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REVIEW ARTICLE
Structural and functional brain alterations in subthreshold
depression: A multimodal coordinate-based meta-analysis
Jingyu Li
1,2
| Shunrong Kuang
1,2
| Yang Liu
1,3
| Yuedong Wu
2
| Haijiang Li
1,2,4
1
School of Psychology, Shanghai Normal
University, Shanghai, China
2
Lab for Educational Big Data and
Policymaking, Ministry of Education, Shanghai
Normal University, Shanghai, China
3
Department of Psychology, University of
Washington, Seattle, Washington, USA
4
The Research Base of Online Education for
Shanghai Middle and Primary Schools,
Shanghai, China
Correspondence
Haijiang Li, School of Psychology, Shanghai
Normal University, No. 100 Guilin Rd. Xuhui
district, Shanghai, 200234, China.
Email: haijiangli@shnu.edu.cn
Yuedong Wu, Lab for Educational Big Data
and Policymaking, Ministry of Education,
Shanghai Normal University, Shanghai, China.
Email: wydong01@shnu.edu.cn
Funding information
National Natural Science Foundation of China,
Grant/Award Number: 31700995; The
Humanity and Social Science Youth
Foundation of Ministry of Education of China,
Grant/Award Number: 17YJC190011
Abstract
Imaging studies of subthreshold depression (StD) have reported structural and func-
tional abnormalities in a variety of spatially diverse brain regions. However, there is
no consensus among different studies. In the present study, we applied a multimodal
meta-analytic approach, the Activation Likelihood Estimation (ALE), to test the
hypothesis that StD exhibits spatially convergent structural and functional brain
abnormalities compared to healthy controls. A total of 31 articles with 25 experiments
were included, collectively representing 1001 subjects with StD. We found consis-
tent differences between StD and healthy controls mainly in the left insula across
studies with various neuroimaging methods. Further exploratory analyses found
structural atrophy and decreased functional activities in the right pallidum and thala-
mus in StD, and abnormal spontaneous activity converged to the middle frontal
gyrus. Coordinate-based meta-analysis found spatially convergent structural and
functional impairments in StD. These findings provide novel insights for understand-
ing the neural underpinnings of subthreshold depression and enlighten the potential
targets for its early screening and therapeutic interventions in the future.
KEYWORDS
activation likelihood estimation, functional magnetic resonance imaging, meta-analysis,
multimodal, subthreshold depression, voxel-based morphometry
1|INTRODUCTION
Subthreshold depression (StD) (also referred to as subclinical depres-
sion, subsyndromal depression, or mild depression) is found to be a
threatening precursor and a risk factor for major depressive disorder
(MDD) (Zhang et al., 2023). StD presents two to four criterion depres-
sive symptoms for 2 weeks or longer (Rodríguez et al., 2012), but does
not meet the diagnostic criteria for MDD.
Depression has been a significant public health issue and a great
health service burden (Liu et al., 2020). The same is true for subthresh-
old depression due to the population's higher prevalence rate than
major depression (Kroenke, 2017;Topuzo
glu et al., 2015). Studies have
consistently demonstrated that individuals with subthreshold depres-
sion are more likely to develop major depression (Lee et al., 2019;
Tuithof et al., 2018). Adolescents with subthreshold depression experi-
ence significant impairment and have striking similarities to adolescents
with MDD, and have also been found to be at risk for developing other
disorders, including dysthymia, social phobia, anxiety disorders, and sui-
cidality (Klein et al., 2009;Pietrzaketal.,2013; Scott et al., 2021).
A rising amount of research has called for psychiatric disorders,
including depressive disorders, to be viewed as a spectrum rather than
categorically (Bakker, 2019; Krueger et al., 2018; Noyes et al., 2022).
Received: 13 December 2023 Revised: 9 April 2024 Accepted: 17 April 2024
DOI: 10.1002/hbm.26702
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any
medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
Hum Brain Mapp. 2024;45:e26702. wileyonlinelibrary.com/journal/hbm 1of14
https://doi.org/10.1002/hbm.26702
From the spectrum perspective, depressive disorders are acknowl-
edged to exist along a spectrum of increasing severity (McElroy
et al., 2021). Therefore, subthreshold depression might be a suitable
model for understanding the pathophysiological mechanisms of
depression, contributing to the development of tailored treatments
for patients at different stages of depression. Subthreshold depression
is of clinical importance. The latest reviews explore the effects of the
psychological treatment and nonpharmacological interventions for
subthreshold depression (Cuijpers et al., 2014; Cuijpers et al., 2021;
He et al., 2022), and one network meta-analysis compares the efficacy
of multiple therapies (Jiang et al., 2021). To provide new insights
into the clinical treatment selection of subthreshold depression and
facilitate the development and application of early prevention and
intervention, there is a great need for a clear understanding of its
neurobiology.
In pursuit of elucidating the neurobiological underpinnings of
StD, numerous neuroimaging studies have investigated localized
abnormalities of the brain structure and function, using voxel-based
morphometry (VBM), resting-state and task-based functional mag-
netic resonance imaging (rs-fMRI and t-fMRI), and positron emission
tomography (PET). Albeit these studies have deepened our under-
standing of the neural correlates of StD, they have often yielded
conflicting and heterogeneous results. For example, compared with
controls, StD subjects showed increased voxel-wise regional homo-
geneity (ReHo) in the bilateral middle frontal gyrus (MFG), superior
frontal gyrus (SFG), precentral gyrus, right precuneus and left hippo-
campus, and increased amplitude of low-frequency fluctuation
(ALFF) in the right precuneus and left MFG (B. Zhang et al., 2021;
2022). However, another study found that StD subjects displayed
higher ReHo only in the bilateral insula and right dorsolateral pre-
frontal cortex (DLPFC), while lower ReHo in the right orbitofrontal
cortex (OFC), left DLPFC, left postcentral gyrus (PCG), left MFG and
inferior temporal gyri (Ma et al., 2013). In addition, structural neuro-
imaging studies have variably reported structural abnormalities in
StD, including decreased grey matter volume (GMV) in right inferior
parietal lobule, bilateral medial prefrontal cortex, right precentral
gyrus, ventromedial prefrontal, rostral anterior cingulate cortices,
bilateral globus pallidus, precentral gyrus and caudates; as well as
increased GMV in the left thalamus, amygdala, right medial prefron-
tal cortex, posterior cingulate cortex, and precuneus (Hayakawa
et al., 2013; Li et al., 2015; J. Li et al., 2017; Taki et al., 2005).
In the context of these inconsistent findings, coordinate-based
meta-analysis (CBMA), a well-established family of methods that
holds a prominent position in neuroimaging research (Samartsidis
et al., 2017), offers a valuable approach to evaluating convergent
spatial findings of previously published neuroimaging studies (Müller
et al., 2018; Tahmasian et al., 2019). Coordinate-based meta-analysis
is applicable to multiple types of imaging data, including task activa-
tion (Wang et al., 2023), voxel-based morphometry (VBM) (Spindler
et al., 2022), diffusion tensor imaging (Z. Zhang et al., 2021), and
resting-state fMRI measures (ReHo or ALFF) (Yuan et al., 2022).
The most popular coordinate-based meta-analytic method is acti-
vation or anatomic likelihood estimation (ALE). It identifies spatial
convergence of reported findings based entirely on location and only
applies to data obtained from the whole brain and with coordinates or
statistic images in standard anatomical space (Eickhoff et al., 2012;Fox
et al., 2014). Although traditionally ALE has been employed in single-
modality meta-analysis, it can flexibly integrate research findings across
various imaging methods, thereby enabling a comprehensive assess-
ment of disease-related effects. For instance, a couple of neuropsychi-
atric diseases-related meta-analyses have utilized this multimodal
approach and resulted in robust findings: anorexia nervosa (Su
et al., 2021), borderline personality disorder (Schulze et al., 2016), major
depressive disorder (Gray et al., 2020), treatment-resistant depression
(Miola et al., 2023), bipolar disorder (Cattarinussi et al., 2019), and anxi-
ety disorder and chronic pain (Brandl et al., 2022).
Up to date, a CBMA focusing on the neural substrates of sub-
threshold depression, in particular, is still lacking. The present study
aims to obtain the overall brain imaging characteristics of individuals
diagnosed with subthreshold depression by integrating multimodal
data. Additionally, it aims to portray the distinctive outcomes yielded
by various brain imaging methods.
Here, we employed the ALE method on the reported brain
differences of people with StD and healthy individuals derived from
whole-brain structural and functional neuroimaging studies to assess
the spatial convergence of brain alterations in subthreshold depression
across published literature. We hypothesized that subthreshold depres-
sion would demonstrate brain alterations detectable across neuroimag-
ing paradigms, mainly manifested as localized convergence of gray/
white matter changes and increased and decreased brain function in
individuals with subthreshold depression relative to control subjects.
2|METHODS
2.1 |Literature search
According to the Preferred Reporting Items for Systematic Reviews
and Meta-Analyses (PRISMA) recommendations (Page et al., 2021),
we searched the following database: PubMed, Web of Science,
Embase for studies published until January 2023, using these key-
words: [“minor depression”OR “subthreshold depress*”OR “subclini-
cal depress*”OR “subsyndromal depress*”] AND [“fMRI”OR
“functional Magnetic Resonance Imaging”OR “PET”OR “Positron-
Emission Tomography”OR “voxel-based morphometry”OR “VBM”
OR “neuroimaging”].
2.2 |Literature screening
After removing duplicated records across databases, a total of
168 unique records were screened by two authors (J.Y.L., H.J.L.)
double-blindly. Literature screening includes titles and abstracts, fol-
lowed by full-text screening.
A study was included in the meta-analysis if it met the following
criteria: (1) subjects were grouped in subthreshold depression with
2of14 LI ET AL .
clear-cut inclusion and exclusion criteria (e.g., presence of least two
DSM depressive symptoms for at least 2 weeks, one symptom of
depressed mood, no MDD or minor depression); (2) subjects with sub-
threshold depression were compared to healthy controls (HC);
(3) whole-brain structural or functional differences were assessed
using rs-fMRI, t-fMRI, VBM, DTI, or PET, and analysis was not limited
to region of interest (ROI) or small volume corrected (SVC), as these
would violate ALE methods assumption and lead to inflated signifi-
cance (Tahmasian et al., 2019); (4) whole-brain coordinates in either
Talairach or Montreal Neurological Institute (MNI) space can be
acquired from the literature or Supplementary Materials, or provided
by authors upon request; (5) no animal study; (6) participants did not
receive treatment yet; and (7) the study was published in English and
peer-reviewed journals.
Notably, considering heterogeneous definitions of StD damage
the meaning of meta-analysis, thus we carefully screened the inclu-
sion criteria for StD in the included studies to ensure that they met
the operational definition by Volz et al. (2022).
2.3 |Data extraction
Two authors (J.Y.L., H.J.L.) coded study characteristics double-blindly.
The extracted data consisted of bibliographic information (e.g., first
author and year), demographic (e.g., sample size, age, sex ratio) and
clinical status, methodological details (e.g., imaging modality, task
name, analysis approach, method of smoothing), assessment of StD,
and the peak coordinates of between-group experiments reported in
each study.
Pooling the data from overlapping samples in ALE meta-analyses
leads to erroneous results by incorrectly amplifying the influence of
that sample (Turkeltaub et al., 2012). Therefore, we were very care-
ful to prevent convergence between analyses conducted on (par-
tially) overlapping samples within and between papers. We reviewed
the included studies for signs of overlap with other studies by exam-
ining their team members, recruitment interval, and sample demo-
graphics. In such cases, We merged their data to ensure that in all
analyses, each sample is only represented by one experiment in ALE
analyses.
2.4 |ALE meta-analysis
We followed the principles of activation likelihood estimation (ALE)
(Eickhoff et al., 2009; Eickhoff et al., 2012). The most recent version
of GingerALE (3.0.2) was used (www.brainmap.org/ale) to compare
coordinates compiled from multiple articles, calculate the degree of
overlap, and produce clusters most statistically likely to become active
across studies. Coordinates reported in Talairach space were trans-
formed into MNI space by Lancaster's transformation (Lancaster
et al., 2007), so all the experiments are in the same reference space.
Imported foci were modeled as three-dimensional Gaussian spatial
probability distributions using a full-width at half-maximum (FWHM)
kernel estimated based on the sample size of the corresponding
experiment. Next, a union map of all modeled activation maps for
each experiment was generated, and above-chance spatial conver-
gence was tested with various available thresholding options. We fol-
lowed the ALE best practices (Müller et al., 2018; Tahmasian
et al., 2019), and corrected the results using cluster-level inference of
p< .05 with a cluster-forming threshold of p< .001 and conducted
1000 permutation test to distinguish between true convergence of
foci and random clustering. Then, we performed separate ALE meta-
analyses for different neuroimaging methods. In the primary analysis,
the coordinates of all included studies were pooled to conduct an all-
effects meta-analysis to investigate all the brain alterations and iden-
tify a neurobiological signature of StD, where neuroimaging changes
colocalize.
2.5 |Exploratory analyses
In the exploratory meta-analyses, to perform complementary analyses
on more homogeneous subsections of the data, we split the experi-
ments by the direction of effect (HC > StD or StD > HC) and imaging
modalities (rs-fMRI, t-fMRI, or VBM/DTI).
Given that the ALE primarily focuses on evaluating the conver-
gence of significant findings in the neuroimaging literature, it lacks
sensitivity towards unpublished null results. To assess the robustness
of identified clusters against potential publication bias, we performed
the Fail-Safe N (FSN) method adapted for ALE analyses as described
by Acar et al. (2018). By adding the noise (randomly generated study
coordinates) to the meta-analysis, the amount of noise study is FSN
value before the results are changed.
3|RESULTS
3.1 |Study selection and characteristics
A flow chart illustrating the detailed study selection process is
depicted in Figure 1. Ultimately, 31 articles with 25 experiments
(after overlap removal) comprising results from 1001 subthreshold
depressed subjects were included in this meta-analysis. The num-
ber of experiments included in each modality was as follows: rs-
fMRI(13),s-MRI(7,5VBM,1DTI,1corticalthickness),andt-fMRI
(11). The more detailed information about each study is shown in
Table 1.
3.2 |Coordinate-based meta-analysis (CBMA)
results
3.2.1 | All-effects analysis
As illustrated in Figure 2, combining all multimodal coordinates and
obtaining 222 foci from 25 experiments, the all-effects meta-analysis
LI ET AL.3of14
results showed that insula is a single region demonstrating convergent
abnormality in StD compared to HC. The peak coordinates, the cluster
sizes, and associated ALE values are reported in Table 2.
3.2.2 | Exploratory analysis
Considering the direction of effects, we pooled over 17 experiments
reflecting increases (i.e., StD > HC), and 21 experiments representing
decreases (i.e., HC > StD) separately.
Compared to HC, significantly decreased activation (i.e., when
resting-state or task) or smaller grey matter volume was found for the
thalamus and pallidum in StD. Whereas StD > HC did not show signif-
icant regional convergence.
In the separate analysis for each neuroimaging modality, only the
resting-state fMRI resulted in a significant cluster in the middle frontal
gyrus. The detailed information is shown in Table 2. Regarding the
robustness of the results, the FSN of extra noise that must be added
to each meta-analysis so that the discovered clusters no longer con-
verge is listed in last column of Table 2.
