Reduced Cerebral Blood Flow Within the Default-Mode Network and Within Total Gray Matter in Major Depression

Abstract

The default-mode network (DMN) was shown to have aberrant blood oxygenation-level-dependent (BOLD) activity in major depressive disorder (MDD). While BOLD is a relative measure of neural activity, cerebral blood flow (CBF) is an absolute measure. Resting-state CBF alterations have been reported in MDD. However, the association of baseline CBF and CBF fluctuations is unclear in MDD. Therefore, the aim was to investigate the CBF within the DMN in MDD, applying a strictly data-driven approach. In 22 MDD patients and 22 matched healthy controls, CBF was acquired using arterial spin labeling (ASL) at rest. A concatenated independent component analysis was performed to identify the DMN within the ASL data. The perfusion of the DMN and its nodes was quantified and compared between groups. The DMN was identified in both groups with high spatial similarity. Absolute CBF values within the DMN were reduced in MDD patients (p<0.001). However, after controlling for whole-brain gray matter CBF and age, the group difference vanished. In patients, depression severity was correlated with reduced perfusion in the DMN in the posterior cingulate cortex and the right inferior parietal lobe. Hypoperfusion within the DMN in MDD is not specific to the DMN. Still, depression severity was linked to DMN node perfusion, supporting a role of the DMN in depression pathobiology. The finding has implications for the interpretation of BOLD functional magnetic resonance imaging data in MDD.

Introduction

M ajor depressive disorder (MDD) is a highly prevalent, disabling psychiatric disease that is characterized by depressed mood, anhedonia, negative self-reference, hopelessness, and feelings of guilt. Disturbances of sleep, concentration, drive, and appetite may occur. MDD has been associated with molecular and cellular alterations in the central nervous system (Krishnan and Nestler, 2010). Particularly, alterations in perfusion, gray matter (GM) volume, function, and connectivity of the prefrontal cortex, basal ganglia, and the limbic system were reported.

Interest has arisen in the brain's resting state in healthy subjects and psychiatric disorders. Resting-state cerebral blood flow (CBF) can be investigated as static (i.e., averaged CBF signal during recording) or dynamic (regarding the CBF signal fluctuations during recording) (Zou et al., 2009). Indeed, static resting-state CBF has been found to be altered in MDD (Fitzgerald et al., 2008). Whole-brain resting-state perfusion studies using positron-emission tomography (PET) indicated reduced CBF in the dorsolateral prefrontal cortex and the anterior cingulate cortex (ACC) (Bench et al., 1993; Drevets et al., 1997; Monkul et al., 2012), and increased CBF in the posterior cingulate cortex (PCC) and the parahippocampal gyrus (Monkul et al., 2012). Slightly different patterns of altered perfusion in MDD were found with magnetic resonance imaging (MRI)-based arterial spin labeling (ASL) studies of whole-brain resting-state CBF: reduced CBF in prefrontal, temporal, and occipital cortices, as well as in the thalamus (Lui et al., 2009; Walther et al., 2012), and increased CBF in amygdala, subgenual anterior cingulate cortex (sgACC), hippocampus, and pallidum (Duhameau et al., 2010; Lui et al., 2009). The divergence of findings in the studies on resting-state perfusion in MDD may be due to different techniques or heterogeneous study populations.

