A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns DOI Creative Commons

Lucie Berkovitch,

Kangjoo Lee, Jie Lisa Ji

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 17, 2023

Abstract Importance Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determine effective personalized treatments. Objective To perform secondary analysis quantifying neural-to-symptom relationships in MDD as function antidepressant treatment. Design Double blind randomized controlled trial. Setting Multicenter. Participants Patients with early onset recurrent depression from public Establishing Moderators and Biosignatures Antidepressant Response Clinical Care (EMBARC) study. Interventions Either sertraline or placebo during 8 weeks (stage 1), according response second line treatment for additional 2). Main Outcomes Measures identify data-driven pattern symptom variations these two stages, we performed Principal Component Analysis (PCA) on individual items four clinical scales measuring depression, anxiety, suicidal ideas manic-like symptoms, resulting univariate measure improvement. We then investigated how initial neural factors predicted this stage 1. do so, extracted resting-state global brain connectivity (GBC) at baseline level using whole-brain functional network parcellation. In turn, computed linear model each parcel scores 1 group. Results 192 patients (127 women), age 37.7 years old (standard deviation: 13.5), were included. The first PC (PC1) capturing 20% variation was similar across groups 2, suggesting reproducible PC1 patients’ significantly differed 1, whereas no difference evidenced between Global Impressions (CGI). Baseline GBC correlated sertraline, but not Conclusions Relevance Using reduction symptoms scales, identified common profile sertraline. However, patterns that mapped onto distinguished placebo. Our results underscore mapping circuits vital detect treatment-responsive profiles may aid optimal patient selection future trials. Key Points Question What antidepressants placebo? Findings has shared behavioral geometry differs terms intensity group only. Meaning There signature can be more robustly by neurobehavioral features when it pharmacologically induced.

Language: Английский

Connectivity-guided intermittent theta burst versus repetitive transcranial magnetic stimulation for treatment-resistant depression: a randomized controlled trial DOI Creative Commons
Richard Morriss, Paul M. Briley, Lucy Webster

et al.

Nature Medicine, Journal Year: 2024, Volume and Issue: 30(2), P. 403 - 413

Published: Jan. 16, 2024

Abstract Disruption in reciprocal connectivity between the right anterior insula and left dorsolateral prefrontal cortex is associated with depression may be a target for neuromodulation. In five-center, parallel, double-blind, randomized controlled trial we personalized resting-state functional magnetic resonance imaging neuronavigated connectivity-guided intermittent theta burst stimulation (cgiTBS) at site based on effective from to cortex. We tested its efficacy reducing primary outcome symptoms measured by GRID Hamilton Depression Rating Scale 17-item over 8, 16 26 weeks, compared structural (MRI) repetitive transcranial (rTMS) delivered standard (F3) patients ‘treatment-resistant depression’. Participants were randomly assigned 20 sessions 4–6 weeks of either cgiTBS ( n = 128) or rTMS 127) MRI baseline weeks. Persistent decreases depressive seen no differences arms score (intention-to-treat adjusted mean, −0.31, 95% confidence interval (CI) −1.87, 1.24, P 0.689). Two serious adverse events possibly related TMS (mania psychosis). MRI-neuronavigated equally treatment-resistant (trial registration no. ISRCTN19674644).

Language: Английский

Citations

32
The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration DOI Creative Commons
Bin Lü, Xiao Chen, F. Xavier Castellanos

et al.

Science Bulletin, Journal Year: 2024, Volume and Issue: 69(10), P. 1536 - 1555

Published: March 6, 2024

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection subtle abnormalities and robust associations, fostering new research methods. Global collaborations imaging have furthered knowledge neurobiological foundations brain disorders aided imaging-based prediction for more targeted treatment. Large-scale magnetic resonance initiatives driving innovation analytics supporting generalizable psychiatric studies. We also emphasize significant role big understanding neural mechanisms early identification precise treatment However, challenges such as harmonization across different sites, privacy protection, effective sharing must be addressed. With proper governance science practices, we conclude with a projection how large-scale resources could revolutionize diagnosis, selection, outcome prediction, contributing to optimal health.

Language: Английский

Citations

9
Individual deviations from normative electroencephalographic connectivity predict antidepressant response DOI
Xiaoyu Tong, Hua Xie, Wei Wu

et al.

Journal of Affective Disorders, Journal Year: 2024, Volume and Issue: 351, P. 220 - 230

Published: Jan. 27, 2024

Language: Английский

Citations

7
The promise of precision functional mapping for neuroimaging in psychiatry DOI
Damion V. Demeter, Deanna J. Greene

Neuropsychopharmacology, Journal Year: 2024, Volume and Issue: 50(1), P. 16 - 28

Published: July 31, 2024

Language: Английский

Citations

6
Discriminative functional connectivity signature of cocaine use disorder links to rTMS treatment response DOI
Kanhao Zhao, Gregory A. Fonzo, Hua Xie

et al.

