Application of data pooling to longitudinal studies of early post-traumatic stress disorder (PTSD): the International Consortium to Predict PTSD (ICPP) project

Large-Scale Data Harmonization Across Prospective Studies

The Preconception Period Analysis of Risks and Exposures Influencing Health and Development (PrePARED) Consortium creates a novel resource for addressing preconception health by merging data from numerous cohort studies. In this paper, we describe our data harmonization methods and results. Individual-level data from 12 prospective studies were pooled. The crosswalk-cataloging-harmonization procedure was used. The index pregnancy was defined as the first postbaseline pregnancy lasting more than 20 weeks. We assessed heterogeneity across studies by comparing preconception characteristics in different types of studies. The pooled data set included 114,762 women, and 25,531 (22%) reported at least 1 pregnancy of more than 20 weeks' gestation during the study period. Babies from the index pregnancies were delivered between 1976 and 2021 (median, 2008), at a mean maternal age of 29.7 (standard deviation, 4.6) years. Before the index pregnancy, 60% of women were nulligravid, 58% had a college degree or more, and 37% were overweight or obese. Other harmonized variables included race/ethnicity, household income, substance use, chronic conditions, and perinatal outcomes. Participants from pregnancy-planning studies had more education and were healthier. The prevalence of preexisting medical conditions did not vary substantially based on whether studies relied on self-reported data. Use of harmonized data presents opportunities to study uncommon preconception risk factors and pregnancy-related events. This harmonization effort laid the groundwork for future analyses and additional data harmonization.

PTSD in the Year Following Sexual Assault: A Meta-Analysis of Prospective Studies

OBJECTIVE

Sexual assault is associated with higher rates of posttraumatic stress disorder (PTSD) than other traumas, and the course of PTSD may differ by trauma type. However, the course of PTSD after sexual assault has not been summarized. The aim of this meta-analysis was to identify the prevalence and severity of PTSD and changes to the average rate of recovery in the 12 months following sexual assault.

METHOD

Authors searched four databases for prospective studies published before April 2020 and sought relevant unpublished data. Eligible studies assessed PTSD in at least 10 survivors of sexual assault in at least two time points, starting within 3 months postassault. Random effects linear-linear piecewise models were used to identify changes in average recovery rate and produce model-implied estimates of monthly point prevalence and mean symptom severity.

RESULTS

Meta-analysis of 22 unique samples (N = 2,106) indicated that 74.58% (95% confidence interval [CI]: [67.21, 81.29]) and 41.49% (95% CI: [32.36, 50.92]) of individuals met diagnostic criteria for PTSD at the first and 12th month following sexual assault, respectively. PTSD symptom severity was 47.94% (95% CI: [41.27, 54.61]) and 29.91% (95% CI: [23.10, 36.73]) of scales' maximum severity at the first and 12th month following sexual assault, respectively. Most symptom recovery occurred within the first 3 months following sexual assault, after which point the average rate of recovery slowed.

CONCLUSIONS

Findings indicate that PTSD is common and severe following sexual assault, and the first 3 months postassault may be a critical period for natural recovery.

Associations between parent posttraumatic stress symptoms (PTSS) and later child PTSS: Results from an international data archive

The extant literature indicates that parent and child posttraumatic stress symptoms (PTSS) are associated. However, the magnitude of this association at different time points and in the context of covariates has been difficult to quantify due to the methodological limitations of past studies, including small sample sizes. Using data from the Prospective studies of Acute Child Trauma and Recovery Data Archive, we harmonized participant-level parent and child data from 16 studies (N = 1,775 parent-child dyads) that included prospective assessment of PTSS during both the acute and later posttrauma periods (i.e., 1-30 days and 3-12 months after exposure to a potentially traumatic event, respectively). Parent and child PTSS demonstrated small-to-moderate cross-sectional, ρs = .22-.27, 95% CI [.16, .32], and longitudinal associations, ρ = .30, CI [.23, .36]. Analyses using actor-partner interdependence models revealed that parent PTSS during the acute trauma period predicted later child PTSS. Regression analyses demonstrated that parent gender did not moderate the association between parent and child PTSS. The findings suggest that parent PTSS during the acute and later posttrauma periods may be one of a constellation of risk factors and indicators for child PTSS.

