Journal of Psychiatric Research 79 (2016) 70e77
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Early life trauma is associated with altered white matter integrity and affective control Vincent Corbo a, b, *, Melissa A. Amick a, William P. Milberg a, c, Regina E. McGlinchey a, c, David H. Salat a, d a Translational Research Center for TBI and Stress Disorders/Geriatric Research Education and Clinical Centers (GRECC), VA Boston Healthcare System, Boston, MA, USA b Boston University School of Medicine, Boston, MA, USA c Department of Psychiatry, Harvard Medical School, Cambridge, MA, USA d Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 26 January 2016 Received in revised form 15 April 2016 Accepted 3 May 2016
Early life trauma (ELT) has been shown to impair affective control and attention well into adulthood. Neuroimaging studies have further shown that ELT was associated with decreased white matter integrity in the prefrontal areas in children and adults. However, no study to date has looked at the relationship between white matter integrity and affective control in individuals with and without a history of ELT. To examine this, we tested 240 Veterans with (ELT N ¼ 80) and without (NoELT N ¼ 160) a history of childhood sexual abuse, physical abuse or family violence. Affective control was measured with the Affective Go/No-Go (AGN) and attention was indexed with the Test of Variable Attention (TOVA). White matter integrity was measured using fractional anisotropy (FA). Results showed greater number of errors on the AGN in ELT compared to NoELT. There was no difference on the TOVA. While there were no mean differences in FA, there was an interaction between FA and reaction time to positive stimuli on the AGN where the ELT group showed a positive relationship between FA and reaction time in right frontal and prefrontal areas, whereas the NoELT group showed a negative or no association between FA and reaction time. This suggests that ELT may be associated with a distinct brain-behavior relationship that could be related to other determinants of FA than those present in healthy adults. Published by Elsevier Ltd.
Keywords: Trauma Childhood Fractional anisotropy Executive function PTSD Attention
1. Introduction The aim of the current study was to examine the association between white matter integrity and cognitive performance in a sample of Veterans with and without a history of early life trauma. Emerging literature has alerted researchers and clinicians to the long-term impact of exposure to early life trauma (ELT) on physical and psychological health and well being (Anda et al., 2006). While the exact deﬁnition of what constitutes ELT or childhood adversity differs between studies, most authors include childhood sexual abuse, physical abuse and family violence under the umbrella term ELT, since these three traumas share an interpersonal dimension (De Bellis and Zisk, 2014; Perry et al., 1995). Importantly, it appears
* Corresponding author. 150 South Huntington Ave, C-11-36, Boston, MA 02120, USA. E-mail address: [email protected]
(V. Corbo). http://dx.doi.org/10.1016/j.jpsychires.2016.05.001 0022-3956/Published by Elsevier Ltd.
