Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Neuropsychologia
Reading between the lines: Combining pause dynamics and semantic coherence for automated assessment of thought disorder.
Formal thought disorder (FTD), a hallmark of schizophrenia spectrum disorders, manifests as incoherent speech and poses challenges for clinical assessment. Traditional clinical rating scales, though validated, are resource-intensive and lack scalability. Automated speech recognition (ASR) allows for objective quantification of linguistic and temporal features of speech, offering scalable alternatives. Furthermore, ASR-derived utterance timestamps provide access to pause dynamics, which are thought to reflect the cognitive processes underlying speech production. Yet, their added value beyond semantic measures remains insufficiently explored. In this study, we evaluated a scalable multimodal framework that integrates pause features with semantic coherence metrics across three datasets: naturalistic self-recorded diaries (AVH, n = 140 participants), structured picture descriptions (TOPSY, n = 72 participants), and dream narratives (PsyCL, n = 43 participants). Pause-related features were evaluated alongside established coherence measures using support vector regression (SVR) to predict clinical FTD scores. Models using pause features alone robustly predict manually rated FTD severity consistently across datasets. Integrating pause features with semantic coherence metrics enhanced predictive performance compared to coherence-only models, with late fusion yielding the most robust and consistent gains in all three datasets. On average across datasets, Spearman correlation increased from ρ = 0.413 for semantic-only models to ρ = 0.455 with late fusion. The performance gains from semantic and pause features integration held consistently across all contexts, though the nature of the most informative pause patterns was dataset-dependent. These findings suggest that both pause dynamics and semantic coherence reflect complementary aspects of thought disorganization. Our integration framework provides a scalable approach for refining the assessment of disorganized speech to advance automated speech analysis in psychosis.
29 July 2026
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rTMS Modulates Static and Dynamic Brain Functional Networks in Children with Autism Spectrum Disorder: An EEG Microstate Study
Repetitive Transcranial Magnetic Stimulation (rTMS) shows promise for treating Autism Spectrum Disorder (ASD), but its impact on the temporal dynamics of large-scale brain networks remains unclear. This study investigated the modulatory effects of rTMS on static and dynamic brain functional networks in children with ASD. Thirty-two children were randomized into an active rTMS group (1 Hz over the dorsolateral prefrontal cortex) and a sham control group. Resting-state EEG and behavioral assessments were conducted before and after a 9-week intervention. We employed a multi-dimensional analysis approach, combining microstate temporal parameters, static functional connectivity based on the weighted Phase Lag Index (wPLI), and dynamic complexity measured by Fuzzy Entropy. Results indicated that intrinsic features of Microstate B were significantly correlated with social relating deficits. Although rTMS did not induce significant interaction effects in standard microstate temporal parameters, it significantly enhanced static functional connectivity strength and increased the dynamic complexity of brain networks across all microstates. These findings suggest that rTMS exerts its therapeutic effects by strengthening network integration and restoring neural flexibility rather than simply altering the duration of brain states. The study underscores the value of network-based EEG metrics in elucidating the neuroplastic changes induced by neuromodulation in ASD.
12 May 2026
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