Persistent MRI Findings Unique to Blast and Repetitive Mild TBI: Analysis of the CENC/LIMBIC Cohort Injury Characteristics.

INTRODUCTION: MRI represents one of the clinical tools at the forefront of research efforts aimed at identifying diagnostic and prognostic biomarkers following traumatic brain injury (TBI). Both volumetric and diffusion MRI findings in mild TBI (mTBI) are mixed, making the findings difficult to interpret. As such, additional research is needed to continue to elucidate the relationship between the clinical features of mTBI and quantitative MRI measurements. MATERIAL AND METHODS: Volumetric and diffusion imaging data in a sample of 976 veterans and service members from the Chronic Effects of Neurotrauma Consortium and now the Long-Term Impact of Military-Relevant Brain Injury Consortium observational study of the late effects of mTBI in combat with and without a history of mTBI were examined. A series of regression models with link functions appropriate for the model outcome were used to evaluate the relationships among imaging measures and clinical features of mTBI. Each model included acquisition site, participant sex, and age as covariates. Separate regression models were fit for each region of interest where said region was a predictor. RESULTS: After controlling for multiple comparisons, no significant main effect was noted for comparisons between veterans and service members with and without a history of mTBI. However, blast-related mTBI were associated with volumetric reductions of several subregions of the corpus callosum compared to non-blast-related mTBI. Several volumetric (i.e., hippocampal subfields, etc.) and diffusion (i.e., corona radiata, superior longitudinal fasciculus, etc.) MRI findings were noted to be associated with an increased number of repetitive mTBIs versus. CONCLUSIONS: In deployment-related mTBI, significant findings in this cohort were only observed when considering mTBI sub-groups (blast mechanism and total number/dose). Simply comparing healthy controls and those with a positive mTBI history is likely an oversimplification that may lead to non-significant findings, even in consortium analyses.

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis.

BACKGROUND: Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting. METHODS: Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier. RESULTS: Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82-0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70-0.73 AUC). CONCLUSIONS: These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.

Microstructural Organization of Distributed White Matter Associated With Fine Motor Control in US Service Members With Mild Traumatic Brain Injury.

Mild traumatic brain injury (mTBI) is the most common form of brain injury. While most individuals recover from mTBI, roughly 20% experience persistent symptoms, potentially including reduced fine motor control. We investigate relationships between regional white matter organization and subcortical volumes associated with performance on the Grooved Pegboard (GPB) test in a large cohort of military Service Members and Veterans (SM&Vs) with and without a history of mTBI(s). Participants were enrolled in the Long-term Impact of Military-relevant Brain Injury Consortium-Chronic Effects of Neurotrauma Consortium. SM&Vs with a history of mTBI(s) (n = 847) and without mTBI (n = 190) underwent magnetic resonance imaging and the GPB test. We first examined between-group differences in GPB completion time. We then investigated associations between GPB performance and regional structural imaging measures (tractwise diffusivity, subcortical volumes, and cortical thickness) in SM&Vs with a history of mTBI(s). Lastly, we explored whether mTBI history moderated associations between imaging measures and GPB performance. SM&Vs with mTBI(s) performed worse than those without mTBI(s) on the non-dominant hand GPB test at a trend level (p < 0.1). Higher fractional anisotropy (FA) of tracts including the posterior corona radiata, superior longitudinal fasciculus, and uncinate fasciculus were associated with better GPB performance in the dominant hand in SM&Vs with mTBI(s). These findings support that the organization of several white matter bundles are associated with fine motor performance in SM&Vs. We did not observe that mTBI history moderated associations between regional FA and GPB test completion time, suggesting that chronic mTBI may not significantly influence fine motor control.

Novel lactoferrin-conjugated gallium complex to treat Pseudomonas aeruginosa wound infection.