Spontaneous dynamic fluctuations of resting-state CBF may be used to identify the so-called default-mode network (DMN) (Viviani et al., 2011; Zou et al., 2009). The DMN consists of midline structures and other brain areas with synchronized neural activity predominantly during resting state (Buckner et al., 2008). The DMN has been demonstrated from blood oxygenation-level-dependent (BOLD) data, ¹⁸F-flourodeoxyglucose PET, H₂ ¹⁵O-PET, or ASL. In resting-state studies, the DMN can be identified either by data-driven independent component analysis (ICA) (Greicius et al., 2004; Jann et al., 2009; Jann et al., 2010b; Meindl et al., 2010; Wirth et al., 2010) or by covariance analysis starting from a selected seed region (Fox et al., 2005; Uddin et al., 2009; Viviani et al., 2011). In healthy subjects, BOLD activity in the DMN has been linked to mental processes focusing on the self (Andrews-Hanna et al., 2010; Gusnard et al., 2001; van Buuren et al., 2010). Functional connectivity based on BOLD signal fluctuations within the DMN was shown to decrease during sad mood induction by self-referential recall (Harrison et al., 2008). In MDD, the DMN has been suggested to be involved in maladaptive rumination and poor executive functioning (Pizzagalli, 2011). In active emotion induction tasks, MDD patients had abnormally high BOLD responses at rest in the anterior DMN (Grimm et al., 2009; Sheline et al., 2009). In addition, increased functional connectivity was reported for the sgACC and the medial thalamus (Greicius et al., 2007). Likewise, a recent study reported increased functional connectivity in the anterior DMN and reduced functional connectivity in the posterior aspects of the DMN in young unmedicated MDD patients (Zhu et al., 2012). The spatial extend of the DMN or the duration of dominance of the DMN over the task network was not different between MDD and controls (Hamilton et al., 2011). However, in MDD patients, DMN domination was correlated with rumination scores, indicating an association of DMN activity and negative self-referential information processing in depression.

Previous studies in MDD investigated the DMN with BOLD functional MRI (fMRI), which provides a relative, rather than absolute, measure of neural activity (Buxton et al., 1998; Obata et al., 2004). As the resting-state perfusion or metabolism is altered in MDD, the BOLD signal could have been disturbed in the patients. This may have influenced the DMN findings. Indeed, absolute measures of neural activity such as CBF or glucose metabolism were increased in the core regions of the DMN in healthy subjects (Pfefferbaum et al., 2011; Raichle et al., 2001; Rilling et al., 2007; Zou et al., 2009). As described above, static resting-state CBF was reduced in MDD in the brain regions associated with the anterior DMN and increased in areas of the posterior DMN (Bench et al., 1993; Fitzgerald et al., 2008; Lui et al., 2009; Monkul et al., 2012).

In a large sample of healthy subjects, a high spatial similarity was demonstrated between connectivity of the DMN derived from BOLD and CBF data (Viviani et al., 2011). However, BOLD led to stronger connectivity values than CBF. Thus, the DMN alterations previously reported in MDD using BOLD fMRI might be overestimated. Here, the aim was to explore the global static and dynamic CBF alterations in MDD patients using a purely data-driven approach (i.e., ICA) on absolute measures of neural activity (ASL).

We sought to identify the DMN in CBF data using ICA, and compare spatial distribution as well as absolute CBF between groups. It was hypothesized that CBF was reduced in the anterior DMN and increased in the posterior DMN in MDD. Furthermore, the correlation of DMN perfusion and depression severity as well as the effect on short-term outcome was explored.

Materials and Methods

Participants

MDD patients were recruited at the inpatient and outpatient departments of the University Hospital of Psychiatry, Bern. Controls were recruited via flyers mainly from hospital staff. In total, 22 right-handed patients (11 men and 11 women) with MDD according to Diagnostic and Statistical Manual Fourth Edition (DSM-IV) and 22 right-handed healthy control subjects (nine men and 13 women) were included. Groups did not differ in age, education, body–mass index, or annual income. Diagnoses were given after semistructured clinical interviews. Patients were screened with the structured clinical interview for DSM-IV part 2 to exclude patients with comorbid personality disorders. Exclusion criteria were depression due to bipolar disorder, history of significant head trauma, history of electroconvulsive therapy, substance abuse, or dependence other than nicotine. Exclusion criteria for controls were lifetime history of depressive episode and first-degree relatives with any affective disorder.

