Adrian M. Owen

Bio: Adrian M. Owen is an academic researcher from University of Western Ontario. The author has contributed to research in topics: Cognition & Working memory. The author has an hindex of 107, co-authored 452 publications receiving 51298 citations. Previous affiliations of Adrian M. Owen include Montreal Neurological Institute and Hospital & Medical Research Council.

N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies

Abstract: One of the most popular experimental paradigms for functional neuroimaging studies of working memory has been the n-back task, in which subjects are asked to monitor the identity or location of a series of verbal or nonverbal stimuli and to indicate when the currently presented stimulus is the same as the one presented n trials previously. We conducted a quantitative meta-analysis of 668 sets of activation coordinates in Talairach space reported in 24 primary studies of n-back task variants manipulating process (location vs. identity monitoring) and content (verbal or nonverbal) of working memory. We found the following cortical regions were activated robustly (voxelwise false discovery rate = 1%): lateral premotor cortex; dorsal cingulate and medial premotor cortex; dorsolateral and ventrolateral prefrontal cortex; frontal poles; and medial and lateral posterior parietal cortex. Subsidiary meta-analyses based on appropriate subsets of the primary data demonstrated broadly similar activation patterns for identity monitoring of verbal stimuli and both location and identity monitoring of nonverbal stimuli. There was also some evidence for distinct frontoparietal activation patterns in response to different task variants. The functional specializations of each of the major cortical components in the generic large-scale frontoparietal system are discussed. We conclude that quantitative meta-analysis can be a powerful tool for combining results of multiple primary studies reported in Talairach space. Here, it provides evidence both for broadly consistent activation of frontal and parietal cortical regions by various versions of the n-back working memory paradigm, and for process- and content-specific frontoparietal activation by working memory.

Detecting Awareness in the Vegetative State

Abstract: We used functional magnetic resonance imaging to demonstrate preserved conscious awareness in a patient fulfilling the criteria for a diagnosis of vegetative state. When asked to imagine playing tennis or moving around her home, the patient activated predicted cortical areas in a manner indistinguishable from that of healthy volunteers.

The problem of functional localization in the human brain

Abstract: Functional imaging gives us increasingly detailed information about the location of brain activity. To use this information, we need a clear conception of the meaning of location data. Here, we review methods for reporting location in functional imaging and discuss the problems that arise from the great variability in brain anatomy between individuals. These problems cause uncertainty in localization, which limits the effective resolution of functional imaging, especially for brain areas involved in higher cognitive function.

An integrative theory of prefrontal cortex function

Abstract: ▪ Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Chapter 11 Working memory

Abstract: Publisher Summary This chapter focuses on the modern notion of short-term memory, called working memory. Working memory refers to the temporary maintenance of information that was just experienced or just retrieved from long-term memory but no longer exists in the external environment. These internal representations are short-lived, but can be maintained for longer periods of time through active rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goal-directed behavior. Working memory is a system that is critically important in cognition and seems necessary in the course of performing many other cognitive functions, such as reasoning, language comprehension, planning, and spatial processing. This chapter demonstrates the functional importance of dopamine to working memory function in several ways. Elucidation of the cognitive and neural mechanisms underlying human working memory is an important focus of cognitive neuroscience and neurology for much of the past decade. One conclusion that arises from research is that working memory, a faculty that enables temporary storage and manipulation of information in the service of behavioral goals, can be viewed as neither a unitary, nor a dedicated system. Data from numerous neuropsychological and neurophysiological studies in animals and humans demonstrates that a network of brain regions, including the prefrontal cortex, is critical for the active maintenance of internal representations.