For a full PDF of the proposal, click here
Danielle R. Sullivan, Ph.D.1,2
Jasmeet P. Hayes, Ph.D. 1,2
Mieke Verfaellie, Ph.D. 2,3
1National Center for PTSD, VA Boston Healthcare System, Boston, MA
2Department of Psychiatry, Boston University School of Medicine, Boston, MA
3Memory Disorders Research Center, VA Boston Healthcare System, Boston, MA
Posttraumatic stress disorder (PTSD) is a psychiatric disorder characterized by debilitating
re-experiencing, avoidance, and hyperarousal symptoms following trauma exposure. Recent
evidence suggests that individuals with PTSD show disrupted functional connectivity in the
default mode network, an intrinsic network that consists of a midline core, a medial temporal
lobe (MTL) subsystem, and a dorsomedial prefrontal cortex (dMPFC) subsystem. Although
there is a lot of work investigating the default mode network in PTSD, there is not much
research focused on the default mode network subsystems in PTSD. Therefore, a large study
investigating whether functional connectivity in these subsystems is differentially disrupted in
PTSD is needed to better understand the role of the default mode network in the disorder. Here,
we propose to use a seed-based approach in the ENIGMA-PGC PTSD sample of ~1500 PTSD
patients and ~1500 controls to examine resting state functional connectivity in the default mode
network subsystems in PTSD.
A. Research Question, Goal, or Specific Aims
Provide a brief description (e.g., 1 paragraph) describing the aims of the proposal and
the research questions to be addressed.
Posttraumatic stress disorder (PTSD) is a debilitating psychiatric disorder that develops
after exposure to highly distressing and life-threatening events. The most common features of
PTSD include re-experiencing of the trauma (e.g. flashbacks), avoidance (e.g., avoiding traumarelated
stimuli or trauma-evoking situations), and hyperarousal symptoms (e.g., hypervigilance).
Current neurocircuitry models of PTSD suggest that the medial prefrontal cortex and
hippocampus are critically involved in mediating the disorder (1-7). According to these models,
abnormal structure and function of the ventromedial prefrontal cortex (vMPFC) in PTSD results
in a failure to regulate activity in brain regions important for fear expression and appraisal,
leading to an exaggerated fear response (3, 4, 8-13). Further, alterations in hippocampal
function in PTSD may contribute to impaired contextual fear learning (3, 4, 9, 10) and impaired
contextual fear extinction recall (11, 14, 15), an adaptive process that relies on both the
hippocampus and vMPFC (16-19). Taken together, these studies suggest that PTSD is
associated with dysregulation of a frontal-medial temporal lobe (MTL) circuit that results in an
exaggerated fear response and an inability to extinguish this fear when the context no longer
More recently, studies have used resting state functional MRI (rs-fMRI) to examine
connectivity among brain regions that form integrated networks in PTSD. One such network is
the default mode network, which includes the MTL, posterior cingulate cortex (PCC), medial
prefrontal cortex, inferior parietal lobule, and lateral temporal cortex (20). Several studies have
found PTSD-related alterations in the default mode network (21-26), and a recent meta-analysis
found that PTSD is consistently associated with reduced functional connectivity (27). Evidence
in healthy individuals suggests that the default mode network can be further fractionated into a
midline core consisting of the PCC and anterior medial prefrontal cortex (aMPFC) and two
functionally and anatomically distinct subsystems (28): a MTL system that includes the vMPFC,
posterior inferior parietal lobule, retrosplenial cortex, parahippocampal cortex, and hippocampal
formation; and a dorsomedial prefrontal cortex (dMPFC) system that includes the dMPFC,
temporoparietal junction, lateral temporal cortex, and temporal pole. These subsystems are
differentially affected by MTL lesions (29) and are thought to be involved in distinct cognitive
processes (28, 30). For example, the MTL subsystem includes regions that are important for
learning and memory (30), while the dMPFC subsystem includes regions that are critical for
mentalizing and social processing of the self and others (30-32). Although there is evidence that
connectivity within the default mode network is compromised in PTSD (21-25, 27, 33), it is
unclear whether the subsystems of the default mode network are differentially disrupted. As
memory alterations appear to be a core feature of the disorder (34, 35), we predict that the MTL
subsystem might be particularly affected in PTSD.
In addition to disruptions to the default mode network, other networks are also altered in
PTSD (23, 25, 27, 36). Networks such as the salience network and central executive network
are engaged during externally-directed and attention-demanding tasks and are anticorrelated
with the default mode network. Daniels et al. (36) found that individuals with PTSD may have
difficulty disengaging the default mode network and engaging salience and central executive
networks during attention-demanding tasks. Further, there appears to be increased crossnetwork
connectivity between the default mode network and salience network in PTSD at rest
(23, 27), which suggests that neural networks may be less differentiated in PTSD.
