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Potential Outcome Measures and Biomarkers: Examples for Brain Disorders of Aging MBIs

Music-Based Intervention Toolkit Potential Outcome Measures

Music-based interventions (MBIs) for brain disorders of aging, including Alzheimer’s disease (AD)/Alzheimer’s disease related dementias (ADRD), Parkinson’s disease (PD), and stroke, provide some of the most compelling evidence for music’s health benefit and create a model for future work across the lifespan[3–10]. The National Institutes of Health (NIH) MBI Toolkit, developed through discussions with our expert panels, suggests core datasets of outcome measures and biomarkers for brain disorders of aging that researchers may select for their MBI studies. 

Mechanistic and Clinical Outcome Measures 

The essential steps in developing hypotheses on the impact of MBIs for brain disorders of aging include adopting a conceptual framework for the outcomes to be measured; choosing the appropriate study design; identifying relevant domains for proximal (short-term) and distal (long-term) outcomes, boundary conditions (i.e., moderators), and mechanisms (i.e., mediators); and determining the relevant biomarkers. For each domain, a range of measurement modalities are possible, including self-report, performance-based measures, direct observation, sensor technology measures, physiological monitoring, and various functional brain measures, each of which has strengths and weaknesses for assessing the domain of interest. Therefore, multimodal assessment of a given domain is often preferable.[52, 53]

Guiding Principles and Practical Implementation Considerations in Choosing Mechanistic and Clinical Outcome Measures* and Identifying Biomarkers for MBIs
In designing music-based interventions, investigators must be guided by the research question, the types and goals of the intervention, and the patient and caregiver experience (population and disease condition) as primary determinants of the choice of primary and secondary outcome measures.
Patient-reported outcomes, mixed methods design, and participatory methods to address contexts (e.g., race, culture, geography) should be taken into consideration.
Basic auditory perception, musical experience, individual choice of music, and other contextual factors (e.g., culture, clinical setting, hearing ability) should be considered and incorporated in the study design.
In choosing a music-based intervention to impact a specific disorder, researchers must take into consideration the match between neural systems involved both in the disorder and in the intervention (e.g., an intervention designed to improve gait abnormalities should target the motor system).
The stage of the disease and disease outcomes, as well as behavioral symptoms such as agitation, frustration, and/or high levels of anxiety, are important factors that may impact data collection of biomarkers. These issues are more likely to be present in mid-to-late-stage dementia.
Investigators must identify the specific domains (sensory, emotional, cognitive, motor) that are affected by the disease condition that they are studying. It is also important to assess the impact of the music intervention on multiple domains—the thinking-moving-feeling triad.
Considerations of time dimension of the intervention are important: symptom exacerbation and disease progression (i.e., in AD, cognition in early stages but behavioral manifestations in middle and later stages) impact MBI outcomes as well as short-term, intermediary, and long-term effects of interventions.
Measures with strong psychometric properties (e.g., test-retest reliability, discriminative reliability/sensitivity to change) must be prioritized.
Investigators must thoughtfully address subject burden and number of outcome measures in their study design.
The caregiver-subject dyad and the impact of the intervention on each is important. Key factors include burnout, empathy, stress relief, engagement, adherence, at-home practice, etc.
The risk/benefit ratio of the MBI must be seriously considered. Potential risks include exacerbation of symptoms, anxiety produced by exposure, expectations of skill learning, and falls and fractures.
Practical factors such as overall cost and resources requirements (e.g., the investigative team’s expertise, available infrastructure) are important implementation considerations.
New tools and m-health technologies should ideally be incorporated into MBIs (e.g., digital measures for facial expressions and movements; wearable devices for sleep quality, activity level, exposure to music, heart rate variability; phone apps for reminders and in-home practice; ecological momentary assessment [EMA] methodology; actigraphy; voice recording; video recordings).

* See definition of mechanistic and clinical outcomes in the Glossary of Terms in the Appendix

Mechanistic and clinical outcomes may be derived from studies using an intervention in healthy subjects or individuals with a specific disease/condition to better understand the clinical aspects of human biology and/or disease. More specifically, mechanistic outcomes provide insights into biological or behavioral processes, the pathophysiology of a disease, or an intervention’s mechanism of action. Clinical outcomes are measurable changes from an objective baseline in health, function, or quality of life that result from a treatment or intervention.