Assessment of early neurocognitive functioning increases the accuracy of predicting chronic PTSD risk

Post-traumatic stress disorder (PTSD) is a protracted and debilitating consequence of traumatic events. Identifying early predictors of PTSD can inform the disorder's risk stratification and prevention. We used advanced computational models to evaluate the contribution of early neurocognitive performance measures to the accuracy of predicting chronic PTSD from demographics and early clinical features. We consecutively enrolled adult trauma survivors seen in a general hospital emergency department (ED) to a 14-month long prospective panel study. Extreme Gradient Boosting algorithm evaluated the incremental contribution to 14 months PTSD risk of demographic variables, 1-month clinical variables, and concurrent neurocognitive performance. The main outcome variable was PTSD diagnosis, 14 months after ED admission, obtained by trained clinicians using the Clinician-Administered PTSD Scale (CAPS). N = 138 trauma survivors (mean age = 34.25 ± 11.73, range = 18-64; n = 73 [53%] women) were evaluated 1 month after ED admission and followed for 14 months, at which time n = 33 (24%) met PTSD diagnosis. Demographics and clinical variables yielded a discriminatory accuracy of AUC = 0.68 in classifying PTSD diagnostic status. Adding neurocognitive functioning improved the discriminatory accuracy (AUC = 0.88); the largest contribution emanating from poorer cognitive flexibility, processing speed, motor coordination, controlled and sustained attention, emotional bias, and higher response inhibition, and recall memory. Impaired cognitive functioning 1-month after trauma exposure is a significant and independent risk factor for PTSD. Evaluating cognitive performance could improve early screening and prevention.

A Systematic Review of Research on Trauma and Women's Health in the Nurses' Health Study II

OBJECTIVE

To synthesize research on interpersonal trauma and women's health from the Nurses' Health Study II (NHS II) and to analyze conceptualization of interpersonal trauma across studies.

DATA SOURCES

A literature review was conducted in PubMed using a systematic search strategy.

STUDY SELECTION

Articles were included in the review if they used data from the NHS II and involved investigations of interpersonal trauma. Theoretical articles, methodologic articles, and other literature reviews involving the NHS II were excluded. Initially, the search returned 61 articles. After exclusions, 45 articles met the criteria for inclusion in the review and data extraction.

DATA EXTRACTION

Information was extracted and consolidated in an evidence table. Data included study time frame, sample, definition of trauma, outcomes studied, and journal of publication.

DATA SYNTHESIS

Trauma was not operationalized consistently across studies, even though the NHS II assessed trauma experiences in childhood, adolescence, and adulthood. Most investigations focused on childhood abuse, with investigations of childhood sexual abuse overrepresented in comparison to other abuse experiences. Authors conducting studies of trauma at any time in the life course consistently found a negative association with physical and mental health outcomes, which were increased by the presence of posttraumatic stress symptoms. Results from a small number of studies suggested a negative intergenerational impact of trauma on the children of women in the NHS II.

CONCLUSION

Interpersonal trauma across the life course was strongly associated with many leading causes of morbidity and mortality among female nurses. Trauma conceptualization and operationalization varied across studies, and future investigations should leverage the full range of trauma measures available in the NHS II data set.

Pragmatic Precision Psychiatry-A New Direction for Optimizing Treatment Selection

IMPORTANCE

Clinical trials have identified numerous prescriptive predictors of mental disorder treatment response, ie, predictors of which treatments are best for which patients. However, none of these prescriptive predictors is strong enough alone to guide precision treatment planning. This has prompted growing interest in developing precision treatment rules (PTRs) that combine information across multiple prescriptive predictors, but this work has been much less successful in psychiatry than some other areas of medicine. Study designs and analysis schemes used in research on PTR development in other areas of medicine are reviewed, key challenges for implementing similar studies of mental disorders are highlighted, and recent methodological advances to address these challenges are described here.

OBSERVATIONS

Discovering prescriptive predictors requires large samples. Three approaches have been used in other areas of medicine to do this: conduct very large randomized clinical trials, pool individual-level results across multiple smaller randomized clinical trials, and develop preliminary PTRs in large observational treatment samples that are then tested in smaller randomized clinical trials. The third approach is most feasible for research on mental disorders. This approach requires working with large real-world observational electronic health record databases; carefully selecting samples to emulate trials; extracting information about prescriptive predictors from electronic health records along with other inexpensive data augmentation strategies; estimating preliminary PTRs in the observational data using appropriate methods; implementing pragmatic trials to validate the preliminary PTRs; and iterating between subsequent observational studies and quality improvement pragmatic trials to refine and expand the PTRs. New statistical methods exist to address the methodological challenges of implementing this approach.

