, activities of daily living, Wireless Technology, computer, healthcare technology
Abstract

Smart homes for the aging population have recently started attracting the attention of the research community. The “health state” of smart homes is comprised of many different levels, starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Published
2015

29. Sonic Intelligence as a Virtual Therapeutic Environment

Author

Dimitrios Adam and Ioannis Tarnanas

Subjects
Adult, Coping (psychology), Communication, Decision Making, Intelligence, Applied psychology, Virtual representation, 610 Medicine & health, Cognition, General Medicine, Environment, Human-Computer Interaction, User-Computer Interface, Sound, Locus of control, Transactional leadership, Adaptation, Psychological, Immersion (virtual reality), Humans, 570 Life sciences, biology, Attribution, Psychology, Social psychology, Applied Psychology, Avatar
Abstract

This paper reports on the results of a research project, on comparing one virtual collaborative environment with a first-person visual immersion (first-perspective interaction) and a second one where the user interacts through a sound-kinetic virtual representation of himself (avatar), as a stress-coping environment in real-life situations. Recent developments in coping research are proposing a shift from a trait-oriented approach of coping to a more situation-specific treatment. We defined as real-life situation a target-oriented situation that demands a complex coping skills inventory of high self-efficacy and internal or external "locus of control" strategies. The participants were 90 normal adults with healthy or impaired coping skills, 25-40 years of age, randomly spread across two groups. There was the same number of participants across groups and gender balance within groups. All two groups went through two phases. In Phase I, Solo, one participant was assessed using a three-stage assessment inspired by the transactional stress theory of Lazarus and the stress inoculation theory of Meichenbaum. In Phase I, each participant was given a coping skills measurement within the time course of various hypothetical stressful encounters performed in two different conditions and a control group. In Condition A, the participant was given a virtual stress assessment scenario relative to a first-person perspective (VRFP). In Condition B, the participant was given a virtual stress assessment scenario relative to a behaviorally realistic motion controlled avatar with sonic feedback (VRSA). In Condition C, the No Treatment Condition (NTC), the participant received just an interview. In Phase II, all three groups were mixed and exercised the same tasks but with two participants in pairs. The results showed that the VRSA group performed notably better in terms of cognitive appraisals, emotions and attributions than the other two groups in Phase I (VRSA, 92%; VRFP, 85%; NTC, 34%). In Phase II, the difference again favored the VRSA group against the other two. These results indicate that a virtual collaborative environment seems to be a consistent coping environment, tapping two classes of stress: (a) aversive or ambiguous situations, and (b) loss or failure situations in relation to the stress inoculation theory. In terms of coping behaviors, a distinction is made between self-directed and environment-directed strategies. A great advantage of the virtual collaborative environment with the behaviorally enhanced sound-kinetic avatar is the consideration of team coping intentions in different stages. Even if the aim is to tap transactional processes in real-life situations, it might be better to conduct research using a sound-kinetic avatar based collaborative environment than a virtual first-person perspective scenario alone. The VE consisted of two dual-processor PC systems, a video splitter, a digital camera and two stereoscopic CRT displays. The system was programmed in C++ and VRScape Immersive Cluster from VRCO, which created an artificial environment that encodes the user's motion from a video camera, targeted at the face of the users and physiological sensors attached to the body.

Published
2003

30. Can a Novel Web-Based Computer Test Predict Poor Simulated Driving Performance? A Pilot Study With Healthy and Cognitive-Impaired Participants

Author

Ioannis Tarnanas, Nora Bethencourt, Rahel Bieri, Tobias Nef, Michael Jäger, René M. Müri, and Urs Peter Mosimann

Subjects
Adult, Automobile Driving, Activities of daily living, Applied psychology, Health Informatics, Pilot Projects, Web-based cognitive test, lcsh:Computer applications to medicine. Medical informatics, Young Adult, Scoring algorithm, Web application, Humans, Computer Simulation, Simulation, cognitive impairment, Original Paper, driving simulation, Internet, business.industry, lcsh:Public aspects of medicine, Driving simulator, lcsh:RA1-1270, Cognition, Middle Aged, Confidence interval, Test (assessment), Test score, Case-Control Studies, computer-based tests, lcsh:R858-859.7, business
Abstract

