Challenges facing this approach
Despite the aforementioned strengths, there are several challenges
facing this approach. As time progresses it may be difficult to
integrate knowledge across studies without a community-agreed
standardisation. For example, methods such as factor analysis (used to
derive transdiagnostic symptom dimensions), are dependent on the
self-report questionnaires used [3]. Furthermore, computational
methods also require significant expertise, and non-experts may find the
tools difficult to navigate.
Using transdiagnostic measurements in clinical trials focusing on
multiple related diagnoses is an approach that may prove the utility of
computational biomarkers. As treatments may differ between diagnoses,
transdiagnostic trials could help clinicians to identify therapies which
work across related diagnoses, and identify comorbid patients who could
benefit from these therapies (Fig 1B). However, despite studies
demonstrating that behaviours, on which diagnosis rests, can be
characterised by computational parameters with specific neurobiological
correlates [6, 7], patients with similar computational parameters
may not all respond to a particular pharmaceutical agent. This group of
individuals may also be heterogeneous and large experimental medicine
studies will be required to establish the mapping between neurobiology,
pharmacology and computation in order for the computational approach to
realise its potential to improve person-specific treatments.
Implementing transdiagnostic tools in clinical practice may also prove
challenging. However, these tools have the potential to complement
current nosological approaches. Although symptom-based diagnoses overlap
and may only provide a rough approximation of underlying pathology,
symptoms are still the primary concern of the patient [2]. The
complexity of individual psychiatric conditions will mean that clinical
assessment will remain vital to providing holistic treatment and will
also help to determine which particular investigative computational
batteries e.g. questionnaires, tasks, imaging modality and
specifications, would be most appropriate, in much the same way that a
physician would run particular diagnostic biomarkers when there is
suspicion of cancer. Because transdiagnostic factors are based on
symptoms, they would augment diagnoses due to their demonstrable links
to underlying psychopathology. As transdiagnostic factors and biomarkers
develop, become standardised, and their predictive value is realised,
greater weight can be placed on these investigative methods as
diagnostic tools in psychiatry.
Finally, to realise the clinical potential of these tools, it will be
imperative to make them both accessible and easy to operate in a
low-resource environment. Factor analysis of self-report questionnaires
may be one way to face this last challenge. Factor scores are robust and
reliable if a consistent set of questions are used. If they are shown to
be robustly related to specific computational descriptions of behaviour
in cognitive tasks, the use of factors derived from self-report
questionnaires could act as a proxy for computational biomarkers (Fig.
1B). This would preclude the need for time-consuming cognitive tasks and
expensive imaging, making precision psychiatry more accessible. This is
a particularly important consideration given the increased risk of
mental illness in low socio-economic groups with limited access to
well-funded mental health centres.
In summary, transdiagnostic computational phenotyping of mental illness
may bridge the gap between biology and clinical practice, providing
investigative and predictive tools which could precipitate a shift away
from relatively crude group-average explanations that have had limited
clinical utility in alleviating mental health symptomatology at the
level of the individual.