Objective Executive function (EF) has been studied using functional magnetic resonance imaging (fMRI) based functional connectivity (FC); however, electroencephalography (EEG) based dynamic FC interactions supporting EF in children and explaining behavioral performance remain underexplored. We used EEG to examine FC within and between seven core brain networks: Default Mode (DMN), Dorsal Attention (DAN), Ventral Attention (VAN), Somatomotor (SMN), Frontoparietal (FPN), Visual (VIN), and Limbic (LIN), during a modified Eriksen flanker task. We aimed to identify dynamic network configurations linked to EF, specifically selective attention and inhibitory control in preadolescent children (8–10 years). Methods Secondary analyses of source-localized EEG data from 84 children (mean age 8.89 ± 0.82 years; 51.2% female) were conducted. A Hidden Markov model extracted spatiotemporal dynamics, quantifying within- and between-network FC correlations. Support vector regression (SVR) with an interpretable kernel identified connectivity patterns predicting behavioral outcomes, including accuracy and reaction time (RT) interference. Results SVR moderately predicted Accuracy Interference (RMSE = 6.51, R2 = 0.48) and RT Interference (RMSE = 31.42, R2 = 0.26). Behavioral performance indices were most strongly associated with interference within VAN, DMN, SMN, and VIN. Notably, within-VAN FC explained between-subject variability in both Accuracy Interference (ρ = −0.28, p = 0.012) and RT Interference (r = 0.26, p = 0.017). Conclusions Dynamic, task-based FC, particularly within-VAN, underpins individual differences in EF among preadolescent children. Stronger within-VAN reconfiguration during high cognitive demand trials supported accuracy but at the cost of slower responses, highlighting a trade-off between stability and efficiency in developing EF.
Introduction Cognitive-behavioural therapy (CBT) is a widely used intervention for depression and anxiety. However, its effectiveness is typically evaluated using subjective self-report symptom scales, which are susceptible to bias. It is hypothesised that improvements in clinical symptoms following CBT may be accompanied by improvements in physiological regulation, particularly heart rate variability (HRV), which has been proposed as a marker of emotion regulation. We conducted a systematic review with meta-analysis to examine the effects of CBT on HRV in adults with depression and/or anxiety disorders. Methods Following a pre-registered protocol (PROSPERO: CRD420251040957), we searched Embase, PsycINFO, PubMed, and Web of Science for randomised controlled trials (RCTs) and non-randomised interventional studies involving adults with depression and/or anxiety in CBT, and reporting HRV before and after the intervention. Random-effects meta-analyses in R estimated pooled effect sizes (Hedges' g). Results After screening 9713 records, we identified 12 studies; 5 RCTs were included in the meta-analysis, and the remaining 7 in the narrative review because they did not provide calculable effect sizes. Meta-analysis results found that adults randomised to CBT had significantly greater pre-to-post intervention increases in HF-HRV, LF-HRV, and SDNN-HRV. No significant changes were observed for the LF/HF ratio, RMSSD-HRV, and the HRV Triangular Index. Conclusion Our meta-analysis provides emerging evidence that CBT might significantly increase selected HRV indices, suggesting that physiological changes may accompany improvements in psychological symptoms. However, further high-quality research is needed to determine the clinical utility of HRV as a predictor of treatment response and a biomarker for treatment effectiveness.
