Caudate Nucleus Is Enlarged in High-Functioning Medication-Naive Subjects with Autism

Langen, Marieke; Durston, Sarah; Staal, Wouter; Palmen, Saskia J. M. C.; Engeland, Hermán van

doi:10.1016/j.biopsych.2006.09.040

Cited by 181 publications

(155 citation statements)

References 36 publications

Supporting

Mentioning

141

Contrasting

Unclassified

Order By: Relevance

“…39 Moreover, although abnormalities in these brain regions seem to be common in ASD, evidence suggests that the diff erences in these regions are not specifi c to the disorder. For example, neuroimaging evidence suggests that the Series caudate nucleus is enlarged in ASD, 40 and that this enlargement is associated with the severity of repetitive and stereotyped behaviours. 41 The amygdala is also signifi cantly enlarged in children with ASD compared with typically developing children.…”

Section: Neural Systems Underlying Autistic Symptoms and Traitsmentioning

confidence: 99%

Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan

Ecker

Bookheimer

Murphy

2015

The Lancet Neurology

374

281

View full text Add to dashboard Cite

Over the past decade, in-vivo MRI studies have provided many invaluable insights into the neural substrates underlying autism spectrum disorder (ASD), which is now known to be associated with neurodevelopmental variations in brain anatomy, functioning, and connectivity. These systems-level features of ASD pathology seem to develop diff erentially across the human lifespan so that the cortical abnormalities that occur in children with ASD diff er from those noted at other stages of life. Thus, investigation of the brain in ASD poses particular methodological challenges, which must be addressed to enable the comparison of results across studies. Novel analytical approaches are also being developed to facilitate the translation of fi ndings from the research to the clinical setting. In the future, the insights provided by human neuroimaging studies could contribute to biomarker development for ASD and other neurodevelopmental disorders, and to new approaches to diagnosis and treatment.

show abstract

Section: Neural Systems Underlying Autistic Symptoms and Traitsmentioning

confidence: 99%

Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan

Ecker

Bookheimer

Murphy

2015

The Lancet Neurology

374

281

View full text Add to dashboard Cite

show abstract

“…We also consulted other detailed protocols, modifying as required. 12,20,21 Volumes obtained were assessed for covariance or normalized in Gray-scale axial reference images for tracing the putamen. A, The first section shows the most inferior point of the putamen, bounded by the anterior limb of the internal capsule anteriorly, the external capsule laterally, the internal capsule medially, and the posterior limb of the internal capsule posteriorly.…”

Section: Imagingmentioning

confidence: 99%

Putaminal Volume in Frontotemporal Lobar Degeneration and Alzheimer Disease: Differential Volumes in Dementia Subtypes and Controls

Looi¹,

Svensson²,

Lindberg³

et al. 2009

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

show abstract

“…The study presented in this section focuses specifically on the shape of the caudate. The overall size of the caudate has previously been observed to correlate with autism diagnoses [20,72]. It is therefore reasonable to hypothesize that significant shape di↵erences might also be observed.…”

Section: Introductionmentioning

confidence: 95%

Shape Modeling and Analysis with Entropy-Based Particle Systems

Cates

Fletcher

Styner

et al. 2007

Lecture Notes in Computer Science

137

224

View full text Add to dashboard Cite

Many important fields of basic research in medicine and biology routinely employ tools for the statistical analysis of collections of similar shapes. Biologists, for example, have long relied on homologous, anatomical landmarks as shape models to characterize the growth and development of species. Increasingly, however, researchers are exploring the use of more detailed models that are derived computationally from three-dimensional images and surface descriptions. While computationally-derived models of shape are promising new tools for biomedical research, they also present some significant engineering challenges, which existing modeling methods have only begun to address.In this dissertation, I propose a new computational framework for statistical shape modeling that significantly advances the state-of-the-art by overcoming many of the limitations of existing methods. The framework uses a particle-system representation of shape, with a fast correspondence-point optimization based on information content. The optimization balances the simplicity of the model (compactness) with the accuracy of the shape representations by using two commensurate entropy metrics and no free parameters. The idea is to maximize both the geometric accuracy and the statistical simplicity of the shape model, in accordance with the principle of parsimony in model selection. The nonparametric representation allows the method to be applied to a larger class of problems than existing methods, including nonspherical surfaces, open surfaces, and sets of multiple surfaces.The relative simplicity of the surface representation and the low number of free parameters results in a framework that is easy to use and can operate directly on image segmentations. In collaboration with scientists from several important areas of biomedicine, I have demonstrated that the proposed method is indeed an e↵ective tool for scientific research.The specific research contributions of this dissertation are as follows. First, I describe a mathematical framework and a robust numerical algorithm for computing optimized correspondence-point shape models using an entropy-based optimization and particle-system technology. Second, I develop a series of extensions of the framework to more general classes of shape analysis problems, including the analysis of multiple-object complexes, the generalization to correspondence based on generic functions of position, an extension to handle surfaces with open boundaries, and shape modeling with simple regression. Third, I describe the application of statistical hypothesis testing, regression analysis, and multiple-analysis of covariance to the proposed shape models. I also introduce new techniques for visualization and interpretation of these statistics. Finally, in cooperation with biomedical researchers, I present validation of the above research contributions by their successful application to real-world research problems.

show abstract

Caudate Nucleus Is Enlarged in High-Functioning Medication-Naive Subjects with Autism

Cited by 181 publications

References 36 publications

Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan

Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan

Putaminal Volume in Frontotemporal Lobar Degeneration and Alzheimer Disease: Differential Volumes in Dementia Subtypes and Controls

Shape Modeling and Analysis with Entropy-Based Particle Systems

Contact Info

Product

Resources

About