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RESEARCH HIGHLIGHT OPEN
Moving towards precision psychiatry: the hard nut of
depression
Juergen Dukart
1,2
✉, Leon D. Lotter
1,2,3
and Simon B. Eickhoff
1,2
Signal Transduction and Targeted Therapy (2024) 9:310 ; https://doi.org/10.1038/s41392-024-02023-8
In a recent study published in Nature, Lynch et al.
1
reported a
consistent spatial expansion of the resting state salience network
in subjects with major depression. The expansion was already
present in children who later developed depression and remained
stable over time in patients with depression, indicating a trait-like
behavior of the observed endophenotype.
Research on major depression is symbolic of the challenges
faced in the study of most, if not all, psychiatric disorders. Whilst
various therapeutic options exist, about a third of patients with
major depression do not respond to existing treatments, and no
validated biomarkers exist for early diagnosis, prognosis, or
monitoring of the disease. Naturally, for a brain disease,
neuroimaging is often the method of choice in studies aiming
to identify biomarkers for depression with, to date, very modest
results. Brain alterations in depression were either reported to be
of small effect size and therefore not clinically meaningful, or they
consistently failed to replicate in later independent cohorts.
Similarly, neuroimaging-based machine learning models have
continuously yielded close to chance-level accuracies for differ-
entiating between major depression patients and healthy controls
in large independent cohorts.
2
Instead of looking at depression-related brain alterations using
classical region- or voxel-wise statistics, Lynch et al. adopted a
network-centered perspective comparing the topology and spatial
extent of well-established resting state connectivity patterns
between patients and healthy controls. Using this approach, the
authors report the intriguing finding of a spatially expanded
salience network with very large effect sizes observed in the
deeply-phenotyped high-density longitudinal discovery cohort.
The authors then replicate this finding in two independent
cohorts with still large, although substantially reduced, effect sizes.
The salience network is well established in the literature and likely
operates under the substantial influence of noradrenergic and
mesolimbic dopaminergic inputs. Functionally, it is implicated in
conscious integration of autonomic feedback with internal goals
as well as social-emotional regulation.
3
These functions align well
with the symptomatology observed in depression, adding face-
validity to the findings by Lynch et al.
The adopted topological approach also illustrates the necessity
of moving away from classical voxel- or region-wise inferential
statistics towards more complex methods such as taking into
account the spatial topology of respective neuroimaging mea-
sures. Despite the promising findings by Lynch et al., the
biological factors that shape network topology, as well as the
pathophysiological mechanisms underlying the observed
expansion of the salience network in major depression, remain
elusive. Understanding both is essential for development of
accurate diagnostic tools and improved interventions. To address
the limited insight into the underlying pathophysiological
mechanisms, the applied topology methods can now be
combined with publicly available gene expression and neuro-
transmitter atlases as well as with other neurophysiological
modalities.
4
Integrating this multimodal information would greatly
facilitate the interpretation of the pathophysiological mechanisms
underlying the respective findings and allow for more guided
hypothesis generation regarding potential interventions (Fig. 1).
Considering the trait-like behavior of the identified network
expansion, normative modeling may be a further promising
avenue to gain a better understanding of the variability of this
endophenotype in the general population. In this regard, follow-
up research will also have to explore the specificity of the
observed salience network expansion to major depression. Both
are important prerequisites for a biomarker to be considered for
clinical applications. For the first, healthy control populations
recruited into clinical studies are often highly selective. Such
preselection may have narrowed the actual variability in network
expansion in the control cohort and thereby inflated the observed
effect sizes. For the second, many of the endophenotypes
reported in the literature for one psychiatric disorder are often
later rediscovered in other psychiatric disorders. Understanding
the specificity of the observed expansion effects is therefore
important for assessing the actual diagnostic or predictive value of
these endophenotypes. Similarly, a crucial part of future research
with respect to the observed network expansion needs to be
directed towards understanding the high variability in clinical
symptomatology and treatment response observed in major
depression. Understanding if and how the observed expansion
aligns with potential depression subtypes and relates to treatment
effects at the individual level will be essential for moving the
findings into clinical applications.
