Li Jing, Lam Linda Chiu Wa, Lu Hanna
Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China.
The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China.
Insights Imaging. 2024 Aug 26;15(1):216. doi: 10.1186/s13244-024-01791-9.
We aimed to develop a standardized method to investigate the relationship between estimated brain age and regional morphometric features, meeting the criteria for simplicity, generalization, and intuitive interpretability.
We utilized T1-weighted magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience project (N = 609) and employed a support vector regression method to train a brain age model. The pre-trained brain age model was applied to the dataset of the brain development project (N = 547). Kraskov (KSG) estimator was used to compute the mutual information (MI) value between brain age and regional morphometric features, including gray matter volume (GMV), white matter volume (WMV), cerebrospinal fluid (CSF) volume, and cortical thickness (CT).
Among four types of brain features, GMV had the highest MI value (8.71), peaking in the pre-central gyrus (0.69). CSF volume was ranked second (7.76), with the highest MI value in the cingulate (0.87). CT was ranked third (6.22), with the highest MI value in superior temporal gyrus (0.53). WMV had the lowest MI value (4.59), with the insula showing the highest MI value (0.53). For brain parenchyma, the volume of the superior frontal gyrus exhibited the highest MI value (0.80).
This is the first demonstration that MI value between estimated brain age and morphometric features may serve as a benchmark for assessing the regional contributions to estimated brain age. Our findings highlighted that both GMV and CSF are the key features that determined the estimated brain age, which may add value to existing computational models of brain age.
Mutual information (MI) analysis reveals gray matter volume (GMV) and cerebrospinal fluid (CSF) volume as pivotal in computing individuals' brain age.
Mutual information (MI) interprets estimated brain age with morphometric features. Gray matter volume in the pre-central gyrus has the highest MI value for estimated brain age. Cerebrospinal fluid volume in the cingulate has the highest MI value. Regarding brain parenchymal volume, the superior frontal gyrus has the highest MI value. The value of mutual information underscores the key brain regions related to brain age.
我们旨在开发一种标准化方法,以研究估计脑龄与区域形态计量特征之间的关系,满足简单性、通用性和直观可解释性的标准。
我们利用了来自剑桥衰老与神经科学中心项目的T1加权磁共振成像(MRI)数据(N = 609),并采用支持向量回归方法训练脑龄模型。将预训练的脑龄模型应用于脑发育项目的数据集(N = 547)。使用克拉斯科夫(KSG)估计器计算脑龄与区域形态计量特征之间的互信息(MI)值,包括灰质体积(GMV)、白质体积(WMV)、脑脊液(CSF)体积和皮质厚度(CT)。
在四种脑特征类型中,GMV的MI值最高(8.71),在中央前回达到峰值(0.69)。CSF体积排名第二(7.76),在扣带回中的MI值最高(0.87)。CT排名第三(6.22),在颞上回中的MI值最高(0.53)。WMV的MI值最低(4.59),脑岛的MI值最高(0.53)。对于脑实质,额上回的体积表现出最高的MI值(0.80)。
这是首次证明估计脑龄与形态计量特征之间的MI值可作为评估区域对估计脑龄贡献的基准。我们的研究结果强调,GMV和CSF都是决定估计脑龄的关键特征,这可能会为现有的脑龄计算模型增添价值。
互信息(MI)分析揭示灰质体积(GMV)和脑脊液(CSF)体积在计算个体脑龄中起关键作用。
互信息(MI)用形态计量特征解释估计脑龄。中央前回的灰质体积对估计脑龄的MI值最高。扣带回的脑脊液体积MI值最高。关于脑实质体积,额上回的MI值最高。互信息值强调了与脑龄相关的关键脑区。