that the effects of trauma are greatest during speciﬁc sensitive periods of brain development (Andersen, 2003). This increased impact of stress on the brain has already been illustrated in animal models, especially in the area of the hippocampus, amygdala and prefrontal cortex (PFC) (McEwen, 2008). Based on these animal models, studies using Magnetic Resonance Imaging (MRI) have provided evidence of the important and lasting impact of ELT on brain integrity potentially suggesting altered development. For example, ELT has been associated with alteration in the fundamental symmetry of the PFC of children exposed to ELT compared to age-matched control subjects (Carrion et al., 2001). Another important region for stress, the anterior cingulate cortex, has been shown to be smaller in children exposed to trauma (Cohen et al., 2006). Similarly, another study (Richert et al., 2006) reported smaller dorso-lateral PFC but larger ventral and middle PFC in children exposed to ELT. Altered gray matter volume within the frontal regions of children exposed to ELT have since been replicated, underscoring the consistency of these effects (Carrion and
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Wong, 2012; Hanson et al., 2010). Studies of adults reporting a history of ELT have shown altered gray matter integrity lasting into adulthood in regions overlapping the ﬁndings of studies in children, such as the PFC (Tomoda et al., 2009), dorsal anterior cingulate cortex (Thomaes et al., 2010), orbito-frontal and anterior cingulate cortices (Dannlowski et al., 2012), as well as midcingulate cortex (Corbo et al., 2014). Brain development presents different windows for gray and white matter. White matter development lasts longer, with myelination in the frontal areas extending into early adulthood (Andersen, 2003). Despite what may be a larger window for the effect of stress on the white matter, studies investigating the impact of ELT on the brain have mainly focused on gray matter and our knowledge of the impact of ELT on white matter development is sparser. Recent studies have used diffusion tensor imaging to investigate the integrity of the underlying white matter tracts across the central nervous system. These studies have shown reduced fractional anisotropy (FA), a marker of white matter integrity, in the anterior and posterior sections of the corpus callosum in maltreated children compared to healthy control subjects (Jackowski et al., 2008), in the left arcuate fasciculus and cingulum bundle in adults victim of verbal abuse during childhood (Choi et al., 2009), and in the inferior longitudinal fasciculus of the occipital region (Choi et al., 2012) of adults reporting a history of domestic abuse. A recent study (Teicher et al., 2014) extended these ﬁndings by showing how ELT (verbal abuse) was associated with reduced FA in the corpus callosum and corona radiata. Similarly, one study (Benedetti et al., 2014) showed that ELT affected the white matter integrity of the bilateral superior longitudinal fasciculi and the left anterior thalamic radiation. Collectively, these studies suggest an impact of ELT on the development of white matter, though the speciﬁc localization of the impact remains largely unclear. Brain imaging studies of ELT are critical to understanding reported behavioral impairments in victims of ELT, especially the cognitive domains of attention and affective control. Studies showed that children reporting maltreatment presented a bias in attention to threat-related stimuli, suggesting altered attention control (Gamble and Rapee, 2009; Pine et al., 2005; Roy et al., 2008; Waters et al., 2008). Beyond the attention bias, children exposed to ELT have been shown to perform more poorly on cognitive tasks involving attention and affective control compared to healthy agematched control subjects (DePrince et al., 2009; Gould et al., 2012; Kaplow et al., 2008; Nikulina and Widom, 2013; Porter et al., 2005). Similar impairments have been shown in neutral (Navalta et al., 2006) and affective (Gould et al., 2012) Go/No-Go paradigms, suggesting that ELT may impair inhibition and affective control, in general, as well as attention. In sum, there is mounting evidence for both impaired attention in individuals with a history of ELT and decreased integrity of brain structures thought to support attention and affective control. However no study to date has investigated how ELT may inﬂuence the association between white matter integrity, especially in tracts located in the PFC (Casey et al., 2000; Manna et