scholarly journals Combining Deep Learning and Graph-Theoretic Brain Features to Detect Posttraumatic Stress Disorder at the Individual Level

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1416
Author(s):  
Ziyu Zhu ◽  
Du Lei ◽  
Kun Qin ◽  
Xueling Suo ◽  
Wenbin Li ◽  
...  
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Deep Learning ◽  
Posttraumatic Stress ◽  
Stress Disorder ◽  
Support Vector ◽  
Single Subject ◽  
Theoretic Approach ◽  
Individual Level ◽  
Graph Theoretic ◽  
The Individual

Previous studies using resting-state functional MRI (rs-fMRI) have revealed alterations in graphical metrics in groups of individuals with posttraumatic stress disorder (PTSD). To explore the ability of graph measures to diagnose PTSD and capture its essential features in individual patients, we used a deep learning (DL) model based on a graph-theoretic approach to discriminate PTSD from trauma-exposed non-PTSD at the individual level and to identify its most discriminant features. Our study was performed on rs-fMRI data from 91 individuals with PTSD and 126 trauma-exposed non-PTSD patients. To evaluate our DL method, we used the traditional support vector machine (SVM) classifier as a reference. Our results showed that the proposed DL model allowed single-subject discrimination of PTSD and trauma-exposed non-PTSD individuals with higher accuracy (average: 80%) than the traditional SVM (average: 57.7%). The top 10 DL features were identified within the default mode, central executive, and salience networks; the first two of these networks were also identified in the SVM classification. We also found that nodal efficiency in the left fusiform gyrus was negatively correlated with the Clinician Administered PTSD Scale score. These findings demonstrate that DL based on graphical features is a promising method for assisting in the diagnosis of PTSD.

scholarly journals Individual Prediction of Psychotherapy Outcome in Posttraumatic Stress Disorder using Neuroimaging Data

2019 ◽  
Author(s):  
Paul Zhutovsky ◽  
Rajat M. Thomas ◽  
Miranda Olff ◽  
Sanne J.H. van Rooij ◽  
Mitzy Kennis ◽  
...  
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Gray Matter ◽  
Resting State ◽  
Stress Disorder ◽  
Gray Matter Volume ◽  
Fmri Data ◽  
Single Subject ◽  
Permutation Testing ◽  
Individual Level

AbstractObjectiveTrauma-focused psychotherapy is the first-line treatment for posttraumatic stress disorder (PTSD) but 30-50% of patients do not benefit sufficiently. We investigated whether structural and resting-state functional magnetic resonance imaging (MRI/rs-fMRI) data could distinguish between treatment responders and non-responders on the group and individual level.MethodsForty-four male veterans with PTSD underwent baseline scanning followed by trauma-focused psychotherapy. Voxel-wise gray matter volumes were extracted from the structural MRI data and resting-state networks (RSNs) were calculated from rs-fMRI data using independent component analysis. Data were used to detect differences between responders and non-responders on the group level using permutation testing, and the single-subject level using Gaussian process classification with cross-validation.ResultsA RSN centered on the bilateral superior frontal gyrus differed between responders and non-responder groups (PFWE < 0.05) while a RSN centered on the pre-supplementary motor area distinguished between responders and non-responders on an individual-level with 81.4% accuracy (P < 0.001, 84.8% sensitivity, 78% specificity and AUC of 0.93). No significant single-subject classification or group differences were observed for gray matter volume.ConclusionsThis proof-of-concept study demonstrates the feasibility of using rs-fMRI to develop neuroimaging biomarkers for treatment response, which could enable personalized treatment of patients with PTSD.

scholarly journals Individual prediction of psychotherapy outcome in posttraumatic stress disorder using neuroimaging data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Paul Zhutovsky ◽  
Rajat M. Thomas ◽  
Miranda Olff ◽  
Sanne J. H. van Rooij ◽  
Mitzy Kennis ◽  
...  
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Gray Matter ◽  
Resting State ◽  
Stress Disorder ◽  
Gray Matter Volume ◽  
Fmri Data ◽  
Single Subject ◽  
Permutation Testing ◽  
Individual Level

