1.1 --- a/grant.bib Mon Apr 20 17:19:47 2009 -0700 1.2 +++ b/grant.bib Tue Apr 21 14:50:10 2009 -0700 1.3 @@ -361,4 +361,112 @@ 1.4 author = {Constance M. Smith and Jacqueline H. Finger and Terry F. Hayamizu and Ingeborg J. {McCright} and Janan T. Eppig and James A. Kadin and Joel E. Richardson and Martin Ringwald}, 1.5 year = {2007}, 1.6 pages = {D618--623} 1.7 +}, 1.8 + 1.9 +@article{annese_myelo-architectonic_2004, 1.10 + title = {A myelo-architectonic method for the structural classification of cortical areas}, 1.11 + volume = {21}, 1.12 + issn = {1053-8119}, 1.13 + url = {http://www.sciencedirect.com/science/article/B6WNP-4B5JN94-1/2/c9519ed20d3002e0b0316bcf0031e7a2}, 1.14 + doi = {10.1016/j.neuroimage.2003.08.024}, 1.15 + abstract = {We describe an automatic and reproducible method to analyze the histological design of the cerebral cortex as applied to brain sections stained to reveal myelinated fibers. The technique provides an evaluation of the distribution of myelination across the width of the cortical mantle in accordance with a model of its curvature and its intrinsic geometry. The profile lines along which the density of staining is measured are generated from the solution of a partial differential equation {(PDE)} that models the intermediate layers of the cortex. Cortical profiles are classified according to significant components that emerge from wavelet analysis. Intensity profiles belonging to each distinct class are normalized and averaged to produce area-specific templates of cortical myelo-architecture.}, 1.16 + number = {1}, 1.17 + journal = {{NeuroImage}}, 1.18 + author = {J. Annese and A. Pitiot and I. D. Dinov and A. W. Toga}, 1.19 + year = {2004}, 1.20 + keywords = {Cerebral {Cortex,Cortical} {areas,Myelo-architecture}}, 1.21 + pages = {15--26} 1.22 +}, 1.23 + 1.24 +@article{schleicher_stereological_2000, 1.25 + title = {A stereological approach to human cortical architecture: identification and delineation of cortical areas}, 1.26 + volume = {20}, 1.27 + issn = {0891-0618}, 1.28 + url = {http://www.sciencedirect.com/science/article/B6T02-43HDYPB-5/2/461101884330ed9e8b29a5f4195a349f}, 1.29 + doi = {{10.1016/S0891-0618(00)00076-4}}, 1.30 + abstract = {Stereology offers a variety of procedures to analyze quantitatively the regional and laminar organization in cytoarchitectonically defined areas of the human cerebral cortex. Conventional anatomical atlases are of little help in localizing specific cortical areas, since most of them are based on a single brain and use highly observer-dependent criteria for the delineation of cortical areas. In consequence, numerous cortical maps exist which greatly differ with respect to number, position, size and extent of cortical areas. We describe a novel algorithm-based procedure for the delineation of cortical areas, which exploits the automated estimation of volume densities of cortical cell bodies. Spatial sampling of the laminar pattern is performed with density profiles, followed by multivariate analysis of the profiles[`] shape, which locates the cytoarchitectonic borders between neighboring cortical areas at sites where the laminar pattern changes significantly. The borders are then mapped to a human brain atlas system comprising tools for three dimensional reconstruction, visualization and morphometric analysis. A sample of brains with labeled cortical areas is warped into the reference brain of the atlas system in order to generate a population map of the cortical areas, which describes the intersubject variability in spatial conformation of cortical areas. These population maps provide a novel tool for the interpretation of images obtained with functional imaging techniques.