Leslie G. Ungerleider

Bio: Leslie G. Ungerleider is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Visual cortex & Temporal cortex. The author has an hindex of 108, co-authored 259 publications receiving 56916 citations. Previous affiliations of Leslie G. Ungerleider include National Institute on Drug Abuse.

Mechanisms of Visual Attention in the Human Cortex

Abstract: A typical scene contains many different objects that, because of the limited processing capacity of the visual system, compete for neural representation. The competition among multiple objects in visual cortex can be biased by both bot- tom-up sensory-driven mechanisms and top-down influences, such as selective atten- tion. Functional brain imaging studies reveal that, both in the absence and in the presence of visual stimulation, biasing signals due to selective attention can modulate neural activity in visual cortex in several ways. Although the competition among stimuli for representation is ultimately resolved within visual cortex, the source of top-down biasing signals derives from a network of areas in frontal and parietal cortex.

Functional MRI evidence for adult motor cortex plasticity during motor skill learning

Abstract: Performance of complex motor tasks, such as rapid sequences of finger movements, can be improved in terms of speed and accuracy over several weeks by daily practice sessions. This improvement does not generalize to a matched sequence of identical component movements, nor to the contralateral hand. Here we report a study of the neural changes underlying this learning using functional magnetic resonance imaging (MRI) of local blood oxygenation level-dependent (BOLD) signals evoked in primary motor cortex (M1). Before training, a comparable extent of M1 was activated by both sequences. However, two ordering effects were observed: repeating a sequence within a brief time window initially resulted in a smaller area of activation (habituation), but later in larger area of activation (enhancement), suggesting a switch in M1 processing mode within the first session (fast learning). By week 4 of training, concurrent with asymptotic performance, the extent of cortex activated by the practised sequence enlarged compared with the unpractised sequence, irrespective of order (slow learning). These changes persisted for several months. The results suggest a slowly evolving, long-term, experience-dependent reorganization of the adult M1, which may underlie the acquisition and retention of the motor skill.

Control of goal-directed and stimulus-driven attention in the brain

Abstract: We review evidence for partially segregated networks of brain areas that carry out different attentional functions. One system, which includes parts of the intraparietal cortex and superior frontal cortex, is involved in preparing and applying goal-directed (top-down) selection for stimuli and responses. This system is also modulated by the detection of stimuli. The other system, which includes the temporoparietal cortex and inferior frontal cortex, and is largely lateralized to the right hemisphere, is not involved in top-down selection. Instead, this system is specialized for the detection of behaviourally relevant stimuli, particularly when they are salient or unexpected. This ventral frontoparietal network works as a 'circuit breaker' for the dorsal system, directing attention to salient events. Both attentional systems interact during normal vision, and both are disrupted in unilateral spatial neglect.

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

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.

Complex brain networks: graph theoretical analysis of structural and functional systems

Abstract: Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.