FIGURE 1 Flow chart of study selection for a meta-analysis of neuroimaging studies in subthreshold depression. ROI: region of interest; rs-
fMRI: resting-state functional magnetic resonance imaging; s-MRI: structural magnetic resonance imaging; t-fMRI: task-based functional magnetic
resonance imaging.
4of14 LI ET AL .
TABLE 1 Characteristics of studies included in the meta-analysis.
Author, year
Participants
(female), nMean age ± SD, year Inclusion criteria Modality Task/methods
FWHM of
smoothing
kernel (mm)
StD HC StD HC
(B. Zhang et al., 2022) 26 (16) 33 (17) 19.69 ± 1.73 19.18 ± 0.87 BDI-II rs-fMRI ReHo 6
(Yang et al., 2022) 26 (16) 33 (17) 19.65 ± 1.77 19.24 ± 0.94 BDI-II > 13 (total score, 14–28) rs-fMRI FC 6
(Huang et al., 2021) 38 (21) 32 (21) 29.84 ± 6.83 28.13 ± 9.68 PHQ-9 ≥5 rs-fMRI ALFF 4
(B. Zhang et al., 2021) 26 (16) 33 (17) 19.69 ± 1.73 19.18 ± 0.87 BDI-II > 10 rs-fMRI ALFF; ReHo; FC 6
(Peng et al., 2020) 59 (30) 59 (31) 20.12 ± 1.39 19.95 ± 1.42 BDI: mild (score of 14–18) or moderate (score of 19–29)
depressive symptoms, with a mean score of 17.52
± 3.43
rs-fMRI FC 6
(Zhu et al., 2019) 34 (23) 40 (19) 19.91 ± 1.64 19.70 ± 0.85 BDI-II > 13 rs-fMRI Functional networks 6
(Hwang et al., 2016) 57 (42) 79 (54) 32.25 ± 15.62 29.52 ± 14.32 CES-D ≥16; HAMD7-17 rs-fMRI Default mode network functional
connectivity
5
(Li et al., 2016) 41 (41) 26 (26) 20.27 ± 0.89 20.35 ± 1.32 BDI-II ≥14 at the two-stage assessment rs-fMRI ALFF 8
(Gao et al., 2016) 37 (23) 34 (19) 19.81 ± 1.56 19.29 ± 1.00 BDI-II > 13 rs-fMRI Degree Centrality 6
(Hwang et al., 2015) 57 (42) 76 (53) 32.25 ± 15.62 29.86 ± 14.49 CES-D ≥16; HAMD7-17 rs-fMRI FC 6
(Zhu et al., 2014) 19 (12) 18 (10) 66.50 ± 5.70 66.40 ± 3.90 CES-D ≥8 rs-fMRI FC 4
(Li et al., 2014) 19 (12) 18 (10) 66.50 ± 5.70 66.40 ± 3.90 CES-D ≥8 rs-fMRI ALFF; FC 4
(Ma et al., 2013) 19 (12) 18 (10) 66.50 ± 5.70 66.40 ± 3.90 CES-D ≥8 rs-fMRI ReHo 4
(Bi et al., 2022) 33 (23) 30 (17) 20.73 ± 2.20 20.23 ± 1.70 BDI-II > 13 t-fMRI Effort-based decision-making Task 6
(S. Zhang et al., 2022) 42 (26) 32 (25) 22.19 ± 1.97 21.50 ± 3.13 CES-D > 16 and BDI-II > 14 at two assessments t-fMRI Passive Viewing Task 6
(Yun et al., 2022) 21 (9) 23 (10) 24.33 ± 3.04 24.65 ± 2.87 (1) depressive mood or (2) loss of interest or pleasure
over the last 2 weeks. DSM-5 2–4
t-fMRI Simon Task 6
(He et al., 2020) 22 (12) 25 (12) 19.50 ± 1.63 19.32 ± 1.38 BDI-II > 13 t-fMRI Social Judgement Task 6
(Yang et al., 2020) 18 20 20.56 ± 1.10 20.35 ± 1.31 BDI-II ≥14 twice t-fMRI TNT paradigm 8
(H. Li et al., 2017) 40 (40) 25 (25) 20.28 ± 0.85 20.32 ± 1.46 BDI-II ≥14 (two stages) t-fMRI Dot-Probe Task 6
(Dedovic et al., 2016) 22 (10) 26 (14) 21.90 ± 2.50 10 ≤BDI-II ≤18 t-fMRI Dot-Probe Task 6
(Mori et al., 2016) 15 (9) 15 (7) 18.50 ± 0.60 19.10 ± 0.70 BDI-II ≥10 t-fMRI Monetary Incentive Delay Task 8
(Stringaris
et al., 2015)
101
(66)
123 (90) 14.50 ± 0.40 14.40 ± 0.40 Three or more depressive symptoms, including at least
one core symptom and at least two other DSM-IV
depressive symptoms, without fulfilling criteria for
clinical depression in terms of duration, number of
symptoms, or impact on functioning in the past
4 weeks
t-fMRI Monetary Incentive Delay Task 5
(Dedovic et al., 2014) 23 (11) 26 (14) 21.90 ± 2.50 10 ≤BDI-II ≤18 t-fMRI Montreal Imaging Stress Task 6
(Modinos et al., 2013) 17 (10) 17 (10) 20.50 ± 2.40 20.70 ± 2.30 BDI-II 11–19 t-fMRI Passive Viewing Task 8
(Continues)
LI ET AL.5of14
TABLE 1 (Continued)
Author, year
Participants
(female), nMean age ± SD, year Inclusion criteria Modality Task/methods
FWHM of
smoothing
kernel (mm)
StD HC StD HC
(Vulser et al., 2018) 96 (62) 336 (217) 14.47 ± 0.38 14.41 ± 0.40 Three or more depressive symptoms, including at least
one core symptom and at least two other DSM-IV