al., 2010; Wood and Grafman, 2003), and performance on tasks of attention/affective control, a brain region and cognitive functions that may be speciﬁcally impacted by ELT. The objectives of the current study were to examine the impact of ELT on prefrontal white matter integrity and to evaluate a possible link between white matter structural changes and behavioral performance on tasks of affective control and attention. White matter integrity was evaluated across the whole brain using diffusion tensor imaging. Attention and affective control were measured using a continuous performance task and affective Go/ No-go task, respectively. We hypothesized that individuals with ELT would show decreased FA in the corpus callosum and the PFC/
anterior cingulate. We also hypothesized that individuals with ELT would evidence impaired attention performance, indexed by greater number of commission errors, on the sustained attention task and greater interference of negative emotional distracters on the affective go/no-go task, indexed by greater number of commission errors for the negative distracter (positive target). 2. Material and methods 2.1. Recruitment Three hundred and seven service members were initially recruited from the cohort of the VA Rehabilitation Research & Development-supported Traumatic Brain Injury (TBI) National Center for TBI Research (NCR) at VA Boston Healthcare System: The Translational Research Center for TBI and Stress Disorders (TRACTS). Participants enrolled in the TRACTS NCR cohort are recruited from the Boston Metropolitan area and the surroundings. Individuals were ineligible for enrollment in TRACTS if they met any of the following criteria: (a) history of neurological illness (other than TBI); (b) history of seizures; (c) current diagnosis of schizophrenia spectrum or other psychotic disorders (not related to PTSD); (d) current active suicidal and/or homicidal ideation, intent, or plan requiring crisis intervention; or (e) cognitive disorder due to general medical condition other than TBI. The Institutional Review Board of Human Studies Research at the VA Boston Healthcare System approved all research procedures and all participants provided informed consent and were reimbursed for their time and travel expenses. From the original 307 participants, we excluded participants with missing ELT rating (N ¼ 13), individuals with missing neuropsychological data (N ¼ 17) and clinical data (N ¼ 10). Additionally, to ensure that performance accurately reﬂected subject’s abilities, the Medical Symptom Validity Test [MSVT, (Green, 2003)] was administered. Subjects who failed the MSVT were excluded from the analyses (N ¼ 27). The ﬁnal sample consisted of two hundred and forty participants, including a total of 22 women and 218 men. 2.2. Participants History of ELT was determined using the Traumatic Life Events Questionnaire (see below). Based on this questionnaire, the ELT group (N ¼ 80) was composed of individuals reporting a history of physical abuse, sexual abuse and/or family violence before the age of 18 coupled with an A2 reaction of fear/helplessness/horror as deﬁned by DSM-IV-TR, (APA, 2004). The age cut-off insured that the traumatic event would be pre-deployment. The control group (NoELT, N ¼ 160) was composed of individuals who reported no interpersonal trauma before the age of 18. This did not preclude exposure to other events not of an interpersonal nature (e.g. natural disaster, motor-vehicle accidents, witness of robbery, criterion A2 of the DSM-IV-TR), though none were diagnosed with PTSD as a result of this pre-deployment event. Effectively, this means that subjects of the ELT group all had trauma exposure before deployment, while some of the participants in the NoELT group reported a ﬁrst trauma exposure before deployment (e.g. earthquake that did not result in PTSD). 2.3. Clinical assessment All clinical assessments were conducted by a doctoral-level psychologist and reviewed by at least three doctoral-level psychologists to achieve consensus Clinician Administered PTSD Scale (CAPS): Current PTSD Diagnosis and symptoms severity were assessed using the Clinician-
