Non-invasive brain stimulation techniques, such as transcranial electric stimulation (tES), are increasingly promoted as methods to enhance attention. However, their efficacy and optimal stimulation targets remain uncertain. We conducted a preregistered meta-analysis of randomized controlled trials in healthy adults (58 trials, 295 outcomes) examining the effects of tES on attentional functions (PROSPERO: CRD42023487035), complemented by a performance-electric field correlation (PEC) analysis to identify brain regions most strongly linked to tES-induced behavioral improvements. In general, tES produced a small but significant improvement in attentional functions compared to control conditions (standardized mean difference [SMD] = 0.24, 95% confidence interval [CI] = 0.12-0.36, I2 = 61%). Small but consistent benefits were observed in trials assessing attentional functions after stimulation (40 trials, SMD = 0.23, 95% CI = 0.12-0.33, I2 = 39%) and in trials applying anodal transcranial direct current stimulation (tDCS) targeting prefrontal regions (vs sham; 31 trials, SMD = 0.26, 95% CI = 0.12-0.39, I2 = 46%) with no evidence of publication bias or serious imprecision. The PEC analysis further revealed that tDCS-induced electric fields in the ventral subregion of the left dorsolateral prefrontal cortex (left vDLPFC) were most strongly associated with improvements in attentional functions following tDCS. Taken together, these findings suggest that tES may enhance attentional functions and the left vDLPFC may be a potential target for future tES studies aiming to improve attention.
Toru Takahashi, Ikko Kimura, S. Vafaei et al.· Biological Psychology· 0 citations
Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are common neurodevelopmental disorders (NDDs) in children and often co-occur (ASD + ADHD), complicating the diagnosis. The diagnostic process is lengthy and subjective, relying heavily on expert knowledge, which limits accessibility. Electroencephalography (EEG) offers potential as a biomarker but requires skilled technicians for measurements and can be stressful for children with NDDs. This study aimed to provide a more accessible diagnostic support tool. We used a portable EEG device with a low participant burden and a deep learning model to distinguish between the typical development group (TD) and the NDD group, comprising children with ASD, ADHD, and ASD + ADHD. Resting-state EEG data were recorded for 5 min using the portable HARU-2 device, which features three channels placed on the forehead of 163 participants (87 TD, 76 NDD). A deep learning model combining a one-dimensional convolutional neural network and a transformer encoder was developed to analyze the EEG data. In 5-fold cross-validation, the model achieved an area under the curve (AUC) of 0.713 and a balanced accuracy (bACC) of 67.5% for classifying NDD and TD. Exploratory evaluation of out-of-fold predictions stratified by clinical phenotype showed an AUC of 0.793 and bACC of 75.0% for ASD vs. TD, 0.817 and 79.5% for ADHD vs. TD, and 0.667 and 62.7% for ASD + ADHD vs. TD. These findings suggest that portable EEG devices combined with deep learning models may serve as accessible adjunctive tools for NDD screening in children.