Lele Xu

Behavioral improvements and brain functional alterations by motor imagery training

Motor imagery training is considered as an effective training strategy for motor skill learning and motor function rehabilitation. However, compared with studies of the neural mechanism underlying motor imagery, neuroimaging examinations of motor imagery training are comparatively few. Using functional magnetic resonance imaging, we designed a 2-week motor imagery training experiment, including execution and imagery tasks, to investigate the effectiveness of motor imagery training on the improvement of motor performance, as well as the neural mechanism associated with motor imagery training. Here, we examined the motor behavior, brain activation, and correlation between the behavior of the motor execution task and the brain activation across task-related region of interests (ROIs) in both pre- and post-test phases. Our results demonstrated that motor imagery training could improve motor performance. More importantly, the brain functional alterations induced by training were found in the fusiform gyrus for both tasks. These findings provide new insights into motor imagery training.

Motor Imagery Learning Modulates Functional Connectivity of Multiple Brain Systems in Resting State

Background Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. Methodology/Principal Findings We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. Conclusions/Significance These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning.

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.

Prediction of Progressive Mild Cognitive Impairment by Multi-Modal Neuroimaging Biomarkers

For patients with mild cognitive impairment (MCI), the likelihood of progression to probable Alzheimers disease (AD) is important not only for individual patient care, but also for the identification of participants in clinical trial, so as to provide early interventions. Biomarkers based on various neuroimaging modalities could offer complementary information regarding different aspects of disease progression. The current study adopted a weighted multi-modality sparse representation-based classification method to combine data from the Alzheimers Disease Neuroimaging Initiative (ADNI) database, from three imaging modalities: Volumetric magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir PET. We included 117 normal controls (NC) and 110 MCI patients, 27 of whom progressed to AD within 36 months (pMCI), while the remaining 83 remained stable (sMCI) over the same time period. Modality-specific biomarkers were identified to distinguish MCI from NC and to predict pMCI among MCI. These included the hippocampus, amygdala, middle temporal and inferior temporal regions for MRI, the posterior cingulum, precentral, and postcentral regions for FDG-PET, and the hippocampus, amygdala, and putamen for florbetapir PET. Results indicated that FDG-PET may be a more effective modality in discriminating MCI from NC and in predicting pMCI than florbetapir PET and MRI. Combining modality-specific sensitive biomarkers from the three modalities boosted the discrimination accuracy of MCI from NC (76.7%) and the prediction accuracy of pMCI (82.5%) when compared with the best single-modality results (73.6% for MCI and 75.6% for pMCI with FDG-PET).

The Altered Triple Networks Interaction in Depression under Resting State Based on Graph Theory

The triple network model (Menon, 2011) has been proposed, which helps with finding a common framework for understanding the dysfunction in core neurocognitive network across multiple disorders. The alteration of the triple networks in the major depression disorder (MDD) is not clear. In our study, the altered interaction of the triple networks, which include default model network (DMN), central executive network (CEN), and salience network (SN), was examined in the MDD by graph theory method. The results showed that the connectivity degree of right anterior insula (rAI) significantly increased in MDD compared with healthy control (HC), and the connectivity degree between DMN and CEN significantly decreased in MDD. These results not only supported the proposal of the triple network model, but also prompted us to understand the dysfunction of neural mechanism in MDD.

Multi-feature kernel discriminant dictionary learning for face recognition

The current study put forward a multi-feature kernel discriminant dictionary learning algorithm for face recognition. It was based on the supervised within-class-similar discriminative dictionary learning algorithm (SCDDL) we introduced previously. The proposed new algorithm was thus named as multi-feature kernel SCDDL (MKSCDDL). In contrast to the weighted combination or the constraint of representation coefficients for the feature combination used by some popular methods, MKSCDDL introduced the multiple kernel learning technique into the dictionary learning scheme. The experimental results on three large well-known face databases suggested that combination multiple features in MKSCDDL improved the recognition rate compared with SCDDL. In addition, adopting multiple kernel learning technique resulted in an excellent multi-feature dictionary learning approach when compared with some state-of-the-art multi-feature algorithms such as multiple kernel learning and multi-task joint sparse representation methods, indicating the effectiveness of the multiple kernel learning technique in the combination of multiple features for classification. A multi-feature kernel discriminant DL algorithm for face recognition is proposed.Multiple kernel framework for multi-feature fusion is adopted into the DL scheme.The MKSCDDL could enhance the recognition rate compared with some other algorithms.

