Pablo Billeke

Background: Cognitive impairment poses a significant challenge to healthcare systems worldwide, impacting patient autonomy, social participation, and quality of life, while placing a considerable burden on caregivers. Non pharmacological interventions, particularly cognitive training and non invasive brain stimulation, have emerged as promising therapeutic strategies. Objective: This study aims to quantify the synergistic effects of transcranial direct current stimulation (tDCS) with cognitive training on cognitive function across a spectrum of pathologies that induce cognitive impairment. Methods: We conducted a systematic review and metaanalysis following PRISMA guidelines. We searched PubMed for randomized controlled trials that investigated the effect of combined tDCS and cognitive training compared with cognitive training alone. The analysis was based on the GRADE framework for systematic reviews and metaanalyses. Results: Across 27 studies including 1,012 participants, tDCS combined with cognitive training showed a small effect compared with cognitive training alone (SMD = 0.36, 95% CI: 0.15 0.56). The effect was found only immediately after the intervention and declined during follow-up. Conclusion: tDCS combined with cognitive training may provide a small, short term benefit for cognitive function, but high heterogeneity across studies and loss of effect at follow up underscore the need for larger, better standardized trials to clarify its clinical value.

Fluctuations in attentional states, such as mind-wandering (MW), are associated with critical variability in task performance. While fMRI studies highlight the opposing roles of task-positive (e.g., dorsal attention network) and task-negative (e.g., default mode network) systems, the electrophysiological mechanisms underlying these dynamics remain poorly understood. Using intracranial electrocorticography in humans performing a sustained attention task, we identified global oscillatory dynamics linked to attentional shifts. MW was characterized by (1) reduced theta (θ) and alpha (α) power, (2) decreased aperiodic signal components, indicating a shift toward cortical inhibition, (3) enhanced phase synchronization across networks, and (4) strengthened θ phase-behavior correlations (ρ). These features support a non-network-specific framework in which low-frequency θ dynamics—captured by both θ power and ρ—are associated with attentional fluctuations, while aperiodic offset relates to attentional state indirectly through its association with ρ (structural equation modeling: power → state β = − 0.118, p = 0.002; ρ → state β = 0.246, p < 0.001; offset → ρ β = − 0.222, p < 0.001). Our study provides a unified neurophysiological framework for understanding how spontaneous neural activity can drive attentional fluctuations and performance variability, with implications for research on attention, learning, and neuropsychiatric disorders.

Summary: Adaptive behavior requires weighting incoming feedback against more stable action policies, but the neural dynamics controlling this balance during social interaction remain unclear. Across 296 experimental sessions in 219 participants, we combined computational modeling, EEG, fMRI, diffusion MRI, and fMRI-guided rhythmic TMS during repeated social bargaining. Behavior was captured by complementary feedback-based updating and reputation-based adjustment, with reproducible individual differences in how instructed social context reweighted feedback. Feedback-based updating showed the clearest convergent neural signature: trial-by-trial updating was associated with alpha–beta dynamics and multivoxel representational structure in right temporoparietal cortex. Critically, rhythmic alpha-TMS over individually localized TPJ sites selectively reduced feedback-based updating relative to active arrhythmic stimulation, while increasing baseline offers and modulating anticipatory alpha activity. Functional and structural connectivity analyses further implicated a distributed temporoparietal–frontal network. Together, these findings link a specific computational operation—feedback weighting—to temporoparietal alpha dynamics and show that rhythmic perturbation alters its behavioral expression.

Fluctuations in attentional states, such as mind-wandering (MW), are associated with critical variability in task performance. While fMRI studies highlight the opposing roles of task-positive (e.g., dorsal attention network) and task-negative (e.g., default mode network) systems, the electrophysiological mechanisms underlying these dynamics remain poorly understood. Using intracranial electrocorticography in humans performing a sustained attention task, we identified global oscillatory dynamics linked to attentional shifts. MW was characterized by (i) reduced theta and alpha power, (ii) decreased aperiodic signal components, indicating a shift toward cortical inhibition, (iii) enhanced phase synchronization across networks, and (iv) strengthened theta phase-behavior correlations. These features support a non-network-specific framework in which low-frequency theta dynamics are associated with attentional fluctuations, while aperiodic offset relates to attentional state indirectly through its association with theta phase-behavior correlations. Our study provides a unified neurophysiological framework for understanding how spontaneous neural activity can drive attentional fluctuations and performance variability, with implications for research on attention, learning, and neuropsychiatric disorders.

Persistent cognitive symptoms following SARS-CoV-2 infection have been widely reported, yet their underlying neural mechanisms remain poorly understood. Here, we applied an integrative framework—combining EEG, diffusion MRI, and computational cognitive modeling—to investigate brain–behavior relationships in patients recovered from COVID-19 (n = 70) and other non-COVID respiratory infections (n = 26). Participants completed tasks probing cognitive control, working memory, and decision-making. Across tasks, individuals with prior COVID-19 showed altered low-frequency oscillatory activity and performance deficits, despite mild or moderate acute illness. These oscillatory alterations were linked to reduced white matter integrity in distinct long-range tracts, including the cingulum and thalamo-occipital fasciculi, depending on the cognitive computation involved. Crucially, model-based clustering revealed two distinct neurophysiological profiles among post-COVID participants, reflecting different combinations of structural and functional alterations. These profiles were not explained by clinical severity markers or other symptoms like anosmia, suggesting the emergence of neurocognitive phenotypes beyond binary classifications. Our findings identify a generalizable principle whereby specific brain network disruptions underlie cognitive dysfunction, highlighting biomarkers that may guide personalized intervention strategies. Beyond COVID-19, this multimodal framework offers a scalable approach for uncovering structure–function–computation coupling in other post-infectious or neuroinflammatory conditions, with broad relevance for the prevention and treatment of cognitive impairment.