Table 1. Potential Measurable Outcomes To Be Considered When Designing MBIs for Brain Disorders of Aging


Measurable Outcomes for AD and ADRD, PD, and Stroke*


Anxiety, depression, emotional regulation, affect, awe, joy, happiness, motivation, interest in life


Language, alertness, short-term memory, long-term memory, autobiographical memory, motivation


Mobility, falls, gait speed


Autonomic function, pain, hearing in noise


Interoceptive awareness, accuracy


Aggressiveness, wandering, agitation, psychosis, apathy, impact of medication


Social connection, social belonging, altruism

Engagement Behaviors

Self-efficacy, responsiveness to music, flow, creativity, artistic identity

Functional Status

Activities of daily living, quality of life, level of independence, well-being, sleep quality


Voice quality, control, volume, level of voice output


Burden, emotional impact

* Not an exhaustive list of measurable outcomes for brain disorders of aging


A biomarker is “a defined characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, a pathogenic process, or pharmacologic responses to a therapeutic intervention.”[54] Biomarkers can be used to parse out the causally active components of an intervention and provide insights not just into whether an intervention works but also how it works. Biomarkers can indicate susceptibility or risk; predict or measure a condition or response; assess safety; or be used for diagnosis, prognosis, or monitoring.

Brain disorders of aging are complex conditions affecting numerous biological, behavioral, and cognitive-affective systems. As such, a wide array of biomarkers can be considered when designing MBIs for brain disorders of aging, including inflammation, brain structure, neurological functioning, gene expression, affect, sensory and motor activity, stress markers (e.g., Galvanic skin response, pupillometry, and cortisol). See above table “Guiding Principles and Practical Implementation Considerations in Choosing Mechanistic and Clinical Outcome Measures and Identifying Biomarkers for MBIs,” which offers guiding principles and identifies factors that merit consideration when selecting biomarkers for MBIs targeting these disorders.

Table 2. Potential Biomarkers To Be Considered When Designing MBIs for Brain Disorders of Aging

CategoryPotential Biomarkers*Methodological Examples (Non-exhaustive)



High-sensitivity C-reactive protein (hs-CRP), proinflammatory cytokines, e.g., interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-alpha)Bioassay (enzyme-linked immunosorbent assay [ELISA])
Brain structureGray and white matter volume, structural connectivityMagnetic resonance imaging (MRI)
Neural circuits and functionFunctional activation and functional connectivityFunctional MRI (fMRI), electroencephalogram (EEG)
Neurotransmitter dynamicsNeural receptor occupancyPositron emission tomography (PET), electrophysiology
Neurotransmitter dynamicsDopamine transporter (DAT)PET
NeuroplasticityBrain derived neurotrophic factor (BDNF)Bioassay (ELISA)
Neurotransmitter dynamicsDopamine (DA)Neurotransmission imaging and levels of DA contained in tears or blood
NeurodegenerationNeurofilament light chain (NFL-1), alpha-synuclein, tauBioassay (ELISA, real-time quaking-induced conversion [RT-QuIC] assay, etc.)
NeurodegenerationAmyloid and tauPET
Gene expressionPresenilin 1 gene methylation, alpha-synuclein DNA methylationBlood and saliva collection
AffectiveTone of voice, quality and control of voice, facial expressionAudiovisual recordings
Affective (anxiety and agitation)Cortisol level, adrenocorticotropic hormone (ACTH), noradrenaline, leptin, proinflammatory cytokines, e.g., IL-6, TNF-alpha


Bioassay (ELISA)

AffectiveActivation in dopaminergic reward systemfMRI
AffectiveConnectivity between auditory and reward systemsfMRI
Affective (autonomic arousal)Galvanic skin response, pupil diameter, heart rate variabilitySkin electrodes and pupillometer
Affective (social engagement)Eye contact, synchronization of body sway across participantsVisual and motion capture (eye tracking, video, wearables)
Affective (social engagement)OxytocinBioassay (ELISA)
SensoryAuditory frequency-following response (FFR)EEG
SensoryRhythmic entrainmentEEG, Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)
MotorBody sway, mobilityBiometrics, MDS-UPDRS, wearables
MotorSpeed of movement

Timed tapping, timed up and go, timed walk, etc.


*Examples of potential biomarkers; some have not yet been validated; some are primarily used in research settings.