Pseudomonas aeruginosa is one of the leading causes of opportunistic infections such as chronic wound infection that could lead to multiple organ failure and death. Gallium (Ga3+) ions are known to inhibit P. aeruginosa growth and biofilm formation but require carrier for localized controlled delivery. Lactoferrin (LTf), a two-lobed protein, can deliver Ga3+ at sites of infection. This study aimed to develop a Ga-LTf complex for the treatment of wound infection. The characterisation of the Ga-LTf complex was conducted using differential scanning calorimetry (DSC), Infra-Red (FTIR) and Inductive Coupled Plasma Optical Emission Spectrometry (ICP-OES). The antibacterial activity was assessed by agar disc diffusion, liquid broth and biofilm inhibition assays using the colony forming units (CFUs). The healing capacity and biocompatibility were evaluated using a P.aeruginosa infected wound in a rat model. DSC analyses showed thermal transition consistent with apo-lactoferrin; FTIR confirmed the complexation of gallium to lactoferrin. ICP-OES confirmed the controlled local delivery of Ga3+. Ga-LTf showed a 0.57 log10 CFUs reduction at 24 h compared with untreated control in planktonic liquid broth assay. Ga-LTf showed the highest antibiofilm activity with a 2.24 log10 CFUs reduction at 24 h. Furthermore, Ga-LTf complex is biocompatible without any adverse effect on brain, kidney, liver and spleen of rats tested in this study. Ga-LTf can be potentially promising novel therapeutic agent to treat pathogenic bacterial infections.

Individual Prediction of Optimal Treatment Allocation Between Electroconvulsive Therapy or Ketamine using the Personalized Advantage Index.

Introduction: Electroconvulsive therapy (ECT) and ketamine are two effective treatments for depression with similar efficacy; however, individual patient outcomes may be improved by models that predict optimal treatment assignment. Here, we adapt the Personalized Advantage Index (PAI) algorithm using machine learning to predict optimal treatment assignment between ECT and ketamine using medical record data from a large, naturalistic patient cohort. We hypothesized that patients who received a treatment predicted to be optimal would have significantly better outcomes following treatment compared to those who received a non-optimal treatment. Methods: Data on 2526 ECT and 235 mixed IV ketamine and esketamine patients from McLean Hospital was aggregated. Depressive symptoms were measured using the Quick Inventory of Depressive Symptomatology (QIDS) before and during acute treatment. Patients were matched between treatments on pretreatment QIDS, age, inpatient status, and psychotic symptoms using a 1:1 ratio yielding a sample of 470 patients (n=235 per treatment). Random forest models were trained and predicted differential patientwise minimum QIDS scores achieved during acute treatment (min-QIDS) scores for ECT and ketamine using pretreatment patient measures. Analysis of Shapley Additive exPlanations (SHAP) values identified predictors of differential outcomes between treatments. Results: Twenty-seven percent of patients with the largest PAI scores who received a treatment predicted optimal had significantly lower min-QIDS scores compared to those who received a non-optimal treatment (mean difference=1.6, t=2.38, q<0.05, Cohen's D=0.36). Analysis of SHAP values identified prescriptive pretreatment measures. Conclusions: Patients assigned to a treatment predicted to be optimal had significantly better treatment outcomes. Our model identified pretreatment patient factors captured in medical records that can provide interpretable and actionable guidelines treatment selection.

Ketamine treatment modulates habenular and nucleus accumbens static and dynamic functional connectivity in major depression.

Dysfunctional reward processing in major depressive disorder (MDD) involves functional circuitry of the habenula (Hb) and nucleus accumbens (NAc). Ketamine elicits rapid antidepressant and alleviates anhedonia in MDD. To clarify how ketamine perturbs reward circuitry in MDD, we examined how serial ketamine infusions (SKI) modulate static and dynamic functional connectivity (FC) in Hb and NAc networks. MDD participants (n=58, mean age=40.7 years, female=28) received four ketamine infusions (0.5mg/kg) 2-3 times weekly. Resting-state fMRI scans and clinical assessments were collected at baseline and 24 hours post-SKI completion. Static FC (sFC) and dynamic FC variability (dFCv) were calculated from left and right Hb and NAc seeds to all other brain regions. Paired t-tests examined changes in FC pre-to-post SKI, and correlations were used to determine relationships between FC changes with mood and anhedonia. Following SKI, significant increases in left Hb-bilateral visual cortex FC, decreases in left Hb-left inferior parietal cortex FC, and decreases in left NAc-right cerebellum FC occurred. Decreased dFCv between left Hb and right precuneus and visual cortex, and decreased dFCv between right NAc and right visual cortex both significantly correlated with improvements in Hamilton Depression Rating Scale. Decreased FC between left Hb and bilateral visual/parietal cortices as well as increased FC between left NAc and right visual/parietal cortices both significantly correlated with improvements in anhedonia. Subanesthetic ketamine modulates functional pathways linking the Hb and NAc with visual, parietal, and cerebellar regions. Overlapping effects between Hb and NAc functional systems were associated with ketamine's therapeutic response.

