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 #
5R01MH076226-16
Application #
9824584
Study Section
Cognition and Perception Study Section (CP)
Program Officer
Rossi, Andrew
Project Start
2006-01-01
Project End
2020-11-30
Budget Start
2019-12-01
Budget End
2020-11-30
Support Year
16
Fiscal Year
2020
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
Bae, Gi-Yeul; Luck, Steven J (2018) Dissociable Decoding of Spatial Attention and Working Memory from EEG Oscillations and Sustained Potentials. J Neurosci 38:409-422
Gaspelin, Nicholas; Luck, Steven J (2018) Inhibition as a potential resolution to the attentional capture debate. Curr Opin Psychol 29:12-18
Bae, Gi-Yeul; Luck, Steven J (2018) What happens to an individual visual working memory representation when it is interrupted? Br J Psychol :
Beck, Valerie M; Luck, Steven J; Hollingworth, Andrew (2018) Whatever you do, don't look at the...: Evaluating guidance by an exclusionary attentional template. J Exp Psychol Hum Percept Perform 44:645-662
Gaspelin, Nicholas; Luck, Steven J (2018) ""Top-down"" Does Not Mean ""Voluntary"". J Cogn 1:
Bacigalupo, Felix; Luck, Steven J (2018) Event-related potential components as measures of aversive conditioning in humans. Psychophysiology 55:
Gaspelin, Nicholas; Luck, Steven J (2018) The Role of Inhibition in Avoiding Distraction by Salient Stimuli. Trends Cogn Sci 22:79-92
Gaspelin, Nicholas; Luck, Steven J (2018) Distinguishing among potential mechanisms of singleton suppression. J Exp Psychol Hum Percept Perform 44:626-644
Gaspelin, Nicholas; Luck, Steven J (2018) Combined Electrophysiological and Behavioral Evidence for the Suppression of Salient Distractors. J Cogn Neurosci 30:1265-1280
Feuerstahler, Leah M; Luck, Steven J; MacDonald 3rd, Angus et al. (2018) A note on the identification of change detection task models to measure storage capacity and attention in visual working memory. Behav Res Methods :

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