Mirta Fabiana Villarreal

Spontaneous thoughts constitute most of everyday inner experience, yet long-standing methodological challenges obscure a thorough exploration of their content and neurophysiological underpinnings. Traditional approaches relying on thought probes impose strict constraints on phenomenological reports, whereas online verbal reports disrupt the natural flow of experience while interfering neural signals with motor artifacts. Here, we designed and tested an alternative approach to assess the neural basis of spontaneous thoughts combining delayed verbal retrospective free reports (RFR) with automated phenomenological ratings generated by large language models (LLMs). Twenty-two participants performed an eyes-closed free-thinking task, providing reports that were evaluated along ten phenomenological dimensions by four state-of-the-art LLMs and a panel of human raters. Machine-learning models (ML) were then trained to decode LLM-derived ratings from EEG spectral, complexity, and connectivity features. Our analyses showed that inter-rater agreement among LLMs exceeded that of human raters whereas ML models achieved above-chance accuracy for the prediction of emotional valence. These findings provide support for the use of LLMs for a scalable phenomenological annotation of spontaneous thoughts and suggest that their affective dimensions can be decoded from concurrent EEG activity.

Background: The macro-social and environmental conditions in which people live, such as the level of a country’s development or inequality, are associated with brain-related disorders. However, the relationship between these systemic environmental factors and the brain remains unclear. We aimed to determine the association between the level of development and inequality of a country and the brain structure of healthy adults. Methods: We conducted a cross-sectional study pooling brain imaging (T1-based) data from 145 magnetic resonance imaging (MRI) studies in 7,962 healthy adults (4,110 women) in 29 different countries. We used a meta-regression approach to relate the brain structure to the country’s level of development and inequality. Results: Higher human development was consistently associated with larger hippocampi and more expanded global cortical surface area, particularly in frontal areas. Increased inequality was most consistently associated with smaller hippocampal volume and thinner cortical thickness across the brain. Conclusions: Our results suggest that the macro-economic conditions of a country are reflected in its inhabitants’ brains and may explain the different incidence of brain disorders across the world. The observed variability of brain structure in health across countries should be considered when developing tools in the field of personalized or precision medicine that are intended to be used across the world.

Background: Factors associated with early life educational performance can be related to neurodevelopment, cognitive functioning and, consequently, the risk for dementia, but have received less attention than adult biomarkers.Method: A systematic review of the evidence available up to October 2020 in PubMed and Scopus was carried out. Original articles addressing both developmental learning disorders (LD) and late‐onset neurodegenerative diseases (NDD) were included.Result: A significant prevalence of LD was found in patients with atypical variants of Alzheimer's disease (AD). Furthermore, an association was found between primary progressive aphasia ‐ logopenic variant and language‐related LD, and also between posterior cortical atrophy and non‐language‐related LD. These results could be explained by the selective vulnerability hypothesis, which states that the vulnerability of a particular brain network or area could predispose both to developmental LD and late‐onset NDD.Conclusion: Developmental LDs can be considered as risk factors for late‐onset NDD, especially for atypical AD variants. Given the scarcity of results and the limitations reported, a greater number of studies are required, with larger samples and, preferably, of longitudinal design in order to address this issue.

Background: One common denominator to the clinical phenotypes of borderline personality disorder (BPD) and major depressive disorder (MDD) is emotion regulation impairment. Although these two conditions have been extensively studied separately, it remains unclear whether their emotion regulation impairments are underpinned by shared or distinct neurobiological alterations. Methods: We contrasted the neural correlates of negative emotion regulation across an adult sample of BPD patients (n = 19), MDD patients (n = 20), and healthy controls (HCs; n = 19). Emotion regulation was assessed using an established functional magnetic resonance imaging cognitive reappraisal paradigm. We assessed both task-related activations and modulations of interregional connectivity. Results: When compared to HCs, patients with BPD and MDD displayed homologous decreased activation in the right ventrolateral prefrontal cortex (vlPFC) during cognitive reappraisal. In addition, the MDD group presented decreased activations in other prefrontal areas (i.e., left dorsolateral and bilateral orbitofrontal cortices), while the BPD group was characterized by a more extended pattern of alteration in the connectivity between the vlPFC and cortices of the visual ventral stream during reappraisal. Conclusions: This study identified, for the first time, a shared neurobiological contributor to emotion regulation deficits in MDD and BPD characterized by decreased vlPFC activity, although we also observed disorder-specific alterations. In MDD, results suggest a primary deficit in the strength of prefrontal activations, while BPD is better defined by connectivity disruptions between the vlPFC and temporal emotion processing regions. These findings substantiate, in neurobiological terms, the different profiles of emotion regulation alterations observed in these disorders.