Digital Brain Biomarkers of Human Cognition and Mood

  • Paul DagumEmail author
Part of the Studies in Neuroscience, Psychology and Behavioral Economics book series (SNPBE)


By comparison to the functional metrics available in other medical disciplines, conventional measures of neuropsychiatric and neurodegenerative disorders have several limitations. They are obtrusive, requiring a subject to break from their normal routine. They are episodic and provide sparse snapshots of a patient only at the time of the assessment. They require subjects to perform a task outside of the context of everyday behavior. And lastly, they are poorly scalable, taxing limited resources. We present validation studies that demonstrate the clinical efficacy of a new approach in reproducing gold-standard neuropsychological measures. We discuss the neuroscience constructs and mathematical underpinnings of cognition and mood measurement from human-computer interaction data. We conclude with a discussion on four areas that we predict will be impacted by these new clinical measurements: (i) understanding of the interdependency between cognition and mood; (ii) nosology of psychiatric illnesses; (iii) drug discovery; and (iv) delivery of healthcare services.


  1. Andone I, Błaszkiewicz K, Eibes M et al (2016) How age and gender affect smartphone usage. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing adjunct—UbiComp ’16. Heidelberg, Germany, 12–16 September 2016Google Scholar
  2. Barshan E, Ghodsi A, Azimifar Z, Zolghadri Jahromi M (2011) Supervised principal component analysis: visualization, classification and regression on subspaces and submanifolds. Pattern Recognit 44(7):1357–1371. Scholar
  3. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 57(1):289–300. Scholar
  4. Bergmeir C, Hyndman RJ, Koo B (2018) A note on the validity of cross-validation for evaluating autoregressive time series prediction. Comput Stat Data Anal 120:70–83. Scholar
  5. Botwinick J, Thompson LW (1966) Premotor and motor components of reaction time. J Exp Psychol 71(1):9–15. Scholar
  6. Business Wire (2017) BlackThorn Therapeutics announces innovative clinical collaboration agreement with Mindstrong Health.
  7. (2017) Testing the value of smartphone assessments of people with mood disorders. A pilot, exploratory, longitudinal study (Identifier No. NCT03429361).
  8. Dagum P (2018a) The long journey to digital brain biomarkers. npj Digital Medicine.
  9. Dagum P (2018b) Digital biomarkers of cognitive function. NPJ Digit Med 1(1):10. Scholar
  10. Insel TR (2017) Digital phenotyping: technology for a new science of behavior. JAMA Netw 318(13):1215–1216. Scholar
  11. Keedwell PA, Andrew C, Williams SCR et al (2005) The neural correlates of anhedonia in major depressive disorder. Biol Psychiatry 58(11):843–853. Scholar
  12. Kerchner GA, Dougherty RF, Dagum P (2015) Unobtrusive neuropsychological monitoring from smart phone use behavior. Alzheimers Dement 11(7):272–273. Scholar
  13. Kubiak T, Smyth JM (2019) Connecting domains—ecological momentary assessment in a mobile sensing framework. Mobile sensing and psychoinformatics. Springer, Berlin, pp x–xGoogle Scholar
  14. Madrid A, Smith D, Alvarez-Horine S et al (2017) Assessing anhedonia with quantitative tasks and digital and patient reported measures in a multi-center double-blind trial with BTRX-246040 for the treatment of major depressive disorder. Neuropsychopharmacology 43:372–372Google Scholar
  15. Markowetz A, Błaszkiewicz K, Montag C et al (2014) Psycho-informatics: big data shaping modern psychometrics. Med Hypotheses 82(4):405–411. Scholar
  16. Mindstrong (2019). Mindstrong Health.
  17. PR Newswire (2018) Mindstrong Health and Takeda partner to explore development of digital biomarkers for mental health conditions.
  18. Smith DG, Saljooqi K, Alvarez-Horine S et al (2018) Exploring novel behavioral tasks and digital phenotyping technologies as adjuncts to a clinical trial of BTRX-246040. International Society of CNS Clinical Trials and MethodologyGoogle Scholar
  19. Staples P, Ouyang M, Dougherty RF et al (2018) Supervised kernel PCA for longitudinal data. arXiv preprint arXiv:180806638
  20. Statistica (2019) StatSoft EUROPE.
  21. Strauss E, Sherman E, Spreen O (2006) A compendium of neuropsychological tests. Administration, norms, and commentary, 3rd edn. Oxford University Press, New YorkGoogle Scholar
  22. Winkler AM, Ridgway GR, Webster MA et al (2014) Permutation inference for the general linear model. Neuroimage 92:381–397. Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Mindstrong HealthMountain ViewUSA

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