Background: Driving a car is a complex instrumental activity of daily living and driving performance is very sensitive to cognitive impairment. The assessment of driving-relevant cognition in older drivers is challenging and requires reliable and valid tests with good sensitivity and specificity to predict safe driving. Driving simulators can be used to test fitness to drive. Several studies have found strong correlation between driving simulator performance and on-the-road driving. However, access to driving simulators is restricted to specialists and simulators are too expensive, large, and complex to allow easy access to older drivers or physicians advising them. An easily accessible, Web-based, cognitive screening test could offer a solution to this problem. The World Wide Web allows easy dissemination of the test software and implementation of the scoring algorithm on a central server, allowing generation of a dynamically growing database with normative values and ensures that all users have access to the same up-to-date normative values. Objective: In this pilot study, we present the novel Web-based Bern Cognitive Screening Test (wBCST) and investigate whether it can predict poor simulated driving performance in healthy and cognitive-impaired participants. Methods: The wBCST performance and simulated driving performance have been analyzed in 26 healthy younger and 44 healthy older participants as well as in 10 older participants with cognitive impairment. Correlations between the two tests were calculated. Also, simulated driving performance was used to group the participants into good performers (n=70) and poor performers (n=10). A receiver-operating characteristic analysis was calculated to determine sensitivity and specificity of the wBCST in predicting simulated driving performance. Results: The mean wBCST score of the participants with poor simulated driving performance was reduced by 52%, compared to participants with good simulated driving performance ( P

Published
2013

31. Our experience with informative and communication technologies (ICT) in dementia

Author

Magda, Tsolaki, Stelios, Zygouris, Ioulietta, Lazarou, Ioannis, Kompatsiaris, Leontios, Chatzileontiadis, Constantinos, Votis, Dimitrios, Tzovaras, Anastasios, Karakostas, Constantina, Karagkiozi, Tatianna, Dimitriou, Thasyvoulos, Tsiatsios, Stavros, Dimitriadis, Ioannis, Tarnanas, Dimitris, Dranidis, and Panagiotis, Bamidis

Abstract

Our research is implementing high quality next generation services for the Prediction, Early Diagnosis, Monitoring, and Support of patients with Cognitive Impairment (Subjective Cognitive Impairment -SCI-, Mild Cognitive Impairment -MCI-, Mild Dementia) and Education and Training for all stakeholders. Prediction, Early Diagnosis and Monitoring: The first idea was to Research and Develop a novel System using motion detection devices, depth cameras, and intelligent objects of everyday use (ranging from cooking implements such as kitchen to furniture (e.g. sofa, bed, etc.) which are appropriately adapted in order to capture changes of subject's Activities of Daily Living -ADL- and behavioural patterns (including mobility, nutrition, exercising and medication schedule). We also demonstrated the potential of a virtual supermarket (VSM) cognitive training game as a screening tool for patients with MCI in a sample of older adults. We have indicated that this VSM application displayed a correct classification rate (CCR) of 87.30%, achieving a level of diagnostic accuracy similar to standardized neuropsychological tests, which are the gold standard for MCI screening http://www.en-noisis.gr/ Support of patients: Cognitive tasks and cognitive exercises for patients suffering from Alzheimer's Disease (AD) through web-based applications. These exercises have been developed in such a way in order to exploit rich interactive multimedia interfaces (including music) based on human computer interaction principles. To this direction we are implementing a web based portal with supportive services such as (a) on-line monitoring of patient's progress by health care professionals, (b) statistical representation of patients' progress. Multimedia enriched cognitive exercises in virtual reality form (i.e. 3D Serious Games) use suitable modalities for such activities through the creation probable of new brain cells and by assisting the brain to find out alternative methods to execute functions, which are controlled by damaged brain regions. Another program the "robot-programming-as-cognitive-training" approach aims to explore the impact that the activity of programming a friendly robot might have on AD and MCI patients' condition. http://aspad.csd.auth.gr. Another study aimed at investigating the benefits of combined physical and cognitive training on global cognition while assessing the effect of training dosage and exploring the role of several potential effect modifiers. The results indicate that combined physical and cognitive training improves global cognition in a dose-responsive manner but these benefits may be less pronounced in older adults with mild dementia. The long-lasting impact of combined training on the incidence and trajectory of cognitive disorders in relation to its severity should be assessed in future long-term trials. www.longlastingmemories.eu. Finally, Symbiosis is a revolutionary system aiming at providing integrated solutions to a series of problems related with MCI and AD. It is the first integrated AD support system that takes into account patient's response in an adaptive way that fulfills each patient's special needs and provides to caregivers and doctors considerable facilitations, unlocking the potential of innovative supporting role. www.youtube.com/watch?v=BDkLz-T-jYE. Education and training for all stakeholders (i.e. health professionals and informal and formal caregivers) through distance education platforms and e-collaboration services. To augment this effort, the research team integrates biofeedback modules for stress measurement in teleconferences in order to support the emotional awareness of the participants. The depression, anxiety and burden of caregivers were reduced significantly in the same way as in a face to face intervention. http://aspad.csd.auth.gr. In conclusion ICT can help health professionals and caregivers to support in a better way the patients with cognitive, functional and behavioral problems.