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Administered PTSD Scale [CAPS; (Blake et al., 1995)]. The CAPS is a 30-item structured interview that corresponds to the DSM-IV criteria for PTSD. Since TRACTS was initiated before the transition to DSM-V criteria, we maintained use of the DSM-IV-TR version (although since released, participants also received a DSM-V diagnosis via the CAPS-V). Severity of symptoms was computed by summing the frequency and intensity of clusters of symptoms B (Flashback and Intrusive Memories), C (Avoidance and Emotional Numbing) and D (Hyperarousal). Structured clinical interview for DSM-IV axis I disorders (SCID-I/ NP): The SCID-I/NP (http://www.scid4.org/psychometric/) was administered to determine eligibility and to characterize the individual’s psychological history. The SCID-I/NP is a semi-structured interview that covers main diagnoses of DSM-IV Axis I psychiatric disorders both currently and as a lifetime condition. Boston Assessment of TBI-Lifetime (BAT-L): Since traumatic brain injuries and exposures to blast munitions are prevalent in military samples, TBI exposure was assessed using the Boston Assessment of TBI-Lifetime (Fortier et al., 2013), a measure validated by TRACTS and developed in collaboration with a number of services at VA BHS. Initial inter-rater reliability and validity have been established for the BAT-L and is comparable to the Ohio State University TBI Identiﬁcation Method (Corrigan and Bogner, 2007; Fortier et al., 2013). For the purpose of this study, number of mild TBIs across lifetime was the variable retained as a measure for brain injuries. A single individual reported moderate/severe TBI and was not excluded from the initial analyses. 2.4. Questionnaires History of interpersonal early life trauma was determined using the Traumatic Life Events Questionnaire [TLEQ, (Kubany et al., 2000)]. The TLEQ is a 23-item self-report measure of 22 types of potentially traumatic events including natural disasters, exposure to warfare, robbery involving a weapon, physical abuse and being stalked. It records age of onset and reaction of horror and helplessness. The TLEQ has good temporal stability, reliability, and validity (Kubany et al., 2000). Total alcohol consumption adjusted for weight was measured using the Lifetime Drinking History (Koenig et al., 2009). Severity of combat exposure was assessed using the Combat scale of the Deployment Risk and Resilience Inventory (Vogt et al., 2008). General IQ was estimated using the Wechsler Test of Adult Reading (WTAR). 2.5. Neuropsychological assessment The Test of Variable Attention (TOVA): The TOVA (Greenberg and Waldmant, 2006) is an objective, neurocognitive measure of sustained attention commonly known as a continuous performance task (CPT). It is a 20-min long computerized, non-language based, ﬁxed interval, visual performance test. The test is comprised of two phases presented in the following order for all participants: infrequent target presentation and frequent target presentation. During each phase, participants are asked to respond as quickly as possible to a geometrical ﬁgure (target) while refraining from responding to the non-target geometrical ﬁgure. Measures of commission errors (responding to non-target), omission errors (not responding to target), reaction time (RT) and reaction time variability (RTV) were recorded for this study. The affective Go/No-Go (AGN): The AGN task of the CANTAB (www.cambridgecognition.com) consists of series of stimuli words of either positive (e.g. Happy, Victory) or negative (e.g. Weakness, Defeat) valence presented on the center of a monitor for 300 ms with a 900 ms inter-stimulus-interval (ISI). Participants are asked to respond to the words of the target valence while not responding
to stimuli of the other valence. Order of presentation was counterbalanced across participants. In total, participants are asked to complete ten blocks. The ﬁrst two blocks of the task served as practice trials. The critical dataset consisted of the remaining eight blocks. Each block contains nine words congruent with the target valence (“Go” target words) and nine incongruent words (“No-Go” distracter words). Dependent measures included, for both the positive and negative valence, the number of omission errors (i.e., failure to respond for a word that matched the targeted valence), commission errors (i.e., responding for a non-matched word for a targeted valence), and mean correct latency (LAT; in ms). 2.6. Statistical analyses All analyses were conducted with SPSS 16.0 for Mac OS X 10.6. Independent t-tests were computed to compare group means on possible continuous variables (except for sex, because of the signiﬁcant discrepancy in sample sizes, in which case we used the Mann-Whitney U non-parametric test), whereas c2 tests were used for discrete measures. For each performance measure on the AGN and TOVA, we used a General Linear Model with ELT (±) as between-subjects factor and age, PTSD severity, level of education and number of mTBI across the lifetime were included as a covariate for all models. All p-values were set at 0.05. 