Figure 1.
(A) A simplified illustration of the different levels which may underlie psychiatric diagnoses. Traditional diagnoses are signified by blue and yellow branches, with a transdiagnostic approach denoted by the green branch. (i) Underlying causes (aetiology), e.g., genetic expression or predisposition, contribute to (ii) pathophysiological mechanisms, such as inflammation, aberrant synaptic gain, perceptual differences, or can result in psychopathology in response to the environment. For example, expression of a particular gene may lead to differences in synaptic gain, affecting output at the circuit level and ultimately influence behaviour through the interaction with other social and psychological factors (environment) to produce (iii) symptomatology. In this way, symptoms can be seen as the product of single or multiple maladaptive processes which can occur across multiple linked levels from biology through to the environment. Notably, many mechanisms can give rise to (v) the same symptom, as can the same mechanism give rise to many symptoms. Traditionally, symptoms are then categorized into a cluster known as a diagnosis (iv) , e.g., A orB . However, a transdiagnostic factor (vi) may recapitulate common symptoms or behaviours across disorders, and is related to a specific mechanism (‘Y’ denoted in green), which can be captured in cognitive experiments and/or neuroimaging (see Fig 1B).
(B) Computational psychiatry techniques implementing a precision approach. (i) Studies can include individuals with a particular diagnosis or across related diagnoses. (ii) Studies may involve factor analysis of questionnaires that aim to encompass a broad range of transdiagnostic behaviours and symptoms. (iii) The battery may involve cognitive tasks and neuroimaging from which model parameters can be estimated, thereby describing the behaviour/neurobiology of an individual. (iv) Model parameters can be used to find new subgroups within diagnoses, or alternatively (v) they can be correlated against factor scores to determine if the factors correspond to an underlying mechanism for disease across diagnoses. (vi) Using these data, subjects can be grouped either based on subgroups within existing disease categories (purple pathway) or across diagnoses (orange pathway). (vii) Longitudinal data from clinical trials can be used in conjunction with machine learning techniques to determine if the parameters produced from the models, or the factors associated with them, have any predictive value in terms of treatment response or prognosis. Looking for cognitive biomarkers within diagnoses is compatible with defining new dimensions of disease as cognitive biomarkers in existing disease subgroups may be common to multiple diagnoses associated with a single dimension. (viii) Data gained from these trials can be used in clinical practice in the form of a decision aid, where patients can undergo clinical investigation in the form of a questionnaire battery or undergo neuroimaging while performing a cognitive task to allow an informed decision about which treatment may be better. This may improve patient outcomes as it facilitates a quicker response to treatment and circumvents the trialling of multiple different therapies.