Global and Regional Structural Differences and Prediction of Autistic Traits during Adolescence

Background: Autistic traits are commonly viewed as dimensional in nature, and as continuously distributed in the general population. In this respect, the identification of predictive values of markers such as subtle autism-related alterations in brain morphology for parameter values of autistic traits could increase our understanding of this dimensional occasion. However, currently, very little is known about how these traits correspond to alterations in brain morphology in typically developing individuals, particularly during a time period where changes due to brain development processes do not provide a bias. Therefore, in the present study, we analyzed brain volume, cortical thickness (CT) and surface area (SA) in a cohort of 14–15-year-old adolescents (N = 285, female: N = 162) and tested their predictive value for autistic traits, assessed with the social responsiveness scale (SRS) two years later at the age of 16–17 years, using a regression-based approach. We found that autistic traits were significantly predicted by volumetric changes in the amygdala (r = 0.181), cerebellum (r = 0.128) and hippocampus (r = −0.181, r = −0.203), both in boys and girls. Moreover, the CT of the superior frontal region was negatively correlated (r = −0.144) with SRS scores. Furthermore, we observed a significant association between the SRS total score and smaller left putamen volume, specifically in boys (r = −0.217), but not in girls. Our findings suggest that neural correlates of autistic traits also seem to lie on a continuum in the general population, are determined by limbic–striatal neuroanatomical brain areas, and are partly dependent on sex. As we imaged adolescents from a large population-based cohort within a small age range, these data may help to increase the understanding of autistic-like occasions in otherwise typically developing individuals

Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids

In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of initial momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1,000 surfaces, and we are able to analyse this dataset in 26 h using a diffeomorphic centroid. Our experiments demonstrate that computing diffeomorphic centroids in place of standard variational templates leads to similar shape analysis results and saves around 70% of computation time. Furthermore, the approach is able to adequately capture the variability of hippocampal shapes with a reasonable number of dimensions, and to predict anatomical features of the hippocampus, only present in 17% of the population, in healthy subjects

Global and Regional Structural Differences and Prediction of Autistic Traits during Adolescence

Background: Autistic traits are commonly viewed as dimensional in nature, and as continuously distributed in the general population. In this respect, the identification of predictive values of markers such as subtle autism-related alterations in brain morphology for parameter values of autistic traits could increase our understanding of this dimensional occasion. However, currently, very little is known about how these traits correspond to alterations in brain morphology in typically developing individuals, particularly during a time period where changes due to brain development processes do not provide a bias. Therefore, in the present study, we analyzed brain volume, cortical thickness (CT) and surface area (SA) in a cohort of 14–15-year-old adolescents (N = 285, female: N = 162) and tested their predictive value for autistic traits, assessed with the social responsiveness scale (SRS) two years later at the age of 16–17 years, using a regression-based approach. We found that autistic traits were significantly predicted by volumetric changes in the amygdala (r = 0.181), cerebellum (r = 0.128) and hippocampus (r = −0.181, r = −0.203), both in boys and girls. Moreover, the CT of the superior frontal region was negatively correlated (r = −0.144) with SRS scores. Furthermore, we observed a significant association between the SRS total score and smaller left putamen volume, specifically in boys (r = −0.217), but not in girls. Our findings suggest that neural correlates of autistic traits also seem to lie on a continuum in the general population, are determined by limbic–striatal neuroanatomical brain areas, and are partly dependent on sex. As we imaged adolescents from a large population-based cohort within a small age range, these data may help to increase the understanding of autistic-like occasions in otherwise typically developing individuals

The Human Brain Is Best Described as Being on a Female/Male Continuum: Evidence from a Neuroimaging Connectivity Study

Psychological androgyny has long been associated with greater cognitive flexibility, adaptive behavior, and better mental health, but whether a similar concept can be defined using neural features remains unknown. Using the neuroimaging data from 9620 participants, we found that global functional connectivity was stronger in the male brain before middle age but became weaker after that, when compared with the female brain, after systematic testing of potentially confounding effects. We defined a brain gender continuum by estimating the likelihood of an observed functional connectivity matrix to represent a male brain. We found that participants mapped at the center of this continuum had fewer internalizing symptoms compared with those at the 2 extreme ends. These findings suggest a novel hypothesis proposing that there exists a neuroimaging concept of androgyny using the brain gender continuum, which may be associated with better mental health in a similar way to psychological androgyny

Fully-automated quality assurance in multi-center studies using MRI phantom measurements [Dataset]

43 measurements acquired in eight different sites within the IMAGEN-project, comprising the following 3 T scanner types: Siemens Verio and TimTrio; General Electric Signa Excite, and Signa HDx; Philips Achieva. Additionally one phantom data set was aquired on a 3 T Siemens Skyra resulting in 44 measurements

Study of genetic variability in Dicrocoelium dendriticum using the random amplified polimorphic DNA

1 page.-- Contributed to: VII European Multicolloquium of Parasitology (Parma, Italia, 2-6 Sep 1996).Dicrocoelium dendriticum (Rudolphi, 1819) Looss, 1899 is a liver fluke (Trematoda, Digenea) species responsible for dicrocoeliosis in its definitive host (usually sheep and cattle), in which it causes economic losses...This study was supported by CICYT, project number AGF92-0588, and Junta de Castilla y León, project number LE 16/94.Peer reviewe

Interaction between striatal volume and DAT1 polymorphism predicts working memory development during adolescence

There is considerable inter-individual variability in the rate at which working memory (WM) develops during childhood and adolescence, but the neural and genetic basis for these differences are poorly understood. Dopamine-related genes, striatal activation and morphology have been associated with increased WM capacity after training. Here we tested the hypothesis that these factors would also explain some of the inter-individual differences in the rate of WM development. We measured WM performance in 487 healthy subjects twice: at age 14 and 19. At age 14 subjects underwent a structural MRI scan, and genotyping of five single nucleotide polymorphisms (SNPs) in or close to the dopamine genes DRD2, DAT-1 and COMT, which have previously been associated with gains in WM after WM training. We then analyzed which biological factors predicted the rate of increase in WM between ages 14 and 19. We found a significant interaction between putamen size and DAT1/SLC6A3 rs40184 polymorphism, such that TC heterozygotes with a larger putamen at age 14 showed greater WM improvement at age 19. The effect of the DAT1 polymorphism on WM development was exerted in interaction with striatal morphology. These results suggest that development of WM partially share neuro-physiological mechanism with training-induced plasticity