AbstractTrauma-focused psychotherapy is the first-line treatment for posttraumatic stress disorder (PTSD) but 30–50% of patients do not benefit sufficiently. We investigated whether structural and resting-state functional magnetic resonance imaging (MRI/rs-fMRI) data could distinguish between treatment responders and non-responders on the group and individual level. Forty-four male veterans with PTSD underwent baseline scanning followed by trauma-focused psychotherapy. Voxel-wise gray matter volumes were extracted from the structural MRI data and resting-state networks (RSNs) were calculated from rs-fMRI data using independent component analysis. Data were used to detect differences between responders and non-responders on the group level using permutation testing, and the single-subject level using Gaussian process classification with cross-validation. A RSN centered on the bilateral superior frontal gyrus differed between responders and non-responder groups (PFWE < 0.05) while a RSN centered on the pre-supplementary motor area distinguished between responders and non-responders on an individual-level with 81.4% accuracy (P < 0.001, 84.8% sensitivity, 78% specificity and AUC of 0.93). No significant single-subject classification or group differences were observed for gray matter volume. This proof-of-concept study demonstrates the feasibility of using rs-fMRI to develop neuroimaging biomarkers for treatment response, which could enable personalized treatment of patients with PTSD.

scholarly journals Culturally informed research design issues in a study for MDMA-assisted psychotherapy for posttraumatic stress disorder

2019 ◽  
Vol 4 (1) ◽  
pp. 40-50 ◽  
Author(s):  
Monnica T. Williams ◽  
Sara Reed ◽  
Ritika Aggarwal
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Stress Disorder ◽  
Late Phase ◽  
Community Outreach ◽  
People Of Color ◽  
Treatment Team ◽  
Open Label ◽  
Individual Level ◽  
Culturally Informed

Recent research suggests that psychedelic drugs can be powerful agents of change when utilized in conjunction with psychotherapy. Methylenedioxymethamphetamine (MDMA)-assisted psychotherapy has been studied as a means of helping people overcome posttraumatic stress disorder, believed to work by reducing fear of traumatic memories and increasing feelings of trust and compassion toward others, without inhibiting access to difficult emotions. However, research studies for psychedelic psychotherapies have largely excluded people of color, leaving important questions unaddressed for these populations. At the University of Connecticut, we participated as a study site in a MAPS-sponsored, FDA-reviewed Phase 2 open-label multisite study, with a focus on providing culturally informed care to people of color. We discuss the development of a study site focused on the ethnic minority trauma experience, including assessment of racial trauma, design of informed consent documents to improve understanding and acceptability to people of color, diversification of the treatment team, ongoing training for team members, validation of participant experiences of racial oppression at a cultural and individual level, examination of the setting and music used during sessions for cultural congruence, training for the independent rater pool, community outreach, and institutional resistance. We also discuss next steps in ensuring that access to culturally informed care is prioritized as MDMA and other psychedelics move into late phase trials, including the importance of diverse sites and training focused on therapy providers of color.

Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood

2020 ◽  
pp. 1-11 ◽  
Author(s):  
Katharina Schultebraucks ◽  
Vijay Yadav ◽  
Arieh Y. Shalev ◽  
George A. Bonanno ◽  
Isaac R. Galatzer-Levy
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Deep Learning ◽  
Posttraumatic Stress ◽  
Free Speech ◽  
Stress Disorder ◽  
Acoustic Analysis ◽  
Traumatic Event ◽  
Weighted Average ◽  
Average Precision ◽  
Video And Audio

