Fusion of fNIRS and fMRI data: Identifying when and where hemodynamic signals are changing in human brains

In this study we implemented a new imaging method to fuse functional near infrared spectroscopy (fNIRS) measurements and functional magnetic resonance imaging (fMRI) data to reveal the spatiotemporal dynamics of the hemodynamic responses with high spatiotemporal resolution across the brain. We evaluated this method using multimodal data acquired from human right finger tapping tasks. And we found the proposed method is able to clearly identify from the linked components of fMRI and fNIRS where and when the hemodynamic signals are changing. In particular, the estimated associations between fNIRS and fMRI will be displayed as time varying spatial fMRI maps along with the fNIRS time courses. In addition, the joint components between fMRI and fNIRS are combined together to generate full spatiotemporal snapshots and movies, which provides an excellent way to examine the dynamic interplay between hemodynamic fNIRS and fMRI measurements

Spatiotemporal brain dynamics of empathy for pain and happiness in friendship

Although a large number of fMRI studies have investigated the neural basis of empathy, little is known about its spatiotemporal dynamics. Moreover, most of the previous studies on empathy have focused on empathy for pain rather than empathy for positive emotions, such as happiness. In the present study, we addressed this question by investigating the spatiotemporal dynamics of different kinds of empathy by combining electrophysiological recordings with a behavioral priming empathy task involving negative and positive emotions. Electrical brain activity and behavioral data were analyzed from 30 subjects (12 males and 18 females). Half of the subjects performed a behavioral task on empathy for pain (EP task), while the other half performed a behavioral task on empathy for happiness (EH task). In each task, participants viewed prime photographs of either: 1) a stranger; or 2) a close friend (primes) followed by target photographs showing either a hand being hurt (or not; targets in the EP task), or a hand in happy circumstances (or not; targets in the EH task). In each task, participants were asked to judge the target situation and report whether they could feel the pain (in EP task) or the happiness (in the EH task), as a function of the primes i.e., either from the close friend’s or from the stranger’s perspective. Overall, our results suggest that taking the perspective of a close friend (compared to that of a stranger), as a prime stimulus, does have a dual-stage effect on empathy that is characterized by an early modulation for pain and later modulations for both pain and happiness. The early differences between friend and stranger primes for pain (but not for happiness) suggest that empathy for pain is an automatic process that has been socially learned and passed among friends. On the other hand, the later differences observed between stranger and friend primes suggest additional cognitive appraisal take place for both pain and happiness. Our results suggest that it takes more cognitive attentional efforts to judge a stranger’s happiness than a friend’s, whereas the opposite is true for pain

Combining Self-Organizing Mapping and Supervised Affinity Propagation Clustering Approach to Investigate Functional Brain Networks Involved in Motor Imagery and Execution with fMRI Measurements

AbstractClustering 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