Mingqi Hui

Motor execution and motor imagery: A comparison of functional connectivity patterns based on graph theory

Motor execution and imagery (ME and MI), as the basic abilities of human beings, have been considered to be effective strategies in motor skill learning and motor abilities rehabilitation. Neuroimaging studies have revealed several critical regions from functional activation for ME as well as MI. Recently, investigations have probed into functional connectivity of ME; however, few explorations compared the functional connectivity between the two tasks. With betweenness centrality (BC) of graph theory, we explored and compared the functional connectivity between two finger tapping tasks of ME and MI. Our results showed that using BC, the key node for the ME task mainly focused on the supplementary motor area, while the key node for the MI task mainly located in the right premotor area. These results characterized the connectivity patterns of ME and MI and may provide new insights into the neural mechanism underlying motor execution and imagination of movements.

Parallel Alterations of Functional Connectivity during Execution and Imagination after Motor Imagery Learning

Background Neural substrates underlying motor learning have been widely investigated with neuroimaging technologies. Investigations have illustrated the critical regions of motor learning and further revealed parallel alterations of functional activation during imagination and execution after learning. However, little is known about the functional connectivity associated with motor learning, especially motor imagery learning, although benefits from functional connectivity analysis attract more attention to the related explorations. We explored whether motor imagery (MI) and motor execution (ME) shared parallel alterations of functional connectivity after MI learning. Methodology/Principal Findings Graph theory analysis, which is widely used in functional connectivity exploration, was performed on the functional magnetic resonance imaging (fMRI) data of MI and ME tasks before and after 14 days of consecutive MI learning. The control group had no learning. Two measures, connectivity degree and interregional connectivity, were calculated and further assessed at a statistical level. Two interesting results were obtained: (1) The connectivity degree of the right posterior parietal lobe decreased in both MI and ME tasks after MI learning in the experimental group; (2) The parallel alterations of interregional connectivity related to the right posterior parietal lobe occurred in the supplementary motor area for both tasks. Conclusions/Significance These computational results may provide the following insights: (1) The establishment of motor schema through MI learning may induce the significant decrease of connectivity degree in the posterior parietal lobe; (2) The decreased interregional connectivity between the supplementary motor area and the right posterior parietal lobe in post-test implicates the dissociation between motor learning and task performing. These findings and explanations further revealed the neural substrates underpinning MI learning and supported that the potential value of MI learning in motor function rehabilitation and motor skill learning deserves more attention and further investigation.

Improved Estimation of the Number of Independent Components for Functional Magnetic Resonance Data by a Whitening Filter

Independent component analysis (ICA) has been widely applied to the analysis of fMRI data. Accurate estimation of the number of independent components (ICs) in fMRI data is critical to reduce over/underfitting. Various methods based on information theoretic criteria (ITC) have been used to estimate the intrinsic dimension of fMRI data. An important assumption of ITC is that the noise is purely white. However, this assumption is often violated by the existence of temporally correlated noise in fMRI data. In this study, we introduced a filtering method into the order selection to remove the autocorrelation from the colored noise by using the whitening filter proposed by Prudon and Weisskoff. Results of the simulated data show that the filtering method has strong robustness to noise and significantly improves the accuracy of order selection from data with colored noise. Moreover, the multifiltering method proposed by us was applied to real fMRI data to improve the performance of ITC. Results of the real fMRI data show that the proposed method can alleviate the overestimation due to the autocorrelation of colored noise. We further compared the stability of IC estimates of real fMRI data at order estimated by minimum description length criterion based on the filtered and unfiltered data by using the software package ICASSO. Results show that ICA yields more stable IC estimates using the reduced order by filtering.

An improvement of independent component analysis with projection method applied to multi-task fMRI data

Independent Component Analysis with projection (ICAp) method proposed by Long et al. Hum. Brain Mapp. 30 (2009) 417-431, can solve the interaction among task-related components of multi-task functional magnetic resonance imaging (fMRI) data. However, the departure of the ideal homodynamic response function (HRF) for projection from the true HRF may worse the ICAp results. In order to improve the performance of ICAp, the deconvolved ICAp (DICAp) method is proposed. Both the simulated and real fMRI experiments demonstrate that DICAp can separate more accurate time course corresponding to each task-related components and is more powerful to detect regions activated by each task only than ICAp.