Abstract Background Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). Methods N = 81 patients admitted to an emergency department (ED) of a Level-1 Trauma Unit following a life-threatening traumatic event participated in an open-ended qualitative interview with a para-professional about their experience 1 month following admission. A deep neural network was utilized to extract facial features of emotion and their intensity, movement parameters, speech prosody, and natural language content. These features were utilized as inputs to classify PTSD and MDD cross-sectionally. Results Both video- and audio-based markers contributed to good discriminatory classification accuracy. The algorithm discriminates PTSD status at 1 month after ED admission with an AUC of 0.90 (weighted average precision = 0.83, recall = 0.84, and f1-score = 0.83) as well as depression status at 1 month after ED admission with an AUC of 0.86 (weighted average precision = 0.83, recall = 0.82, and f1-score = 0.82). Conclusions Direct clinical observation during post-trauma free speech using deep learning identifies digital markers that can be utilized to classify MDD and PTSD status.

scholarly journals Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level

2013 ◽  
Vol 43 (12) ◽  
pp. 2547-2562 ◽  
Author(s):  
W. Pettersson-Yeo ◽  
S. Benetti ◽  
A. F. Marquand ◽  
F. Dell‘Acqua ◽  
S. C. R. Williams ◽  
...  
Keyword(s):  
High Risk ◽  
First Episode Psychosis ◽  
First Episode ◽  
Support Vector ◽  
Single Subject ◽  
Individual Level ◽  
Episode Psychosis ◽  
Ultra High Risk ◽  
The Individual ◽  
Cognitive Data

BackgroundGroup-level results suggest that relative to healthy controls (HCs), ultra-high-risk (UHR) and first-episode psychosis (FEP) subjects show alterations in neuroanatomy, neurofunction and cognition that may be mediated genetically. It is unclear, however, whether these groups can be differentiated at single-subject level, for instance using the machine learning analysis support vector machine (SVM). Here, we used a multimodal approach to examine the ability of structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor neuroimaging (DTI), genetic and cognitive data to differentiate between UHR, FEP and HC subjects at the single-subject level using SVM.MethodThree age- and gender-matched SVM paired comparison groups were created comprising 19, 19 and 15 subject pairs for FEPversusHC, UHRversusHC and FEPversusUHR, respectively. Genetic, sMRI, DTI, fMRI and cognitive data were obtained for each participant and the ability of each to discriminate subjects at the individual level in conjunction with SVM was tested.ResultsSuccessful classification accuracies (p < 0.05) comprised FEPversusHC (genotype, 67.86%; DTI, 65.79%; fMRI, 65.79% and 68.42%; cognitive data, 73.69%), UHRversusHC (sMRI, 68.42%; DTI, 65.79%), and FEPversusUHR (sMRI, 76.67%; fMRI, 73.33%; cognitive data, 66.67%).ConclusionsThe results suggest that FEP subjects are identifiable at the individual level using a range of biological and cognitive measures. Comparatively, only sMRI and DTI allowed discrimination of UHR from HC subjects. For the first time FEP and UHR subjects have been shown to be directly differentiable at the single-subject level using cognitive, sMRI and fMRI data. Preliminarily, the results support clinical development of SVM to help inform identification of FEP and UHR subjects, though future work is needed to provide enhanced levels of accuracy.

Posttraumatic Stress Disorder and the MCMI-II

1995 ◽  
Vol 76 (3) ◽  
pp. 939-944 ◽  
Author(s):  
Patrick H. Munley ◽  
Dharm S. Bains ◽  
William D. Bloem ◽  
Rebecca M. Busby ◽  
Steve Pendziszewski
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Stress Disorder ◽  
Clinical Syndrome ◽  
Individual Personality ◽  
The Mean ◽  
Profile Characteristics ◽  
The Individual ◽  
Group Profile ◽  
Univariate Analyses

