Evaluation of the effective connectivity of supplementary motor areas during motor imagery using Granger causality mapping

Deciphering Functional Connectivity Differences Between Motor Imagery and Execution of Target-Oriented Grasping

This study aimed to delineate overlapping and distinctive functional connectivity in visual motor imagery, kinesthetic motor imagery, and motor execution of target-oriented grasping action of the right hand. Functional magnetic resonance imaging data were obtained from 18 right-handed healthy individuals during each condition. Seed-based connectivity and multi-voxel pattern analyses were employed after selecting seed regions with the left primary motor cortex and supplementary motor area. There was equivalent seed-based connectivity during the three conditions in the bilateral frontoparietal and temporal areas. When the seed region was the left primary motor cortex, increased connectivity was observed in the left cuneus and superior frontal area during visual and kinesthetic motor imageries, respectively, compared with that during motor execution. Multi-voxel pattern analyses revealed that each condition was differentiated by spatially distributed connectivity patterns of the left primary motor cortex within the right cerebellum VI, cerebellum crus II, and left lingual area. When the seed region was the left supplementary motor area, the connectivity patterns within the right putamen, thalamus, cerebellar areas IV-V, and left superior parietal lobule were significantly classified above chance level across the three conditions. The present findings improve our understanding of the spatial representation of functional connectivity and its specific patterns among motor imagery and motor execution. The strength and fine-grained connectivity patterns of the brain areas can discriminate between motor imagery and motor execution.

Effective Connectivity of Right Amygdala Subregions Predicts Symptom Improvement Following 12-Week Pharmacological Therapy in Major Depressive Disorder

The low rates of treatment response still exist in the pharmacological therapy of major depressive disorder (MDD). Exploring an optimal neurological predictor of symptom improvement caused by pharmacotherapy is urgently needed for improving response to treatment. The amygdala is closely related to the pathological mechanism of MDD and is expected to be a predictor of the treatment. However, previous studies ignored the heterogeneousness and lateralization of amygdala. Therefore, this study mainly aimed to explore whether the right amygdala subregion function at baseline can predict symptom improvement after 12-week pharmacotherapy in MDD patients. We performed granger causality analysis (GCA) to identify abnormal effective connectivity (EC) of right amygdala subregions in MDD and compared the EC strength before and after 12-week pharmacological therapy. The results show that the abnormal EC mainly concentrated on the frontolimbic circuitry and default mode network (DMN). With relief of the clinical symptom, these abnormal ECs also change toward normalization. In addition, the EC strength of right amygdala subregions at baseline showed significant predictive ability for symptom improvement using a regularized least-squares regression predict model. These findings indicated that the EC of right amygdala subregions may be functionally related in symptom improvement of MDD. It may aid us to understand the neurological mechanism of pharmacotherapy and can be used as a promising predictor for symptom improvement in MDD.

Resting-state causal connectivity of the bed nucleus of the stria terminalis in panic disorder

Panic disorder (PD) is associated with anticipatory anxiety, a sustained threat response that appears to be related to the bed nucleus of the stria terminalis (BNST). Individuals with panic disorder may demonstrate significant differences in causal connectivity of the BNST in comparison to healthy controls. To test this hypothesis, resting-state functional magnetic resonance imaging (fMRI) was used to identify aberrant causal connectivity of the BNST in PD patients. 19 PD patients and 18 healthy controls (HC) matched for gender, age and education were included. Granger causality analysis (GCA) utilizing the BNST as a seed region was used to investigate changes in directional connectivity. Relative to healthy controls, PD patients displayed abnormal directional connectivity of the BNST including enhanced causal connectivity between the left parahippocampal gyrus and left BNST, the right insula and the right BNST, the left BNST and the right dorsolateral prefrontal cortex (dlPFC) and right BNST to the left and right dlPFC. Furthermore, PD patients displayed weakened causal connectivity between the right dlPFC and the left BNST, the left dlPFC and the right BNST, the left BNST and the left dorsomedial prefrontal cortex (dmPFC), right insula, right fusiform, and right BNST to the right insula. The results suggest that PD strongly correlates with increased causal connectivity between emotional processing regions and the BNST and enhanced causal connectivity between the BNST and cognitive control regions.

The Insula Is a Hub for Functional Brain Network in Patients With Anti-N-Methyl-D-Aspartate Receptor Encephalitis

BACKGROUND

In recent years, imaging technologies have been rapidly evolving, with an emphasis on the characterization of brain structure changes and functional imaging in patients with autoimmune encephalitis. However, the neural basis of anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis and its linked cognitive decline is unclear. Our research aimed to assess changes in the functional brain network in patients with anti-NMDAR encephalitis and whether these changes lead to cognitive impairment.

METHODS

Twenty-one anti-NMDAR encephalitis patients and 22 age-, gender-, and education status-matched healthy controls were assessed using resting functional magnetic resonance imaging (fMRI) scanning and neuropsychological tests, including the Hamilton Depression Scale (HAMD24), the Montreal Cognitive Assessment (MoCA), and the Hamilton Anxiety Scale (HAMA). A functional brain network was constructed using fMRI, and the topology of the network parameters was analyzed using graph theory. Next, we extracted the aberrant topological parameters of the functional network as seeds and compared causal connectivity with the whole brain. Lastly, we explored the correlation of aberrant topological structures with deficits in cognitive performance.

RESULTS

Relative to healthy controls, anti-NMDAR encephalitis patients exhibited decreased MoCA scores and increased HAMA and HAMD24 scores (p < 0.05). The nodal clustering coefficient and nodal local efficiency of the left insula (Insula_L) were significantly decreased in anti-NMDAR encephalitis patients (p < 0.05 following Bonferroni correction). Moreover, anti-NMDAR encephalitis patients showed a weakened causal connectivity from the left insula to the left inferior parietal lobe (Parietal_Inf_L) compared to healthy controls. Conversely, the left superior parietal lobe (Parietal_sup_L) exhibited an enhanced causal connectivity to the left insula in anti-NMDAR encephalitis patients compared to controls. Unexpectedly, these alterations were not correlated with any neuropsychological test scores.

CONCLUSION

This research describes topological abnormalities in the functional brain network in anti-NMDAR encephalitis. These results will be conducive to understand the structure and function of the brain network of patients with anti-NMDAR encephalitis and further explore the neuropathophysiological mechanisms.

Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions

Motor imagery brain–computer interfaces (MI‐BCIs) have great potential value in prosthetics control, neurorehabilitation, and gaming; however, currently, most such systems only operate in controlled laboratory environments. One of the most important obstacles is the MI‐BCI inefficiency phenomenon. The accuracy of MI‐BCI control varies significantly (from chance level to 100% accuracy) across subjects due to the not easily induced and unstable MI‐related EEG features. An MI‐BCI inefficient subject is defined as a subject who cannot achieve greater than 70% accuracy after sufficient training time, and multiple survey results indicate that inefficient subjects account for 10%–50% of the experimental population. The widespread use of MI‐BCI has been seriously limited due to these large percentages of inefficient subjects. In this review, we summarize recent findings of the cause of MI‐BCI inefficiency from resting‐state brain function, task‐related brain activity, brain structure, and psychological perspectives. These factors help understand the reasons for inter‐subject MI‐BCI control performance variability, and it can be concluded that the lower resting‐state sensorimotor rhythm (SMR) is the key factor in MI‐BCI inefficiency, which has been confirmed by multiple independent laboratories. We then propose to divide MI‐BCI inefficient subjects into three categories according to the resting‐state SMR and offline/online accuracy to apply more accurate approaches to solve the inefficiency problem. The potential solutions include developing transfer learning algorithms, new experimental paradigms, mindfulness meditation practice, novel training strategies, and identifying new motor imagery‐related EEG features. To date, few studies have focused on improving the control accuracy of MI‐BCI inefficient subjects; thus, we appeal to the BCI community to focus more on this research area. Only by reducing the percentage of inefficient subjects can we create the opportunity to expand the value and influence of MI‐BCI.

