Clustering techniques for patients suffering acquired brain injury inneuro personal trainer

The study of the effectiveness of the cognitive rehabilitation processes and the identification of cognitive profiles, in order to define comparable populations, is a controversial area, but concurrently it is strongly needed in order to improve therapies. There is limited evidence about cognitive rehabilitation efficacy. Many of the trials conclude that in spite of an apparent clinical good response, differences do not show statistical significance. The common feature in all these trials is heterogeneity among populations. In this situation, observational studies on very well controlled cohort of studies, together with innovative methods in knowledge extraction, could provide methodological insights for the design of more accurate comparative trials. Some correlation studies between neuropsychological tests and patients capacities have been carried out -1---2- and also correlation between tests and morphological changes in the brain -3-. The procedures efficacy depends on three main factors: the affectation profile, the scheduled tasks and the execution results. The relationship between them makes up the cognitive rehabilitation as a discipline, but its structure is not properly defined. In this work we present a clustering method used in Neuro Personal Trainer (NPT) to group patients into cognitive profiles using data mining techniques. The system uses these clusters to personalize treatments, using the patients assigned cluster to select which tasks are more suitable for its concrete needs, by comparing the results obtained in the past by patients with the same profile.

INDICIa: a new distributed clustering protocol

Many data streaming applications produces massive amounts of data that must be processed in a distributed fashion due to the resource limitation of a single machine. We propose a distributed data stream clustering protocol. Theoretical analysis shows preliminary results about the quality of discovered clustering. In addition, we present results about the ability to reduce the time complexity respect to the centralized approach.

Clustering basado en redes bayesianas con predictoras continuas: aplicaciones en neurociencia

Desentrañar el funcionamiento del cerebro es uno de los principales desafíos a los que se enfrenta la ciencia actual. Un área de estudio que ha despertado muchas expectativas e interés es el análisis de la estructura cortical desde el punto de vista morfológico, de manera que se cree una simulación del cerebro a nivel molecular. Con ello se espera poder profundizar en el estudio de numerosas enfermedades neurológicas y patológicas. Con el desarrollo de este proyecto se persigue el estudio del soma y de las espinas desde el punto de vista de la neuromorfología teórica. Es común en el estado del arte que en el análisis de las características morfológicas de una neurona en tres dimensiones el soma sea ignorado o, en el mejor de los casos, que sea sustituido por una simple esfera. De hecho, el concepto de soma resulta abstracto porque no se dispone de una dfinición estricta y robusta que especifique exactamente donde finaliza y comienzan las dendritas. En este proyecto se alcanza por primera vez una definición matemática de soma para determinar qué es el soma. Con el fin de simular somas se ahonda en los atributos utilizados en el estado del arte. Estas propiedades, de índole genérica, no especifican una morfología única. Es por ello que se propone un método que agrupe propiedades locales y globales de la morfología. En disposición de las características se procede con la categorización del cuerpo celular en distintas clases a partir de un nuevo subtipo de red bayesiana dinámica adaptada al espacio. Con ello se discute la existencia de distintas clases de somas y se descubren las diferencias entre los somas piramidales de distintas capas del cerebro. A partir del modelo matemático se simulan por primera vez somas virtuales. Algunas morfologías de espinas han sido atribuidas a ciertos comportamientos cognitivos. Por ello resulta de interés dictaminar las clases existentes y relacionarlas con funciones de la actividad cerebral. La clasificación más extendida (Peters y Kaiserman-Abramof, 1970) presenta una definición ambigua y subjetiva dependiente de la interpretación de cada individuo y por tanto discutible. Este estudio se sustenta en un conjunto de descriptores extraídos mediante una técnica de análisis topológico local para representaciones 3D. Sobre estos datos se trata de alcanzar el conjunto de clases más adecuado en el que agrupar las espinas así como de describir cada grupo mediante reglas unívocas. A partir de los resultados, se discute la existencia de un continuo de espinas y las propiedades que caracterizan a cada subtipo de espina. ---ABSTRACT---Unravel how the brain works is one of the main challenges faced by current science. A field of study which has aroused great expectations and interest is the analysis of the cortical structure from a morphological point of view, so that a molecular level simulation of the brain is achieved. This is expected to deepen the study of many neurological and pathological diseases. This project seeks the study of the soma and spines from the theoretical neuromorphology point of view. In the state of the art it is common that when it comes to analyze the morphological characteristics of a three dimension neuron the soma is ignored or, in the best case, it is replaced by a simple sphere. In fact, the concept of soma is abstract because there is not a robust and strict definition on exactly where it ends and dendrites begin. In this project a mathematical definition is reached for the first time to determine what a soma is. With the aim to simulate somas the atributes applied in the state of the art are studied. These properties, generic in nature, do not specify a unique morphology. It is why it was proposed a method to group local and global morphology properties. In arrangement of the characteristics it was proceed with the categorization of the celular body into diferent classes by using a new subtype of dynamic Bayesian network adapted to space. From the result the existance of different classes of somas and diferences among pyramidal somas from distinct brain layers are discovered. From the mathematical model virtual somas were simulated for the first time. Some morphologies of spines have been attributed to certain cognitive behaviours. For this reason it is interesting to rule the existent classes and to relate them with their functions in the brain activity. The most extended classification (Peters y Kaiserman-Abramof, 1970) presents an ambiguous and subjective definition that relies on the interpretation of each individual and consequently it is arguable. This study was based on the set of descriptors extracted from a local topological analysis technique for 3D representations. On these data it was tried to reach the most suitable set of classes to group the spines as well as to describe each cluster by unambiguous rules. From these results, the existance of a continuum of spines and the properties that characterize each spine subtype were discussed .

