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State-Space Analysis of Working Memory in Schizophrenia: An FBIRN Study

Published online by Cambridge University Press:  01 January 2025

Firdaus Janoos ,
Gregory Brown ,
Istvan Á. Mórocz  and
William M. Wells III
Firdaus Janoos*
Affiliation:
Brigham and Women’s Hospital Harvard Medical School
Gregory Brown
Affiliation:
University of California, San Diego
Istvan Á. Mórocz
Affiliation:
Brigham and Women’s Hospital
William M. Wells III
Affiliation:
Brigham and Women’s Hospital Harvard Medical School
*
Requests for reprints should be sent to Firdaus Janoos, Brigham and Women’s Hospital, Boston, MA, USA. E-mail: fjanoos@bwh.harvard.edu
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Abstract

The neural correlates of working memory (WM) in schizophrenia (SZ) have been extensively studied using the multisite fMRI data acquired by the Functional Biomedical Informatics Research Network (fBIRN) consortium. Although univariate and multivariate analysis methods have been variously employed to localize brain responses under differing task conditions, important hypotheses regarding the representation of mental processes in the spatio-temporal patterns of neural recruitment and the differential organization of these mental processes in patients versus controls have not been addressed in this context. This paper uses a multivariate state-space model (SSM) to analyze the differential representation and organization of mental processes of controls and patients performing the Sternberg Item Recognition Paradigm (SIRP) WM task. The SSM is able to not only predict the mental state of the subject from the data, but also yield estimates of the spatial distribution and temporal ordering of neural activity, along with estimates of the hemodynamic response. The dynamical Bayesian modeling approach used in this study was able to find significant differences between the predictability and organization of the working memory processes of SZ patients versus healthy subjects. Prediction of some stimulus types from imaging data in the SZ group was significantly lower than controls, reflecting a greater level of disorganization/heterogeneity of their mental processes. Moreover, the changes in accuracy of predicting the mental state of the subject with respect to parametric modulations, such as memory load and task duration, may have important implications on the neurocognitive models for WM processes in both SZ and healthy adults. Additionally, the SSM was used to compare the spatio-temporal patterns of mental activity across subjects, in a holistic fashion and to derive a low-dimensional representation space for the SIRP task, in which subjects were found to cluster according to their diagnosis.

Keywords

state-space analysisMarkov chain modelsschizophreniaworking memorymulti-site functional MRI
Type
Original Paper
Information
Psychometrika , Volume 78 , Issue 2 , April 2013 , pp. 279 - 307
Copyright
Copyright © 2012 The Psychometric Society

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