This study investigated the MCMI-II profile characteristics of 39 veterans diagnosed with Posttraumatic Stress Disorder. Characteristics of the mean group profile were similar to prior findings reported in the literature on the MCMI and Posttraumatic Stress Disorder with highest mean elevations found on the Avoidant, Passive-Aggressive, Schizoid, and Antisocial basic personality scales, the Borderline and Schizotypal pathological personality scales, and with elevations on the Anxiety, Dysthymia, Alcohol Dependence, Drug Dependence, and Major Depression clinical syndrome scales. A multivariate analysis of variance comparing the group with Posttraumatic Stress Disorder with a non-PTSD comparison group of 39 on the basic personality, pathological personality, and the clinical syndrome scales of the MCMI-II was not statistically significant. Nonetheless, univariate analyses of variance comparing the two groups on the individual modifier scales and the individual personality and clinical syndrome scales of the MCMI-II using a Bonferroni adjusted probability indicated significant differences on the Desirability and Histrionic scales. Response-style bias as a possible factor in MCMI-II profiles for the group with Posttraumatic Stress Disorder is also discussed.

scholarly journals Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach (Preprint)

2019 ◽  
Author(s):  
Safwan Wshah ◽  
Christian Skalka ◽  
Matthew Price
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Stress Disorder ◽  
Traumatic Event ◽  
Area Under The Curve ◽  
The United States ◽  
Support Vector ◽  
Ptsd Symptoms

BACKGROUND A majority of adults in the United States are exposed to a potentially traumatic event but only a handful go on to develop impairing mental health conditions such as posttraumatic stress disorder (PTSD). OBJECTIVE Identifying those at elevated risk shortly after trauma exposure is a clinical challenge. The aim of this study was to develop computational methods to more effectively identify at-risk patients and, thereby, support better early interventions. METHODS We proposed machine learning (ML) induction of models to automatically predict elevated PTSD symptoms in patients 1 month after a trauma, using self-reported symptoms from data collected via smartphones. RESULTS We show that an ensemble model accurately predicts elevated PTSD symptoms, with an area under the curve (AUC) of .85, using a bag of support vector machines, naive Bayes, logistic regression, and random forest algorithms. Furthermore, we show that only 7 self-reported items (features) are needed to obtain this AUC. Most importantly, we show that accurate predictions can be made 10 to 20 days posttrauma. CONCLUSIONS These results suggest that simple smartphone-based patient surveys, coupled with automated analysis using ML-trained models, can identify those at risk for developing elevated PTSD symptoms and thus target them for early intervention.

scholarly journals Using deep learning to classify pediatric posttraumatic stress disorder at the individual level

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jing Yang ◽  
Du Lei ◽  
Kun Qin ◽  
Walter H. L. Pinaya ◽  
Xueling Suo ◽  
...  
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Deep Learning ◽  
Posttraumatic Stress ◽  
Partial Correlation ◽  
Stress Disorder ◽  
Functional Neuroimaging ◽  
Correlation Coefficients ◽  
Brain Regions ◽  
Functional Connectome ◽  
Topological Measures

Abstract Background Children exposed to natural disasters are vulnerable to developing posttraumatic stress disorder (PTSD). Previous studies using resting-state functional neuroimaging have revealed alterations in graph-based brain topological network metrics in pediatric PTSD patients relative to healthy controls (HC). Here we aimed to apply deep learning (DL) models to neuroimaging markers of classification which may be of assistance in diagnosis of pediatric PTSD. Methods We studied 33 pediatric PTSD and 53 matched HC. Functional connectivity between 90 brain regions from the automated anatomical labeling atlas was established using partial correlation coefficients, and the whole-brain functional connectome was constructed by applying a threshold to the resultant 90 * 90 partial correlation matrix. Graph theory analysis was used to examine the topological properties of the functional connectome. A DL algorithm then used this measure to classify pediatric PTSD vs HC. Results Graphic topological measures using DL provide a potentially clinically useful classifier for differentiating pediatric PTSD and HC (overall accuracy 71.2%). Frontoparietal areas (central executive network), cingulate cortex, and amygdala contributed the most to the DL model’s performance. Conclusions Graphic topological measures based on fMRI data could contribute to imaging models of clinical utility in distinguishing pediatric PTSD from HC. DL model may be a useful tool in the identification of brain mechanisms PTSD participants.

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