Predicting Response to Brain Stimulation in Depression: a Roadmap for Biomarker Discovery

Nord, Camilla L.

doi:10.1007/s40473-021-00226-9

Cited by 7 publications

(2 citation statements)

References 90 publications

(73 reference statements)

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“…Potential biomarkers for diagnosing depression are usually performed when a patient is suspected of having a major depressive disorder. These biomarkers are growth factors, cytokines, other inflammatory markers, oxidative stress markers, endocrine markers, energy balance hormones, genetic and epigenetic features, and neuroimaging [36].…”

Section: Discussionmentioning

confidence: 99%

Coping mechanism and depression among Moslem patients treated with chemotherapy

Anita¹,

Titipangesti²,

Fadhillah³

et al. 2022

Palliat Med Pract

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Background: Chemotherapy patients commonly experience depression due to various physical complaints suffered and the length of treatment therapy. Depression is closely related to morbidity and mortality. Therefore, a good coping mechanism is needed to overcome it. This research aims to determine the relationship between coping mechanisms and depression in chemotherapy patients. Patients and methods: The design used was a quantitative correlation with a cross-sectional approach. The respondents recruited were 52 patients using an accidental sampling technique. The research instrument used was a questionnaire. Data were analysed using the contingency coefficient test. Results:The results showed that most respondents had maladaptive coping mechanisms (53.2%), experienced depression (59.6%), and there was no relationship between coping mechanisms and depression (p = 0.213). Conclusions: many factors can influence the depression level of cancer patients. These factors contribute together, but some of them do not directly contribute to depression. One of the factors that do not directly contribute is the coping mechanism.

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Section: Discussionmentioning

confidence: 99%

Coping mechanism and depression among Moslem patients treated with chemotherapy

Anita¹,

Titipangesti²,

Fadhillah³

et al. 2022

Palliat Med Pract

View full text Add to dashboard Cite

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“…Some neural biomarkers have been developed via transcranial magnetic stimulation (TMS) in concurrent combination with imaging-based localization (fMRI), electroencephalography (EEG) or measurement in intracortical and corticospinal electrophysiology for treatment response in depression. [ 4 , 5 ] Utilizing such measures could enable the evaluation of the extent to which the treatment effectively engages the intended targets, and they could also serve as early indicators of treatment outcome. However, it is important to note that a significant number of individuals with depression also experience chronic pain symptoms, which have been linked to less favorable treatment outcomes with repetitive transcranial magnetic stimulation (rTMS).…”

Section: Introductionmentioning

confidence: 99%

Biomarker development perspective: Exploring comorbid chronic pain in depression through deep transcranial magnetic stimulation

Ju,

Zhao,

et al. 2024

Journal of Translational Internal Medicine

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The promise of machine learning in predicting treatment outcomes in psychiatry

Bondar²,

et al. 2021

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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.

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Predicting Response to Brain Stimulation in Depression: a Roadmap for Biomarker Discovery

Cited by 7 publications

References 90 publications

Coping mechanism and depression among Moslem patients treated with chemotherapy

Coping mechanism and depression among Moslem patients treated with chemotherapy

Biomarker development perspective: Exploring comorbid chronic pain in depression through deep transcranial magnetic stimulation

The promise of machine learning in predicting treatment outcomes in psychiatry

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