Digital cognitive assessments (DCAs) provide insight into cognition and behavior that remains inaccessible through standard assessment approaches. However, the availability of DCAs and the requisite toolkits to extract and analyze meaningful features from these datasets are largely constrained to technical specialists or through fee-for-service commercial entities. The NKI- Rockland Sample provides a large- scale lifespan data sample featuring DCAs, and also openly shares its DCA tasks through the open-source MindLogger platform along with pipelines for feature extraction and analyses. Here we present normative performance from a digital version of Archimedes Spiral Drawing.

NKI-RS2 participants were largely drawn from the existing NKI-RS participant pool (n= 1,500), aged 885. The NKI-RS2 is in year 1 of data collection; here, we report on a subset of participants (n= 9) who performed a digitized version of the Archimedes Spiral Drawing task. This graphomotor task with well-established research and clinical utility in movement disordered populations was adapted for use for off-the-shelf tablet devices. The NKI-RS2 implements these tasks on an Apple iPad Pro2, sampling participant drawing at 120Hz, and featuring pixel- and millisecond- level resolution for all tasks. On the Spiral Drawing and Recall Tests participants traced five Archimedes spirals from the center outward through four windings presented on the iPad. They were then asked to replicate the spiral freehand three times. From these spiral drawings, we extracted time to completion, distance covered, speed/ speed variability, rotational smoothness, number of crossings, mean absolute error, bias, and goodness of fit to the ideal Archimedes spiral.

Comparing the tracing and recall conditions, participants showed significantly faster drawing speed (t[8]=5.32, p< .001), more variable drawing speed (t[8]=5.93, p< .001), reduced goodness of fit to the template t[8]=4.99, p< .002, and reduced rotational smoothness (t[8]=7.43, p< .0003) in the recall conditions. Collapsing across conditions, age predicted more variable drawing speed: t[8]= 2.77 p< .019, greater tracing error (t[8] = 2.69, p< .0227), and reduced rotational smoothness (t[8] = 2.67, p< .024). Between conditions, age predicts a greater increase in drawing speed variability (t[8] = 9.76, p< .0006).

Using the open source MindLogger platform and off-the-shelf digital tablets, we were able to replicate classic paper and pen neuropsychological tests. By adapting these tasks to DCA, we were able to extract meaningful features that are not otherwise accessible (drawing speed, variability, etc.), or that would require additional hardware solutions (e.g., dwell time). By making these tasks and their processing pipelines available, the NKI-RS2 can facilitate the democratization of DCA and DCA analysis to a broader range of researchers and clinicians.