Trends in research on novel antidepressant treatments

Frontiers in Pharmacology, Год журнала: 2025, Номер 16

Опубликована: Янв. 27, 2025

Mood disorders, such as major depressive disorder and bipolar disorder, are among the most common mental illnesses a leading cause of disability worldwide. Key symptoms these conditions include depressed mood or anhedonia, sleep psychomotor disturbances, changes in appetite weight, fatigue loss energy. Prolonged cognitive disturbances further impair ability to think concentrate often accompanied by persistent feelings worthlessness excessive guilt. Collectively, underscore depression serious, long-term global health issue. In addition, clinical studies indicate growing number patients experiencing difficulties responding treatment, even long term. This phenomenon poses significant challenges for healthcare professionals, families, alike. As result, there is an urgent need therapies that both rapid-acting safe. review aims summarize prevailing trends research on novel antidepressants, emphasizing their diversity multi-directional mechanisms action. The development drugs increasingly focused achieving high efficacy, particularly treatment-resistant depression. Such advances offer potential rapid therapeutic effects without prolonged tedious administration older generation antidepressants. Findings from using animal models continue play crucial role predicting designing new strategies. These remain indispensable understanding physiological newly developed compounds, thereby guiding creation innovative treatments.

Язык: Английский

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Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

Neuropsychopharmacology, Год журнала: 2024, Номер 50(1), С. 230 - 245

Опубликована: Июль 1, 2024

Язык: Английский

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Home-based transcranial direct current stimulation (tDCS) in major depressive disorder: Enhanced network synchronization with active relative to sham and deep learning-based predictors of remission

Personalized Medicine in Psychiatry, Год журнала: 2025, Номер 49-50, С. 100147 - 100147

Опубликована: Март 1, 2025

Язык: Английский

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Neuroanatomical dimensions in major depression: external validation and links with cognition, adverse life events, self-harm, metabolomics and genetics

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Март 26, 2025

Major depressive disorder (MDD) is a leading cause of disability worldwide, yet its diagnosis relies on clinical symptoms alone. Using machine learning applied to deeply phenotyped, medication-free participants with MDD, we identified two neuroanatomical dimensions. Dimension 2 (D2), compared 1 (D1), was characterized by reductions in grey and white matter associated limited treatment response both antidepressant placebo medications. Validation UK Biobank general population cohort (n = 37,235) confirmed that D2 reduced matter, alongside widespread cognitive impairments, adverse events adulthood childhood, increased self-harm suicide attempts, pro-atherogenic lipid profile, genetic associations neurodegenerative traits. These findings suggest D1 reflect distinct neurobiological mechanisms underlying important implications for outcomes. External validation demonstrated population-based delineated heterogeneity identifying potential biomarkers could aid personalising approaches this debilitating disorder.

Язык: Английский

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Optimizing Antidepressant Efficacy: Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Апрель 12, 2024

Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages demographic groups. Unfortunately, current antidepressant psychotherapeutic treatments exhibit limited efficacy unsatisfactory response rates in substantial number patients. The development effective therapies MDD hindered by the insufficiently understood heterogeneity within its elusive underlying mechanisms. To address these challenges, we present target-oriented multimodal fusion framework robustly predicts integrating structural functional connectivity data (sertraline: R

Язык: Английский

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Biomarkers of cognitive and memory decline in psychotropic drug users

Journal of Neural Transmission, Год журнала: 2024, Номер unknown

Опубликована: Окт. 8, 2024

Abstract Psychotropic drugs are vital in psychiatry, aiding the management of mental health disorders. Their use requires an understanding their pharmacological properties, therapeutic applications, and potential side effects. Ongoing research aims to improve efficacy safety. Biomarkers play a crucial role predicting memory decline psychotropic drug users. A comprehensive biomarkers, including neuroimaging, biochemical, genetic, cognitive assessments, is essential for developing targeted interventions preventive strategies. In this narrative review, we performed search on PubMed Google using review-specific terms. Clinicians should multifaceted approach, neurotransmitter analysis, neurotrophic factors, miRNA profiling, tasks early intervention personalized treatment. Anxiolytics' mechanisms involve various systems emerging targets. Research biomarkers anxiolytic users can lead detection intervention, enhancing clinical practices aligning with precision medicine. Mood stabilizer benefit from through RNA, neurophysiological, inflammatory promoting timely interventions. Performance-enhancing may boost athletic performance short term, but long-term risks ethical issues make problematic. Long-term enhancers athletes shows changes decline, necessitating ongoing monitoring Understanding these genetic influences helps pave way approaches prevent or mitigate deterioration, emphasizing importance screening based individual's profile. Future focus refining protective measures against deterioration. Overall,

