Towards Portable Large-Scale Image Processing with High-Performance Computing.

High-throughput, large-scale medical image computing demands tight integration of high-performance computing (HPC) infrastructure for data storage, job distribution, and image processing. The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has constructed a large-scale image storage and processing infrastructure that is composed of (1) a large-scale image database using the eXtensible Neuroimaging Archive Toolkit (XNAT), (2) a content-aware job scheduling platform using the Distributed Automation for XNAT pipeline automation tool (DAX), and (3) a wide variety of encapsulated image processing pipelines called "spiders." The VUIIS CCI medical image data storage and processing infrastructure have housed and processed nearly half-million medical image volumes with Vanderbilt Advanced Computing Center for Research and Education (ACCRE), which is the HPC facility at the Vanderbilt University.

Prefrontal-Thalamic Anatomical Connectivity and Executive Cognitive Function in Schizophrenia.

Background: Executive cognitive functions, including working memory, cognitive flexibility, and inhibition, are impaired in schizophrenia. Executive functions rely on coordinated information processing between the prefrontal cortex (PFC) and thalamus, particularly the mediodorsal nucleus. This raises the possibility that anatomical connectivity between the PFC and mediodorsal thalamus may be 1) reduced in schizophrenia and 2) related to deficits in executive function.

DAX - The Next Generation: Towards One Million Processes on Commodity Hardware.

Large scale image processing demands a standardized way of not only storage but also a method for job distribution and scheduling. The eXtensible Neuroimaging Archive Toolkit (XNAT) is one of several platforms that seeks to solve the storage issues. Distributed Automation for XNAT (DAX) is a job control and distribution manager.

Performance Management of High Performance Computing for Medical Image Processing in Amazon Web Services.

Adopting high performance cloud computing for medical image processing is a popular trend given the pressing needs of large studies. Amazon Web Services (AWS) provide reliable, on-demand, and inexpensive cloud computing services. Our research objective is to implement an affordable, scalable and easy-to-use AWS framework for the Java Image Science Toolkit (JIST).

Increased myocardial native T1 and extracellular volume in patients with Duchenne muscular dystrophy.

Background: Duchenne muscular dystrophy (DMD) cardiomyopathy is a progressive disease for which there is no cure. Disease-specific therapies are needed that can be initiated before irreversible myocardial damage ensues. In order to evaluate therapeutic efficacy, surrogate endpoints other than ejection fraction must be found.

Sex differences in psychophysical and neurophysiological responses to pain in older adults: a cross-sectional study.

Background: Neuroimaging studies in younger adults have demonstrated sex differences in brain processing of painful experimental stimuli. Such differences may contribute to findings that women suffer disproportionately from pain. It is not known whether sex-related differences in pain processing extend to older adults.

Vanderbilt University Institute of Imaging Science Center for Computational Imaging XNAT: A multimodal data archive and processing environment.

The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has developed a database built on XNAT housing over a quarter of a million scans. The database provides framework for (1) rapid prototyping, (2) large scale batch processing of images and (3) scalable project management. The system uses the web-based interfaces of XNAT and REDCap to allow for graphical interaction.

Subjective memory complaint only relates to verbal episodic memory performance in mild cognitive impairment.

Background: A cognitive concern from the patient, informant, or clinician is required for the diagnosis of mild cognitive impairment (MCI); however, the cognitive and neuroanatomical correlates of complaint are poorly understood.

Objective: We assessed how self-complaint relates to cognitive and neuroimaging measures in older adults with MCI.

Method: MCI participants were drawn from the Alzheimer's Disease Neuroimaging Initiative and dichotomized into two groups based on the presence of self-reported memory complaint (no complaint n = 191, 77 ± 7 years; complaint n = 206, 73 ± 8 years).

Gray & white matter tissue contrast differentiates Mild Cognitive Impairment converters from non-converters.

The clinical relevance of gray/white matter contrast ratio (GWR) in mild cognitive impairment (MCI) remains unknown. This study examined baseline GWR and 3-year follow-up diagnostic status in MCI. Alzheimer's Disease Neuroimaging Initiative MCI participants with baseline 1.

Antipsychotic drug-like effects of the selective M4 muscarinic acetylcholine receptor positive allosteric modulator VU0152100.

Accumulating evidence suggests that selective M4 muscarinic acetylcholine receptor (mAChR) activators may offer a novel strategy for the treatment of psychosis. However, previous efforts to develop selective M4 activators were unsuccessful because of the lack of M4 mAChR subtype specificity and off-target muscarinic adverse effects. We recently developed VU0152100, a highly selective M4 positive allosteric modulator (PAM) that exerts central effects after systemic administration.