THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

https://surfer.nmr.mgh.harvard.edu/ftp/articles/desikan06-parcellation.pdf

An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

http://midag.cs.unc.edu/pubs/papers/Neuroimage06_Yushkevich.pdf

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Two key aspects of coupled multi-object shape analysis and atlas generation are the choice of representation and subsequent registration methods used to align the sample set For example, a typical brain image can be labeled into three structures: grey matter, white matter and cerebrospinal fluid Many manipulations such as interpolation, transformation, smoothing, or registration need to be performed on these images before they can be used in further analysis Current techniques for such analysis tend to trade off performance between the two tasks, performing well for one task but developing problems when used for the other This article proposes to use a representation that is both flexible and well suited for both tasks We propose to map object labels to vertices of a regular simplex, eg  the unit interval for two labels, a triangle for three labels, a tetrahedron for four labels, etc This representation, which is routinely used in fuzzy classification, is ideally suited for representing and registering multiple shapes On closer examination, this representation reveals several desirable properties: algebraic operations may be done directly, label uncertainty is expressed as a weighted mixture of labels (probabilistic interpretation), interpolation is unbiased toward any label or the background, and registration may be performed directly We demonstrate these properties by using label space in a gradient descent based registration scheme to obtain a probabilistic atlas While straightforward, this iterative method is very slow, could get stuck in local minima, and depends heavily on the initial conditions To address these issues, two fast methods are proposed which serve as coarse registration schemes following which the iterative descent method can be used to refine the results Further, we derive an analytical formulation for direct computation of the "group mean" from the parameters of pairwise registration of all the images in the sample set We show results on richly labeled 2D and 3D data sets

Affine Registration of label maps in Label Space.

Schizophrenia spectrum disorders: Electrophysiological and structural MRI features

We introduce a new approach to medical image analysis that combines deformable model methodologies with concepts from the field of artificial life. In particular, we propose 'deformable organisms', autonomous agents whose task is the automatic segmentation, labeling, and quantitative analysis of anatomical structures in medical images. Analogous to natural organisms capable of voluntary movement, our artificial organisms possess deformable bodies with distributed sensors, as well as (rudimentary) brains with motor, perception, behavior, and cognition centers. Deformable organisms are perceptually aware of the image analysis process. Their behaviors, which manifest themselves in voluntary movement and alteration of body shape, are based upon sensed image features, pre-stored anatomical knowledge, and a deliberate cognitive plan. We demonstrate several prototype deformable organisms based on a multiscale axisymmetric body morphology, including a 'corpus callosum worm' that can overcome noise, incomplete edges, considerable anatomical variation, and interference from collateral structures to segment and label the corpus callosum in 2D mid-sagittal MR brain images.  2002 Elsevier Science B.V. All rights reserved.

/pdf/d-organisms-for-automatic-medical-image-analysis-27vifab938.pdf

D organisms for automatic medical image analysis

Is there p300 asymmetry in schizophrenia ?. reply

Objective: We combined neuropsychological and structural magnetic resonance imaging (MRI) measures to examine the neural and informational processes underlying episodic memory in healthy participants. Method: The Doors-and-People Test (DPT) provided a detailed assessment of episodic memory, including recall and recognition tasks of matched difficulty for social (e.g., people) and non-social (e.g., shape) content. The Wisconsin Card Sorting (WCS) test provided a measure of category learning that relies heavily on executive control and inhibition. A subset of participants also had available high-resolution, 3-T MRI gray matter volume studies of prefrontal cortex (PFC) parcellated into four regions: 1) frontal pole; 2) superior frontal gyrus; 3) middle frontal gyrus; and 4) inferior frontal gyrus. Results: Bivariate neuropsychological correlations revealed a highly statistically significant relationship of reduced WCS perseverative errors and stronger recall for people but not for shapes. By contrast, WCS perseveration did not correlate with any recognition measures. Hierarchical regression revealed that perseverative errors and people recall test scores combined to account for approximately 29.98% to 57.78% of the variance in left PFC gray matter volume. Conclusions: These results may point to an important role of the PFC in mnemonic process of retrieval inhibition in episodic memory for recall of social content in healthy participants.

Martha E. Shenton

Papers

Affine Registration of label maps in Label Space.

Schizophrenia spectrum disorders: Electrophysiological and structural MRI features

D organisms for automatic medical image analysis

Is there p300 asymmetry in schizophrenia ?. reply

Prefrontal Lobe Gray Matter, Cognitive Control and Episodic Memory in Healthy Cognition