Pre-treatment Resting-State Functional MR Imaging Predicts the Long-Term Clinical Outcome After Short-Term Paroxtine Treatment in Post-traumatic Stress Disorder

Yuan, Minlan; Qiu, Changjian; Meng, Yajing; Ren, Zhengjia; Yuan, Cui; Li, Yuchen; Gao, Meng; Lui, Su; Zhu, Hongru; Gong, Qiyong; Zhang, Wei

doi:10.3389/fpsyt.2018.00532

Cited by 28 publications

(18 citation statements)

References 56 publications

(69 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further SVR results revealed that the high NH levels at baseline in the bilateral superior MPFC could predict symptomatic improvement of PANSS after 8 weeks of DPP treatment. Previous studies revealed that the orbital frontal cortex and dorsal MPFC were related to predicting the long-term clinical outcome in the post-traumatic stress disorder (47). The hemodynamic activities of the frontotemporal cortex may predict treatment response to selective serotonin reuptake inhibitor in MDD (48).…”

Section: Discussionmentioning

confidence: 99%

Metacognitive Training Modulates Default-Mode Network Homogeneity During 8-Week Olanzapine Treatment in Patients With Schizophrenia

Shan

Liao

et al. 2020

Front. Psychiatry

View full text Add to dashboard Cite

Background: Previous studies have revealed the efficacy of metacognitive training for schizophrenia. However, the underlying mechanisms of metacognitive training on brain function alterations, including the default-mode network (DMN), remain unknown. The present study explored treatment effects of metacognitive training on functional connectivity of the brain regions in the DMN.Methods: Forty-one patients with schizophrenia and 20 healthy controls were scanned using resting-state functional magnetic resonance imaging. Patients were randomly assigned to drug plus psychotherapy (DPP) and drug therapy (DT) groups. The DPP group received olanzapine and metacognitive training, and the DT group received only olanzapine for 8 weeks. Network homogeneity (NH) was applied to analyze the imaging data, and pattern classification techniques were applied to test whether abnormal NH deficits at baseline might be used to discriminate patients from healthy controls. Abnormal NH in predicting treatment response was also examined in each patient group.Results: Compared with healthy controls, patients at baseline showed decreased NH in the bilateral ventral medial prefrontal cortex (MPFC), right posterior cingulate cortex (PCC)/ precuneus, and bilateral precuneus and increased NH in the right cerebellum Crus II and bilateral superior MPFC. NH values in the right PCC/precuneus increased in the DPP group after 8 weeks of treatment, whereas no substantial difference in NH value was observed in the DT group. Support vector machine analyses showed that the accuracy, sensitivity, and specificity for distinguishing patients from healthy controls were more than 0.7 in the NH values of the right PCC/precuneus, bilateral ventral MPFC, bilateral superior MPFC, and bilateral precuneus regions. Support vector regression analyses showed that high NH levels at baseline in the bilateral superior MPFC could predict symptomatic improvement of positive and negative syndrome scale (PANSS) after 8 weeks of DPP treatment. No correlations were found between alterations in the NH values and changes in the PANSS scores/cognition parameters in the patients. Conclusion:This study provides evidence that metacognitive training is related to the modulation of DMN homogeneity in schizophrenia.

show abstract

Section: Discussionmentioning

confidence: 99%

Metacognitive Training Modulates Default-Mode Network Homogeneity During 8-Week Olanzapine Treatment in Patients With Schizophrenia

Shan

Liao

et al. 2020

Front. Psychiatry

View full text Add to dashboard Cite

show abstract

“…Resting‐state fMRI is a popular alternative, because it measures behaviourally‐relevant, synchronized brain network activity at rest, and the imaging protocols can be more easily harmonized across scanners 165 . Studies in this field have demonstrated similar accuracies for CBT 166 , trauma‐focused psychotherapy 167 , antidepressant treatment 168 , and antipsychotic therapy 169 , while also showing predictive accuracy for ECT 165,170 .…”

Section: The Use Of Data From Genetics Electrophysiology Neuroimaging and Cognitive Testingmentioning

confidence: 99%

The promise of machine learning in predicting treatment outcomes in psychiatry

Bondar²,

et al. 2021

View full text Add to dashboard Cite

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real‐world clinical practice. Relatively few retrospective studies to‐date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

show abstract

“…However, only two studies to date have used machine learning analyses to predict future outcome at an individual level. One study aimed to predict clinical status two years after treatment with 12 weeks of paroxetine in a sample of 20 civilian PTSD patients 24 . This study used pre- and post-treatment rs-fMRI derived measures, namely amplitude of low-frequency fluctuations and whole-brain degree centrality maps, and the results showed that pre- but not post-treatment measures were able to predict remission status after two years with an accuracy of 72.5%.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

Trauma-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.

show abstract

Pre-treatment Resting-State Functional MR Imaging Predicts the Long-Term Clinical Outcome After Short-Term Paroxtine Treatment in Post-traumatic Stress Disorder

Cited by 28 publications

References 56 publications

Metacognitive Training Modulates Default-Mode Network Homogeneity During 8-Week Olanzapine Treatment in Patients With Schizophrenia

Metacognitive Training Modulates Default-Mode Network Homogeneity During 8-Week Olanzapine Treatment in Patients With Schizophrenia

The promise of machine learning in predicting treatment outcomes in psychiatry

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

Contact Info

Product

Resources

About