Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2017 Aug 1.
Published in final edited form as: J Psychiatr Res. 2016 May 13;79:93–100. doi: 10.1016/j.jpsychires.2016.05.003

Early Life Stress Predicts Thalamic Hyperconnectivity: a Transdiagnostic Study of Global Connectivity

Noah S Philip a,b,*, Audrey R Tyrka b, Sarah E Albright a, Lawrence H Sweet c, Jorge Almeida b, Lawrence H Price b, Linda L Carpenter b
PMCID: PMC4894492  NIHMSID: NIHMS789197  PMID: 27214526

Abstract

Early life stress (ELS) is an established risk factor for psychiatric illness and is associated with altered functional connectivity within- and between intrinsic neural networks. The widespread nature of these disruptions suggests that broad imaging measures of neural connectivity, such as global based connectivity (GBC), may be particularly appropriate for studies of this population. GBC is designed to identify brain regions having maximal functional connectedness with the rest of the brain, and alterations in GBC may reflect a restriction or broadening of network synchronization. We evaluated whether ELS severity predicted GBC in a sample (N=46) with a spectrum of ELS exposure. Participants included healthy controls without ELS, those with at least moderate ELS but without psychiatric disorders, and a group of patients with ELS− related psychiatric disorders. The spatial distribution of GBC peaked in regions of the salience and default mode networks, and ELS severity predicted increased GBC of the left thalamus (corrected p < .005, r = .498). Thalamic connectivity was subsequently evaluated and revealed reduced connectivity with the salience network, particularly the dorsal anterior cingulate cortex (corrected p < .005), only in the patient group. These findings support a model of disrupted thalamic connectivity in ELS and trauma-related negative affect states, and underscore the importance of a transdiagnostic, dimensional neuroimaging approach to understanding the sequelae of trauma exposure.

Keywords: early life stress, thalamus, functional connectivity, salience network, dorsal anterior cingulate cortex, default mode network

INTRODUCTION

Early life stress (ELS), often defined as childhood maltreatment, abuse, and neglect, is associated with up to 45% of all childhood-onset and 30% of later-onset psychiatric disorders (Green et al., 2010). There is a transdiagnostic impact of stress exposure during development, as ELS is commonly associated with disorders characterized by negative affect states, such as major depressive disorder (MDD) and posttraumatic stress disorder (PTSD), that are often chronic and resistant to treatment (Gilbert et al., 2009; Kaffman & Meaney, 2007; Numeroff et al., 2003; Zeanah et al., 2009).

Characterizing the functional neuroimaging characteristics of adults with prior ELS is an important area of recent research, as it is not completely undertstood how ELS leads to subsequent illness or psychopathology. Our group and others have reported altered resting state functional connectivity (RSFC) in subjects with a history of ELS and ELS-related disorders. A prominent finding is reduction of RSFC in the default mode network (DMN) (Bluhm et al., 2009; Cisler et al., 2012; Philip et al., 2013; van der Werff et al., 2013). Other key findings include disruptions in emotional processing networks in subjects with ELS. Cisler et al. (2012) found reduced integration between the left amygdala and frontal regions when comparing participants with ELS but without MDD to those with both ELS and MDD (Cisler et al., 2012). RSFC changes within the executive network have also been implicated in ELS: Cisler et al. (2012) reported that dorsolateral prefrontal cortex (DLFPC) connectivity correlated negatively with ELS severity, but did not correlate with MDD symptom severity, and our group recently reported that ELS was associated with greater anti-correlated RSFC between the DLPFC and DMN (Philip et al., 2014), indicating a deterioration in the intrinsic relationships associated with healthy function (Fox et al., 2005). ELS is also associated with reduced negative connectivity between the amygdala and DMN, as well as with decreased RSFC between regions of the salience network and hippocampus (van der Werff et al., 2013). This body of functional connectivity research is complemented by diffusion and morphometry studies of ELS that consistently demonstrate that ELS adversely impacts neural circuits regulating stress response to threatening stimuli. These effects are prominently observed as reductions in corpus callosum integrity, hippocampal size, and reduced volume of the prefrontal cortex, (Reviewed in Teicher & Samson, 2013). Taken together, these findings illustrate that ELS impacts a broad range of cortical and subcortical networks, and indicate a broad-based effect of ELS on multiple intrinsic resting state networks that implies a relationship between this exposure and multiple domains of psychopathology.

These findings have advanced the development of neural circuit-based models of psychiatric illness, and the involvement of multiple networks suggests a central mechanism of disrupted connectivity. The literature reviewed above largely relied upon seed-based approaches to RSFC, a method that focuses on data from within a single seed voxel or brain region and correlates it with that of all other brain voxels, but may not be sufficient to evaluate broad, multi-network effects. Global-based connectivity (GBC) may be better suited to this application. Based upon graph theory, GBC is a data-driven method that describes the connectivity of each voxel with every other voxel of the brain (Bullmore & Sporns et al., 2009; Cole et al., 2010; Cole et al., 2012; Rubinov & Sporns et al., 2011). As such, changes in GBC can be interpreted to mean a pathological restriction (i.e., reduced GBC) or broadening (i.e., increased GBC) of large-scale network synchronization. GBC therefore may be an ideal method to evaluate exposure, such as ELS, and its impact on voxel inter-connectedness that may be simultaneously affecting multiple intrinsic resting state networks.

To date, few studies have used graph-based analyses in ELS and ELS-related disorders characterized by negative affect. Cisler et al. (2012), described above, used a graph approach to evaluate multiple nodes of the emotional processing network, but this analysis was, by design, restricted to a limited number of brain regions. To our knowledge, only one study has used GBC in MDD: Wang et al. (2014) applied GBC in a sample of MDD patients with or without a history of childhood neglect and matched controls. Compared to controls, the MDD group demonstrated increased global connectivity within the DMN, occipital, and sensorimotor regions. Furthermore, relative to MDD patients without neglect, the MDD group with neglect demonstrated lower global connectivity within the dorsomedial and lateral prefrontal cortex, ventrolateral prefrontal cortex, thalamus, and limbic regions; these reductions were correlated with the severity of childhood neglect. The authors concluded that their findings provided empirical neural evidence of the role of childhood neglect in adult MDD, although the principal limitation of this study was the absence of a group with childhood neglect and without MDD.

In light of these findings, we conducted a study that evaluated whether ELS severity predicted GBC, to investigate whether there might be a central mechanism behind prior observations of disrupted connectivity in ELS and ELS-related disorders. This approach was designed to be transdiagnostic, by including healthy participants with a broad range of ELS and patients with multiple types of trauma-related diagnoses. We hypothesized that ELS would predict disruptions in multiple intrinsic resting state networks, with the most prominent effects observed in our patient sub-sample.

