The Translational Value of Psychophysiology Methods and Mechanisms: Multilevel, Dynamic, Personalized

Abstract

Objective: It has been nearly 15 years since Kazdin and Nock published methodological and research recommendations for understanding mechanisms of change in child and adolescent therapy. Their arguments and enthusiasm for research on mechanisms of behavior change (MOBCs) resonated across disciplines and disorders, as it shined a light on the crucial importance of understanding how and for whom treatments instigate behavior change and how therapeutic mechanisms might be extended to "situations and settings of everyday life." Initial efforts focused on how psychotherapy works and linear models, yet the use of theory to guide the study of mechanisms, and laboratory experiments to manipulate them, is broadly applicable.

Method: This article considers dynamic physiological processes that support behavior change. Specifically, it examines the utility of psychophysiological methods to measure and promote behavior change. Moreover, it embeds the baroreflex mechanism, a well-defined heart-brain feedback loop, within the theories and strategies of MOBC research.

Results and conclusion: Individuals' subjective and expressive experience of change does not always align with their physiological reactivity. Thus, behavior change may be best understood when concurrently assessed across multiple biobehavioral levels. Further, behavior is initiated in the moment, often before conscious deliberation, suggesting that multilevel behavior change research may benefit from real-time methodological designs. Last, substance use trajectories vary widely, suggesting that different MOBCs are more or less active in individuals depending on their personal constituency and the functional need that their substance use serves; thus, methods that are amenable to personalized modeling approaches are important.

Figure 1.

Person-centered heat maps of subjective and physiological reactivity to negative picture cues. Each row represents one individual across three concurrent measures. The left heat map shows average self-reported arousal ratings (measured on a 9-point Likert scale) to a block of 15 negative picture cues (Lang et al., 1999) from students who were mandated to a brief intervention for violating university alcohol and drug policies. The middle and right heat maps show two measures of heart rate variability (HRV) reactivity (measured as the variability in the R-to-R intervals of the electrocardiogram): high frequency HRV, a measure of vagal activity, and 0.1 Hz HRV, a proxy of baroreflex activity. The left heat map was sorted in descending order to better visualize individual differences in magnitude of self-reported arousal and HRV reactivity and, when compared with the middle and right columns, the degree of dissociation between subjective and objective physiological reactions.

Figure 2.

Person-centered heat maps of expressive and physiological reactivity to negative picture cues. Each row denotes data from one individual across measures. Social drinking college students participated in a cue reactivity study during which their facial expressiveness (left heat map) and physiological reactivity (right heat map) were simultaneously measured. Facial expressiveness was rated by trained observers of videotaped face recordings taken during negative emotional cues exposures. Scores ranged from 1 (low expressiveness) to 5 (high expressiveness) based on facial movement (not specific emotional expressions). Physiological reactivity was derived from a computational model parameter (Fonoberova et al., 2014) that captured vagal nerve firing rate. The left heat map was sorted in descending order to allow visualization of individual differences and comparison of the heat maps that showed substantial discrepancy between physiological and facial responsivity to negative emotional cues in many individuals.

Figure 3.

Instantaneous physiological reactions to resonance breathing, the “active ingredient” of HRV biofeedback, in one representative individual. The top panel shows that during 5 minutes of resonance breathing, heart rate oscillations were magnified and more rhythmic, giving rise to substantially increased HRV. The middle panel shows similar enhancements in rhythmicity and variability in systolic arterial pressure. In addition, systolic pressure decreased dramatically. The bottom panel illustrates that during resonance breathing, heart rate becomes more sensitive to changes in blood pressure, such that 1-mmHg change in blood pressure elicits a greater response in heart rate. Baroreflex sensitivity was measured from heart rate (input) – systolic pressure (output) transfer functions in the low frequency range (0.05–0.15 Hz) where coherence was > 0.5.

Figure 4.

Individual differences in physiological responding to resonance breathing derived from a computation model. Physiological data were modeled in young adult social drinkers across two low-cognitive demand baseline tasks (B1, B2) and a six-breath-per-minute resonance breathing task (6P). On average, resonance breathing significantly increased splanchnic peripheral compliance (C_sp, top panel), arterial receptor gain on sympathetic control of heart period (G_aTs, middle panel), and minimum left ventricular elastance (E_lv,min, bottom panel) compared with baseline tasks (B1 and B2). When characterized at the level of the individual, however, many (modal, left), but not all (non-modal, right), individuals responded in a manner consistent with enhanced physiological functioning during resonance breathing. The non-modal responses varied, showing either less change across tasks or atypical patterns of change, suggesting that these individuals may receive less benefit from a behavioral intervention designed to activate the baroreflex mechanism. Adapted from Fonoberova, M. Mezić, I., Buckman, J. F., Fonoberov, V., Mezić, A., Vaschillo, E. G., … Bates, M. E. (2014). A computational physiology approach to personalized treatment models: The beneficial effects of slow breathing on the human cardiovascular system. American Journal of Physiology – Heart & Circulation, 307, H1073–H1091. Used with permission.