CONCLUSIONS AND RELEVANCE

Advances in pragmatic precision psychiatry will require moving beyond the current focus on randomized clinical trials and adopting an iterative discovery-confirmation process that integrates observational and experimental studies in real-world clinical populations.

Deep learning model of fMRI connectivity predicts PTSD symptom trajectories in recent trauma survivors

Early intervention following exposure to a traumatic life event could change the clinical path from the development of post traumatic stress disorder (PTSD) to recovery, hence the interest in early detection and underlying biological mechanisms involved in the development of post traumatic sequelae. We introduce a novel end-to-end neural network that employs resting-state and task-based functional MRI (fMRI) datasets, obtained one month after trauma exposure, to predict PTSD symptoms at one-, six- and fourteen-months after the exposure. FMRI data, as well as PTSD status and symptoms, were collected from adults at risk for PTSD development, after admission to emergency room following a traumatic event. Our computational method utilized a per-region encoder to extract brain regions embedding, which were subsequently updated by applying the algorithmic technique of pairwise attention. The affinities obtained between each pair of regions were combined to create a pairwise co-activation map used to perform multi-label classification. The results demonstrate that the novel method's performance in predicting PTSD symptoms, in a prospective manner, outperforms previous analytical techniques reported in the fMRI literature, all trained on the same dataset. We further show a high predictive ability for predicting PTSD symptom clusters and PTSD persistence. To the best of our knowledge, this is the first deep learning method applied on fMRI data with respect to prospective clinical outcomes, to predict PTSD status, severity and symptom clusters. Future work could further delineate the mechanisms that underlie such a prediction, and potentially improve single patient characterization.

Posttraumatic stress disorder symptom trajectories within the first year following emergency department admissions: pooled results from the International Consortium to predict PTSD

BACKGROUND

Research exploring the longitudinal course of posttraumatic stress disorder (PTSD) symptoms has documented four modal trajectories (low, remitting, high, and delayed), with proportions varying across studies. Heterogeneity could be due to differences in trauma types and patient demographic characteristics.

METHODS

This analysis pooled data from six longitudinal studies of adult survivors of civilian-related injuries admitted to general hospital emergency departments (EDs) in six countries (pooled N = 3083). Each study included at least three assessments of the clinician-administered PTSD scale in the first post-trauma year. Latent class growth analysis determined the proportion of participants exhibiting various PTSD symptom trajectories within and across the datasets. Multinomial logistic regression analyses examined demographic characteristics, type of event leading to the injury, and trauma history as predictors of trajectories differentiated by their initial severity and course.

RESULTS

Five trajectories were found across the datasets: Low (64.5%), Remitting (16.9%), Moderate (6.7%), High (6.5%), and Delayed (5.5%). Female gender, non-white race, prior interpersonal trauma, and assaultive injuries were associated with increased risk for initial PTSD reactions. Female gender and assaultive injuries were associated with risk for membership in the Delayed (v. Low) trajectory, and lower education, prior interpersonal trauma, and assaultive injuries with risk for membership in the High (v. Remitting) trajectory.

CONCLUSIONS

The results suggest that over 30% of civilian-related injury survivors admitted to EDs experience moderate-to-high levels of PTSD symptoms within the first post-trauma year, with those reporting assaultive violence at increased risk of both immediate and longer-term symptoms.