Predicting Dimensional Antidepressant Response to Repetitive Transcranial Magnetic Stimulation using Pretreatment Resting-state Functional Connectivity.

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression and has been shown to modulate resting-state functional connectivity (RSFC) of depression-relevant neural circuits. To date, however, few studies have investigated whether individual treatment-related symptom changes are predictable from pretreatment RSFC. We use machine learning to predict dimensional changes in depressive symptoms using pretreatment patterns of RSFC. We hypothesized that changes in dimensional depressive symptoms would be predicted more accurately than scale total scores. Patients with depression (n=26) underwent pretreatment RSFC MRI. Depressive symptoms were assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17). Random forest regression (RFR) models were trained and tested to predict treatment-related symptom changes captured by the HDRS-17, HDRS-6 and three previously identified HDRS subscales: core mood/anhedonia (CMA), somatic disturbances, and insomnia. Changes along the CMA, HDRS-17, and HDRS-6 were predicted significantly above chance, with 9%, 2%, and 2% of out-of-sample outcome variance explained, respectively (all p<0.01). CMA changes were predicted more accurately than the HDRS-17 (p<0.05). Higher baseline global connectivity (GC) of default mode network (DMN) subregions and the somatomotor network (SMN) predicted poorer symptom reduction, while higher GC of the right dorsal attention (DAN) frontoparietal control (FPCN), and visual networks (VN) predicted reduced CMA symptoms. HDRS-17 and HDRS-6 changes were predicted with similar GC patterns. These results suggest that RSFC spanning the DMN, SMN, DAN, FPCN, and VN subregions predict dimensional changes with greater accuracy than syndromal changes following rTMS. These findings highlight the need to assess more granular clinical dimensions in therapeutic studies, particularly device neuromodulation studies, and echo earlier studies supporting that dimensional outcomes improve model accuracy.

An Improved Average Atomic Number Calculation for Estimating Backscatter and Continuum Production in Compounds.

It is often assumed that electron backscatter and continuum (bremsstrahlung) productions emitted from electron-solid interactions during X-ray microanalysis in compounds can be extrapolated from pure element observations by means of the assumption of average atomic number, or Z-bar (Z¯). For pure elements the average Z is equal to the atomic number, but this direct approach fails for compounds. The use of simple atomic fractions yields completely spurious results, and while the commonly used mass fraction Z averaging produces fairly reasonable results, we know from physical considerations that the mass of the neutron plays only a negligible role in such interactions below ∼1 MeV. Therefore, including the mass or atomic weight in such calculations can only introduce further errors in these models. We present an expression utilizing atomic fractions of the atomic numbers of the elements in the compound (Z fraction), with an exponent to account for the variation in nuclear screening as a function of the element Z value.

Comparing resting-state connectivity of working memory networks in U.S. Service members with mild traumatic brain injury and posttraumatic stress disorder.

Mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) are prevalent among military populations, and both have been associated with working memory (WM) impairments. Previous resting-state functional connectivity (rsFC) research conducted separately in PTSD and mTBI populations suggests that there may be similar and distinct abnormalities in WM-related networks. However, no studies have compared rsFC of WM brain regions in participants with mTBI versus PTSD. We used resting-state fMRI to investigate rsFC of WM networks in U.S. Service Members (n = 127; ages 18-59) with mTBI only (n = 46), PTSD only (n = 24), and an orthopedically injured (OI) control group (n = 57). We conducted voxelwise rsFC analyses with WM brain regions to test for differences in WM network connectivity in mTBI versus PTSD. Results revealed reduced rsFC between ventrolateral prefrontal cortex (vlPFC), lateral premotor cortex, and dorsolateral prefrontal cortex (dlPFC) WM regions and brain regions in the dorsal attention and somatomotor networks in both mTBI and PTSD groups versus controls. When compared to those with mTBI, individuals with PTSD had lower rsFC between both the lateral premotor WM seed region and middle occipital gyrus as well as between the dlPFC WM seed region and paracentral lobule. Interestingly, only vlPFC connectivity was significantly associated with WM performance across the samples. In conclusion, we found primarily overlapping patterns of reduced rsFC in WM brain regions in both mTBI and PTSD groups. Our finding of decreased vlPFC connectivity associated with WM is consistent with previous clinical and neuroimaging studies. Overall, these results provide support for shared neural substrates of WM in individuals with either mTBI or PTSD.