Язык: Английский

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Contrastive learning enhances the links between functional signatures and antidepressant treatment

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Апрель 18, 2024

Major depressive disorder (MDD) is highly heterogeneous in terms of responses to treatment, which hinders the improvement treatment effectiveness and outcomes for MDD. Identifying MDD subtypes associated with could inform interventions facilitate personalized treatment. Here, we sought identify reproducible characterized by distinct neurofunctional (i.e., neuroimaging) patterns delineate heterogeneity explored relationship between antidepressant response. We used contrastive variational autoencoders (CVAEs) two REST-meta-MDD II dataset (1660 participants, 1340 HCs). Subtype 1 exhibited increased functional activity occipital, parietal, temporal, frontal areas, while subtype 2 showed decreased these areas. The number were validated a further large multi-center (1276 1104 Notably, patients be considered "treatment-sensitive" group, response rate over 50% all antidepressants better repetitive transcranial magnetic stimulation (rTMS) compared 2. In contrast, as "treatment-resistant" below most medications. ensuing MDD-specific features from CVAEs may serve neuroimaging biomarker predicting both medication rTMS treatments. Our study shows that learning can establish predictive validity brain signatures — offering potential new targets optimizing strategies treatment-resistant depression, lay path toward higher outcomes.

Язык: Английский

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Home-based transcranial direct current stimulation (tDCS) in major depressive disorder: enhanced network synchronization with active relative to sham and deep learning-based predictors of remission

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июнь 10, 2024

Abstract Aim To investigate neural oscillatory networks in major depressive disorder (MDD), effects of home-based transcranial direct current stimulation (tDCS) treatment, and potential predictors treatment remission. Methods In a randomised controlled trial (RCT) tDCS EEG data were acquired subset: 21 MDD participants (16 women) (mean age 36.63 ± 9.71 years) episode moderate to severe severity Hamilton Depression Rating Scale (HAMD) score 18.42 1.80). Participants either active (n=11) or sham (n=8). Treatment was for 10 weeks bifrontal montage (anode over left dorsolateral prefrontal cortex) consisting 5 sessions per week 3 7 weeks. Active 2mA 0mA with brief ramp up down period mimic stimulation. Each session 30 minutes. Clinical remission defined as HAMD ≤ 7. Resting-state at baseline, prior the start 10-week end treatment. using portable 4-channel device (electrode positions: AF7, AF8, TP9, TP10). band power extracted each electrode functional connectivity phase synchronization by locking value (PLV). Deep learning applied baseline PLV features identify Results Main effect group observed gamma frontal temporal regions, which showed higher compared group. group, significant positive correlations between changes delta, theta, alpha, beta improvement depression observed. The highest prediction achieved combining from beta: accuracy 71.94% (sensitivity 52.88%, specificity 83.06%). Conclusions Synchronized brain activity across large-scale reflected is mechanism placebo-sham tDCS. Baseline resting-state predictor Home-based measures are feasible potentially useful clinical outcome.

Язык: Английский

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Prediction of Treatment Outcome to Transcranial Direct Current Stimulation in Major Depression Based on Deep Learning of EEG Data

Опубликована: Июнь 25, 2024

Язык: Английский

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Contrastive functional connectivity defines neurophysiology-informed symptom dimensions in major depression

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Окт. 7, 2024

Background: Major depressive disorder (MDD) is a prevalent psychiatric characterized by substantial clinical and neurobiological heterogeneity. Conventional studies that solely focus on symptoms or neuroimaging metrics often fail to capture the intricate relationship between these modalities, limiting their ability disentangle complexity in MDD. Moreover, patient data typically contains normal sources of variance shared with healthy controls, which can obscure disorder-specific complicate delineation disease Methods: We employed contrastive principal component analysis extract variations fMRI-based resting-state functional connectivity (RSFC) contrasting MDD patients (N=233) age-matched controls (N=285). then applied sparse canonical correlation identify latent dimensions linking extracted features patients. Results: Two significant generalizable distinct brain circuits profiles were discovered. The first dimension, associated an apparent internalizing-externalizing symptom was self-connections within visual network also choice reaction times cognitive tasks. second personality facets such as extraversion conscientiousness inversely depression symptoms, primarily driven dorsal attention network. This depression-protective dimension multiple task performances related psychomotor slowing control. Conclusions: Our RSFC-based dimensional approach offers new avenue dissect heterogeneity underlying By identifying two stable, neurophysiology-informed patients, our findings may enhance mechanism insights facilitate precision phenotyping, thus advancing development targeted therapeutics for mental health.

Язык: Английский

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