Neuroimaging studies suggest that resilience to adversity is linked to reduced emotional reactivity or enhanced emotion regulation. However, such studies are scarce and mainly use adult samples and categorical definitions of resilience. Using a novel, data-driven approach to define resilience dimensionally, based on cumulative adversity exposure across childhood and psychopathology, we investigated associations between resilience and brain activation during facial emotion processing in youth. We...

Endogenous retroviruses (ERVs) are genetic elements derived from a process of germline infection by exogenous retroviruses. Some ERVs have been co-opted for physiological functions, and their activation has been associated with complex diseases, including Autism Spectrum Disorder (ASD). We have already demonstrated an abnormal expression of ERVs in the BTBR T + tf/J (BTBR) mouse model of ASD during intrauterine life till adulthood. Thus, starting from the assumptions that ERVs may contribute to...

Functional Magnetic Resonance Imaging (fMRI) serves as a unique non-invasive tool for investigating brain function by analyzing blood oxygenation level-dependent (BOLD) series. These signals result from the complex interplay between deterministic and stochastic components underpinning biological brain activity. In this context, the quantification of the stochastic component, here defined as brain noise, is challenging without making assumptions on the deterministic dynamics. Leveraging on...

CONCLUSIONS: These results not only validate our method's accuracy and reliability but also establish a foundation for a streamlined, non-invasive approach to dynamic PET data quantification. By offering a precise and less invasive alternative to traditional quantification methods, our technique holds significant promise for expanding the applicability of PET imaging across a wider range of tracers, thereby enhancing its utility in both clinical research and diagnostic settings.

Decoding visual representations from human brain activity has emerged as a thriving research domain, particularly in the context of brain-computer interfaces. Our study presents an innovative method that employs knowledge distillation to train an EEG classifier and reconstruct images from the ImageNet and THINGS-EEG 2 datasets using only electroencephalography (EEG) data from participants who have viewed the images themselves (i.e. "brain decoding"). We analyzed EEG recordings from 6...