Machine learning-based identification of a psychotherapy-predictive electroencephalographic signature in PTSD

Yu, Zhang; Naparstek, Sharon; Gordon, Joseph R.; Watts, Mallissa; Shpigel, Emmanuel; EI-Said, Dawlat; Badami, Faizan; Eisenberg, Mark; Toll, Russell T.; Gage, Allyson; Goodkind, Madeleine S.; Etkin, Amit; Wu, Wei

doi:10.1038/s44220-023-00049-5

Cited by 6 publications

(3 citation statements)

References 73 publications

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“…Although these variables were able to capture 39%-45% of the variance in the outcomes, there may be additional baseline measures not available in these data which could further improve prognostic performance in a purpose-designed study. Therapeutic process variables such as early treatment response (Stuke et al, 2021) or biological variables PREDICTING OUTCOMES OF A COACHED WEB INTERVENTION such as skin conductance (Mallol-Ragolta et al, 2018) or brain connectivity (Zhang et al, 2023) have also shown promise as prognostic predictors of PTSD treatment outcomes, although the latter category may be challenging to implement in real-world clinical settings. A final limitation is that the ML algorithm we used cannot identify possible nonlinear effects between predictors and outcomes.…”

Section: Discussionmentioning

confidence: 99%

A prognostic index to predict symptom and functional outcomes of a coached, web-based intervention for trauma-exposed veterans.

Hallenbeck,

Wielgosz,

Cohen

et al. 2024

Psychological Services

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Researchers at the Department of Veterans Affairs (VA) have studied interventions for posttraumatic stress disorder and co-occurring conditions in both traditional and digital formats. One such empirically supported intervention is web skills training in affective and interpersonal regulation (webSTAIR), a coached, 10-module web program based on STAIR. To understand which patient characteristics were predictive of webSTAIR outcomes in a sample of trauma-exposed veterans (N = 189), we used machine learning (ML) to develop a prognostic index from among 18 baseline characteristics (i.e., demographic, military, trauma history, and clinical) to predict posttreatment posttraumatic stress disorder severity, depression severity, and psychosocial functioning impairment. We compared the ML models to a benchmark of linear regression models in which the only predictor was the baseline severity score of the outcome measure. The ML and "severity-only" models performed similarly, explaining 39%-45% of the variance in outcomes. This suggests that baseline symptom severity and functioning are strong indicators for webSTAIR outcomes in veterans, with higher severity indicating worse prognosis, and that the other variables examined did not contribute significant added predictive signal. Findings also highlight the importance of comparing ML models to an appropriate benchmark. Future research with larger samples could potentially detect smaller patient-level effects as well as effects driven by other types of variables (e.g., therapeutic process variables). As a transdiagnostic, digital intervention, webSTAIR can potentially serve a diverse veteran population with varying trauma histories and may be best conceptualized as a beneficial first step of a stepped care model for those with heightened symptoms or impairment. Impact StatementWeb skills training in affective and interpersonal regulation (webSTAIR) is a coached, web-based intervention for trauma survivors that improves posttraumatic stress disorder, depression, and psychosocial functioning. Veterans receiving webSTAIR who begin treatment with more severe symptoms or functioning impairment tend to have more symptoms or impairment after treatment; no other patient characteristics including age, gender, race, or type of trauma were predictive of treatment outcomes. webSTAIR may be offered to veterans with diverse backgrounds and trauma histories, and for those with more initial symptoms, can be a helpful first treatment before potentially pursuing higher intensity treatment.

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Section: Discussionmentioning

confidence: 99%

A prognostic index to predict symptom and functional outcomes of a coached, web-based intervention for trauma-exposed veterans.

Hallenbeck,

Wielgosz,

Cohen

et al. 2024

Psychological Services

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“…This, in turn, can be translated into clinical practice for pathology classification and treatment issues. In this vein, Zhang et al [ 62 ] developed a treatment-predictive EEG signature using ML techniques for military veterans with post-traumatic stress disorder. The findings provided by authors address the importance in the biological definition of patients with post-traumatic stress disorders that are resistant to psychotherapy.…”

Section: Artificial Intelligence and Machine Learning In The Neuroreh...mentioning

confidence: 99%

Bridging the Gap between Basic Research and Clinical Practice: The Growing Role of Translational Neurorehabilitation

Bonanno,

Calabrò

2023

Medicines

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Translational neuroscience is intended as a holistic approach in the field of brain disorders, starting from the basic research of cerebral morphology and with the function of implementing it into clinical practice. This concept can be applied to the rehabilitation field to promote promising results that positively influence the patient’s quality of life. The last decades have seen great scientific and technological improvements in the field of neurorehabilitation. In this paper, we discuss the main issues related to translational neurorehabilitation, from basic research to current clinical practice, and we also suggest possible future scenarios.

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“…Recently, graph theory and machine learning techniques have been widely applied in neuroscience for brain analysis and disease detection [9,[12][13][14][15]. Devika and Ramana Murthy Oruganti [16] developed a machine learning framework based on support vector machine (SVM) to diagnose neurological disorders using rfMRI.…”

Section: Introductionmentioning

confidence: 99%

Early prediction of dementia using fMRI data with a graph convolutional network approach

Han,

Sun,

Zhao

et al. 2024

J. Neural Eng.

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Objective: Alzheimer's disease is a progressive neurodegenerative dementia that poses a significant global health threat. It is imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions to prevent further deterioration of dementia. This study focuses on the early prediction of dementia utilizing Magnetic Resonance Imaging (MRI) data, using the proposed Graph Convolutional Networks (GCNs).Approach: Specifically, we developed a functional connectivity (FC) based GCN framework for binary classifications using resting-state fMRI data. We explored different types and processing methods of FC and evaluated the performance on the OASIS-3 dataset.We developed the GCN model for two different purposes: 1) MCI diagnosis: classifying MCI from normal controls; and 2) dementia risk prediction: classifying normal controls from subjects who have the potential for developing MCI but have not been clinically diagnosed as MCI.Main results: The results of the experiments revealed several important findings: First, the proposed GCN outperformed both the baseline GCN and Support Vector Machine (SVM). It achieved the best average accuracy of 80.3% (11.7% higher than the baseline GCN and 23.5% higher than SVM) and the highest accuracy of 91.2%. Secondly, the GCN framework with (absolute) individual FC performed slightly better than that with global FC generally. However, GCN using global graphs with appropriate connectivity can achieve equivalent or superior performance to individual graphs in some cases, which highlights the significance of suitable connectivity for achieving performance. Additionally, the results indicate that the self-network connectivity of specific brain network regions (such as default mode network, visual network, ventral attention network and somatomotor network) may play a more significant role in GCN classification.Significance: Overall, this study offers valuable insights into the application of GCNs in brain analysis and early diagnosis of dementia. This contributes significantly to the understanding of MCI and has substantial potential for clinical applications in early diagnosis and intervention for dementia and other neurodegenerative diseases. Our code for GCN implemention is available at: https://github.com/Shuning-Han/FC-based-GCN.

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Machine learning-based identification of a psychotherapy-predictive electroencephalographic signature in PTSD

Cited by 6 publications

References 73 publications

A prognostic index to predict symptom and functional outcomes of a coached, web-based intervention for trauma-exposed veterans.

A prognostic index to predict symptom and functional outcomes of a coached, web-based intervention for trauma-exposed veterans.

Bridging the Gap between Basic Research and Clinical Practice: The Growing Role of Translational Neurorehabilitation

Early prediction of dementia using fMRI data with a graph convolutional network approach

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