Hans Zauner

**A new article [published today in
*GigaScience*](https://academic.oup.com/gigascience/article/10/10/giab071/6396189?searchresult=1)
demonstrates that machine learning can yield "proxy measures" for
brain-related health issues from large population data, without the need
for a specialist's assessment. To develop AI models sensitive to mental
health, [Denis Engemann](https://denis-engemann.de/) and his team at
[Inria (Saclay --
Île-de-France)](https://www.inria.fr/en/centre-inria-saclay-ile-de-france)
combined questionnaires and brain imaging data from the UK Biobank (read
the press release
[here](https://www.eurekalert.org/news-releases/931340)).**

Mental health issues have been increasing worldwide, with the WHO
determining that there has been a 13% increase in mental health
conditions and substance abuse disorders between 2007 and 2017. We
interviewed Dr. Engemann via email about his research and the emerging
role of machine learning for mental health assessment:

***You are using brain signals and questionnaire data to derive measures
of mental health, using machine learning. Could you explain in simple
terms what the goal of this research is?***

::: {.wp-block-image}
<figure class="alignleft size-large is-resized">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/EngemannBlog.jpg"
class="wp-image-4172" loading="lazy" decoding="async" width="323"
height="371" />
</figure>
:::

Mental health is a construct that is difficult to quantify and relates
to human reality at multiple levels of description: It is like a
kaleidoscope.

Research on its biological causes focuses on multiple levels such as
alterations in neurotransmitter concentrations, large-scale patterns of
neuronal activity or brain anatomy.

Yet, when it comes to inferring the mental health status, practitioners
rely on overt behavior and speech, potentially revealing individual
life-style patterns, but also the social environment. In this research,
we want to build richer measures of mental health by benefiting from
heterogeneous data describing individuals from categorically different
points of view.

***Without going into too much technical detail, how does the Machine
Learning method derive "proxy measures" of psychological concepts from
questionnaire data?***

In its essence, we see machine learning as a statistical tool for
estimating functions that robustly map outcomes of interest (like
results of cognitive tests or self-reported mood) to a broad range of
input data; even if the precise data generating mechanisms are unknown.

For instance, we can try to explain classic assessments of mood, based
on questionnaires, with brain imaging. Given the brain image of a
person, the resulting model will provide a prediction by returning the
most probable questionnaire result by extrapolating from people whose
brains "looked" similar. Thus, the predicted questionnaire result can
become a proxy for the construct measured by the questionnaire. This
reflects the statistical link between questionnaire data and brain
images and, therefore, can enrich the original questionnaire measure.

::: {.wp-block-image}
<figure class="alignleft size-large is-resized">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/proxymeasures.jpg"
class="wp-image-4169" loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/proxymeasures.jpg 826w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/proxymeasures-300x285.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/proxymeasures-768x729.jpg 768w"
sizes="(max-width: 298px) 100vw, 298px" width="298" height="283"
alt="proxy measures for mental health " />
<figcaption>Adapted from Fig.1 in <a
href="http://https//doi.org/10.1093/gigascience/giab071">Dadi et
al.</a></figcaption>
</figure>
:::

Once the model is built, this proxy can be obtained even if the
questionnaire of interest has never been assessed. This is a promising
method for finding large-scale statistical patterns of health within the
general population to then enhance smaller clinical studies in which
machine learning may not be directly applicable, due to limited training
data.

In the past decade, "brain age", based on this proxy measure
methodology, has led to promising studies in clinical neurology. In this
work, we generalized this methodology in two ways:

First, we showed that, beyond biological aging, the same proxy-measure
framework is applicable to constructs more directly related to mental
health. Second, we showed that useful proxy measures can be derived from
other inputs than brain images such as sociodemographic and behavioral
data.

***What's in the UK biobank data, and how did it help you for this
project?***

The United Kingdom Biobank contains the key ingredients to address our
research question. It provides an epidemiological dataset encompassing a
large volume of heterogeneous biomedical and sociodemographic data
sampled from the general population. This dataset reaches a size that is
appropriate for a machine learning approach.