In our previous work (37), we examined whether functional connectivity in the default
mode network subsystems was differentially disrupted in a cohort of 69 veterans with PTSD
compared to 44 trauma-exposed veterans without PTSD. We found selective alterations in
functional connectivity in the MTL subsystem of the default mode network in PTSD, with
reduced correlation between the PCC and the hippocampus and reduced anticorrelation
between the vMPFC and the dorsal anterior cingulate cortex. Further, we found that functional
connectivity between the PCC and hippocampus was associated with avoidance/numbing
symptoms (i.e., avoidance of thoughts and feelings associated with the trauma, avoidance of
reminders of the trauma, or inability to recall an important aspect of the trauma), such that
PTSD individuals with reduced PCC-hippocampal functional connectivity exhibited more
symptoms. In contrast, no alterations were observed in the dMPFC subsystem of the default
mode network. Although this work is a good first step in further understanding the role of the
default mode network in PTSD, more research is needed to confirm these initial findings. The
ENIGMA PGC-PTSD neuroimaging group offers an unprecedented opportunity to do so in a
large sample. Moreover, PTSD and depression were highly comorbid in our original sample,
raising a question concerning the specificity of our findings. Complicating this question, PTSD
encompasses an array of symptoms, some shared with depression and some unique. Thus, the
ENIGMA PGC-PTSD neuroimaging group also offers an opportunity to investigate the specificity
of these initial findings by including depression as a variable of interest.
The objective of this study is to use seed based resting state functional magnetic
resonance imaging (rs-fMRI) in a large cohort to examine how PTSD affects the default mode
network subsystems. Given the critical role of the vMPFC and hippocampus in PTSD, two areas
associated with the MTL subsystem of the default mode network, as well as our initial findings,
we hypothesize that PTSD will be associated with decreased default mode network functional
connectivity specific to the MTL subsystem. Additionally, in light of evidence for diminished
network segregation in PTSD (23, 27, 37), we hypothesize that PTSD will be associated with
increased connectivity (i.e., reduced anticorrelation) between the default mode network and
regions outside of the default mode network, such as those in the salience and central executive
networks. Lastly, we hypothesize that MTL subsystem disruptions will be specific to PTSD when
accounting for depression.
B. Analyses Plan
To use seed based resting state functional magnetic resonance imaging (rs-fMRI) in a
large cohort to examine how PTSD affects the default mode network subsystems.
1. We hypothesize that PTSD will be associated with decreased default mode network
functional connectivity specific to the MTL subsystem.
2. We hypothesize that PTSD will be associated with increased connectivity (i.e., reduced
anticorrelation) between the default mode network and regions outside of the default
mode network, such as those in the salience and central executive networks.
3. We hypothesize that MTL subsystem disruptions will be specific to PTSD when
accounting for depression.
Variables to be used in the analysis (the main predictor and outcome variables, and
potential covariates must be identified)
• Diagnosis (PTSD vs healthy controls)
• PTSD symptom severity (including symptom subscores)
• For depression sub analysis- depression diagnosis (comorbid depression PTSD, PTSD
only, depression only, controls)
• DMN subsystem functional connectivity
o Hubs of core network (PCC and aMPFC)
o Hub of dorsal medial prefrontal cortex subsystem (dMPFC)
o Hub of medial temporal lob sybsystem (vMPFC)
• Depression (yes/no)
• mTBI (yes/no)
PTSD x Age
Childhood Trauma (number of categories from CTQ)
PTSD x Childhood Trauma
Comorbidity (depression and alcohol use disorder)
Some of the thalamic nuclei defined by Iglesias and colleagues are very small. To minimize
floor effects and segmentation failures, we recombine these subnuclei to five larger groups
of thalamic subnuclei per hemisphere (see table below).
Eligible participants will be accessed through the ENIGMA-PGC PTSD consortium.
Resting state scans are estimated to include ~1500 PTSD patients and ~1500 controls. PTSD
diagnosis will be obtained from individual studies and was assessed with the Clinician-
Administered PTSD Scale (CAPS), the PTSD Symptom Scale (PSS), or equivalent.
Exclusionary criteria will include (a) past or current Axis I disorders other than PTSD or MDD,
(b) current substance disorder, (c) history of moderate or severe TBI, and/or (d) history of a
significant neurological condition (e.g., stroke).
Resting State fMRI Analyses
Preprocessing. Resting state data will be analyzed centrally and preprocessed using
the ENIGMA resting-state pipeline (38). First, we will use Marchenko-Pastur principal
components analysis for denoising to improve the signal-to-noise ratio of the data. Next, we will
correct for spatial distortion associated with long-TE gradient echo imaging (i.e., using gradientecho
fieldmap or reversed-gradient approach). Third, we will compute a transformation by
registering the base volume to the ENIGMA EPI template to develop a spatial template and
spatial atlas, which will be used for regression of the global signal and an anatomical spatial
reference frame. Correction for head motion will then be performed by registering each volume
to the volume with the minimum outlier fraction. Nuisance variables including linear trend, the 6
motion parameters and their derivatives, and the time courses of white matter and cerebrospinal
fluid (CSF) from the lateral ventricles will be modelled in multiple linear regression analyses.
Time points of excessive motion (>0.2mm) will be further censored from the analyses. Images
will be spatially normalized to the ENIGMA EPI template in MNI standard space and smoothed
for group-level analyses.