In the MDD group, 13 patients had experienced one to three depressive episodes, and nine patients had a history of more than three episodes. All patients were on stable medication at the time of scanning. All but one patient received antidepressant drugs (amitriptyline 25–225 mg, n=3; escitalopram 20 mg, n=2; clomipramine 75–225 mg, n=2; doxepin 100–200 mg, n=2; mirtazapine 15–45 mg, n=4; sertraline 100–200 mg, n=2; venlafaxine 150–300 mg, n=2; and venlafaxine and mirtazapine combination 150–300 mg/30 mg, n=4). Six patients received substances for augmentation (lithium n=2; lamotrigine n=1 and quetiapine n=3), and seven patients received zolpidem 10 mg at night. The study protocol was approved by the local ethics committee (KEK-BE 196/09) and was in accordance with the Declaration of Helsinki. All participants provided written informed consent. Before scanning, participants were assessed with the Beck Depression Inventory (Beck et al., 1961), the Hamilton Depression Rating Scale (HAMD) (Hamilton, 1960), the Montgomery-Asberg Depression Rating Scale (Montgomery and Asberg, 1979), and the Edinburgh Handedness Inventory (Oldfield, 1971). To investigate the effects on short-term outcome, clinical assessment was repeated in 20 MDD patients (91% of the initial sample) 4 weeks after the MRI scan. Eight patients had improved more than 50% in the HAMD (responders), while 12 were considered nonresponders. Descriptive and clinical data of the participants are given in Table 1.

Table 1.

Descriptive Statistics

	Controls	Patients	T	p
Age (years)	41.45±13.65	45.73±14.1	−1.021	0.313
Education (years)	14.8±4.2	13.4±2.6	1.305	0.199
BMI (kg/m²)	23.5±3.8	24.9±5.1	1.040	0.304
Annual income (thousand SFr)	42.0±23.6	50.5±37.0	0.912	0.367
BDI	1.5±2.1	30.0±8.6	−14.996	<0.001
HAMD week 0	0.5±1.1	26.1±5.4	−21.949	<0.001
HAMD week 4	—	16.0±9.7	—	—
MADRS	0.9±1.4	26.9±4.8	−24.308	<0.001

Numbers given are mean and standard deviation.

BMI, body–mass index; BDI, Beck Depression Inventory; HAMD, Hamilton Depression Rating Scale; MADRS, Montgomery-Asberg Depression Rating Scale.

MR scanning

Scanning was performed at a 3T Siemens Trio unit (Erlangen, Germany) in the morning of workdays between 08:00 and 10:00. First, a set of high-resolution T1-weighted MR images were obtained with a 3D-modified driven equilibrium Fourier transform sequence (Deichmann et al., 2004). The optimized acquisition parameters included 176 sagittal slices with 256×224 matrix points with a noncubic field of view (FOV) of 256×224 mm, yielding a nominal isotropic resolution of 1 mm³ (i.e., 1×1×1 mm), repetition time (TR)=7.92 msec, echo time (TE)=48 msec, flip angle=16°, inversion with symmetric timing (inversion time 910 msec), fat saturation, and 12-min total acquisition time.

Afterward, a pseudocontinuous ASL (pCASL) sequence was acquired (Dai et al., 2008; Wu et al., 2007). This technique magnetically labels the endogenous water molecules in the blood flowing into the brain, thus providing a noninvasive tracer to quantify perfusion of blood into brain regions. The pCASL parameters were set as follows: the gap between the labeling slab and the proximal slice was 90 mm; TR=4000 msec; TE=18 msec; FOV=230×230 mm²; matrix size=128×128; 18 axial slices at a distance of 1.0 mm; slice thickness=6.0 mm; gradient-echo echo-planar readout; ascending order; acquisition time 45 msec per slice; number of measurements N=100; and 7-min total acquisition time. Slice-selective gradient 6 mT/m; postlabeling delay ω=1250 msec; and tagging duration τ=1600 msec.

CBF-quantification

Preprocessing of the pCASL images included coregistration to the individual anatomical scans and normalization into the standard Talairach space. Quantification of CBF flow time series was based on the equation \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{portland, xspace} \usepackage{amsmath, amsxtra} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} \begin{align*} CBF = \left( \frac { \lambda \cdot \Delta M } { 2 \cdot \alpha \cdot M_0 \cdot T_ { 1b } } \right) \cdot \left( \frac {1} { e^{-w / T_{1b }} - e^ { -(\tau + w) / T_ { 1b })}} \right) \end{align*}\end{document}

The blood/tissue water partition coefficient λ was set at 0.9 g/mL, and the labeling efficiency α at 0.85 (Wu et al., 2007); the decay time for labeled blood T1b at 3T is 1490 msec. M ₀ are the equilibrium brain tissue magnetization images (Federspiel et al., 2006; Jann et al., 2010a; Wang et al., 2003). ΔM was calculated by simple subtraction of label and control images. Simple subtraction has been demonstrated to efficiently minimize spurious BOLD contaminations within the CBF signal in the case of resting-state recordings (Liu and Wong, 2005; Wang et al., 2003). Furthermore, Liu and Wong (2005) demonstrated that simple subtraction in resting-state CBF data works with the same performance as special filtering approaches (Chuang et al., 2008; Liu and Wong, 2005). Finally, the CBF time series were spatially smoothed with a Gaussian kernel (full width at half maximum 10 mm) to reduce interindividual anatomical differences and further increase the signal-to-noise ratio.

