Andreas Hess

Superposition of horseshoe-like periodicity and linear tonotopic maps in auditory cortex of the Mongolian gerbil.

The segregation of an individual sound from a mixture of concurrent sounds, the so‐called cocktail‐party phenomenon, is a fundamental and largely unexplained capability of the auditory system. Speaker recognition involves grouping of the various spectral (frequency) components of an individuals voice and segregating them from other competing voices. The important parameter for grouping may be the periodicity of sound waves because the spectral components of a given voice have one periodicity, viz. fundamental frequency, as their common denominator. To determine the relationship between the representations of spectral content and periodicity in the primary auditory cortex (AI), we used optical recording of intrinsic signals and electrophysiological mapping in Mongolian gerbils (Meriones unguiculatus). We found that periodicity maps as an almost circular gradient superimposed on the linear tonotopic gradient in the low frequency part of AI. This geometry of the periodicity map may imply competitive signal processing in support of the theory of ‘winner‐takes‐all’.

A new method for reliable and efficient reconstruction of 3-dimensional images from autoradiographs of brain sections

Valuable information on metabolism and function of distinct brain regions can be extracted from autoradiographs of 2D brain sections, e.g. after labelling with the non-metabolisable sugar derivate [14C]-2-fluoro-deoxyglucose (2-FDG). For a more complete comprehension of the data and for a comparison with information obtained by modern functional imaging techniques it is essential to have a reliable and efficient method for the 3D reconstruction of autoradiographs of serial sections. This paper describes a new method for the alignment of 2D 2-FDG images, that combines two established algorithms, i.e. principal axes alignment followed by consistent matrix transformation. The power and efficiency of this new 2-step method is compared to those of various previously described procedures.

Functional mapping of transsynaptic effects of local manipulation of inhibition in gerbil auditory cortex.

Cortical networks are under the tonic influence of inhibition which is mainly mediated by GABA. The state of inhibition of small neuronal populations in the auditory cortex (AC) field AI of gerbils was altered by local microinjection of GABA, of the GABA(A)-receptor agonist 4-piperidine-sulfonic acid (P4S) and the GABA(A)-receptor antagonists bicuculline methiodide (BMI) and SR-95531. In order to elucidate direct and transsynaptic effects of the alterations of inhibition produced by these substances we used the 2-fluoro-2-deoxy-D-[(14)C(U)] glucose (FDG) mapping method. The injection of GABA (10 mM) caused no significant changes in FDG labeling but P4S caused a marked decrease of local FDG uptake in a small region surrounding the injection site but in no other region. The injection of the GABA(A)-receptor antagonists caused massive increases of FDG uptake within the entire ipsilateral AC, whereas the contralateral AC was not significantly affected in spite of prominent callosal connections. However, disinhibited excitatory output from the ipsilateral AC is suggested by a strong increase in FDG labeling of the corticothalamic fiber tract and ipsilateral structures like medial geniculate nucleus, caudal striatum, and lateral amygdaloid nucleus and a structure at the caudoventral margin of the thalamic reticular nucleus, presumably the subgeniculate nucleus, a structure with hitherto unknown connections and function. No alteration of FDG uptake could be detected in the inferior colliculus, another main descending target structure of the AC. In summary, the effects resulting from microinjection of GABA(A)-receptor antagonists reflect a differential influence of the AC on its anatomically connected target regions. The findings demonstrate the potential of the method of focal application of neuroactive substances in combination with the FDG technique for mapping their transsynaptic influences which are hard to derive from anatomical tracing studies alone.

BrainView: a computer program for reconstruction and interactive visualization of 3D data sets

A computer program, BrainView, is presented which has been developed to reconstruct, visualize, and evaluate three dimensional (3D) biological and medical imaging data, such as images from histological sections, confocal microscopy, or magnetic resonance tomography. The program allows the simultaneous display of three orthogonal sectional planes, i.e. the horizontal, frontal, and sagittal planes, of reconstructed data and to move interactively through the brain to optimally assess the 3D data set. Furthermore, any arbitrary sectioning plane through a data set can be visualized. Implemented warping algorithms allow the geometric normalization of data sets of different animals, modalities or developmental stages as a preprocessing for the comparative evaluation of the data. For a quantitative analysis, data sets can be segmented based on equal grey levels and the corresponding equidensities were calculated. The program works on Apple-Macintosh computers and has a user-friendly graphical interface. The BrainView program is discussed in comparison to related programs.

3D edge detection to define landmarks for point-based warping in brain imaging

The accurate comparison of inter-individual 3D image datasets of brains requires warping techniques to reduce geometric variations. In this study we use a point-based method of warping with weighted sums of displacement vectors, which is extended by an optimization process. To improve the practicability of 3D warping, we investigate 3D operators as landmark detectors for the applicability to our image datasets. The combined approach was tested on 3D autoradiographs of brains of Mongolian gerbils. The warping function is distance-weighted with landmark-specific weighting factors. These weighting factors are optimized by a computational evolution strategy. Within this optimization process the quality of warping is quantified by the sum of spatial differences of manually predefined registration points (registration error). The described approach combines a highly suitable procedure to detect landmarks in brain images and a point-based warping technique, which optimizes local weighting factors.

