Digital biomarkers of Major Depressive Disorder

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Digital biomarkers of Major Depressive Disorder

Major Depressive Disorder is associated with heterogeneous symptomatology, which traditional clinical measures aim to quantify through observation of patient behavior [1-3]. Similarly, OpenDBM captures visual and auditory behavior to calculate digital biomarkers indicative of MDD symptom severity. An overview of MDD biomarkers calculated by OpenDBM and the methods used is provided below. These biomarkers can be used to assess MDD severity and treatment efficacy in clinical trials.

Emotional expressivity and mood

Patients with MDD show decreased expressivity in the face [4,5], a possible result of psychomotor retardation and changes in mood [6-8]. Increased MDD severity is associated with decreased expressivity during both free behavior and expressions evoked on cue [9-13]. Using a computer vision-based approach to the Facial Action Coding System [14-16], OpenDBM objectively quantifies facial expressivity and derives digital biomarkers of mood that have been shown to reflect disease severity in patients with MDD.

Acoustic properties of voice

Analysis of the voice of individuals with MDD has revealed distinct acoustic properties that are indicative of MDD severity [17-19]. These properties, recorded both during free speech and sustained vowel phonations, include loudness, fundamental frequency, jitter, shimmer, and harmonics-to-noise ratio [20-28]. OpenDBM uses established digital signal processing tools [29] to measure acoustic properties of voice as biomarkers of MDD that can be used to assess disease severity and treatment response.

Characteristics of speech

Observation of speech of individuals with MDD has revealed measurable characteristics that are indicative of disease severity [30,31]. Established natural language processing tools can be used to transcribe and analyze speech from patient audio. This includes speech characteristics such as the rate of speech, parts of speech used, lexical diversity, pauses between words, and the overall sentiment of words spoken [32-41]. Each of these speech characteristics can be used as digital biomarkers of MDD. Note: The current version of OpenDBM does not transcribe speech or calculate NLP features but we're hoping future versions will.

Patterns of movement

MDD has been shown to be associated with changes in motor functioning [42-45]. Indeed, the use of digital tools such as wearables for proxy measurements of motor functioning is gaining popularity [46-48]. OpenDBM instead uses computer vision-based quantification of head movement and positioning [14-16] during active assessments as proxies of motor functioning. These behaviors have existing support in the literature as having a direct relationship to MDD severity and can be used as digital biomarkers of MDD [46-53].

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