Independent component analysis

The DMN was computed with a concatenated group ICA approach (Calhoun et al., 2001, 2004; Horn et al., 2012). The subjects' CBF time series were normalized (scaled between 0 and 1) to account for intersubject variability in the CBF data while maintaining the relative temporal signal fluctuations. The normalized CBF datasets then were temporally concatenated and subjected to the ICA algorithm [FastICA developed by Hyvarinen and Oja (2000) implemented in BrainVoyager QX]. The ICA resulted in 30 components, and the one displaying the spatial pattern of the DMN was selected for further analyses. The selection was based upon visual inspection of the spatial distribution and similarity to the DMN descriptions in the literature, for example, Figures 1 and 4 of Buckner (2012). Further, for each subject, a specific spatial map was generated based on a back-reconstruction approach (Calhoun et al., 2004; Horn et al., 2012). For this purpose, the temporal signal of the DMN component was segmented into the time periods corresponding to the individual subjects and then used as predictors in single-subject GLMs [motion parameters were not included in the GLM (Erhardt et al., 2010)]. This yielded the spatial pattern of voxels that correlate with the dynamics of the DMN in the specific single subject. The statistical maps of the DMN were corrected for multiple comparisons using the cluster-size estimation procedure implemented in Brainvoyager QX with 1000 permutations, yielding a threshold of 216 mm³. At this statistical threshold, the resulting DMN map consisted of four separate regions equivalent to four nodes of the core DMN known from the literature (Buckner et al., 2008), which was then defined as regions of interest.

CBF data analyses

CBF values were extracted for the whole-brain gray matter (GM), the DMN, as well as for its nodes (ACC, PCC, right inferior parietal lobe [IPL], and left IPL). CBF values of the DMN and the global GM were compared between groups using independent sample T-tests. A repeated-measure analysis of covariance (ANCOVA) with group (controls, depression) as the between-subject factor and the DMN nodes (ACC, PCC, right IPL, and left IPL) as the within-subject factor was performed. As there was a wide range in age (controls: 25–64 years; patients: 22–67 years), age was included as a covariate. Moreover, as the known correlation between age and global perfusion could be replicated (r=−0.32, p<0.05) (Liu et al., 2012; Marchal et al., 1992), GM-CBF was also added as a covariate. To explore clinical correlates, partial Pearson correlations were computed for HAMD and the four DMN nodes, including age and GM-CBF, as control variables.

In a second analysis step, the patient group was split into responders and nonresponders. The DMN-CBF and GM-CBF between groups were compared using one-way analysis of variances (ANOVAs). The ANCOVA and partial correlations were performed as described above, but with three groups (controls, responders, and nonresponders). All analyses were performed with IBM SPSS Statistics 19.

Results

DMN reconstruction

The concatenated group ICA is shown in Figure 1. Both ICA on the group level (Fig. 1, top row) as well as on the level of the group-wise individual subjects (Fig. 1, rows 2 and 3) identified the DMN. Groups differed only in posterior white-matter regions with respect to DMN distribution.

FIG. 1.

ICA component of group analyses and single-subject analyses. Top row: DMN distribution from the ICA of the whole data set. Second row: DMN distribution of the individual ICA maps in healthy controls. Third row: DMN distribution of the individual ICA maps in MDD patients. Bottom row: T-test of DMN spatial distribution between groups. T-tests corrected for multiple comparisons using cluster-size thresholds of >216 mm³. Z-scores above the top row indicate the z-coordinate of the axial slice in Talairach space. Images given in the radiological convention, that is, left are right. ICA, independent component analysis; DMN, default-mode network; MDD, major depressive disorder.

