Insight processes that peak in “unpredictable moments of exceptional thinking” are often referred to as Aha! or Eureka moments. During insight, connections between previously unrelated concepts are made and new patterns arise at the perceptual level while new solutions to apparently insolvable problems suddenly emerge to consciousness. Given its unpredictable nature, the definition, and behavioral and neurophysiological measurement of insight problem solving represent a major challenge in contemporary cognitive neuroscience. Numerous attempts have been made, yet results show limited consistency across experimental approaches. Here we provide a comprehensive overview of available neuroscience of insight, including: i) a discussion about the theoretical definition of insight and an overview of the most widely accepted theoretical models, including those debating its relationship with creativity and intelligence; ii) an overview of available tasks used to investigate insight; iii) an ad-hoc quantitative meta-analysis of functional magnetic resonance imaging studies investigating the Eureka moment, using activation likelihood estimation maps; iv) a review of electroencephalographic evidence in the time and frequency domains, as well as v) an overview of the application of non-invasive brain stimulation techniques to causally assess the neurobiological basis of insight as well as enhance insight-related cognition

Coping abilities represent the individual set of mental and behavioral strategies adopted when facing stress or traumatic experiences. Coping styles related to avoidance have been linked to a disposition to develop psychiatric disorders such as PTSD, anxiety, and major depression, whereas problem-oriented coping skills have been positively correlated with well-being and high quality of life. Even though coping styles constitute an important determinant of resilience and can impact many aspects of everyday living, no study has investigated their brain functional connectivity underpinnings in humans. Here we analyzed both psychometric scores of coping and resting-state fMRI data from 102 healthy adult participants. Controlling for personality and problem-solving abilities, we identified significant links between the propensity to adopt different coping styles and the functional connectivity profiles of regions belonging to the default mode (DMN) and anterior salience (AS) networks-namely, the anterior cingulate cortex, left frontopolar cortex, and left angular gyrus. Also, a reduced negative correlation between AS and DMN nodes explained variability in one specific coping style, related to avoiding problems while focusing on the emotional component of the stressor at hand, instead of relying on cognitive resources. These results might be integrated with current neurophysiological models of resilience and individual responses to stress, in order to understand the propensity to develop clinical conditions (e.g., PTSD) and predict the outcomes of psychotherapeutic interventions.

Recent studies have revealed the possible role of choroid plexus (ChP) in Alzheimer's disease (AD). T1-weighted MRI is the modality of choice for the segmentation of ChP in humans. Manual segmentation is considered the gold-standard technique, but given its time-consuming nature, large-scale neuroimaging studies of ChP would be impossible. In this study, we introduce a lightweight segmentation algorithm based on the Gaussian Mixture Model (GMM). We compared its performance against manual segmentation as well as automated segmentation by Freesurfer in three separate datasets: 1) patients with structural MRIs enhanced with contrast (n = 19), 2) young healthy subjects (n = 20), and 3) patients with AD (n = 20). GMM outperformed Freesurfer and showed high similarity with manual segmentation. To further assess the algorithm's performance in large scale studies, we performed GMM segmentations in young healthy subjects from the Human Connectome Project (n = 1,067), as well as healthy controls, mild cognitive impairment (MCI), and AD patients from the Alzheimer's Disease Neuroimaging Initiative (n = 509). In both datasets, GMM segmented ChP more accurately than Freesurfer. To show the clinical importance