MATERIALS AND METHODS

Participants and Measures

MRI images from 46 right-handed participants were used for this study. This sample represents an imaging database comprised of subjects enrolled in studies of ELS, MDD and PTSD at Brown University-affiliated hospitals. This group included n = 18 medication-free healthy controls (ELS−/DIS−), n = 14 medication-free participants with ELS and without psychiatric disorders (ELS+/DIS−), and n = 14 patients with trauma-related disorders associated with negative affect states (ELS+/DIS+). Data from a portion of these participants have been previously reported (Philip et al., 2014), but no analyses in this study have been previously performed. Participants received diagnostic interviews using the Structured Clinical Interview for DSM-IV-TR (SCID) (First et al., 1994). Severity of childhood maltreatment was measured using the Childhood Trauma Questionnaire (CTQ) (Bernstein & Fink, 1998). Inclusion criteria for the ELS+/DIS− group required a report of physical, emotional, or sexual abuse as a child, operationally defined as a score of at least “moderate/severe” on any of the five CTQ subscales (i.e., emotional, physical, or sexual abuse, emotional or physical neglect); participants meeting criteria for MDD, PTSD, or any other major Axis I diagnosis were excluded. Inclusion in the ELS+/DIS+ group required meeting criteria for both MDD and PTSD, with currently active symptoms of both disorders and sufficient symptom severity to warrant an illness rating of at least moderate severity on the Inventory of Depressive Symptoms, Self-Report (IDSSR) (Rush et al., 2003) and the PTSD checklist (PCL) (Weathers et al., 2013). Participants in this group did not have to meet a particular threshold of childhood trauma for inclusion. The ELS−/DIS− group was defined by the absence of childhood trauma, confirmed with the CTQ, and absence of any major Axis I diagnosis. Participants in the ELS− and ELS+/DIS− groups were required to be free from any psychotropic medication, whereas participants in the ELS+/DIS+ group were required to be stable on their medications for at least 6 weeks prior to MRI scanning. Exclusion criteria for all groups were standard MRI contraindications and any uncontrolled or active medical illness that could compromise neuroimaging results. Negative urine toxicology screens (and pregnancy tests, when appropriate) were required prior to scanning for all participants. Analysis of variance (ANOVA) was used to examine potential group differences in demographic and associated variables in SPSS (v21, Armonk, NY), followed by post-hoc comparisons to identify between-group demographic differences. Institutional Review Boards of Brown University, Butler Hospital, and the Providence VA Medical Center approved this study. All procedures were conducted in accordance with the Declaration of Helsinki, and all participants provided voluntary written informed consent following full explanation of study procedures.

Image Acquisition

Neuroimaging data were acquired at the Brown University MRI Research Facility (mri.brown.edu) using a Siemens TIM TRIO 3T scanner (Siemens, Erlangen, Germany) equipped with a 32-channel head coil. Whole-brain high-resolution (1 mm3) T1 images were acquired for anatomic reference (TR = 1900ms, TE = 2.98ms, and FOV 256mm2). Resting state echoplanar images were acquired for 8 minutes, during which participants were instructed to remain awake and observe a white fixation cross against a black background (TR = 2500ms, TE = 28ms, FOV = 192mm2, and matrix size 642 in 3-mm axial slices; this sequence yielded a total of 192 whole brain volumes with spatial resolution of 3 mm3/voxel).

Image Preprocessing

All preprocessing and imaging data analyses utilized the Analysis of Functional NeuroImages (AFNI) (Cox, 1996), unless otherwise indicated. After image acquisition, anatomic data were transformed to standard Talairach stereotaxic space (Talairach & Tournoux, 1988). Echoplanar data were slice-time corrected and reconstructed into 3D + time datasets, which were concatenated and registered to the fourth volume of the first series to minimize movement artifact. TRs with head motion >1.5mm (informed by Posner et al., 2013) or >10% outliers were censored during preprocessing, and any participants with < 85% retained time points were excluded a priori. Bandpass filtering was performed at .009<f<0.08 to reduce the effect of high-frequency noise and low-frequency drift, and functional data were scaled to normalize within-run intensity. Nuisance variables for each voxel included average ventricle and white matter time series, and six-parameter estimates of head motion (demeaned and derivative values). The predicted time course of these nuisance variables was subtracted from the full voxel time series to yield a residual time series. Smoothing up to a 6mm FWHM Gaussian distribution was performed after preprocessing. Global signal regression was not implemented due to the concern that this preprocessing step may spuriously influence anticorrelations (Murphy et al., 2009; Fox & Greicius, 2010; Saad et al., 2012).

Due to concern about the effect of head motion on RSFC results (Satterthwaite et al., 2012; Power et al., 2012; van Dijk et al., 2012), in addition to censoring TRs with motion and outliers during preprocessing, we evaluated the bivariate correlation between head motion and CTQ score, and compared head motion between the three groups. These analyses were performed using a combination of AFNI and SPSS, and found no significant correlation between CTQ and head motion, and no significant group differences in head motion (see supplemental information).

Global-Based Connectivity

We used GBC methods based on prior studies (Cole et al., 2012; Anticevic et al., 2014). First, for each participant, gray matter masks were generated using automatic segmentation in Freesurfer (v.5.3; surfer.nmr.mgh.harvard.edu). We used the same software version, workstation type and operating system for all participants (Gronenschild et al., 2012). Only gray matter in which >85% of participants had non-zero voxels (defined by their freesurfer segmentation) were used in subsequent steps (see supplemental information). GBC was calculated within AFNI (using the command 3dTCorrMap), which computed the connectedness of each voxel in the brain with every other voxel in the brain, within gray matter masks. Fisher-transformed correlation values of GBC served as the principal unit for subsequent analysis. We initially performed a one-sample, voxel-wise t-test to describe the spatial distribution of GBC, which was followed by a predictor analysis, where we evaluated those voxels in the brain where CTQ score significantly predicted GBC, (using age, gender, and IDSSR score as a priori-defined covariates in the model) within AFNI. Output from the latter analysis provided an estimate of the unique variance associated with CTQ score, when controlling other variables and corrected for multiple comparisons. Because of the concern that pharmacology might influence imaging results (Lanius et al., 2010) we converted medication doses into imipramine equivalents (Bollini et al., 1999; Jakubovski et al., 2016), and used this as a covariate in a sensitivity analysis to estimate the impact of medication load on GBC. Voxel-wise GBC analyses were thresholded at Family-Wise Error (FWE)-corrected p < .05 (Z > 2.33, cluster size > 64 voxels. The Z threshold was informed by prior studies of GBC (Anticevic et al., 2014), with empiric correction for the cluster size. This multiple comparison correction was generated with AFNI’s ClusterSim program, which uses 10,000 Monte Carlo simulations to generate corrections based on height and cluster extents, using a group-appropriate matrix and smoothness of the data. Positive and negative GBC clusters were considered separately within ClusterSim to avoid potentially cancelling out opposing connectivity. This dimensional (i.e., inclusive of a range of ELS severity), transdiagnostic (i.e., inclusive of MDD and PTSD) GBC approach was the primary outcome measure of this study.