DMN quantification

Controls versus depression

Mean CBF values of the GM, the DMN, and the DMN nodes are listed in Table 2. When comparing the mean CBF values of the DMN between groups, the MDD patients have significantly lower CBF than healthy subjects (T=7.58, p<0.0001). However, as GM-CBF also differed significantly between groups (T=6.97, p<0.0001), the observed CBF group difference in the DMN might not be specific to the DMN. For further analysis, the CBF values in the patient group were therefore adjusted to the global GM-CBF of the healthy controls. For this purpose, the mean GM-CBF of the healthy controls was divided by the patients' mean GM-CBF. The CBF values of the DMN and its separate nodes in the patient group were then multiplied by the resulting factor (1.527). Indeed, the comparison of the corrected CBF values finally did not produce any difference in the DMN between groups (T=0.168, n.s.).

Table 2.

Perfusion and Distribution of Default-Mode Network Nodes in Patients and Controls

	Mean CBF		Talairach coordinates of center of gravity±SD
	Controls	Patients	x	y	z	Number of voxels (mm³)
GM	64.3±12.2	42.1±8.5				933,957
DMN	84.1±13.0	54.5±12.9				32,861
Adjusted DMN	84.1±13.0	83.3±19.7				32,861
Adjusted ACC	78.5±18.7	75.2±23.7	−0.94±5.7	48±9.5	29±8.8	7454
Adjusted PCC	87.8±18.4	86.3±32.5	−1.9±3.5	−61±8.7	30±6.3	5658
Adjusted right IPL	91.2±17.3	94.6±25.4	53±2.1	−62±2.6	26±4.4	782
Adjusted left IPL	85.3±12.4	85.4±18.7	−48±7.9	−63±8.9	24±9.8	18,537

Adjusted CBF values include correction for whole-brain gray matter CBF. Numbers given are mean and standard deviation.

CBF, cerebral blood flow; SD, standard deviation; GM, gray matter; DMN, default-mode network; ACC, anterior cingulate cortex; PCC, posterior cingulate cortex; IPL, inferior parietal lobe.

The repeated-measure ANCOVA showed no statistically significant difference between groups [F(1,40)=0.009; n.s.) or the DMN nodes [F(3,40)=0.84; n.s.) and no significant interaction between the groups and DMN nodes [F(3,40)=0.338; n.s.).

Controls versus responders versus nonresponders

Mean CBF values of the GM, the DMN, and the DMN nodes are listed in Table 3. As in the first group analysis, uncorrected GM perfusion differed between groups [F(2,41)=30.26, p<0.0001). The Tukey-HSD post hoc test indicated higher values in controls as compared to responders (p<0.0001) and nonresponders (p<0.0001). Similarly, the one-way ANOVA over the DMN-CBF data yielded a significant group effect [F(2,41)=39.25, p<0.0001) originating from significantly higher CBF values in the healthy controls compared to responders (p<0.0001) and nonresponders (p<0.0001), respectively. Thus, the CBF values of the two patient groups were adjusted to the GM-CBF of the healthy subjects in the same procedure as described above. The one-way ANOVA comparing the corrected DMN-CBF between groups demonstrated no significant difference [F(2,41)=0.57, n.s.).

Table 3.

Perfusion and Distribution of Default-Mode Network Nodes in Responders, Nonresponders, and Controls

	Mean CBF
	Controls	Responders	Nonresponders
GM	64.3±12.2	37.6±4.2	42.7±7.2
DMN	84.1±13.0	46.3±7.3	57.1±11.5
Adjusted DMN	84.1±13.0	79.1±12.5	85.9±17.3
Adjusted ACC	78.5±18.7	75.4±15.9	72.3±18.6
Adjusted PCC	87.8±18.4	77.5±39.2	92.8±28.4
Adjusted right IPL	91.2±17.3	88.4±25.0	100.1±19.1
Adjusted left IPL	85.3±12.4	80.7±12.1	89.2±16.7

Adjusted CBF values include correction for whole-brain gray matter CBF. Numbers given are mean and standard deviation.

The subsequent repeated-measure ANCOVA over the DMN nodes showed an effect of neither group [F(2,37)=1.99, n.s.) nor the DMN node [F(3,37)=0.96, n.s.). The interaction between these two factors did not reach significance, either [F(6,37)=0.89, n.s.).

