Naturalizing psychopathology—towards a quantitative real-world psychiatry

Abstract

Psychiatric disorders continue to be on the rise around the globe. Meanwhile, efforts and investments directed to early diagnosis and appropriate interventions for mental health problems are lagging resulting in ‘substantial loss of human capabilities and avoidable suffering’ [1]. A major component of our inability to address mental health problems resides in the persistent lack of objective measures for evaluating such difficulties in the daily life of individuals, complicating the detection of clinically relevant changes in the patients’ well-being [2]. Similarly, most studies into the neurobiology of psychiatric disorders lack a detailed description of individual functioning despite influential calls for quantitative approaches to psychopathology. Psychiatric conditions are often particularly reflected through, and exacerbated by, difficulties in social functioning in everyday life [3]. Yet, current diagnostic evaluations primarily take the form of limited interactions in artificial clinical settings. While these types of diagnostic procedures have clear clinical value, they are often subjective and qualitative in nature. Reports provided by the patient may suffer from memory biases and can also depend on the rapport and trust between the patient and the clinician. Concurrently, standardized questionnaires probe a limited set of functional impairments and are administered only infrequently during clinical consultations. With the slow onset and the nonspecific and transient nature of functional impairments in psychiatric conditions, these limitations may prevent a more comprehensive description and early detection of psychopathology [1]. We argue that a thorough understanding of the real-world manifestations and implications of psychiatric disorders is the key for the field to move towards the development of more effective personalized assessments and interventions. To address this, we outline a general multilevel framework for deep behavioural phenotyping to guide the scientific inquiry into the behavioural and neural mechanisms of psychopathology and to delineate specific therapeutic interventions (Fig. 1). This approach emphasises an objective and continuous evaluation of psychopathology in everyday life and naturalistic social interactions (green box, top left) to guide clinical practice and scientific research. The most debilitating symptoms of psychiatric disorders may only manifest in everyday life, where patients must cope with multiple aspects of life simultaneously (illustrated by the red scale at the bottom of Fig. 1). Current technologies can enable detailed and objective monitoring of the impact of mental health problems on everyday life of the patient based on their natural behavioural patterns. One way to achieve this is by measuring daily behaviour using personal smart devices, often referred to as “digital phenotyping” [4]. Using these devices, everyday life behaviour can be assessed passively, for example by objectively measuring the amount of physical activity or phone-based social interaction. Moreover, temporal patterns of user interface inputs may provide valuable insights into psychomotor symptoms such as agitation or retardation. Importantly, these passive measures of daily behaviour can be complemented by subjective ecological momentary assessments providing real-time self-reported measures of patients’ well-being. Such subjective momentary evaluations may prove crucial to understanding changes in passively monitored behavioural patterns. For example, while single passive measures may not be sufficiently discriminative, negative mood evaluations in combination with reductions in locomotive activity, social application usage and speed of typing may represent early signs of a depressive episode. Detecting such patterns offers a chance for early intervention to avoid hospitalization (arrow 1. In Fig. 1). While such digital phenotyping can address macroscopic aspects of social and motor functioning, it lacks specificity for assessing how these problems relate to difficulties the patients face during real-life social interactions. To address this, recent studies have started to objectively measure behaviour during social interactions using motion tracking techniques to detect e.g. individual differences related to interaction success [5] and behavioural predictors for psychiatric disorders and therapeutic outcomes [6]. Such tracking techniques can be used to evaluate psychomotor symptoms in more detail based on face and wholebody movements. Moreover, some of the symptoms or behavioural tendencies of a person may manifest more strongly during a dyadic real-time interaction rather than during interaction with a digital user interface. In a clinical setting, such fine-grained characterization of behaviour during dyadic interactions may assist a clinician to reveal subtle early signs for interpersonal difficulties before symptoms develop into a fully-fledged psychiatric disorder. Such behavioural measures can also be extremely beneficial for elucidating the neurobiological underpinnings of psychiatric disorders and the behavioural and neural mechanisms of psychotherapeutic interventions (arrow 2. In Fig. 1). Moreover,