Abnormalities of effective connectivity and white matter microstructure in the triple network in patients with schizophrenia

Disorganized communication among large-scale brain networks, especially in the salience network, default mode network and central executive network, have been consistently reported in schizophrenia (SZ) patients. However, abnormal patterns of the effective connectivity and abnormalities in the white matter of these networks remains unclear in patients with SZ. Fifty-six SZ patients and fifty-five healthy controls were enrolled in the present study and underwent resting state functional magnetic resonance and diffusion tensor imaging. Twelve main nodes within the triple networks were defined by independent components analysis. Effective connectivity between these main nodes was computed using Granger causality analysis. Voxel-based analysis of the diffusion tensor imaging data was conducted to explore white matter changes. The SZ patients showed abnormal effective connectivity between the anterior cingulate cortex and the dorsolateral prefrontal cortex. The abnormal white matter showed decreased fractional anisotropy localized in the bilateral anterior corona radiate and left superior long fasciculus in patients with SZ. These findings shed light on the importance of the triple network in the pathogenesis of SZ, which may facilitate the understanding of SZ.

Classification of pure conduct disorder from healthy controls based on indices of brain networks during resting state

Conduct disorder (CD) is an important mental health problem in childhood and adolescence. There is presently a trend of revealing neural mechanisms using measures of brain networks. This study goes further by presenting a classification scheme to distinguish subjects with CD from typically developing healthy subjects based on measures of small-world networks. In this study, small-world networks were constructed, and feature data were generated for both the CD and healthy control (HC) groups. Two methods of feature selection, including the F-score and feature projection with singular value decomposition (SVD), were used to extract the feature data. Furthermore, and importantly, the classification performances were compared between the results from the two methods of feature selection. The selected feature data by SVD were employed to train three classifiers-least squares support vector machine (LS-SVM), naive Bayes and K-nearest neighbour (KNN)-for CD classification. Cross-validation results from 36 subjects showed that CD patients can be separated from HC with a sensitivity, specificity and overall accuracy of 88.89%, 100% and 94.44%, respectively, by using the LS-SVM classifier. These findings suggest that the combination of the LS-SVM classifier with SVD can achieve a higher degree of accuracy for CD diagnosis than the naive Bayes and KNN classifiers. Graphical abstract.

Sensory Feedback in Upper Limb Amputees Impacts Cortical Activity as Revealed by Multiscale Connectivity Analysis

Sensory feedback in upper limb amputees is crucial for improving movement decoding and also to enhance embodiment of the prosthetic limb. Recently, an increasing number of invasive and noninvasive solutions for sensory stimulation have demonstrated the capability of providing a range of sensations to upper limb amputees. However, the cortical impact of restored sensation is not clearly understood. Particularly, understanding the cortical connectivity changes at multiple scales (nodal and modular) in response to sensory stimulation, can reveal crucial information on how amputees brain process the sensory stimuli. Using Electroencephalography (EEG) signals, we compared the cortical connectivity network in response to sensory feedback provided by targeted transcutaneous electrical nerve stimulation (tTENS) in an upper limb amputee during phantom upper limb movements. We focused our cortical connectivity analysis on four functional modules comprising of 20 brain regions that are primarily associated with a visually guided motor task (visual, motor, somatosensory and multisensory integration (MI)) used in this study. At the modular level, we observed that the hubness (a graph theoretic measure quantifying the importance of brain regions in integrating brain function) of the motor module decreases whereas that of the somatosensory module increases in presence of tTENS feedback. At the nodal level, similar observations were made for the visual and MI regions. This is the first work to reveal the impact of sensory feedback at multiple scales in the cortex of amputees in response to sensory stimulation.

EEG-based Classification of Lower Limb Motor Imagery with Brain Network Analysis

This study aims to investigate the difference in cortical signal characteristics between the left and right foot imaginary movements and to improve the classification accuracy of the experimental tasks. Raw signals were gathered from 64-channel scalp electroencephalograms of 11 healthy participants. Firstly, the cortical source model was defined with 62 regions of interest over the sensorimotor cortex (nine Brodmann areas). Secondly, functional connectivity was calculated by phase lock value for α and β rhythm networks. Thirdly, network-based statistics were applied to identify whether there existed stable and significant subnetworks that formed between the two types of motor imagery tasks. Meanwhile, ten graph theory indices were investigated for each network by t-test to determine statistical significance between tasks. Finally, sparse multinomial logistic regression (SMLR)-support vector machine (SVM), as a feature selection and classification model, was used to analyze the graph theory features. The specific time-frequency (α event-related desynchronization and β event-related synchronization) difference network between the two tasks was congregated at the midline and demonstrated significant connections in the premotor areas and primary somatosensory cortex. A few of statistically significant differences in the network properties were observed between tasks in the α and β rhythm. The SMLR-SVM classification model achieved fair discrimination accuracy between imaginary movements of the two feet (maximum 75% accuracy rate in single-trial analyses). This study reveals the network mechanism of the discrimination of the left and right foot motor imagery, which can provide a novel avenue for the BCI system by unilateral lower limb motor imagery.

Hand, foot and lip representations in primary sensorimotor cortex: a high-density electroencephalography study

The primary sensorimotor cortex plays a major role in the execution of movements of the contralateral side of the body. The topographic representation of different body parts within this brain region is commonly investigated through functional magnetic resonance imaging (fMRI). However, fMRI does not provide direct information about neuronal activity. In this study, we used high-density electroencephalography (hdEEG) to map the representations of hand, foot, and lip movements in the primary sensorimotor cortex, and to study their neural signatures. Specifically, we assessed the event-related desynchronization (ERD) in the cortical space. We found that the performance of hand, foot, and lip movements elicited an ERD in beta and gamma frequency bands. The primary regions showing significant beta- and gamma-band ERD for hand and foot movements, respectively, were consistent with previously reported using fMRI. We observed relatively weaker ERD for lip movements, which may be explained by the fact that less fine movement control was required. Overall, our study demonstrated that ERD based on hdEEG data can support the study of motor-related neural processes, with relatively high spatial resolution. An interesting avenue may be the use of hdEEG for deeper investigations into the pathophysiology of neuromotor disorders.

