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Functional connectivity analysis of working memory during a mental arithmetic task

Rina Hagiwara1*, Satoru Hiwa2 and Tomoyuki Hiroyasu2
  • 1 Doshisha University, Graduate School of Life and Medical Sciences, Japan
  • 2 Doshisha University, Faculty of Life and Medical Sciences, Japan

We spend daily life by using a storage system called working memory. Working memory (WM) has been defined as a system for the temporary holding and manipulation of the information [1 - 2]. It plays an important role in cognitive functions such as language recognition and reasoning ability. A limited memory buffer of retaining and processing the information is called as working memory capacity (WMC), and it is different between individuals. The individual differences in WMC affect a variety of cognitive activities. For example, poor WMC is said to be related to attention deficit hyperactivity disorder (ADHD), and working memory training to make WMC increased is used as treatment for ADHD patients [3]. Working memory is used in the case of reading and solving mental arithmetic. Reading span test and N-back task are often used as an assessment of WMC. However, these paradigms are far from our daily life. Therefore, in this study, we adopted mental arithmetic task often used in everyday life. In a complex system such as working memory, each brain region does not always activate individually but often works cooperatively with other regions. Although the brain regions associated with working memory during mental arithmetic task had been revealed [4], cooperative relationship among these regions have not been investigated enough. Therefore, in this study, we investigated the cooperative relationship among the brain regions during mental arithmetic task using a functional connectivity magnetic resonance imaging (fcMRI) study. Fourteen healthy adults (average age: 22.5 ± 1.5 years, 13 right-handed, 11 male) participated in this study. Participants performed the mental arithmetic task, which was consisted of the easy (non-working memory) and difficult (working memory) task, in the fMRI scanner. We calculated the correlation matrix and analyzed the functional connectivity. Acquired images were preprocessed by SPM8, and the activated regions were extracted and analyzed. A functional connectivity matrix of the individual data was calculated using Conn toolbox [5]. Each image was partitioned into 116 regions using automatic anatomical labeling (AAL) atlas, and ROI-to-ROI connectivity was calculated for 116 regions. Moreover, graph theory metrics (degree and clustering coefficient) were calculated for each functional connectivity matrix using Brain Connectivity Toolbox [6]. Degree is the number of edges connected to other nodes and indicates the centrality of a certain node (the brain region). Clustering coefficient indicates the degree to which nodes tend to cluster together. Moreover, the community of the brain network is extracted using Newman algorithm, which is a graph partitioning method for network analysis. These metrics allow us to quantitatively analyze the structure of brain network. Average correct answers of easy and difficult tasks were 30.7±4.9% and 3.88±1.17%, respectively, and it was confirmed that the answers of easy task were significantly higher than those of difficult task (t (13) = 22.5, p < 0.01). By group analysis we performed a paired t-test to examine the difference of activation between easy task and difficult task. Supplementary motor area and middle temporal gyrus were extracted as the regions whose activations were significantly higher for difficult task than for easy task. Significant activated regions during the difficult task were both cuneus and left precuneus. Since these regions are associated with working memory [7 - 9], they activated during the difficult task.Moreover, degree measures of these regions were significantly higher for difficult task than for easy task (paird t-test, p < 0.05). This suggested that these regions worked cooperatively with other regions during the difficult task compared with during the easy task. On the other hand, the clustering coefficient is higher for difficult task than for easy task for all regions (p < 0.05). This indicated that brain regions tended to form a cluster during the difficult task.Furthermore, 116 brain regions were partitioned into four communities, the frontal lobe, the parietal lobe, the temporal lobe and the occipital lobe in easy task. In difficult task, the frontal lobe and the parietal lobe were categorized into the same group, so that three communities were identified. These results suggested that the frontal lobe and the parietal lobe needed to work together during working memory task.In addition, observation of the regions connected from the both cuneus revealed that there were connections from the both cuneus to the frontal lobe (left middle frontal gyrus) and the parietal lobe (left inferior parietal lobule, left paracentral lobule) during difficult task. The frontal-parietal network is associated with visual attention, and controls the occipital visual cortex to selectively process only the required visual information [10]. Since our results found connections between the both cuneus and the frontal and parietal lobes, it suggested that the cuneus (the occipital visual cortex) was controlled by the frontal lobe and the parietal lobe.

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Keywords: functional connectivity, working memory, Mental arithmetic task, graph theory, fMRI

Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.

Presentation Type: Poster

Topic: Neuroimaging

Citation: Hagiwara R, Hiwa S and Hiroyasu T (2016). Functional connectivity analysis of working memory during a mental arithmetic task. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00032

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Received: 28 Apr 2016; Published Online: 18 Jul 2016.

* Correspondence: Ms. Rina Hagiwara, Doshisha University, Graduate School of Life and Medical Sciences, Kyoto, Japan, rhagiwara@mis.doshisha.ac.jp