On a more cautious note, the authors trained a machine
learning-based classification framework using the extracted net-
work expansion features. They report rather high accuracies of
about 78% for differentiation between depression cases and
healthy controls, with salience network expansion unsurprisingly
providing the largest contribution. Despite these promising
findings, it is important to note that the search for machine
learning biomarkers in psychiatry has been continuously ham-
pered by failures to replicate and generalize. For example, several
promising machine learning models for schizophrenia diagnosis
Received: 11 October 2024 Revised: 16 October 2024 Accepted: 18 October 2024
1
Institute of Neurosciences and Medicine, Brain & Behaviour (INM-7), Research Centre Juelich, Juelich, Germany;
2
Institute of Systems Neuroscience, Medical Faculty, Heinrich
Heine University, Düsseldorf, Germany and
3
Max Planck School of Cognition, Leipzig, Germany
Correspondence: Juergen Dukart (j.dukart@fz-juelich.de)
www.nature.com/sigtrans
Signal Transduction and Targeted Therapy
©The Author(s) 2024
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initially performed well in large multi-centric datasets, but all failed
to replicate in truly independent cohorts.
5
Small sample sizes,
study-specific confounding effects such as differences in demo-
graphics or acquisition parameters, and processing and analyses
choices contributed to limited generalization of previous machine
learning models. Several of these limitations including the limited
sample size and specific processing and analysis choices certainly
also apply to the study by Lynch et al. In this respect, whilst
optimism is warranted, a prospective and independent replication
of the findings is imperative for further pursuit into clinical
translation.
The innovative approach introduced by Lynch et al. certainly
provides a promising path to study psychiatric disorders and, more
specifically, to translate neuroimaging measures into clinically
relevant biomarkers for major depression. In this regard, the
demonstrated applicability to individual patients indicates its high
potential for moving the field towards true precision psychiatry.
ACKNOWLEDGEMENTS
J.D. and S.B.E. were supported by the Ministry for Culture and Research NRW
(Kooperationsplattformen 2022). L.D.L. was supported by the Federal Ministry of
Education and Research (BMBF) and the Max Planck Society (MPG).
AUTHOR CONTRIBUTIONS
J.D. designed and wrote the draft manuscript. L.D.L. and S.B.E. revised the manuscript.
L.D.L. prepared the figure. All authors have read and approved the article.
FUNDING
Open Access funding enabled and organized by Projekt DEAL.
ADDITIONAL INFORMATION
Competing interests: The authors declare no competing interests.
REFERENCES
1. Lynch, C. J. et al. Frontostriatal salience network expansion in individuals in
depression. Nature 633, 624–633 (2024).
2. Winter, N. R. et al. A systematic evaluation of machine learning–based biomarkers
for major depressive disorder. JAMA Psychiatry 81, 386–395 (2024).
3. Seeley, W. W. The salience network: a neural system for perceiving and responding
to homeostatic demands. J. Neurosci. 39, 9878–9882 (2019).
4. Lotter, L. D. et al. Regional patterns of human cortex development correlate with
underlying neurobiology. Nat. Commun. 15, 7987 (2024).
5. Chekroud, A. M. et al. Illusory generalizability of clinical prediction models. Science
383, 164–167 (2024).
Fig. 1 Conceptual overview of the next steps for translation of the findings by Lynch et al.
1
into clinical applications for major depression. To
enable clinical translation, the observed endophenotypes need to be integrated with other neurophysiological modalities to gain insights into
the pathomechanisms underlying the network expansion observed in major depression. Normative modeling and subtyping studies are
further required to gain insights into the variability and reliability of expansion endophenotypes in the general as well as in the major
depression population. Both needs to be combined with prospective clinical studies to validate the findings as potential biomarkers for
diagnosis and prognosis of major depression
Moving towards precision psychiatry: the hard nut of depression
Dukart et al.
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Signal Transduction and Targeted Therapy (2024) 9:310
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© The Author(s) 2024
Moving towards precision psychiatry: the hard nut of depression
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Signal Transduction and Targeted Therapy (2024) 9:310