Background: Sedentarism is prevalent and associated with poorer mental and physical health. Whether everyday physical activity (PA) maps onto computational decision strategies and brain structure/function in non-elderly adults remains unclear. Methods: Seventy-one healthy adults (39 women; 18–45 years) completed the Multi-Source Interference Task (MSIT) during fMRI. PA was quantified with the short IPAQ and participants were classified as Active or Sedentary. Behavior (RT, accuracy) was analyzed with frequentist models and a hierarchical Bayesian drift–diffusion model (DDM) estimating drift rate ( v ), boundary separation ( α ), and non-decision time (τ). First-level fMRI modeled congruent/incongruent trials; group-level analyses used FLAME1 with cluster-wise FWE correction (z>3.1, p <.05). Structural MRI was processed with FreeSurfer 7.4.1 (cortical thickness, hippocampal subfields); surface-based GLMs tested group and DDM effects. Results: Active participants responded faster overall; incongruent accuracy showed a speed– accuracy trade-off (accuracy increased with slower RTs), with a significant RT × group interaction. In the DDM, boundary separation ( α ) was higher in sedentary individuals (greater caution), whereas drift rate ( v ) differed by sex (males > females). Structurally, the active group showed larger left hippocampal subfields and thicker cortex in posterior temporal & anterior cingulate regions that are negatively related to α . Functionally, boundary-related BOLD modulation encompassed fusiform, posterior cingulate, superior temporal cortex, and SMA; Active > Sedentary contrasts highlighted left occipital and inferior parietal lobe. Conclusions: Everyday PA aligns with lower decision thresholds, select structural advantages, and more efficient task-engaged networks, suggesting PA fosters adaptive, resource-efficient cognitive control. These mechanistic links support PA-based strategies to mitigate risks of sedentarism.

Docosahexaenoic acid (DHA) is a polyunsaturated fatty acid enriched in neuronal membranes and myelin and associated with cognitive performance. However, nutritional interventions show inconsistent cognitive effects, partly due to limited knowledge of the neural pathways linking DHA status to human cognition during sensitive periods of white-matter maturation, such as adolescence. We addressed this gap by studying 99 adolescents drawn from both extremes of performance on a national scholastic examination. Participants completed assessments of scholastic achievement (SA) and intellectual ability (IA), provided erythrocyte DHA samples, and underwent multimodal MRI, including diffusion, T1-weighted, and T2-weighted imaging. Independent component analysis and Bayesian multivariate LASSO models identified brain components jointly associated with DHA and cognition. Across four MRI modalities, a single deep white-matter component consistently emerged as the strongest shared pathway linking DHA with cognition. Tract-resolved analyses highlighted predominant contributions from the fornix and thalamus–temporal fasciculus, with additional subcortical and cortical involvement. In joint models, these components predicted SA and IA after accounting for DHA and other fatty acids, consistent with an indirect, mediation-like pathway. These findings move beyond DHA–behavior correlations by identifying specific neuroanatomical pathways through which a modifiable dietary factor relates to adolescent learning and intellectual performance, offering mechanistic insight relevant to neuroscience, nutrition, and education. Significance Statement: Adolescence is a sensitive period for the maturation of white-matter pathways that support learning and reasoning. Docosahexaenoic acid (DHA), an essential dietary fatty acid enriched in neuronal membranes and myelin, has been linked to cognitive performance, yet the neural mechanisms underlying this association remain unclear. Using multimodal MRI and Bayesian multivariate modeling in adolescents with high or low scholastic performance, identify a specific deep white-matter pathway—centered on the fornix and the thalamus–temporal fasciculus—as the principal route connecting DHA status with scholastic achievement and intellectual ability. Additional subcortical and cortical contributions reveal a coordinated system-level architecture. These findings move beyond correlations by providing mechanistic insight into how a modifiable nutritional factor relates to cognitive development, with implications for neuroscience, public health, and education.

During childhood, schools are crucial environments for social interactions, making them ideal for evaluating the inclusion of students with special educational needs (SEN). In particular, autistic children often face challenges in peer relationships, yet the impact of autism on social dynamics in schools is not well understood. To address this issue, we examined social dynamics within elementary schools. We hypothesized that, compared to their non-autistic peers, autistic children occupy more peripheral positions in social networks and engage less in reciprocal relationships. To test these hypotheses, we introduced a novel ecological approach using experimental game theory to quantify social integration and reciprocity among autistic children. Social networks were constructed for each classroom based on the children’s peer selections during a distributive game where they had to send tokens to their peers. Six elementary schools took part in this study. From these schools, 26 classrooms from first to fourth grade were included, comprising a total of 625 students aged 6 to 11. Among them, 464 were students without SEN, 143 were students with SEN excluding autism, and 18 were autistic students. Our analysis showed that autistic children and children with SEN were significantly less central and less involved in reciprocal peer relationships compared to children without SEN. Due to the small sample of autistic students, further research with greater statistical power is needed to clarify the specificity of the results. These findings highlight the need for support in promoting social inclusion while also emphasizing the importance of exploring the intersection of neurodevelopmental conditions and social dynamics.