ArticleFrontiers in psychiatry2022
Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study.
Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Biomarker use in predicting transcranial direct-current stimulation (tDCS) response: a systematic scoping review.Frontiers in psychiatry · 2026Pooled it
- Baseline Functional Connectivity Predicts Who Will Benefit From Neuromodulation: Evidence From Primary Progressive Aphasia.Neurorehabilitation and neural repair · 2026Trial
- Recent Advancements of Transcranial Direct Current Stimulation and Machine Learning: Methods, Challenges, and Opportunities.Transactions on artificial intelligence · 2026Article
- Differences in cortical gyrification and fractional amplitude of low-frequency fluctuations between early- and late-onset obsessive-compulsive disorder.Journal of psychiatry & neuroscience : JPN · 2026Article
- Neuromodulation and neural networks in psychiatric disorders: current status and emerging prospects.Psychological medicine · 2025Review
- Temporal dynamics of offline transcranial ultrasound stimulation.Current research in neurobiology · 2025Article
- Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.BMC neurology · 2024Review
- Dynamical and individualised approach of transcranial ultrasound neuromodulation effects in non-human primates.Scientific reports · 2024Article
- Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2024Article
- Study protocol for identifying resting brain functional connectivity markers of response to continuous Theta Burst Stimulation and cathodal transcranial Direct Current Stimulation in patients with schizophrenia with persistent auditory hallucinations.Wellcome open research · 2024Article
- Reduced resting-state functional connectivity between insula and inferior frontal gyrus and superior temporal gyrus in hoarding disorder.Frontiers in psychiatry · 2024Article
- Identifying biomarkers for tDCS treatment response in Alzheimer's disease patients: a machine learning approach using resting-state EEG classification.Frontiers in human neuroscience · 2023Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
Abstract
Transcranial direct current stimulation (tDCS) is a promising adjuvant treatment for persistent auditory verbal hallucinations (AVH) in Schizophrenia (SZ). Nonetheless, there is considerable inter-patient variability in the treatment response of AVH to tDCS in SZ. Machine-learned models have the potential to predict clinical response to tDCS in SZ. This study aims to examine the feasibility of identifying SZ patients with persistent AVH (SZ-AVH) who will respond to tDCS based on resting-state functional connectivity (rs-FC). Thirty-four SZ-AVH patients underwent resting-state functional MRI at baseline followed by add-on, twice-daily, 20-min sessions with tDCS (conventional/high-definition) for 5 days. A machine learning model was developed to identify tDCS treatment responders based on the rs-FC pattern, using the left superior temporal gyrus (LSTG) as the seed region. Functional connectivity between LSTG and brain regions involved in auditory and sensorimotor processing emerged as the important predictors of the tDCS treatment response. L1-regularized logistic regression model had an overall accuracy of 72.5% in classifying responders vs. non-responders. This model outperformed the state-of-the-art convolutional neural networks (CNN) model-both without (59.41%) and with pre-training (68.82%). It also outperformed the L1-logistic regression model trained with baseline demographic features and clinical scores of SZ patients. This study reports the first evidence that rs-fMRI-derived brain connectivity pattern can predict the clinical response of persistent AVH to add-on tDCS in SZ patients with 72.5% accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.