Large-Scale Granger Causal Brain Network based on Resting-State fMRI data

The causal connections among small-scale regions based on resting-state fMRI data have been extensively studied and a lot of achievements have been demonstrated. However, the causal connection among large-scale regions was seldom discussed. In this paper, we applied global Granger causality analysis to construct the causal connections in the whole-brain network among 103 healthy subjects (33 M/66F, ages 20-23) based on a resting-state fMRI dataset. We further explored four large-scale cognitive networks which have been widely known: central executive network (CEN), default mode network (DMN), dorsal attention network (DAN) and salience network (SN). These four cognitive networks are particularly important for understanding higher cognitive functions and dysfunction. Based on the above research, Out-In degree were introduced to identify the driving and driven hubs. Studying the driving and driven hub of brain network is of great significance for assessing the functional mechanism of the brain network. There were 817 directed edges identified as significant among the 8010 possible causal connections; seven driving hubs and ten driven hubs were identified in the whole-brain network. In CEN, dorsolateral prefrontal cortex (DlPFC) and superior parietal cortex (SPC) were the driven and driving hubs, respectively; in DMN, they were posterior cingulate cortex (PCC) and medial prefrontal cortex (MPFC); in DAN, they were frontal eye fields (FEF) and intraparietal sulcus (IPS); and in SN, they were frontoinsular cortex (FIC) and medial frontal cortex (MFC). These findings may provide insights into our understanding of human brain function mechanisms and the diagnosis of brain diseases.

Altered Granger causality connectivity within motor-related regions of patients with Parkinson's disease: a resting-state fMRI study

PURPOSE

Although numerous clinical neuroimaging studies have demonstrated that there are functional abnormalities of motor-related regions in patients with Parkinson's disease (PD) by resting-state functional magnetic resonance imaging (fMRI), little studies have explored the causal interactions within these motor-related regions. The present study aimed to examine Granger causality connectivity patterns within motor-related regions in PD patients.

METHODS

Resting-state fMRI was conducted to investigate the causal connectivity differences within motor-related regions between 17 PD patients and 17 matched healthy controls. Subsequently, the relationship between the Unified Parkinson's Disease Rating Scale scores and causal connectivity values within motor-related regions was examined in PD patients.

RESULTS

An increased causal connectivity from the left premotor cortex (PMC) to right primary motor cortex (M1) was found in PD patients compared with that of healthy controls. Also, increased causal flow from the PMC to M1 was negatively correlated with motor scores.

CONCLUSION

PD patients have abnormal causal connectivity in specific motor-related regions, which may reflect a compensatory role of motor deficits in PD patients.

Aging Changes Effective Connectivity of Motor Networks During Motor Execution and Motor Imagery

Age-related neurodegenerative and neurochemical changes are considered to be the basis for the decline of motor function; however, the change of effective connections in cortical motor networks that come with aging remains unclear. Here, we investigated the age-related changes of the dynamic interaction between cortical motor regions. Twenty young subjects and 20 older subjects underwent both right hand motor execution (ME) and right hand motor imagery (MI) tasks by using functional magnetic resonance imaging. Conditional Granger causality analysis (CGCA) was used to compare young and older adults’ effective connectivity among regions of the motor network during the tasks. The more effective connections among motor regions in older adults were found during ME; however, effective within-domain hemisphere connections were reduced, and the blood oxygenation level dependent (BOLD) signal was significantly delayed in older adults during MI. Supplementary motor area (SMA) had a significantly higher In+Out degree within the network during ME and MI in older adults. Our results revealed a dynamic interaction within the motor network altered with aging during ME and MI, which suggested that the interaction with cortical motor neurons caused by the mental task was more difficult with aging. The age-related effects on the motor cortical network provide a new insight into our understanding of neurodegeneration in older individuals.

Hippocampal connectivity in the aftermath of acute social stress

The hippocampus is a core brain region that responds to stress. Previous studies have found a dysconnectivity between hippocampus and other brain regions under acute and chronic stress. However, whether and how acute social stress influences the directed connectivity patterns from and to the hippocampus remains unclear. In this study, using a within-subject design and Granger causal analysis (GCA), we investigated the alterations of resting state effective connectivity from and to hippocampal subregions after an acute social stressor (the Trier Social Stress Test). Participants were engaged in stress and control conditions spaced approximately one month apart. Our findings showed that stress altered the information flows in the thalamus-hippocampus-insula/midbrain circuit. The changes in this circuit could also predict with high accuracy the stress and control conditions at the subject level. These hippocampus-related brain networks have been documented to be involved in emotional information processing and storage, as well as habitual responses. We speculate that alterations of the effective connectivity between these brain regions may be associated with the registering and encoding of threatening stimuli under stress. Our investigation of hippocampal functional connectivity at a subregional level may help elucidate the functional neurobiology of stress-related psychiatric disorders.

Evaluation of synchronization measures for capturing the lagged synchronization between EEG channels: A cognitive task recognition approach

During cognitive, perceptual and sensory tasks, connectivity profile changes across different regions of the brain. Variations of such connectivity patterns between different cognitive tasks can be evaluated using pairwise synchronization measures applied to electrophysiological signals, such as electroencephalography (EEG). However, connectivity-based task recognition approaches achieving viable recognition performance have been lacking from the literature. By using several synchronization measures, we identify time lags between channel pairs during different cognitive tasks. We employed mutual information, cross correntropy, cross correlation, phase locking value, cosine similarity and nonlinear interdependence measures. In the training phase, for each type of cognitive task, we identify the time lags that maximize the average synchronization between channel pairs. These lags are used to calculate pairwise synchronization values with which we construct the train and test feature vectors for recognition of the cognitive task carried out using Fisher's linear discriminant (FLD) analysis. We tested our framework in a motor imagery activity recognition scenario on PhysioNet Motor Movement/Imagery and BCI Competition-III Ⅳa datasets. For PhysioNet dataset, average performance results ranging between % 51 and % 61 across 20 subjects. For BCI Competition-Ⅲ dataset, we achieve an average recognition performance of % 76 which is above the minimum reliable communication rate (% 70). We achieved an average accuracy over the minimum reliable communication rate on the BCI Competition-Ⅲ dataset. Performance levels were lower on the PhysioNet dataset. These results indicate that a viable task recognition system is achievable using pairwise synchronization measures evaluated at the proper task specific lags.

The Ventral Intermediate Nucleus Differently Modulates Subtype-Related Networks in Parkinson's Disease

Background: Posture instability gait difficulty-dominant (PIGD) and tremor-dominant (TD) are two subtypes of Parkinson's disease (PD). The thalamus is involved in the neural circuits of both subtypes. However, which subregion of the thalamus has an influence on the PD subtypes remains unclear. Objective: To explore the core subregion of the thalamus showing a significant influence on the PD subtypes and its directional interaction between the PD subtypes. Methods: A total of 79 PD patients (43 TD and 36 PIGD) and 31 normal controls (NC) were enrolled, and the gray matter volume and perfusion characteristics in the thalamus were compared between the three groups. The subregion of the thalamus with significantly different perfusion and volume among three groups was used as the seed of a Granger causality analysis (GCA) to compare the causal connectivity between different subtypes. Results: Perfusion with an increased gradient among the three groups (TD > PIGD > NC) in the bilateral ventral intermediate nucleus (Vim) was observed, which was positively correlated with the clinical tremor scores. The GCA revealed that TD patients had enhanced causal connectivity from the bilateral Vim to the bilateral paracentral gyrus, M1 and the cerebellum compared with the NC group, while the PIGD subtype revealed an increased causal connectivity from the bilateral Vim to the bilateral premotor cortex (preM) and putamen. Additionally, there were positive correlations between the tremor scores and a causal connectivity from the Vim to the cerebellum. The connectivity from the right Vim to the right preM and the right putamen was positively correlated with the PIGD scores. Conclusion: This multilevel analysis showed that the Vim had a significant influence on the PD subtypes and that it differentially mediated the TD and PIGD-related causal connectivity pattern in PD.

