A data-driven reconceptualization of the RDoC construct of working memory: Neural correlates of underlying factors and implications for schizophrenia

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Project Summary / Abstract Over two decades of research has focused on elucidating the pathophysiology of working memory (WM) deficits in patients with schizophrenia in the hopes of finding effective treatments, as these deficits are more closely linked to functional outcomes in patients than are psychotic symptoms, and they are presumed to arise from alterations in dopaminergic function that may be fundamental to the pathogenesis of schizophrenia. However, the overwhelming majority of research in this area has treated WM as a unitary cognitive ability that is assayed equally well by any of the wide range of tasks commonly employed in functional Magnetic Resonance Imaging (fMRI) studies of patients with schizophrenia. In contrast, empirical evidence suggests that at least three distinct cognitive abilities contribute to performance on WM tasks: attentional control, short-term memory capacity, and long-term (or secondary) memory retrieval. These are presumed to have dissociable neural substrates and may be differentially impaired in different patients, which would serve to obscure potential biomarkers of WM deficit in case-control fMRI studies of patients. The NIMH Research Domain Criteria (RDoC) Matrix attempts to capture some of this complexity by defining four subconstructs of WM (active maintenance, flexible updating, limited capacity, and interference controls), although these differ from those based on empirical work described above, and no empirical work to date has attempted to determine the extent to which various WM tasks tap these four putative subconstructs. The overarching goal of this application is to conduct a large-scale latent-variable analysis of the most commonly employed WM tasks in the fMRI literature of schizophrenia, along with a broad array of other cognitive tasks, in order to clarify the underlying cognitive abilities (or subconstructs) that subserve WM task performance and to identify neural correlates of these empirically identified subconstructs. To this end, 500 participants will undergo behavioral testing on 9 WM and 12 other cognitive tasks in order to provide a robust dataset for latent variable analysis using factor analytic and structural equation modeling techniques that will identify the subconstructs that underlie performance on each of the WM tasks. Next, 80 patients with schizophrenia and 80 matched control participants will undergo the same battery of tasks, but will perform 7 of the WM tasks during fMRI scanning, in order to identify neural correlates of the subconstructs that are specifically disrupted in schizophrenia. This work will help to advance our understanding of WM deficits in schizophrenia and will identify specific neural targets, and the optimal tasks that future investigators can employ to target them, that are disrupted in schizophrenia. These neural targets can then form the basis for establishing target engagement in clinical trials aimed at finding treatments for cognitive deficits in schizophrenia.