Abstract

Adult performance of high ability individuals has seldom been researched. Current results suggest that adult excellence occurs at lower rates than high ability individuals identified in their infancy or youth, with few cases of high intellectual abilities among adults that yield excellence products. This paper focuses on the analysis of the relative frequency of biographical traits that are associated with high ability as well as to the opportunities that allow building particular knowledge structures that are non-conventional and support innovation in people who excelled. A retrospective biographical analysis was performed on a sample of 120 individuals that generated renowned excellence products, in different fields, in the XXth century. Variables associated to high abilities were: precocity; learning problems; social problems in school; and academic excellence. And the variables associated with the generation of knowledge structures that support innovation were: academic-professional continuity; strong influence of particular individuals; and high productivity. Significant differences were found, showing a low presence of the first four variables and a higher presence of the last three. It follows that the trajectory towards excellence does not seem to correlate with a high level of intellectual resources but with a certain use of sufficient resources, whether cerebral or external technological support.

Abstract

Structural brain networks may be reconstructed from diffusion MRI tractography data and have great potential to further our understanding of the topological organisation of brain structure in health and disease. Network reconstruction is complex and involves a series of processesing methods including anatomical parcellation, registration, fiber orientation estimation and whole-brain fiber tractography. Methodological choices at each stage can affect the anatomical accuracy and graph theoretical properties of the reconstructed networks, meaning applying different combinations in a network reconstruction pipeline may produce substantially different networks. Furthermore, the choice of which connections are considered important is unclear. In this study, we assessed the similarity between structural networks obtained using two independent state-of-the-art reconstruction pipelines. We aimed to quantify network similarity and identify the core connections emerging most robustly in both pipelines. Similarity of network connections was compared between pipelines employing different atlases by merging parcels to a common and equivalent node scale. We found a high agreement between the networks across a range of fiber density thresholds. In addition, we identified a robust core of highly connected regions coinciding with a peak in similarity across network density thresholds, and replicated these results with atlases at different node scales. The binary network properties of these core connections were similar between pipelines but showed some differences in atlases across node scales. This study demonstrates the utility of applying multiple structural network reconstrution pipelines to diffusion data in order to identify the most important connections for further study. Document type: Article