Shared and Distinct Neural Bases of Large- and Small-Scale Spatial Ability: A Coordinate-Based Activation Likelihood Estimation Meta-Analysis

Background: Spatial ability is vital for human survival and development. However, the relationship between large-scale and small-scale spatial ability remains poorly understood. To address this issue from a novel perspective, we performed an activation likelihood estimation (ALE) meta-analysis of neuroimaging studies to determine the shared and distinct neural bases of these two forms of spatial ability. Methods: We searched Web of Science, PubMed, PsycINFO, and Google Scholar for studies regarding "spatial ability" published within the last 20 years (January 1988 through June 2018). A final total of 103 studies (Table 1) involving 2,085 participants (male = 1,116) and 2,586 foci were incorporated into the meta-analysis. Results: Large-scale spatial ability was associated with activation in the limbic lobe, posterior lobe, occipital lobe, parietal lobe, right anterior lobe, frontal lobe, and right sub-lobar area. Small-scale spatial ability was associated with activation in the parietal lobe, occipital lobe, frontal lobe, right posterior lobe, and left sub-lobar area. Furthermore, conjunction analysis revealed overlapping regions in the sub-gyrus, right superior frontal gyrus, right superior parietal lobule, right middle occipital gyrus, right superior occipital gyrus, left inferior occipital gyrus, and precuneus. The contrast analysis demonstrated that the parahippocampal gyrus, left lingual gyrus, culmen, right middle temporal gyrus, left declive, left superior occipital gyrus, and right lentiform nucleus were more strongly activated during large-scale spatial tasks. In contrast, the precuneus, right inferior frontal gyrus, right precentral gyrus, left inferior parietal lobule, left supramarginal gyrus, left superior parietal lobule, right inferior occipital gyrus, and left middle frontal gyrus were more strongly activated during small-scale spatial tasks. Our results further indicated that there is no absolute difference in the cognitive strategies associated with the two forms of spatial ability (egocentric/allocentric). Conclusion: The results of the present study verify and expand upon the theoretical model of spatial ability proposed by Hegarty et al. Our analysis revealed a shared neural basis between large- and small-scale spatial abilities, as well as specific yet independent neural bases underlying each. Based on these findings, we proposed a more comprehensive version of the behavioral model.

Brain activation during repeated imagining of chocolate consumption: a functional magnetic resonance imaging study

OBJECTIVE

To assess brain activation during mental visualization of eating chocolate.

DESIGN

Twenty-one subjects were included. FMRI was acquired with a single-shot, multislice, gradient echo-planar sequence, while subjects were performing two specific imaginary tasks.

RESULTS

Activation of motor-associated brain areas was observed during both mental visualization tasks. Increased activation of the right dorsolateral prefrontal cortex, the thalamus, the postcentral gyrus and the left anterior cingulate cortex, and the precuneus was observed during imagining eating chocolate.

CONCLUSIONS

Repeated imagination of chocolate consumption results in activation of brain areas associated with hedonic effects of food and satiety and inhibition of orexigenic areas.

Distinction of directional coupling in sensorimotor networks between active and passive finger movements using fNIRS

The purpose of this study is to investigate cerebral cortex activation during active movement and passive movement by using a functional near-infrared spectroscopy (fNIRS). Tasks were the flexion/extension of the right hand finger by active movement and passive movement. Oxy-hemoglobin concentration changes calculated from fNIRS and analyzed the activation and connectivity so as to understand dynamical brain relationship. The results demonstrated that the brain activation in passive movements is similar to motor execution. During active movement, the estimated causality patterns showed significant causality value from the supplementary motor area (SMA) to the primary motor cortex (M1). During the passive movement, the causality from the primary somatosensory cortex (S1) to the primary motor cortex (M1) was stronger than active movement. These results demonstrated that active and passive movements had a direct effect on the cerebral cortex but the stimulus pathway of active and passive movement is different. This study may contribute to better understanding how active and passive movements can be expressed into cortical activation by means of fNIRS.

Changes in network connectivity during motor imagery and execution

Background

Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were to analyze the effective connectivity between motor and cognitive networks in order to define network dynamics during motor execution and motor imagery in healthy individuals. Second, we analyzed the differences in effective connectivity between correct and incorrect responses during motor execution and imagery using dynamic causal modeling (DCM) of electroencephalography (EEG) data.

Method

Twenty healthy subjects performed a sequence of finger tapping trials using either motor execution or motor imagery, and the performances were recorded. Changes in effective connectivity between the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), and dorsolateral prefrontal cortex (DLPFC) were estimated using dynamic causal modeling. Bayesian model averaging with family-level inference and fixed-effects analysis was applied to determine the most likely connectivity model for these regions.

Results

Motor execution and imagery showed inputs to distinct brain regions, the premotor cortex and the supplementary motor area, respectively. During motor execution, the coupling strength of a feedforward network from the DLPFC to the PMC was greater than that during motor imagery. During motor imagery, the coupling strengths of a feedforward network from the PMC to the SMA and of a feedback network from M1 to the PMC were higher than that during motor execution. In imagined movement, although there were connectivity differences between correct and incorrect task responses, each motor imagery task that included correct and incorrect responses showed similar network connectivity characteristics. Correct motor imagery responses showed connectivity from the PMC to the DLPFC, while the incorrect responses had characteristic connectivity from the SMA to the DLPFC.

Conclusions

These findings provide an understanding of effective connectivity between motor and cognitive areas during motor execution and imagery as well as the basis for future connectivity studies for patients with stroke.

Investigation of the Changes in the Power Distribution in Resting-State Brain Networks Associated with Pure Conduct Disorder

Conduct disorder (CD) is a psychiatric disorder in children and adolescence. To investigate changes in the power distribution in brain networks between CD and typically developing (TD) groups, resting-state functional magnetic resonance imaging (rsfMRI) data of thirty-six subjects were first recorded, and then the data were preprocessed using DPARSF and SPM8. Meanwhile, the power of the blood oxygenation level-dependent (BOLD) signals of ninety brain regions was acquired using the integral of the Welch power spectral density (PSD). Additionally, the powers of the brain regions that reached significance (p < 0.05) were extracted using the bootstrap statistics, in which the standardized z-scores of the powers were used as a reference. The results of the analysis of the changes in power exhibited that there were significant power differences in some pairs of brain regions between the CD and TD groups, indicating a change in the power distribution. In addition, the results also suggest that the total power consumption of brain networks in CD patients is less than that observed in the TD group. Consequently, the study provided a paradigm for establishing quantifiable indicators via the power spectrum approach for the comparison and analysis of the BOLD signal power between CD patients and healthy controls.