depressive symptoms, without fulfilling criteria for
clinical depression in terms of duration, number of
symptoms, or impact on functioning in the past
4 weeks
s-MRI Fractional anisotropy 10
(J. Li et al., 2017) 57 (42) 76 (53) NA NA CES-D ≥16; HAMD 7—17 s-MRI GMV 10
(Vulser et al., 2015) 119
(78)
461 (303) 14.45 ± 0.36 14.40 ± 0.41 Three or more depressive symptoms, including at least
one core symptom and at least two other DSM-IV
depressive symptoms, without fulfilling criteria for
clinical depression in terms of duration, number of
symptoms, or impact on functioning in the past
4 weeks
s-MRI GMV, WMV 10
(Li et al., 2015) 42 (42) 30 (30) 20.26 ± 0.89 20.20 ± 1.30 BDI-II ≥14 (two stages) s-MRI GMV, WMV 10
(Kumar et al., 2014) 16 (9) 16 (9) 76.25 ± 7.54 75.06 ± 5.42 HAMD 8—14 s-MRI Cortical thinning NA
(Hayakawa
et al., 2013)
21 (12) 21 (12) 51.00 ± 7.50 51.50 ± 6.90 CES-D ≥16 s-MRI GMV 10
(Taki et al., 2005)13 (Male) 55 (Male) 72.92 ± 1.71 72.38 ± 1.30 GDS ≥15; MMSE ≥22 s-MRI
GMV 12
Abbreviations: ALFF, amplitude of low-frequency fluctuation; FC, functional connectivity; FWHM, full-width at half-maximum; GMV, grey matter volume; HC, healthy control; ReHo, regional homogeneity; rs-fMRI, resting-
state functional magnetic resonance imaging; SD, Standard Deviation; s-MRI, structural magnetic resonance imaging; StD, subthreshold depression; t-fMRI, task-based functional magnetic resonance imaging; WMV, white
matter volume.
6of14 LI ET AL .
FIGURE 2 Results of the meta-analyses. (a) Brain difference between StD and HC from all experiments with different modalities (red);
(b) Structural atrophy or decreased functional activities in StD compared to HC (blue); (c) Resting-state functional activity difference between StD
and HC (green). HC, healthy controls; rs-fMRI, resting-state functional magnetic resonance imaging; StD, subthreshold depression. This image was
created with Mango (v4.1., http://ric.uthscsa.edu/mango/).
TABLE 2 Results of ALE meta-analyses after cluster-level FWE correction for multiple comparisons.
Analysis Cluster Cluster size (mm
3
) Anatomical region BA Hemisphere
Peak MNI
coordinates ALE value Fail-safe N (%)
xyz
All-effects
1 808 Insula 13 Left 42 18 10 0.0160 7 (28)
1 Insula 45 Left 42 18 4 0.0156
1 Claustrum/insula 13 Left 34 20 2 0.0151
Subgroup
StD > HC
No sig.
HC > StD
1 720 Thalamus Right 20 10 0 0.0177 5 (23.8)
1 Pallidum Right 16 26 0.0106
Modality
rs-fMRI
1 504 Middle Frontal Gyrus 9 Left 44 16 34 0.0158 2 (15.4)
t-fMRI
No sig.
s-MRI
No sig.
Abbreviations: BA: Brodmann area; MNI: Montreal Neurological Institute; no sig.: no significant results after correction for multiple comparisons; StD:
subthreshold depression; HC: healthy control; rs-fMRI: resting-state functional magnetic resonance imaging; s-MRI: structural magnetic resonance imaging;
t-fMRI: task-based functional magnetic resonance imaging.
LI ET AL.7of14
4|DISCUSSION
The need to better describe the human brain alterations in subthresh-
old depression has long been acknowledged, whereby meta-analyses
serve as a crucial tool for consolidating evidence. To our knowledge,
the present work provides the first comprehensive assessment of
multimodal imaging data to investigate the convergence of findings in
subthreshold depression. Identification of convergent brain abnormal-
ities across structural and functional data sets supports our hypothesis
for the co-localization of disease effects in StD.
The present meta-analysis indicated clusters encompassing the
left insula, right thalamus, right pallidum, and middle frontal gyrus, cor-
roborating the prominent role of these functions and structures in
StD. Furthermore, our methods of pooling multimodal data, separating
single modalities and exploring different directions, deepened our
understanding of the neuropathology of StD.
