Forde Natalie J, Llera Alberto, Beckmann Christian
Radboud University Medical Centre, Donders Centre for Brain, Cognition and Behaviour, Nijmegen, Netherlands.
bioRxiv. 2024 Jul 4:2024.07.04.602076. doi: 10.1101/2024.07.04.602076.
Multimodal data integration approaches, such as Linked Independent Component Analysis (LICA), increase sensitivity to brain-behaviour relationships and allow us to probe the relationship between modalities. Here we focus on inter-regional functional and structural organisation to determine if organisational patterns persist across modalities and if investigating multi-modality organisations provides increased sensitivity to brain-behaviour associations. We utilised multimodal magnetic resonance imaging (MRI; T1w, resting-state functional [fMRI] and diffusion weighted [DWI]) and behavioural data from the Human Connectome Project (HCP, n=676; 51% female). Unimodal features were extracted to produce individual grey matter density maps, probabilistic tractography connectivity matrices and connectopic maps from the T1w, DWI and fMRI data, respectively. DWI and fMRI analyses were restricted to subcortical regions for computational reasons. LICA was then used to integrate features, generating 100 novel independent components. Associations between these components and demographic/behavioural (n=308) variables were examined. 15 components were significantly associated with various demographic/behavioural measures. 2 components were strongly related to various measures of intoxication, driven by DWI and resemble components previously identified. Another component was driven by striatal functional data and related to working memory. A small number of components showed shared variance between structure and function but none of these displayed any significant behavioural associations. Our working memory findings provide support for the use of fMRI connectopic mapping in future research of working memory. Given the lack of behaviourally relevant shared variance between functional and structural organisation, as indexed here, we question the utility of integrating connectopic maps and tractography data.
多模态数据整合方法,如链接独立成分分析(LICA),提高了对脑-行为关系的敏感性,并使我们能够探究不同模态之间的关系。在这里,我们专注于区域间的功能和结构组织,以确定组织模式是否在不同模态间持续存在,以及研究多模态组织是否能提高对脑-行为关联的敏感性。我们使用了多模态磁共振成像(MRI;T1加权成像、静息态功能磁共振成像[fMRI]和扩散加权成像[DWI])以及来自人类连接组计划(HCP,n = 676;51%为女性)的行为数据。分别从T1加权成像、DWI和fMRI数据中提取单模态特征,以生成个体灰质密度图、概率纤维束成像连接矩阵和连接图谱。出于计算原因,DWI和fMRI分析仅限于皮质下区域。然后使用LICA整合特征,生成100个新的独立成分。检查了这些成分与人口统计学/行为学(n = 308)变量之间的关联。15个成分与各种人口统计学/行为学指标显著相关。2个成分与各种中毒指标密切相关,由DWI驱动,类似于先前确定的成分。另一个成分由纹状体功能数据驱动,与工作记忆有关。少数成分在结构和功能之间显示出共享方差,但这些均未显示出任何显著的行为关联。我们关于工作记忆的研究结果为在未来工作记忆研究中使用fMRI连接图谱提供了支持。鉴于此处所示功能和结构组织之间缺乏与行为相关的共享方差,我们质疑整合连接图谱和纤维束成像数据的效用。