Multi-voxel pattern analysis of amygdala functional connectivity at rest predicts variability in posttraumatic stress severity

Machine learning applied to fMRI patterns of brain activation in response to mutilation pictures predicts PTSD symptoms

BACKGROUND

The present study aimed to apply multivariate pattern recognition methods to predict posttraumatic stress symptoms from whole-brain activation patterns during two contexts where the aversiveness of unpleasant pictures was manipulated by the presence or absence of safety cues.

METHODS

Trauma-exposed participants were presented with neutral and mutilation pictures during functional magnetic resonance imaging (fMRI) collection. Before the presentation of pictures, a text informed the subjects that the pictures were fictitious ("safe context") or real-life scenes ("real context"). We trained machine learning regression models (Gaussian process regression (GPR)) to predict PTSD symptoms in real and safe contexts.

RESULTS

The GPR model could predict PTSD symptoms from brain responses to mutilation pictures in the real context but not in the safe context. The brain regions with the highest contribution to the model were the occipito-parietal regions, including the superior parietal gyrus, inferior parietal gyrus, and supramarginal gyrus. Additional analysis showed that GPR regression models accurately predicted clusters of PTSD symptoms, nominal intrusion, avoidance, and alterations in cognition. As expected, we obtained very similar results as those obtained in a model predicting PTSD total symptoms.

CONCLUSION

This study is the first to show that machine learning applied to fMRI data collected in an aversive context can predict not only PTSD total symptoms but also clusters of PTSD symptoms in a more aversive context. Furthermore, this approach was able to identify potential biomarkers for PTSD, especially in occipitoparietal regions.

Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning

Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.

Towards assessing subcortical "deep brain" biomarkers of PTSD with functional near-infrared spectroscopy

Assessment of brain function with functional near-infrared spectroscopy (fNIRS) is limited to the outer regions of the cortex. Previously, we demonstrated the feasibility of inferring activity in subcortical "deep brain" regions using cortical functional magnetic resonance imaging (fMRI) and fNIRS activity in healthy adults. Access to subcortical regions subserving emotion and arousal using affordable and portable fNIRS is likely to be transformative for clinical diagnostic and treatment planning. Here, we validate the feasibility of inferring activity in subcortical regions that are central to the pathophysiology of posttraumatic stress disorder (PTSD; i.e. amygdala and hippocampus) using cortical fMRI and simulated fNIRS activity in a sample of adolescents diagnosed with PTSD (N = 20, mean age = 15.3 ± 1.9 years) and age-matched healthy controls (N = 20, mean age = 14.5 ± 2.0 years) as they performed a facial expression task. We tested different prediction models, including linear regression, a multilayer perceptron neural network, and a k-nearest neighbors model. Inference of subcortical fMRI activity with cortical fMRI showed high prediction performance for the amygdala (r > 0.91) and hippocampus (r > 0.95) in both groups. Using fNIRS simulated data, relatively high prediction performance for deep brain regions was maintained in healthy controls (r > 0.79), as well as in youths with PTSD (r > 0.75). The linear regression and neural network models provided the best predictions.

Altered functional connectivity in children with ADHD while performing cognitive control task

Response inhibition is one of the crucial cognitive domains that exhibit deficit in children with ADHD. To further elucidate it, this study examines the task-based functional-connectivity in children with attention deficit hyperactive disorder (ADHD). We acquired the fMRI data of 16 unmedicated children with ADHD and 16 typically developing (TD) children who performed the flanker task. MVPA and seed-based connectivity analysis was performed to identify the abnormal connectivity pattern across the whole brain. MVPA revealed that six important regions, namely the right IFG, right SMA, bilateral precentral gyrus, left DLPFC, and left cerebellum, had abnormal connectivity in children with ADHD while they performed the cognitive control task. Out of these six regions, four were further used for whole-brain seed-based functional connectivity analyses, which revealed patterns of significantly altered connectivity across multiple regions. Signal intensities changes were also extracted to perform BOLD- reaction time (RT) correlation analysis, that suggest positive correlation between left DLPFC and right IFG. Overall, the results suggest that children with ADHD are unable to endure high cognitive control demand. Our findings highlight the utility of analyzing brain connectivity data in identifying the abnormal connectivity in children with ADHD.

Hippocampal Resting-State Functional Connectivity Forecasts Individual Posttraumatic Stress Disorder Symptoms: A Data-Driven Approach

BACKGROUND

Posttraumatic stress disorder (PTSD) is a debilitating disorder, and there is no current accurate prediction of who develops it after trauma. Neurobiologically, individuals with chronic PTSD exhibit aberrant resting-state functional connectivity (rsFC) between the hippocampus and other brain regions (e.g., amygdala, prefrontal cortex, posterior cingulate), and these aberrations correlate with severity of illness. Previous small-scale research (n < 25) has also shown that hippocampal rsFC measured acutely after trauma is predictive of future severity using a region-of-interest-based approach. While this is a promising biomarker, to date, no study has used a data-driven approach to test whole-brain hippocampal FC patterns in forecasting the development of PTSD symptoms.