The Relationship between Intrinsic Couplings of the Visual Word Form Area with Spoken Language Network and Reading Ability in Children and Adults

Reading plays a key role in education and communication in modern society. Learning to read establishes the connections between the visual word form area (VWFA) and language areas responsible for speech processing. Using resting-state functional connectivity (RSFC) and Granger Causality Analysis (GCA) methods, the current developmental study aimed to identify the difference in the relationship between the connections of VWFA-language areas and reading performance in both adults and children. The results showed that: (1) the spontaneous connectivity between VWFA and the spoken language areas, i.e., the left inferior frontal gyrus/supramarginal gyrus (LIFG/LSMG), was stronger in adults compared with children; (2) the spontaneous functional patterns of connectivity between VWFA and language network were negatively correlated with reading ability in adults but not in children; (3) the causal influence from LIFG to VWFA was negatively correlated with reading ability only in adults but not in children; (4) the RSFCs between left posterior middle frontal gyrus (LpMFG) and VWFA/LIFG were positively correlated with reading ability in both adults and children; and (5) the causal influence from LIFG to LSMG was positively correlated with reading ability in both groups. These findings provide insights into the relationship between VWFA and the language network for reading, and the role of the unique features of Chinese in the neural circuits of reading.

Disrupted Causal Connectivity Anchored in the Posterior Cingulate Cortex in Amnestic Mild Cognitive Impairment

Amnestic mild cognitive impairment (aMCI) is a transitional stage between normal cognitive aging and Alzheimer’s disease. Previous studies have found that neuronal activity and functional connectivity impaired in many functional networks, especially in the default mode network (DMN), which is related to significantly impaired cognitive and memory functions in aMCI patients. However, few studies have focused on the effective connectivity of the DMN and its subsystems in aMCI patients. The posterior cingulate cortex (PCC) is considered a crucial region in connectivity of the DMN and its key subsystem. In this study, using the coefficient Granger causality analysis approach and using the PCC as the region of interest, we explored changes in the DMN and its subsystems in effective connectivity with other brain regions as well as in correlations among them in 16 aMCI patients and 15 age-matched cognitively normal elderly. Results showed decreased effective connectivity from PCC to whole brain in the left prefrontal cortex, the left medial temporal lobe (MTL), the left fusiform gyrus (FG), and the left cerebellar hemisphere, meanwhile, right temporal lobe showed increased effective connectivity from PCC to the whole brain in aMCI patients compared with normal control. In addition, compared with the normal controls, increased effective connectivity of the whole brain to the PCC in aMCI patients was found in the right thalamus, left medial temporal lobe, left FG, and left cerebellar hemisphere. Compared with the normal controls, no reduced effective connectivity was found in any brain regions from the whole brain to the PCC in aMCI patients. The reduced effective connectivity of the PCC to left MTL showed negative correlation trend with neuropsychological tests (Auditory Verbal Learning Test-immediate recall and clock drawing test) in aMCI patients. Our study shows that aMCI patients have abnormalities in effective connectivity within the PCC-centered DMN network and its posterior subsystems as well as in the cerebellar hemisphere and thalamus. Abnormal integration of networks may be related to cognitive and memory impairment and compensation mechanisms in aMCI patients.

Differential neural encoding of sensorimotor and visual body representations

Sensorimotor processing specifically impacts mental body representations. In particular, deteriorated somatosensory input (as after complete spinal cord injury) increases the relative weight of visual aspects of body parts’ representations, leading to aberrancies in how images of body parts are mentally manipulated (e.g. mental rotation). This suggests that a sensorimotor or visual reference frame, respectively, can be relatively dominant in local (hands) versus global (full-body) bodily representations. On this basis, we hypothesized that the recruitment of a specific reference frame could be reflected in the activation of sensorimotor versus visual brain networks. To this aim, we directly compared the brain activity associated with mental rotation of hands versus full-bodies. Mental rotation of hands recruited more strongly the supplementary motor area, premotor cortex, and secondary somatosensory cortex. Conversely, mental rotation of full-bodies determined stronger activity in temporo-occipital regions, including the functionally-localized extrastriate body area. These results support that (1) sensorimotor and visual frames of reference are used to represent the body, (2) two distinct brain networks encode local or global bodily representations, and (3) the extrastriate body area is a multimodal region involved in body processing both at the perceptual and representational level.

Altered Effective Connectivity of the Primary Motor Cortex in Stroke: A Resting-State fMRI Study with Granger Causality Analysis

The primary motor cortex (M1) is often abnormally recruited in stroke patients with motor disabilities. However, little is known about the alterations in the causal connectivity of M1 following stroke. The purpose of the present study was to investigate whether the effective connectivity of the ipsilesional M1 is disturbed in stroke patients who show different outcomes in hand motor function. 23 patients with left-hemisphere subcortical stroke were selected and divided into two subgroups: partially paralyzed hands (PPH) and completely paralyzed hands (CPH). Further, 24 matched healthy controls (HCs) were recruited. A voxel-wise Granger causality analysis (GCA) on the resting-state fMRI data between the ipsilesional M1 and the whole brain was performed to explore differences between the three groups. Our results showed that the influence from the frontoparietal cortices to ipsilesional M1 was diminished in both stroke subgroups and the influence from ipsilesional M1 to the sensorimotor cortices decreased greater in the CPH group than in the PPH group. Moreover, compared with the PPH group, the decreased influence from ipsilesional M1 to the contralesional cerebellum and from the contralesional superior parietal lobe to ipsilesional M1 were observed in the CPH group, and their GCA values were positively correlated with the FMA scores; Conversely, the increased influence from ipsilesional M1 to the ipsilesional middle frontal gyrus and middle temporal gyrus were observed, whose GCA values were negatively correlated with the FMA scores. This study suggests that the abnormalities of casual flow in the ipsilesional M1 are related to the severity of stroke-hand dysfunction, providing valuable information to understand the deficits in resting-state effective connectivity of motor execution and the frontoparietal motor control network during brain plasticity following stroke.

An Investigation of the Differences and Similarities between Generated Small-World Networks for Right- and Left-Hand Motor Imageries

In this study, small-world network analysis was performed to identify the similarities and differences between functional brain networks for right- and left-hand motor imageries (MIs). First, Pearson correlation coefficients among the nodes within the functional brain networks from healthy subjects were calculated. Then, small-world network indicators, including the clustering coefficient, the average path length, the global efficiency, the local efficiency, the average node degree, and the small-world index, were generated for the functional brain networks during both right- and left-hand MIs. We identified large differences in the small-world network indicators between the functional networks during MI and in the random networks. More importantly, the functional brain networks underlying the right- and left-hand MIs exhibited similar small-world properties in terms of the clustering coefficient, the average path length, the global efficiency, and the local efficiency. By contrast, the right- and left-hand MI brain networks showed differences in small-world characteristics, including indicators such as the average node degree and the small-world index. Interestingly, our findings also suggested that the differences in the activity intensity and range, the average node degree, and the small-world index of brain networks between the right- and left-hand MIs were associated with the asymmetry of brain functions.

