Strength Of Effective Connectivity Research Articles

Dopamine Modulates Effective Connectivity in Frontal Cortex.

There is increasing evidence that the left lateral frontal cortex is hierarchically organized such that higher-order regions have an asymmetric top-down influence over lower order regions. However, questions remain about the underlying neuroarchitecture of this hierarchical control organization. Within the frontal cortex, dopamine plays an important role in cognitive control functions, and we hypothesized that dopamine may preferentially influence top-down connections within the lateral frontal hierarchy. Using a randomized, double-blind, within-subject design, we analyzed resting-state fMRI data of 66 healthy young participants who were scanned once each after administration of bromocriptine (a dopamine agonist with preferential affinity for D2 receptor), tolcapone (an inhibitor of catechol-O-methyltransferase), and placebo, to determine whether dopaminergic stimulation modulated effective functional connectivity between hierarchically organized frontal regions in the left hemisphere. We found that dopaminergic drugs modulated connections from the caudal middle frontal gyrus and the inferior frontal sulcus to both rostral and caudal frontal areas. In dorsal frontal regions, effectivity connectivity strength was increased, whereas in ventral frontal regions, effective connectivity strength was decreased. These findings suggest that connections within frontal cortex are differentially modulated by dopamine, which may bias the influence that frontal regions exert over each other.

Disrupted Effective Connectivity within the Fronto-Thalamic Circuit in Pontine Infarction: A Spectral Dynamic Causal Modeling Study.

This study aims to investigate alterations in effective connectivity (EC) within the fronto-thalamic circuit and their associations with motor and cognitive declines in pontine infarction (PI). A total of 33 right PI patients (RPIs), 38 left PI patients (LPIs), and 67 healthy controls (HCs) were recruited. The spectral dynamic causal modeling (spDCM) approach was used for EC analysis within the fronto-thalamic circuit, including the thalamus, caudate, supplementary motor area (SMA), medial prefrontal cortex (mPFC), and anterior cingulate cortex (ACC). The EC differences between different sides of the patients and HCs were assessed, and their correlations with motor and cognitive dysfunctions were analyzed. The LPIs showed increased EC from the mPFC to the R-SMA and decreased EC from the L-thalamus to the ACC, the L-SMA to the R-SMA, the R-caudate to the R-thalamus, and the R-thalamus to the ACC. For RPIs, the EC of the R-caudate to the mPFC, the L-thalamus and L-caudate to the L-SMA, and the L-caudate to the ACC increased obviously, while a lower EC strength was shown from the L-thalamus to the mPFC, the LSMA to the R-caudate, and the R-SMA to the L-thalamus. The EC from the R-caudate to the mPFC was negatively correlated with the MoCA score for RPIs, and the EC from the R-caudate to the R-thalamus was negatively correlated with the FMA score for LPIs. The results demonstrated EC within the fronto-thalamic circuit in PI-related functional impairments and reveal its potential as a novel imaging marker.

Investigating altered brain functional hubs and causal connectivity in coronary artery disease with cognitive impairment.

Coronary artery disease (CAD) and cognitive impairment (CI) have become significant global disease and medical burdens. There have been several reports documenting the alterations in regional brain function and their correlation with CI in CAD patients. However, there is limited research on the changes in brain network connectivity in CAD patients. To investigate the resting-state connectivity and further understand the effective connectivity strength and directionality in patients with CAD, we utilized degree centrality (DC) and spectral dynamic causal modeling (spDCM) to detect functional hubs in the whole brain network, followed by an analysis of directional connections. Using the aforementioned approaches, it is possible to investigate the hub regions and aberrant connections underlying the altered brain function in CAD patients, providing neuroimaging evidence for the cognitive decline in patients with coronary artery disease. This study was prospectively conducted involving 24 patients diagnosed with CAD and 24 healthy controls (HC) who were matched in terms of age, gender, and education. Functional MRI (fMRI) scans were utilized to investigate brain activity in these individuals. Neuropsychological examinations were performed on all participants. DC analysis and spDCM were employed to investigate abnormal brain networks in patients with CAD. Additionally, the association between effective connectivity strength and cognitive function in patients with CAD was examined based on the aforementioned results. By assessing cognitive functions, we discovered that patients with CAD exhibited notably lower cognitive function compared to the HC group. By utilizing DC analysis and spDCM, we observed significant reductions in DC values within the left parahippocampal cortex (PHC) and the left medial temporal gyrus (MTG) in CAD patients when compared to the control group. In terms of effective connectivity, we observed the absence of positive connectivity between the right superior frontal gyrus (SFG) and PHC in CAD patients. Moreover, there was an increase in negative connectivity from PHC and MTG to SFG, along with a decrease in the strength of positive connectivity between PHC and MTG. Furthermore, we identified a noteworthy positive correlation (r = 0.491, p = 0.015) between the strength of connectivity between the PHC and the MTG and cognitive function in CAD patients. These research findings suggest that alterations in the connectivity of the brain networks involving SFG, PHC, and MTG in CAD patients may mediate changes in cognitive function.

