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

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.

Conflict of interest statement

A.S. and A.Ç. declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article. The remaining authors declare the following financial interests/personal relationships, which may be considered potential competing interests: M.B. and I.T. are employees and shareholders of Altodia, Inc. M.B., I.B.M., R.H., and I.T. report personal fees from Altoida, Inc., outside the submitted work. Finally, I.T., R.H., and M.B. have issued and pending patents with respect to Altoida DNS.

Figures

Fig. 1. Day-to-day variability in testing can overshadow true performance due to external factors (environment) and internal factors (anxiety, motivation, etc.).

Reprint courtesy of Martin Sliwinsk, permission granted.

Fig. 2

Dispersion index based on LTRS and NP plotted for for the HC (A), MCI (B), and AD (C) groups translated into standard deviation. The A–C graphs show a nonlinear increase in standard deviation as a function of disease trajectories. Comparing the overall mean of LTRS vs NP per group yields the following values: HC: t = 10.00106, p < 0.00001; MCI: t = 7.02195, p < 0.00001; AD: t = 6.65272, p = 0.000011, the results are statistically significant at p < 0.001. Details of the individual time points are shown in Table 1.

Fig. 3

Dispersion index plotted across tasks, showing group intraindividual standard deviation (iSD) for the HC (A), MCI (B), and AD (C) groups. The A–C graphs show a nonlinear increase in SD as a function of disease trajectories. IIV is consistently and significantly more sensitive for the disease trajectory trends than conventional NP assessments, especially at the pre-conversion events (spikes in B predict a likely conversion by next assessment). LIIV longitudinal IIV.

Fig. 4

An overview of the procedure used in both studies, except for the unsupervised Altoida DNS use, which was conducted only in Study B in HCs and participants with MCI.

Fig. 5. An illustration of dispersion.

Left: Individual patient data over time. Right: patient performance dispersion (dots) at different time points (A, B, and C) represented in population mean (line) picked up using Altoida (blue, top curve) and conventional NP (red, bottom curve). SD is related to the dispersion of a given subject over time (LTRS). Black (dashed line): true dispersion.

Fig. 6. Illustrations of the LTRS (top) and LDVS (bottom) on the left (numbers 17.3 and 41.5 are random examples).

The two measures can be used in a matrix to obtain a combined longitudinal risk matrix (right). Green indicates low risk, yellow medium, and red high risk. In the risk matrix (right), the overlapping areas allow for a more nuanced interpretation.

Fig. 7. The IIV quantifies the variability of cognitive domain percentiles over time.

The value corresponds to the average variability of the subject’s test in multiples of the variability of healthy subjects for each domain. The value is only reliable for at least five tests done by the same participant.