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

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 < 0.001). The digital score was associated with the clinical cognitive score (rho = 0.56, p < 0.001). No learning effects were found. Here we report the AR app distinguishes HC from otherwise healthy Aß-positive individuals, both in the outpatient setting and at home, which is currently not possible with standard cognitive tests.

Dynamic Reconfiguration of Dominant Intrinsic Coupling Modes in Elderly at Prodromal Alzheimer's Disease Risk.

Large-scale human brain networks interact across both spatial and temporal scales. Especially for electro- and magnetoencephalography (EEG/MEG), there are many evidences that there is a synergy of different subnetworks that oscillate on a dominant frequency within a quasi-stable brain temporal frame. Intrinsic cortical-level integration reflects the reorganization of functional brain networks that support a compensation mechanism for cognitive decline. Here, a computerized intervention integrating different functions of the medial temporal lobes, namely, object-level and scene-level representations, was conducted. One hundred fifty-eight patients with mild cognitive impairment underwent 90 min of training per day over 10 weeks. An active control (AC) group of 50 subjects was exposed to documentaries, and a passive control group of 55 subjects did not engage in any activity. Following a dynamic functional source connectivity analysis, the dynamic reconfiguration of intra- and cross-frequency coupling mechanisms before and after the intervention was revealed. After the neuropsychological and resting state electroencephalography evaluation, the ratio of inter versus intra-frequency coupling modes and also the contribution of ß1 frequency was higher for the target group compared to its pre-intervention period. These frequency-dependent contributions were linked to neuropsychological estimates that were improved due to intervention. Additionally, the time-delays of the cortical interactions were improved in {δ, θ, α2, ß1} compared to the pre-intervention period. Finally, dynamic networks of the target group further improved their efficiency over the total cost of the network. This is the first study that revealed a dynamic reconfiguration of intrinsic coupling modes and an improvement of time-delays due to a target intervention protocol.

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.

SARS-CoV-2 effects on cognition are 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 pre-diagnostic 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.

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

Background: Mixed 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. Methods: We 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). Results: DNS-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. Conclusion: Our 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.

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

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.

Digital biomarkers and sex impacts in Alzheimer's disease management - potential utility for innovative 3P medicine approach.

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. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-022-00284-3.

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.

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.

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

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.

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

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.

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.

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.