数字地形分析方法在大脑皮层形态研究中的应用

doi:10.3969/j.issn.1674-8115.2023.03.010

上海交通大学学报（医学版） ›› 2023, Vol. 43 ›› Issue (3): 342-349.doi: 10.3969/j.issn.1674-8115.2023.03.010

• 论著 · 技术与方法 • 上一篇

数字地形分析方法在大脑皮层形态研究中的应用

杨子豪¹^,²(), 陈楠¹^,²(), 林偲蔚³

^1.福州大学空间数据挖掘与信息共享教育部重点实验室，福州 350108
^2.福州大学数字中国研究院（福建），福州 350108
^3.南京大学地理与海洋科学学院，南京 210023

收稿日期:2022-09-29 接受日期:2023-02-25 出版日期:2023-03-28 发布日期:2023-03-28
通讯作者: 陈楠 E-mail:172732351@qq.com;chennan@fzu.edu.cn
作者简介:杨子豪（1997—），男，硕士生；电子信箱：172732351@qq.com。
基金资助:
国家自然科学基金(41771423)

Application of digital terrain analysis to the study of cerebral cortex morphology

YANG Zihao¹^,²(), CHEN Nan¹^,²(), LIN Siwei³

^1.Key Lab for Spatial Data Mining and Information Sharing of Education Ministry, Fuzhou University, Fuzhou 350108, China
^2.The Academy of Digital China, Fuzhou University, Fuzhou 350108, China
^3.School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China

Received:2022-09-29 Accepted:2023-02-25 Online:2023-03-28 Published:2023-03-28
Contact: CHEN Nan E-mail:172732351@qq.com;chennan@fzu.edu.cn
Supported by:
National Nature Science Fundation of China(41771423)

摘要/Abstract

摘要：

目的·为大脑皮层表面形态的表达和定量描述提出一种基于地学的理论方法。方法·从阿尔茨海默病神经成像倡议（Alzheimer′s Disease Neuroimaging Initiative，ADNI）数据库下载85个正常认知（normal cognition，NC）样本和84个阿尔茨海默病（Alzheimer′s disease，AD）样本作为研究对象，使用SPM 12软件包，提取脑磁共振成像（magnetic resonance imaging，MRI）影像的大脑皮层数据。将起伏的大脑皮层影像映射为三维重建后的点云数据，并根据数字地形分析的理论方法，进一步构建大脑数字高程模型（digital elevation model，DEM），实现对大脑皮层形态的数字化表达。引入地学中的粗糙度、起伏度、高程等地形因子指标，用于定量描述大脑皮层的形态特征。以年龄为协变量进行协方差分析；再分别根据性别分组，以NC组与AD组为变量；根据地形因子分组，以左右半脑为变量，进行独立样本t检验，揭示大脑皮层形态的变化规律。结果·大脑皮层粗糙度、起伏度与皮层厚度数值随年龄增长呈线性分布，且AD组与NC组各指标间差异均具有统计学意义（均P<0.05）；男性和女性的粗糙度和起伏度指标均表现出AD组大于NC组（均P<0.05），皮层厚度表现出AD组小于NC组（P=0.000），高程则只在女性中具备组间差异（P=0.043）；NC组的左半脑粗糙度、起伏度与高程数值均大于右半脑（均P<0.05），AD则只在高程指标中具备组间差异（P=0.000）。结论·地形因子指标能够显著区分AD与NC患者间大脑皮层的差异，并且在以年龄、性别和左右半脑作为变量时，有较好的区分表现，有望为医学量化大脑皮层形态提供辅助手段。

关键词: 数字地形分析, 地形因子, 大脑皮层, 阿尔茨海默病, 皮层厚度

Abstract:

Objective ·To propose a theoretical method based on geosciences for the expression and quantitative description of the surface morphology of the cerebral cortex. Methods ·Eighty-five samples of normal cognition (NC) and 84 samples of Alzheimer′s disease (AD) were downloaded from the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database, and the cerebral cortex data of magnetic resonance imaging (MRI) images of the human brain were extracted by using SPM 12 software package. The undulating cerebral cortex image was mapped into the point cloud data after three-dimensional reconstruction, and the digital elevation model (DEM) of the brain was further constructed according to the theoretical method of digital terrain analysis to realize the digital expression of the cerebral cortex morphology. The study introduced terrain factors′ indicators such as roughness, relief amplitude and elevation in geoscience to quantitatively describe the morphological characteristics of the cerebral cortex. Covariance analysis was conducted with age as the covariate, and independent sample t test was conducted with gender into groups and NC samples and AD samples as the variables, terrain factors as the group and left and right hemispheres as the variables to reveal the change rule of cerebral cortex morphology. Results ·The roughness, relief amplitude and thickness of the cerebral cortex were linearly distributed with age, and the differences between the AD group and the NC group were all statistically significant (all P<0.05); the roughness and relief amplitude of both men and women showed that the AD group was larger than the NC group (both P<0.05), the cortical thickness showed that the AD group was smaller than the NC group (P=0.000), and the elevation only had an inter-group difference in women (P=0.043); the left hemisphere roughness, relief amplitude and elevation values of NC group were higher than those of the right hemisphere (all P<0.05), while AD only had inter-group differences in elevation indicators (P=0.000). Conclusion ·Terrain factors can significantly distinguish the cerebral cortex differences between AD and NC patients, and has a good distinction when age, gender and left and right hemisphere are used as variables. It is expected to provide an auxiliary means for the medical quantification of cerebral cortex morphology.