METHODS

A total of 98 adults at risk of PTSD were recruited from the emergency department after traumatic injury and completed resting-state functional magnetic resonance imaging (8 min) within 1 month; 6 months later, they completed the Clinician-Administered PTSD Scale for DSM-5 for assessment of PTSD symptom severity. Whole-brain rsFC values with bilateral hippocampi were extracted (using CONN) and used in a machine learning kernel ridge regression analysis (PRoNTo); a k-folds (k = 10) and 70/30 testing versus training split approach were used for cross-validation (1000 iterations to bootstrap confidence intervals for significance values).

RESULTS

Acute hippocampal rsFC significantly predicted Clinician-Administered PTSD Scale for DSM-5 scores at 6 months (r = 0.30, p = .006; mean squared error = 120.58, p = .006; R² = 0.09, p = .025). In post hoc analyses, hippocampal rsFC remained significant after controlling for demographics, PTSD symptoms at baseline, and depression, anxiety, and stress severity at 6 months (B = 0.59, SE = 0.20, p = .003).

CONCLUSIONS

Findings suggest that functional connectivity of the hippocampus across the brain acutely after traumatic injury is associated with prospective PTSD symptom severity.

Functional near-infrared spectroscopy brain imaging predicts symptom severity in youth exposed to traumatic stress

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique with the potential to enable the assessment of posttraumatic stress disorder (PTSD) brain biomarkers in an affordable and portable manner. Consistent with biological models of PTSD, functional magnetic resonance imaging (fMRI) and fNIRS studies of adults with trauma exposure and PTSD symptoms suggest increased activation in the dorsolateral prefrontal cortex (dlPFC) and ventrolateral PFC (vlPFC) in response to negative emotion stimuli. We tested this theory with fNIRS assessment among youth exposed to traumatic stress and experiencing PTSD symptoms (PTSS). A portable fNIRS system collected hemodynamic responses from (N = 57) youth with PTSS when engaging in a classic emotion expression task that included fearful and neutral faces stimuli. The General Linear Model was applied to identify cortical activations associated with the facial stimuli. Subsequently, a prediction model was established via a Support Vector Regression to determine whether PTSS severity could be predicted based on fNIRS-derived cortical response measures and individual demographic information. Results were consistent with findings from adult fMRI and fNIRS studies of PTSS showing increased activation in the dlPFC and vlPFC in response to negative emotion stimuli. Subsequent prediction analysis revealed ten features (i.e., cortical responses from eight frontocortical fNIRS channels, age and sex) strongly correlated with PTSS severity (r = 0.65, p < .001). Our findings suggest the potential utility of fNIRS as a portable tool for the detection of putative PTSS brain biomarkers.

Multi-voxel pattern analysis of amygdala functional connectivity at rest predicts variability in posttraumatic stress severity

INTRODUCTION

Resting state functional magnetic resonance imaging (rsfMRI) studies demonstrate that individuals with posttraumatic stress disorder (PTSD) exhibit atypical functional connectivity (FC) between the amygdala, involved in the generation of emotion, and regions responsible for emotional appraisal (e.g., insula, orbitofrontal cortex [OFC]) and regulation (prefrontal cortex [PFC], anterior cingulate cortex). Consequently, atypical amygdala FC within an emotional processing and regulation network may be a defining feature of PTSD, although altered FC does not seem constrained to one brain region. Instead, altered amygdala FC involves a large, distributed brain network in those with PTSD. The present study used a machine-learning data-driven approach, multi-voxel pattern analysis (MVPA), to predict PTSD severity based on whole-brain patterns of amygdala FC.

METHODS

Trauma-exposed adults (N = 90) completed the PTSD Checklist-Civilian Version to assess symptoms and a 5-min rsfMRI. Whole-brain FC values to bilateral amygdala were extracted and used in a relevance vector regression analysis with a leave-one-out approach for cross-validation with permutation testing (1,000) to obtain significance values.

RESULTS

Results demonstrated that amygdala FC predicted PCL-C scores with statistically significant accuracy (r = .46, p = .001; mean sum of squares = 130.46, p = .001; R²  = 0.21, p = .001). Prediction was based on whole-brain amygdala FC, although regions that informed prediction (top 10%) included the OFC, amygdala, and dorsolateral PFC.

CONCLUSION

Findings demonstrate the utility of MVPA based on amygdala FC to predict individual severity of PTSD symptoms and that amygdala FC within a fear acquisition and regulation network contributed to accurate prediction.