Investigating time-varying functional connectivity derived from the Jackknife Correlation method for distinguishing between emotions in fMRI data

Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI

The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09907-x.

A multi-stage dynamical fusion network for multimodal emotion recognition

In recent years, emotion recognition using physiological signals has become a popular research topic. Physiological signal can reflect the real emotional state for individual which is widely applied to emotion recognition. Multimodal signals provide more discriminative information compared with single modal which arose the interest of related researchers. However, current studies on multimodal emotion recognition normally adopt one-stage fusion method which results in the overlook of cross-modal interaction. To solve this problem, we proposed a multi-stage multimodal dynamical fusion network (MSMDFN). Through the MSMDFN, the joint representation based on cross-modal correlation is obtained. Initially, the latent and essential interactions among various features extracted independently from multiple modalities are explored based on specific manner. Subsequently, the multi-stage fusion network is designed to split the fusion procedure into multi-stages using the correlation observed before. This allows us to exploit much more fine-grained unimodal, bimodal and trimodal intercorrelations. For evaluation, the MSMDFN was verified on multimodal benchmark DEAP. The experiments indicate that our method outperforms the related one-stage multi-modal emotion recognition works.

Investigation of global brain dynamics depending on emotion regulation strategies indicated by graph theoretical brain network measures at system level

In the present study, new findings reveal the close association between graph theoretic global brain connectivity measures and cognitive abilities the ability to manage and regulate negative emotions in healthy adults. Functional brain connectivity measures have been estimated from both eyes-opened and eyes-closed resting-state EEG recordings in four groups including individuals who use opposite Emotion Regulation Strategies (ERS) as follow: While 20 individuals who frequently use two opposing strategies, such as rumination and cognitive distraction, are included in 1st group, 20 individuals who don't use these cognitive strategies are included in 2nd group. In 3rd and 4th groups, there are matched individuals who use both Expressive Suppression and Cognitive Reappraisal strategies together frequently and never use them, respectively. EEG measurements and psychometric scores of individuals were both downloaded from a public dataset LEMON. Since it is not sensitive to volume conduction, Directed Transfer Function has been applied to 62-channel recordings to obtain cortical connectivity estimations across the whole cortex. Regarding well defined threshold, connectivity estimations have been transformed into binary numbers for implementation of Brain Connectivity Toolbox. The groups are compared to each other through both statistical logistic regression models and deep learning models driven by frequency band specific network measures referring segregation, integration and modularity of the brain. Overall results show that high classification accuracies of 96.05% (1st vs 2nd) and 89.66% (3rd vs 4th) are obtained in analyzing full-band ( 0.5 - 45 H z ) EEG. In conclusion, negative strategies may upset the balance between segregation and integration. In particular, graphical results show that frequent use of rumination induces the decrease in assortativity referring network resilience. The psychometric scores are found to be highly correlated with brain network measures of global efficiency, local efficiency, clustering coefficient, transitivity and assortativity in even resting-state.

Conflicts influence affects: an FMRI study of emotional effects in a conflict task

Although prior research has confirmed that conflict itself is likely to be aversive, it is unclear whether and how emotional conflicts influence an individual's affective processing. The current fMRI study adopted a lexical valence conflict task via instructing participants to shift lexical valence or not. We found that the involvement of positive emotions enhanced the activation of the middle right temporal gyrus (R-MTG) in the non-conflict condition, whereas such activation attenuated in the conflict condition. In addition, the R-MTG was activated in the opposite way when negative emotions were involved. The functional connectivity and correlation analyses further revealed that the faster the participants processed positive emotional words, the weaker the connectivity between R-MTG and positive emotion-related areas of left MTG in the non-conflict condition would be. In contrast, the faster the participants processed negative emotional words, the stronger the connectivity between R-MTG and negative emotion-related areas of the right cerebellum in the conflict condition would become. These findings suggest that conflicts have different influences on emotional processing.

Dynamic Reorganization of Functional Connectivity During Post-break Task Reengagement

Because of the undesired fatigue-related consequences, accumulating efforts have been made to find an effective intervention to alleviate the suboptimal cognitive function caused by mental fatigue. Nonetheless, limitations of intervention and evaluation methods may hinder the revealing of underlying neural mechanisms of fatigue recovery. Through the newly-developed dynamic functional connectivity (FC) analysis framework, this study aims to investigate the effects of two types of mid-task interventions (i.e., rest-break and moderate-intensity exercise-break) on the dynamic reorganization of FC during the execution of psychomotor vigilance test (PVT). Using a sliding window approach, temporal brain networks within each frequency band (i.e., δ, θ, α, & β) were estimated before and immediate after the intervention, and towards the end of the task to investigate the immediate and delayed effects respectively during post-break task reengagement. Behaviourally, similar beneficial effects of exercise- and rest-break on performance were observed, manifested by the immediate improvements after both interventions and a long-lasting influence towards the end of tasks. Moreover, temporal brain networks assessment showed significant immediate decreases of fluctuability, which followed by an increase of fluctuability towards the end of intervention tasks. Furthermore, the temporal nodal measure revealed the channels with significant differences across tasks were mainly resided in the fronto-parietal areas that exhibited interesting frequency-dependent distribution. The observations of immediate and delayed dynamic FC reorganizations extend previous fatigue-related intervention and static FC studies, and provide new insight into the dynamic characteristics of FC during post-break task reengagement.

