Prediction of response to transcranial magnetic stimulation treatment for depression using electroencephalography and statistical learning methods, including an out-of-sample validation DOI Creative Commons
Neil W. Bailey, Ben Fulcher, Martijn Arns

и другие.

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Окт. 25, 2023

Abstract Background Repetitive transcranial magnetic stimulation (rTMS) has shown efficacy for treating depression, but not all patients. Accurate treatment response prediction could lower burden. Research suggests machine learning trained with electroencephalographic (EEG) data may predict response, only a limited range of measures have been tested. Objectives We used >7000 time-series features to comprehensively test whether rTMS be predicted in discovery dataset and an independent dataset. Methods Baseline EEG from 188 patients depression treated (125 responders) were decomposed into the top five principal components (PCs). The hctsa toolbox was extract 7304 each participant PC. A classification algorithm responders feature matrix separately classifier applied ( N = 58) generalizability on unseen sample. Results Within dataset, third PC (which showed posterior-maximum prominent alpha power) above-chance accuracy (68%, p FDR 0.005, normalised positive predictive value 114%). Other PCs did outperform chance. model generalized balanced (60%, 0.046, Analysis feature-clusters suggested more high frequency power relative total power, negative skew distribution their values. Conclusion dynamical properties PC3 moderate accuracy, which suggest stratification pre-treatment possible, potentially enabling better outcomes than ‘one-size-fits-all’ approaches.

Язык: Английский

The use of artificial intelligence in psychotherapy: development of intelligent therapeutic systems DOI Creative Commons
Liana Spytska

BMC Psychology, Год журнала: 2025, Номер 13(1)

Опубликована: Фев. 28, 2025

The increasing demand for psychotherapy and limited access to specialists underscore the potential of artificial intelligence (AI) in mental health care. This study evaluates effectiveness AI-powered Friend chatbot providing psychological support during crisis situations, compared traditional psychotherapy. A randomized controlled trial was conducted with 104 women diagnosed anxiety disorders active war zones. Participants were randomly assigned two groups: experimental group used daily support, while control received 60-minute sessions three times a week. Anxiety levels assessed using Hamilton Rating Scale Beck Inventory. T-tests analyze results. Both groups showed significant reductions levels. receiving therapy had 45% reduction on scale 50% scale, 30% 35% group. While provided accessible, immediate proved more effective due emotional depth adaptability by human therapists. particularly beneficial settings where therapists limited, proving its value scalability availability. However, engagement notably lower in-person therapy. offers scalable, cost-effective solution situations may not be accessible. Although remains reducing anxiety, hybrid model combining AI interaction could optimize care, especially underserved areas or emergencies. Further research is needed improve AI's responsiveness adaptability.

Язык: Английский

Процитировано

7
Non-invasive brain stimulation in cognitive sciences and Alzheimer's disease DOI Creative Commons
Claudia Carrarini, Chiara Pappalettera,

Domenica Le Pera

и другие.

Frontiers in Human Neuroscience, Год журнала: 2025, Номер 18

Опубликована: Янв. 14, 2025

Over the last four decades, non-invasive brain stimulation techniques (NIBS) have significantly gained interest in fields of cognitive sciences and dementia care, including neurorehabilitation, for its emerging potential increasing insights over functions boosting residual functions. In present paper, basic physiological technical mechanisms different applications NIBS were reviewed discussed to highlight importance multidisciplinary translational approaches clinical research settings neurodegenerative diseases, especially Alzheimer's disease. Indeed, strategies may represent a promising opportunity increase neuromodulation as efficacious interventions individualized patients care.

Язык: Английский

Процитировано

2
Generalizability of Treatment Outcome Prediction Across Antidepressant Treatment Trials in Depression DOI Creative Commons
Peter Zhukovsky, Madhukar H. Trivedi, Myrna M. Weissman

и другие.

JAMA Network Open, Год журнала: 2025, Номер 8(3), С. e251310 - e251310

Опубликована: Март 20, 2025

Importance Although several predictive models for response to antidepressant treatment have emerged on the basis of individual clinical trials, it is unclear whether such generalize different and geographical contexts. Objective To assess neuroimaging features predict sertraline escitalopram in patients with major depressive disorder (MDD) across 2 multisite studies using machine learning change depression severity independent studies. Design, Setting, Participants This prognostic study included structural functional resting-state magnetic resonance imaging demographic data from Establishing Moderators Biosignatures Antidepressant Response Clinical Care (EMBARC) randomized trial (RCT), which administered (in stage 1 2) placebo, Canadian Biomarker Integration Network Depression (CANBIND-1) RCT, escitalopram. EMBARC recruited participants MDD (aged 18-65 years) at 4 academic sites US between August 2011 December 2015. CANBIND-1 6 outpatient centers Canada 2013 2016. Data were analyzed October 2023 May 2024. Main Outcomes Measures Prediction performance was assessed balanced classification accuracy area under curve (AUC). In secondary analyses, prediction observed vs predicted correlations severity. Results 363 adult (225 138 CANBIND-1; mean [SD] age, 36.6 [13.1] years; 235 women [64.7%]), best-performing pretreatment connectivity dorsal anterior cingulate had moderate cross-trial generalizability (trained tested EMBARC, AUC = 0.62 0.67 2; trained CANBIND-1, 0.66). The addition improved compared only. use early-treatment (week instead scores resulted best generalization performance, comparable within-trial performance. Multivariate regressions showed substantial (predicted r ranging 0.31 0.39). Conclusions Relevance this outcomes, predicting antidepressants show RCTs MDD.