}, 1.31 + number = {1}, 1.32 + journal = {Journal of Chemical Neuroanatomy}, 1.33 + author = {A. Schleicher and K. Amunts and S. Geyer and T. Kowalski and T. Schormann and N. {Palomero-Gallagher} and K. Zilles}, 1.34 + month = oct, 1.35 + year = {2000}, 1.36 + keywords = {Cerebral {Cortex,Density} {profile,Multivariate} {statistics,Quantitative} {cytoarchitecture,Stereology-brain} mapping}, 1.37 + pages = {31--47} 1.38 +}, 1.39 + 1.40 +@article{schmitt_detection_2003, 1.41 + title = {Detection of cortical transition regions utilizing statistical analyses of excess masses}, 1.42 + volume = {19}, 1.43 + issn = {1053-8119}, 1.44 + url = {http://www.sciencedirect.com/science/article/B6WNP-488VX9X-2/2/4a7467890b69d13dec8261a4f6fc66d5}, 1.45 + doi = {{10.1016/S1053-8119(03)00040-5}}, 1.46 + abstract = {A new statistical approach for observer-assisted detection of transition regions of adjacent cytoarchitectonic areas within the human cerebral cortex was developed. This method analyzes the structural information of cytoarchitectural profiles (e.g., the modality of a gray level intensity distribution) based on observed excess mass differences verified by a suitable statistical test. Profiles were generated by scanning the cerebral cortex over respective regions of interest that were oriented to trajectories running parallel to the orientation of cell columns. For each single profile, determination of excess masses provided evidence for a certain number of peaks in the cell density, thereby avoiding fluctuation due solely to sampling anomalies. Comparing such excess mass measurements by means of multiple local rank tests over a wide range of profiles allowed for the detection of cytoarchitectural inhomogeneities at respective given confidence levels. Special parameters (e.g., level of significance, width of targeted region, number of peaks) then could be adapted to specific pattern recognition problems in lamination analyses. Such analyses of excess masses provided a general tool for observer-assisted evaluation of profile arrays. This observer-assisted statistical method was applied to five different cortical examples. It detected the same transition regions that had been determined earlier through direct examination of samples, despite cortical convexities, concavities, and some minor staining inhomogeneities.}, 1.47 + number = {1}, 1.48 + journal = {{NeuroImage}}, 1.49 + author = {Oliver Schmitt and Lars Hömke and Lutz Dümbgen}, 1.50 + month = may, 1.51 + year = {2003}, 1.52 + keywords = {Brain {mapping,Cerebral} {Cortex,Cytoarchitecture,Excess} {mass,Lamination,Multiple} local rank {test,Neuroimaging,Profiles,Trajectories,Transition} {regions,Traverses}}, 1.53 + pages = {42--63} 1.54 +}, 1.55 + 1.56 +@article{schleicher_quantitative_2005, 1.57 + title = {Quantitative architectural analysis: a new approach to cortical mapping}, 1.58 + volume = {210}, 1.59 + url = {http://dx.doi.org/10.1007/s00429-005-0028-2}, 1.60 + doi = {10.1007/s00429-005-0028-2}, 1.61 + abstract = {Abstract Recent progress in anatomical and functional {MRI} has revived the demand for a reliable, topographic map of the human cerebral 1.62 +cortex. Till date, interpretations of specific activations found in functional imaging studies and their topographical analysis 1.63 +in a spatial reference system are, often, still based on classical architectonic maps. The most commonly used reference atlas 1.64 +is that of Brodmann and his successors, despite its severe inherent drawbacks. One obvious weakness in traditional, architectural 1.65 +mapping is the subjective nature of localising borders between cortical areas, by means of a purely visual, microscopical 1.66 +examination of histological specimens. To overcome this limitation, more objective, quantitative mapping procedures have been 1.67 +established in the past years. The quantification of the neocortical, laminar pattern by defining intensity line profiles 1.68 +across the cortical layers, has a long tradition. During the last years, this method has been extended to enable a reliable, 1.69 +reproducible mapping of the cortex based on image analysis and multivariate statistics. Methodological approaches to such 1.70 +algorithm-based, cortical mapping were published for various architectural modalities. In our contribution, principles of 1.71 +algorithm-based mapping are described for cyto- and receptorarchitecture. In a cytoarchitectural parcellation of the human 1.72 +auditory cortex, using a sliding window procedure, the classical areal pattern of the human superior temporal gyrus was modified 1.73 +by a replacing of Brodmann’s areas 41, 42, 22 and parts of area 21, with a novel, more detailed map. An extension and optimisation 1.74 +of the sliding window procedure to the specific requirements of receptorarchitectonic mapping, is also described using the 1.75 +macaque central sulcus and adjacent superior parietal lobule as a second, biologically independent example. Algorithm-based 1.76 +mapping procedures, however, are not limited to these two architectural modalities, but can be applied to all images in which 1.77 +a laminar cortical pattern can be detected and quantified, e.g. myeloarchitectonic and in vivo high resolution {MR} imaging. 