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\begin{document}

\title{Transdiagnostic approaches to precision medicine:\\
A Computational Psychiatry Primer}



\author[1]{Benjamin Illingworth}%
\author[1]{Timothy Sandhu}%
\author[1]{Eleanor Smith}%
\author[1]{Claudia Lage}%
\author[1]{Brónagh McCoy}%
\author[1]{Rebecca Lawson}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 11, 2024}


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\sloppy


Benjamin J. G. Illingworth MBBS BSc\textsuperscript{a,b}, Timothy R.
Sandhu\textsuperscript{b,c} BSc, Eleanor S. Smith PhD\textsuperscript{b}
, Claudia Lage MSc\textsuperscript{b}, Br\selectlanguage{ngerman}ónagh McCoy
PhD\textsuperscript{b}, Rebecca P. Lawson PhD\textsuperscript{b}

\textsuperscript{a} Cambridge University Hospitals NHS Foundation Trust,
Cambridge, United Kingdom

\textsuperscript{b} Department of Psychology, University of Cambridge,
Cambridge, United Kingdom

\textsuperscript{c} MRC Cognition and Brain Sciences Unit, Cambridge,
United Kingdom

\textbf{Word count for the body of the text:} 1484

\textbf{Declaration of Interest:} None

\par\null

\textbf{Funding:}

This work was supported by a `Royal Society Wellcome Trust Henry Dale
Fellowship' (RPL, grant number 10.35802/206691) and `The Portuguese
Foundation for Science and Technology (FCT)' (C.L., grant number
SFRH/BD/144811/2019).

\textbf{Ethical approval:}

Ethical approval not required.

\textbf{Correspondence to:}

Dr Benjamin J. G. Illingworth

Department of Psychology, University of Cambridge

Downing Street, Cambridge, CB2 3EB, United Kingdom

\href{mailto:bi232@cam.ac.uk}{\nolinkurl{bi232@cam.ac.uk}}

@DocIllingworth ~

\par\null

\pagebreak

\textbf{Abstract}

Psychiatry's foremost challenge is that its symptom-based classification
has insufficient predictive value for clinical outcomes. Precision
psychiatry attempts to improve treatment response by searching for
predictive biomarkers, which should be described in the context of the
neurobiology of illness to help facilitate the development of new
treatments.

This primer for clinicians aims to outline the problems that precision
psychiatry faces, how computational methods can overcome them and how
these methods can be used in clinical practice. Clinicians have an
important role in translating this research and incorporating
computationally-derived transdiagnostic biomarkers in clinical trials
opening the possibility for personalised therapies.

\textbf{Keywords:}

Diagnosis; biomarkers; precision medicine;

\par\null

\pagebreak

\textbf{Why current diagnostic categorisation may impair the progress of
precision psychiatry}

Precision medicine aims to increase treatment success by tailoring
therapies based on individual differences. As in other specialities,
psychiatry has attempted to implement approaches from precision medicine
by seeking biomarkers to inform treatment and therefore improve
prognosis. There is a need for a precision approach in psychiatry as
current treatments have been shown to have poor initial success rates
for patients within well-defined diagnostic categories {[}1{]}.

However, in contrast to other areas of medicine, psychiatric patients
may not fit any diagnostic category clearly, or may have multiple
diagnoses (co-morbidity) {[}1{]}. This implies that in psychiatry,
diagnostic categories may not be sensitive enough to individual
differences or representative of underlying pathologies. Using a
symptom-based nosology may impair the search for biomarkers, as
diagnostic groups are likely to be heterogeneous in terms of aetiology
and pathogenesis. Here, multiple neurobiological mechanisms may lead to
similar symptoms or a single mechanism may lead to different symptoms
depending on their interaction with other biopsychosocial factors
{[}2{]} (Fig 1A).