Язык: Английский

Процитировано

2
Advances in biosensors for major depressive disorder diagnostic biomarkers DOI
Tao Dong,

Chenghui Yu,

Qi Mao

и другие.

Biosensors and Bioelectronics, Год журнала: 2024, Номер 258, С. 116291 - 116291

Опубликована: Апрель 16, 2024

Язык: Английский

Процитировано

9
Opportunities for use of neuroimaging in de-risking drug development and improving clinical outcomes in psychiatry: an industry perspective DOI Creative Commons
Amit Etkin,

Jessica Powell,

Adam Savitz

и другие.

Neuropsychopharmacology, Год журнала: 2024, Номер 50(1), С. 258 - 268

Опубликована: Авг. 21, 2024

Abstract Neuroimaging, across positron emission tomography (PET), electroencephalography (EEG), and magnetic resonance imaging (MRI), has been a mainstay of clinical neuroscience research for decades, yet penetrated little into psychiatric drug development beyond often underpowered phase 1 studies, or care. Simultaneously, there is pressing need to improve the probability success in development, increase mechanistic diversity, enhance efficacy. These goals can be achieved by leveraging neuroimaging precision psychiatry framework, wherein effects drugs on brain are measured early understand dosing indication, then later-stage trials identify likely responders enrich trials, ultimately improving outcomes. Here we examine key variables important using from lens biotechnology pharmaceutical companies developing deploying new psychiatry. We argue that clear paths incorporating different modalities de-risk subsequent phases near intermediate term, culminating use select care prescription drugs. Better outcomes through biomarkers, however, require wholesale commitment approach will necessitate cultural shift align biopharma orientation already routine other areas medicine.

Язык: Английский

Процитировано

7
It is time to personalize rTMS targeting for the treatment of pain DOI
Jean‐Pascal Lefaucheur

Neurophysiologie Clinique, Год журнала: 2024, Номер 54(1), С. 102950 - 102950

Опубликована: Фев. 1, 2024

Язык: Английский

Процитировано

6
Neuroprediction of violence and criminal behavior using neuro-imaging data: From innovation to considerations for future directions DOI Creative Commons
Josanne D. M. van Dongen,

Yudith R. A. Haveman,

Carmen S. Sergiou

и другие.

Aggression and Violent Behavior, Год журнала: 2024, Номер unknown, С. 102008 - 102008

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

6
Treatment of Depression with Acupuncture Based on Pathophysiological Mechanism DOI Creative Commons

Bo Sun,

Xue‐Wei Cao,

Xin Ming

и другие.

International Journal of General Medicine, Год журнала: 2024, Номер Volume 17, С. 347 - 357

Опубликована: Янв. 1, 2024

Abstract: Depression is a prevalent mental disorder and has profound impact on an individual's psychological physical well-being. It characterized by persistently depressed mood, loss of interest, energy loss, cognitive dysfunction. In recent years, more people have changed to diseases, such as depression, anxiety, mania so on. the incidence covering all ages, but still mainly young middle-aged women. Traditional treatments for depression rely medication psychotherapy, these methods are not effective patients often accompanied certain side effects. Therefore, finding safe alternative or adjuvant become priority. Here we highlight research progress acupuncture in treatment explore mechanism depression. Acupuncture ancient method, involves multiple biological pathways, example, regulating neurotransmitter levels, neuroendocrine axis, improving neuroplasticity, anti-inflammatory other effects, emotional state play antidepressant role. To provide evidence support widespread use clinical practice. We hope new ideas with even reduce Keywords: acupuncture,

Язык: Английский

Процитировано

5
Probing prefrontal-sgACC connectivity using TMS-induced heart–brain coupling DOI
Eva Dijkstra, Summer Frandsen, Hanneke van Dijk

и другие.

Nature Mental Health, Год журнала: 2024, Номер 2(7), С. 809 - 817

Опубликована: Апрель 26, 2024

Язык: Английский

Процитировано

5
Frontal Alpha Asymmetry and Its Modulation by Monoaminergic Neurotransmitters in Depression DOI Open Access
Paul J. Fitzgerald

Clinical Psychopharmacology and Neuroscience, Год журнала: 2024, Номер 22(3), С. 405 - 415

Опубликована: Март 4, 2024

Frontal alpha asymmetry (FAA) is an electroencephalography (EEG) measure that quantifies trait-like left versus right hemisphere lateralization in power. Increased FAA indicates relatively greater than frontal cortex activation and associated with enhanced reward-related approach behaviors rather avoidance or withdrawal. Studies dating back several decades have often suggested having supports positive affect protection against major depressive disorder (MDD), whereas (i.e., reduced FAA) negative risk for MDD. While this hypothesis widely known, a number of other studies instead found increased MDD, evidence either leftward rightward bias depression. Here we briefly review the literature on find much MDD not always characterized by FAA. We also limited monoaminergic neurotransmitter systems, including pharmacologic agents act them. serotonin particular provide genetic modulation FAA, where some these data may suggest reduces In synthesis collective monoamines, norepinephrine differentially tending to promote biased toward activation. These putative differences influence phenotypes potential subtypes disorder, treatment strategies.

Язык: Английский

Процитировано

4