Human sensory cortex structure and top-down controlling brain network determine individual differences in perceptual alternations

Bistable perception is a type of subjective perception that spontaneously alternates between two perceptual interpretations of an ambiguous sensory input. Past functional magnetic resonance imaging (fMRI) studies have examined the activation patterns underlying bistable perception, yet the variability between individuals in the alternations is not well understood. Therefore, voxel-based morphometry (VBM) was introduced in this study to correlate the GM of the sensory cortex with the alternations of Rubin face-vase illusion in a large group of young adults. We found that the GM volume and density (GMV/GMD) of the left fusiform face area (FFA) were significantly positively correlated with the alternations. Next, Granger causality analysis (GCA) was introduced to investigate the top-down modulation from high-level areas to the sensory cortex using resting-state fMRI data. Correlations between the perceptual alternations and Granger causalities showed that the top-down modulations from high-level brain regions, such as the superior parietal lobule (SPL) to the left FFA, were positive. Together, these findings indicated that the anatomical structure of the face-selective area may determine individual alternations of the Rubin face-vase illusion. This process may be controlled by a high-level cortex associated with attentional modulation, such as the SPL or Posterior Cingulate Cortex (PCC).

Extraversion and neuroticism related to the resting-state effective connectivity of amygdala

The amygdala plays a key role in emotion processing. Its functional connectivity with other brain regions has been extensively demonstrated to be associated with extraversion and neuroticism. However, how the amygdala affects other regions and is affected by others within these connectivity patterns associated with extraversion and neuroticism remains unclear. To address this issue, we investigated the effective connectivity of the amygdala using Granger causality analysis on the resting-state functional magnetic resonance imaging data of 70 participants. Results showed that extraversion was positively correlated with the influence from the right inferior occipital gyrus (IOG) to the left amygdala, and from the bilateral IOG to the right amygdala; such result may represent the neural correlates of social interactions in extraverts. Conversely, neuroticism was associated with an increased influence from right amygdala to right middle frontal gyrus and a decreased influence from right precuneus to right amygdala. This influence might affect the modulations of cognitive regulation function and self-referential processes in neurotic individuals. These findings highlight the importance of the causal influences of amygdala in explaining the individual differences in extraversion and neuroticism, and offer further insights into the specific neural networks underlying personality.

Neural Representations of Physics Concepts

We used functional MRI (fMRI) to assess neural representations of physics concepts (momentum, energy, etc.) in juniors, seniors, and graduate students majoring in physics or engineering. Our goal was to identify the underlying neural dimensions of these representations. Using factor analysis to reduce the number of dimensions of activation, we obtained four physics-related factors that were mapped to sets of voxels. The four factors were interpretable as causal motion visualization, periodicity, algebraic form, and energy flow. The individual concepts were identifiable from their fMRI signatures with a mean rank accuracy of .75 using a machine-learning (multivoxel) classifier. Furthermore, there was commonality in participants' neural representation of physics; a classifier trained on data from all but one participant identified the concepts in the left-out participant (mean accuracy = .71 across all nine participant samples). The findings indicate that abstract scientific concepts acquired in an educational setting evoke activation patterns that are identifiable and common, indicating that science education builds abstract knowledge using inherent, repurposed brain systems.

Conditional Granger Causality Analysis of Effective Connectivity during Motor Imagery and Motor Execution in Stroke Patients

Aims. Motor imagery has emerged as a promising technique for the improvement of motor function following stroke, but the mechanism of functional network reorganization in patients during this process remains unclear. The aim of this study is to evaluate the cortical motor network patterns of effective connectivity in stroke patients. Methods. Ten stroke patients with right hand hemiplegia and ten normal control subjects were recruited. We applied conditional Granger causality analysis (CGCA) to explore and compare the functional connectivity between motor execution and motor imagery. Results. Compared with the normal controls, the patient group showed lower effective connectivity to the primary motor cortex (M1), the premotor cortex (PMC), and the supplementary motor area (SMA) in the damaged hemisphere but stronger effective connectivity to the ipsilesional PMC and M1 in the intact hemisphere during motor execution. There were tighter connections in the cortical motor network in the patients than in the controls during motor imagery, and the patients showed more effective connectivity in the intact hemisphere. Conclusions. The increase in effective connectivity suggests that motor imagery enhances core corticocortical interactions, promotes internal interaction in damaged hemispheres in stroke patients, and may facilitate recovery of motor function.

Disrupted causal connectivity anchored on the anterior cingulate cortex in first-episode medication-naive major depressive disorder

In recent years, major depressive disorder (MDD) has been demonstrated to be associated with abnormalities in neural networks, particularly the prefrontal-limbic network (PLN). However, there are few current studies that have examined information flow in the PLN. In this study, Granger causality analysis (GCA), based on signed regression coefficient, was used to explore changes in causal connectivity in resting-state PLNs of MDD patients. A total of 23 first-episode medication-naïve MDD patients and 20 normal control participants were subjected to resting-state functional magnetic resonance imaging (RS-fMRI) scans. Increased causal effects of the right insular cortex, right putamen and right caudate on the rostral anterior cingulate cortex (rACC) and reduced causal effects of bilateral dorsolateral prefrontal cortex (DLPFC) and left orbitofrontal cortex (OFC) on the rACC were found in MDD patients compared to normal controls. The extensive reduction in the causal effect of the prefrontal cortex (PFC) demonstrates impaired top-down cognitive control in MDD patients. Changes in the causal relationship between the right insula and rACC suggest problems in coordination of the default mode network by the right anterior insular cortex (rAI). These findings provide valuable insight into MDD-related neural network disorders reported in previous RS-fMRI studies and may potentially guide clinical treatment of MDD in the future.

The Study of Object-Oriented Motor Imagery Based on EEG Suppression

Motor imagery is a conventional method for brain computer interface and motor learning. To avoid the great individual difference of the motor imagery ability, object-oriented motor imagery was applied, and the effects were studied. Kinesthetic motor imagery and visual observation were administered to 15 healthy volunteers. The EEG during cue-based simple imagery (SI), object-oriented motor imagery (OI), non-object-oriented motor imagery (NI) and visual observation (VO) was recorded. Study results showed that OI and NI presented significant contralateral suppression in mu rhythm (p < 0.05). Besides, OI exhibited significant contralateral suppression in beta rhythm (p < 0.05). While no significant mu or beta contralateral suppression could be found during VO or SI (p > 0.05). Compared with NI, OI showed significant difference (p < 0.05) in mu rhythm and weak significant difference (p = 0.0612) in beta rhythm over the contralateral hemisphere. The ability of motor imagery can be reflected by the suppression degree of mu and beta frequencies which are the motor related rhythms. Thus, greater enhancement of activation in mirror neuron system is involved in response to object-oriented motor imagery. The object-oriented motor imagery is favorable for improvement of motor imagery ability.