Investigation of functional brain network reconfiguration during exposure to naturalistic stimuli using graph-theoretical analysis

Objective.One of the most significant features of the human brain is that it can dynamically reconfigure itself to adapt to a changing environment. However, dynamic interaction characteristics of the brain networks in naturalistic scenes remain unclear.Approach.We used open-source functional magnetic resonance imaging (fMRI) data from 15 participants who underwent fMRI scans while watching an audio-visual movie 'Forrest Gump'. The community detection algorithm based on inter-subject functional correlation was used to study the time-varying functional networks only induced by the movie stimuli. The whole brain reconfiguration patterns were quantified by the temporal co-occurrence matrix that describes the probability of two brain regions engage in the same community (or putative functional module) across time and the time-varying brain modularity. Four graph metrics of integration, recruitment, spatio-temporal diversity and within-community normalised centrality were further calculated to summarise the brain network dynamic roles and hub features in their spatio-temporal topology.Main results.Our results suggest that the networks that were involved in attention and audio-visual information processing, such as the visual network, auditory network, and dorsal attention network, were considered to play a role of 'stable loners'. By contrast, 'unstable loner' networks such as the default mode network (DMN) and fronto-parietal network tended to interact more flexibly with the other networks. In addition, global brain network showed significant fluctuations in modularity. The 'stable loner' networks always maintained high functional connectivity (FC) strength while 'unstable loner' networks, especially the DMN, exhibited high intra- and inter-network FC only during a low modularity period. Finally, changes in brain modularity were significantly associated with variations in emotions induced by the movie.Significance.Our findings provide new insight for understanding the dynamic interaction characteristics of functional brain networks during naturalistic stimuli.

Spatial Connectivity and Temporal Dynamic Functional Network Connectivity of Musical Emotions Evoked by Dynamically Changing Tempo

Music tempo is closely connected to listeners' musical emotion and multifunctional neural activities. Music with increasing tempo evokes higher emotional responses and music with decreasing tempo enhances relaxation. However, the neural substrate of emotion evoked by dynamically changing tempo is still unclear. To investigate the spatial connectivity and temporal dynamic functional network connectivity (dFNC) of musical emotion evoked by dynamically changing tempo, we collected dynamic emotional ratings and conducted group independent component analysis (ICA), sliding time window correlations, and k-means clustering to assess the FNC of emotion evoked by music with decreasing tempo (180-65 bpm) and increasing tempo (60-180 bpm). Music with decreasing tempo (with more stable dynamic valences) evoked higher valence than increasing tempo both with stronger independent components (ICs) in the default mode network (DMN) and sensorimotor network (SMN). The dFNC analysis showed that with time-decreasing FNC across the whole brain, emotion evoked by decreasing music was associated with strong spatial connectivity within the DMN and SMN. Meanwhile, it was associated with strong FNC between the DMN-frontoparietal network (FPN) and DMN-cingulate-opercular network (CON). The paired t-test showed that music with a decreasing tempo evokes stronger activation of ICs within DMN and SMN than that with an increasing tempo, which indicated that faster music is more likely to enhance listeners' emotions with multifunctional brain activities even when the tempo is slowing down. With increasing FNC across the whole brain, music with an increasing tempo was associated with strong connectivity within FPN; time-decreasing connectivity was found within CON, SMN, VIS, and between CON and SMN, which explained its unstable valence during the dynamic valence rating. Overall, the FNC can help uncover the spatial and temporal neural substrates of musical emotions evoked by dynamically changing tempi.

Dynamic functional network connectivity associated with musical emotions evoked by different tempi

Background：Music tempo has strong clinical maneuverability and positive emotional effect in music therapy, which can directly evoke multiple emotions and dynamic neural changes in the whole-brain. However, the precise relationship between music tempo and its emotional effects remains unclear. The present study aimed to investigate the dynamic functional network connectivity (dFNC) associated with emotions elicited by music at different tempi.

METHODS

We obtained emotion ratings of fast- (155-170 bpm), middle- (90 bpm), and slow-tempo (50-60 bpm) piano music from 40 participants both during and after functional magnetic resonance imaging (fMRI). Group independent component analysis (ICA), sliding time window correlations, and k-means clustering were used to assess dFNC of fMRI data. Paired t-tests were conducted to compare the difference of neural networks.

RESULTS

(1) Fast music was associated with higher ratings of emotional valence and arousal, which were accompanied with increasing dFNC between somatomotor (SM) and cingulo-opercular (CO) networks and decreasing dFNC between fronto-parietal and SM networks. (2) Even with stronger activation in auditory (AUD) networks, slow music was associated with weaker emotion than fast music, with decreasing FNC across the brain and the participation of default mode (DM). (3) Middle-tempo music elicited moderate emotional activation with the most stable dFNC in the whole brain.

CONCLUSION

Faster music increases neural activity in the SM and CO regions, increasing the intensity of the emotional experience. In contrast, slower music was associated with decreasing engagement of AUD and stable engagement of DM, resulting in a weak emotional experience. These findings suggested that the time-varying aspects of functional connectivity can help to uncover the dynamic neural substrates of tempo-evoked emotion while listening to music.