Anterior default mode network and posterior insular connectivity is predictive of depressive symptom reduction following serial ketamine infusion.

BACKGROUND: Ketamine is a rapidly-acting antidepressant treatment with robust response rates. Previous studies have reported that serial ketamine therapy modulates resting state functional connectivity in several large-scale networks, though it remains unknown whether variations in brain structure, function, and connectivity impact subsequent treatment success. We used a data-driven approach to determine whether pretreatment multimodal neuroimaging measures predict changes along symptom dimensions of depression following serial ketamine infusion. METHODS: Patients with depression (n = 60) received structural, resting state functional, and diffusion MRI scans before treatment. Depressive symptoms were assessed using the 17-item Hamilton Depression Rating Scale (HDRS-17), the Inventory of Depressive Symptomatology (IDS-C), and the Rumination Response Scale (RRS) before and 24 h after patients received four (0.5 mg/kg) infusions of racemic ketamine over 2 weeks. Nineteen unaffected controls were assessed at similar timepoints. Random forest regression models predicted symptom changes using pretreatment multimodal neuroimaging and demographic measures. RESULTS: Two HDRS-17 subscales, the HDRS-6 and core mood and anhedonia (CMA) symptoms, and the RRS: reflection (RRSR) scale were predicted significantly with 19, 27, and 1% variance explained, respectively. Increased right medial prefrontal cortex/anterior cingulate and posterior insula (PoI) and lower kurtosis of the superior longitudinal fasciculus predicted reduced HDRS-6 and CMA symptoms following treatment. RRSR change was predicted by global connectivity of the left posterior cingulate, left insula, and right superior parietal lobule. CONCLUSIONS: Our findings support that connectivity of the anterior default mode network and PoI may serve as potential biomarkers of antidepressant outcomes for core depressive symptoms.

Parsing the Network Mechanisms of Electroconvulsive Therapy.

Electroconvulsive therapy (ECT) is one of the oldest and most effective forms of neurostimulation, wherein electrical current is used to elicit brief, generalized seizures under general anesthesia. When electrodes are positioned to target frontotemporal cortex, ECT is arguably the most effective treatment for severe major depression, with response rates and times superior to other available antidepressant therapies. Neuroimaging research has been pivotal in improving the field's mechanistic understanding of ECT, with a growing number of magnetic resonance imaging studies demonstrating hippocampal plasticity after ECT, in line with evidence of upregulated neurotrophic processes in the hippocampus in animal models. However, the precise roles of the hippocampus and other brain regions in antidepressant response to ECT remain unclear. Seizure physiology may also play a role in antidepressant response to ECT, as indicated by early positron emission tomography, single-photon emission computed tomography, and electroencephalography research and corroborated by recent magnetic resonance imaging studies. In this review, we discuss the evidence supporting neuroplasticity in the hippocampus and other brain regions during and after ECT, and their associations with antidepressant response. We also offer a mechanistic, circuit-level model that proposes that core mechanisms of antidepressant response to ECT involve thalamocortical and cerebellar networks that are active during seizure generalization and termination over repeated ECT sessions, and their interactions with corticolimbic circuits that are dysfunctional prior to treatment and targeted with the electrical stimulus.

Modulation of the functional connectome in major depressive disorder by ketamine therapy.