A new point-based warping method for enhanced and simplified analysis of functional brain image data☆

Comparison of brain imaging data requires the exact matching of data sets from different individuals. Warping methods, used to optimize matching of data sets, can exploit either local gray value distribution or identifiable reference points within the images to be compared. Gray value-based warping, which is more comfortable, cannot be used if gray values include functional information that should be compared between images. A major drawback in the use of point-based warping methods is the lack of methods for efficient and precise definition of reference points (landmarks) within comparable data sets. Here, we present a novel approach to automatically detect sufficient numbers of landmarks, which is based on 3D differential operators. In addition, we have developed a new distance-weighted warping method, which optimizes individual local weighting factors of displacement vectors. The quality of the methods was evaluated using a set of autoradiographs documenting the metabolic activity of gerbil brains after acoustic stimulation. The new warping method was compared with known methods of landmark-based warping, i.e., warping with radial basis functions and with distance-weighted methods. For the data sets presented in this study our new optimized warping method produced an increase in linear cross correlation of 4.44%, an increase in volume overlap index of 1.55%, and a decrease in the registration error of 36.2%. In addition, the detection of functional differences was improved after warping. Therefore, the new method is a powerful tool, which enhances the comparison of complex biological structures and the quantitative evaluation of functional imaging data.

Warping with optimized weighting factors of displacement vectors-a new method to reduce inter-individual variations in brain imaging

An accurate comparison of multimodal and/or inter-individual 3D image datasets of brains requires geometric transformation techniques (warping) to reduce geometric variations. Here, a subset of warping techniques, namely point-based warping, is investigated. For this kind of warping landmarks between datasets have to be defined. In large 3D datasets manual setting of landmarks is time-consuming and therefore impracticable. Consequently we approach this problem by investigating fast automatic procedures for determining landmarks, based on Monte Carlo techniques. The combined methods were tested on 3D autoradiographs of the brains of gerbils. The results are evaluated by three different similarity functions. We found that the combined approach is highly applicable in processing brain images.

A New Approach to Define Landmarks for Point-Based Warping in Brain Imaging

An accurate comparison of inter-individual 3D image datasets of brains requires warping techniques to reduce geometric variations. In this study we use a point-based method of warping with weighted sums of displacement vectors, which is extended by an optimization process. To improve the practicability of 3D warping, we investigate fast automatic procedures for determining landmarks. The combined approach was tested on 3D autoradiographs of brains of Mongolian gerbils. The landmark-generator is based on Monte-Carlo-techniques to detect corresponding reference points at edges of anatomical structures. The warping function is distance-weighted with landmark-specific weighting factors. These weighting factors are optimized by a computational evolution strategy. Within this optimization process the quality of warping is quantified by the sum of spatial differences of manually predefined registration points (registration error). The described approach combines a highly suitable procedure to detect landmarks in brain images and a point-based warping technique, which optimizes local weighting factors. The optimization of the weighting factors improves the similarity between the warped and the target image.

Point-based warping with optimized weighting factors of displacement vectors

The accurate comparison of inter-individual 3D image brain datasets requires non-affine transformation techniques (warping) to reduce geometric variations. Constrained by the biological prerequisites we use in this study a landmark-based warping method with weighted sums of displacement vectors, which is enhanced by an optimization process. Furthermore, we investigate fast automatic procedures for determining landmarks to improve the practicability of 3D warping. This combined approach was tested on 3D autoradiographs of Gerbil brains. The autoradiographs were obtained after injecting a non-metabolized radioactive glucose derivative into the Gerbil thereby visualizing neuronal activity in the brain. Afterwards the brain was processed with standard autoradiographical methods. The landmark-generator computes corresponding reference points simultaneously within a given number of datasets by Monte-Carlo-techniques. The warping function is a distance weighted exponential function with a landmark- specific weighting factor. These weighting factors are optimized by a computational evolution strategy. The warping quality is quantified by several coefficients (correlation coefficient, overlap-index, and registration error). The described approach combines a highly suitable procedure to automatically detect landmarks in autoradiographical brain images and an enhanced point-based warping technique, optimizing the local weighting factors. This optimization process significantly improves the similarity between the warped and the target dataset.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Comparison of Different 3D Edge Detection Methods to Define Landmarks for Point-Based Warping in Autoradiographic Brain Imaging

Warping can be used to reduce interindividual structural variations of 3D image datasets of brains by generating a standard brain and subsequent matching of individual datasets to this reference system. Point-based warping uses structural information (landmarks) to construct the spatial correspondence between the datasets. For this we compare the performance of three landmark detection algorithms. The first two approaches use a threshold-based definition of landmarks, the third spatial derivations of voxels. The warping is based on a distance-weighted method with an exponential weighting function. All methods tested are able to reduce structural variations, best results are obtained by the derivation approach.