4.1 |All-effects analysis
The coordinates of all included studies across imaging modalities (s-
MRI, rs-fMRI, and t-fMRI) were pooled to conduct an all-effects meta-
analysis. The results showed one large cluster located in the left
insula. Identification of consistent structural and functional abnormal-
ity within the insula in this meta-analysis is a potentially important
finding for subthreshold depression research.
A brain system known as the salience network, with key nodes in
the insular cortices, is central in detecting behaviorally relevant stimuli
and coordinating neural resources. Emerging evidence suggests that
atypical engagement of specific insula subdivisions within the salience
network is a feature of many neuropsychiatric disorders (Uddin, 2015).
The insula, engaged in the perception of emotions and monitoring the
continuous internal emotional state of the body (Harvey et al., 2007), is
regarded to be an essential neurological correlate of the core symptoms
of MDD (Stratmann et al., 2014).
The latest study, using the causal structural covariance network
method, finds that the GMV reduction in MDD originated from the
right insula with a greater duration of illness. It revealed that the right
insula was the prominent node projecting positive causal influences
(i.e., GMV decrease) to the frontal lobe, temporal lobe, postcentral
gyrus, putamen, and precuneus (Lu et al., 2023). As the precursor
period of MDD, subthreshold depression is similarly associated with
progressive brain structural alterations. Some studies also find the
brain structural changes in StD. Compared to healthy controls,
the young StD patients show a significant grey matter volume (GMV)
decrease in the left insula and right putamen (J. Li et al., 2017).
Depressive and anxious symptoms in late-life depression are also
linked to reduced insula volumes (Laird et al., 2019).
These abnormal functional alterations in StD were also found in
rs-fMRI studies. Compared to controls, StD displayed higher ReHo
in the bilateral insula and right DLPFC (Ma et al., 2013), as well as the
increased ALFF in the anterior portion of the dorsal ACC (adACC),
which also displayed decreased functional connectivity (FC) with the
anterior insula, thalamus, and putamen (Li et al., 2014). Hwang et al.
(2015) found a significant resting-state FC decrease within the cogni-
tive control network (CNN), especially between DLPFC and the insula
in StD subjects, and further found FC between the default mode net-
work (DMN) and left insula is enhanced in StD, which might reflect
self-compensation for the lowered reward function of the left insula
(Hwang et al., 2016). All these findings represented impaired cognitive
control and salience detection in StD, and the insula played a central
role (Cole & Schneider, 2007; Manoliu et al., 2014).
Insula is also a part of the affective network and has functional
interconnections to regions associated with the experience of emo-
tion (Wager et al., 2008). Reduced connectivity between the bilateral
amygdala and the left insula within the affective network is one of the
distinguishing features of MDD (Veer et al., 2010). Similarly, individ-
uals with StD exhibited decreased functional connectivity between
the left amygdala and the left insula (Peng et al., 2020), which means
reduced amygdala–insula functional connectivity at rest might play a
central role in maladaptation of emotion processing and autonomic
regulation in StD.
A resting-state fMRI study found that attentional bias modification
training significantly reduced ALFF of the right anterior insula (AI) and
right middle frontal gyrus, which showed greater ALFF than healthy
controls before training (Li et al., 2016). It may shed light on the thera-
peutic interventions for StD, though the effect of attentional bias modi-
fication training is inconsistent to a large extent (Li et al., 2023).
The insula has been given specific roles in various tasks that
span a wide range of cognitive and affective processes. During the
passive viewing task, StD showed a decreased activation in the left
insula and a significantly increased functional connectivity between
the superior frontal gyrus (SFG) and insula, pallidum, and caudate
(Modinos et al., 2013; S. Zhang et al., 2022). Consistently, individuals
with StD exhibit diminished insula responses and positive connectiv-
ity between the insula and ventral lateral prefrontal cortex (VLPFC)
in consuming social loss (He et al., 2019). However, faced with sig-
nificant financial loss, greater functional activation of the left insula
in StD is found (Yun et al., 2022). These suggest that neural alter-
ations in subthreshold depression are associated with the processing
of conflict control against loss, mainly characterized by dysfunction
within the social pain network, particularly the insula. Further studies
that examine the functional activation patterns at the insula are
warranted.
In common with impaired motivational effort decision-making
and self-relevant processing in depressive individuals (Hobbs
et al., 2021; Horne et al., 2021), StD displayed blunted activity in the
bilateral anterior insula and right putamen-left dorsolateral prefrontal
cortex functional connectivity when choosing to exert effort for
themselves, while greater activation in the bilateral anterior insula
when choosing to exert effort for others (Bi et al., 2022).
Note that, though numerous researchers have found significant
effects of bilateral insula in StD, the left and right insula are connected
to distinct networks and carry out diverse functions (Menon &
Uddin, 2010; Wang et al., 2020). A latest study found left–right insula
thickness difference and left insula thickness significantly predicted
8of14 LI ET AL .
MDD risk in middle-aged to older adults, but right insula thickness did
not (Jones et al., 2019).