To interpret the GBC results, any brain region identified by this predictor analysis was explored using a traditional seed-based functional connectivity (following methods used in Philip et al., 2014). This approach was designed to describe differences in RSFC between the three participant groups (i.e., ELS−/DIS−, ELS+/DIS−, ELS+/DIS+). First, we saved the spatial extent of the GBC findings as a binary mask, extracted the average BOLD time series within this mask, and then used voxel-based general linear modeling (GLM) to quantify the relationship between the seed and all other voxels in the brain. GLM results yielded individual R2 values for time series data, which were normalized into Z scores of connectivity, and independent samples t-tests were used to compare RSFC between groups. These contrasts were controlled for a priori covariates age, gender and depressive symptom severity. Results of all group contrasts were thresholded at FWE-corrected p < .05, utilizing the height and cluster correction reported above. Z scores of RSFC were extracted from any regions that differed between groups to describe the direction of observed effects.

RESULTS

Participants

Demographics are reported in Table 1. The ELS+/DIS+ group was significantly older than the other two groups, who did not differ in age, and there was a non-significant trend towards more females in the ELS−/DIS− and ELS+/DIS− groups. ELS+/DIS+ participants were on psychiatric medications used for MDD and PTSD, inclusive of first- and second-line therapeutic agents in multiple classes (see supplemental information for descriptions of psychopharmacology and psychiatric comorbidity). As expected, CTQ severity was greater in the ELS+/DIS+ and ELS+/DIS− compared to ELS−/DIS−, and no significant differences in CTQ score were observed between the ELS+/DIS− and ELS+/DIS+ groups. Comparable distributions of ELS sub-types were observed in both ELS+/DIS− and ELS+/DIS− groups, except for physical neglect, which differed significantly across groups, with the most frequent reports occurring in the ELS+/DIS+ group. As expected, IDSSR score was highest in the ELS+/DIS+ group.

Table 1.

Demographic Characteristics (N = 46)

Characteristic ELS−/DIS−
(n = 18)		 ELS+/DIS−
(n = 14)		 ELS+/DIS+
(n = 14)		 F P
Age (Mean ± SD years) 32 ± 9 37 ± 10 51 ± 13 12.31 < .001a
Gender (n, % Female) 11 (61) 7 (50) 4 (29) 1.71 .194
College Education (%) 61 64 43 .54 .584
CTQ Score (Mean ± SD) 22 ± 15 59 ± 16 64 ± 9 43.79 < .001b
CTQ Sub-Scale (Mean ± SD)
  Emotional Abuse 5 ± 4 12 ± 4 14 ± 5 21.62 < .001b
  Physical Abuse 4 ± 3 11 ± 5 13 ± 6 15.50 < .001b
  Sexual Abuse 4 ± 3 12 ± 8 10 ± 5 10.48 < .001b
  Emotional Neglect 6 ± 4 14 ± 4 14 ± 6 19.49 < .001b
  Physical Neglect 4 ± 3 10 ± 4 13 ± 2 33.90 < .001c
IDSSR Score (Mean ± SD) 2 ± 2 10 ± 6 49 ± 11 180.42 < .001c
Open in a new tab

Key: ELS, early life stress; DIS, disorder; SD, standard deviation; CTQ, Childhood Trauma Questionnaire; IDSSR, Inventory of Depressive Symptoms, Self-Report

a

significant at p < .001 across groups, no difference observed between ELS−/DIS− and ELS+/DIS− groups.

b

significant at p < .001 across groups, no difference observed between ELS+/DIS− and ELS+/DIS+ groups.

c

significant at p < .001, with statistically significant differences observed between all groups.

Global-Based Connectivity

Initial one-sample t-tests revealed a broad distribution of GBC that peaked in the right parahippocampal gyrus (BA 19) and left insula (BA 13) (Figure S1). CTQ significantly predicted increased GBC within a 94-voxel cluster located in the left thalamus (corrected p < .005) (Figure 1). CTQ score accounted for 24.8% of the variance (partial correlation r = .498; p < .001) (Figure 2). This effect persisted even when not controlling for age (r = .422, p = .004). When performing a sensitivity analysis on the effect of medications on these results, the outcome was nearly identical, with a 71-voxel cluster in the same location (corrected p = .025); no unique effects of medication were observed. When we explored whether any individual sub-types of ELS were associated with this thalamic finding, results from emotional neglect and sexual abuse nominally survived correction for multiple comparisons (both corrected p = .05). There was no correlation between IDSSR score and GBC (p > .1).

Figure 1.

Figure 1

Open in a new tab

Severity of childhood trauma predicts increased thalamic global-based connectivity. Image is shown using radiologic convention and Z coordinates (Talairach) are shown on the bottom left. Image is corrected p < .05. Color bars represent Z scores.

Figure 2.

Figure 2

Open in a new tab

Scatterplot of correlation between strength of thalamic seed global based connectivity and severity of childhood trauma, adjusted for age, gender and depressive symptom severity. Partial correlation p < .001. Key: GBC, global-based connectivity; CTQ, childhood trauma questionnaire score.

Exploring Increased Thalamic RSFC: Comparing Participant Groups

In the contrast of ELS+/DIS− vs. ELS− there was significantly positive RSFC in the ELS+/DIS− group between the thalamus and lingual gyrus (corrected p < .005), and right middle temporal gyrus (corrected p < .05). For the contrast ELS+/DIS+ vs. ELS−/DIS−, there was significantly positive RSFC in the ELS+/DIS+ group between the thalamus and left postcentral gyrus (corrected p < .01), and when comparing ELS+/DIS+ vs. ELS+/DIS−, the ELS+/DIS+ group had significantly negative RSFC between the thalamus and two regions of the salience network (defined by Shirer et al., 2012), the bilateral dorsal anterior cingulate (dACC) (corrected p < .005) and left cerebellar culmen (corrected p < .005), as well as within the cerebellar vermis extending into the left nodule (corrected p < .05)(Table 2)(Figure 3A-C). Negative thalamus to dACC connectivity was also observed in the ELS+/DIS+ vs. ELS−/DIS− contrast, which survived correction for height (Z = −2.37), although not cluster (k = 25 voxels). Observed effects were due to opposite direction in connectivity in almost all participant group contrasts, except in the ELS+/DIS− vs. ELS+/DIS+ where the dACC result reflected attenuated positive connectivity (Figure 4A-C).

