The Autism Related Protein Contactin-Associated Protein-Like 2 (CNTNAP2) Stabilizes New Spines: An In Vivo Mouse Study.

The establishment and maintenance of neuronal circuits depends on tight regulation of synaptic contacts. We hypothesized that CNTNAP2, a protein associated with autism, would play a key role in this process. Indeed, we found that new dendritic spines in mice lacking CNTNAP2 were formed at normal rates, but failed to stabilize. Notably, rates of spine elimination were unaltered, suggesting a specific role for CNTNAP2 in stabilizing new synaptic circuitry

Hotspots of dendritic spine turnover facilitate clustered spine addition and learning and memory.

Modeling studies suggest that clustered structural plasticity of dendritic spines is an efficient mechanism of information storage in cortical circuits. However, why new clustered spines occur in specific locations and how their formation relates to learning and memory (L&M) remain unclear. Using in vivo two-photon microscopy, we track spine dynamics in retrosplenial cortex before, during, and after two forms of episodic-like learning and find that spine turnover before learning predicts future L&M performance, as well as the localization and rates of spine clustering. Consistent with the idea that these measures are causally related, a genetic manipulation that enhances spine turnover also enhances both L&M and spine clustering. Biophysically inspired modeling suggests turnover increases clustering, network sparsity, and memory capacity. These results support a hotspot model where spine turnover is the driver for localization of clustered spine formation, which serves to modulate network function, thus influencing storage capacity and L&M

Level Set Methods in an EM Framework for Shape Classification and Estimation

Abstract. In this paper, we propose an Expectation-Maximization (EM) approach to separate a shape database into different shape classes, while simultaneously estimating the shape contours that best exemplify each of the different shape classes. We begin our formulation by employ-ing the level set function as the shape descriptor. Next, for each shape class we assume that there exists an unknown underlying level set func-tion whose zero level set describes the contour that best represents the shapes within that shape class. The level set function for each exam-ple shape is modeled as a noisy measurement of the appropriate shape class’s unknown underlying level set function. Based on this measure-ment model and the judicious introduction of the class labels as hidden data, our EM formulation calculates the labels for shape classification and estimates the shape contours that best typify the different shape classes. This resulting iterative algorithm is computationally efficient, simple, and accurate. We demonstrate the utility and performance of this algorithm by applying it to two medical applications.

From Images to Shape Models for Object Detection

This research was supported by the EADS foundation, INRIA, CNRS, and SNSF. V. Ferrari was funded by a fellowship of the EADS foundation and by SNSF.International audienceWe present an object class detection approach which fully integrates the complementary strengths offered by shape matchers. Like an object detector, it can learn class models directly from images, and can localize novel instances in the presence of intra-class variations, clutter, and scale changes. Like a shape matcher, it finds the boundaries of objects, rather than just their bounding-boxes. This is achieved by a novel technique for learning a shape model of an object class given images of example instances. Furthermore, we also integrate Hough-style voting with a non-rigid point matching algorithm to localize the model in cluttered images. As demonstrated by an extensive evaluation, our method can localize object boundaries accurately and does not need segmented examples for training (only bounding-boxes)

DLK induces developmental neuronal degeneration via selective regulation of proapoptotic JNK activity

DLK is part of a specialized JNK signaling complex in axons that promotes apoptosis via c-Jun but axon degeneration via distinct JNK substrates

Global Developmental Gene Expression and Pathway Analysis of Normal Brain Development and Mouse Models of Human Neuronal Migration Defects

Heterozygous LIS1 mutations are the most common cause of human lissencephaly, a human neuronal migration defect, and DCX mutations are the most common cause of X-linked lissencephaly. LIS1 is part of a protein complex including NDEL1 and 14-3-3ε that regulates dynein motor function and microtubule dynamics, while DCX stabilizes microtubules and cooperates with LIS1 during neuronal migration and neurogenesis. Targeted gene mutations of Lis1, Dcx, Ywhae (coding for 14-3-3ε), and Ndel1 lead to neuronal migration defects in mouse and provide models of human lissencephaly, as well as aid the study of related neuro-developmental diseases. Here we investigated the developing brain of these four mutants and wild-type mice using expression microarrays, bioinformatic analyses, and in vivo/in vitro experiments to address whether mutations in different members of the LIS1 neuronal migration complex lead to similar and/or distinct global gene expression alterations. Consistent with the overall successful development of the mutant brains, unsupervised clustering and co-expression analysis suggested that cell cycle and synaptogenesis genes are similarly expressed and co-regulated in WT and mutant brains in a time-dependent fashion. By contrast, focused co-expression analysis in the Lis1 and Ndel1 mutants uncovered substantial differences in the correlation among pathways. Differential expression analysis revealed that cell cycle, cell adhesion, and cytoskeleton organization pathways are commonly altered in all mutants, while synaptogenesis, cell morphology, and inflammation/immune response are specifically altered in one or more mutants. We found several commonly dysregulated genes located within pathogenic deletion/duplication regions, which represent novel candidates of human mental retardation and neurocognitive disabilities. Our analysis suggests that gene expression and pathway analysis in mouse models of a similar disorder or within a common pathway can be used to define novel candidates for related human diseases

Shape description and matching using integral invariants on eccentricity transformed images

Matching occluded and noisy shapes is a problem frequently encountered in medical image analysis and more generally in computer vision. To keep track of changes inside the breast, for example, it is important for a computer aided detection system to establish correspondences between regions of interest. Shape transformations, computed both with integral invariants (II) and with geodesic distance, yield signatures that are invariant to isometric deformations, such as bending and articulations. Integral invariants describe the boundaries of planar shapes. However, they provide no information about where a particular feature lies on the boundary with regard to the overall shape structure. Conversely, eccentricity transforms (Ecc) can match shapes by signatures of geodesic distance histograms based on information from inside the shape; but they ignore the boundary information. We describe a method that combines the boundary signature of a shape obtained from II and structural information from the Ecc to yield results that improve on them separately

Role of cytoskeletal abnormalities in the neuropathology and pathophysiology of type I lissencephaly

Type I lissencephaly or agyria-pachygyria is a rare developmental disorder which results from a defect of neuronal migration. It is characterized by the absence of gyri and a thickening of the cerebral cortex and can be associated with other brain and visceral anomalies. Since the discovery of the first genetic cause (deletion of chromosome 17p13.3), six additional genes have been found to be responsible for agyria–pachygyria. In this review, we summarize the current knowledge concerning these genetic disorders including clinical, neuropathological and molecular results. Genetic alterations of LIS1, DCX, ARX, TUBA1A, VLDLR, RELN and more recently WDR62 genes cause migrational abnormalities along with more complex and subtle anomalies affecting cell proliferation and differentiation, i.e., neurite outgrowth, axonal pathfinding, axonal transport, connectivity and even myelination. The number and heterogeneity of clinical, neuropathological and radiological defects suggest that type I lissencephaly now includes several forms of cerebral malformations. In vitro experiments and mutant animal studies, along with neuropathological abnormalities in humans are of invaluable interest for the understanding of pathophysiological mechanisms, highlighting the central role of cytoskeletal dynamics required for a proper achievement of cell proliferation, neuronal migration and differentiation

Volitional modulation of optically recorded calcium signals during neuroprosthetic learning

Brain-machine interfaces are not only promising for neurological applications, but also powerful for investigating neuronal ensemble dynamics during learning. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with 2-photon imaging in motor and somatosensory cortex. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across- and within-sessions. Learning was accompanied by striking modifications of firing correlations within spatially localized networks at fine scales