Considering the important role of the left anterior insula in
social affect (Uddin et al., 2017) and emotion regulation, it might be
an important identifying indicator of major depression. There was a
significant and robust volume reduction of the left insular cortex
andgreymattervolumeinMDD(Schnellbächeretal.,2022;
Sprengelmeyeretal.,2011; Takahashi et al., 2010;Zhang
et al., 2016). Patients with MDD has also shown decreased func-
tional connectivity in the left insula (Guo et al., 2015; Veer
et al., 2010), which would disrupt the function of the fronto-limbic
circuit and result in social withdrawal. In consistent with these find-
ings, our results also emphasize the left insula as a region of specific
interest in StD. And the investigation of both structural and func-
tional alterations, as well as lateralization of the insula in StD,
warrants further exploration in future.
We also found some researches have linked the insula to inter-
ventions in schizophrenia and depression. One month of music
intervention could facilitate improvement of the insular FC in schizo-
phrenia (He et al., 2018). Individuals in the early course of schizophre-
nia showed changes in the functional connectivity between the
resting-state brain network and the insula as well as the dorsolateral
prefrontal cortex after receiving cognitive enhancement therapy (CET)
(Eack et al., 2016). Increased fMRI brain response to interoception in
anterior insula was found after mindfulness training in anxiety or
depressed patients (Datko et al., 2022). And even in general popula-
tion, short-term mindfulness meditation enhanced cerebral blood flow
(CBF) in left ACC and insula (Tang et al., 2015). In short, the improve-
ment in mindfulness were accompanied by functional alterations in
the insula (Mooneyham et al., 2017; Mrazek et al., 2016).
Especially, after modified electroconvulsive therapy, there were
structural changes in the insula, manifested as enhanced GMV in bilat-
eral posterior insula in schizophrenia (Jiang et al., 2019). Therefore,
the future study could scrutinize potential alterations within the insula
concerning the efficacy of these interventions in StD.
4.2 |Convergence of different direction
Coordinates were assigned to two categories based on the direction-
ality of findings to avoid conclusions opposed to the original studies
enhancing each other in the ALE analysis (Sundermann et al., 2014).
Direction-specific CBMAs only found a large cluster in the HC > StD
comparison, while there was no significant convergence in StD > HC.
StD showed decreased GMV in the orbitofrontal cortex, left tem-
poral gyrus, bilateral globus pallidus, and precentral gyrus (J. Li
et al., 2017; Zhang et al., 2020). Previous studies suggested that the
volumetric reduction in the globus pallidus in depressed individuals
(Griffiths et al., 2015) is associated with reduced awareness of the
causal efficacy of goal-directed actions. The pallidum is also an essen-
tial part of the affective network (including the amygdala, temporal
poles), which plays a vital role in identifying MMD, StD, and HC
(B. Zhang et al., 2022). The severity of depressive symptoms was
associated with reduced gray matter volume in the orbitofrontal cor-
tex, anterior cingulate, thalamus, superior temporal gyrus/temporal
pole, superior frontal gyrus and the bilateral globus pallidus (J. Li
et al., 2017; Webb et al., 2014).
Cortico-striatal-pallidal-thalamic (CSPT) circuits are highly orga-
nized and integrated to support diverse motor, cognitive, and emo-
tional processes (Haber & Calzavara, 2009). A meta-analysis found
abnormalities in CSPT have been implicated in MDD, and late-life
depression (LLD) tended to be associated with smaller volumes in cir-
cumscribed frontal and subcortical structures, especially in thalamus
(Bora et al., 2012). Abnormalities in CSPT might be the neural mecha-
nism of subthreshold depression that needs to be explored in more
studies in the future.
4.3 |Convergence of imaging modalities
In the separate meta-analyses for each modality approach, only the rs-
fMRI studies converged on a single cluster in the middle frontal gyrus,
showing a functional alteration between StD and HC that contributed
by three experiments. Compared to HC, increased ReHo and ALFF in
the middle frontal gyrus were found in StD (B. Zhang et al., 2021;
2022), and the middle frontal gyrus also showed an elevated degree
centrality of the brain network (Gao et al., 2016). However, one
included study reported the opposite result: elderly StD subjects dis-
play lower ReHo in the left middle frontal (Ma et al., 2013), probably
due to the heterogeneity of subjects and inclusion criteria.
In addition, one study using dynamic casual modeling found that
medicated depressed patients had significantly reduced effective
connectivity from the anterior insula to the MFG (Kandilarova
et al., 2018). Our results also suggest a possible intrinsic link between
the MFG and the insula.
Notably, though studies based on t-fMRI failed to get a signifi-
cantly convergent result, reduced activations in the MFG when StD
performed a passive viewing task (S. Zhang et al., 2022), stronger
activity in the MFG during suppressing neutral and negative stimuli
(Yang et al., 2020), and increased activation during gain anticipation
(Mori et al., 2016) we documented. It implies that MFG is a core area
that influences social, emotional, and cognitive functioning, and its
abnormality is closely related to subthreshold depression.
It also suggests the potential for improved convergence of
resting-state data compared to task-activated and structure data. The
lack of convergence might be partially caused by confounders intro-
duced through inconsistency in various tasks' procedures.
However, the number of single modality experiments is relatively
small, so the conclusions would be cautiously drawn.
