A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages.
A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages.
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DOI：
10.1016/j.neuroimage.2017.03.057
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发表时间：
2017-07-15
期刊：
影响因子：
5.7
通讯作者：
Davatzikos C
中科院分区：
文献类型：
--
作者：
Rathore S;Habes M;Iftikhar MA;Shacklett A;Davatzikos C
神经影像学使得在体内测量与阿尔茨海默病（AD）相关的病理性脑变化成为可能。在过去的十年中，这些措施已越来越多地通过分类框架整合到AD的成像特征中，为个性化诊断和预后提供了有前途的工具。我们回顾了基于神经影像学的AD分类和轻度认知功能障碍研究，这些研究是在Google Scholar和PubMed（1985年1月至2016年6月）在线数据库检索后选择的。我们根据以下神经影像学方式对这些研究进行了分类（并且基于作为后处理步骤从这些模态提取的特征进行子分类）：i）结构磁共振成像（组织密度、皮质表面和海马测量），ii）功能性MRI（不同大脑区域的功能一致性，以及功能连接的强度），iii）扩散张量成像（沿白色纤维的沿着图案），iv）氟脱氧葡萄糖正电子发射断层扫描（脑葡萄糖的代谢率），和v）淀粉样蛋白-PET（淀粉样蛋白负荷）。回顾的研究表明，基于这些特征制定的分类框架显示出个性化诊断和预测临床进展的希望。最后，我们提供了一个详细的说明AD分类的挑战，并提出了一些未来的研究方向。
Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer’s disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD classification and mild cognitive impairment, selected after online database searches in Google Scholar and PubMed (January, 1985 to June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and address some future research directions.
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影响因子：
5.7
作者：
Bron EE;Smits M;van der Flier WM;Vrenken H;Barkhof F;Scheltens P;Papma JM;Steketee RM;Méndez Orellana C;Meijboom R;Pinto M;Meireles JR;Garrett C;Bastos-Leite AJ;Abdulkadir A;Ronneberger O;Amoroso N;Bellotti R;Cárdenas-Peña D;Álvarez-Meza AM;Dolph CV;Iftekharuddin KM;Eskildsen SF;Coupé P;Fonov VS;Franke K;Gaser C;Ledig C;Guerrero R;Tong T;Gray KR;Moradi E;Tohka J;Routier A;Durrleman S;Sarica A;Di Fatta G;Sensi F;Chincarini A;Smith GM;Stoyanov ZV;Sørensen L;Nielsen M;Tangaro S;Inglese P;Wachinger C;Reuter M;van Swieten JC;Niessen WJ;Klein S;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
神经影像学中脑部疾病的单受试者预测：前景和陷阱
影响因子：
5.7
作者：
Arbabshirani MR;Plis S;Sui J;Calhoun VD
通讯作者：
Calhoun VD
DOI：
10.1109/tbme.2015.2404809
发表时间：
2015-07
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Cheng B;Liu M;Zhang D;Munsell BC;Shen D
通讯作者：
Shen D
影响因子：
14
作者：
Brookmeyer, Ron;Johnson, Elizabeth;Arrighi, H. Michael
通讯作者：
Arrighi, H. Michael
影响因子：
7.7
作者：
Cabral, Carlos;Morgado, Pedro M.;Silveira, Margarida
通讯作者：
Silveira, Margarida