Forecasting individual risk for long-term Posttraumatic Stress Disorder in emergency medical settings using biomedical data: A machine learning multicenter cohort study

The necessary requirement of a traumatic event preceding the development of Posttraumatic Stress Disorder, theoretically allows for administering preventive and early interventions in the early aftermath of such events. Machine learning models including biomedical data to forecast PTSD outcome after trauma are highly promising for detection of individuals most in need of such interventions. In the current study, machine learning was applied on biomedical data collected within 48 h post-trauma to forecast individual risk for long-term PTSD, using a multinominal approach including the full spectrum of common PTSD symptom courses within one prognostic model for the first time. N = 417 patients (37.2% females; mean age 46.09 ± 15.88) admitted with (suspected) serious injury to two urban Academic Level-1 Trauma Centers were included. Routinely collected biomedical information (endocrine measures, vital signs, pharmacotherapy, demographics, injury and trauma characteristics) upon ED admission and subsequent 48 h was used. Cross-validated multi-nominal classification of longitudinal self-reported symptom severity (IES-R) over 12 months and bimodal classification of clinician-rated PTSD diagnosis (CAPS-IV) at 12 months post-trauma was performed using extreme Gradient Boosting and evaluated on hold-out sets. SHapley Additive exPlanations (SHAP) values were used to explain the derived models in human-interpretable form. Good prediction of longitudinal PTSD symptom trajectories (multiclass AUC = 0.89) and clinician-rated PTSD at 12 months (AUC = 0.89) was achieved. Most relevant prognostic variables to forecast both multinominal and dichotomous PTSD outcomes included acute endocrine and psychophysiological measures and hospital-prescribed pharmacotherapy. Thus, individual risk for long-term PTSD was accurately forecasted from biomedical information routinely collected within 48 h post-trauma. These results facilitate future targeted preventive interventions by enabling future early risk detection and provide further insights into the complex etiology of PTSD.

Identification of key genes involved in post-traumatic stress disorder: Evidence from bioinformatics analysis

BACKGROUND

Post-traumatic stress disorder (PTSD) is a serious stress-related disorder.

AIM

To identify the key genes and pathways to uncover the potential mechanisms of PTSD using bioinformatics methods.

METHODS

Gene expression profiles were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified by using GEO2R. Gene functional annotation and pathway enrichment were then conducted. The gene-pathway network was constructed with Cytoscape software. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis was applied for validation, and text mining by Coremine Medical was used to confirm the connections among genes and pathways.

RESULTS

We identified 973 DEGs including 358 upregulated genes and 615 downregulated genes in PTSD. A group of centrality hub genes and significantly enriched pathways (MAPK, Ras, and ErbB signaling pathways) were identified by using gene functional assignment and enrichment analyses. Six genes (KRAS, EGFR, NFKB1, FGF12, PRKCA, and RAF1) were selected to validate using qRT-PCR. The results of text mining further confirmed the correlation among hub genes and the enriched pathways. It indicated that these altered genes displayed functional roles in PTSD via these pathways, which might serve as key signatures in the pathogenesis of PTSD.

CONCLUSION

The current study identified a panel of candidate genes and important pathways, which might help us deepen our understanding of the underlying mechanism of PTSD at the molecular level. However, further studies are warranted to discover the critical regulatory mechanism of these genes via relevant pathways in PTSD.

Evaluating a screener to quantify PTSD risk using emergency care information: a proof of concept study

BACKGROUND

Previous work has indicated that post-traumatic stress disorder (PTSD) symptoms, measured by the Clinician-Administered PTSD Scale (CAPS) within 60 days of trauma exposure, can reliably produce likelihood estimates of chronic PTSD among trauma survivors admitted to acute care centers. Administering the CAPS is burdensome, requires skilled professionals, and relies on symptoms that are not fully expressed upon acute care admission. Predicting chronic PTSD from peritraumatic responses, which are obtainable upon acute care admission, has yielded conflicting results, hence the rationale for a stepwise screening-and-prediction practice. This work explores the ability of peritraumatic responses to produce risk likelihood estimates of early CAPS-based PTSD symptoms indicative of chronic PTSD risk. It specifically evaluates the Peritraumatic Dissociative Experiences Questionnaire (PDEQ) as a risk-likelihood estimator.

METHODS

We used individual participant data (IPD) from five acute care studies that used both the PDEQ and the CAPS (n = 647). Logistic regression calculated the probability of having CAPS scores ≥ 40 between 30 and 60 days after trauma exposure across the range of initial PDEQ scores, and evaluated the added contribution of age, sex, trauma type, and prior trauma exposure. Brier scores, area under the receiver-operating characteristic curve (AUC), and the mean slope of the calibration line evaluated the accuracy and precision of the predicted probabilities.