Of note, our study was limited by the recruiting and reporting
methods employed at the individual study level. Although there are
subtle differences in the definition of subthreshold depression across
studies, mainly in the operational definition of subthreshold depres-
sion and the inclusion criteria for subjects, there is still an essential
consistency among different studies. Twenty-eight studies (87.5% of
the total) use the standard self-reported scales, such as the Centre for
LI ET AL.9of14
Epidemiological Studies Depression Scale-Revised (CESD), Beck
Depression Inventory (BDI-II), or Brief Patient Health Questionnaire
Mood Scale (PHQ-9) to identify depressive symptoms and the exclu-
sion of DSM-IV MDD. Four studies use clinical structural interviews.
Consistent with Volz's proposal (Volz et al., 2022), to achieve
consensus on the definition of subthreshold depression, we recom-
mend that:
•Presence of at least two DSM depressive symptoms for at least
2 weeks, one symptom of depressed mood, no major depressive
disorder or minor depression.
•Brief Patient Health Questionnaire Mood Scale (PHQ-9) between
five and nine, CES-D at least 16 and/or BDI-II at least 14.
•Montgomery-Ăsberg Depression Rating Scale (MADRS) between
10 and 18 (for 2 weeks).
Adopting more standardized and consistent definitions of StD
can lead to more homogeneous study populations, further improve
the interpretation of findings at the individual study level, and pro-
mote more meaningful investigations at the meta-analytic level.
We believe that hypotheses confirmed by this meta-analysis
should be regarded as providing direction for further primary data
studies rather than as established conclusions.
5|LIMITATIONS
This study has several limitations that should be considered when inter-
preting the results. First, ALE analyses with few experiments are unsta-
ble and can be largely influenced by a single experiment (Eickhoff
et al., 2016). Therefore, the exploratory analysis results with various
modalities separately may be underpowered. Notably, with more neu-
roimaging studies on StD published, an updated meta-analysis would
be able to undertake additional subgroup analyses and identify modal-
specific convergence across more homogenous data. Second, it is sig-
nificant to highlight the controversy about multimodal CBMAs due to
the differences among the various imaging modalities. However, from
the technical perspective, the results from any imaging modality that
provides stereotaxic coordinates in a standard reference space for the
peak locations of those clusters that became significant in a voxel-wise
whole-brain analysis are readily includable in a CBMA (Tahmasian
et al., 2018). Inherent to all meta-analytic approaches, it requires a
trade-off between the power or generalizability of the findings versus
the homogeneity of the included experiments. Third, the ALE approach
is not sensitive to non-significant results and thus susceptible to publi-
cation bias. To address this issue, we performed the Fail-Safe N method
for each identified clusters and most clusters show stability and robust-
ness against additional noise studies. Fourth, our study focused on spe-
cific imaging modalities and abnormal brain changes among individuals
with subthreshold depression, and these conclusions may not general-
ize to other samples like MDD. A meta-analysis concentrating on
the abnormal brain connections reported the thalamus as the causal
hub of intervention in patients with major depressive disorder
(Yang et al., 2023). Finally, as previously discussed, demographic
characteristics such as age (adolescents, adults or the elderly),
inclusion criteria or measures of StD, and depression severity may
act as potential confounding factors. Accordingly, testing of sub-
groupsbasedonmorestandardizeddefinitionwouldhaveprovided
more clinically meaningful findings and the clinical heterogeneity of
StD warrants further investigation in neuroimaging studies.
6|CONCLUSIONS
By summarizing studies across modalities to date, this meta-analysis
demonstrated that subthreshold depression exhibited a concordance of
structural and functional brain alterations in the insula, which is consis-
tent with dysfunction in major depression, likely reflecting subthreshold
depression existing on a spectrum with major depressive disorder.
Moreover, resting-state functional abnormality in the middle frontal
gyrus may also play a significant role in subthreshold depression. These
results may contribute to the understanding of the neuropathologic
mechanism in subthreshold depression and provide additional potential
targets for therapeutic intervention. Finally, a well-recognized, unique
definition of subthreshold depression is still lacking. A more precise and
valid definition is needed for future studies, which recruit more homog-
enous populations and aim better to characterize the behavioral and
neural features of subthreshold depression.
AUTHOR CONTRIBUTIONS
J.Y.L. and H.J.L. formed the idea and designed the study. J.Y.L. and
H.J.L. contributed to the literature search and data extraction based
on suggestions from Y.L. J.Y.L. performed the analyses. J.Y.L. drafted
the manuscript, S.R.K., Y.L., Y.D.W., and H.J.L. revised and approved
the article.
ACKNOWLEDGMENTS
This study was supported by the National Natural Science Foundation
of China (31700995); The Humanity and Social Science Youth Foun-
dation of Ministry of Education of China (17YJC190011).
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the
corresponding author upon reasonable request.
ORCID
Haijiang Li https://orcid.org/0000-0003-2551-3278
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How to cite this article: Li, J., Kuang, S., Liu, Y., Wu, Y., & Li, H.
(2024). Structural and functional brain alterations in
subthreshold depression: A multimodal coordinate-based
meta-analysis. Human Brain Mapping,45(7), e26702. https://
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