INTRODUCTION

About 20% of military service members develop posttraumatic stress disorder (PTSD) (Hayes et al., 2012). PTSD is characterized by high anxiety, re‐experiencing traumatic memories, hypervigilance, and hyperarousal. In combat veterans, PTSD has high comorbidity with mild‐traumatic brain injury (mTBI) (Hoge et al., 2008, 2009) due to the risk of being exposed to improvised explosive devices (IEDs) and nonblast events. A significant percentage of those who sustain mTBI suffer from chronic symptoms (postconcussion syndrome [PCS] (Cicerone and Kalmar, 1995)). With current diagnostic procedures and treatments centering on subjective assessments, a thorough understanding of the mechanistic basis for PTSD and PCS is essential for accurate diagnosis, targeted treatment and for making return‐to‐duty decisions. Owing to largely overlapping symptomatology between PTSD and PCS (Eierud et al., 2014), it is necessary to identify and validate objective biomarkers of the respective neurologic and neuropsychiatric conditions to improve clinical evaluation and, ultimately, treatment outcomes.

We employed resting‐state functional MRI (rs‐fMRI), which avoids task dependency and subsequent performance differences. We performed connectivity analysis on rs‐fMRI data, without a priori assumptions concerning regions of interest (ROIs). Functional connectivity (FC) refers to measures of instantaneous correlation between a pair of fMRI time series obtained from different brain regions. There have been several recent fMRI FC studies with PTSD (Hayes et al., 2012; Simmons and Matthews, 2012; Spielberg et al., 2015) and PCS (Costanzo et al., 2014; Eierud et al., 2014; Hoge et al., 2008). However, very little work has been done on comorbid PTSD and PCS, even though comorbidity is the norm rather than the exception in military populations (Spielberg et al., 2015). Existing findings have been mixed (Simmons and Matthews, 2012).

Hyperconnectivity is seen as a response to neurological disruption (Hillary et al., 2015) and is observed in individuals with PTSD (Cisler et al., 2014; Hayes et al., 2012; Simmons and Matthews, 2012). Most studies employ only static FC (SFC) and ignore dynamic variation of connectivity over time, known as dynamic FC (DFC). Recent studies have highlighted the enormous importance of dynamics in deciphering brain functioning (Hansen et al., 2015). Several studies show that DFC signatures in subjects with mental disorders are different from those in healthy subjects (Deshpande et al., 2006; Keilholz et al., 2013; Li et al., 2014; Majeed et al., 2011). DFC is also related to real world cognitive behaviors (Thompson et al., 2013), which may make it a good tool for studying disorders such as PTSD and PCS where cognitive functioning is compromised. SFC and DFC provide different types of information regarding connectivity between two brain regions (Hutchison et al., 2013). Reduced temporal variance in DFC is associated with psychiatric disorders as well as compromised behavioral performance in healthy individuals (Jia et al., 2014; Sakoğlu et al., 2010). This reduction is associated with compromised ability to dynamically adjust (e.g., behavior, thoughts, etc.) to changing conditions. This phenomenon is well recognized in other biological systems such as reduced heart rate variability being a risk factor of cardiovascular disease (Greiser et al., 2009). As external influences and internal body states are continually changing, a healthy biological system varies its activity in real‐time to accommodate these changes. In these terms, “frozen” connectivity reflects compromised brain health. This study uses these principles to identify functional connectivities in the brains of soldiers with PTSD and PCS which are in a “frozen” hyperconnected state compared to healthy soldiers.

Active duty, U.S. Army soldiers who screened positive for PTSD, both PCS and PTSD (PCS + PTSD), and healthy combat controls were recruited. We tested an overarching hypothesis that PTSD with and without PCS is associated with higher connectivity strength (SFC) but lower connectivity variance (variance of DFC [vDFC] calculated over time, Fig. 4) as compared to healthy controls (Fig. 1). Furthermore, we hypothesized that the connectivities would be more extreme (i.e., higher SFC and lower vDFC) in PCS + PTSD subjects compared to PTSD subjects, indicative of greater symptom severity. We notably tested the hypothesis on whole‐brain connectivity data without imposition of any priors or assumptions. Figure 1 provides an illustration of our hypothesis.