The contribution of different frequency bands of fMRI data to the correlation with EEG alpha rhythm.

Alpha rhythm is a prominent EEG rhythm observed during resting state and is thought to be related to multiple cognitive processes. Previous simultaneous electroencephalography (EEG)/functional magnetic resonance imaging (fMRI) studies have demonstrated that alpha rhythm is associated with blood oxygen level dependent (BOLD) signals in several different functional networks. How these networks influence alpha rhythm respectively is unclear. The low-frequency oscillations (LFO) in spontaneous BOLD activity are thought to contribute to the local correlations in resting state. Recent studies suggested that either LFO or other components of fMRI can be further divided into sub-components on different frequency bands. We hypothesized that those BOLD sub-components characterized the contributions of different brain networks to alpha rhythm. To test this hypothesis, EEG and fMRI data were simultaneously recorded from 17 human subjects performing an eyes-close resting state experiment. EEG alpha rhythm was correlated with the filtered fMRI time courses at different frequency bands (0.01-0.08 Hz, 0.08-0.25 Hz, 0.01-0.027 Hz, 0.027-0.073 Hz, 0.073-0.198 Hz, and 0.198-0.25 Hz). The results demonstrated significant relations between alpha rhythm and the BOLD signals in the visual network and in the attention network at LFO band, especially at the very low frequency band (0.01-0.027 Hz).

A pooling-LiNGAM algorithm for effective connectivity analysis of fMRI data.

The Independent Component Analysis (ICA)—linear non-Gaussian acyclic model (LiNGAM), an algorithm that can be used to estimate the causal relationship among non-Gaussian distributed data, has the potential value to detect the effective connectivity of human brain areas. Under the assumptions that (a): the data generating process is linear, (b) there are no unobserved confounders, and (c) data have non-Gaussian distributions, LiNGAM can be used to discover the complete causal structure of data. Previous studies reveal that the algorithm could perform well when the data points being analyzed is relatively long. However, there are too few data points in most neuroimaging recordings, especially functional magnetic resonance imaging (fMRI), to allow the algorithm to converge. Smiths study speculates a method by pooling data points across subjects may be useful to address this issue (Smith et al., 2011). Thus, this study focus on validating Smiths proposal of pooling data points across subjects for the use of LiNGAM, and this method is named as pooling-LiNGAM (pLiNGAM). Using both simulated and real fMRI data, our current study demonstrates the feasibility and efficiency of the pLiNGAM on the effective connectivity estimation.

Supervised within-class-similar discriminative dictionary learning for face recognition

A discriminative dictionary learning algorithm is put forward for face recognition.The algorithm combines the linear classification error with the within-class scatter.The fisher ratios of the coefficients are enhanced.The proposed method outperforms some state-of-the-art dictionary learning algorithms. The current study puts forward a supervised within-class-similar discriminative dictionary learning (SCDDL) algorithm for face recognition. Some popular discriminative dictionary learning schemes for recognition tasks always incorporate the linear classification error term into the objective function or make some discriminative restrictions on representation coefficients. In the presented SCDDL algorithm, we propose to directly restrict the representation coefficients to be similar within the same class and simultaneously include the linear classification error term in the supervised dictionary learning scheme to derive a more discriminative dictionary for face recognition. The experimental results on three large well-known face databases suggest that our approach can enhance the fisher ratio of representation coefficients when compared with several dictionary learning algorithms that incorporate linear classifiers. In addition, the learned discriminative dictionary, the large fisher ratio of representation coefficients and the simultaneously learned classifier can improve the recognition rate compared with some state-of-the-art dictionary learning algorithms.