Remote digital cognitive assessment reveals cognitive deficits related to hippocampal atrophy in autoimmune limbic encephalitis: a cross-sectional validation study

Remote cognitive tests predict neurodegenerative biomarkers in the Insight 46 cohort

BACKGROUND

Alzheimer's disease-related biomarkers detect pathology years before symptoms emerge, when disease-modifying therapies might be most beneficial. Remote cognitive testing provides a means of assessing early cognitive changes. We explored the relationship between neurodegenerative biomarkers and cognition in cognitively normal individuals.

METHODS

We remotely deployed 13 computerized Cognitron tasks in 255 Insight 46 participants. We generated amyloid load and positivity, white matter hyperintensity volume (WMHV), whole brain and hippocampal volumes at age 73, plus rates of change over 2 years. We examined the relationship between Cognitron, biomarkers, and standard neuropsychological tests.

RESULTS

Slower response time on a delayed recognition task predicted amyloid positivity (odds ratio [OR] = 1.79, confidence interval [CI]: 1.15, 2.95), and WMHV (1.23, CI: 1.00, 1.56). Brain and hippocampal atrophy rates correlated with poorer visuospatial performance (b = -0.42, CI: -0.80, -0.05) and accuracy on immediate recognition (b = -0.01, CI: -0.012, -0.001), respectively. Standard tests correlated with Cognitron composites (rho = 0.50, p < 0.001).

DISCUSSION

Remote computerized testing correlates with standard supervised assessments and holds potential for studying early cognitive changes associated with neurodegeneration.

HIGHLIGHTS

70% of the Online 46 cohort performed a set of remote online cognitive tasks. Response time and accuracy on a memory task predicted amyloid status and load (SUVR). Accuracy on memory and spatial span tasks correlated with longitudinal atrophy rate. The Cognitron tasks correlated with standard supervised cognitive tests. Online cognitive testing can help identify early AD-related memory deficits.

Performance and validation of a digital memory test across the Alzheimer's disease continuum

Digital cognitive testing using online platforms has emerged as a potentially transformative tool in clinical neuroscience. In theory, it could provide a powerful means of screening for and tracking cognitive performance in people at risk of developing conditions such as Alzheimer's disease. Here we investigate whether digital metrics derived from an in-person administered, tablet-based short-term memory task-the 'What was where?' Oxford Memory Task-were able to clinically stratify patients at different points within the Alzheimer's disease continuum and to track disease progression over time. Performance of these metrics compared to traditional neuropsychological pen-and-paper screening tests of cognition was also analysed. A total of 325 people participated in this study: 49 patients with subjective cognitive decline, 57 with mild cognitive impairment, 63 with Alzheimer's disease dementia and 156 elderly healthy controls. Most digital metrics were able to discriminate between healthy controls and patients with mild cognitive impairment and between mild cognitive impairment and Alzheimer's disease patients. Some, including Absolute Localization Error, also differed significantly between patients with subjective cognitive decline and mild cognitive impairment. Identification accuracy was the best predictor of hippocampal atrophy, performing as well as standard screening neuropsychological tests. A linear support vector model combining digital metrics achieved high accuracy and performed at par with standard testing in discriminating between elderly healthy controls and subjective cognitive decline (area under the curve 0.82) and between subjective cognitive decline and mild cognitive impairment (area under the curve 0.92), while performing worse in classifying between mild cognitive impairment and Alzheimer's disease patients (area under the curve 0.75). Memory imprecision was able to predict cognitive decline on standard cognitive tests over one year. Overall, these findings show how it might be possible to use a digital memory test in clinics and clinical trial contexts to stratify and track performance across the Alzheimer's disease continuum.

An iterative approach for estimating domain-specific cognitive abilities from large scale online cognitive data

Online cognitive tasks are gaining traction as scalable and cost-effective alternatives to traditional supervised assessments. However, variability in peoples' home devices, visual and motor abilities, and speed-accuracy biases confound the specificity with which online tasks can measure cognitive abilities. To address these limitations, we developed IDoCT (Iterative Decomposition of Cognitive Tasks), a method for estimating domain-specific cognitive abilities and trial-difficulty scales from task performance timecourses in a data-driven manner while accounting for device and visuomotor latencies, unspecific cognitive processes and speed-accuracy trade-offs. IDoCT can operate with any computerised task where cognitive difficulty varies across trials. Using data from 388,757 adults, we show that IDoCT successfully dissociates cognitive abilities from these confounding factors. The resultant cognitive scores exhibit stronger dissociation of psychometric factors, improved cross-participants distributions, and meaningful demographic's associations. We propose that IDoCT can enhance the precision of online cognitive assessments, especially in large scale clinical and research applications.

Online cognitive monitoring technology for people with Parkinson's disease and REM sleep behavioural disorder

Automated online cognitive assessments are set to revolutionise clinical research and healthcare. However, their applicability for Parkinson's Disease (PD) and REM Sleep Behavioural Disorder (RBD), a strong PD precursor, is underexplored. Here, we developed an online battery to measure early cognitive changes in PD and RBD. Evaluating 19 candidate tasks showed significant global accuracy deficits in PD (0.65 SD, p = 0.003) and RBD (0.45 SD, p = 0.027), driven by memory, language, attention and executive underperformance, and global reaction time deficits in PD (0.61 SD, p = 0.001). We identified a brief 20-min battery that had sensitivity to deficits across these cognitive domains while being robust to the device used. This battery was more sensitive to early-stage and prodromal deficits than the supervised neuropsychological scales. It also diverged from those scales, capturing additional cognitive factors sensitive to PD and RBD. This technology offers an economical and scalable method for assessing these populations that can complement standard supervised practices.