2 Methods
2.1 Participants
The group of 68 participants consisted of 23 healthy controls with no
history of psychiatric disease (HC), 20 patients with a current
diagnosis of MDD (MDD), and 25 patients with a current comorbid
diagnosis of MDD and at least one anxiety disorder (10 patients with
agoraphobia, 6 with social phobia, 13 with panic disorder and 7 with
generalized anxiety disorder; 8 patients exhibited more than one AD at
the time of testing) (MDD+AD). Patients were recruited from the Clinic
of Psychiatry and Psychotherapy at the University Hospital Magdeburg and
affiliated mental health services in the area. Healthy controls were
recruited from the local community and matched for age and sex. Table 1
shows participant characteristics split by participant group. The mean
age of the participants was 35.25 (12.4) years. 52.9 % of the
participants were male and the mean years of education was 15.5 (3.4).
Of the patient groups, 73.4 % were currently receiving
psychopharmacotherapy and the mean duration of illness was 57.14 months.
Exclusion criteria were: (1) age under 18 or over 65, (2) a history of
other comorbid psychiatric disorders, substance abuse or dependence, (3)
other severe medical illness, head injury or neurological disorders, (4)
MRI contraindications. Demographic variables and inclusion/exclusion
criteria were assessed using a standardized questionnaire and a
structured interview conducted by trained medical staff. Diagnoses were
confirmed according to DSM-IV (American Psychiatric Association, 2013)
criteria using the Structured Clinical Interview for DSM-IV (SCID I and
II) (First et al., 1996). In addition, the Beck Depression Inventory II
(BDI-II) (Beck et al., 1996) and the Beck Anxiety Inventory (BAI) (Beck
et al., 1988) were filled out by each participant as a measure of
symptom severity. Neurological disorders were examined via a
neuropsychological test battery.
All participants received detailed oral and written information about
the study and gave written informed consent prior to participation.
Ethical approval for the study was granted by the Ethics Committee of
the Otto-von-Guericke University. The study was conducted in accordance
with The Code of Ethics of the World Medical Association (Declaration of
Helsinki).
2.2 MRI data acquisition
Magnetic resonance images were obtained using a 3 Tesla Siemens MRI
scanner (MAGNETOM Prisma Syngo MR D13D; Siemens, Erlangen, Germany) with
a 64-channel phased array head coil. All subjects received earplugs to
protect them from the noise of the head coil. Structural images were
recorded using a magnetization-prepared rapid gradient echo (MPRAGE)
sequence. High-resolution T1-weighted three-dimensional anatomical scans
of 192 sagittal slices with a voxel size of 1.0 x 1.0 x 1.0
mm3 of the whole brain were obtained (repetition time
(TR): 2500 ms, echo time (TE): 2.82 ms, inversion time (TI): 1100 ms,
field of view (FOV) from foot to head (FH): 256 mm, anterior to
posterior (AP): 256 mm and right to left (RL): 192 mm, flip angle of 7°,
matrix size = 256 x 256). The scan time required for structural
acquisition was 9 min 20 s.
T2-weighted functional images were collected in single runs using
echo-planar imaging (TE = 30 ms; TR = 2000 ms; field of view = 216 mm;
flip angle = 90°, voxel size 3 × 3 × 3 mm, EPI-Factor 72) using blood
oxygenation level-dependent contrast. Covering the whole brain, 36
contiguous 3-mm-thick slices were acquired sequentially in ascending
order parallel to the anterior–posterior-commissure. Within a total
scan time of 10:06 min, 306 volumes were acquired
continuously. Participants were
instructed to stay awake with their eyes closed during the resting-state
scans.
2.3 MRI data pre-processing
Functional data were preprocessed using Data Processing Assistant for
Resting-State fMRI (DPARSF 4.3) (Chao-Gan & Yu-Feng, 2010). Imaging
preprocessing included: interleaved slice time correction, realignment
to temporal mean image, motion regression, normalization to Montreal
Neurological Institute (MNI) reference space combined with reslicing to
an isotropic resolution of 3-mm, temporal band-pass filtering (0.01
< f < 0.10 Hz) and smoothing using a
full-width-at-half-maximum Gaussian-kernel of 4 mm. Spurious variance
was removed by nuisance regression including covariates for head motion
using the Friston 24-parameter model as well as averaged signals from
global brain signal, cerebrospinal fluid and white matter signal.
To compute RSFC within and between the three networks, we proceeded as
follows. First, for each network, we utilized established a priori
defined, literature-based seed regions (Table 2) belonging to the DMN,
VAN and ECN to compute seed-to-whole-brain connectivity maps by
calculating the Pearson correlation coefficient of the average time
series of each seed region to the rest of the brain. Then, we converted
the correlation coefficient maps using Fisher’s z-transformation to
improve the Gaussianity of their distribution.
2.4 Statistical MRI data
analysis
Following preprocessing of the data, using Statistical Parametric
Mapping (SPM 12) (Friston, 2007) functional connectivity maps were
compared using analyses of variance, where age and sex were included as
covariates. For each network-pair, we then computed group-wise
comparisons between all the three groups using post-hoc t-tests for
independent samples. All clusters were formed using a
cluster-forming-threshold of p < .001 and controlled
for Family-Wise-Error (pFWE-corr <
.05). Additonally, all clusters were subsequently corrected for multiple
comparisons using Bonferroni-correction and considered significant atpcorr < .05. The atlas of Yeo et al.
(2011) was used to assign the resulting clusters to one of the three
networks. For all 5 seeds, the within-network (seed to other structures
within the network the seed belonged to) and the between-network
connectivity (seed to structures belonging to the other networks
included in this study) was examined. For post-hoc analysis using SPSS
27.0 (IBM Corp., 2022), we extracted the beta values of these clusters
indicating the degree of connectivity from our first level models using
the REX toolbox (Gabrieli Lab). Automated anatomical labelling atlas
(AAL 3) (Rolls et al., 2020) was used to identify and label clusters
with aberrant RSFC. Connectivities were visualized using Brain Net
Viewer (2013).
SPSS (IBM Corp., 2022) software was used for comparison of group
characteristics as well as correlation and regression analysis. All
results were considered to be significant atpcorr < .05 after correcting for
multiple comparisons using Bonferroni-correction. Characteristics of the
participant groups were summarized using mean and standard deviation for
continuous variables and percentage for categorial variables. Variables
were tested for normal distribution using Shapiro-Wilk-Test. Age and
clinical scores were compared between all groups using ANOVA. Between
patient groups duration of illness and clinical scores were compared
using Mann-Whitney-U -test. Differences in the distribution of sex
and medication were assessed using χ2 -tests.
To further investigate the nature of the relationship of RSFC-values and
symptom severity, we performed regression analyses using linear models,
for all clusters found to be significant within the patient group (MDD
and MDD+AD). Mean beta values were used as dependent variables and
severity of symptoms as measured by BDI-II and BAI-scores as independent
variables. Additionally, since an effect of pharmacotherapy on RFSC has
commonly been found (Rolls et al., 2019), we performed an
independent-sample-t-test within the patient group, testing for a
difference in RSFC-values between medication groups.
Bonferroni-correction was used to correct for multiple comparisons.