2.7. MRI acquisition and processing Imaging procedures for this protocol have been described elsewhere (Trotter et al., 2015). All scans were performed at the Neuroimaging Research for Veterans (NeRVe) Center at the VA Boston Healthcare System in Jamaica Plains, using a Siemens Tim Trio 3-T scanner with 12 RF channels head coil. Image acquisition was based on a 2 mm isotropic, 60 direction single shot echo planar sequence with a twice-refocused spin echo pulse sequence (Reese et al., 2003), 10 T2 (b ¼ 0)þ60 diffusion directions; b ¼ 700, TR ¼ 10,000 ms, TE ¼ 103 ms; bandwidth ¼ 1395 Hz/px, slice thickness ¼ 2.0 mm, FOV ¼ 256 256 mm; 128 128 matrix; 64 slices; with 0 gap]. To increase signal-to-noise ratio, the 60 diffusion-weighted directions were obtained using the electrostatic shell method (Jones et al., 1999). The diffusion tensor was calculated on a voxel-by-voxel basis (Basser et al., 1994; Fischl et al., 1999; Smith et al., 2004). The FreeSurfer image analysis suite (http://surfer.nmr.mgh.harvard.edu/) and FSL Diffusion Toolbox (http://www.fmrib.ox.ac.uk.fsl/) were used to preprocess the diffusion data. Brieﬂy, the raw data was motion and eddy corrected using FSL. A T2-weighted structural volume (b0) volume, collected using identical sequence parameters, was then used as an afﬁne, and rigid body registration target for eddy current, and motion correction of the raw DTI data, respectively (Jenkinson et al., 2002). The ﬁtting of a tensor model to the corrected images was performed using FreeSurfer. The brain extraction toolkit of FSL was then used to extract the diffusion images (Smith et al., 2004). Data were then prepared for statistical analysis using Tract-Based Spatial Statistics [TBSS, (Smith et al., 2006)]. For the purpose of this study, we used the FMRIB58 FA skeleton template after coregistering the data. Voxel-wise analyses of the white matter skeleton was performed with the TBSS tool Randomise (Winkler et al., 2014), a nonparametric permutation testing method for statistical analysis that avoids assumptions about the distribution of DTI data. To compensate for multiple comparisons issues, threshold-free cluster enhancements were used (Smith et al., 2006; Winkler et al., 2014). Analyses tested for main effect of ELT on FA as well as formal interaction of ELT on the association between FA and measures of performance on the TOVA/AGN.
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3. Results 3.1. Clinical/demographic group differences All clinical and demographic information is summarized in Table 1. Analyses revealed that the ELT þ group was older [T(239) ¼ 2.45, p ¼ 0.015], had an earlier age of ﬁrst trauma exposure [T(239) ¼ 10.14, p < 0.001], and had lower premorbid estimated IQ [T(239) ¼ 2.44, p ¼ 0.02]. While groups did not differ in current severity of PTSD symptoms, the ELT þ group presented with lower levels of combat exposure [T(239) ¼ 3.02, p ¼ 0.003]. 3.2. Cognitive performance All performance measures are displayed in Table 1. Individuals in the ELT þ group committed more commission errors for both positive and negative target in the AGN (see Fig. 1). This result remained signiﬁcant after controlling for age, PTSD severity and education level. However, groups did not differ on omission errors and in reaction time for either positive or negative valence. When measuring performance on the TOVA, both groups performed similarly in errors, reaction time and reaction time variability. Across both groups on the TOVA, women presented a slower RT compared to male subjects [Mann-Whitney U Z ¼ 2.162, p < 0.03]. There was no effect of sex on AGN performance. 3.3. FA and behavior association There was no signiﬁcant difference in mean FA between groups across the whole brain. When looking at the impact of ELT on the association between performance on the AGN and FA, a signiﬁcant interaction was found for the mean correct latency for positive target (see Fig. 2) in clusters located in the right frontal/prefrontal white matter. Speciﬁcally, ELT þ participants presented a positive association between mean correct latency and FA, while the NoELT participants presented a negative or no association, in the right caudal/rostral middle frontal gyrus white matter (xyz-peak 56/157/ 98; ELT þ r ¼ 0.217, n.s.; NoELT r ¼ 0.160, p < 0.045)(xyz-peak 63/ Table 1 Sociodemographic, clinical and behavioral results per group.
