Stephen C. Strother

Bio: Stephen C. Strother is an academic researcher from University of Toronto. The author has contributed to research in topics: Medicine & Dementia. The author has an hindex of 51, co-authored 231 publications receiving 9141 citations. Previous affiliations of Stephen C. Strother include Veterans Health Administration & Baycrest Hospital.

Measuring PET scanner sensitivity: relating countrates to image signal-to-noise ratios using noise equivalents counts

Abstract: True coincidence count (TCC) and noise equivalent count (NEC) curves were measured with a standardized 20-cm-diameter nylon cylinder for five different CTI/Siemens PET (positron emission tomography) scanners with several scanner-collimator combinations: (1) 831/08-12 with 1-mm collimator septa; (2) 933/08-12 and 933/08-16 with 3 to 1-mm tapered collimator septa; and (3) 931/08-12 with 3 to 1-mm tapered and a 1-mm collimator septa and the 931/08-16 with 3 to 1-mm tapered collimator septa In addition, TCC and NEC curves on the 933/08-12 were compared with those from an Alderson brain phantom In general, it is found that the TCC curves indicated peak count rates and activity levels that were as much as 50% higher than the corresponding values from NEC curves The primary factor causing this difference is the noise effect of the randoms component It is demonstrated that, compared to the Alderson brain phantom, the standard 20-cm cylinder is a poor predictor of count rate performance for PET brain imaging >

A Multivariate Analysis of Age-Related Differences in Default Mode and Task-Positive Networks across Multiple Cognitive Domains

Abstract: We explored the effects of aging on 2 large-scale brain networks, the default mode network (DMN) and the task-positive network (TPN). During functional magnetic resonance imaging scanning, young and older participants carried out 4 visual tasks: detection, perceptual matching, attentional cueing, and working memory. Accuracy of performance was roughly matched at 80% across tasks and groups. Modulations of activity across conditions were assessed, as well as functional connectivity of both networks. Younger adults showed a broader engagement of the DMN and older adults a more extensive engagement of the TPN. Functional connectivity in the DMN was reduced in older adults, whereas the main pattern of TPN connectivity was equivalent in the 2 groups. Age-specific connectivity also was seen in TPN regions. Increased activity in TPN areas predicted worse accuracy on the tasks, but greater expression of a connectivity pattern associated with a right dorsolateral prefrontal TPN region, seen only in older adults, predicted better performance. These results provide further evidence for age-related differences in the DMN and new evidence of age differences in the TPN. Increased use of the TPN may reflect greater demand on cognitive control processes in older individuals that may be partially offset by alterations in prefrontal functional connectivity.

Machine Learning in Medical Imaging

Abstract: This article will discuss very different ways of using machine learning that may be less familiar, and we will demonstrate through examples the role of these concepts in medical imaging. Although the term machine learning is relatively recent, the ideas of machine learning have been applied to medical imaging for decades, perhaps most notably in the areas of computer-aided diagnosis (CAD) and functional brain mapping. We will not attempt in this brief article to survey the rich literature of this field. Instead our goals will be 1) to acquaint the reader with some modern techniques that are now staples of the machine-learning field and 2) to illustrate how these techniques can be employed in various ways in medical imaging.

Representation Learning: A Review and New Perspectives

Abstract: The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.

Principles of Neural Science

Abstract: 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

The brainweb: phase synchronization and large-scale integration.

Abstract: The emergence of a unified cognitive moment relies on the coordination of scattered mosaics of functionally specialized brain regions. Here we review the mechanisms of large-scale integration that counterbalance the distributed anatomical and functional organization of brain activity to enable the emergence of coherent behaviour and cognition. Although the mechanisms involved in large-scale integration are still largely unknown, we argue that the most plausible candidate is the formation of dynamic links mediated by synchrony over multiple frequency bands.