Background: This study investigated the relation between cognition and the neural connection from injured cingulum to brainstem cholinergic nuclei in patients with traumatic brain injury (TBI), using diffusion tensor tractography (DTT).
Methods: Among 353 patients with TBI, 20 chronic patients who showed discontinuation of both anterior cingulums from the basal forebrain on DTT were recruited for this study. The Wechsler Intelligence Scale and the Memory Assessment Scale (MAS; short-term, verbal, visual and total memory) were used for assessment of cognition. Patients were divided into two groups according to the presence of a neural connection between injured cingulum and brainstem cholinergic nuclei.
Results: Eight patients who had a neural connection between injured cingulum and brainstem cholinergic nuclei showed better short-term memory on MAS than 12 patients who did not (p < 0.05). However, other results of neuropsychological testing showed no significant difference (p > 0.05).
Conclusions: Better short-term memory in patients who had the neural connection between injured cingulum and brainstem cholinergic nuclei appears to have been attributed to the presence of cholinergic innervation to the cerebral cortex through the neural connection instead of the injured anterior cingulum. The neural connection appears to compensate for the injured anterior cingulum in obtaining cholinergic innervation.
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http://dx.doi.org/10.3109/02699052.2014.901557 | DOI Listing |
J Cheminform
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Department of Intelligent Electronics and Computer Engineering, Chonnam National University, Gwangju, Republic of Korea.
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Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region, China.
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View Article and Find Full Text PDFBrain Imaging Behav
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Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang, Wuhan, Hubei, 430071, China.
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View Article and Find Full Text PDFCommun Biol
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Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Whether working memory (WM) is encoded by persistent activity using attractors or by dynamic activity using transient trajectories has been debated for decades in both experimental and modeling studies, and a consensus has not been reached. Even though many recurrent neural networks (RNNs) have been proposed to simulate WM, most networks are designed to match respective experimental observations and show either transient or persistent activities. Those few which consider networks with both activity patterns have not attempted to directly compare their memory capabilities.
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