Age Sex (women:men) Education CAPS Curr Depl Duration Combat exposure Lifetime mTBI Depression IQ AGN Lat Positive AGN Lat Negative AGN Comm Positive AGN Comm Negative AGN Omiss Positive AGN Omiss Negative TOVA Comm TOVA Omis TOVA RT TOVA RTV
NoELT (N ¼ 160)
ELT (N ¼ 80)
30.85 (8.39) 10:150 14.07 (1.97) 42.71 (29.24) 13.78 (8.73) 16.87 (12.13) 1.31 (2.00) 6.82 (8.81) 103.24 (11.21) 499.88 (70.72) 502.21 (68.72) 5.16 (4.25) 4.81 (3.94) 3.15 (4.06) 2.61 (3.38) 11.83 (9.89) 16.24 (37.69) 354.93 (62.52) 100.73 (38.45)
33.73 (8.18) 12:68 13.73 (1.86) 49.37 (26.59) 12.86 (5.90) 12.64 (9.80) 1.77 (2.79) 7.73 (7.17) 99.41 (12.83) 489.08 (72.46) 495.26 (76.63) 6.89 (5.58) 6.37 (5.70) 3.56 (4.06) 3.44 (4.17) 13.12 (9.87) 18.68 (37.14) 380.05 (111.35) 111.45 (52.27)
2.40 4.90 1.30 4.63 0.92 2.76 1.38 0.76 2.26 1.05 0.68 2.33 2.09 0.701 1.47 0.913 0.453 1.79 1.55
0.017* 0.027* 0.20 0.10 0.36 0.006* 0.17 0.45 0.025* 0.29 0.50 0.021* 0.039* 0.48 0.145 0.362 0.651 0.077 0.123
Sociodemographic, clinical and behavioral results per group: Curr ¼ current, within the last month; Depl ¼ deployment (in months); AGN ¼ Affective Go/No-Go; Lat ¼ mean correct latency; Comm ¼ errors of commission; Omis ¼ errors of omission; TOVA ¼ Test of Variable Attention; RT ¼ reaction time; RTV ¼ reaction time variability. * ¼ p < 0.05.
Fig. 1. Number of errors per group on the Affective Go/No-Go (both commission and omission errors). Individuals with a history of ELT showed on average more errors of commission, independent of the valence of the stimulus presented.
140/102; ELT þ r ¼ 0.480, p < 0.001; NoELT r ¼ 0.194, p < 0.015)(xyz-peak 59/151/108; ELT þ r ¼ 0.341, p < 0.002; NoELT r ¼ 0.087, n.s.)(xyz-peak 52/125/114; ELT þ r ¼ 0.202, n.s.; NoELT r ¼ 0.139, n.s.), precentral gyrus white matter (xyz-peak 52/119/ 104; ELT þ r ¼ 0.477, p < 0.001; NoELT r ¼ 0.110, n.s.), external capsule/pars orbitalis of the inferior frontal gyrus (xyz-peak 60/135/ 86; ELT þ r ¼ 0.355, p < 0.002; NoELT r ¼ 0.164, p < 0.040), postcentral gyrus white matter (xyz-peak 46/108/105; ELT þ r ¼ 0.450, p < 0.001; NoELT r ¼ 0.200, p < 0.012), anterior corona radiata (xyz-peak 63/156/84; ELT þ r ¼ 0.350, p < 0.002; NoELT r ¼ 0.046, n.s.), pars triangularis of the inferior frontal gyrus (xyz-peak 56/161/71; ELT þ r ¼ 0.338, p < 0.003; NoELT r ¼ 0.181, p < 0.024), putamen white matter (xyz-peak 72/129/64; ELT þ r ¼ 0.274, p < 0.016; NoELT r ¼ 0.270, p < 0.001), lateral orbitofrontal gyrus white matter/anterior corona radiata (xyz-peak 68/181/65; ELT þ r ¼ 0.245, p < 0.032; NoELT r ¼ 0.155, p < 0.052), lateral orbitofrontal cortex (xyz-peak 64/151/61; ELT þ r ¼ 0.313, p < 0.006; NoELT r ¼ 0.219, p < 0.006), superior/anterior corona radiata (xyz-peak 64/139/92; ELT þ r ¼ 0.375, p < 0.001; NoELT r ¼ 0.022, n.s.), and anterior limb of the external capsule/superior fronto-occipital fasciculus (xyz-peak 68/136/89; ELT þ r ¼ 0.363, p < 0.001; NoELT r ¼ 0.077, n.s.). All other regions of the white matter skeleton were not associated with the mean correct latency of the AGN. Furthermore, there was no voxel-wise association between FA and other metrics of the AGN or of the TOVA. 4. Discussion In this study, we examined how, in a sample of Veterans, a history of ELT inﬂuenced the association between white matter integrity and performance on attention and affective control tasks. Our ﬁndings indicated impaired affective control in subjects with ELT, as revealed by greater errors of commission on the AGN. Additionally, reaction time for positive targets on the AGN was positively associated with white matter integrity in the ELT þ group while negatively associated in the NoELT group. This suggests that stressful events occurring during periods of brain development (e.g. myelination) may inﬂuence how behavior becomes associated with markers of brain integrity. This ﬁnding offers a new perspective on the impact of ELT on the brain. Prior studies using