BACKGROUND: Subanesthetic ketamine infusion therapy can produce fast-acting antidepressant effects in patients with major depression. How single and repeated ketamine treatment modulates the whole-brain functional connectome to affect clinical outcomes remains uncharacterized. METHODS: Data-driven whole brain functional connectivity (FC) analysis was used to identify the functional connections modified by ketamine treatment in patients with major depressive disorder (MDD). MDD patients (N = 61, mean age = 38, 19 women) completed baseline resting-state (RS) functional magnetic resonance imaging and depression symptom scales. Of these patients, n = 48 and n = 51, completed the same assessments 24 h after receiving one and four 0.5 mg/kg intravenous ketamine infusions. Healthy controls (HC) (n = 40, 24 women) completed baseline assessments with no intervention. Analysis of RS FC addressed effects of diagnosis, time, and remitter status. RESULTS: Significant differences (p < 0.05, corrected) in RS FC were observed between HC and MDD at baseline in the somatomotor network and between association and default mode networks. These disruptions in FC in MDD patients trended toward control patterns with ketamine treatment. Furthermore, following serial ketamine infusions, significant decreases in FC were observed between the cerebellum and salience network (SN) (p < 0.05, corrected). Patient remitters showed increased FC between the cerebellum and the striatum prior to treatment that decreased following treatment, whereas non-remitters showed the opposite pattern. CONCLUSION: Results support that ketamine treatment leads to neurofunctional plasticity between distinct neural networks that are shown as disrupted in MDD patients. Cortico-striatal-cerebellar loops that encompass the SN could be a potential biomarker for ketamine treatment.

Ketamine's modulation of cerebro-cerebellar circuitry during response inhibition in major depression.

Patients with major depressive disorder (MDD) exhibit impaired control of cognitive and emotional systems, including deficient response selection and inhibition. Though these deficits are typically attributed to abnormal communication between macro-scale cortical networks, altered communication with the cerebellum also plays an important role. Yet, how the circuitry between the cerebellum and large-scale functional networks impact treatment outcome in MDD is not understood. We thus examined how ketamine, which elicits rapid therapeutic effects in MDD, modulates cerebro-cerebellar circuitry during response-inhibition using a functional imaging NoGo/Go task in MDD patients (N = 46, mean age: 39.2, 38.1% female) receiving four ketamine infusions, and healthy controls (N = 32, mean age:35.2, 71.4% female). We fitted psychophysiological-interaction (PPI) models for a functionally-derived cerebellar-seed and extracted average PPI in three target functional networks, frontoparietal (FPN), sensory-motor (SMN) and salience (SN) networks. Time and remission status were then evaluated for each of the networks and their network-nodes. Follow-up tests examined whether PPI-connectivity differed between patient remitter/non-remitters and controls. Results showed significant decreases in PPI-connectivity after ketamine between the cerebellum and FPN (p < 0.001) and SMN networks (p = 0.008) in remitters only (N = 20). However, ketamine-related changes in PPI-connectivity between the cerebellum and the SN (p = 0.003) did not vary with remitter status. Cerebellar-FPN, -SN PPI values at baseline were also associated with treatment outcome. Using novel methodology to quantify the functional coupling of cerebro-cerebellar circuitry during response-inhibition, our findings highlight that these loops play distinct roles in treatment response and could potentially serve as novel biomarkers for fast-acting antidepressant therapies in MDD.

Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy.

Depression symptom heterogeneity limits the identifiability of treatment-response biomarkers. Whether improvement along dimensions of depressive symptoms relates to separable neural networks remains poorly understood. We build on work describing three latent symptom dimensions within the 17-item Hamilton Depression Rating Scale (HDRS) and use data-driven methods to relate multivariate patterns of patient clinical, demographic, and brain structural changes over electroconvulsive therapy (ECT) to dimensional changes in depressive symptoms. We included 110 ECT patients from Global ECT-MRI Research Collaboration (GEMRIC) sites who underwent structural MRI and HDRS assessments before and after treatment. Cross validated random forest regression models predicted change along symptom dimensions. HDRS symptoms clustered into dimensions of somatic disturbances (SoD), core mood and anhedonia (CMA), and insomnia. The coefficient of determination between predicted and actual changes were 22%, 39%, and 39% (all p < .01) for SoD, CMA, and insomnia, respectively. CMA and insomnia change were predicted more accurately than HDRS-6 and HDRS-17 changes (p < .05). Pretreatment symptoms, body-mass index, and age were important predictors. Important imaging predictors included the right transverse temporal gyrus and left frontal pole for the SoD dimension; right transverse temporal gyrus and right rostral middle frontal gyrus for the CMA dimension; and right superior parietal lobule and left accumbens for the insomnia dimension. Our findings support that recovery along depressive symptom dimensions is predicted more accurately than HDRS total scores and are related to unique and overlapping patterns of clinical and demographic data and volumetric changes in brain regions related to depression and near ECT electrodes.