Motor execution and imagination: a comparison of functional connectivity based on connection strength

Motor tasks, in our daily life, could be performed through execution and imagination. The brain response underlying these movements has been investigated by many studies. Neuroimaging studies have reported that both execution and imagination could activate several brain regions including supplementary motor area (SMA), premotor area (PMA), primary sensorimotor area (M1/S1), posterior parietal lobe (PPL), striatum, thalamus and cerebellum. These findings were based on the regional activation, and brain regions have been indicated to functionally interact with each other when performing tasks. Therefore further investigation in these brain regions with functional connectivity measurements may provide new insights into the neural mechanism of execution and imagination. As a fundamental measurement of functional connectivity, connection strength of graph theory has been used to identify the key nodes of connection and their strength-priorities. Thus, we performed a comparative investigation between execution and imagination tasks with functional magnetic resonance imaging (fMRI), and further explored the key nodes of connection and their strength-priorities based on the results of functional activations. Our results revealed that bilateral SMA, contralateral PMA, thalamus and M1/S1 were involved in both tasks as key nodes of connection. These nodes may play important roles in motor control and motor coordination during execution and imagination. Notably, the strength-priorities of contralateral PMA and thalamus were different between the two tasks. Higher strength-priority was detected in PMA for imagination, implicating that motor planning may be more involved in the imagination task.

The application of independent component analysis with projection method to two-task fMRI data over multiple subjects

Spatial Independent component analysis (sICA) has been successfully used to analyze functional magnetic resonance (fMRI) data. However, the application of ICA was limited in multi-task fMRI data due to the potential spatial dependence between task-related components. Long et al. (2009) proposed ICA with linear projection (ICAp) method and demonstrated its capacity to solve the interaction among task-related components in multi-task fMRI data of single subject. However, its unclear that how to perform ICAp over a group of subjects. In this study, we proposed a group analysis framework on multi-task fMRI data by combining ICAp with the temporal concatenation method reported by Calhoun (2001). The results of real fMRI experiment containing multiple visual processing tasks demonstrated the feasibility and effectiveness of the group ICAp method. Moreover, compared to the GLM method, the group ICAp method is more sensitive to detect the regions specific to each task.

Corrigendum to: Modulation of functional network with real-time fMRI feedback training of right premotor cortex activity [Neuropsychologia (2014) 111–123]

a State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China b Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China c Paul C. Lauterbur Research Centers for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China d School of Information Science and Technology, Beijing Normal University, Xin Jie Kou Wai Da Jie 19#, Beijing 100875, China

Alterations in Resting-State after Motor Imagery Training: A Pilot Investigation with Eigenvector Centrality Mapping

Motor training, including motor execution and motor imagery training, has been indicated to be effective in mental disorders rehabilitation and motor skill learning. In related neuroimaging studies, resting-state has been employed as a new perspective besides task-state to examine the neural mechanism of motor execution training. However, motor imagery training, as another part of motor training, has been few investigated. To address this issue, eigenvector centrality mapping (ECM) was applied to explore resting-state before and after motor imagery training. ECM could assess the computational measurement of eigenvector centrality for capturing intrinsic neural architecture on a voxel-wise level without any prior assumptions. Our results revealed that the significant increases of eigenvector centrality were in the precuneus and medial frontal gyrus (MFG) for the experimental group but not for the control group. These alterations may be associated with the sensorimotor information integration and inner state modulation of motor imagery training.

Motor execution and imagery: a comparison of the functional network based on ICA and hierarchical integration

Neuroimaging studies have revealed that motor imagery (MI) shared similar neural substrates with motor execution (ME) though there are some differences in the activation pattern. Most previous studies generally focused on voxel-wise based analysis. However, the congruence and difference in functional brain network relevant to MI and ME task has been rarely investigated. In this study, independent component analysis (ICA) was applied to characterize the functional brain networks underlying MI and ME. Results shows that the brain networks underlying MI and ME shared similar brain regions consisted of supplementary motor area (SMA), contralateral primary sensorimotor area (M1/S1), striatum, bilateral premotor area (PMA), posterior parietal lobule (PPL), and cerebellum. However, the ME task induced stronger activities in SMA-proper, bilateral M1/S1 and cerebellum while the MI task produced greater activities in preSMA, right cerebellum, bilateral PMA, parietal cortex and striatum. These findings are in accordance with the model proposed by Hikosaka (2002) that includes the parietal–prefrontal cortical loops for a spatial sequence and the motor cortical loops for a motor sequence. Moreover, the functional connectivity within the MI/ME-relevant network was evaluated using hierarchical integration that can quantify the total amount of interaction within the network and further assess the information exchanges within/between sub-networks. Results of hierarchical integration further indicate that parietalprefrontal areas contributes more to the integration of MI network than that of ME network while motor cortical areas contributes more to the integration of ME network than that of MI network.