\textbf{How this can be overcome by redefining groups around mechanisms
of disease}

Motivated by the National Institute of Mental Health Research Domain
Criteria (RDoC) initiative, a recent approach to this problem considers
mental health transdiagnostically. The aim is to better capture the
complexities of individuals' mental health conditions and explain the
prevalence of deficits across multiple disorders {[}3{]}. Computational
methods have been utilised to better characterise variation in
psychopathology and to define new sub-groups of mental illness {[}3{]}.
These groups should be built around the presence of physiological or
cognitive biomarkers and have roots in the neuro-biological mechanisms
of mental illnesses (Fig 1A).

Factor analysis is one method of discovering new dimensions of
psychopathology. To this end, large online population studies have
illustrated novel and robust dimensions of psychiatric disease derived
from a consistent set of self-report questionnaires which, independent
of traditional scoring systems, are also associated with specific
deficits on cognitive tasks (Fig. 1B) {[}3{]}.

\textbf{Theory-driven approaches: advancing understanding of disease
processes}

Cognitive tasks and neuroimaging are combined with theoretical
approaches in computational psychiatry to learn more about differences
in behaviour, which in extremis are associated with symptoms of mental
illness. Computational models are created with the aim of explaining
observed behaviour (e.g. responses on tasks) in a mathematical
framework. These models are composed of free variables, i.e. parameters
which vary on a per individual basis and represent unobservable
neurobiological processes, such as learning or decision-making. When
these models are applied to imaging and to cognitive tasks, the
resulting parameters can illustrate individual differences in
performance at a mechanistic level, capturing the heterogeneity that is
a cardinal feature of complex conditions.

Reinforcement learning models represent a promising example of this
approach. Under this framework, the relationship between two modes of
learning about rewards can be assessed e.g., goal-directed vs. habitual.
Fitting these models to each individual's data produces parameters,
i.e., summaries of the learning process, which can be compared to reveal
if there is a significant difference between two predefined groups or if
novel groups exist within the population.

These models have a biological basis, and provide further mechanistic
insight into the processes underlying aberrant behaviour. For example,
mismatches between what is expected and what is actually observed by an
individual generates prediction errors, which are encoded by
dopaminergic neurons in subcortical regions {[}1{]}. Deficits in
goal-directed learning, and therefore a tendency towards habits, have
been found across disorders such as binge eating, addiction, social
anxiety and obsessive-compulsive disorder (OCD), and have also been
related to a specific transdiagnostic factor - compulsive behaviour and
intrusive thought - which underlies all of these disorders {[}3{]}.

\textbf{Data-driven approaches: Creating applications in clinical
settings}

Another approach in the search for predictive biomarkers, is the use of
data-driven approaches such as machine learning. These methods have been
used successfully on neuroimaging and clinical data to answer questions
about prognosis and treatment outcomes. In one study, using existing
clinical trial data, clinical variables were used to predict remission
in depression following treatment with selective serotonin re-uptake
inhibitors (SSRIs){[}4{]}. Supervised dimensionality reduction enabled
large numbers of clinical variables to be reduced to a select few
predictive variables, which were then fed into a machine learning
algorithm to predict remission in patients with depression. Applying
this algorithm had a number needed to treat (NNT) of 14 to achieve
remission in depression{[}1{]}. Ultimately, machine learning methods
could provide the basis for the creation of tools to support clinical
decision making.

\textbf{Better together: A combined theory-driven and data-driven
approach}

To realise the full potential of parameters derived from computational
models as biomarkers, theory-driven models will have to be combined with
data-driven (machine learning) approaches. Generative embedding is a
process which exemplifies this combined approach. Here, models of brain
connectivity or cognitive tasks are used to fit parameters, representing
physiological or cognitive processes, which can then be classified into
subgroups by machine learning techniques. These groups are validated
against clinical variables, and assessed for prognostic value. One
example uses dynamic causal modelling, which infers synaptic coupling
between large neuronal populations based on connection strengths between
brain regions, to predict participant disease trajectory in depression
{[}5{]}.