Table 2.

Group Contrasts: Thalamic RSFC of Participant Groups

ELS+/DIS− vs. ELS-/DIS−
Region BA Peak Coordinates Cluster Size Z Score
Bilateral Lingual Gyrus 18 −4, −70, 5 93 3.2
R Middle Temporal Gyrus 37 41, −58, −4 66 3.0
ELS+/DIS+ vs. ELS−/DIS−
Region BA Peak Coordinates Cluster Size Z Score
L Postcentral Gyrus 4 −61, −13, 20 85 3.0
ELS+/DIS+ vs. ELS+/DIS−
Region BA Peak Coordinates Cluster Size Z Score
Bilateral dACC 24/32 −1, −1, 47 100 −3.2
L Culmen - −34, −49, −22 93 −4.6
L Nodule / Vermis - −1, −43, −34 67 −3.7
Open in a new tab

Key: ELS, early life stress; DIS, disorder; R, right; L, left; dACC, bilateral dorsal anterior cingulate cortex. Results from group contrasts, corrected at p < .05. For each comparison, the second group served as the reference (i.e., contrasts of ELS+/DIS− vs. ELS−/DIS− used ELS−/DIS− as the reference group).

Figure 3A-C.

Figure 3A-C

Open in a new tab

Group contrasts comparing A) ELS+/DIS− vs. reference ELS−, showing increased RSFC from the thalamus to the lingual gyrus and medial temporal gyrus, B) ELS+/DIS+ group vs. reference ELS− group, showing increased RSFC to the postcentral gyrus, and C) ELS+/DIS+ group vs. reference ELS+/DIS− group showing reduced RSFC to the dorsal anterior cingulate and vermis. The cerebellar culmen result is not displayed. Images are shown using radiologic convention and Z slice coordinates (Talairach) are shown on the bottom left of the corresponding image, except for C that uses X slice coordinates. Images are corrected p < .05.

Figure 4A-C.

Figure 4A-C

Figure 4A-C

Figure 4A-C

Open in a new tab

Bar graphs of connectivity strength, illustrating the directions of effect of observed changes in RSFC, in regions that significantly differed between groups. Y-axis refers to Z scores of connectivity. Letters correspond to contrast images shown in Figure 3A-C.

DISCUSSION

Using a data-driven global connectivity approach, we found that the severity of childhood trauma predicted thalamic hyperconnectivity in a transdiagnostic sample inclusive of healthy controls, an intermediate phenotype (ELS exposure but without disorder), and a disorder group composed of the two major psychiatric diagnoses associated with childhood trauma. These results may shed light on imaging correlates of childhood trauma, and provide a theoretical explanation for the neural network disruptions associated with ELS− and related disorders observed in prior imaging studies.

Our finding of increased thalamic GBC is consistent with previous reports in MDD and PTSD. Multiple groups have reported increased thalamocortical connectivity or increased thalamic activity in MDD. Greicius et al. (2007) first reported that increased thalamocortical activity was associated with MDD, and Lui et al. (2011) later reported disruptions in functional connectivity between thalamic and prefrontal regions. A meta-analysis of imaging findings in MDD also found increased resting cerebral blood flow in the thalamus (Hamilton et al., 2012), although Wang et al. (2014) found results in the opposite direction, with reduced GBC in the thalamus in their sample of MDD patients with neglect. It is possible that different types of trauma impact GBC, as we found that emotional neglect and sexual abuse were important components of GBC effects. Altered thalamic connectivity has also been found in PTSD. Ramage et al. (2013) conducted a meta-analysis of trauma processing in PTSD and found important thalamic mediation of emotional processing in PTSD. Zhong et al. (2014) found increased localized subcortical connectivity inclusive of the thalamus, and Lanius et al. (2005) reported increased connectivity between the ventrolateral thalamus and frontal regions, and between the DMN and insular cortex, during PTSD-related dissociation symptoms. Taken together, the results reported here and the prior literature in MDD and PTSD support the notion of that changes in thalamic connectivity may be a shared trait between psychiatric disorders and prior ELS, suggesting that thalamic hyperconnectivity might represent a marker of earlier exposure.

One important question is how to place these findings in the broader context of the literature. A central thalamic function is to regulate the oscillatory pattern of cortical networks (Buzsaki et al., 2004). Since correlational RSFC methods depend upon the oscillatory pattern of the BOLD time series, it is possible that changes in thalamic oscillations, observed here as increased GBC, may have led to our previous results of reduced DMN RSFC (Philip et al., 2013) and altered RSFC between the DMN and executive networks (Philip et al., 2014). This interpretation is consistent with the theory of thalamocortical dysrhythmia (TCD) proposed by Llinas et al. (1999), in which abnormal thalamic oscillations adversely affect the frequency domains of neural networks. TCD is hypothesized to underlie several neuropsychiatric disorders, including those associated with ELS (Schulman et al., 2011), and it is possible that the observed increase in thalamic GBC may represent an imaging correlate of TCD. While this interpretation remains speculative, it is supported by work by Hamilton et al. (2015), which postulated that aberrant thalamic activity within the DMN could result in a shift of this network into pathological rumination by creating connections between the DMN and subgenual anterior cingulate. Future multimodal imaging studies, combining functional connectivity and thalamic signaling (i.e., magneto-encephalography) would be potential approaches to further evaluate this hypothesis.

Results from our follow-up seed-based exploratory analysis provide insight into the impact of altered GBC. Most remarkable was the contrast between the ELS+/DIS− and ELS+/DIS+ groups, which found reduced connectivity between the thalamus and dACC. The dACC plays a well-known role in error detection/conflict monitoring (Ridderinkhof et al., 2004; Bush et al., 2000) and reward-based decision-making (Bush et al., 2002). From the perspective of neural networks, this area is a hub in both the salience network and DMN (Sheline et al., 2009; Shirer et al., 2012), and is a key area in the “rich club” of brain regions that mediate within- and between network information flow (van den Heuvel & Sporns, 2013). This is aligned with the extensive body of prior work that found abnormal dACC function in both PTSD and MDD (Hamilton et al., 2012; Kaiser et al., 2015; Lei et al., 2015; reviewed in Patel et al., 2012). Our results indicate that disruption of the dACC may constitute a crucial threshold associated with development of clinical symptoms, mediated by pathologically increased thalamic GBC. This explanation would suggest that, in patients with ELS-related disorders, the thalamus is over-connected to the cortex, and a consequence of this over-connectedness is a reduction in needed connectivity with the dACC. This interpretation is consistent with the literature linking ELS with reduced pruning during development (reviewed in McEwen et al., 2015).

