Nair Sangeeta, Szaflarski Jerzy P, Wang Yingying, Pizarro Diana, Killen Jeffrey F, Allendorfer Jane B
University of Alabama at Birmingham, Department of Psychology, USA.
University of Alabama at Birmingham, Department of Neurology, USA.
Neuroimage Rep. 2023 Feb 1;3(1):100154. doi: 10.1016/j.ynirp.2022.100154. eCollection 2023 Mar.
The abilities of individual neuroimaging methods to resolve spatial and temporal contributions of brain regions during cognitive processes are limited. Co-processing of functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) may overcome some of the limitations by utilizing Multiple Sparse Priors (MSP) in a Bayesian framework that takes advantage of the temporal resolution of MEG and spatial resolution of fMRI.
24 healthy participants were recruited to perform a paired-associate verbal learning task during fMRI and MEG scans. FMRI data were processed within Group ICA fMRI Toolbox. Independent components (ICs) were temporally sorted by task time series (|r|>0.30 threshold identified task-related ICs). Task-positive ("generate") ICs were retained as spatial priors for MEG analyses. MEG data were processed by an event-related potential (ERP) approach and with a theta power approach. MEG source reconstructions were constrained within the task-positive ICs for both ERP and theta-power approaches.
For fMRI, five networks were identified as task-related. Four ICs underlying active generation spanned bilateral parietal, orbitofrontal, medial frontal and superior temporal regions, and occipital lobe. FMRI-constrained MEG source reconstructions using the ERP approach yielded early visual cortex activity followed by left inferior frontal gyrus (IFG) and orbito-frontal cortex (OFC) recruitment to coalesce in the left inferior temporal lobe. For the theta approach, MEG source reconstructions showed a progression of activity from bilateral temporal areas to left OFC and middle temporal gyrus, followed by right IFG.
MSP analyses informed by fMRI produced more focused regional activity than reconstructions without priors suggesting this approach may result in identifying more relevant semantic information during active generation. Constraining MEG source reconstruction to fMRI priors during active generation indicates fronto-temporal and fronto-parietal networks are interconnected across time and space.
在认知过程中,个体神经成像方法解析脑区空间和时间贡献的能力有限。功能磁共振成像(fMRI)和脑磁图(MEG)的联合处理可以通过在贝叶斯框架中利用多重稀疏先验(MSP)来克服一些局限性,该框架利用了MEG的时间分辨率和fMRI的空间分辨率。
招募24名健康参与者,在fMRI和MEG扫描期间执行配对联想言语学习任务。fMRI数据在Group ICA fMRI Toolbox中进行处理。独立成分(ICs)通过任务时间序列进行时间排序(|r|>0.30阈值确定与任务相关的ICs)。任务阳性(“生成”)ICs被保留作为MEG分析的空间先验。MEG数据通过事件相关电位(ERP)方法和θ功率方法进行处理。对于ERP和θ功率方法,MEG源重建都被限制在任务阳性ICs内。
对于fMRI,五个网络被确定为与任务相关。四个活跃生成的ICs跨越双侧顶叶、眶额叶、内侧额叶和颞上区域以及枕叶。使用ERP方法的fMRI约束MEG源重建产生早期视觉皮层活动,随后左侧额下回(IFG)和眶额皮层(OFC)被激活,在左侧颞下回合并。对于θ方法,MEG源重建显示活动从双侧颞区发展到左侧OFC和颞中回,随后是右侧IFG。
由fMRI提供信息的MSP分析比没有先验的重建产生更集中的区域活动,表明这种方法可能导致在活跃生成过程中识别更多相关的语义信息。在活跃生成过程中将MEG源重建约束到fMRI先验表明额颞和额顶网络在时间和空间上相互连接。