There are several benefits to a combined theoretical and data-driven
approach. Data-driven models are dependent on the quality of the input
data, i.e. clean signals with little noise, and how sensitive they are
to relevant biological, cognitive, and environmental processes that
shape behaviour. Theory-driven models provide a specific mechanistic
description for the input data, based on prior research and expertise.
Parameters of a theory-driven model can therefore provide a framework
that reduces the dimensionality, i.e. the total number of variables, of
the input data, leading to more stable and accurate output from
data-driven machine learning techniques.

\textbf{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.

\par\null

\pagebreak

\textbf{Figure 1:}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.}

\textbf{(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., \emph{A} or\emph{B} . However,
a transdiagnostic factor (vi) may recapitulate common symptoms or
behaviours across disorders, and is related to a specific mechanism
(\emph{`Y'} denoted in green), which can be captured in cognitive
experiments and/or neuroimaging (see Fig 1B).

\textbf{(B)} Computational psychiatry techniques implementing a
precision approach. \emph{(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.

\textbf{}

\textbf{}

\pagebreak

\textbf{Bibliography}

1.~ Huys Q, Maia T, Frank M. Computational psychiatry as a bridge from
neuroscience to clinical applications. Nature Neuroscience.
2016;19(3):404-413.

2. Stephan K, Binder E, Breakspear M, Dayan P, Johnstone E,
Meyer-Lindenberg A et al. Charting the landscape of priority problems in
psychiatry, part 2: pathogenesis and aetiology. The Lancet Psychiatry.
2016;3(1):84-90.

3. Gillan C, Seow T. Carving Out New Transdiagnostic Dimensions for
Research in Mental Health. Biological Psychiatry: Cognitive Neuroscience
and Neuroimaging. 2020;5(10):932-934.

4. Chekroud A, Zotti R, Shehzad Z, Gueorguieva R, Johnson M, Trivedi M
et al. Cross-trial prediction of treatment outcome in depression: a
machine learning approach. The Lancet Psychiatry. 2016;3(3):243-250

5. Fr\selectlanguage{ngerman}ässle S, Marquand A, Schmaal L, Dinga R, Veltman D, van der Wee N
et al. Predicting individual clinical trajectories of depression with
generative embedding. NeuroImage: Clinical. 2020;26:102213.{[}6{]}

6. Maia T, Conceição V. The Roles of Phasic and Tonic Dopamine in Tic
Learning and Expression. Biological Psychiatry. 2017;82(6):401-412.

7. O'Callaghan C, Hezemans F, Ye R, Rua C, Jones P, Murley A et al.
Locus coeruleus integrity and the effect of atomoxetine on response
inhibition in Parkinson's disease. Brain. 2021;144(8):2513-2526.