::: {.wp-block-image}
<figure class="alignright size-large is-resized">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/fig2-1024x499.jpg"
class="wp-image-4186" loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/fig2-1024x499.jpg 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/fig2-300x146.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/fig2-768x374.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/fig2.jpg 1530w"
sizes="(max-width: 345px) 100vw, 345px" width="345" height="167" />
</figure>
:::

Moreover, it contains direct measures of classical psychological
constructs in academic and experimental psychology established
throughout the 20th century, including fluid intelligence (cognitive
capacity) and neuroticism (persistent negative mood and feelings). These
constructs are intimately related to mental health and have been
intensely studied in psychiatric populations.

***In your opinion, what is the most important implication of your
work?***

Our results open an entirely new set of opportunities for biomarker
development and clinical applications by providing evidence for the
generality of the proxy measure framework. Previously, we have been
actively studying brain age as an empirically derived measure of
biological aging. While we intuitively understand "brain age" as a
statistical phenomenon, we did not yet conceptualize it in more general
terms as a proxy measure.

In the present work, we have revised the framework by proposing a more
general terminology and extending the scope successfully through novel
empirical benchmarks. We not only found that the proxy could enrich the
original target, but multiple proxies of different constructs like
aging, intelligence or mood were complementary at characterizing
real-world health behavior like exercise, sleeping, or drinking habits.

***Assessing psychological constructs, such as intelligence or
neuroticism, today usually requires a human expert. Will machine
learning be able to "replace" the human expert in the future, at least
for certain questions?***

Thank you for this fascinating question. One can doubt that this type of
approach is going to replace psychologists.

Instead, the psychologist will have a richer toolkit including proxy
measures derived from large populations next to classical questionnaires
and interviews. If we think about this for a moment, already the
classical intelligence quotient, the IQ score, is obtained from
referencing the raw results of the intelligence test by comparison
against test results obtained in different age cohorts, leading to norm
tables.

Our approach generalizes this process and allows mental health
researchers and practitioners to consider additional social and
biomedical contexts for referencing individual results. Machine learning
helps here to consider a much larger and more complete volume of
observations.

***How could work such as yours change the practice of psychology and
related fields?***

In the future, psychologists and machine learning models may form a
dynamic duo for tackling mental assessments. This may lead to
increasingly fine-grained and personalized mental assessments. One can
imagine that next to completing tests, questionnaires, and interviews,
clients or patients grant a machine learning model secured access to
their social media accounts or their mobile phone data to then return
proxy measures useful to both the client and the mental health or
education expert.

However, what is not going to change is that practicing psychologists
and other mental health practitioners will need to carefully interpret
and contextualize test results on a case-by-case basis and through
social interaction; whether these results will be derived from
expert-based questionnaires or machine learning.

**Read the *GigaScience* article:**

Dadi K; Varoquaux G; Houenou J; Bzdok D; Thirion B; Engemann D (2021)  
Population modeling with machine learning can enhance measures of mental
health. *GigaScience* (October 2021).
[https//doi.org/10.1093/gigascience/giab071](https://academic.oup.com/gigascience/article/10/10/giab071/6396189?searchresult=1)

**Media coverage:**

*L'Express* (in French): *[Une intelligence artificielle pour évaluer la
santé mentale des
patients](https://www.lexpress.fr/actualite/sciences/une-intelligence-artificielle-pour-evaluer-la-sante-mentale-des-patients_2160541.html)*,
by Victor Garcia

*All GigaScience content (including articles, blogs, and peer reviews)
is open access and issued under a Commons Attribution License (CC-BY
4.0), which permits unrestricted reuse, distribution, and reproduction
in any medium, provided the original work is properly cited.*

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<strong>
 A new article published today in
 <em>
  GigaScience
 </em>
 demonstrates that machine learning can yield “proxy measures” for brain-related health issues from large population data, without the need for a specialist’s assessment.
</strong>

Messaggi di Rogue Scholar

AI for mental health assessment: Author Q&A