Schizophrenia is a prevalent mental health disorder that creates enormous social, economic, and interpersonal hardships for patients and their families. Although hallucinations and delusions are the most salient symptoms of this disease, schizophrenia also involves cognitive deficits that predict long-term outcome and are not ameliorated by current medications. In particular, we have shown that schizophrenia patients exhibit large reductions in working memory capacity that predict the degree of overall cognitive dysfunction. Animal models of working memory capacity focus on the idea that working memory is implemented by means of sustained neural activity (active maintenance) that arises from recurrent neural connections, and this has formed the basis of computational models of the microcircuitry of schizophrenia. However, research in humans indicates that both passive and active maintenance mechanisms underlie working memory. These passive and active maintenance mechanisms have different neural substrates and have been hypothesized to play very different roles in broader cognitive function. If we are to understand and treat impaired cognition in schizophrenia, we must develop a better understanding of these active and passive maintenance subsystems. The purpose of the present proposal is to advance our understanding of the active and passive components of working memory in healthy individuals and simultaneously lay the groundwork for the next steps in our program of schizophrenia research. Specifically, the proposed project will test the hypothesis that the active maintenance component of working memory plays the role traditionally ascribed to working memory in general, namely serving as a temporary buffer for non-automated cognitive operations. For example, our preliminary data indicate that the active maintenance of information in a working memory task terminates when a simple discrimination must be performed during the delay period. However, passive maintenance does not appear to be disrupted by the intervening task. Thus, active maintenance is used to perform individual cognitive operations, and passive maintenance is used to maintain other relevant information while these cognitive operations are being performed. Our pilot data also indicate that active maintenance can be used to store precise, metric information about visual features, whereas passive maintenance is more categorical. To differentiate active maintenance from passive storage, we will use multiple converging measures of sustained memory-related brain activity, including sustained electrical potentials in ERP recordings, sustained BOLD activity in fMRI, decoding of memory content from single-trial EEG signals, and decoding of memory content from the single-trial BOLD signal. In addition, advanced psychophysical methods will be used to assess differences in the informational content of active and passive working memory representations. This research will feed directly into our program of translational research on cognitive dysfunction in schizophrenia, providing concepts and methods than can be used in the development and assessment of new treatments.

Public Health Relevance

Recent research shows that reduced working memory capacity plays a key role in schizophrenia, and this is guiding the development of new treatments. However, recent research shows that working memory involves at least two distinct neural mechanisms (active and passive maintenance), which may have important implications for treatment development. The present project aims to provide a clearer understanding of these two mechanisms in the healthy brain, which will provide the basic science backbone for research designed to understand and treat cognitive dysfunction in schizophrenia.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH076226-14
Application #
9385004
Study Section
Cognition and Perception Study Section (CP)
Program Officer
Rossi, Andrew
Project Start
2006-01-01
Project End
2020-11-30
Budget Start
2017-12-01
Budget End
2018-11-30
Support Year
14
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of California Davis
Department
Neurology
Type
Schools of Medicine
DUNS #
047120084
City
Davis
State
CA
Country
United States
Zip Code
95618
Oakes, Lisa M; Baumgartner, Heidi A; Kanjlia, Shipra et al. (2017) An eye tracking investigation of color-location binding in infants' visual short-term memory. Infancy 22:584-607
Luck, Steven J; Gaspelin, Nicholas (2017) How to get statistically significant effects in any ERP experiment (and why you shouldn't). Psychophysiology 54:146-157
Bacigalupo, Felix; Luck, Steven J (2017) Event-related potential components as measures of aversive conditioning in humans. Psychophysiology :
Gaspelin, Nicholas; Luck, Steven J (2017) Distinguishing Among Potential Mechanisms of Singleton Suppression. J Exp Psychol Hum Percept Perform :
Beck, Valerie M; Luck, Steven J; Hollingworth, Andrew (2017) Whatever You Do, Don't Look at the . . .: Evaluating Guidance by an Exclusionary Attentional Template. J Exp Psychol Hum Percept Perform :
Bae, Gi-Yeul; Luck, Steven J (2017) Interactions between visual working memory representations. Atten Percept Psychophys 79:2376-2395
Gaspelin, Nicholas; Leonard, Carly J; Luck, Steven J (2017) Suppression of overt attentional capture by salient-but-irrelevant color singletons. Atten Percept Psychophys 79:45-62
Bengson, Jesse J; Luck, Steven J (2016) Effects of strategy on visual working memory capacity. Psychon Bull Rev 23:265-70
Tas, A Caglar; Luck, Steven J; Hollingworth, Andrew (2016) The relationship between visual attention and visual working memory encoding: A dissociation between covert and overt orienting. J Exp Psychol Hum Percept Perform 42:1121-38
Miller, Claire E; Shapiro, Kimron L; Luck, Steven J (2015) Electrophysiological measurement of the effect of inter-stimulus competition on early cortical stages of human vision. Neuroimage 105:229-37

Showing the most recent 10 out of 50 publications