Anatomical and functional connectivity in the default mode network of post-traumatic stress disorder patients after civilian and military-related trauma

Posttraumatic stress disorder (PTSD) is characterized by unwanted intrusive thoughts and hyperarousal at rest. As these core symptoms reflect disturbance in resting-state mechanisms, we investigated the functional and anatomical involvement of the default mode network (DMN) in this disorder. The relation between symptomatology and trauma characteristics was considered. Twenty PTSD patients and 20 matched trauma-exposed controls that were exposed to a similar traumatic event were recruited for this study. In each group, 10 patients were exposed to military trauma, and 10 to civilian trauma. PTSD, anxiety, and depression symptom severity were assessed. DMN maps were identified in resting-state scans using independent component analysis. Regions of interest (medial prefrontal, precuneus, and bilateral inferior parietal) were defined and average z-scores were extracted for use in the statistical analysis. The medial prefrontal and the precuneus regions were used for cingulum tractography whose integrity was measured and compared between groups. Similar functional and anatomical connectivity patterns were identified in the DMN of PTSD patients and trauma-exposed controls. In the PTSD group, functional and anatomical connectivity parameters were strongly correlated with clinical measures, and there was evidence of coupling between the anatomical and functional properties. Type of trauma and time from trauma were found to modulate connectivity patterns. To conclude, anatomical and functional connectivity patterns are related to PTSD symptoms and trauma characteristics influence connectivity beyond clinical symptoms. Hum Brain Mapp 37:589-599, 2016. © 2015 Wiley Periodicals, Inc.

Combining self-organizing mapping and supervised affinity propagation clustering approach to investigate functional brain networks involved in motor imagery and execution with fMRI measurements

Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering (SAPC), is proposed and implemented to identify the motor execution (ME) and motor imagery (MI) networks. In SOM-SAPC, SOM was first performed to process fMRI data and SAPC is further utilized for clustering the patterns of functional networks. As a result, SOM-SAPC is able to significantly reduce the computational cost for brain network analysis. Simulation and clinical tests involving ME and MI were conducted based on SOM-SAPC, and the analysis results indicated that functional brain networks were clearly identified with different response patterns and reduced computational cost. In particular, three activation clusters were clearly revealed, which include parts of the visual, ME and MI functional networks. These findings validated that SOM-SAPC is an effective and robust method to analyze the fMRI data with multitasks.

Activity of right premotor-parietal regions dependent upon imagined force level: an fMRI study

In this study, we utilized functional magnetic resonance imaging (fMRI) to measure blood oxygenation level-dependent (BOLD) signals. This allowed us to evaluate the relationship between brain activity and imagined force level. Subjects performed motor imagery of repetitive right hand grasping with three different levels of contractile force; 10%, 30%, and 60% of their maximum voluntary contraction (MVC). We observed a common activation among each condition in the following brain regions; the dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), supplementary motor area (SMA), premotor area (PM), insula, and inferior parietal lobule (IPL). In addition, the BOLD signal changes were significantly larger at 60% MVC than at 10% MVC in the right PM, the right IPL, and the primary somatosensory cortex (SI). These findings indicate that during motor imagery right fronto-parietal activity increases as the imagined contractile force level is intensified. The present finding that the right brain activity during motor imagery is clearly altered depending on the imagined force level suggests that it may be possible to decode intended force level during the motor imagery of patients or healthy subjects.

Adaptation of motor imagery EEG classification model based on tensor decomposition

OBJECTIVE

Session-to-session nonstationarity is inherent in brain-computer interfaces based on electroencephalography. The objective of this paper is to quantify the mismatch between the training model and test data caused by nonstationarity and to adapt the model towards minimizing the mismatch.

APPROACH

We employ a tensor model to estimate the mismatch in a semi-supervised manner, and the estimate is regularized in the discriminative objective function.

MAIN RESULTS

The performance of the proposed adaptation method was evaluated on a dataset recorded from 16 subjects performing motor imagery tasks on different days. The classification results validated the advantage of the proposed method in comparison with other regularization-based or spatial filter adaptation approaches. Experimental results also showed that there is a significant correlation between the quantified mismatch and the classification accuracy.

SIGNIFICANCE

The proposed method approached the nonstationarity issue from the perspective of data-model mismatch, which is more direct than data variation measurement. The results also demonstrated that the proposed method is effective in enhancing the performance of the feature extraction model.

Quantification of the power changes in BOLD signals using Welch spectrum method during different single-hand motor imageries

Motor imagery is an experimental paradigm implemented in cognitive neuroscience and cognitive psychology. To investigate the asymmetry of the strength of cortical functional activity due to different single-hand motor imageries, functional magnetic resonance imaging (fMRI) data from right handed normal subjects were recorded and analyzed during both left-hand and right-hand motor imagery processes. Then the average power of blood oxygenation level-dependent (BOLD) signals in temporal domain was calculated using the developed tool that combines Welch power spectrum and the integral of power spectrum approach of BOLD signal changes during motor imagery. Power change analysis results indicated that cortical activity exhibited a stronger power in the precentral gyrus and medial frontal gyrus with left-hand motor imagery tasks compared with that from right-hand motor imagery tasks. These observations suggest that right handed normal subjects mobilize more cortical nerve cells for left-hand motor imagery. Our findings also suggest that the approach based on power differences of BOLD signals is a suitable quantitative analysis tool for quantification of asymmetry of brain activity intensity during motor imagery tasks.

Evaluation of net causal influences in the circuit responding to premotor control during the movement-readiness state using conditional Granger causality

As an initialization procedure for brain responding to subsequent movement execution (ME), the movement-readiness (MR) state is important for understanding the formation processes from daily movement training to long-term memory of movement pattern. As such, based on functional magnetic resonance imaging (fMRI), the net causal influences among regions contributing to premotor control during the MR state were explored by means of conditional Granger causality (CGC) and graph-theory methods in the present study. Our results found that net causal circuits responding to unimanual MR were identified during right-hand or left-hand MR, involving in the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), upper precuneus (UPCU), caudate nucleus (CN), cingulate motor area (CMA), supplementary motor area (SMA) and primary sensorimotor area (S1M1). Moreover, the contralateral CN, SMA and S1M1 revealed greater net causal influences during unimanual MR, which highlighted the contralateral dominant modulations during unimanual MR. Furthermore, according as the graph-theory analysis, the higher In+Out degrees of upper precuneus (UPCU) during right-hand MR or higher In+Out degrees of cingulate motor area (CMA) and posterior cingulate cortex (PCC) during left-hand MR implied the brain asymmetry of causal connectivity in the circuit responding to right-hand or left-hand MR. This article is part of a Special Issue entitled SI: Brain and Memory.

Effector-independent brain activity during motor imagery of the upper and lower limbs: an fMRI study

We utilized functional magnetic resonance imaging (fMRI) to evaluate the common brain region of motor imagery for the right and left upper and lower limbs. The subjects were instructed to repeatedly imagined extension and flexion of the right or left hands/ankles. Brain regions, which included the supplemental motor area (SMA), premotor cortex and parietal cortex, were activated during motor imagery. Conjunction analysis revealed that the left SMA and inferior frontal gyrus (IFG)/ventral premotor cortex (vPM) were commonly activated with motor imagery of the right hand, left hand, right foot, and left foot. This result suggests that these brain regions are activated during motor imagery in an effector independent manner.