Interleukin-8 and lower severity of depression in females, but not males, with treatment-resistant depression.

INTRODUCTION: In cross-sectional studies of depressed patients, relationships between depression and levels of IL-8 are inconsistent, and have not been examined in relation to sex. Given identified sex differences in longitudinal data, it is important to evaluate sex-specific cross-sectional relationships between IL-8 and depressive symptoms, which may explain some inconsistency in the extant literature. It is further unknown whether IL-8 levels may relate to specific symptom profiles among depressed patients, with or without regard to sex. METHODS: Among 108 patients with treatment resistant depression (50 females), we evaluated cross-sectional relationships between IL-8 and depression severity, as measured by the Hamilton Depression Rating Scale [HAM-D] Score, and examined sex-specific relationships, as well as relationships with depressive symptom profiles. Other inflammatory markers (IL-6, IL-10, TNF-α, CRP) were also explored in relation to HAM-D. RESULTS: Higher IL-8 was associated with lower total HAM-D score (standardized ß = -0.19, p = 0.049). Sex-specific effects were identified (IL-8 x sex interaction: p = 0.03), in which higher IL-8 related to lower HAM-D score in females (standardized ß = -0.41, p = 0.004, effect size (sr2) = 0.17), but not males (standardized ß = 0.02, p = 0.91). Among a subset of 94 patients (41 females) who had individual HAM-D items available, we evaluated relationships between IL-8 and HAM-D factor subscores. Across sexes, higher IL-8 was associated with lower anxiety/hypochondriasis subscores (standardized ß = -0.31, p = 0.002; sex interaction: p = 0.99). Sex differences were identified for relationships between IL-8 and two other HAM-D factor subscores. CONCLUSIONS: IL-8 may be related to anxiety symptoms across sexes, but may have a sex-specific relationship with other depressive symptoms. Further evaluation of sex-specific relationships between IL-8, depression symptom profiles, treatment response, and potential neurobiological correlates, may inform mechanisms of depression pathophysiology and aid in development of precision medicine strategies.

White Matter Disruption in Pediatric Traumatic Brain Injury: Results from ENIGMA Pediatric Moderate to Severe Traumatic Brain Injury.

OBJECTIVE: Our study addressed aims: (1) test the hypothesis that moderate-severe TBI in pediatric patients is associated with widespread white matter (WM) disruption; (2) test the hypothesis that age and sex impact WM organization after injury; and (3) examine associations between WM organization and neurobehavioral outcomes. METHODS: Data from ten previously enrolled, existing cohorts recruited from local hospitals and clinics were shared with the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Pediatric msTBI working group. We conducted a coordinated analysis of diffusion MRI (dMRI) data using the ENIGMA dMRI processing pipeline. RESULTS: Five hundred and seven children and adolescents (244 with complicated mild to severe TBI [msTBI] and 263 controls) were included. Patients were clustered into three post-injury intervals: acute/subacute - <2 months, post-acute - 2-6 months, chronic - 6+ months. Outcomes were dMRI metrics and post-injury behavioral problems as indexed by the Child Behavior Checklist (CBCL). Our analyses revealed altered WM diffusion metrics across multiple tracts and all post-injury intervals (effect sizes ranging between d=-0.5 to -1.3). Injury severity is a significant contributor to the extent of WM alterations but explained less variance in dMRI measures with increasing time post-injury. We observed a sex-by-group interaction: females with TBI had significantly lower fractional anisotropy in the uncinate fasciculus than controls (𝞫=0.043), which coincided with more parent-reported behavioral problems (𝞫=-0.0027). CONCLUSIONS: WM disruption after msTBI is widespread, persistent, and influenced by demographic and clinical variables. Future work will test techniques for harmonizing neurocognitive data, enabling more advanced analyses to identify symptom clusters and clinically-meaningful patient subtypes.

Distinct patterns of resting-state connectivity in U.S. service members with mild traumatic brain injury versus posttraumatic stress disorder.