\par\null

\pagebreak

\textbf{Supplementary Material}

\textbf{Table 1}Key of useful terms\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
\textbf{Term}\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
\textbf{Definition}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Precision Medicine\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
A form of practice using specific therapies which are selected
(`personalised') for patients based on their individual characteristics
or the characteristics of a group to which they belong {[}1{]}. The aim
of personalised therapy is to maximise patient outcomes whilst reducing
adverse effects. An example of precision psychiatry, could be taken as
selecting a therapy for depression e.g. cognitive behavioural therapy
(CBT) versus an SSRI based on the likelihood of success for that
treatment given a patient's characteristics (clinical/biological).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Biomarkers\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
``A defined characteristic that is measured as an indicator of normal
biological processes, pathogenic processes or responses to an exposure
or intervention.'' {[}2{]} Biomarkers refer to substances which, found
in the body, indicate information about a disease. These substances are
usually component parts or bi-products of the disease process itself,
and have traditionally been biological substrates such as proteins e.g.
C-reactive protein as a biomarker for inflammation. As part of the
disease process, biomarkers have value as they indicate the presence or
prognosis of a disease. In psychiatric disease, due to a lack of
traditional biomarkers for disease prognostication, increasing attention
is being paid to computational parameters which can capture a
behavioural process related to a particular disease.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Transdiagnostic Psychiatry\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Transdiagnostic psychiatry aims to look across diagnoses to discover new
dimensions of disease based on biological and behavioural mechanisms
{[}3{]}.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Nosology\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Related to the classification of disease\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Factor Analysis\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Modelling observed variables as a weighted combination of a smaller
number of latent variables (e.g. modelling scores from 9 questionnaires
as 3 factors).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Reinforcement Learning\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
A framework for adaptive decision-making in the context of rewards and
punishment.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Computational Model\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
In neuroscientific terms, a computational model is a mathematical
description that can be used to characterize complex cognitive
processes, such as learning or decision-making. Parameters (see below)
of the model can be estimated and quantify a specific part of the
learning process, e.g. the weighting of new information compared to old.
These parameters are estimated based on an individual's behavioural
responses during a cognitive task.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Parameter\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Models are composed of parameters which represent a specific part of the
learning process. When models are fitted to data from a task, parameters
can be used to describe an individual's performance mechanistically. For
example, two people with major depressive disorder may show the same
negative emotional bias on a cognitive task, but that behaviour may be
caused by two different mechanisms - captured as differences in model
parameters.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
High dimensionality\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Data can be described as highly dimensional when there are more
measurable features or variables than there are independent samples. In
these scenarios, machine learning algorithms perform poorly. Reducing
the number of variables is important as it improves the performance of
the algorithm. {[}4{]}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Machine Learning\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Machine learning involves applying algorithms to data in order to make
predictions or classifications based on input data, which either does
(supervised) or does not (unsupervised) have known labels. A machine
learning algorithm will produce an estimate about pattern or structure
in the data.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Dynamic Causal Modelling (DCM)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
A method commonly used for the quantification of effective connectivity
(e.g. the influence that one neural population exerts over another), DCM
allows comparison between models of interconnected networks of neuronal
populations in order to explain data gained from dynamic imaging during
cognitive tasks. {[}5{]}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Overfitting\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Overfitting is a process in which models become extremely sensitive to
noise when they are fitted to a training data set. The model
inaccurately treats noise as signal of interest, so that it can better
predict outcomes for the data that it is trained on. Highly dimensional
data sets can lead to overfitting which in turn leads to poor
predictions in new data (poor generalisability). {[}6{]}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Number needed to treat (NNT)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
The number needed to treat describes the number of patients needing to
receive a particular intervention so that one additional patient has a
positive outcome. For example in a computational context, applying an
algorithm which can predict remission in depression, the number needed
to treat describes the number of patients the algorithm has to be
applied to for the algorithm predict remission in an additional
patient.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Supplementary Bibliography (Supporting References for the
Glossary)}

\textbf{}

\begin{enumerate}
\tightlist
\item
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  2020. p. 127--40.
\item
  FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and
  other Tools) Resource {[}Internet{]}.
\item
  Cuthbert BN, Insel TR. Toward the future of psychiatric diagnosis: The
  seven pillars of RDoC. BMC Med. 2013 May 14;11(1).
\item
  Narisetty NN. Bayesian model selection for high-dimensional data.
  Handb Stat. 2019;
\item
  Brodersen KH, Deserno L, Schlagenhauf F, Lin Z, Penny WD, Buhmann JM,
  et al. Dissecting psychiatric spectrum disorders by generative
  embedding. NeuroImage Clin. 2014;4:98--111.
\item
  Dwyer DB, Falkai P, Koutsouleris N. Machine Learning Approaches for
  Clinical Psychology and Psychiatry. 2018; Available from:
  https://doi.org/10.1146/annurev-clinpsy-032816-
\item
  Paulus MP, Huys QJM, Maia T V. A Roadmap for the Development of
  Applied Computational Psychiatry. Vol. 1, Biological Psychiatry:
  Cognitive Neuroscience and Neuroimaging. Elsevier Inc; 2016. p.
  386--92.
\item
  Wang XJ, Krystal JH. Computational psychiatry. Vol. 84, Neuron. Cell
  Press; 2014. p. 638--54.
\item
  Astle DE, Holmes J, Kievit R, Gathercole SE. Annual Research Review:
  The transdiagnostic revolution in neurodevelopmental disorders. J
  Child Psychol Psychiatry. 2021 Jul 23;
\end{enumerate}

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