Classifying GABAergic interneurons with semi-supervised projected model-based clustering

Objectives: A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names.We sought to automatically classify digitally reconstructed interneuronal morphologies according tothis scheme. Simultaneously, we sought to discover possible subtypes of these types that might emergeduring automatic classification (clustering). We also investigated which morphometric properties weremost relevant for this classification.Materials and methods: A set of 118 digitally reconstructed interneuronal morphologies classified into thecommon basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of theworld?s leading neuroscientists, quantified by five simple morphometric properties of the axon and fourof the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. Wethen removed this class information for each type separately, and applied semi-supervised clustering tothose cells (keeping the others? cluster membership fixed), to assess separation from other types and lookfor the formation of new groups (subtypes). We performed this same experiment unlabeling the cells oftwo types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixtureof Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performedthe described experiments on three different subsets of the data, formed according to how many expertsagreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least26 (47 neurons).Results: Interneurons with more reliable type labels were classified more accurately. We classified HTcells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy,respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, andno subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette widthand ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively,confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a singletype also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometricproperties were more relevant that dendritic ones, with the axonal polar histogram length in the [pi, 2pi) angle interval being particularly useful.Conclusions: The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heteroge-neous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones fordistinguishing among the CB, HT, LB, and MA interneuron types.

Identification of pore spaces in 3D CT soil images using a PFCM partitional clustering

Recent advances in non-destructive imaging techniques, such as X-ray computed tomography (CT), make it possible to analyse pore space features from the direct visualisation from soil structures. A quantitative characterisation of the three-dimensional solid-pore architecture is important to understand soil mechanics, as they relate to the control of biological, chemical, and physical processes across scales. This analysis technique therefore offers an opportunity to better interpret soil strata, as new and relevant information can be obtained. In this work, we propose an approach to automatically identify the pore structure of a set of 200-2D images that represent slices of an original 3D CT image of a soil sample, which can be accomplished through non-linear enhancement of the pixel grey levels and an image segmentation based on a PFCM (Possibilistic Fuzzy C-Means) algorithm. Once the solids and pore spaces have been identified, the set of 200-2D images is then used to reconstruct an approximation of the soil sample by projecting only the pore spaces. This reconstruction shows the structure of the soil and its pores, which become more bounded, less bounded, or unbounded with changes in depth. If the soil sample image quality is sufficiently favourable in terms of contrast, noise and sharpness, the pore identification is less complicated, and the PFCM clustering algorithm can be used without additional processing; otherwise, images require pre-processing before using this algorithm. Promising results were obtained with four soil samples, the first of which was used to show the algorithm validity and the additional three were used to demonstrate the robustness of our proposal. The methodology we present here can better detect the solid soil and pore spaces on CT images, enabling the generation of better 2D?3D representations of pore structures from segmented 2D images.

Fast 2D to 3D conversion using a clustering-based hierarchical search in a machine learning framework

Automatic 2D-to-3D conversion is an important application for filling the gap between the increasing number of 3D displays and the still scant 3D content. However, existing approaches have an excessive computational cost that complicates its practical application. In this paper, a fast automatic 2D-to-3D conversion technique is proposed, which uses a machine learning framework to infer the 3D structure of a query color image from a training database with color and depth images. Assuming that photometrically similar images have analogous 3D structures, a depth map is estimated by searching the most similar color images in the database, and fusing the corresponding depth maps. Large databases are desirable to achieve better results, but the computational cost also increases. A clustering-based hierarchical search using compact SURF descriptors to characterize images is proposed to drastically reduce search times. A significant computational time improvement has been obtained regarding other state-of-the-art approaches, maintaining the quality results.