Clinical correlates

The patient group showed negative correlations between HAMD and the CBF in the PCC (r=−0.47, p<0.05), and the right IPL (r=−0.49, p<0.05), when corrected for age and GM-CBF. There were no effects of the number of episodes or duration of illness.

Discussion

The DMN in perfusion data was identified using a truly data-driven approach, that is, a concatenated ICA in MDD. With this approach, it was possible in both healthy controls and MDD patients to demonstrate the DMN (Raichle et al., 2001). In line with previous reports, the spatial extend of the identified DMN did not differ between groups (Hamilton et al., 2011). While absolute perfusion values of the DMN were reduced in MDD patients, particularly in treatment responders, the correction for whole-brain GM CBF and age abolished the perfusion group differences. Therefore, CBF is reduced within the DMN in MDD, but also in total GM. The corrected CBF values were inversely correlated with depression severity as determined by the HAMD in two DMN nodes, namely the PCC and right IPL.

The majority of the pioneering studies demonstrated the DMN with BOLD fMRI data, producing fascinating results (Buckner et al., 2008). However, instead of relative measures of neural activity, we were interested in absolute measures of neural activity, such as CBF. Few studies on the DMN that have also used ASL perfusion data applied correlation analyses from a seed region (e.g., PCC) (Pfefferbaum et al., 2011; Viviani et al., 2011; Zou et al., 2009), which is a hypothesis-driven approach. Here, it was demonstrated that the DMN can be derived from CBF data by means of an ICA. Our approach is data driven, and therefore less susceptible to selection problems such as the correct location of seed voxels. Furthermore, absolute measures of resting-state neural activity were used. In the present study, the DMN consisted of the common areas such as the anterior and PCC as well as the bilateral IPL. The DMN in our study was highly similar to the DMN described in ICAs of BOLD data (Damoiseaux et al., 2006; Greicius et al., 2004; Jann et al., 2010b; Zhu et al., 2012), task-negative activation BOLD data (Buckner et al., 2008; Gusnard et al., 2001; van Buuren et al., 2010), or functional connectivity analyses in healthy subjects (Fox et al., 2005; Viviani et al., 2011). As in previous studies, perfusion in the DMN was higher than whole-brain perfusion (Pfefferbaum et al., 2011; Raichle et al., 2001; Zou et al., 2009). However, some BOLD fMRI studies indicated alterations of the DMN extent in MDD using different measures of connectivity (Bluhm et al., 2009; Greicius et al., 2007; Zhou et al., 2010; Zhu et al., 2012).

CBF quantification indicated reduced static CBF in the DMN of MDD patients. Furthermore, CBF in the DMN was reduced in later treatment responders compared to later nonresponders. However, the group differences are not specific to the DMN, but apply to whole-brain GM perfusion. This is in line with resting-state studies of CBF or glucose metabolism, indicating predominantly reductions in the ventromedial cortex in MDD patients as compared to controls (Drevets et al., 1997; Fitzgerald et al., 2008; Mayberg et al., 1994; Videbech, 2000). The finding of reduced global GM perfusion in MDD is in line with earlier studies (Dunn et al., 2005; Iidaka et al., 1997; Sackeim et al., 1990; Videbech, 2000), but at odds with other reports using different CBF quantification methods (Bench et al., 1993; Lui et al., 2009). However, while the global perfusion analyses to GM was restricted, most studies investigated whole-brain perfusion, irrespective of the tissue (Lui et al., 2009). Others have not reported on global CBF (Monkul et al., 2012; Videbech et al., 2002).

Regarding the DMN in MDD, findings from resting-state BOLD analyses of functional connectivity have been conflicting: two studies reported increased connectivity in depression (Greicius et al., 2007; Zhu et al., 2012), one decreased connectivity (Bluhm et al., 2009), and two studies found no group difference (Hamilton et al., 2011; Veer et al., 2010). It remains unclear, however, whether CBF reductions within the DMN in patients contribute to alterations in BOLD signal or changes in functional connectivity as determined by BOLD fMRI.

