Poster No:
2351
Submission Type:
Abstract Submission
Authors:
Tyrone Ly1, Linda Chen1, Todd Woodward1
Institutions:
1University of British Columbia, Vancouver, British Columbia
First Author:
Tyrone Ly
University of British Columbia
Vancouver, British Columbia
Co-Author(s):
Linda Chen
University of British Columbia
Vancouver, British Columbia
Todd Woodward
University of British Columbia
Vancouver, British Columbia
Introduction:
In cognitive neuroscience, fMRI investigation of the working memory was most often acquired through analyzing the neural bases of behavior in performance of the Sternberg task. Originally developed by Robert J. Sternberg in 1966, this is a choice reaction time task that requires the usage of working memory. Though this has been a prominent research area for decades, the field has yet to arrive at a consensus for a set of functional brain networks. In the present study, we utilized a version of the task called Sternberg Item Recognition Paradigm to identify and characterize brain networks which demonstrated distinct patterns of activation during participants' performances of this task.
We hypothesized that the Working Memory Big Five set of task-based functional brain networks will be detectable upon analysis of fMRI data for the performance of the Sternberg task: Response, Focus on Visual Features, Initiation, Internal Attention, and Default Mode Networks.
Methods:
Thanks to open science, the fMRI data used in this project was obtained from the Function Biomedical Informatics Research Network phase II multi-site study, which is a publicly available database comprised of fMRI data collected at six different study sites across the US. Subjects included 54 healthy participants and 54 patients with schizophrenia or schizoaffective disorder who performed the Sternberg task while undergoing fMRI scanning sessions. For this task, subjects were instructed to memorize a set of one, three, or five digits (numbers between 0 and 9) in the beginning. After a short break, they would be presented with a series of digits and asked to indicate whether they have seen those before.
Constrained Principal Component Analysis for fMRI (fMRI-CPCA) will be applied on the data to extract and characterize functional brain networks that emerged during task performance and their associated hemodynamic profiles.
Results:
Upon fMRI-CPCA analysis, four functional brain networks were found to have been elicited during participants' performance of the Sternberg task. They included Auditory Attention for Response Network together with three of the Big Five which are Response, Initiation, and Traditional Default Mode Networks. Discovery of the Auditory Attention for Response network was an unexpected and surprising outcome. However, through further examination, we determined that this finding was reasonable because this network underwent deactivation when participants had to shift focus away from auditory stimuli and towards visual details of digits on the screen. All four networks demonstrated load dependency, meaning the more digits subjects had to memorized, the higher the response in their brain network activity. Initiation and Traditional Default Mode Networks both showed increased activity in schizophrenia patients compared to healthy participants, indicating their dysfunction may underlie cognitive impairments in schizophrenia. Finally, analyzing the features of the extracted Response network yielded that it was potentially merged with the Internal Attention network, which has been a common finding in our previous studies.
Conclusions:
From these preliminary results, future research will investigate the effects of individual differences in demographics, personality, behavior, cognitive performance, and symptom of mental illnesses on the observed patterns of network activation. Additionally, since we were able to obtain a reasonably large sample of healthy subjects, further analyses of their data will serve as important implications for broadening our understanding about working memory deficits in several other neurological diseases. Hence, biological underpinnings in these diseases can be identified so that effective treatments can be developed to specifically target the impaired brain networks and move them to a healthier state.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Learning and Memory:
Working Memory 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Novel Imaging Acquisition Methods:
BOLD fMRI 1
Keywords:
Computational Neuroscience
Data analysis
FUNCTIONAL MRI
Learning
Memory
Multivariate
Neurological
Open Data
Psychiatric Disorders
Schizophrenia
1|2Indicates the priority used for review
Provide references using author date format
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