Intrusive Traumatic Re-Experiencing Domain (ITRED) – Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium

Background Intrusive Traumatic Re-Experiencing Domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective. Methods Data was collected from nine sites taking part in the ENIGMA-PTSD Consortium (n=584) and included itemized PTSD symptoms scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and Trauma-exposed (TE)-only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. Random forest classification model was built on a training set using cross-validation (CV), and the averaged CV model performance for classification was evaluated using area-under-the-curve (AUC). The model was tested using a fully independent portion of the data (test dataset), and the test AUC was evaluated. Results RsFC signatures differentiated TE-only participants from PTSD and from ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and from ITRED-only participants mainly involved default mode network-related pathways. Some unique features, such as connectivity within the frontal-parietal network, differentiated TE-only participants from one group (PTSD or ITRED-only), but to a lesser extent from the other. Conclusion Neural network connectivity supports ITRED as a novel neurobiologically-based approach to classifying post-trauma psychopathology

Neuropsychological predictors of functional disability in Gulf War Illness.

Functional disability refers to the degree to which a health condition impacts day-to-day physical, social, and emotional functioning. Veterans with Gulf War Illness (GWI) may be particularly at risk for developing high functional disability due to their illness. However, few have examined potential factors associated with functional disability in GWI. Cognitive impairment is potentially one determinant of functional disability. Moreover, cognitive impairment is one of the core symptoms of GWI. Thus, the current study proposed to examine neuropsychological and deployment-related correlates of functional disability in veterans with GWI. Data from N = 28 veterans with GWI were used for the current study. We hypothesized that three neuropsychological domains impaired in GWI—verbal memory, visuospatial ability, and working memory—would significantly predict functional disability over and above deployment trauma (probable PTSD and depressive symptoms). Hierarchical linear regression suggested our hypothesis was only partially supported. The combination of neuropsychological predictors did not account for a significant addition of variance (ΔR2 = .85, F(8, 25) = 1.73, p = .163), with deployment trauma accounting for the majority of variance (b = .43, SE = .05, p < .001). However, immediate visuospatial memory (b = -.02, SE = .01, p = .037) and visuospatial organizational ability (b = .01, SE = .00, p = .040) were both significantly associated with functional disability in the full model (R2 = .90, F(10, 17) = 14.55, p < .001). Our results suggest that veterans with GWI could benefit from treatment targeting both mental and cognitive health in addition to typical treatment targeting physical symptoms. Historically, veterans with GWI have struggled to find appropriate support and treatment for their illness. Identifying factors impacting their day-to-day functioning will help target resources to this underserved population

Probabilistic category learning and memory systems functioning in those at-risk for alcohol abuse.

The transition from non-problematic to problematic drinking may be related to neuropsychological functioning, especially difficulties in memory functioning. The goal of this study was to examine declarative and non-declarative memory functioning in high-risk drinking college students using the Weather Prediction Task (WPT). We recruited 20 high-risk and 44 low-risk participants. We hypothesized that high-risk drinkers would perform worse on the WPT, as well as a reversal learning component of the WPT. We also examined the relationship between executive functioning and WPT performance as well as if impulsivity was related to WPT performance. Overall our results did not confirm our primary hypotheses. However, our exploratory analyses revealed in interesting relationship between WPT performance and facets of impulsivity. Future research is needed to further examine declarative and non-declarative memory process in high-risk drinkers, as there were several limitations in our study

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium.

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60&nbsp;% test AUC for s-MRI, 59&nbsp;% for rs-fMRI and 56&nbsp;% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75&nbsp;% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable