Journal of neuroscience methods
-
J. Neurosci. Methods · May 2013
Pig lumbar spine anatomy and imaging-guided lateral lumbar puncture: a new large animal model for intrathecal drug delivery.
Intrathecal (IT) administration is an important route of drug delivery, and its modelling in a large animal species is of critical value. Although domestic swine is the preferred species for preclinical pharmacology, no minimally invasive method has been established to deliver agents into the IT space. While a "blind" lumbar puncture (LP) can sample cerebrospinal fluid (CSF), it is unreliable for drug delivery in pigs. ⋯ Effective IT delivery was validated by the injection of contrast media to obtain a CT myelogram. LLP represents a safe and reliable method to deliver agents to the lumbar pig IT space, which can be implemented in a straightforward way by any laboratory with access to CT equipment. Therefore, LLP is an attractive large animal model for preclinical studies of IT therapies.
-
J. Neurosci. Methods · May 2013
SACICA: a sparse approximation coefficient-based ICA model for functional magnetic resonance imaging data analysis.
Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data to evaluate the functional connectivity, which assumes that the sources of functional networks are statistically independent. Recently, many researchers have demonstrated that sparsity is an effective assumption for fMRI signal separation. In this research, we present a sparse approximation coefficient-based ICA (SACICA) model to analyse fMRI data, which is a promising combination model of sparse features and an ICA technique. ⋯ The hybrid data experimental results demonstrated that the SACICA method exhibited the stronger spatial source reconstruction ability with respect to the unsmoothed fMRI data and better detection sensitivity of the functional signal on the smoothed fMRI data than the FastICA method. Furthermore, task-related experiments also revealed that SACICA was not only effective in discovering the functional networks but also exhibited a better detection sensitivity of the visual-related functional signal. In addition, the SACICA combined with Fast-FENICA proposed by Wang et al. (2012) was demonstrated to conduct the group analysis effectively on the resting-state data set.