Biomedical subjects

Arno Klein

Publications and source records attributed to Arno Klein.

3 recordsLinked to original sources

PubMed2005

Mindboggle: automated brain labeling with multiple atlases.

BACKGROUND: To make inferences about brain structures or activity across multiple individuals, one first needs to determine the structural correspondences across their image data. We have recently developed Mindboggle as a fully automated, feature-matching approach to assign anatomical labels to cortical structures and activity in human brain MRI data. Label assignment is based on structural correspondences between labeled atlases and unlabeled image data, where an atlas consists of a set of labels manually assigned to a single brain image. In the present work, we study the influence of using variable numbers of individual atlases to nonlinearly label human brain image data. METHODS: Each brain image voxel of each of 20 human subjects is assigned a label by each of the remaining 19 atlases using Mindboggle. The most common label is selected and is given a confidence rating based on the number of atlases that assigned that label. The automatically assigned labels for each subject brain are compared with the manual labels for that subject (its atlas). Unlike recent approaches that transform subject data to a labeled, probabilistic atlas space (constructed from a database of atlases), Mindboggle labels a subject by each atlas in a database independently. RESULTS: When Mindboggle labels a human subject's brain image with at least four atlases, the resulting label agreement with coregistered manual labels is significantly higher than when only a single atlas is used. Different numbers of atlases provide significantly higher label agreements for individual brain regions. CONCLUSION: Increasing the number of reference brains used to automatically label a human subject brain improves labeling accuracy with respect to manually assigned labels. Mindboggle software can provide confidence measures for labels based on probabilistic assignment of labels and could be applied to large databases of brain images.

Journal Article↗

PubMed2004

Mindboggle: a scatterbrained approach to automate brain labeling.

Mindboggle (http://www.binarybottle.com/mindboggle.html) is a fully automated, feature matching approach to label cortical structures and activity anatomically in human brain MRI data. This approach does not assume that the existence of component structures and their relative spatial relationship is preserved from brain to brain, but instead disassembles a labeled atlas and reassembles its pieces to match corresponding pieces in an unlabeled subject brain before labeling. Mindboggle: (1) converts linearly coregistered subject and atlas MRI data into sulcus pieces, (2) matches each atlas piece with a combination of subject pieces by minimizing a cost function, (3) transforms atlas label boundaries to the matching subject pieces, (4) warps atlas labels to their transformed boundaries, and (5) propagates labels to fill remaining gaps in a mask derived from the subject brain. We compared Mindboggle with four registration methods: linear registration, and nonlinear registration using SPM2, AIR, and ANIMAL. Automated labeling by all of the nonlinear methods was found to be at least comparable with linear registration. Mindboggle outperformed every other method, as measured by the agreement between overlapping atlas labels and manually assigned subject labels, with respect to the union or the intersection of voxels. After applying the same procedure that Mindboggle uses to fill a subject's segmented gray matter mask with labels (step 5), the results of the other methods improved. However, after performing a one-way ANOVA (and Tukey's honestly significant difference criterion) in a multiple comparison between the results obtained by the different methods, Mindboggle was still found to be the only nonlinear method whose labeling performance was significantly better than that of linear registration or SPM2. Further advantages to Mindboggle include a high degree of robustness against image artifacts, poor image quality, and incomplete brain data. We tested the latter hypothesis by conducting all of the tests again, this time registering the atlas to an artificially lesioned version of itself, and found that Mindboggle was the only method whose performance did not degrade significantly as the lesion size increased.

Animals↗

PubMed2002

Zolpidem pharmacokinetic properties in young females: influence of smoking and oral contraceptive use.

Sixteen normal healthy female volunteers, ages 22 to 42 years, participated in this study to determine the pharmacokinetic characteristics of zolpidem and the influence of oral contraceptives and smoking. Five of the volunteers were cigarette smokers, and 8 were on oral contraceptive preparations (OCP). All subjects received a 5 mg oral dose of zolpidem tartrate, followed by multiple blood samples that were assayed by HPLC with fluorescence detection. Among all subjects, mean apparent oral clearance was 376 ml/min (5.8 ml/min/kg), elimination half-life was 2.4 hours, and maximum serum zolpidem concentration was 60 ng/ml. Clearance was higher (445 vs. 345 ml/min) and half-life was shorter (1.8 vs. 2.7 h) in smokers than nonsmokers, although the differences were not statistically significant. Likewise, zolpidem clearance was higher and half-life shorter in women using OCP but differences were not significant. Differences in zolpidem kinetics associated with smoking may be explained by the small contribution of cytochrome P450-1A2 to net clearance of zolpidem. In any case, the differences were quantitatively small and not likely to be of clinical importance.

Administration, Oral↗