AIMC Topic: Depressive Disorder, Treatment-Resistant

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Role of the rostral anterior cingulate cortex in emotion processing in Treatment Resistant Depression.

Translational psychiatry
The rostral anterior cingulate cortex (rACC) has been identified as a key region in treatment-resistant depression (TRD), potentially influencing the adaptive interplay between the default mode network and other critical neural networks. This study a...

Applications of machine learning in deep brain stimulation for major depressive disorder: a systematic review and meta-analysis.

Neurosurgical review
Depression is a significant public health issue, consistently ranking among the leading causes of mortality, reduced quality of life, and economic burden. Despite available treatments, approximately one-third of patients exhibit resistance to standar...

Clinical predictors of treatment resistant depression.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
Despite advances in the treatment of major depressive disorder (MDD) yet a substantial proportion of patients fail to achieve remission and instead develop treatment-resistant depression (TRD). Identifying robust clinical predictors of response is es...

Detecting the clinical features of difficult-to-treat depression using synthetic data from large language models.

Computers in biology and medicine
Difficult-to-treat depression (DTD) has been proposed as a broader and more clinically comprehensive perspective on a person's depressive disorder where, despite treatment, they continue to experience significant burden. We sought to develop a tool c...

Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression.

Translational psychiatry
Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (TRD). Leveraging electronic medical records (EMR), this retrospective cohort study applied supervise...

Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases with High Accuracy.

Clinical EEG and neuroscience
Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistant depression (TRD), which is characterized by an inadequate response to antidepressant treatment. Id...

Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1.

PloS one
OBJECTIVES: Antidepressants are first-line treatments for major depressive disorder (MDD), but 40-60% of patients will not respond, hence, predicting response would be a major clinical advance. Machine learning algorithms hold promise to predict trea...

Can Machine Learning help us in dealing with treatment resistant depression? A review.

Journal of affective disorders
BACKGROUND: About one third of patients treated with antidepressant do not show sufficient symptoms relief and up to 15% of patients remain symptomatic even after multiple trials are applied, configuring a state called treatment resistant depression ...

[Psilocybin in the setting of treatment-resistant unipolar depression: A case report].

L'Encephale
BACKGROUND: Current antidepressants have shown certain limitations in the treatment of unipolar depression. Their long onset of action, interactions, and side effects are obstacles to achieving lasting remission of this prevalent, often chronic or re...

Multiband EEG signatures decoded using machine learning for predicting rTMS treatment response in MDD.

Journal of affective disorders
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for major depression disorder (MDD), particularly for treatment-resistant cases. However, identifying translatable biomarkers predictive of treatment outcomes re...