Zhiying Long

Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data

Background Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Methodology/Principal Findings Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. Conclusions/Significance The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.

Improved working memory performance through self-regulation of dorsal lateral prefrontal cortex activation using real-time fMRI.

Working memory is important for a wide range of high-level cognitive activities. Previous studies have shown that the dorsal lateral prefrontal cortex (DLPFC) plays a critical role in working memory and that behavioral training of working memory can alter the activity of DLPFC. However, it is unclear whether the activation in the DLPFC can be self-regulated and whether any self-regulation can affect working memory behavior. The recently emerged real-time functional magnetic resonance imaging (rtfMRI) technique enables the individuals to acquire self-control of localized brain activation, potentially inducing desirable behavioral changes. In the present study, we employed the rtfMRI technique to train subjects to up-regulate the activation in the left DLPFC, which is linked to verbal working memory. After two rtfMRI training sessions, activation in the left DLPFC was significantly increased, whereas the control group that received sham feedback did not show any increase in DLPFC activation. Pre- and post-training behavioral tests indicated that performance of the digit span and letter memory task was significantly improved in the experimental group. Between-group comparison of behavioral changes showed that the increase of digit span in the experimental group was significantly greater than that in the control group. These findings provide preliminary evidence that working memory performance can be improved through learned regulation of activation in associated brain regions using rtfMRI.

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).

Motor Imagery Learning Induced Changes in Functional Connectivity of the Default Mode Network

Numerous studies provide evidences that motor skill learning changes the activity of some brain regions during task as well as some resting networks during rest. However, it is still unclear how motor learning affects the resting-state default-mode network (DMN). Using functional magnetic resonance imaging, this study investigated the alteration of the DMN after motor skill learning with mental imagery practice. Fourteen participants in the experimental group learned to imagine a sequential finger movement over a two-week period while twelve control participants did not undergo motor imagery learning. For the experimental group, interregional connectivity, estimated by the graph theory method, between the medial temporal lobe, lateral temporal, and lateral parietal cortex within the DMN was increased after learning, whereas activity of the DMN network, estimated by the independent component analysis method, remained stable. Moreover, the experimental group showed significant improvement in motor performance after learning and a negative correlation between the alteration of the execution rate and changes in activity in the lateral parietal cortex. These results indicate that the DMN could be sculpted by motor learning in a manner of altering interregional connectivity and may imply that the DMN plays a role in improving behavioral performance.

Functional Alteration of the DMN by Learned Regulation of the PCC Using Real-Time fMRI

The default mode network (DMN) is a network of brain regions that are active during rest and suppressed during a cognitively demanding task. Previous studies have shown that the DMN can be altered by development, aging, disorder, cognitive tasks and offline training. However, its unclear whether activity in the DMN can be altered by real-time training. Recently, real-time functional magnetic resonance imaging (rtfMRI), as a novel neurofeedback technique, has been applied to train subjects to voluntarily control activities in specific brain regions. In the current study, it was found that by using rtfMRI to guide training, subjects were able to learn to decrease activity in the posterior cingulate cortex (PCC), which is a “key hub” in the DMN, using motor imagery strategy. After the real-time training, activity in the medial prefrontral cortex/ anterior cingulate cortex (MPFC/ACC) of the resting state DMN was decreased. By contrast, the control group without neurofeedback produced increased activity in the MPFC/ACC of the DMN during the post-training resting state. These findings suggest that this rtfMRI technique has great potential to be used in the regulation of the DMN and may be a novel approach for studying functional plasticity of the cortex.

Altered effective connectivity model in the default mode network between bipolar and unipolar depression based on resting-state fMRI

BACKGROUND Bipolar depression (BD) is characterized by alternating episodes of depression and mania. Patients who spend the majority of their time in episodes of depression rather than mania are often misdiagnosed with unipolar depression (UD) that only exhibits depressive episodes. It would be important to explore the construction of more objective biomarkers which can be used to more accurately differentiate BD and UD. METHODS The effective connectivity model of BD and UD in the default mode network (DMN) was constructed based on resting-state fMRI data of 17 BD (32.12±8.57 years old) and 17 UD (32.59±9.77 years old) patients using a linear non-Gaussian acyclic model (LiNGAM). The effective connectivity differences were obtained by conducting a permutation test. RESULTS The following connections were stronger in the BD group than in the UD group: medial prefrontal cortex (MPFC) →posterior cingulate cortex (PCC), right inferior parietal cortex (rIPC)→left hippocampus (lHC) and rIPC→right insula (rInsula). In contrast, the following connections were weak or unapparent in the BD group: MPFC→lHC, rHC→MPFC, rHC→rInsula and rInsula→lHC. LIMITATIONS First, the medication effect is a confounding factor. Second, as with most fMRI studies, the subjects׳ thoughts during imaging are difficult to control. CONCLUSIONS The brain regions in these altered connections, such as the HC, insula, MPFC and IPC, all play important roles in emotional processing, suggesting that these altered connections may be conducive to better distinguish between BD and UD.