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Fig. 2. Clusters of signiﬁcant interaction between ELT/NoELT on the relationship between FA and mean correct latency for positive stimuli on the Affective Go/No-Go, controlling for PTSD symptoms severity, age and level of education. Fourteen (clusters 1e4 illustrated here) clusters were detected within the right frontal tracts, all signiﬁcant at p < 0.05 corrected for multiple comparisons.
brain imaging techniques have generally approached the question of the impact of ELT on white matter by testing for mean differences in FA between groups (Benedetti et al., 2014; Choi et al., 2012; Choi et al., 2009; Jackowski et al., 2008; Teicher et al., 2014). The
assumption behind these studies was that stress and trauma should reduce FA levels. Instead, what our ﬁndings suggest is that ELT may act to shape differently the association between brain and behavior. As was noted by Beaulieu (Beaulieu, 2002), diffusion of water in the
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cortex is inﬂuenced by many factors outside myelination. One study illustrated how gliosis, i.e. the process of generation of glial cell, contributed signiﬁcantly to observed levels of fractional anisotropy (Budde et al., 2011). Furthermore, stress hormones have been shown to stimulate gliosis and even be protective of the myelin sheet, thus contributing potentially to higher FA (Jauregui-Huerta et al., 2010). This proliferation of glial cells would effectively lead to an increase in FA concomitant to an increase in reaction time, as noted in our study. In adults without ELT, the decrease in FA would likely be due to neural death, leading to slower processing speed and therefore longer reaction times. Thus, the opposite patterns observed between FA and behavioral performances would be due to different cellular mechanisms inﬂuencing FA. The association of white matter integrity and cognitive performance has been shown in healthy young adults and in aging. Initial studies showed a moderate association between decline in cognitive function and non-speciﬁc white matter lesions (GunningDixon and Raz, 2000). However, the studies based on diffusion are less clear regarding the presence and directionality of the association between white matter integrity and reaction time. One study of healthy adults ranging from 35 to 74 years old found that, controlling for the effect of age, an increase in fractional anisotropy was related to a decrease in reaction time (Grieve et al., 2007). This inverse association, which echoes the ﬁnding in our NoELT group, was replicated in a different sample of healthy young adults, aged on average 23 years old (Konrad et al., 2009). Sasson et al. 2012 extended these ﬁndings by showing the inverse relationship between FA and a composite measure of reaction time drawn from multiple cognitive tests. Children and adolescents who survived brain tumors also presented this inverse relationship (Rueckriegel et al., 2015), illustrating that such an association is not present only in healthy individuals. However, some other groups have found that the relationship between FA and performance is not always inversed. In a later study, results showed that FA in areas of the cerebellar white matter correlated positively with auditory reaction time in young healthy adults (Bohr et al., 2007). In a sample of children diagnosed with Williams syndrome, Hoeft et al. (Hoeft et al., 2007) showed that worse performance on a visuospatial task was associated with greater FA. A similar relationship was observed in a mixed sample of healthy individuals and patients with brain lesions (Turken et al., 2008). Our ﬁndings seem to support the latter observations whereby a lesion, illness or, in our case, excessive stress may be associated with a linear relationship between FA and performance on cognitive testing, whereas in healthy cases, no relationship appears between white matter and performance. Interestingly, for our study, the association was present only for the reaction time to positive targets, and thus to negative distracters. This is in line with studies that have shown that individuals exposed to trauma present an attention bias to negative or