Project description:BackgroundExploring neural network dynamics during social interaction could help to identify biomarkers of Autism Spectrum Disorders (ASD). A cerebellar involvement in autism has long been suspected and recent methodological advances now enable studying cerebellar functioning in a naturalistic setting. Here, we investigated the electrophysiological activity of the cerebro-cerebellar network during real-time social interaction in ASD. We focused our analysis on theta oscillations (3-8 Hz), which have been associated with large-scale coordination of distant brain areas and might contribute to interoception, motor control, and social event anticipation, all skills known to be altered in ASD.MethodsWe combined the Human Dynamic Clamp, a paradigm for studying realistic social interactions using a virtual avatar, with high-density electroencephalography (HD-EEG). Using source reconstruction, we investigated power in the cortex and the cerebellum, along with coherence between the cerebellum and three cerebral-cortical areas, and compared our findings in a sample of participants with ASD (n = 107) and with typical development (TD) (n = 33). We developed an open-source pipeline to analyse neural dynamics at the source level from HD-EEG data.ResultsIndividuals with ASD showed a significant increase in theta band power over the cerebellum and the frontal and temporal cortices during social interaction compared to resting state, along with significant coherence increases between the cerebellum and the sensorimotor, frontal and parietal cortices. However, a phase-based connectivity measure did not support a strict activity increase in the cortico-cerebellar functional network. We did not find any significant differences between the ASD and the TD group.ConclusionsThis exploratory study uncovered increases in the theta band activity of participants with ASD during social interaction, pointing at the presence of neural interactions between the cerebellum and cerebral networks associated with social cognition. It also emphasizes the need for complementary functional connectivity measures to capture network-level alterations. Future work will focus on optimizing artifact correction to include more participants with TD and increase the statistical power of group-level contrasts.

Project description:BCN02 was an exploratory, single arm study designed to evaluate a kick-and-kill therapeutic vaccine strategy in early-treated HIV infected individuals. The participants in the study received a MVA.HIVconsv vaccination before and after three cycles of infusion of romidepsin (RMD), an inhibitor of histone deacetylases that acts as a viral latency reversing agent. The inclusion of a monitored antiretroviral pause (MAP) identified 8 individuals with an early viral rebound (pVL > 2,000 HIV RNA copies/mL, < 4 weeks of MAP initiation) and 4, with a late viral rebound (pVL > 2,000 HIV RNA copies/mL, > 4 weeks of MAP initiation). In the present study, a systems biology analysis was conducted to identify the pathways modulated at transcriptomic and epigenetic level during the clinical intervention, involving vaccination and RMD, and to identify biomarkers predictive of HIV viral rebound during MAP. To this end, we studied gene expression and whole-genome DNA methylation in longitudinal PBMC samples prior and after the intervention. The main impact on host transcriptional and epigenetic signatures was observed after vaccination and RMD infusion (Vacc+RMD) with multiple pathways related to HIV infection and immune response showing changes at gene expression and DNA methylation level. Analysis of an independent cohort of individuals treated with RMD alone, showed that these changes were mainly driven by CD4 T cells. DNA methylation patterns in full PBMC obtained after full intervention (Vacc+RMD) allowed to discriminate individuals with an Early versus Late viral rebound. These individuals also showed a differential enrichment on chromatin accessibility and transcription factor binding sites. Intriguingly, 30 of the DMPs between Early and Late rebounders at Vacc+RMD were present already at baseline and were more accentuated after RMD infusion. Overall, this study provides with a deeper understanding of the mechanisms modulated during the BCN02 study and highlights the importance of DNA methylation profiles in HIV cure.

Project description:IntroductionApproximately one in six people will experience an episode of major depressive disorder (MDD) in their lifetime. Effective treatment is hindered by subjective clinical decision-making and a lack of objective prognostic biomarkers. Functional MRI (fMRI) could provide such an objective measure but the majority of MDD studies has focused on static approaches, disregarding the rapidly changing nature of the brain. In this study, we aim to predict depression severity changes at 3 and 6 months using dynamic fMRI features.MethodsFor our research, we acquired a longitudinal dataset of 32 MDD patients with fMRI scans acquired at baseline and clinical follow-ups 3 and 6 months later. Several measures were derived from an emotion face-matching fMRI dataset: activity in brain regions, static and dynamic functional connectivity between functional brain networks (FBNs) and two measures from a wavelet coherence analysis approach. All fMRI features were evaluated independently, with and without demographic and clinical parameters. Patients were divided into two classes based on changes in depression severity at both follow-ups.ResultsThe number of coherence clusters (nCC) between FBNs, reflecting the total number of interactions (either synchronous, anti-synchronous or causal), resulted in the highest predictive performance. The nCC-based classifier achieved 87.5% and 77.4% accuracy for the 3- and 6-months change in severity, respectively. Furthermore, regression analyses supported the potential of nCC for predicting depression severity on a continuous scale. The posterior default mode network (DMN), dorsal attention network (DAN) and two visual networks were the most important networks in the optimal nCC models. Reduced nCC was associated with a poorer depression course, suggesting deficits in sustained attention to and coping with emotion-related faces. An ensemble of classifiers with demographic, clinical and lead coherence features, a measure of dynamic causality, resulted in a 3-months clinical outcome prediction accuracy of 81.2%.DiscussionThe dynamic wavelet features demonstrated high accuracy in predicting individual depression severity change. Features describing brain dynamics could enhance understanding of depression and support clinical decision-making. Further studies are required to evaluate their robustness and replicability in larger cohorts.

Dataset Information

Cerebellar Neurodynamics Predict Decision Timing and Outcome on the Single-Trial Level.

Publications

Cerebellar Neurodynamics Predict Decision Timing and Outcome on the Single-Trial Level.

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