Training-Induced Functional Activation Changes in Dual-Task Processing: An fMRI Study

Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61810, USA.
Cerebral Cortex (Impact Factor: 8.67). 02/2007; 17(1):192-204. DOI: 10.1093/cercor/bhj137
Source: PubMed


Although training-induced changes in brain activity have been previously examined, plasticity associated with executive functions remains understudied. In this study, we examined training-related changes in cortical activity during a dual task requiring executive control. Two functional magnetic resonance imaging (fMRI) sessions, one before training and one after training, were performed on both a control group and a training group. Using a region-of-interest analysis, we examined Time x Group and Time x Group x Condition interactions to isolate training-dependent changes in activation. We found that most regions involved in dual-task processing before training showed reductions in activation after training. Many of the decreases in activation were correlated with improved performance on the task. We also found an area in the dorsolateral prefrontal cortex that showed an increase in activation for the training group for the dual-task condition, which was also correlated with improved performance. These results are discussed in relation to the efficacy of training protocols for modulating attention and executive functions, dual-task processing, and fMRI correlates of plasticity.

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Available from: Jennifer Kim, Oct 08, 2015
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    • "Therefore, our results showing immediate reduction of cortical activation appear to be distinguished from those of previous studies on motor learning and common sense (Hund-Georgiadis and von Cramon 1999; Lang and Bastian 2002; Erickson et al. 2007; Picard et al. 2013). Many functional neuroimaging studies have reported on the differences of cortical activation between one motor task and the addition of another task (Gordon et al. 1998; Johansen-Berg and Matthews 2002; Erickson et al. 2007; Lee et al. 2010; Johannsen et al. 2013; Wu et al. 2013; Beurskens et al. 2014). Among these studies, only a few have reported on change in activation in the SM1, like the current study (Johansen-Berg and Matthews 2002; Lee et al. 2010). "
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    ABSTRACT: Nine right-handed normal subjects were recruited for this study. We compared the cortical activation during execution of hand movements (right finger flexion-extension) with that during execution of hand movements while chewing gum (right side chewing). We found that execution of hand movements while chewing gum induced less activation in the contralateral SM1 than hand movements alone. Based on our findings, it appears chewing gum during execution of hand movements enhanced the efficiency of hand movements.
    Somatosensory & Motor Research 06/2015; 32(2):110-113. DOI:10.3109/08990220.2014.991782 · 0.64 Impact Factor
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    • "Interestingly, studies have demonstrated that practice and cognitive training can diminish the decline in performance, or multitasking cost, which occurs when engaged in multitasking behavior (Anguera et al., 2013; Bherer et al., 2005; Kramer, Larish, & Strayer, 1995). Importantly, training-based improvements in multitasking performance are thought to arise from cortical activity changes in dorsolateral prefrontal cortex (DLPFC) (Erickson et al., 2007). A collective view of these findings led us to hypothesize that multitasking performance may be influenced by neuroplastic changes in prefrontal cortical function, and that the DLPFC is a critical node involved in the dynamic changes of this cognitive control system. "
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    ABSTRACT: The dorsolateral prefrontal cortex (DLPFC) has been proposed to play an important role in neural processes that underlie multitasking performance. However, this claim is underexplored in terms of direct causal evidence. The current study aimed to delineate the causal involvement of the DLPFC during multitasking by modulating neural activity with transcranial direct current stimulation (tDCS) prior to engagement in a demanding multitasking paradigm. The study is a single-blind, crossover, sham-controlled experiment. Anodal tDCS or sham tDCS was applied over left DLPFC in forty-one healthy young adults (aged 18-35 years) immediately before they engaged in a 3-D video game designed to assess multitasking performance. Participants were separated into three subgroups: real-sham (i.e., real tDCS in the first session, followed by sham tDCS in the second session 1 h later), sham-real (sham tDCS first session, real tDCS second session), and sham-sham (sham tDCS in both sessions). The real-sham group showed enhanced multitasking performance and decreased multitasking cost during the second session, compared to first session, suggesting delayed cognitive benefits of tDCS. Interestingly, performance benefits were observed only for multitasking and not on a single-task version of the game. No significant changes were found between the first and second sessions for either the sham-real or the sham-sham groups. These results suggest a causal role of left prefrontal cortex in facilitating the simultaneous performance of more than one task, or multitasking. Moreover, these findings reveal that anodal tDCS may have delayed benefits that reflect an enhanced rate of learning. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Cortex 05/2015; 69. DOI:10.1016/j.cortex.2015.05.014 · 5.13 Impact Factor
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    • "omation of processing ( Hempel et al . , 2004 ) . Furthermore , a training of multi - task processing revealed training - induced reductions in activity of brain areas responsible for stimulus - response associations , attentional control , and response selection process as well as an increase of activity in a region related to executive control ( Erickson et al . , 2007 ) . Such reductions in brain activity induced by training may reflect increased task selectivity within the areas ( Dux et al . , 2009 ) . Even thirty hours of training on a video game can induce reduction of activation in attentional control areas , suggesting a reduction of attentional demands after the training ( Lee et al . , 2012 )"
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    ABSTRACT: Owing to the recent advances in neurotechnology and the progress in understanding of brain cognitive functions, improvements of cognitive performance or acceleration of learning process with brain enhancement systems is not out of our reach anymore, on the contrary, it is a tangible target of contemporary research. Although a variety of approaches have been proposed, we will mainly focus on cognitive training interventions, in which learners repeatedly perform cognitive tasks to improve their cognitive abilities. In this review article, we propose that the learning process during the cognitive training can be facilitated by an assistive system monitoring cognitive workloads using electroencephalography (EEG) biomarkers, and the brain connectome approach can provide additional valuable biomarkers for facilitating leaners' learning processes. For the purpose, we will introduce studies on the cognitive training interventions, EEG biomarkers for cognitive workload, and human brain connectome. As cognitive overload and mental fatigue would reduce or even eliminate gains of cognitive training interventions, a real-time monitoring of cognitive workload can facilitate the learning process by flexibly adjusting difficulty levels of the training task. Moreover, cognitive training interventions should have effects on brain sub-networks, not on a single brain region, and graph theoretical network metrics quantifying topological architecture of the brain network can differentiate with respect to individual cognitive states as well as to different individuals' cognitive abilities, suggesting that the connectome is a valuable approach for tracking the learning progress. Although only a few studies have exploited the connectome approach for studying alterations of the brain network induced by cognitive training interventions so far, we believe that it would be a useful technique for capturing improvements of cognitive functions.
    Frontiers in Systems Neuroscience 04/2015; 9:44. DOI:10.3389/fnsys.2015.00044
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