trauma-related stimuli, potentially interfering with performance (Fani et al., 2012; Pannu Hayes et al., 2009; Pine et al., 2005). This study does have limitations. Our clinical characterization of early life trauma did not allow us to measure some dimensions of adversity in childhood that could have affected the performance and integrity of white matter tracts, such as emotional abuse and physical/emotional neglect. Similarly, while there may be an association between the number of trauma exposures/types and behavioral outcomes, we were unable to test for this. Further, our study sample was composed of Veterans who have been deployed and thus re-exposed to trauma. While we controlled statistically for the impact of current severity of PTSD symptoms, further studies should include a non-deployed sample. Lastly, while the male:female ratio in this sample was representative of the Veterans from recent conﬂicts, because of the low number of women, it was impossible to properly assess for the impact of sex on the effects
observed. Future studies should address this speciﬁc question. Despite these limitations, our study does contribute to the important ﬁeld of the cognitive neuroscience of ELT. Since our ﬁndings were exclusive to the AGN, this suggests that the affective control domain may suffer more importantly from the impact of ELT when contrasted to sustained attention. As mentioned earlier, no study so far has investigated the impact of ELT on how behavior is associated with white matter integrity. This approach may prove helpful in revealing new and subtler effects of stress and trauma. It may also help us understand further how various aspects of brain structural integrity are associated with different components of cognition, from processing of the stimuli to decision-making and its behavioral result. Through these ﬁndings, we may better understand the long-lasting emotional and cognitive impact of ELT. Author contributions Vincent Corbo: Collection of data, data analysis, and writing of manuscript. Melissa A Amick: Provided clinical expertise for clinical variables, contributed to data analyses, manuscript editing. William P Milberg: provided expertise for neuropsychological assessment, data analyses and manuscript editing. Regina E McGlinchey: supervised clinical and neuropsychological data collection, provided expertise for trauma and PTSD, provided expertise for neuropsychological assessment, provided expertise for statistical analyses, manuscript editing. David H Salat: supervised MRI data collection, provided expertise in MRI data analyses, provided expertise in statistical modeling, manuscript editing.
Role of funding agencies The funding agencies (VA Rehabilitation, Research and Development & the National Institute of Health) were not involved in the study development, data analysis, writing of report or in the decision to submit this manuscript for publication. Acknowledgements The authors would like to thank Mr. Wally Musto for his championship of our work among military personnel and his tireless recruitment efforts on our behalf. We would further like to acknowledge the work of Drs. Alexandra Kenna, Catherine Fortier, Ann Rasmusson, Brad Brummett, Sara Lippa and Colleen Jackson for the clinical assessments and diagnoses. The authors would also like to thank Dr. Jennifer Fonda for assistance in the statistical support and database management. This research was support by the Translational Research Center for TBI and Stress Disorders (TRACTS), a VA Rehabilitation Research and Development Traumatic Brain Injury Center of Excellence (B9254-C) provided by the United States (U.S.) Department of Veterans Affairs, as well as NIH grant #R01NR10827 (Dr. David Salat). The contents do not represent the views of the U.S. Department of Veterans Affairs or the United States Government. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jpsychires.2016.05.001. References Anda, R.F., Felitti, V.J., Bremner, J.D., Walker, J.D., Whitﬁeld, C., Perry, B.D., et al.,
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