Mild traumatic brain injury (mTBI) is highly prevalent in military populations, with many service members suffering from long-term symptoms. Posttraumatic stress disorder (PTSD) often co-occurs with mTBI and predicts worse clinical outcomes. Functional neuroimaging research suggests there are both overlapping and distinct patterns of resting-state functional connectivity (rsFC) in mTBI versus PTSD. However, few studies have directly compared rsFC of cortical networks in military service members with these two conditions. In the present study, U.S. service members (n = 137; ages 19-59; 120 male) underwent resting-state fMRI scans. Participants were divided into three study groups: mTBI only, PTSD only, and orthopedically injured (OI) controls. Analyses investigated group differences in rsFC for cortical networks: default mode (DMN), frontoparietal (FPN), salience, somatosensory, motor, auditory, and visual. Analyses were family-wise error (FWE) cluster-corrected and Bonferroni-corrected for number of network seeds regions at the whole brain level (pFWE < 0.002). Both mTBI and PTSD groups had reduced rsFC for DMN and FPN regions compared with OI controls. These group differences were largely driven by diminished connectivity in the PTSD group. rsFC with the middle frontal gyrus of the FPN was increased in mTBI, but decreased in PTSD. Overall, these results suggest that PTSD symptoms may have a more consistent signal than mTBI. Our novel findings of opposite patterns of connectivity with lateral prefrontal cortex highlight a potential biomarker that could be used to differentiate between these conditions.

Depression treatment response to ketamine: sex-specific role of interleukin-8, but not other inflammatory markers.

Inflammation plays a role in depression pathophysiology and treatment response, with effects varying by sex and therapeutic modality. Lower levels of interleukin(IL)-8 predict depression response to antidepressant medication and to electroconvulsive therapy (ECT), although ECT effects are specific to females. Whether IL-8 predicts depression response to ketamine and in a sex-specific manner is not known. Here, depressed patients (n = 46; female, n = 17) received open label infusion of ketamine (0.5 mg/kg over 40 min; NCT02165449). Plasma levels of IL-8 were evaluated at baseline and post-treatment. Baseline levels of IL-8 had a trending association with response to ketamine, depending upon sex (responder status × sex interaction: p = 0.096), in which lower baseline levels of IL-8 in females (p = 0.095) but not males (p = 0.96) trended with treatment response. Change in levels of IL-8 from baseline to post-treatment differed significantly by responder status (defined as ≥50% reduction in Hamilton Depression Rating Scale [HAM-D] Score), depending upon sex (responder status × sex × time interaction: F(1,42)=6.68, p = 0.01). In addition, change in IL-8 interacted with sex to predict change in HAM-D score (ß = -0.63, p = 0.003); increasing IL-8 was associated with decreasing HAM-D score in females (p = 0.08) whereas the inverse was found in males (p = 0.02). Other inflammatory markers (IL-6, IL-10, tumor necrosis factor-α, C-reactive protein) were explored with no significant relationships identified. Given these preliminary findings, further evaluation of sex differences in the relationship between IL-8 and treatment response is warranted to elucidate mechanisms of response and aid in the development of personalized approaches to depression treatment.

Coordinating Global Multi-Site Studies of Military-Relevant Traumatic Brain Injury: Opportunities, Challenges, and Harmonization Guidelines.

Traumatic brain injury (TBI) is common among military personnel and the civilian population and is often followed by a heterogeneous array of clinical, cognitive, behavioral, mood, and neuroimaging changes. Unlike many neurological disorders that have a characteristic abnormal central neurologic area(s) of abnormality pathognomonic to the disorder, a sufficient head impact may cause focal, multifocal, diffuse or combination of injury to the brain. This inconsistent presentation makes it difficult to establish or validate biological and imaging markers that could help improve diagnostic and prognostic accuracy in this patient population. The purpose of this manuscript is to describe both the challenges and opportunities when conducting military-relevant TBI research and introduce the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Military Brain Injury working group. ENIGMA is a worldwide consortium focused on improving replicability and analytical power through data sharing and collaboration. In this paper, we discuss challenges affecting efforts to aggregate data in this patient group. In addition, we highlight how "big data" approaches might be used to understand better the role that each of these variables might play in the imaging and functional phenotypes of TBI in Service member and Veteran populations, and how data may be used to examine important military specific issues such as return to duty, the late effects of combat-related injury, and alteration of the natural aging processes.