北京生物醫(yī)學(xué)工程

基于體素的和基于形變的形態(tài)學(xué)測量在輕度認知障礙識別上的比較研究

Comparative study of voxel-basedand deformation-based morphometry in the identification of mild cognitive impairment

作者：周震景斌

單位：首都醫(yī)科大學(xué)生物醫(yī)學(xué)工程學(xué)院（北京 100069） 通信作者：周震。E-mail: [email protected]

關(guān)鍵詞：基于體素的形態(tài)學(xué)測量；基于形變的形態(tài)學(xué)測量；磁共振成像；輕度認知障礙；模式分類

分類號：R318.04

出版年·卷·期（頁碼）：2021·40·5（494-498）

摘要：

目的比較基于體素的形態(tài)學(xué)測量(voxel-based morphometry ,VBM)和基于形變的形態(tài)學(xué)測量(DBM)在檢測輕度認知功能障礙(deformation-based morphometry ,MCI)灰質(zhì)異常及相應(yīng)分類識別性能上的差異，為結(jié)構(gòu)態(tài)分析方法的選擇提供依據(jù)。方法利用VBM和DBM對27例MCI患者及30例健康對照的磁共振結(jié)構(gòu)像進行分析，分別統(tǒng)計比較獲得相應(yīng)的組間結(jié)構(gòu)異常腦區(qū)，并將異常腦區(qū)作為分類特征構(gòu)建相應(yīng)的MCI診斷識別模型，最終通過評價異常腦區(qū)的空間分布特征及分類識別準(zhǔn)確率來評估兩種方法的差異。結(jié)果VBM和DBM均發(fā)現(xiàn)MCI患者在海馬、海馬旁回、杏仁核、島葉等腦區(qū)發(fā)生結(jié)構(gòu)改變，但VBM方法還在額中回、顳中回等腦區(qū)發(fā)現(xiàn)異常。VBM確定的結(jié)構(gòu)異常得到了86.0%的最佳準(zhǔn)確度，而DBM方法的準(zhǔn)確度為77.2%，雖然在性能表現(xiàn)上稍差，但發(fā)現(xiàn)的特征與VBM的最優(yōu)特征具有一致性。結(jié)論VBM方法可以發(fā)現(xiàn)更多的MCI結(jié)構(gòu)異常，而DBM方法則能發(fā)現(xiàn)具有較強敏感性的結(jié)構(gòu)異常，因而提示在磁共振結(jié)構(gòu)像研究中應(yīng)將兩者結(jié)合應(yīng)用。

Objective To make comparisons between voxel-based morphometry(VBM) and deformation-based morphometry(DBM) in investigating gray matter abnormalities and corresponding identification performances for mild cognitive impairment (MCI), which may provide evidences for method selection in structural analysis studies. Methods The structural magnetic resonance imaging data of 27 MCI patients and 30 normal controls(NC) were analyzed with VBM and DBM methods,and the group differences were detected by statistical analysis, which was then used to construct the classification model for MCI. The spacial patterns of the detected abnormalities and the classification accuracies would be compared between two methods. Results Both VBM and DBM methods discovered structural abnormalities in regions such as parahippocampal gyrus, hippocampus, amygdala and insula. In addition,VBM also detected structural changes in regions like middle frontal gyrus and middle temporal gyrus. Furthermore,the proposed classification model for VBM achieved the best accuracy of 86.0%, while DBM model got an accuracy of 77.2%. Although DBM performed slightly worse than VBM, it detected consistent features as the optimal ones that VBM detected. Conclusion More structural abnormalities can be detected by VBM method in MCI patients, while DBM method can discover structural abnormalities with relatively high sensitivity, which suggests both methods should be combined together to study structural alterations.

參考文獻：

[1] Zhang W, Song LH, Yin XT, et al. Grey Matter Abnormalities in untreated hyperthyroidism: a voxel-based morphometry study using the DARTEL approach [J]. European Journal of Radiology, 2014, 83(1): 43-48.

[2] Yoon B, Shim Y.S, Hong Y.J, et al. Comparison of diffusion tensor imaging and voxel-based morphometry to detect white matter damage in alzheimers’s disease [J]. Journal of the Neurological Sciences, 2011, 302(1-2): 89-95. ?

[3] Chetelat G, Landeau B, Eustache F, et al. Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI:a longitudinal MRI study[J]. Neuroimage, 2005, 27(4): 934-946.

[4] Can D D, Richard T, Kuhl P K. Early grey-matter and white matter concentration in infancy predict later language skills: a whole brain voxel-based morphometry study [J]. Brain and Language, 2013, 124(1): 34-44. ?

[5] Gaser?C,?Nenadic I, Buchsbaum B R，et al. Deformation-based morphometry and its relation to conventional volumetry of brain lateral ventricles in MRI [J]. NeuroImage, 2001, 13(6): 1140-1145.

[6] Savio A, Grana M. Deformation based feature selection for computer aided diagnosis of alzheimer’s disease[J]. Expert Systems with Applications, 2013, 40(5): 1619-1628.

[7] Long ZQ,?Huang JC, Li B,?et al. A comparative atlas-based recognition of mild cognitive impairment with voxel-based morphometry?[J]. Frontiers in Neuroscience, 2018, 12：916.

[8] Long ZQ, Jing B, Guo R, et al. A brainnetome atlas based mild cognitive impairment identification using hurst exponent [J]. Frontiers in Aging Neuroscience, 2018, 10:103.

[9] Liu, H, Jing B, Han WJ et al. A?comparative texture analysis based on NECT and CECT?images to differentiate lung adenocarcinoma from squamous cell carcinoma [J]. Journal of Medical Systems,2019,.43(3):59.

[10] Ferreira?LK,?Diniz BS, Forlenza OV,?et al. Neurostructural predictors of Alzheimer's disease: A meta-analysis of VBM studies?[J].?Neurobiology of Aging,2011, 32(10):1733-1741.

[11] Nickl-Jockschat T, Kleiman A, Schulz JB, et al. Neuroanatomic changes and their association with cognitive decline in mild cognitive impairment: a meta-analysis [J]. Brain Structure & Function, 2012,217(1):115-125.

[12] Tapiola T,?Pennanen C, Tapiola M,?et al. MRI of hippocampus and entorhinal cortex in mild cognitive impairment: A follow-up study?[J]. Neurobiology of Aging,?2008, 29.1:31-38.

[13] Hirjak D,?Wolf RC, Remmele B,?et al. Hippocampal formation alterations differently contribute to autobiographic memory deficits in mild cognitive impairment and Alzheimer's disease?[J]. Hippocampus,2017, 27(6):702-715.

[14] Ren P,?Anthony M, Chapman BP,?et al. Amygdala functional connectivity is associated with locus of control in the context of cognitive aging?[J]. Neuropsychologia,?2017,99:199-206.

[15] Yao HX,?Liu Y, Zhou B,?et al. Decreased functional connectivity of the amygdala in Alzheimer's disease revealed by resting-state fMRI?[J]. European Journal of Radiology, 2013,82（9）:1531-1538.

[16] Fan Y,?Batmanghelich N,Clark CM,?et al. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline?[J]. Neuroimage,?2008,39（4）:1731-1743.

服務(wù)與反饋：

【文章下載】【加入收藏】

提示：您還未登錄，請登錄！點此登錄

51黑料吃瓜在线观看,51黑料官网|51黑料捷克街头搭讪_51黑料入口最新视频