Other brain regions implicated in the significant group contrasts are consistent with other studies of trauma-related disorders. For example, multiple groups have reported trauma-related changes to the medial temporal gyrus, with its putative role in integrating memory function and imagery (e.g., Shin et al., 2001; Dickie et al., 2008; Moores et al., 2008). The lingual gyrus, with its likely role in visual processing and integration, has also been implicated in imaging studies of depression, conflict resolution, and impaired reward response (Dichter et al., 2009; Kessler et al., 2011; Chechko et al., 2013), whereas the postcentral gyrus has been associated with depression and pain (Bar et al., 2007). Taken together, we interpret this to mean that thalamic hyperconnectivity is associated with broad-based disruptions between important brain functions, inclusive of salience, memory and visual processing and sensation, that corresponds to both the literature and our clinical experience with this population. Future longitudinal studies should evaluate the predictive validity of this multi-network matrix as a risk marker of future clinical illnesses and potential target for intervention.

This study has several limitations. As with any study using novel methods, replication will be needed to confirm the reported results. This study also included a modest sample size, particularly for subgroup analyses, and small studies can produce inflated effect sizes (Yarkoni, 2009). That stated, the results obtained from whole-brain evaluations of GBC matched connectivity patterns of intrinsic neural networks and were congruent with prior findings from similar samples. Unfortunately, no participants in the DIS+ group had low CTQ scores, which would have allowed a more nuanced analysis regarding the effects of childhood vs. adult trauma, as prior work has indicated differential sequelae of these exposures (Birn et al., 2014). While both ELS− and ELS+/DIS− groups in this study were medication-free, the ELS+/DIS+ group was taking medications, and as such we are unable to separate illness status from the confounding effect of pharmacotherapy. While medications themselves may impact neuroimaging (e.g., as discussed in Lanius et al., 2010), the fact that participants had active psychiatric symptoms despite stable pharmacotherapy, and modest impact of medication as a covariate suggests an attenuated effect of medications on observed results. DIS+ participants were older, which was expected as these disorders by definition occur over time, and it is possible that imaging results were confounded by age, although we a priori statistically adjusted for this variable; sensitivity analysis showed increased thalamic GBC remained strongly significant when not adjusting for age. Prior work has revealed reduced thalamus to salience network connectivity associated with healthy aging (Cao et al., 2014), although this reduction was seen in older participants than in our sample, and was not evaluated in the context of childhood trauma. Other limitations include those inherent to cross-sectional studies and retrospective self-reports of ELS.

In summary, this study found that ELS predicted increased thalamic global connectivity, which can explain previous observations of multiple network disruption. This work suggests thalamic hyperconnectivity may be a shared trait across ELS-related psychiatric disorders, and may represent an imaging biomarker of threshold exposure. Future work is needed to further evaluate its utility as a diagnostic marker of prior exposure, or as a potential predictor of treatment response.

Supplementary Material

1

Fig. S1. Global-based connectivity distribution across the brain. High involvement of the salience network and posterior default mode network regions are observed. Images are shown using radiologic convention, with slice coordinates (Talairach) on the bottom left. Image is corrected p < .05. Color bars represent Z scores.

2

ACKNOWLEDGEMENTS

This work was supported by a Career Development Award (IK2 CX000724) from the U.S. Department of Veterans Affairs (Clinical Sciences Research and Development) (NSP), NIH grants 5R01MH068767 (LLC) and R25 5R25MH101076 (JA), and Rhode Island Foundation (NSP). The opinions herein represent those of the authors and not the Department of Veterans Affairs or the NIH. The funders had no involvement in the collection, analysis and interpretation of the data, in the writing of the report, or in the decision to submit these results for publication. We thank all of the participants.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

CONFLICT OF INTEREST STATEMENT

The authors report no relevant conflicts of interest.

CONTRIBUTORS

Dr. Philip designed the study, wrote the protocol and led the manuscript writing. Ms. Albright and Dr. Almeida drafted the manuscript; Ms. Albright managed the literature searches and participant recruitment. Dr. Sweet designed the imaging protocol. Drs. Tyrka and Price contributed to study conceptualization and design. Dr. Carpenter assisted with study design and facilitated participant recruitment and evaluation. All authors contributed to, and have approved, the final manuscript.

