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Asymmetry of prefrontal cortical convolution complexity in males with attention-deficit/hyperactivity disorder using fractal information dimension

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机构: [a]National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, PR China [b]Department of Radiology, Xuanwu Hospital, Beijing, PR China [c]Institute of Mental Health, Peking University, Beijing, PR China
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关键词: anatomic MRI asymmetry pattern attention-deficit hyperactivity disorder cortical convolution complexity information dimension prefrontal cortex

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Background and purpose: Prefrontal cortex, known to be a crucial region for the function of attention, is generally thought to be largely associated with the pathogenesis of attention-deficit hyperactivity disorder (ADHD). Most previous structural imaging studies of ADHD reported abnormality of grey matter volume in prefrontal region. However, volume measure is affected by the size of the interrogated brain, which may cause the inconsistence of the volume based findings. The purpose of the current paper is to use a scale-free measure, fractal information dimension (FID), to assess the prefrontal cortical convolution complexity and asymmetry in ADHD patients. Methods: MRI scans from 12 boys with ADHD and 11 controls were carefully processed. Prefrontal cortex was outlined manually. FIDs of bilateral prefrontal cortical surface were examined in each case. Group differences of the bilateral prefrontal cortical convolution complexities and the asymmetry pattern were statistically tested. Results: We found a left-greater-than-right prefrontal cortical convolution complexity pattern in both groups. However, compared with healthy controls, the left prefrontal cortical convolution complexities of ADHD patients were significantly reduced, resulting in significant reduction of the normal left-greater-than-right cortical convolution complexity asymmetry pattern. Conclusion: This study confirms and extends the existing anatomical knowledge about the brains of people with ADHD. The cortical convolution analysis method may also be applied to quantitatively assess changes in other neuropsychiatric syndromes as well. (C) 2007 Elsevier B.V. All rights reserved.

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出版当年[2006]版:
大类 | 4 区 医学
最新[2023]版:
大类 | 4 区 医学
小类 | 4 区 临床神经病学 4 区 儿科
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出版当年[2005]版:
Q3 CLINICAL NEUROLOGY
最新[2023]版:
Q2 PEDIATRICS Q3 PEDIATRICS Q3 CLINICAL NEUROLOGY Q4 CLINICAL NEUROLOGY

影响因子: 最新[2023版] 最新五年平均 出版当年[2005版] 出版当年五年平均 出版前一年[2004版] 出版后一年[2006版]

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第一作者机构: [a]National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, PR China
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通讯机构: [a]National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, PR China
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