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Certain personality traits and cognitive domains of executive functions (EF) are differentially related to attention deficit hyperactivity disorder (ADHD) symptoms in adolescents. This study aimed to analyze the five-factor model (FFM) personality characteristics in adolescents with ADHD, and to examine whether EF mediate the relationships between FFM personality traits and ADHD symptoms. A comprehensive diagnostic assessment, including ADHD clinical interviews, ADHD rating scales, neuropsychological EF testing (i.e., working memory, flexibility and inhibition) and a personality assessment was carried out in a sample of 118 adolescents (75 ADHD and 43 control participants, 68% males), aged 12 to 16 years, and their parents and teachers. Adolescents with ADHD had lower scores than control participants on Conscientiousness and Agreeableness, and higher scores on Neuroticism. Structural equation models (SEM) showed that Conscientiousness directly influenced inattentive and hyperactive-impulsive symptoms, while Neuroticism, Agreeableness, and Extraversion directly affected hyperactive-impulsive symptoms. Only Conscientiousness exerted indirect effects on inattention, but not on hyperactivity-impulsivity symptoms, via EF; higher scores on Conscientiousness were related to higher scores on EF, which in turn were related to lower scores on inattentive symptoms. These findings corroborate the relationships between ADHD symptoms, FFM personality traits and EF and indicate the mediating effect of EF on the relationship between Conscientiousness and inattention., Competing Interests: The authors have declared that no competing interests exist.

The assessment of task-independent functional connectivity (FC) after a lesion causing hemianopia remains an uncovered topic and represents a crucial point to better understand the neural basis of blindsight (i.e. unconscious visually triggered behavior) and visual awareness. In this light, we evaluated functional connectivity (FC) in 10 hemianopic patients and 10 healthy controls in a resting state paradigm. The main aim of this study is twofold: first of all we focused on the description and assessment of density and intensity of functional connectivity and network topology with and without a lesion affecting the visual pathway, and then we extracted and statistically compared network metrics, focusing on functional segregation, integration and specialization. Moreover, a study of 3-cycle triangles with prominent connectivity was conducted to analyze functional segregation calculated as the area of each triangle created connecting three neighboring nodes. To achieve these purposes we applied a graph theory-based approach, starting from Pearson correlation coefficients extracted from pairs of regions of interest. In these analyses we focused on the FC extracted by the whole brain as well as by four resting state networks: The Visual (VN), Salience (SN), Attention (AN) and Default Mode Network (DMN), to assess brain functional reorganization following the injury. The results showed a general decrease in density and intensity of functional connections, that leads to a less compact structure characterized by decrease in functional integration, segregation and in the number of interconnected hubs in both the Visual Network and the whole brain, despite an increase in long-range inter-modules connections (occipito-frontal connections). Indeed, the VN was the most affected network, characterized by a decrease in intra- and inter-network connections and by a less compact topology, with less interconnected nodes. Surprisingly, we observed a higher functional integration in the DMN and in the AN regardless of the lesion extent, that may indicate a functional reorganization of the brain following the injury, trying to compensate for the general reduced connectivity. Finally we observed an increase in functional specialization (lower between-network connectivity) and in inter-networks functional segregation, which is reflected in a less compact network topology, highly organized in functional clusters. These descriptive findings provide new insight on the spontaneous brain activity in hemianopic patients by showing an alteration in the intrinsic architecture of a large-scale brain system that goes beyond the impairment of a single RSN., Competing Interests: The authors have declared that no competing interests exist.

Background: Type 1 diabetes mellitus (T1D) affects the entire cellular network of the organism. Some patients develop cognitive disturbances due to the disease, but several authors have suggested that the brain develops compensatory mechanisms to minimize or prevent neuropsychological decline. The present study aimed to assess the effective connectivity underlying visuospatial working memory performance in young adults diagnosed with T1D using neuroimaging techniques (fMRI)., Methods: Fifteen T1D right-handed, young adults with sustained metabolic clinical stability and a control group matched by age, sex, and educational level voluntarily participated. All participants performed 2 visuospatial working memory tasks using a block design within an MRI scanner. Regions of interest and their signal values were obtained. Effective connectivity-by means of structural equations models-was evaluated for each group and task through maximum likelihood estimation, and the model with the best fit was chosen in each case., Results: Compared to the control group, the patient group showed a significant reduction in brain activity in the two estimated networks (one for each group and task). The models of effective connectivity showed greater brain connectivity in healthy individuals, as well as a more complex network. T1D patients showed a pattern of connectivity mainly involving the cerebellum and the red nucleus. In contrast, the control group showed a connectivity network predominantly involving brain areas that are typically activated while individuals are performing working memory tasks., Conclusion: Our results suggest a specific effective connectivity between the cerebellum and the red nucleus in T1D patients during working memory tasks, probably reflecting a compensatory mechanism to fulfill task demands., Competing Interests: The authors have declared that no competing interests exist.

Type 1 diabetes (T1D) is commonly diagnosed in childhood and adolescence, and the developing brain has to cope with its deleterious effects. Although brain adaptation to the disease may not result in evident cognitive dysfunction, the effects of T1D on neurodevelopment could alter the pattern of BOLD fMRI activation. The aim of this study was to explore the neural BOLD activation pattern in patients with T1D versus that of healthy matched controls while performing two visuospatial working memory tasks, which included a pair of assignments administered through a block design. In the first task (condition A), the subjects were shown a trial sequence of 3 or 4 white squares positioned pseudorandomly around a fixation point on a black background. After a fixed delay, a second corresponding sequence of 3 or 4 red squares was shown that either resembled (direct, 50%) or differed from (50%) the previous stimulation order. The subjects were required to press one button if the two spatial sequences were identical or a second button if they were not. In condition B, the participants had to determine whether the second sequence of red squares appeared in inverse order (inverse, 50%) or not (50%) and respond by pressing a button. If the latter sequence followed an order distinct from the inverse sequence, the subjects were instructed to press a different button. Sixteen patients with normal IQ and without diabetes complications and 16 healthy control subjects participated in the study. In the behavioral analysis, there were no significant differences between the groups in the pure visuo-spatial task, but the patients with diabetes exhibited poorer performance in the task with verbal stimuli (p < .001). However, fMRI analyses revealed that the patients with T1D showed significantly increased activation in the prefrontal inferior cortex, subcortical regions and the cerebellum (in general p < .001). These different activation patterns could be due to adaptive compensation mechanisms that are devoted to improving efficiency while solving more complex cognitive tasks.