RESULTS

Twenty percent of the sample had CAPS ≥ 40. PDEQ severity significantly predicted having CAPS ≥ 40 symptoms (p < 0.001). Incremental PDEQ scores produced a reliable estimator of CAPS ≥ 40 likelihood. An individual risk estimation tool incorporating PDEQ and other significant risk indicators is provided.

CONCLUSION

Peritraumatic reactions, measured here by the PDEQ, can reliably quantify the likelihood of acute PTSD symptoms predictive of chronic PTSD and requiring clinical attention. Using them as a screener in a stepwise chronic PTSD prediction strategy may reduce the burden of later CAPS-based assessments. Other peritraumatic metrics may perform similarly and their use requires similar validation.

TRIAL REGISTRATION

Jerusalem Trauma Outreach and Prevention Study (J-TOPS): NCT00146900.

Differentiating PTSD from anxiety and depression: Lessons from the ICD-11 PTSD diagnostic criteria

OBJECTIVE

Posttraumatic stress disorder (PTSD) is frequently associated with depression and anxiety, but the nature of the relationship is unclear. By removing mood and anxiety diagnostic criteria, the 11th edition of the International Classification of Diseases (ICD-11) aims to delineate a distinct PTSD phenotype. We examined the effect of implementing ICD-11 criteria on rates of codiagnosed depression and anxiety in survivors with recent PTSD.

METHOD

Participants were 1,061 survivors of traumatic injury admitted to acute care centers in Israel. ICD-10 and ICD-11 diagnostic rules were applied to the Clinician-Administered PTSD Scale for DSM-IV. Co-occurring disorders were identified using the Structured Clinical Interview for DSM-IV (SCID). Depression severity was measured by the Beck Depression Inventory-II (BDI-II). Assessments were performed 0-60 ("wave 1") and 90-240 ("wave 2") days after trauma exposure.

RESULTS

Participants identified by ICD-11 PTSD criteria were equally or more likely than those identified by the ICD-10 alone to meet depression or anxiety disorder diagnostic criteria (for wave 1: depressive disorders, OR [odds ratio] = 1.98, 95% CI [confidence interval] = [1.36, 2.87]; anxiety disorders, OR = 1.04, 95% CI = [0.67, 1.64]; for wave 2: depressive disorders, OR = 1.70, 95% CI = [1.00, 2.91]; anxiety disorders, OR = 1.04, 95% CI = [0.54, 2.01]). ICD-11 PTSD was associated with higher BDI scores (M = 23.15 vs. 17.93, P < 0.001 for wave 1; M = 23.93 vs. 17.94, P < 0.001 for wave 2). PTSD symptom severity accounted for the higher levels of depression in ICD-11 PTSD.

CONCLUSIONS

Despite excluding depression and anxiety symptom criteria, the ICD-11 identified equal or higher proportion of depression and anxiety disorders, suggesting that those are inherently associated with PTSD.

Facts on psychotraumatology

Daily news is dominated by reports of traumatic events across the world. Is trauma indeed rather the norm than the exception? What are the facts? How can we better understand, prevent and treat the consequences of trauma? This past year the European Journal of Psychotraumatology (EJPT) has again tried to address these questions. With the gold Open Access model articles in the journal are being made immediately available without any barriers to access. In Europe, promising developments with regard to Open Science emerged in 2018, for instance, cOAlition S with their ambitious Plan S boosting the transition to full Open Access. In this editorial these and other developments in the journal, such as Registered Reports as a way to reduce Questionable Research Practices (QRPs), journal metrics, and the ESTSS EJPT award finalists for best paper of 2018 are being presented.

Identification of Key Genes and Pathways in Post-traumatic Stress Disorder Using Microarray Analysis