REFERENCES

  1. Anticevic A, Hu S, Zhang S, Savic A, Billingslea E, Wasylink S, Pittenger C. Global resting-state functional magnetic resonance imaging analysis identifies frontal cortex, striatal, and cerebellar dysconnectivity in obsessive-compulsive disorder. Biol Psychiatry. 2014;75:595–605. doi: 10.1016/j.biopsych.2013.10.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bär KJ, Wagner G, Koschke M, Boettger S, Boettger MK, Schlösser R, Sauer H. Increased prefrontal activation during pain perception in major depression. Biol Psychiatry. 2007;62:1281–7. doi: 10.1016/j.biopsych.2007.02.011. [DOI] [PubMed] [Google Scholar]
  3. Bernstein D, Fink L. Childhood Trauma Questionnaire: A Retrospective Self-report. Pearson Education, Inc.; San Antonio, Texas: 1998. [Google Scholar]
  4. Birn RM, Patriat R, Phillips ML, Germain A, Herringa RJ. Childhood maltreatment and combat posttraumatic stress differentially predict fear-related fronto-subcortical connectivity. Depress Anxiety. 2014;31:880–92. doi: 10.1002/da.22291. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bluhm RL, Williamson PC, Osuch EA, Frewen PA, Stevens TK, Boksman K, Lanius RA. Alterations in default network connectivity in posttraumatic stress disorder related to early-life trauma. J Psychiatry Neurosci. 2009;34:187–194. [PMC free article] [PubMed] [Google Scholar]
  6. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008;1124:1–38. doi: 10.1196/annals.1440.011. doi: 10.1196/annals.1440.011. [DOI] [PubMed] [Google Scholar]
  7. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10:186–198. doi: 10.1038/nrn2575. [DOI] [PubMed] [Google Scholar]
  8. Bollini P, Pampallona S, Tibaldi G, Kupelnick B, Munizza C. Effectiveness of antidepressants. Meta-analysis of dose-effect relationships in randomised clinical trials. Br J Psychiatry. 1999;174:297–303. doi: 10.1192/bjp.174.4.297. [DOI] [PubMed] [Google Scholar]
  9. Bush G, Luu P, Posner MI. Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci. 2000;4:215–222. doi: 10.1016/s1364-6613(00)01483-2. [DOI] [PubMed] [Google Scholar]
  10. Bush G, Vogt BA, Holmes J, Dale AM, Greve D, Jenike MA, Rosen BR. Dorsal anterior cingulate cortex: a role in reward-based decision making. Proc Natl Acad Sci U S A. 2002;99:523–8. doi: 10.1073/pnas.012470999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Buzsáki G, Draguhn A. Neuronal oscillations in cortical networks. Science. 2004;304:1926–9. doi: 10.1126/science.1099745. [DOI] [PubMed] [Google Scholar]
  12. Cao W, Luo C, Zhu B, Zhang D, Dong L, Gong J, Gong D, He H, Tu S, Yin W, Li J, Chen H, Yao D. Resting-state functional connectivity in anterior cingulate cortex in normal aging. Front Aging Neurosci. 2014;6:280. doi: 10.3389/fnagi.2014.00280. doi: 10.3389/fnagi.2014.00280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Chechko N, Augustin M, Zvyagintsev M, Schneider F, Habel U, Kellermann T. Brain circuitries involved in emotional interference task in major depression disorder. J Affect Disord. 2013;149:136–45. doi: 10.1016/j.jad.2013.01.013. [DOI] [PubMed] [Google Scholar]
  14. Cisler JM, James GA, Tripathi S, Mletzko T, Heim C, Hu XP, Kilts CD. Differential functional connectivity within an emotion regulation neural network among individuals resilient and susceptible to the depressogenic effects of early life stress. Psychol Med. 2012:1–12. doi: 10.1017/S0033291712001390. [DOI] [PubMed] [Google Scholar]
  15. Cole MW, Pathak S, Schneider W. Identifying the brain’s most globally connected regions. Neuroimage. 2010;49:3132–48. doi: 10.1016/j.neuroimage.2009.11.001. [DOI] [PubMed] [Google Scholar]
  16. Cole MW, Yarkoni T, Repovs G, Anticevic A, Braver TS. Global connectivity of prefrontal cortex predicts cognitive control and intelligence. J Neurosci. 2012;32:8988–99. doi: 10.1523/JNEUROSCI.0536-12.2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 1996;29:162–173. doi: 10.1006/cbmr.1996.0014. [DOI] [PubMed] [Google Scholar]
  18. Dannlowski U, Stuhrmann A, Beutelmann V, Zwanzger P, Lenzen T, Grotegerd D, Domschke K, Hohoff C, Ohrmann P, Bauer J, Lindner C, Postert C, Konrad C, Arolt V, Heindel W, Suslow T, Kugel H. Limbic scars: long-term consequences of childhood maltreatment revealed by functional and structural magnetic resonance imaging. Biol Psychiatry. 2012;71:286–93. doi: 10.1016/j.biopsych.2011.10.021. doi: 10.1016/j.biopsych.2011.10.021. [DOI] [PubMed] [Google Scholar]
  19. Dichter GS, Felder JN, Petty C, Bizzell J, Ernst M, Smoski MJ. The effects of psychotherapy on neural responses to rewards in major depression. Biol Psychiatry. 2009;66:886–97. doi: 10.1016/j.biopsych.2009.06.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Dickie EW, Brunet A, Akerib V, Armony JL. An fMRI investigation of memory encoding in PTSD: influence of symptom severity. Neuropsychologia. 2008;46:1522–31. doi: 10.1016/j.neuropsychologia.2008.01.007. [DOI] [PubMed] [Google Scholar]
  21. Epstein J, Pan H, Kocsis JH, Yang Y, Butler T, Chusid J, Hochberg H, Murrough J, Strohmayer E, Stern E, Silbersweig DA. Lack of ventral striatal response to positive stimuli in depressed versus normal subjects. Am J Psychiatry. 2006;163:1784–90. doi: 10.1176/ajp.2006.163.10.1784. [DOI] [PubMed] [Google Scholar]
  22. Hart H, Rubia K. Neuroimaging of child abuse: a critical review. Front Hum Neurosci. 2012;6:52. doi: 10.3389/fnhum.2012.00052. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. First MB, Spitzer RL, Gibbon M, Williams JBW. Structured clinical interview for axis I DSM-IV disorders. Biometrics Research, New York State Psychiatric Institute; New York: 1994. [Google Scholar]
  24. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A. 2005;102:9673–8. doi: 10.1073/pnas.0504136102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Fox MD, Greicius M. Clinical applications of resting state functional connectivity. Front Syst Neurosci. 2010;4:19. doi: 10.3389/fnsys.2010.00019. doi: 10.3389/fnsys.2010.00019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Gilbert R, Widom CS, Browne K, Fergusson D, Webb E, Janson S. Burden and consequences of child maltreatment in high-income countries. Lancet. 2009;373:68–81. doi: 10.1016/S0140-6736(08)61706-7. [DOI] [PubMed] [Google Scholar]
  27. Grahn JA, Parkinson JA, Owen AM. The cognitive functions of the caudate nucleus. Prog Neurobiol. 2008;86:141–55. doi: 10.1016/j.pneurobio.2008.09.004. doi: 10.1016/j.pneurobio.2008.09.004. [DOI] [PubMed] [Google Scholar]
