Spatial component analysis of MRI data for Alzheimer's disease diagnosis: a Bayesian network approach

This work presents a spatial-component (SC) based approach to aid the diagnosis of Alzheimer's disease (AD) using magnetic resonance images. In this approach, the whole brain image is subdivided in regions or spatial components, and a Bayesian network is used to model the dependencies between affected regions of AD. The structure of relations between affected regions allows to detect neurodegeneration with an estimated performance of 88% on more than 400 subjects and predict neurodegeneration with 80% accuracy, supporting the conclusion that modeling the dependencies between components increases the recognition of different patterns of brain degeneration in AD.This work was partly supported by the MICINN under the TEC2012-34306 project and the Consejería de Innovación, Ciencia y Empresa (Junta de Andaluca, Spain) under the Excellence Projects P09-TIC-4530 and P11-TIC-7103

Altered Resting State in Diabetic Neuropathic Pain

BACKGROUND: The spontaneous component of neuropathic pain (NP) has not been explored sufficiently with neuroimaging techniques, given the difficulty to coax out the brain components that sustain background ongoing pain. Here, we address for the first time the correlates of this component in an fMRI study of a group of eight patients suffering from diabetic neuropathic pain and eight healthy control subjects. Specifically, we studied the functional connectivity that is associated with spontaneous neuropathic pain with spatial independent component analysis (sICA). PRINCIPAL FINDINGS: Functional connectivity analyses revealed a cortical network consisting of two anti-correlated patterns: one includes the left fusiform gyrus, the left lingual gyrus, the left inferior temporal gyrus, the right inferior occipital gyrus, the dorsal anterior cingulate cortex bilaterally, the pre and postcentral gyrus bilaterally, in which its activity is correlated negatively with pain and positively with the controls; the other includes the left precuneus, dorsolateral prefrontal, frontopolar cortex (both bilaterally), right superior frontal gyrus, left inferior frontal gyrus, thalami, both insulae, inferior parietal lobuli, right mammillary body, and a small area in the left brainstem, in which its activity is correlated positively with pain and negatively with the controls. Furthermore, a power spectra analyses revealed group differences in the frequency bands wherein the sICA signal was decomposed: patients' spectra are shifted towards higher frequencies. CONCLUSION: In conclusion, we have characterized here for the first time a functional network of brain areas that mark the spontaneous component of NP. Pain is the result of aberrant default mode functional connectivity

Pinning observability of competitive neural networks with different timeconstants

The new concept called pinning observability is proposed for competitive neural networks with different time-scales and a distributed observer structure, which is determined to estimate the states of this large scale network. This network observer has local distinct sub-observers that process local information at the node level but exchange their state estimates with the neighboring observers and thus reflect the interconnection structure of the neural network. The goal is to employ only a minimum number of measurements at certain nodes within the whole neural network, however to be able to estimate the entire state of the network. This can be interpreted as the dual problem to the longer studied pinning control problem. In this paper, we formulate the proposed approach for the two most common competitive neural networks with different time-scales and derive some decoupled and simple conditions for pinning observability. The sub-observers are driven by only local neuron level information but communicate the estimated local states with the neighboring observers. This exchange of local information is the basis of cortical neural processing. The monitoring of few signals from the network holds important practical application for brain signal processing. Simulation examples are given to illustrate the theoretical concepts

Auditory Neuron Models for Cochlea Implants

Auditory perception neurons, also called inner hair cells (IHCs), because of their physical shape, transform the mechanical movements of the basilar membrane into electrical impulses. The impulse coding of the IHC is the main information carrier in the auditory process and is the basis for improvements of cochlea implants as well as for low rate, high quality speech processing and compression. This paper compares biologically motivated models (Meddis, Cooke, Brachman-Payton) with a newly developed model which is transfer function oriented. The new model has only three reservoirs and the parameters can be controlled through five small ROM tables. We compare this model with the often used Meddis model in terms of accuracy, system parameter flexibility, and hardware effort in an FPGA implementation. Keywords: Inner Hair Cell (IHC), Cochlea Implants, Auditory Neurons, FPGA design 1. INTRODUCTION Cochlea implants (CIs) have been successfully used to treat deaf patients having defective ..

Proteomic Data Analysis of Glioma Cancer Stem-Cell Lines Based on Novel Nonlinear Dimensional Data Reduction Techniques

Glioma-derived cancer stem cells (GSCs) are tumor-initiating cells and may be refractory to radiation and chemotherapy and thus have important implications for tumor biology and therapeutics. The analysis and interpretation of large proteomic data sets requires the development of new data mining and visualization approaches. Traditional techniques are insufficient to interpret and visualize these resulting experimental data. The emphasis of this paper lies in the application of novel approaches for the visualization, clustering and projection representation to unveil hidden data structures relevant for the accurate interpretation of biological experiments. These qualitative and quantitative methods are applied to the proteomic analysis of data sets derived from the GSCs. The achieved clustering and visualization results provide a more detailed insight into the protein-level fold changes and putative upstream regulators for the GSCs. However the extracted molecular information is insufficient in classifying GSCs and paving the pathway to an improved therapeutics of the heterogeneous glioma

Multi-level analysis of spatio-temporal features in non-mass enhancing breast tumors

Diagnostically challenging breast tumors and Non-Mass-Enhancing (NME) lesions are often characterized by spatial and temporal heterogeneity, thus difficult to detect and classify. Differently from mass enhancing tumors they have an atypical temporal enhancement behavior that does not enable a straight-forward lesion classification into benign or malignant. The poorly de fined margins do not support a concise shape description thus impacting morphological characterizations. A multi-level analysis strategy capturing the features of Non-Mass-Like-Enhancing (NMLEs) is shown to be superior to other methods relying only on morphological and kinetic information. In addition to this, the NMLE features such as NMLE distribution types and NMLE enhancement pattern, can be employed in radiomics analysis to make robust models in the early prediction of the response to neo-adjuvant chemotherapy in breast cancer. Therefore, this could predict treatment response early in therapy to identify women who do not benefit from cytotoxic therapy

Dynamical Graph Theory Networks Techniques for the Analysis of Sparse Connectivity Networks in Dementia

Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links.In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies

The Driving Regulators of the Connectivity Protein Network of Brain Malignancies

An important problem in modern therapeutics at the proteomic level remains to identify therapeutic targets in a plentitude of high-throughput data from experiments relevant to a variety of diseases. This paper presents the application of novel modern control concepts, such as pinning controllability and observability applied to the glioma cancer stem cells (GSCs) protein graph network with known and novel association to glioblastoma (GBM). The theoretical frameworks provides us with the minimal number of "driver nodes", which are necessary, and their location to determine the full control over the obtained graph network in order to provide a change in the network's dynamics from an initial state (disease) to a desired state (non-disease). The achieved results will provide biochemists with techniques to identify more metabolic regions and biological pathways for complex diseases, to design and test novel therapeutic solutions