The severity of post-traumatic stress disorder (PTSD) – a behavioral and mental disorder that occurs after a person suffers a traumatic event such as a terror attack, a natural disaster, domestic violence, a traffic accident, violence by a partner, or other threats on a person’s life or well-being – often can’t be diagnosed quickly by professionals.
This is unfortunate, because the earlier it is identified, the easier it is to treat it. Currently, the first-line of treatment is giving drugs and psychotherapy, but these are not very effective in some people.
Now, a joint study conducted by Dr. Ziv Ben-Zion, a brain researcher at the University of Haifa’s School of Public Health and Yale University in New Haven, Connecticut, and published in the prestigious journal JAMA Network Open, has discovered that it’s possible to predict the severity of PTSD by analyzing brain connectivity patterns as early as one month after a traumatic event.
Symptoms include flashbacks, hyperarousal, avoidance of trauma-related stimuli, and changes in emotional and cognitive responses. Despite numerous studies in the field, PTSD diagnosis is still based mostly on subjective reports from victims, with no objective biological measures available for the early prediction of the severity of the disorder.
“Our study showed that early brain connectivity patterns can predict the severity of PTSD symptoms at a later stage,” he stated. “Early identification of brain patterns associated with PTSD may make it easier to develop personalized tools that make it possible to intervene early and in a targeted intervention for people at high risk of developing the chronic disorder,” he told The Jerusalem Post.
The traumatic events included car accidents, physical assaults, robberies, terror attacks, electric shocks, fires, drownings, work accidents, terror attacks or other hostilities, and large-scale disasters.
The study in light of Oct. 7, Israel-Hamas War
The findings of this study could be especially timely as Israel grapples with many IDF soldiers and civilians suffering from trauma as a result of the Israel-Hamas War.
“In light of the October 7 events and their aftermath, many Israelis have been exposed to severe trauma,” said Ben-Zion. “While more studies to replicate our results are needed, our findings may eventually help identify those at risk of developing long-term PTSD. Early identification could allow more timely and personalized interventions to support recovery and prevent the disorder from becoming chronic. We hope that integrating these approaches into medical assessments will make possible the early identification of at-risk victims and provide personalized treatment to prevent the worsening of symptoms and the development of the chronic disorder.”
In the current study, Ben-Zion sought to identify the brain networks involved in post-traumatic responses and examine their ability to predict the improvement or worsening of PTSD symptoms over time, based on brain connectivity patterns measured one month after exposure to trauma. Potential participants were civilians aged 18 to 65 years who had been admitted to Tel Aviv Sourasky Medical Center’s emergency department after experiencing a traumatic event.
“We worked for five years on the theory that one needn’t look at a specific brain region but at large-scale networks in the whole brain; each network has a specific role,” he said. “We contacted 7,000 people who had reached the emergency room after such an event, but not all had initial PTSD symptoms, and not all were willing to come several times afterward for an fMRI scan of a few minutes. But those who did participate said they thought it would help people, so they agreed. It will take time until the technique is used clinically, but already one large Israeli hospital has shown interest in doing such tests.”
The research included 162 participants who went to the hospital and underwent functional MRI (fMRI) scans one month after the event. Clinical assessments were also conducted to measure symptom severity at three time points – at one month, six months, and 14 months after the trauma.
The researchers analyzed the participants’ whole-brain connectivity patterns using an advanced method based on machine learning that identifies patterns of functional connectivity between all regions and networks in the brain, enabling the identification of a “neural signature” associated with the development of the disorder or recovery from early symptoms.
Using advanced calculations, the ability of these patterns to predict the severity of PTSD symptoms was examined over the three time points to understand how early brain connectivity can serve as a measure for predicting recovery from early symptoms or, conversely, the development of chronic post-trauma.
The data analysis revealed that connectivity patterns between certain brain regions were linked with the onset of avoidance symptoms and mood changes within the first month.
The study was published in the JAMA Network Open under the title “Deep learning model of fMRI connectivity predicts PTSD symptom trajectories in recent trauma survivors.”
“The results show that the novel method’s performance in predicting PTSD symptoms in relating to the future outperforms previous analytical techniques reported in the fMRI literature. 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,” he concluded.