Altered effective connectivity from cerebellum to motor cortex in chronic low back pain: A multivariate pattern analysis and spectral dynamic causal modeling study

To explore the central processing mechanism of pain perception in chronic low back pain (cLBP) using multi-voxel pattern analysis (MVPA) based on the static and dynamic fractional amplitude of low-frequency fluctuations (fALFF) analysis, and spectral dynamic causal modeling (spDCM). Thirty-two patients with cLBP and 29 matched healthy controls (HCs) for the first cohort and 24 patients with cLBP and 22 HCs for the validation cohort underwent resting-state fMRI scan. The alterations in static and dynamic fALFF were as classification features to distinguish patients with cLBP from HCs. The brain regions gotten from the MVPA results were used for further spDCM analysis. We found that the most discriminative brain regions that contributed to the classification were the right supplementary motor area (SMA.R), left paracentral lobule (PCL.L), and bilateral cerebellar Crus II. The spDCM results displayed decreased excitatory influence from the bilateral cerebellar Crus II to PCL.L in patients with cLBP compared with HCs. Moreover, the conversion of effective connectivity from the bilateral cerebellar Crus II to SMA.R from excitatory influence to inhibitive influence, and the effective connectivity strength exhibited partially mediated effects on Chinese Short Form Oswestry Disability Index Questionnaire (C-SFODI) scores. Our findings suggest that the cerebellum and its weakened or inhibited connections to the motor cortex may be one of the underlying feedback pathways for pain perception in cLBP, and partially mediate the degree of dysfunction.

Improving transfer entropy and partial transfer entropy for relative detection of effective connectivity strength between time series

Information theory-based measures have recently been considered in determining functional and effective connectivity. Transfer Entropy (TE) and Partial Transfer Entropy (Partial TE) are two such measures that investigate causal connectivity in bivariate and multivariate systems, respectively. The existence or absence of causal connectivity between variables is determined through statistical testing that compares TE or Partial TE values under different conditions or between the original TE or Partial TE values and surrogate data. However, using normal TE or Partial TE provides no information about coupling strength between variables. In this study, we present a repeating statistical test method for TE and Partial TE values in linear dynamic systems to determine the relative coupling strength between connectivity in the networks. In this method, segments with different time lengths (N) were considered for each variable in a system. Using the proposed method, the number of segments with statistically significant TE or partial TE values for each pair of time series was counted and analyzed as N increased. Our research results demonstrated that the proposed statistical test distinguishes stronger linear couplings with fewer segments and shorter time lengths (N) from weaker couplings. The proposed method also confirmed that Partial TE is more capable of determining direct causal connectivity than TE. Therefore, our proposed method can not only detect direct causal connections, but also determine the causal effect size of linear connectivity between time series relative to each other in a system, thus improving the performance of TE and Partial TE criteria.

Alteration in resting-state effective connectivity within the Papez circuit in Presbycusis.

Previous studies have suggested that the Papez circuit may be involved in the cognitive impairment observed after hearing loss in presbycusis patients, yet relatively little is known about the pattern of changes in effective connectivity within the circuit. The aim of this study was to investigate abnormal alterations in resting-state effective connectivity within the Papez circuit and their association with cognitive decline in presbycusis patients. The spectral dynamic causal modelling (spDCM) approach was used for resting-state effective connectivity analysis in 61 presbycusis patients and 52 healthy controls (HCs) within the Papez circuit. The hippocampus (HPC), mamillary body (MB), anterior thalamic nuclei (ATN), anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), entorhinal cortex (ERC), subiculum (Sub) and parahippocampal gyrus (PHG) were selected as the regions of interest (ROIs). The fully connected model difference in effective connectivity between the two groups was assessed, and the correlation between effective connectivity alteration and cognitive scale was analysed. We found that presbycusis patients demonstrated decreased effective connectivity from MB, PCC, and Sub to ACC relative to HCs, whereas higher effective connectivity strength was shown from HPC to MB, from ATN to PHG and from PHG to Sub. The effective connectivity from PHG to Sub was significantly negatively correlated with the complex figure test (CFT)-delay score (rho = -0.259, p = 0.044). The results support and reinforce the role of abnormal effective connectivity within the Papez circuit in the pathophysiology of presbycusis-related cognitive impairment and reveal its potential as a novel imaging marker.