  28. Green JG, McLaughlin KA, Berglund PA, Gruber MJ, Sampson NA, Zaslavsky AM, Kessler RC. Childhood adversities and adult psychiatric disorders in the national comorbidity survey replication I: associations with first onset of DSM-IV disorders. Arch Gen Psychiatry. 2010;67:113–123. doi: 10.1001/archgenpsychiatry.2009.186. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Greicius MD, Flores BH, Menon V, Glover GH, Solvason HB, Kenna H, Schatzberg AF. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry. 2007;62:429–437. doi: 10.1016/j.biopsych.2006.09.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Gronenschild EH, Habets P, Jacobs HI, Mengelers R, Rozendaal N, van Os J, Marcelis M. The effects of FreeSurfer version, workstation type, and Macintosh operating system version on anatomical volume and cortical thickness measurements. PLoS One. 2012;7:e38234. doi: 10.1371/journal.pone.0038234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Hamilton JP, Etkin A, Furman DJ, Lemus MG, Johnson RF, Gotlib IH. Functional neuroimaging of major depressive disorder: a meta-analysis and new integration of base line activation and neural response data. Am J Psychiatry. 2012;169:693–703. doi: 10.1176/appi.ajp.2012.11071105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hamilton JP, Farmer M, Fogelman P, Gotlib IH. Depressive Rumination, the Default-Mode Network, and the Dark Matter of Clinical Neuroscience. Biol Psychiatry. 2015;78(4):224–30. doi: 10.1016/j.biopsych.2015.02.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Jakubovski E, Varigonda AL, Freemantle N, Taylor MJ, Bloch MH. Systematic Review and Meta-Analysis: Dose-Response Relationship of Selective Serotonin Reuptake Inhibitors in Major Depressive Disorder. Am J Psychiatry. 2016;173(2):174–83. doi: 10.1176/appi.ajp.2015.15030331. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kaffman A, Meaney M. Neurodevelopmental sequelae of postnatal maternal care in rodents: clinical and research implications of molecular insights. J Child Psychol Psychiatry. 2007;48:224–244. doi: 10.1111/j.1469-7610.2007.01730.x. [DOI] [PubMed] [Google Scholar]
  35. Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA. Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. JAMA Psychiatry. 2015;72:603–11. doi: 10.1001/jamapsychiatry.2015.0071. doi: 10.1001/jamapsychiatry.2015.0071. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Keedwell PA, Andrew C, Williams SC, Brammer MJ, Phillips ML. The neural correlates of anhedonia in major depressive disorder. Biol Psychiatry. 2005;58:843–53. doi: 10.1016/j.biopsych.2005.05.019. [DOI] [PubMed] [Google Scholar]
  37. Kessler H, Taubner S, Buchheim A, Münte TF, Stasch M, Kächele H, Roth G, Heinecke A, Erhard P, Cierpka M, Wiswede D. Individualized and clinically derived stimuli activate limbic structures in depression: an fMRI study. PLoS One. 2011;6:e15712. doi: 10.1371/journal.pone.0015712. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Lanius RA, Williamson PC, Bluhm RL, Densmore M, Boksman K, Neufeld RW, Menon RS. Functional connectivity of dissociative responses in posttraumatic stress disorder: a functional magnetic resonance imaging investigation. Biol Psychiatry. 2005;57:873–884. doi: 10.1016/j.biopsych.2005.01.011. [DOI] [PubMed] [Google Scholar]
  39. Lanius RA, Brewin CR, Bremner JD, Daniels JK, Friedman MJ, Liberzon I, McFarlane A, Schnurr PP, Shin L, Stein M, Vermetten E. Does neuroimaging research examining the pathophysiology of posttraumatic stress disorder require medication-free patients? J Psychiatry Neurosci. 2010;35(2):80–9. doi: 10.1503/jpn.090047. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lei D, Li K, Li L, Chen F, Huang X, Lui S, Li J, Bi F, Gong Q. Disrupted Functional Brain Connectome in Patients with Posttraumatic Stress Disorder. Radiology. 2015;276:818–27. doi: 10.1148/radiol.15141700. doi: 10.1148/radiol.15141700. [DOI] [PubMed] [Google Scholar]
  41. Linnman C, Zeffiro TA, Pitman RK, Milad MR. An fMRI study of unconditioned responses in post-traumatic stress disorder. Biol Mood Anxiety Disord. 2011;1:8. doi: 10.1186/2045-5380-1-8. doi: 10.1186/2045-5380-1-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Llinas RR, Ribary U, Jeanmonod D, Kronberg E, Mitra PP. Thalamocortical dysrhythmia: A neurological and neuropsychiatric syndrome characterized by magnetoencephalography. Proc Natl Acad Sci U S A. 1999;96:15222–15227. doi: 10.1073/pnas.96.26.15222. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Lui S, Wu Q, Qiu L, Yang X, Kuang W, Chan RC, Gong Q. Resting-state functional connectivity in treatment-resistant depression. Am J Psychiatry. 2011;168:642–648. doi: 10.1176/appi.ajp.2010.10101419. [DOI] [PubMed] [Google Scholar]
  44. McEwen BS, Nasca C, Gray JD. Stress Effects on Neuronal Structure: Hippocampus, Amygdala and Prefrontal Cortex. Neuropsychopharmacol. 2015 doi: 10.1038/npp.2015.171. doi: 10.1038/npp.2015.171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Michl LC, McLaughlin KA, Shepherd K, Nolen-Hoeksema S. Rumination as a mechanism linking stressful life events to symptoms of depression and anxiety: longitudinal evidence in early adolescents and adults. J Abnorm Psychol. 2013;122:339–52. doi: 10.1037/a0031994. doi: 10.1037/a0031994. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Moores KA, Clark CR, McFarlane AC, Brown GC, Puce A, Taylor DJ. Abnormal recruitment of working memory updating networks during maintenance of trauma-neutral information in post-traumatic stress disorder. Psychiatry Res. 2008;163:156–70. doi: 10.1016/j.pscychresns.2007.08.011. [DOI] [PubMed] [Google Scholar]
  47. Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage. 2009;44:893–905. doi: 10.1016/j.neuroimage.2008.09.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Nemeroff CB, Heim CM, Thase ME, Klein DN, Rush AJ, Schatzberg AF. Differential responses to psychotherapy vs pharmacotherapy in patients with chronic forms of major depression and childhood trauma. Proc Natl Acad Sci USA. 2003;100:14293–14296. doi: 10.1073/pnas.2336126100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Olino TM, McMakin DL, Dahl RE, Ryan ND, Silk JS, Birmaher B, Axelson DA, Forbes EE. “I won, but I’m not getting my hopes up”: depression moderates the relationship of outcomes and reward anticipation. Psychiatry Res. 2011;194:393–5. doi: 10.1016/j.pscychresns.2011.04.009. doi: 10.1016/j.pscychresns.2011.04.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Packard MG, Knowlton BJ. Learning and memory functions of the Basal Ganglia. Annu Rev Neurosci. 2002;25:563–93. doi: 10.1146/annurev.neuro.25.112701.142937. [DOI] [PubMed] [Google Scholar]
  51. Patel R, Spreng RN, Shin LM, Girard TA. Neurocircuitry models of posttraumatic stress disorder and beyond: a meta-analysis of functional neuroimaging studies. Neurosci Biobehav Rev. 2012;36:2130–42. doi: 10.1016/j.neubiorev.2012.06.003. [DOI] [PubMed] [Google Scholar]