Nature Mental Health, Journal Year: 2024, Volume and Issue: 2(4), P. 388 - 400

Published: Feb. 16, 2024

Language: Английский

Citations

5
A Multiview Brain Network Transformer Fusing Individualized Information for Autism Spectrum Disorder Diagnosis DOI
Qunxi Dong, Hong-Xin Cai, Zhigang Li

et al.

IEEE Journal of Biomedical and Health Informatics, Journal Year: 2024, Volume and Issue: 28(8), P. 4854 - 4865

Published: May 3, 2024

Functional connectivity (FC) networks, built from analyses of resting-state magnetic resonance imaging (rs-fMRI), serve as efficacious biomarkers for identifying Autism Spectrum Disorders (ASD) patients. Given the neurobiological heterogeneity across individuals and unique presentation ASD symptoms, fusion individualized information into diagnosis becomes essential. However, this aspect is overlooked in most methods. Furthermore, existing methods typically focus on studying direct pairwise connections between brain ROIs, while disregarding interactions indirectly connected neighbors. To overcome above challenges, we build common FC by tangent pearson embedding (TP) orthogonal basis extraction (COBE) respectively, present a novel multiview transformer (MBT) aimed at effectively fusing subjects. MBT mainly constructed layers with diffusion kernel (DK), quality-inspired weighting module (FQW), similarity loss orthonormal clustering readout (OCFRead). DK can incorporate higher-order random walk to capture wider among regions. FQW promotes adaptive features views, OCFRead are placed last layer accomplish ultimate integration information. In our method, TP, modules all help model that make up shortcomings traditional We conducted experiments public ABIDE dataset based AAL CC200 respectively. Our framework has shown promising results, outperforming state-of-the-art both templates. This suggests its potential valuable approach clinical diagnosis.

Language: Английский

Citations

4
Resolving heterogeneity of early-onset major depressive disorder through individual differential structural covariance network analysis DOI

Zhanjie Luo,

Zhibo Hu, Weicheng Li

et al.

Journal of Affective Disorders, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
Modulation of cerebellar homotopic connectivity by modified electroconvulsive therapy at rest: Study of first-episode, drug-naive adolescent major depressive disorder DOI
Yujun Gao, Sanwang Wang, Tingting Li

et al.

Journal of Affective Disorders, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0
Effects of the KCNQ (Kv7) Channel Opener Ezogabine on Resting-State Functional Connectivity of Striatal Brain Reward Regions, Depression and Anhedonia in Major Depressive Disorder: Results from a Randomized Controlled Trial DOI
Avijit Chowdhury, Sarah Boukezzi, Sara Costi

et al.

Biological Psychiatry, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0
Deep graph learning of multimodal brain networks defines treatment-predictive signatures in major depression DOI Creative Commons
Yong Jiao, Kanhao Zhao, Xinxu Wei

et al.

Molecular Psychiatry, Journal Year: 2025, Volume and Issue: unknown

Published: March 31, 2025

Abstract Major depressive disorder (MDD) presents a substantial health burden with low treatment response rates. Predicting antidepressant efficacy is challenging due to MDD’s complex and varied neuropathology. Identifying biomarkers for requires thorough analysis of clinical trial data. Multimodal neuroimaging, combined advanced data-driven methods, can enhance our understanding the neurobiological processes influencing outcomes. To address this, we analyzed resting-state fMRI EEG connectivity data from 130 patients treated sertraline 135 placebo Establishing Moderators Biosignatures Antidepressant Response in Clinical Care (EMBARC) study. A deep learning framework was developed using graph neural networks integrate data-augmented cross-modality correlation, aiming predict individual symptom changes by revealing multimodal brain network signatures. The results showed that model demonstrated promising prediction accuracy, an R 2 value 0.24 0.20 placebo. It also exhibited potential transferring predictions only EEG. Key regions identified predicting included inferior temporal gyrus (fMRI) posterior cingulate cortex (EEG), while response, precuneus supplementary motor area (EEG) were critical. Additionally, both modalities superior as significant anterior postcentral common predictors arm. variations frontoparietal control, ventral attention, dorsal limbic notably associated MDD treatment. By integrating EEG, study established novel signatures responses MDD, providing interpretable circuit patterns may guide future targeted interventions. Trial Registration: Depression ClinicalTrials.gov Identifier: NCT#01407094.

Language: Английский

Citations

0