Measuring the optical characteristics of medulloblastoma with optical coherence tomography.

Medulloblastoma is the most common malignant pediatric brain tumor. Standard treatment consists of surgical resection, followed by radiation and high-dose chemotherapy. Despite these efforts, recurrence is common, leading to reduced patient survival. Even with successful treatment, there are often severe long-term neurologic impacts on the developing nervous system. We present two quantitative techniques that use a high-resolution optical imaging modality: optical coherence tomography (OCT) to measure refractive index, and the optical attenuation coefficient. To the best of our knowledge, this study is the first to demonstrate OCT analysis of medulloblastoma. Refractive index and optical attenuation coefficient were able to differentiate between normal brain tissue and medulloblastoma in mouse models. More specifically, optical attenuation coefficient imaging of normal cerebellum displayed layers of grey matter and white matter, which were indistinguishable in the structural OCT image. The morphology of the tumor was distinct in the optical attenuation coefficient imaging. These inherent properties may be useful during neurosurgical intervention to better delineate tumor boundaries and minimize resection of normal tissue.

Visualization of trunk muscle synergies during sitting perturbations using self-organizing maps (SOM).

The purpose of this study was to demonstrate the use of the self-organizing map (SOM) method for visualization, modeling, and comparison of trunk neuromuscular synergies during perturbed sitting. Thirteen participants were perturbed at the level of the sternum, in eight directions during sitting. Electromyographic (EMG) responses of ten trunk muscles involved in postural control were recorded. The SOM was used to encode the EMG responses on a 2-D projection (i.e., visualization). The result contains similar patterns mapped close together on the plot therefore forming clusters of data. Such visualization of ten EMG responses, following eight directional perturbations, allows for comparisons of direction-dependent postural synergies. Direction-dependent neuromuscular response models for each muscle were then constructed from the SOM visualization. The results demonstrated that the SOM was able to encode neuromuscular responses, and the SOM visualization showed direction-dependent differences in the postural synergies. Moreover, each muscle was modeled using the SOM-based method, and derived models showed that all muscles, except for one, produced a Gaussian fit for direction-dependent responses. Overall, SOM analysis offers a reverse engineering method for exploration and comparison of complex neuromuscular systems, which can describe postural synergies at a glance.

Hybrid-controlled neurofuzzy networks analysis resulting in genetic regulatory networks reconstruction.

Reverse engineering of gene regulatory networks (GRNs) is the process of estimating genetic interactions of a cellular system from gene expression data. In this paper, we propose a novel hybrid systematic algorithm based on neurofuzzy network for reconstructing GRNs from observational gene expression data when only a medium-small number of measurements are available. The approach uses fuzzy logic to transform gene expression values into qualitative descriptors that can be evaluated by using a set of defined rules. The algorithm uses neurofuzzy network to model genes effects on other genes followed by four stages of decision making to extract gene interactions. One of the main features of the proposed algorithm is that an optimal number of fuzzy rules can be easily and rapidly extracted without overparameterizing. Data analysis and simulation are conducted on microarray expression profiles of S. cerevisiae cell cycle and demonstrate that the proposed algorithm not only selects the patterns of the time series gene expression data accurately, but also provides models with better reconstruction accuracy when compared with four published algorithms: DBNs, VBEM, time delay ARACNE, and PF subjected to LASSO. The accuracy of the proposed approach is evaluated in terms of recall and F-score for the network reconstruction task.

Refining competition in the self-organising tree map for unsupervised biofilm image segmentation.

The Self Organising Tree Map (SOTM) neural network is investigated as a means of segmenting micro-organisms from confocal microscope image data. Features describing pixel and regional intensities, phase congruency and spatial proximity are explored in terms of their impact on the segmentation of bacteria and other micro-organisms. The significance of individual features is investigated, and it is proposed that, within the context of micro-biological image segmentation, better object delineation can be achieved if certain features are more dominant in the initial stages of learning. In this way, other features are allowed to become more/less significant as learning progresses: as more knowledge is acquired about the data being segmented. We argue that the efficiency and flexibility of the SOTM in adapting to, and preserving the topology of input space, makes it an appropriate candidate for implementing this idea. We propose a refinement to the competitive search strategy that allows for a more appropriate fusion of signal and proximal features, thereby promoting a segmentation that is more sensitive to the regional associations of different microbial matter. A refined stop criterion is also suggested such that the dynamically generated number of classes becomes more data dependant. Preliminary experiments are presented and it is found that favouring intensity characteristics in the early phases of learning, whilst relaxing proximity constraints in later phases of learning, offers a general mechanism through which we can improve the segmentation of microbial constituents.