First-level processing. Whole-brain resting-state fMRI analyses will be performed using
a seed based approach. Seeds will consist of four 8-mm spherical regions of interest (ROIS)
obtained from Andrews-Hanna et al. (28): PCC (MNI coordinates = -8, -56, 26), aMPFC (MNI
coordinates = -6, 52, -2), dMPFC (MNI coordinates = 0, 52, 26), and vMPFC (MNI coordinates =
0, 26, -18). The PCC and aMPFC seeds were chosen because they represent the two core
hubs of the default mode network; the dMPFC and vMPFC seeds were selected because they
represent the core hubs of the dMPFC and MTL subsystems, respectively. All seeds and ROIs
of CSF, white matter, and whole brain will be first transformed to each individual’s native space
and then the mean time series (based on all of the voxels within the region) will be computed.
Next, we will complete a whole-brain voxel-wise analysis assessing the correlation between the
seed region and the rest of the brain, with nuisance regressors (CSF, white matter, and whole
brain time-series along with the motion parameters) included in the model.
Group-level processing. To determine connectivity differences across groups (PTSD v.
controls), group level connectivity maps will be generated for each seed. Age, sex, depression
(yes/no), mTBI (yes/no), and scanner site will be entered into the model as covariates. Statistic
images will be thresholded using clusters determined by p<0.001 with a corrected cluster
significance threshold of p=0.05.
To examine associations between functional connectivity and PTSD symptom subscores,
Z-values of significant functional connectivity clusters in the group contrast will be
extracted from connectivity maps and entered into SPSS. For the subset of participants that
have more detailed information regarding PTSD including PTSD symptom sub-scores, Pearson
correlations will be calculated between functional connectivity Z-values and CAPS reexperiencing,
avoidance/numbing, and hyperarousal scores. Bonferroni correction will be used
to correct for multiple comparisons.
To determine whether the observed PTSD alterations could be linked specifically to
PTSD rather than depression, additional analyses will be limited to participants with depression
information and groups will be further divided into comorbid depression and PTSD, PTSD only,
depression only, and controls. Group level connectivity maps will be generated for each seed.
Age, sex, mTBI (yes/no), and scanner site will be entered into the model as covariates. Statistic
images will be thresholded using clusters determined by p<0.001 with a corrected cluster
significance threshold of p=0.05.
C. Investigative Team
1. Danielle Sullivan
2. Jasmeet Hayes
3. Mieke Verfaellie
4. Mark Miller
5. Erika Wolf
6. Mark Logue
7. David Salat
D. Resources Needed
Describe the resources needed to achieve the aims of the analysis, including variables
needed, analysis support, and any other issues that may affect the feasibility of the plan.
Resting state data will be analyzed centrally and preprocessed using the ENIGMA
resting-state pipeline using FSL’s FEAT program. Whole-brain resting-state fMRI analyses will be
performed using a seed based approach. Seeds will consist of four 8-mm spherical regions of
interest (ROIS) obtained from Andrews-Hanna et al. (28): PCC (MNI coordinates = -8, -56, 26),
aMPFC (MNI coordinates = -6, 52, -2), dMPFC (MNI coordinates = 0, 52, 26), and vMPFC (MNI
coordinates = 0, 26, -18). All seeds and ROIs of CSF, white matter, and whole brain will be first
transformed to each individual’s native space and then the mean time series (based on all of the
voxels within the region) will be computed. Next, we will complete a whole-brain voxel-wise
analysis assessing the correlation between the seed region and the rest of the brain, with
nuisance regressors (CSF, white matter, and whole brain time-series along with the motion
parameters) included in the model. Then, group level connectivity maps will be generated
examining our variable of interest along with covariates.
The following is the standard PGC policy about secondary analyses. Any deviation from this
policy needs to be described and justified, and could negatively impact the proposal.
PGC investigators who are not named on this proposal but who wish to substantively contribute
to the analysis and manuscript may contact the proposing group to discuss joining the proposal.
We will follow the authorship policy of the PGC-PTSD which can be found at https://pgcptsd.
(a) are you following the authorship policies of the groups involved? YES see https://pgcptsd.
(b) will there be a writing group and if so, who will be included? The writing group will be
comprised of the investigative team (#1 – #5) listed above.
(c) what groups or individuals will be listed as authors? Authors will include the writing group
plus individual and group contributors of data and analysis from each site (generally 2-3
co-authors from each site).
(d) will PGC members not listed as named authors be listed at the end of the manuscript?
All individuals who meet the criteria established in the PGC-PTSD authorship policy will
be co-authors. Other PGC members will not be listed at the end of the manuscript.
(e) will PGC members or groups be listed as “collaborators” on the PubMed abstract page?
All individuals and groups who meet the authorship criteria of the PGC-PTSD authorship
policy will be listed as collaborators on the PubMed abstract page. No other individuals
or groups will be listed.
(f) how will funding sources be handled or acknowledged? All funding sources that
supported data collection and analysis will be listed in the manuscript.
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