A clustering technique for partial discharge and noise sources identification in power cables by means of waveform parameters

On-line partial discharge (PD) measurements have become a common technique for assessing the insulation condition of installed high voltage (HV) insulated cables. When on-line tests are performed in noisy environments, or when more than one source of pulse-shaped signals are present in a cable system, it is difficult to perform accurate diagnoses. In these cases, an adequate selection of the non-conventional measuring technique and the implementation of effective signal processing tools are essential for a correct evaluation of the insulation degradation. Once a specific noise rejection filter is applied, many signals can be identified as potential PD pulses, therefore, a classification tool to discriminate the PD sources involved is required. This paper proposes an efficient method for the classification of PD signals and pulse-type noise interferences measured in power cables with HFCT sensors. By using a signal feature generation algorithm, representative parameters associated to the waveform of each pulse acquired are calculated so that they can be separated in different clusters. The efficiency of the clustering technique proposed is demonstrated through an example with three different PD sources and several pulse-shaped interferences measured simultaneously in a cable system with a high frequency current transformer (HFCT).

Postsynaptic clustering of γ-aminobutyric acid type A receptors by the γ3 subunit in vivo

Synaptic localization of γ-aminobutyric acid type A (GABAA) receptors is a prerequisite for synaptic inhibitory function, but the mechanism by which different receptor subtypes are localized to postsynaptic sites is poorly understood. The γ2 subunit and the postsynaptic clustering protein gephyrin are required for synaptic localization and function of major GABAA receptor subtypes. We now show that transgenic overexpression of the γ3 subunit in γ2 subunit-deficient mice restores benzodiazepine binding sites, benzodiazepine-modulated whole cell currents, and postsynaptic miniature currents, suggesting the formation of functional, postsynaptic receptors. Moreover, the γ3 subunit can substitute for γ2 in the formation of GABAA receptors that are synaptically clustered and colocalized with gephyrin in vivo. These clusters were formed even in brain regions devoid of endogenous γ3 subunit, indicating that the factors present for clustering of γ2 subunit-containing receptors are sufficient to cluster γ3 subunit-containing receptors. The GABAA receptor and gephyrin-clustering properties of the ectopic γ3 subunit were also observed for the endogenous γ3 subunit, but only in the absence of the γ2 subunit, suggesting that the γ3 subunit is at a competitive disadvantage with the γ2 subunit for clustering of postsynaptic GABAA receptors in wild-type mice.

Activity-dependent regulation of synaptic clustering in a hippocampal culture system

Currently, there is a limited understanding of the factors that influence the localization and density of individual synapses in the central nervous system. Here we have studied the effects of activity on synapse formation between hippocampal dentate granule cells and CA3 pyramidal neurons in culture, taking advantage of FM1–43 as a fluorescent marker of synaptic boutons. We observed an early tendency for synapses to group together, quickly followed by the appearance of synaptic clusters on dendritic processes. These events were strongly influenced by N-methyl-d-aspartic acid receptor- and cyclic AMP-dependent signaling. The microstructure and localization of the synaptic clusters resembled that found in hippocampus, at mossy fiber synapses of stratum lucidum. Activity-dependent clustering of synapses represents a means for synaptic targeting that might contribute to synaptic organization in the brain.

Kinase domain of the muscle-specific receptor tyrosine kinase (MuSK) is sufficient for phosphorylation but not clustering of acetylcholine receptors: Required role for the MuSK ectodomain?

Formation of the neuromuscular junction (NMJ) depends upon a nerve-derived protein, agrin, acting by means of a muscle-specific receptor tyrosine kinase, MuSK, as well as a required accessory receptor protein known as MASC. We report that MuSK does not merely play a structural role by demonstrating that MuSK kinase activity is required for inducing acetylcholine receptor (AChR) clustering. We also show that MuSK is necessary, and that MuSK kinase domain activation is sufficient, to mediate a key early event in NMJ formation—phosphorylation of the AChR. However, MuSK kinase domain activation and the resulting AChR phosphorylation are not sufficient for AChR clustering; thus we show that the MuSK ectodomain is also required. These results indicate that AChR phosphorylation is not the sole trigger of the clustering process. Moreover, our results suggest that, unlike the ectodomain of all other receptor tyrosine kinases, the MuSK ectodomain plays a required role in addition to simply mediating ligand binding and receptor dimerization, perhaps by helping to recruit NMJ components to a MuSK-based scaffold.

A link density clustering algorithm based on automatically selecting density peaks for overlapping community detection

Peer reviewed

Implications of a possible clustering of highest-energy cosmic rays

Recently, a possible clustering of a subset of observed ultra-high energy cosmic rays above ≃40 EeV (4 × 1019 eV) in pairs near the supergalactic plane was reported. We show that a confirmation of this effect would provide information on the origin and nature of these events and, in case of charged primaries, imply interesting constraints on the extragalactic magnetic field. Possible implications for the most common models of ultra-high energy cosmic ray production in the literature are discussed.