  52. Philip NS, Sweet LH, Tyrka AR, Price LH, Bloom RF, Carpenter LL. Decreased default network connectivity is associated with early life stress in medication-free healthy adults. Eur Neuropsychopharmacol. 2013;23:24–32. doi: 10.1016/j.euroneuro.2012.10.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Philip NS, Valentine TR, Sweet LH, Tyrka AR, Price LH, Carpenter LL. Early life stress impacts dorsolateral prefrontal cortex functional connectivity in healthy adults: informing future studies of antidepressant treatments. J Psychiatr Res. 2014;52:63–69. doi: 10.1016/j.jpsychires.2014.01.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Posner J, Marsh R, Maia TV, Peterson BS, Gruber A, Simpson HB. Reduced functional connectivity within the limbic cortico-striato-thalamo-cortical loop in unmedicated adults with obsessive-compulsive disorder. Hum Brain Mapp. 2013;35:60. doi: 10.1002/hbm.22371. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 2012;59:2142–54. doi: 10.1016/j.neuroimage.2011.10.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Ramage AE, Laird AR, Eickhoff SB, Acheson A, Peterson AL, Williamson DE, Fox PT. A coordinate-based meta-analytic model of trauma processing in posttraumatic stress disorder. Hum Brain Mapp. 2013;34:3392–3399. doi: 10.1002/hbm.22155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Ridderinkhof KR, Ullsperger M, Crone EA, Nieuwenhuis S. The role of the medial frontal cortex in cognitive control. Science. 2004;306:443–7. doi: 10.1126/science.1100301. [DOI] [PubMed] [Google Scholar]
  58. Rubinov M, Sporns O. Weight-conserving characterization of complex functional brain networks. Neuroimage. 2011;56:2068–79. doi: 10.1016/j.neuroimage.2011.03.069. [DOI] [PubMed] [Google Scholar]
  59. Rush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, Markowitz JC, Ninan PT, Kornstein S, Manber R, Thase ME, Kocsis JH, Keller MB. The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry. 2003;54:573–83. doi: 10.1016/s0006-3223(02)01866-8. [DOI] [PubMed] [Google Scholar]
  60. Saad ZS, Gotts SJ, Murphy K, Chen G, Jo HJ, Martin A, Cox RW. Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect. 2012;2:25–32. doi: 10.1089/brain.2012.0080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Satterthwaite TD, Wolf DH, Loughead J, Ruparel K, Elliott MA, Hakonarson H, Gur RC, Gur RE. Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. Neuroimage. 2012;60:623–32. doi: 10.1016/j.neuroimage.2011.12.063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Schulman JJ, Cancro R, Lowe S, Lu F, Walton KD, Llinas RR. Imaging of thalamocortical dysrhythmia in neuropsychiatry. Front Hum Neurosci. 2011;5:69. doi: 10.3389/fnhum.2011.00069. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Sheline YI, Barch DM, Price JL, Rundle MM, Vaishnavi SN, Snyder AZ, Mintun MA, Wang S, Coalson RS, Raichle ME. The default mode network and self-referential processes in depression. Proc Natl Acad Sci U S A. 2009;106:1942–7. doi: 10.1073/pnas.0812686106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Shin LM, Whalen PJ, Pitman RK, Bush G, Macklin ML, Lasko NB, Orr SP, McInerney SC, Rauch SL. An fMRI study of anterior cingulate function in posttraumatic stress disorder. Biol Psychiatry. 2001;50:932–42. doi: 10.1016/s0006-3223(01)01215-x. [DOI] [PubMed] [Google Scholar]
  65. Shirer WR, Ryali S, Rykhlevskaia E, Menon V, Greicius MD. Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb Cortex. 2012;22:158–65. doi: 10.1093/cercor/bhr099. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Talairach J, Tournoux P. Co-planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging. Thieme Medical Publishers, Inc; Stuggart, Germany: 1988. [Google Scholar]
  67. Teicher MH, Samson JA. Childhood maltreatment and psychopathology: A case for ecophenotypic variants as clinically and neurobiologically distinct subtypes. Am J Psychiatry. 2013;170(10):1114–33. doi: 10.1176/appi.ajp.2013.12070957. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. van den Heuvel MP, Sporns O. An anatomical substrate for integration among functional networks in human cortex. J Neurosci. 2013;33:14489–500. doi: 10.1523/JNEUROSCI.2128-13.2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. van der Werff SJ, Pannekoek JN, Veer IM, van Tol MJ, Aleman A, Veltman DJ, van der Wee NJ. Resilience to childhood maltreatment is associated with increased resting-state functional connectivity of the salience network with the lingual gyrus. Child Abuse Negl. 2013;37:1021–1029. doi: 10.1016/j.chiabu.2013.07.008. [DOI] [PubMed] [Google Scholar]
  70. Van Dijk KR, Sabuncu MR, Buckner RL. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage. 2012;59:431–8. doi: 10.1016/j.neuroimage.2011.07.044. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Wang L, Dai Z, Peng H, Tan L, Ding Y, He Z, Zhang Y, Xia M, Li Z, Li W, Cai Y, Lu S, Liao M, Zhang L, Wu W, He Y, Li L. Overlapping and segregated resting-state functional connectivity in patients with major depressive disorder with and without childhood neglect. Hum Brain Mapp. 2014;35:1154–66. doi: 10.1002/hbm.22241. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Weathers FW, Blake DD, Schnurr PP, Kaloupek DG, Marx BP, Keane TM. The PTSD Checklist for DSM-5 (PCL-5) 2013 Instrument available from the National Center for PTSD. www.ptsd.va.gov.
  73. Yarkoni T. Big Correlations in Little Studies: Inflated fMRI Correlations Reflect Low Statistical Power-Commentary on Vul et al. Perspect Psychol Sci. 2009;4:294–8. doi: 10.1111/j.1745-6924.2009.01127.x. [DOI] [PubMed] [Google Scholar]
  74. Zeanah CH, Egger HL, Smyke AT, Nelson CA, Fox NA, Marshall PJ. Institutional rearing and psychiatric disorders in romanian preschool children. Am J Psychiatry. 2009;166:777–785. doi: 10.1176/appi.ajp.2009.08091438. [DOI] [PubMed] [Google Scholar]
  75. Zhong Y, Zhang R, Li K, Qi R, Zhang Z, Huang Q, Lu G. Altered cortical and subcortical local coherence in PTSD: evidence from resting-state fMRI. Acta Radiol. 2014;56:746–53. doi: 10.1177/0284185114537927. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1

Fig. S1. Global-based connectivity distribution across the brain. High involvement of the salience network and posterior default mode network regions are observed. Images are shown using radiologic convention, with slice coordinates (Talairach) on the bottom left. Image is corrected p < .05. Color bars represent Z scores.

NIHMS789197-supplement-1.tiff (174KB, tiff)
2
NIHMS789197-supplement-2.docx (99.7KB, docx)

RESOURCES