Key words: digital terrain analysis, terrain factor, cerebral cortex, Alzheimer′s disease (AD), cortical thickness

中图分类号:

R445.2

杨子豪, 陈楠, 林偲蔚. 数字地形分析方法在大脑皮层形态研究中的应用[J]. 上海交通大学学报（医学版）, 2023, 43(3): 342-349.

YANG Zihao, CHEN Nan, LIN Siwei. Application of digital terrain analysis to the study of cerebral cortex morphology[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(3): 342-349.

图/表 9

图1 预处理流程图Note： The brain MRI image data in the figure was from the ADNI public database. GM—gray matter; WM—white matter; CSF—cerebrospinal fluid.

Fig 1 Pretreatment flow chart

图2 大脑点云生成示意图Note： The brain MRI image data in the figure was from the ADNI public database. A. MRI image of gray matter obtained after pretreatment. B. Brain model after three-dimensional reconstruction. C. Extracted brain point cloud data. The color in the picture transits from green to yellow and then to red representing the elevation from low to high.

Fig 2 Schematic diagram of brain point cloud generation

图3 三维坐标转换经度及纬度示意图

Fig 3 Schematic diagram of 3D coordinates turning to longitude and latitude

图4 大脑格网化示意图Note： A. The brain was divided according to longitude and latitude. B. Multiple points in the cell network. C. Cell net after downsampling. D. The final 3×3 analysis window.

Fig 4 Schematic diagram of brain grid

表1 研究对象基本信息

Tab 1 Basic information of the research objects

Item	AD (n=84)	NC (n=85)	χ² /t value	P value
Gender/n			0.006	0.940
Male	43	44
Female	41	41
Age/year	75.44±8.64	74.13±8.49	0.995	0.321
MMSE/score	22.87±3.09	29.12±1.15	-17.394	0.001

图5 样本总体分布图

Fig 5 Overall distribution of samples

表2 各因子协方差分析

Tab 2 Covariance analysis of each factor

Item	NC	AD	Age-factor		Between-group
Item	NC	AD	F value	P value	F value	P value
Roughness	2.154±0.088	2.238±0.087	92.267	0.000	48.884	0.000
Elevation/mm	72.115±0.852	71.832±0.761	1.526	0.127	4.647	0.033
Relief amplitude/mm	3.828±0.193	4.002±0.187	84.103	0.000	42.894	0.000
Cortical thickness/mm	2.534±0.142	2.384±0.152	22.545	0.000	44.318	0.000

表3 各因子性别分组对比

Tab 3 Comparison of gender groups of each factor

Item	Male				Female
Item	AD	NC	t value	P value	AD	NC	t value	P value
Roughness	2.254±0.085	2.166±0.091	4.626	0.000	2.221±0.087	2.140±0.083	4.284	0.000
Elevation/mm	71.929±0.811	72.132±0.821	-1.159	0.250	71.730±0.701	72.096±0.894	-2.060	0.043
Relief amplitude/mm	4.033±0.180	3.847±0.202	5.946	0.000	3.969±0.190	3.808±0.184	3.902	0.000
Cortical thickness/mm	2.344±0.158	2.475±0.115	-4.514	0.000	2.426±0.142	2.597±0.141	-5.476	0.000

表4 左右半脑地形因子指标比较

Tab 4 Comparison of indicators of left and right brain terrain factors

Item	Right brain	Left brain	t value	P value
Roughness
NC	2.139±0.085	2.169±0.097	2.171	0.031
AD	2.228±0.086	2.247±0.094	1.431	0.154
Relief amplitude/mm
NC	3.788±0.189	3.868±0.216	2.574	0.011
AD	3.977±0.190	4.027±0.202	1.633	0.104
Elevation/mm
NC	71.800±0.890	72.429±0.846	-4.722	0.000
AD	71.513±0.780	72.151±0.779	-5.309	0.000