Altered neural associations with cognitive and emotional functions in cannabis dependence.

Negative emotional state has been found to correlate with poor cognitive performance in cannabis-dependent (CD) individuals, but not healthy controls (HCs). To examine the neural substrates underlying such unusual emotion-cognition coupling, we analyzed the behavioral and resting state fMRI data from the Human Connectome Project and found opposite brain-behavior associations in the CD and HC groups: (i) although the cognitive performance was positively correlated with the within-network functional connectivity strength and segregation (i.e. clustering coefficient and local efficiency) of the cognitive network in HCs, these correlations were inversed in CDs; (ii) although the cognitive performance was positively correlated with the within-network Granger effective connectivity strength and integration (i.e. characteristic path length) of the cognitive network in CDs, such associations were not significant in HCs. In addition, we also found that the effective connectivity strength within cognition network mediated the behavioral coupling between emotional state and cognitive performance. These results indicate a disorganization of the cognition network in CDs, and may help improve our understanding of substance use disorder.

The timing of transcranial magnetic stimulation relative to the phase of prefrontal alpha EEG modulates downstream target engagement

BackgroundThe communication through coherence model posits that brain rhythms are synchronized across different frequency bands and that effective connectivity strength between interacting regions depends on their phase relation. Evidence to support the model comes mostly from electrophysiological recordings in animals while evidence from human data is limited. MethodsHere, an fMRI-EEG-TMS (fET) instrument capable of acquiring simultaneous fMRI and EEG during noninvasive single pulse TMS applied to dorsolateral prefrontal cortex (DLPFC) was used to test whether prefrontal EEG alpha phase moderates TMS-evoked top-down influences on subgenual, rostral and dorsal anterior cingulate cortex (ACC). Six runs (276 total trials) were acquired in each participant. Phase at each TMS pulse was determined post-hoc using single-trial sorting. Results were examined in two independent datasets: healthy volunteers (HV) (n = 11) and patients with major depressive disorder (MDD) (n = 17) collected as part of an ongoing clinical trial. ResultsIn both groups, TMS-evoked functional connectivity between DLPFC and subgenual ACC (sgACC) depended on the EEG alpha phase. TMS-evoked DLPFC to sgACC fMRI-derived effective connectivity (EC) was modulated by EEG alpha phase in healthy volunteers, but not in the MDD patients. Top-down EC was inhibitory for TMS pulses during the upward slope of the alpha wave relative to TMS timed to the downward slope of the alpha wave. Prefrontal EEG alpha phase dependent effects on TMS-evoked fMRI BOLD activation of the rostral anterior cingulate cortex were detected in the MDD patient group, but not in the healthy volunteer group. DiscussionResults demonstrate that TMS-evoked top-down influences vary as a function of the prefrontal alpha rhythm, and suggest potential clinical applications whereby TMS is synchronized to the brain's internal rhythms in order to more efficiently engage deep therapeutic targets.

Enhanced electroencephalography effective connectivity in frontal low-gamma band correlates of emotional regulation after mindfulness training.

Practicing mindfulness, focusing attention on the internal and external experiences occurring in the present moment with open and nonjudgement stance, can lead to the development of emotional regulation skills. Yet, the effective connectivity of brain regions during mindfulness has been largely unexplored. Studies have shown that mindfulness practice promotes functional connectivity in practitioners, potentially due to improved emotional regulation abilities and increased connectivity in the lateral prefrontal areas. To examine the changes in effective connectivity due to mindfulness training, we analyzed electroencephalogram (EEG) signals taken before and after mindfulness training, focusing on training-related effective connectivity changes in the frontal area. The mindfulness training group participated in an 8-week mindfulness-based stress reduction (MBSR) program. The control group did not take part. Regardless of the specific mindfulness practice used, low-gamma band effective connectivity increased globally after the mindfulness training. High-beta band effective connectivity increased globally only during Breathing. Moreover, relatively higher outgoing effective connectivity strength was seen during Resting and Breathing and Body-scan. By analyzing the changes in outgoing and incoming connectivity edges, both F7 and F8 exhibited strong parietal connectivity during Resting and Breathing. Multiple regression analysis revealed that the changes in effective connectivity of the right lateral prefrontal area predicted mindfulness and emotional regulation abilities. These results partially support the theory that the lateral prefrontal areas have top-down modulatory control, as these areas have high outflow effective connectivity, implying that mindfulness training cultivates better emotional regulation.

