Digital brain atlases are used in neuroscience to characterize the spatial organization of neuronal structures [1]–[3], for planning and guidance during neurosurgery [4], [5], and as a reference for interpreting other modalities such as gene expression or proteomic data [6]–[9]. The field of digital atlasing is extensive, and includes high quality brain atlases of the mouse [10], rat [11], rhesus macaque [12], human [13], [14], and several other model organisms. In addition to atlases based on histology, [11], [15], [16], magnetic resonance imaging [10], [17], and positron emission tomography [11], modern digital atlases often use probabilistic and multimodal techniques [18], [19], as well as sophisticated visualization software [20], [21]. Whether atlases involve detailed visualization of structures of a single or small group of specimens [6], [22], [23] or averages over larger populations [18], [24], much of the work in developing digital brain atlases is from the perspective of the user of a single resource. This is often due largely to the challenges of data generation, maintenance, and resources management [25], [26]. A more recent goal of many neuroscientists is to connect multiple and diverse resources to work in a collaborative manner using an atlas based framework [2], [19]. This vision is appealing as, ideally, researchers would be able to share their data and analyses with others, regardless of where they or the data are located. An important step in this direction is the specification of a common frame of reference across specimens and resources (either as coordinate, ontology, or region of interest) that is adopted by the community. In this perspective, we propose a collaborative digital atlasing framework for coordinating mouse brain research that allows access to data, tools, and analyses from multiple sources.

BACKGROUND: Spatially mapped large scale gene expression databases enable quantitative comparison of data measurements across genes, anatomy, and phenotype. In most ongoing efforts to study gene expression in the mammalian brain, significant resources are applied to the mapping and visualization of data. This paper describes the implementation and utility of Brain Explorer, a 3D visualization tool for studying in situ hybridization-based (ISH) expression patterns in the Allen Brain Atlas, a genome-wide survey of 21,000 expression patterns in the C57BL\6J adult mouse brain. RESULTS: Brain Explorer enables users to visualize gene expression data from the C57Bl/6J mouse brain in 3D at a resolution of 100 microm3, allowing co-display of several experiments as well as 179 reference neuro-anatomical structures. Brain Explorer also allows viewing of the original ISH images referenced from any point in a 3D data set. Anatomic and spatial homology searches can be performed from the application to find data sets with expression in specific structures and with similar expression patterns. This latter feature allows for anatomy independent queries and genome wide expression correlation studies. CONCLUSION: These tools offer convenient access to detailed expression information in the adult mouse brain and the ability to perform data mining and visualization of gene expression and neuroanatomy in an integrated manner.

Large scale gene expression studies in the mammalian brain offer the promise of understanding the topology, networks and ultimately the function of its complex anatomy, opening previously unexplored avenues in neuroscience. High-throughput methods permit genome-wide searches to discover genes that are uniquely expressed in brain circuits and regions that control behavior. Previous gene expression mapping studies in model organisms have employed situ hybridization (ISH), a technique that uses labeled nucleic acid probes to bind to specific mRNA transcripts in tissue sections. A key requirement for this effort is the development of fast and robust algorithms for anatomically mapping and quantifying gene expression for ISH. We describe a neuroinformatics pipeline for automatically mapping expression profiles of ISH data and its use to produce the first genomic scale 3-D mapping of gene expression in a mammalian brain. The pipeline is fully automated and adaptable to other organisms and tissues. Our automated study of over 20,000 genes indicates that at least 78.8 percent are expressed at some level in the adult C56BL/6J mouse brain. In addition to providing a platform for genomic scale search, high-resolution images and visualization tools for expression analysis are available at the Allen Brain Atlas web site (http://www.brain-map.org).

Gradient-based optical flow estimation methods typically do not take into account errors in the spatial derivative estimates. The presence of these errors causes an errors-in-variables (EIV) problem. Moreover, the use of finite difference methods to calculate these derivatives ensures that the errors are strongly correlated between pixels. Total least squares (TLS) has often been used to address this EIV problem. However, its application in this context is flawed as TLS implicitly assumes that the errors between neighborhood pixels are independent. In this paper, a new optical flow estimation method (EIVM) is formulated to properly treat the EIV problem in optical flow. EIVM is based on Sprent's (1966) procedure which allows the incorporation of a general EIV model in the estimation process. In EIVM, the neighborhood size acts as a smoothing parameter. Due to the weights in the EIVM objective function, the effect of changing the neighborhood size is more complex than in other local model methods such as Lucas and Kanade (1981). These weights, which are functions of the flow estimate, can alter the effective size and orientation of the neighborhood. In this paper, we also present a data-driven method for choosing the neighborhood size based on Stein's unbiased risk estimators (SURE).

Local methods have long been used to estimate optical flow by fitting measurements in a small neighbourhood to a simple model. What is less well known are procedures to choose the neighbourhood size, weights and model order. In this paper, we show that the choice of these local model tuning variables can have a significant effect on the flow estimate. The optimal choice of these variables will depend on the image content, the noise level and the type of motion in the sequence. Hence, the development of a data-driven selection method is important research goal. This paper presents such a procedure based on Stein's unbiased risk estimators (SURE).

Motion fields of real image sequences are typically piecewise smooth with discontinuities at object boundaries. Further, because of the aperture problem, only the normal component of the optical flow can be observed. Direct application of wavelet shrinkage to the normal component flow may yield undesirable results due to the correlated noise affecting the wavelet coefficients. In this paper, we present a new technique for estimating optical flow based on L/sub 1/ regularisation. The resulting flow estimate typically has many zero wavelet coefficients, but unlike wavelet shrinkage some of the remaining coefficients are allowed to "grow". To highlight this difference, we have named our new technique wavelet zeroing. Additionally, we present a data-driven, sequence adaptive method for optimally choosing the penalty parameter.

Although still in practice, the use of total least squares (TLS) in optical flow estimation is unreliable. The TLS implicitly assumes that the error terms affecting the partial derivatives of the image intensities are independent. The usual methods for estimating the partial derivatives ensures that the errors are strongly correlated. Due to this correlation, an alternative method is required to treat the resulting errors-in-variables (EN) problem. We propose a new method for estimating optical flow based on Sprent's (1966, 1969) procedure. This method incorporates a general EIV model and provides a far simpler computational procedure than found in previous solutions.

This paper brings together two research issues in optical flow estimation: the selection of the smoothing parameter and the proper treatment of the correlated noise in the partial derivatives of the image intensity. In previous work, the authors have developed an optical flow estimation method which properly treats the errors-in-variables problem cause by the correlated noise in the spatio-temporal derivatives of the image intensity. This method requires the specification of a neighbourhood size to use when optical flow calculations are made. In this paper we present a data-driven method for choosing the optimal neighbourhood size.

In optical flow estimation, an additional constraint to the constant brightness assumption is required to uniquely determine both components of the flow. Typically, these constraints impose a smoothness requirement on the flow estimate. Since the smoothness constraint may be inconsistent with the brightness constraint, a smoothing parameter is introduced to control the tradeoff between satisfying the requirements of both constraints. Previously, there have only been heuristic discussions on how to choose the smoothing parameter. We show that the choice of the smoothing parameter can have a significant effect on the flow estimate and present a data-driven method based on estimated risk to select the smoothing parameter in the Horn and Schunck (1981) algorithm.