Introduction: Post-traumatic stress disorder (PTSD) is characterized by impaired fear extinction, excessive anxiety, and depression. However, the potential pathogenesis and cause of PTSD are not fully understood. Hence, the purpose of this study was to identify key genes and pathway involved in PTSD and reveal underlying molecular mechanisms by using bioinformatics analysis. Methods: The mRNA microarray expression profile dataset was retrieved and downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened using GEO2R. Gene ontology (GO) was used for gene function annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was performed for enrichment analysis. Subsequently, protein-protein interaction (PPI) network and module analysis by the plugin MCODE were mapped by Cytoscape software. Finally, these key genes were verified in stress-exposed models by Real-Time quantitative (qRT-PCR). In addition, we performed text mining among the key genes and pathway with PTSD by using COREMINE. Results: A total of 1004 DEGs were identified. Gene functional annotations and enrichment analysis indicated that the most associated pathway was closely related to the Wnt signaling pathway. Using PPI network and module analysis, we identified a group of "seed" genes. These genes were further verified by qRT-PCR. In addition, text mining indicated that the altered CYP1A2, SYT1, and NLGN1 affecting PTSD might work via the Wnt signaling pathway. Conclusion: By using bioinformatics analysis, we identified a number of genes and relevant pathway which may represent key mechanisms associated with PTSD. However, these findings require verification in future experimental studies.

Estimating the risk of PTSD in recent trauma survivors: results of the International Consortium to Predict PTSD (ICPP)

A timely determination of the risk of post-traumatic stress disorder (PTSD) is a prerequisite for efficient service delivery and prevention. We provide a risk estimate tool allowing a calculation of individuals' PTSD likelihood from early predictors. Members of the International Consortium to Predict PTSD (ICPP) shared individual participants' item-level data from ten longitudinal studies of civilian trauma survivors admitted to acute care centers in six countries. Eligible participants (N=2,473) completed an initial clinical assessment within 60 days of trauma exposure, and at least one follow-up assessment 4-15 months later. The Clinician-Administered PTSD Scale for DSM-IV (CAPS) evaluated PTSD symptom severity and diagnostic status at each assessment. Participants' education, prior lifetime trauma exposure, marital status and socio-economic status were assessed and harmonized across studies. The study's main outcome was the likelihood of a follow-up PTSD given early predictors. The prevalence of follow-up PTSD was 11.8% (9.2% for male participants and 16.4% for females). A logistic model using early PTSD symptom severity (initial CAPS total score) as a predictor produced remarkably accurate estimates of follow-up PTSD (predicted vs. raw probabilities: r=0.976). Adding respondents' female gender, lower education, and exposure to prior interpersonal trauma to the model yielded higher PTSD likelihood estimates, with similar model accuracy (predicted vs. raw probabilities: r=0.941). The current model could be adjusted for other traumatic circumstances and accommodate risk factors not captured by the ICPP (e.g., biological, social). In line with their use in general medicine, risk estimate models can inform clinical choices in psychiatry. It is hoped that quantifying individuals' PTSD risk will be a first step towards systematic prevention of the disorder.

Neurobehavioral moderators of post-traumatic stress disorder (PTSD) trajectories: study protocol of a prospective MRI study of recent trauma survivors

Background: Post-traumatic stress disorder (PTSD) is triggered by distinct events and is therefore amenable to studies of its early pathogenesis. Longitudinal studies during the year that follows trauma exposure revealed typical symptom trajectories leading to either recovery or protracted PTSD. Thezneurobehavioral correlates of early PTSD symptoms' trajectories have not been longitudinally explored. Objective: To present the rationale and design of a longitudinal study exploring the relationship between evolving PTSD symptoms and co-occurring cognitive functioning and structural and functional brain imaging parameters. Method: Adult civilians consecutively admitted to a general hospital emergency room (ER) for traumatic injury will be screened for early PTSD symptoms suggestive of chronic PTSD risk, and consecutively evaluated 1, 6 and 14 months following the traumatic event. Consecutive assessments will include structured clinical interviews for PTSD and comorbid disorders, self-reported depression and anxiety symptoms, a web-based assessment of cognitive domains previously linked with PTSD (e.g., memory, executive functions, cognitive flexibility), high-resolution structural MRI of both grey and white matter, functional resting-state connectivity, and fMRI tasks examining emotional reactivity and regulation, as well as motivation processing and sensitivity to risk and reward. Data analyses will explore putative cognitive predictors of non-remitting PTSD, and brain structural and functional correlates of PTSD persistence or recovery. Conclusion: This work will longitudinally document patterns of brain structures, connectivity, and functioning, predictive of (or associated with) emerging PTSD during the critical first year of after the traumatic event. It will thereby inform our understanding of the disorder's pathogenesis and underlying neuropathology. Challenges to longitudinal MRI studies of recent survivors, and methodological choices used to optimize the study's design are discussed.