Connectivity based on dynamic CBF fluctuations is not necessarily correlated with static CBF. In healthy controls, functional connectivity between most DMN portions was not correlated with baseline CBF with the exception of the PCC—cingulate and the middle temporal—calcarine cortex associations (Viviani et al., 2011). Likewise, a study in 12 younger subjects demonstrated increased amplitudes of low-frequency fluctuations in core regions of the DMN that also had the highest baseline CBF values (Zou et al., 2009). Even though connectivity maps determined by BOLD and CBF analyses were highly similar in healthy subjects, CBF maps had smaller spatial extend, thus indicating reduced connectivity strength (Viviani et al., 2011). It may be only speculated on whether global baseline CBF was correlated with BOLD signal connectivity during resting state. Findings from visual stimulation studies seem to support this notion. Calibrated fMRI has demonstrated an inverse correlation of BOLD response and baseline CBF in humans during visual stimulation (Liau and Liu, 2009; Lu et al., 2008), that is, lower baseline CBF was associated with increased BOLD variability. Furthermore, exsanguination in rats reduces mean arterial pressure and enhances the amplitude of low-frequency BOLD fluctuations, an effect that was stronger in the cortex than in basal ganglia (Kannurpatti et al., 2008). In line with that, inverse correlations were found between CBF in the white matter and functional connectivity in the connected GM (Aslan et al., 2011). These findings argue in favor of an association of static CBF and BOLD connectivity and suggest that diminished static CBF in the DMN in MDD could lead to enhanced functional connectivity in BOLD studies.

Calibrated fMRI in healthy subjects demonstrated a positive linear association between perfusion changes and metabolic changes within the DMN, suggesting that task-negative BOLD activation is indeed a result of neural activity instead of cardiovascular changes (Lin et al., 2011). Thus, the baseline CBF needs consideration when comparing DMN BOLD signals across subject groups. However, in MDD, an uncoupling of CBF and glucose metabolism was demonstrated (Dunn et al., 2005), suggesting that neural activity and CBF might be unrelated in some brain areas as an effect of the disease.

Higher depression severity was associated with reduced CBF within the PCC portion and the right IPL portion of the DMN. The inverse correlation of HAMD and CBF in the PCC is in line with a previous study examining the effects of depression severity on cerebral glucose metabolism (Dunn et al., 2002). Furthermore, PCC within the DMN was shown to be engaged in self-referential processing in healthy subjects (Andrews-Hanna et al., 2010; Gusnard et al., 2001). The IPL has not been related to depression severity before. Still, the IPL within the DMN was associated with self-referential processing regarding the personal future (Andrews-Hanna et al., 2010). Furthermore, the right IPL was shown to be critical for the discrimination of self versus other (Uddin et al., 2006) and might therefore play a role in attribution processes in MDD. In addition, BOLD fMRI indicated a positive association of right-IPL activity with changes in galvanic skin conductance response, which is related to emotional or cognitive arousal (Critchley et al., 2000).

However, resting-state CBF or glucose metabolism has not been found to correlate with depression severity, for example, the HAMD score in most studies (Videbech, 2000). However, most of these studies did not control for age and global CBF.

Other resting-state networks might also be of interest to MDD, but the scope of this study was the DMN. All patients received pharmacological antidepressant treatment that may have an impact on resting-state CBF quantification in the insula and amygdala using ASL (Chen et al., 2011). Some of the previous studies have investigated patients that were drug naïve or free of medication for 1–4 weeks (Grimm et al., 2009; Sheline et al., 2009; Veer et al., 2010; Zhu et al., 2012), while other studies tested medicated patients (Bluhm et al., 2009; Greicius et al., 2007; Wu et al., 2011). Furthermore, the applied pCASL sequence has poor sensitivity for the most caudal and medial aspects of the sgACC. Therefore, CBF effects in this region may have been underestimated.

Conclusion

In summary, the current results indicate that the hypoperfusion of DMN portions in MDD may not be specific to the DMN, but more general to GM perfusion. However, depression severity correlates inversely with CBF in the PCC and right-IPL components of the DMN in MDD after correction for global effects. These findings further support a role of the DMN in the pathophysiology of MDD.

Footnotes

Author Disclosure Statement

No conflicts of interest exist with this work.

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