1.78 +Defining cortical borders, based on changes in cortical lamination in high resolution, in vivo structural {MR} images will result 1.79 +in a rapid increase of our knowledge on the structural parcellation of the human cerebral cortex.}, 1.80 + number = {5}, 1.81 + journal = {Anatomy and Embryology}, 1.82 + author = {A. Schleicher and N. {Palomero-Gallagher} and P. Morosan and S. Eickhoff and T. Kowalski and K. Vos and K. Amunts and K. Zilles}, 1.83 + month = dec, 1.84 + year = {2005}, 1.85 + pages = {373--386} 1.86 +}, 1.87 + 1.88 +@article{kruggel_analyzingneocortical_2003, 1.89 + title = {Analyzing the neocortical fine-structure}, 1.90 + volume = {7}, 1.91 + issn = {1361-8415}, 1.92 + url = {http://www.sciencedirect.com/science/article/B6W6Y-48FSTG9-3/2/5a6f5b703630037afeea6067c27b42be}, 1.93 + doi = {{10.1016/S1361-8415(03)00006-9}}, 1.94 + abstract = {Cytoarchitectonic fields of the human neocortex are defined by characteristic variations in the composition of a general six-layer structure. It is commonly accepted that these fields correspond to functionally homogeneous entities. Diligent techniques were developed to characterize cytoarchitectonic fields by staining sections of post-mortem brains and subsequent statistical evaluation. Fields were found to show a considerable interindividual variability in extent and relation to macroscopic anatomical landmarks. With upcoming new high-resolution magnetic resonance imaging {(MRI)} protocols, it appears worthwhile to examine the feasibility of characterizing the neocortical fine-structure from anatomical {MRI} scans, thus, defining neocortical fields by in vivo techniques. A fixated brain hemisphere was scanned at a resolution of approximately 0.3 mm. After correcting for intensity inhomogeneities in the dataset, the cortex boundaries (the white/grey matter and grey matter/background interfaces) were determined as a triangular mesh. Radial intensity profiles following the shortest path through the cortex were computed and characterized by a sparse set of features. A statistical similarity measure between features of different regions was defined, and served to define the extent of Brodmann's Areas 4, 17, 44 and 45 in this dataset.}, 1.95 + number = {3}, 1.96 + journal = {Medical Image Analysis}, 1.97 + author = {F. Kruggel and M. K. Brückner and Th. Arendt and C. J. Wiggins and D. Y. von Cramon}, 1.98 + month = sep, 1.99 + year = {2003}, 1.100 + pages = {251--264} 1.101 +}, 1.102 + 1.103 +@inbook{adamson_tracking_2005, 1.104 + series = {Lecture Notes in Computer Science}, 1.105 + title = {A Tracking Approach to Parcellation of the Cerebral Cortex}, 1.106 + volume = {Volume 3749/2005}, 1.107 + isbn = {978-3-540-29327-9}, 1.108 + url = {http://dx.doi.org/10.1007/11566465_37}, 1.109 + abstract = {The cerebral cortex is composed of regions with distinct laminar structure. Functional neuroimaging results are often reported with respect to these regions, usually by means of a brain “atlas”. Motivated by the need for more precise atlases, and the lack of model-based approaches in prior work in the field, this paper introduces a novel approach to parcellating the cortex into regions of distinct laminar structure, based on the theory of target tracking. The cortical layers are modelled by hidden Markov models and are tracked to determine the Bayesian evidence of layer hypotheses. This model-based parcellation method, evaluated here on a set of histological images of the cortex, is extensible to {3-D} images.}, 1.110 + booktitle = {Medical Image Computing and {Computer-Assisted} Intervention – {MICCAI} 2005}, 1.111 + publisher = {Springer Berlin / Heidelberg}, 1.112 + author = {Chris Adamson and Leigh Johnston and Terrie Inder and Sandra Rees and Iven Mareels and Gary Egan}, 1.113 + year = {2005}, 1.114 + pages = {294--301} 1.115 } 1.116 \ No newline at end of file