Alpha band functional connectivity correlates with the performance of brain-machine interfaces to decode real and imagined movements

Brain signals recorded from the primary motor cortex (M1) are known to serve a significant role in coding the information brain–machine interfaces (BMIs) need to perform real and imagined movements, and also to form several functional networks with motor association areas. However, whether functional networks between M1 and other brain regions, such as these motor association areas, are related to the performance of BMIs is unclear. To examine the relationship between functional connectivity and performance of BMIs, we analyzed the correlation coefficient between performance of neural decoding and functional connectivity over the whole brain using magnetoencephalography. Ten healthy participants were instructed to execute or imagine three simple right upper limb movements. To decode the movement type, we extracted 40 virtual channels in the left M1 via the beam forming approach, and used them as a decoding feature. In addition, seed-based functional connectivities of activities in the alpha band during real and imagined movements were calculated using imaginary coherence. Seed voxels were set as the same virtual channels in M1. After calculating the imaginary coherence in individuals, the correlation coefficient between decoding accuracy and strength of imaginary coherence was calculated over the whole brain. The significant correlations were distributed mainly to motor association areas for both real and imagined movements. These regions largely overlapped with brain regions that had significant connectivity to M1. Our results suggest that use of the strength of functional connectivity between M1 and motor association areas has the potential to improve the performance of BMIs to perform real and imagined movements.

The handyman's brain: a neuroimaging meta-analysis describing the similarities and differences between grip type and pattern in humans

BACKGROUND

Handgrip is a ubiquitous human movement that was critical in our evolution. However, the differences in brain activity between grip type (i.e. power or precision) and pattern (i.e. dynamic or static) are not fully understood. In order to address this, we performed Activation Likelihood Estimation (ALE) analysis between grip type and grip pattern using functional magnetic resonance imaging (fMRI) data. ALE provides a probabilistic summary of the BOLD response in hundreds of subjects, which is often beyond the scope of a single fMRI experiment.

METHODS

We collected data from 28 functional magnetic resonance data sets, which included a total of 398 male and female subjects. Using ALE, we analyzed the BOLD response during power, precision, static and dynamic grip in a range of forces and age in right handed healthy individuals without physical impairment, cardiovascular or neurological dysfunction using a variety of grip tools, feedback and experimental training.

RESULTS

Power grip generates unique activation in the postcentral gyrus (areas 1 and 3b) and precision grip generates unique activation in the supplementary motor area (SMA, area 6) and precentral gyrus (area 4a). Dynamic handgrip generates unique activation in the precentral gyrus (area 4p) and SMA (area 6) and of particular interest, both dynamic and static grip share activation in the area 2 of the postcentral gyrus, an area implicated in the evolution of handgrip. According to effect size analysis, precision and dynamic grip generates stronger activity than power and static, respectively.

CONCLUSION

Our study demonstrates specific differences between grip type and pattern. However, there was a large degree of overlap in the pre and postcentral gyrus, SMA and areas of the frontal-parietal-cerebellar network, which indicates that other mechanisms are potentially involved in regulating handgrip. Further, our study provides empirically based regions of interest, which can be downloaded here within, that can be used to more effectively study power grip in a range of populations and conditions.

Imaging tactile imagery: changes in brain connectivity support perceptual grounding of mental images in primary sensory cortices

Constructing mental representations in the absence of sensory stimulation is a fundamental ability of the human mind and has been investigated in numerous brain imaging studies. However, it is still unclear how brain areas facilitating mental construction processes interact with brain regions related to specific sensory representations. In this fMRI study subjects formed mental representations of tactile stimuli either from memory (imagery) or from presentation of actual corresponding vibrotactile patterned stimuli. First our analysis addressed the question of whether tactile imagery recruits primary somatosensory cortex (SI), because the activation of early perceptual areas is classically interpreted as perceptual grounding of the mental image. We also tested whether a network, referred to as 'core construction system', is involved in the generation of mental representations in the somatosensory domain. In fact, we observed imagery-induced activation of SI. We further found support for the notion of a modality independent construction network with the retrosplenial cortices and the precuneus as core components, which were supplemented with the left inferior frontal gyrus (IFG). Finally, psychophysiological interaction (PPI) analyses revealed robust imagery-modulated changes in the connectivity of these construction related areas, which suggests that they orchestrate the assembly of an abstract mental representation. Interestingly, we found increased coupling between prefrontal cortex (left IFG) and SI during mental imagery, indicating the augmentation of an abstract mental representation by reactivating perceptually grounded sensory details.

Differential contribution of bilateral supplementary motor area to the effective connectivity networks induced by task conditions using dynamic causal modeling

Functional imaging studies have indicated hemispheric asymmetry of activation in bilateral supplementary motor area (SMA) during unimanual motor tasks. However, the hemispherically special roles of bilateral SMAs on primary motor cortex (M1) in the effective connectivity networks (ECN) during lateralized tasks remain unclear. Aiming to study the differential contribution of bilateral SMAs during the motor execution and motor imagery tasks, and the hemispherically asymmetric patterns of ECN among regions involved, the present study used dynamic causal modeling to analyze the functional magnetic resonance imaging data of the unimanual motor execution/imagery tasks in 12 right-handed subjects. Our results demonstrated that distributions of network parameters underlying motor execution and motor imagery were significantly different. The variation was mainly induced by task condition modulations of intrinsic coupling. Particularly, regardless of the performing hand, the task input modulations of intrinsic coupling from the contralateral SMA to contralateral M1 were positive during motor execution, while varied to be negative during motor imagery. The results suggested that the inhibitive modulation suppressed the overt movement during motor imagery. In addition, the left SMA also helped accomplishing left hand tasks through task input modulation of left SMA→right SMA connection, implying that hemispheric recruitment occurred when performing nondominant hand tasks. The results specified differential and altered contributions of bilateral SMAs to the ECN during unimanual motor execution and motor imagery, and highlighted the contributions induced by the task input of motor execution/imagery.

Constructing fMRI connectivity networks: a whole brain functional parcellation method for node definition

BACKGROUND

Functional Magnetic Resonance Imaging (fMRI) is used for exploring brain functionality, and recently it was applied for mapping the brain connection patterns. To give a meaningful neurobiological interpretation to the connectivity network, it is fundamental to properly define the network framework. In particular, the choice of the network nodes may affect the final connectivity results and the consequent interpretation.

NEW METHOD

We introduce a novel method for the intra subject topological characterization of the nodes of fMRI brain networks, based on a whole brain parcellation scheme. The proposed whole brain parcellation algorithm divides the brain into clusters that are homogeneous from the anatomical and functional point of view, each of which constitutes a node. The functional parcellation described is based on the Tononi's cluster index, which measures instantaneous correlation in terms of intrinsic and extrinsic statistical dependencies.

RESULTS

The method performance and reliability were first tested on simulated data, then on a real fMRI dataset acquired on healthy subjects during visual stimulation. Finally, the proposed algorithm was applied to epileptic patients' fMRI data recorded during seizures, to verify its usefulness as preparatory step for effective connectivity analysis. For each patient, the nodes of the network involved in ictal activity were defined according to the proposed parcellation scheme and Granger Causality Analysis (GCA) was applied to infer effective connectivity.

CONCLUSIONS

We showed that the algorithm 1) performed well on simulated data, 2) was able to produce reliable inter subjects results and 3) led to a detailed definition of the effective connectivity pattern.