Figure 1 Open in figure viewer PowerPoint Illustration of our primary hypothesis: increasing font size of SFC implies increasing connectivity strength from the control group to the PTSD group to the PCS + PTSD group. Decreasing font size of DFC implies decreasing variance of connectivity from the control group to the PTSD group to the PCS + PTSD group. [Color figure can be viewed at wileyonlinelibrary.com]

In addition to the primary hypothesis, there were multiple corollary hypotheses addressed in this study. First, if the connectivities were indeed more extreme (i.e., higher SFC and lower vDFC) in PCS + PTSD subjects compared to PTSD subjects, it raises the question as to whether the PCS + PTSD group's condition is being driven by PTSD. Alternatively, is this comorbid group's state unique, potentially due to the addition of mTBI sequelae? We attempt to address this question by investigating structural alterations of white‐matter tracts in all the three groups with the hypothesis that the changes in axonal integrity must be exclusive to the PCS + PTSD group, likely attributed to the mTBI suffered by these subjects. MRI diffusion tensor imaging (DTI) tractography provides meaningful information concerning diffusion of water molecules in white matter as a measure of tract trajectory, integrity, and directionality. White‐matter neuropathology can result in increased diffusivity, for example, with inflammation from demyelination (Harsan et al., 2006; Henry et al., 2003). In a recent study involving veterans from the Iraq and Afghanistan wars who were diagnosed with PCS (Morey et al., 2013), DTI showed differences in white‐matter diffusivity associated with the regions that also had abnormal functional connectivity. In line with this finding, we predicted that there would be congruently greater diffusivity in the tracts connecting regions with altered functional connectivity; therefore, supporting the argument that the PCS + PTSD group is etiologically different from the PTSD group.

Second, if we are successful in finding functional connectivities in the brain which satisfy our overarching hypothesis, it will be important to determine their relevance to behavior and clinical diagnostics. Our subjects are traditionally assigned diagnostic groups based on clinical observation and symptom reporting. While self‐report symptom scores provide subjective assessments of severity of the disorders (i.e., psychopathology), neuroimaging data provide mechanistic characterization of underlying pathophysiology. Hence, as our secondary hypothesis, we hypothesized that grouping of subjects based on significant connectivity values would be superior (in terms of how the groups map to behavioral clusters) than conventional diagnostic grouping. Indeed, this approach has been actively promoted by the National Institute of Mental Health (NIMH) in the United States by publication of “Research Domain Criteria” (RDoC, http://www.nimh.nih.gov/research-priorities/rdoc/nimh-research-domain-criteria-rdoc.shtml). RDoC is agnostic about current disorder categories, and the intent is to generate classifications in a data‐driven way. The “core unit of analysis” advanced by RDoC is the “measurements of particular circuits as studied by neuroimaging techniques.” In line with this ideology, a recent report demonstrates how data‐driven definition of groups in psychiatric spectrum disorders can identify new groups which map better onto behavioral clusters (Brodersen et al., 2013). Our regrouping strategy is inspired by these recent developments.

To address our secondary hypothesis, behavioral measures obtained from a neurocognitive battery were separately grouped using both conventional grouping and the proposed imaging‐based grouping methods. Next, the statistical separation between the groups was compared for both grouping methods. This comparison was done to test the hypothesis that the imaging‐based grouping, based on underlying neurobiology (as inferred from connectivity), will map better onto neurobehavior than conventional grouping, based on symptom severity scores.

Third, both our primary hypothesis and corollary hypotheses are based on an analysis framework which relies on statistical separation between the groups. However, statistical separation of between‐group connectivities does not necessarily imply that they have predictive diagnostic ability (Craddock et al., 2009; Deshpande et al., 2010); that is, they may not be able to predict group membership at an individual level with reasonable accuracy. Consequently, those connectivities which are statistically significant as well as possess the discriminative power to classify subjects with high accuracy are more powerful. Several studies report that machine learning classifiers can be successfully used on fMRI data for diagnostic prediction, including, but not limited to, major depressive disorder (Deshpande et al., 2009), Parkinson's disease (Marquand et al., 2013), PTSD (Liu et al., 2015), dementia (Chen et al., 2011), autism (Deshpande et al., 2013), and prenatal cocaine exposure syndrome (Deshpande et al., 2010). However, to the best of our knowledge, there are no studies which have used connectivity markers in the classification of both PTSD and PCS subjects. For neuropsychiatric disorders such as PTSD and PCS, which are currently diagnosed solely through clinical observation, classification using neuroimaging signatures could be applied to obtain more accurate diagnoses in these highly comorbid conditions. We thus employed a machine learning technique which, in a data‐driven way, recursively eliminates unimportant features from whole‐brain connectivity data to identify those connectivities (i.e., top predictors) which predict the diagnostic membership of a novel subject with high accuracy. We specifically investigated whether there was an overlap between connectivity paths satisfying the overarching hypothesis and those identified as having high predictive ability. We hypothesize that these paths (i) will better predict the diagnostic membership of a novel subject than the available non‐imaging measures and (ii) will predict the group membership of a novel subject with significantly better accuracy for the proposed imaging‐based grouping (as elucidated in the previous paragraph), as compared to the conventional grouping.

Figure 2 illustrates the complete analysis pipeline with a hierarchical flowchart (outcomes are discussed in the Results section).