Published
2013

32. Ecological validity of virtual reality daily living activities screening for early dementia: longitudinal study

Author

Magda Tsolaki, René M. Müri, Tobias Nef, Urs Peter Mosimann, Winfried Schlee, and Ioannis Tarnanas

Subjects
Activities of daily living, Ecological validity, Biomedical Engineering, Physical Therapy, Sports Therapy and Rehabilitation, 610 Medicine & health, Information technology, Developmental psychology, medicine, Dementia, Cognitive decline, Original Paper, Rehabilitation, Construct validity, Cognition, Executive functions, medicine.disease, T58.5-58.64, Computer Science Applications, Psychiatry and Mental health, Finger tapping, 570 Life sciences, biology, Public aspects of medicine, RA1-1270, Psychology, Clinical psychology
Abstract

BackgroundDementia is a multifaceted disorder that impairs cognitive functions, such as memory, language, and executive functions necessary to plan, organize, and prioritize tasks required for goal-directed behaviors. In most cases, individuals with dementia experience difficulties interacting with physical and social environments. The purpose of this study was to establish ecological validity and initial construct validity of a fire evacuation Virtual Reality Day-Out Task (VR-DOT) environment based on performance profiles as a screening tool for early dementia. ObjectiveThe objectives were (1) to examine the relationships among the performances of 3 groups of participants in the VR-DOT and traditional neuropsychological tests employed to assess executive functions, and (2) to compare the performance of participants with mild Alzheimer’s-type dementia (AD) to those with amnestic single-domain mild cognitive impairment (MCI) and healthy controls in the VR-DOT and traditional neuropsychological tests used to assess executive functions. We hypothesized that the 2 cognitively impaired groups would have distinct performance profiles and show significantly impaired independent functioning in ADL compared to the healthy controls. MethodsThe study population included 3 groups: 72 healthy control elderly participants, 65 amnestic MCI participants, and 68 mild AD participants. A natural user interface framework based on a fire evacuation VR-DOT environment was used for assessing physical and cognitive abilities of seniors over 3 years. VR-DOT focuses on the subtle errors and patterns in performing everyday activities and has the advantage of not depending on a subjective rating of an individual person. We further assessed functional capacity by both neuropsychological tests (including measures of attention, memory, working memory, executive functions, language, and depression). We also evaluated performance in finger tapping, grip strength, stride length, gait speed, and chair stands separately and while performing VR-DOTs in order to correlate performance in these measures with VR-DOTs because performance while navigating a virtual environment is a valid and reliable indicator of cognitive decline in elderly persons. ResultsThe mild AD group was more impaired than the amnestic MCI group, and both were more impaired than healthy controls. The novel VR-DOT functional index correlated strongly with standard cognitive and functional measurements, such as mini-mental state examination (MMSE; rho=0.26, P=.01) and Bristol Activities of Daily Living (ADL) scale scores (rho=0.32, P=.001). ConclusionsFunctional impairment is a defining characteristic of predementia and is partly dependent on the degree of cognitive impairment. The novel virtual reality measures of functional ability seem more sensitive to functional impairment than qualitative measures in predementia, thus accurately differentiating from healthy controls. We conclude that VR-DOT is an effective tool for discriminating predementia and mild AD from controls by detecting differences in terms of errors, omissions, and perseverations while measuring ADL functional ability.

Published
2013
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