参考文献 24

1	VAN DER MEER D, KAUFMANN T, SHADRIN A A, et al. The genetic architecture of human cortical folding[J]. Sci Adv, 2021, 7(51): eabj9446.
2	汤国安, 李发源, 熊礼阳. 黄土高原数字地形分析研究进展[J]. 地理与地理信息科学, 2017, 33(4): 1-7.
	TANG G A, LI F Y, XIONG L Y. Progress of digital terrain analysis in the Loess Plateau of China[J]. Geography and Geo-Information Science, 2017, 33(4): 1-7.
3	汤国安. 我国数字高程模型与数字地形分析研究进展[J]. 地理学报, 2014, 69(9): 1305-1325.
	TANG G A. Progress of DEM and digital terrain analysis in China[J]. Acta Geographica Sinica, 2014, 69(9): 1305-1325.
4	GOEDERT M, SPILLANTINI M G. A century of Alzheimer's disease[J]. Science, 2006, 314(5800): 777-781.
5	THOMPSON P M, MOUSSAI J, ZOHOORI S, et al. Cortical variability and asymmetry in normal aging and Alzheimer′s disease[J]. Cereb Cortex, 1998, 8(6): 492-509.
6	LV Y T, ZHAO W S, YAO X F, et al. Analyses of brain cortical changes of Alzheimer's disease[J]. J Mech Med Biol, 2021, 21(5): 2140025.
7	RUIZ DE MIRAS J, COSTUMERO V, BELLOCH V, et al. Complexity analysis of cortical surface detects changes in future Alzheimer′s disease converters[J]. Hum Brain Mapp, 2017, 38(12): 5905-5918.
8	PINI L, PIEVANI M, BOCCHETTA M, et al. Brain atrophy in Alzheimer′s disease and aging[J]. Ageing Res Rev, 2016, 30: 25-48.
9	WU Z X, PENG Y, HONG M, et al. Gray matter deterioration pattern during Alzheimer′s disease progression: a regions-of-interest based surface morphometry study[J]. Front Aging Neurosci, 2021, 13: 593898.
10	NICASTRO N, MALPETTI M, COPE T E, et al. Cortical complexity analyses and their cognitive correlate in Alzheimer′s disease and frontotemporal dementia[J]. J Alzheimers Dis, 2020, 76(1): 331-340.
11	YOUN H, CHOI M, LEE S J, et al. Decreased cortical thickness and local gyrification in individuals with subjective cognitive impairment[J]. Clin Psychopharmacol Neurosci, 2021, 19(4): 640-652.
12	SCHMITT J E, WATTS K, ELIEZ S, et al. Increased gyrification in Williams syndrome: evidence using 3D MRI methods[J]. Dev Med Child Neurol, 2002, 44(5): 292-295.
13	GASER C, LUDERS E, THOMPSON P M. Increased local gyrification mapped in Williams syndrome[J]. NeuroImage, 2006, 33(1): 46-54.
14	LANCASTER J L, TORDESILLAS-GUTIÉRREZ D, MARTINEZ M, et al. Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template[J]. Hum Brain Mapp, 2007, 28(11): 1194-1205.
15	朱会义, 刘述林, 贾绍凤. 自然地理要素空间插值的几个问题[J]. 地理研究, 2004, 23(4): 425-432.
	ZHU H Y, LIU S L, JIA S F. Problems of the spatial interpolation of physical geographical elements[J]. Geographical Research, 2004, 23(4): 425-432.
16	周艳莲, 孙晓敏, 朱治林, 等. 几种典型地表粗糙度计算方法的比较研究[J]. 地理研究, 2007, 26(5): 887-896.
	ZHOU Y L, SUN X M, ZHU Z L, et al. Comparative research on four typical surface roughness length calculation methods[J]. Geographical Research, 2007, 26(5): 887-896.
17	YIN J H, CHEN J, XU X W, et al. The characteristics of the landslides triggered by the Wenchuan Ms 8.0 earthquake from Anxian to Beichuan[J]. J Asian Earth Sci, 2010, 37(5/6): 452-459.
18	SALAT D H, BUCKNER R L, SNYDER A Z, et al. Thinning of the cerebral cortex in aging[J]. Cereb Cortex, 2004, 14(7): 721-730.
19	THOMPSON P M, LEE A D, DUTTON R A, et al. Abnormal cortical complexity and thickness profiles mapped in Williams syndrome[J]. J Neurosci, 2005, 25(16): 4146-4158.
20	HADJIKHANI N, JOSEPH R M, SNYDER J, et al. Anatomical differences in the mirror neuron system and social cognition network in autism[J]. Cereb Cortex, 2006, 16(9): 1276-1282.
21	DU A T, SCHUFF N, KRAMER J H, et al. Different regional patterns of cortical thinning in Alzheimer′s disease and frontotemporal dementia[J]. Brain, 2007, 130(Pt 4): 1159-1166.
22	RITCHIE S J, COX S R, SHEN X Y, et al. Sex differences in the adult human brain: evidence from 5216 UK biobank participants[J]. Cereb Cortex, 2018, 28(8): 2959-2975.
23	LUDERS E, NARR K L, THOMPSON P M, et al. Gender effects on cortical thickness and the influence of scaling[J]. Hum Brain Mapp, 2006, 27(4): 314-324.
24	TOGA A W, THOMPSON P M. Mapping brain asymmetry[J]. Nat Rev Neurosci, 2003, 4(1): 37-48.

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数字地形分析方法在大脑皮层形态研究中的应用

Application of digital terrain analysis to the study of cerebral cortex morphology

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