Attenuated effective connectivity of large-scale brain networks in children with autism spectrum disorders.

Understanding the neurological basis of autism spectrum disorder (ASD) is important for the diagnosis and treatment of this mental disorder. Emerging evidence has suggested aberrant functional connectivity of large-scale brain networks in individuals with ASD. However, whether the effective connectivity which measures the causal interactions of these networks is also impaired in these patients remains unclear. The main purpose of this study was to investigate the effective connectivity of large-scale brain networks in patients with ASD during resting state. The subjects were 42 autistic children and 127 age-matched normal children from the ABIDE II dataset. We investigated effective connectivity of 7 large-scale brain networks including visual network (VN), default mode network (DMN), cerebellum, sensorimotor network (SMN), auditory network (AN), salience network (SN), frontoparietal network (FPN), with spectral dynamic causality model (spDCM). Parametric empirical Bayesian (PEB) was used to perform second-level group analysis and furnished group commonalities and differences in effective connectivity. Furthermore, we analyzed the correlation between the strength of effective connectivity and patients' clinical characteristics. For both groups, SMN acted like a hub network which demonstrated dense effective connectivity with other large-scale brain network. We also observed significant causal interactions within the "triple networks" system, including DMN, SN and FPN. Compared with healthy controls, children with ASD showed decreased effective connectivity among some large-scale brain networks. These brain networks included VN, DMN, cerebellum, SMN, and FPN. In addition, we also found significant negative correlation between the strength of the effective connectivity from right angular gyrus (ANG_R) of DMN to left precentral gyrus (PreCG_L) of SMN and ADOS-G or ADOS-2 module 4 stereotyped behaviors and restricted interest total (ADOS_G_STEREO_BEHAV) scores. Our research provides new evidence for the pathogenesis of children with ASD from the perspective of effective connections within and between large-scale brain networks. The attenuated effective connectivity of brain networks may be a clinical neurobiological feature of ASD. Changes in effective connectivity of brain network in children with ASD may provide useful information for the diagnosis and treatment of the disease.

Anhedonia symptoms: The assessment of brain functional mechanism following music stimuli using functional magnetic resonance imaging

PurposeThis study aimed to investigate the effect of music stimulation on the brain functional mechanism of depressed patients with anhedonia symptoms using functional magnetic resonance imaging (fMRI). MethodsParticipants in this study included 20 healthy subjects as the control group, 25 subjects with depression and no anhedonia as the intervention group A, and 24 subjects with depression and anhedonia as the intervention group B. The safely emotional stimulation was done by Iranian music. To investigate the effect of music therapy on the brain, a task including 50 tracks of 12 s Iranian music (traditional and pop) was used. Finally, the data were analyzed using SPM Toolbox in MATLAB software. ResultsThe results showed that brain patterns in depressed patients with and without anhedonia could be distinguished based on positive and negative musical stimuli (p < 0.05). Important fMRI biomarker such as effective connectivity strength related to the fronto-limbic network, including the supragenual ACC, subgenual ACC, AMYG, and FFG were evaluated in depressed patients with anhedonia. ConclusionThis was the first study to investigate the neural circuits involved in music-related emotional processing in patients with anhedonia symptoms. These findings could help advance neurological understandings of anhedonia and suggest new treatments.

Specific subsystems of the inferior parietal lobule are associated with hand dysfunction following stroke: A cross-sectional resting-state fMRI study.

The inferior parietal lobule (IPL) plays important roles in reaching and grasping during hand movements, but how reorganizations of IPL subsystems underlie the paretic hand remains unclear. We aimed to explore whether specific IPL subsystems were disrupted and associated with hand performance after chronic stroke. In this cross-sectional study, we recruited 65 patients who had chronic subcortical strokes and 40 healthy controls from China. Each participant underwent the Fugl-Meyer Assessment of Hand and Wrist and resting-state fMRI at baseline. We mainly explored the group differences in resting-state effective connectivity (EC) patterns for six IPL subregions in each hemisphere, and we correlated these EC patterns with paretic hand performance across the whole stroke group and stroke subgroups. Moreover, we used receiver operating characteristic curve analysis to distinguish the stroke subgroups with partially (PPH) and completely (CPH) paretic hands. Stroke patients exhibited abnormal EC patterns with ipsilesional PFt and bilateral PGa, and five sensorimotor-parietal/two parietal-temporal subsystems were positively or negatively correlated with hand performance. Compared with CPH patients, PPH patients exhibited abnormal EC patterns with the contralesional PFop. The PPH patients had one motor-parietal subsystem, while the CPH patients had one sensorimotor-parietal and three parietal-occipital subsystems that were associated with hand performance. Notably, the EC strength from the contralesional PFop to the ipsilesional superior frontal gyrus could distinguish patients with PPH from patients with CPH. The IPL subsystems manifest specific functional reorganization and are associated with hand dysfunction following chronic stroke.