Involvement of unique leucine-zipper motif of PSD-Zip45 (Homer 1c/vesl-1L) in group 1 metabotropic glutamate receptor clustering

Several scaffold proteins for neurotransmitter receptors have been identified as candidates for receptor targeting. However, the molecular mechanism underlying such receptor clustering and targeting to postsynaptic specializations remains unknown. PSD-Zip45 (also named Homer 1c/vesl-1L) consists of the NH2 terminus containing the enabled/VASP homology 1 domain and the COOH terminus containing the leucine zipper. Here, we demonstrate immunohistochemically that metabotropic glutamate receptor 1α (mGluR1α) and PSD-Zip45/Homer 1c are colocalized to synapses in the cerebellar molecular layer but not in the hippocampus. In cultured hippocampal neurons, PSD-Zip45/Homer1c and N-methyl-d-aspartate receptors are preferentially colocalized to dendritic spines. Cotransfection of mGluR1α or mGluR5 and PSD-Zip45/Homer 1c into COS-7 cells results in mGluR clustering induced by PSD-Zip45/Homer 1c. An in vitro multimerization assay shows that the extreme COOH-terminal leucine zipper is involved in self-multimerization of PSD-Zip45/Homer 1c. A clustering assay of mGluRs in COS-7 cells also reveals a critical role of this leucine-zipper motif of PSD-Zip45/Homer 1c in mGluR clustering. These results suggest that the leucine zipper of subsynaptic scaffold protein is a candidate motif involved in neurotransmitter receptor clustering at the central synapse.

Clustering of meiotic double-strand breaks on yeast chromosome III

In the yeast Saccharomyces cerevisiae, meiotic recombination is initiated by transient DNA double-strand breaks (DSBs) that are repaired by interaction of the broken chromosome with its homologue. To identify a large number of DSB sites and gain insight into the control of DSB formation at both the local and the whole chromosomal levels, we have determined at high resolution the distribution of meiotic DSBs along the 340 kb of chromosome III. We have found 76 DSB regions, mostly located in intergenic promoter-containing intervals. The frequency of DSBs varies at least 50-fold from one region to another. The global distribution of DSB regions along chromosome III is nonrandom, defining large (39–105 kb) chromosomal domains, both hot and cold. The distribution of these localized DSBs indicates that they are likely to initiate most crossovers along chromosome III, but some discrepancies remain to be explained.

Calcium-dependent Clustering of Inositol 1,4,5-Trisphosphate Receptors

Rat basophilic leukemia (RBL-2H3) cells predominantly express the type II receptor for inositol 1,4,5-trisphosphate (InsP3), which operates as an InsP3-gated calcium channel. In these cells, cross-linking the high-affinity immunoglobulin E receptor (FcεR1) leads to activation of phospholipase C γ isoforms via tyrosine kinase- and phosphatidylinositol 3-kinase-dependent pathways, release of InsP3-sensitive intracellular Ca2+ stores, and a sustained phase of Ca2+ influx. These events are accompanied by a redistribution of type II InsP3 receptors within the endoplasmic reticulum and nuclear envelope, from a diffuse pattern with a few small aggregates in resting cells to large isolated clusters after antigen stimulation. Redistribution of type II InsP3 receptors is also seen after treatment of RBL-2H3 cells with ionomycin or thapsigargin. InsP3 receptor clustering occurs within 5–10 min of stimulus and persists for up to 1 h in the presence of antigen. Receptor clustering is independent of endoplasmic reticulum vesiculation, which occurs only at ionomycin concentrations >1 μM, and maximal clustering responses are dependent on the presence of extracellular calcium. InsP3 receptor aggregation may be a characteristic cellular response to Ca2+-mobilizing ligands, because similar results are seen after activation of phospholipase C-linked G-protein-coupled receptors; cholecystokinin causes type II receptor redistribution in rat pancreatoma AR4–2J cells, and carbachol causes type III receptor redistribution in muscarinic receptor-expressing hamster lung fibroblast E36M3R cells. Stimulation of these three cell types leads to a reduction in InsP3 receptor levels only in AR4–2J cells, indicating that receptor clustering does not correlate with receptor down-regulation. The calcium-dependent aggregation of InsP3 receptors may contribute to the previously observed changes in affinity for InsP3 in the presence of elevated Ca2+ and/or may establish discrete regions within refilled stores with varying capacity to release Ca2+ when a subsequent stimulus results in production of InsP3.