Digital Biomarkers Based Individualized Prognosis for People at Risk of Dementia: the AltoidaML Multi-site External Validation Study.

Research investigating treatments and interventions for cognitive decline and Alzheimer's disease (AD) suffer due to difficulties in accurately identifying individuals at risk of AD in the pre-symptomatic stages of the disease. There is an urgent need for better identification of such individuals in order to enable earlier treatment and to properly stage and stratify participants for clinical trials and intervention studies. Although some biological measures (biomarkers) can identify Alzheimer's-related changes before significant changes in cognitive function occur, such biomarkers are not ideal as they are only able to place individuals in rudimentary stages of the disease/cognitive decline (Tarnanas et al., Alzheimers Dement (Amst) 1(4):521-532, 2015) and sometimes mistakenly diagnose individuals (Edmonds et al. 2015). Two tests, based on real-world functioning, which have been used to screen for pre-symptomatic AD are (i) dual-task walking tests (Belghali et al. 2017) and (ii) day-out tasks (Tarnanas et al. 2013). A novel digital biomarker, the Altoida ADPS app, which implements gamified versions of these tests has been shown to accurately discriminate between healthy controls and individuals in prodromal stages of Alzheimer's disease (Tarnanas et al. 2013) and can differentiate between people with mild cognitive impairment who convert to Alzheimer's disease and those who don't (Tarnanas et al. 2015b). The aim of this study is the validation of a novel digital biomarker of cognitive decline.

A Combination of Impulsivity Subdomains Predict Alcohol Intoxication Frequency.

BACKGROUND: Impulsivity, broadly characterized as the tendency to act prematurely without foresight, is linked to alcohol misuse in college students. However, impulsivity is a multidimensional construct and different subdomains likely underlie different patterns of alcohol misuse. Here, we quantified the association between alcohol intoxication frequency and alcohol consumption frequency and choice, action, cognitive, and trait domains of impulsivity. METHODS: University student drinkers (n = 106) completed a battery of demographic and alcohol-related items, as well as self-report and task-based measures indexing different facets of impulsivity. Two orthogonal latent factors, intoxication frequency and alcohol consumption frequency, were generated. Their validity was demonstrated with respect to adverse consequences of alcohol use. Machine learning with penalized regression and feature selection was then utilized to predict intoxication and alcohol consumption frequency using all impulsivity subdomains. Out-of-sample validation was used to quantify model performance. RESULTS: Impulsivity measures alone were significant predictors of intoxication frequency, but not consumption frequency. Propensity for increased intoxication frequency was characterized by increased trait impulsivity, including the Disinhibition subscale of the Sensation Seeking Scale, Attentional and Non-planning subscales of the Barratt Impulsiveness Scale, increased task-based cognitive impulsivity (response time variability), and increased choice impulsivity (steeper delay discounting on a delay discounting questionnaire). A model combining impulsivity domains with other risk factors (gender; nicotine, cannabis, and other drug use; executive functioning; and learning processes) was also significant but did not outperform the model comprising of impulsivity alone. CONCLUSIONS: Intoxication frequency, but not consumption frequency, was characterized by a number of impulsivity subdomains.

Functional EEG connectivity is a neuromarker for adult attention deficit hyperactivity disorder symptoms.

OBJECTIVE: Altered brain functional connectivity has been shown in youth with attention-deficit/hyperactivity disorder (ADHD). However, relatively little is known about functional connectivity in adult ADHD, and how it is linked with the heritability of ADHD. METHODS: We measured eyes-open and eyes-closed resting electroencephalography (EEG) from 38 adults with ADHD, 45 1st degree relatives of people with ADHD and 51 healthy controls. Functional connectivity among all scalp channels was calculated using a weighted phase lag index for delta, theta, alpha, beta and gamma frequency bands. A machine learning analysis using penalized linear regression was used to identify if connectivity features (10,080 connectivity pairs) could predict ADHD symptoms. Furthermore, we examined if EEG connectivity could accurately classify participants into ADHD, 1st degree relatives and/or control groups. RESULTS: Hyperactive symptoms were best predicted by eyes-open EEG connectivity in delta, beta and gamma bands. Inattentive symptoms were predicted by eyes-open EEG connectivity in delta, alpha and gamma bands, and eyes-closed EEG connectivity in delta and gamma bands. EEG connectivity features did not reliably classify participants into groups. CONCLUSIONS: EEG connectivity may represent a neuromarker for ADHD symptoms. SIGNIFICANCE: EEG connectivity may help elucidate the neural basis of adult ADHD symptoms.

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

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.

Individual differences in learning from probabilistic reward and punishment predicts smoking status.

INTRODUCTION: The ability to update reward and punishment contingencies is a fundamental aspect of effective decision-making, requiring the ability to successfully adapt to the changing demands of one's environment. In the case of nicotine addiction, research has predominantly focused on reward- and punishment-based learning processes among current smokers relative to non-smokers, whereas less is known about these processes in former smokers. METHODS: In a total sample of 105 students, we used the Probabilistic Selection Task to examine differences in reinforcement learning among 41 current smokers, 29 ex-smokers, and 35 non-smokers. The PST was comprised of a training and test phase that allowed for the comparison of learning from positive versus negative feedback. RESULTS: The test phase of the Probabilistic Selection Task significantly predicted smoking status. Current and non-smokers were classified with moderate accuracy, whereas ex-smokers were typically misclassified as smokers. Lower rates of learning from rewards were associated with an increased likelihood of being a smoker or an ex-smoker compared with being a non-smoker. Higher rates of learning from punishment were associated with an increased likelihood of being a smoker relative to non-smoker. However, learning from punishment did not predict ex-smoker status. CONCLUSIONS: Current smokers and ex-smokers were less likely to learn from rewards, supporting the hypothesis that deficient reward processing is a feature of chronic addiction. In addition, current smokers were more sensitive to punishment than ex-smokers, contradicting some recent findings.