3,333 research outputs found
Brain morphometry in autism spectrum disorders: a unified approach for structure-specific statistical analysis of neuroimaging data - biomed 2011.
No full textAutism spectrum disorders (ASD) are a neurodevelopmental condition with multiple causes, comorbid conditions, and a wide range in the type and severity of symptoms expressed by different individuals. This makes the neuroanatomy of autism inherently difficult to describe. It has been assumed in the scientific literature that deviations in regional brain size in clinical samples are directly related to maldevelopment or pathogenesis. The performed clinical studies analyzed specific brain structures that are assumed to be correlated to autistic brain behaviors. Examples of performed analyses, based upon manual or semi-automated segmentation from magnetic resonance imaging (MRI) scans, include volumetric measures of specific brain structures, or small groups of structures, as caudate, corpus callosum, putamen, hippocampus, nucleus accumbens, evaluating differences between groups of subjects with autism and control subjects. Nonetheless, the brain regions analyzed that differ between patients and control subjects have not been always consistent over the performed studies. This inconsistency might be due to the fact that the specific single volume differences that have been reported in the literature for the different brain structures under investigation may, instead, be not independent during pathogenesis. Hence, this issue comes into play in logically framing a comprehensive assessment of putative abnormalities in regional brain volumes. To this aim, a whole brain investigation system for a semi-automated morphometric statistical analysis of brain anatomy is presented in this paper and validated on a selected group of patients diagnosed with ASD that completed a 1.5 T magnetic resonance image (MRI) of the brain. The proposed system, which is mainly built basing upon the FreeSurfer and the 3D Slicer software frameworks for the volumetric analysis of brain imaging data, lies its foundations on the higher statistical power of the region of interest (ROI) approach, but equally aims at a higher exploratory power as it doesn t restrict its focus to a small number of specific regions, thanks to a whole brain unified approach
Evaluating the performance of a multiple signal classification algorithm for detection of BOLD activation from fMRI data
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Metamodel deprecation to manage technical debt in model co-evolution
No full textModel-Driven Engineering helps formalize problem-domains by using metamodels. Modeling ecosystems consisting of purposely designed editors, transformations, and code generators are defined on top of the metamodels. Analogously to other software forms, metamodels can evolve - -consequently, the validity of existing artifacts might be compromised. Coupled evolution provides techniques for restoring artifacts' validity in response to metamodel evolution. In this paper, we propose using deprecation in metamodeling to mitigate the difficulties in performing a class of adaptations that must be operated manually. Technical debt in co-evolution can be regarded as the outcome of procrastinating the migration of artifacts and, thus, must be reduced if not eliminated. Tool support for the adoption of deprecation and technical debt is used to demonstrate the feasibility of the methods
Cortical control of saccadic eye movements as assessed by functional magnetic resonance imaging
No full textIn humans, anatomy and physiology of the saccadic eye movement (SEM) system have been studied by invasive techniques that suffer from limited spatial resolution. Functional magnetic resonance imaging (fMRI) provides maps of human brain activations with high spatial resolution. This technique is based on the increase of magnetic resonance signal in cerebral areas activated during a task or a stimulation. Six healthy volunteers underwent fMRI examination while performing visually guided and memory-guided saccades and antisaccades. To assess the activation areas we used a dedicated software for image statistical analysis including z-score, t-test, correlation and cluster activation analysis. Activation areas were found in cortical areas involved in SEM planning and execution, such as the frontal eye fields, the supplementary eye fields, the prefrontal cortex, the parietal eye fields, the striate and the extra-striate cortex. The activation areas showed considerable spatial interindividual variability and no or slight pattern differences between saccade tasks. The high spatial resolution of fMRI allowed the location of the frontal eye fields in the banks and fundus of the precentral sulcus. as well as the location of the parietal eye fields in the intraparietal sulcus. We anticipate that fMRI will provide new insights into the understanding of SEM control
Decay-sampling design for echo-planar functional Magnetic Resonance Imaging (fMRI) of the auditory cortex
No full textThe main limiting factor to the application of the functional Magnetic Resonance Imaging (fMRI) to the study of the auditory cortex is the presence of the loud background acoustic noise in the MR scanner during functional measurements. In the present work, we propose an averaged single-trial experimental design for EPI-fMRI (decay-sampling design) which does not require the presentation of stimuli during echo-planar acquisitions and allows for mapping of auditory cortex without the interference of scanner noise. We apply the decay-sampling technique to the study of the cortical responses to amplitude modulated tones in healthy volunteers. Results point out the presence, within the auditory cortex, of neuronal clusters that correspond to different models of responses to the stimulus and to the EPI noise. Furthermore, some of these clusters show a clear tonotopic organization
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