Baseline Activity Predicts Working Memory Load of Preceding Task Condition

Department of Psychiatry and Psychotherapy, University of Marburg, Germany. .
Human Brain Mapping (Impact Factor: 5.97). 11/2013; 34(11). DOI: 10.1002/hbm.22121
Source: PubMed


The conceptual notion of the so-called resting state of the brain has been recently challenged by studies indicating a continuing effect of cognitive processes on subsequent rest. In particular, activity in posterior parietal and medial prefrontal areas has been found to be modulated by preceding experimental conditions. In this study, we investigated which brain areas show working memory dependent patterns in subsequent baseline periods and how specific they are for the preceding experimental condition. During functional magnetic resonance imaging, 94 subjects performed a letter-version of the n-back task with the conditions 0-back and 2-back followed by a low-level baseline in which subjects had to passively observe the letters appearing. In a univariate analysis, 2-back served as control condition while 0-back, baseline after 0-back and baseline after 2-back were modeled as regressors to test for activity changes between both baseline conditions. Additionally, we tested, using Gaussian process classifiers, the recognition of task condition from functional images acquired during baseline. Besides the expected activity changes in the precuneus and medial prefrontal cortex, we found differential activity in the thalamus, putamen, and postcentral gyrus that were affected by the preceding task. The multivariate analysis revealed that images of the subsequent baseline block contain task related patterns that yield a recognition rate of 70%. The results suggest that the influence of a cognitive task on subsequent baseline is strong and specific for some areas but not restricted to areas of the so-called default mode network. Hum Brain Mapp, 2012. © 2012 Wiley Periodicals, Inc.

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    • "The potential applications range from spatial priors (Groves et al., 2009), cortical maps (Macke et al., 2011), image denoising (Zhu et al., 2012), parameter estimation (Wang et al., 2012), white matter fiber clustering (Wassermann et al., 2010) and meta-analysis (Salimi-Khorshidi et al., 2011). GP models were shown to be particularly powerful for clinical applications, providing probabilistic predictions of symptom severity, pain states, recovery, cognitive and disease states using regression (Doyle et al., 2013a; Hope et al., 2013; Marquand et al., 2010) and classification (Hahn et al., 2011; Marquand et al., 2010; Mourao-Miranda et al., 2012; Pyka et al., 2012; Young et al., 2013) using functional and structural MR images as inputs. "
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    • "An association of resting-state cortical activity and WM performance has previously been observed using hemodynamic and neurophysiological markers [17]–[19], [30]–[32]. More specifically, some investigators reported that resting-state activation in the parietal region is consistently linked to an attention network and n-back performance [19], [33]–[35]. "
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