1 Introduction
Major depressive disorder (MDD) and anxiety disorders (AD), one of its
most common comorbid groups of disorders (Fava et al., 2000), are each
among the most common psychiatric disorders worldwide, (Malhi & Mann,
2018; van Tol et al., 2021) with devastating effects on virtually all
areas of a patient´s life as well as contributing significantly to the
overall burden of disease (James et al., 2018; Kessler & Greenberg,
2002; Silverstone & Studnitz, 2003). Comorbid MDD and AD are shown to
have significantly less favourable treatment outcome, more severe
symptom presentation and higher impact on quality of life (van Tol et
al., 2021).
Despite extensive research, both disorders are still lacking reliable
biomarkers for diagnosis, leading to uncertainty both in diagnosis and
treatment (Mulders et al., 2015) and high rates of patients failing to
achieve remission (Mulders et al., 2015; Trivedi et al., 2008). In
particular, linking differences in the engagement of individual brain
areas to psychiatric disorders has been shown to be insufficient in
explaining the full symptomatic complexity of these disorders (Menon,
2019; Menon, 2011). One of the most consistent findings has been a
hyperactivation of the amygdala as a common core mechanism of anxiety
disorders (Shin & Liberzon, 2010). However, this finding could not
account for the dynamics and individual heterogeneity of symptoms
associated with anxiety disorders (Menon, 2011; Sylvester et al., 2012).
Therefore, in recent years, the focus has shifted to alterations in
neural networks. One important tool for quantifying the activity of
neural circuits has been resting-state functional connectivity (RSFC),
which measures the correlation of spontaneous fluctuations of the blood
oxygen level-dependent (BOLD) signal between brain regions at rest
(Cullen et al., 2014; Greicius et al., 2007; Kelly et al., 2012; Ogawa
et al., 1990; van den Heuvel & Hulshoff Pol, 2010). RSFC has proven to
be an advantageous and reliable method to examine psychiatric disorders,
with disruptions of brain networks being consistently reported in both
MDD (Frodl et al., 2010; Javaheripour et al., 2021; Kaiser et al., 2015;
Schirmer et al., 2023; Sharpley & Bitsika, 2013) and AD (Northoff,
2020; Peterson et al., 2014; Sylvester et al., 2012; Xu et al., 2019).
Of particular interest in this area has been the so-called triple
network, encompassing the default mode network (DMN), the ventral
attention network (VAN) and the executive control network (ECN).
According to Menon´s (2011) model, the VAN, with key nodes in the insula
and dorsal anterior cingulate cortex (Sridharan et al., 2008),
associated with filtering and integrating information (Fan et al., 2017)
and assigning saliency to stimuli, operates as a switch between the
task-negative DMN and the task-positive ECN (Menon & Uddin, 2010). The
DMN, with key nodes located in the ventromedial prefrontal cortex
(VMPFC) and posterior cingulate cortex (PCC) (Buckner et al., 2008;
Manoliu et al., 2013; Raichle et al., 2001), is activated during times
of cognitive rest and associated with self-referential thoughts (Buckner
et al., 2008; Raichle, 2015) and internal attention (Li et al., 2021).
The ECN on the other hand includes areas of the dorsolateral prefrontal
cortex (dlPFC) and posterior parietal cortex (PPC) (Fan et al., 2017;
Jiang et al., 2017) and is associated with working memory,
decision-making, and cognitive control (Ernst et al., 2019; Lerman et
al., 2014).
A failure to adequately switch between the DMN and the ECN could lead to
“deficits in engagement and disengagement” of these networks (Menon,
2011). Aberrant functional connectivity between these networks
consistently found for both MDD (Kaiser et al., 2015; Mulders et al.,
2015; Zheng et al., 2015) and AD (Etkin & Wager, 2007; Massullo et al.,
2020; Sylvester et al., 2012; Williams, 2016) could therefore be related
to a set of various core symptoms in these disorders, such as
rumination, cognitive deficits or emotional dysregulation (Hamilton et
al., 2013; Menon, 2011).
While MDD and AD have both separately been extensively studied in
regards to alterations in RSFC, research examining comorbid MDD and AD
has been scarce, with only three studies covering the topic to the
authors knowledge at this time: Pannekoek et al. (2015) examined
patients with comorbid MDD and one or more anxiety disorder compared to
those with only MDD or anxiety disorders alone and healthy controls. For
the comorbid group, they found significantly increased RSFC between the
limbic network and a cluster containing the bilateral precuneus,
intracalcarine cortex, lingual gyrus, and posterior cingulate, and with
a cluster including the right precentral gyrus, inferior frontal gyrus,
and middle frontal gyrus. No difference was found for the other groups.
From the same cohort study, Nawjin et al. (2022) on the other hand,
found no significant differences when comparing patients with comorbid
MDD and anxiety disorders with patients with MDD and healthy controls.
Oathes et al. (2015) did not find any significant results within the
triple network.
While the posterior cingulate region found to be significant in
Pannekoek et al. (2015) is considered part of the DMN, no study has yet
examined the triple network specifically in the context of comorbid MDD
and AD. For this reason, we investigated the RSFC between the three core
networks belonging to the triple network, DMN, VAN and ECN, comparing
patients with MDD, patients with comorbid MDD and AD and healthy
controls, using a seed-based approach utilizing commonly used
seed-regions of all three networks. The RSFC was compared both within
and between all three networks studied and its relationship to symptom
severity and medication status was explored.