Background Developmental dyslexia is classically divided into Boder's dysphonetic (DD), dyseidetic (DYD) and mixed (MD) subtypes, assigned by direct reading-and-spelling testing. Its computerised Italian adaptation, the Direct Test of Reading and Spelling (DTRS), assigns the subtype algorithmically from the reading quotient and the percentage of correctly spelt known and unknown words. Aims Using the same source clinical cohort and the same stable and sparse regression classifier (SSRC) as our companion, we asked whether the subtypes form natural clusters or lie on a continuum, and whether reading-time (severity) or qualitative-error (route) information discriminates them. Methods We analysed one DTRS assessment each from 520 children (378 DD, 41 DYD, 101 MD), deriving reading-time indices, including a severity/fluency index (SFI), and Boder's error taxonomies for reading and spelling. Subtype structure was probed with Gaussian-mixture clustering (adjusted Rand index against clinical labels) and Hartigan's dip test; discrimination with the SSRC and robust ROC under participant-level Monte-Carlo cross-validation, with subsampling stability intervals (5th–95th percentile over 300 iterations). Full-sample pairwise AUCs were additionally evaluated with percentile bootstrap 95% confidence intervals (10,000 subject-level resamples of the full-sample classifier scores) and label-permutation tests (10,000 permutations, one-sided and Bonferroni-corrected within each model). Results No feature space yielded label-aligned mixture solutions (adjusted Rand index ≈ 0); SFI was unimodal (dip p = 0.36). Reading-time indices separated the severity contrast (DD vs MD, stable AUC 0.73 [0.66–0.80]; p < 0.001) but were weak and non-significant for the route contrast (DYD vs DD, 0.58 [0.47–0.69]; p = 0.543, non-significant); reading qualitative errors showed a weak route signal (0.61 [0.50–0.73]; p = 0.062) that did not reach Bonferroni-corrected significance. Unknown-word spelling, the basis of the diagnostic algorithm, separated DD from DYD almost perfectly (0.94–0.98), but partialling out unknown-word accuracy collapsed this to 0.60: the separation is definitional. The severity contrast was likewise definitional: the reading quotient alone separated DD from MD at 0.86, and residualising the reading-time indices on the reading quotient within each training fold reduced the DD–MD AUC from 0.73 to 0.50. In the EEG subsample, no behavioural classifier separated DD from DYD significantly (TIME: 0.52 [0.31–0.75], p = 0.41; ERR: weak and non-significant 0.62 [0.41–0.81], p = 0.15), whereas qEEG cross-spectral connectivity did so at 0.85 in the published qEEG sample of 227 children (169 DD, 18 DYD, 40 MD; Chiarenza et al., 2026). Conclusions No evidence for natural clusters emerged with the present methods: the subtypes behave as overlapping regions of graded continuous distributions. Both axes of Boder's scheme are threshold-defined — severity by the reading quotient, route by unknown-word spelling — and both are continuously distributed. Once the defining variable is removed, reading dynamics carry no residual severity signal (0.50) and independent behaviour carries only a faint route signal (0.58–0.61); the route distinction is clearest in brain connectivity, which predicts the behaviourally defined labels rather than independently validating a latent route construct. Together, the two papers advance a dimensional, mechanism-aware account of dyslexia subtyping.
Failures in inhibitory control over negative emotional memories can lead to intrusive recollections that interfere with cognitive functioning and well-being. The present study examined whether acute exercise modulates the electrophysiological dynamics underlying inhibitory control during the suppression of negative emotional memories. Seventy healthy participants were randomly assigned to an acute exercise group or a seated-rest control group. The exercise group completed 30 min of moderate-intensity exercise, whereas the control group remained seated at rest. Participants completed the emotional Think/No-Think paradigm both before and after the intervention, while electroencephalography (EEG) was recorded. Event-related potentials (ERPs), time-frequency analyses, and multivariate pattern analysis (MVPA) were used to characterize neural processes associated with inhibitory control. Results showed that acute exercise selectively modulated late-stage neural activity during negative memory suppression, particularly within the 2400–3000 ms time window. This modulation was most evident over central site and left parietal site, and was accompanied by changes in alpha- and beta-band power at the left parieto-occipital junction. Overall, these findings suggest that acute exercise modulates late-stage neural dynamics during the suppression of negative memories. The observed neural changes were accompanied by a reduction in intrusion frequency during suppression attempts, consistent with the possibility that acute exercise facilitates late-stage sustained inhibition.