Six-dimensional dynamic tractography atlas of language connectivity in the developing brain.

During a verbal conversation, our brain moves through a series of complex linguistic processing stages: sound decoding, semantic comprehension, retrieval of semantically coherent words, and overt production of speech outputs. Each process is thought to be supported by a network consisting of local and long-range connections bridging between major cortical areas. Both temporal and extratemporal lobe regions have functional compartments responsible for distinct language domains, including the perception and production of phonological and semantic components. This study provides quantitative evidence of how directly connected inter-lobar neocortical networks support distinct stages of linguistic processing across brain development. Novel six-dimensional tractography was used to intuitively visualize the strength and temporal dynamics of direct inter-lobar effective connectivity between cortical areas activated during each linguistic processing stage. We analysed 3401 non-epileptic intracranial electrode sites from 37 children with focal epilepsy (aged 5-20 years) who underwent extra-operative electrocorticography recording. Principal component analysis of auditory naming-related high-gamma modulations determined the relative involvement of each cortical area during each linguistic processing stage. To quantify direct effective connectivity, we delivered single-pulse electrical stimulation to 488 temporal and 1581 extratemporal lobe sites and measured the early cortico-cortical spectral responses at distant electrodes. Mixed model analyses determined the effects of naming-related high-gamma co-augmentation between connecting regions, age, and cerebral hemisphere on the strength of effective connectivity independent of epilepsy-related factors. Direct effective connectivity was strongest between extratemporal and temporal lobe site pairs, which were simultaneously activated between sentence offset and verbal response onset (i.e. response preparation period); this connectivity was approximately twice more robust than that with temporal lobe sites activated during stimulus listening or overt response. Conversely, extratemporal lobe sites activated during overt response were equally connected with temporal lobe language sites. Older age was associated with increased strength of inter-lobar effective connectivity especially between those activated during response preparation. The arcuate fasciculus supported approximately two-thirds of the direct effective connectivity pathways from temporal to extratemporal auditory language-related areas but only up to half of those in the opposite direction. The uncinate fasciculus consisted of <2% of those in the temporal-to-extratemporal direction and up to 6% of those in the opposite direction. We, for the first time, provided an atlas which quantifies and animates the strength, dynamics, and direction specificity of inter-lobar neural communications between language areas via the white matter pathways. Language-related effective connectivity may be strengthened in an age-dependent manner even after the age of 5.

Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy.

Objective: This study aimed to investigate differences in brain networks between healthy children and children with attention deficit hyperactivity disorder (ADHD) during an attention test. Method : To fulfill this, we constructed weighted directed graphs based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children with the same age. Nodes of graphs were 19 EEG electrodes, and the edges were phase transfer entropy (PTE) between each pair of electrodes. PTE is a measure for directed connectivity that determines the effective relationship between signals in linear and nonlinear coupling. Connectivity graphs of each sample were constructed using PTE in the five frequency bands as follows: delta, theta, alpha, beta, and gamma. To investigate the differences in connectivity strength of each node after the sparsification process with two values (0.5 and 0.25), the permutation statistical test was used with the statistical significance level of p<0.01. Results: The results indicate stronger inter-regional connectivity in the prefrontal brain regions of the control group compared to the ADHD group. However, the strength of inter-regional connectivity in the central regions of the ADHD group was higher. A comparison of the prefrontal regions between the two groups revealed that the areas of the Fp1 electrode (left prefrontal) in healthy individuals play stronger transmission roles. Conclusion: Our research can provide new insights into the strength and direction of connectivity in ADHD and healthy individuals during an attention task.

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.

Decreased effective connectivity between insula and anterior cingulate cortex during a working memory task after prolonged sleep deprivation

Total sleep deprivation (TSD) causes a decline in almost all cognitive domains, especially working memory. However, we do not have a clear understanding of the degree working memory is impaired under prolonged TSD, nor do we know the underlying neurophysiological mechanism. In this study, we recorded EEG data from 64 subjects while they performed a working memory task during resting wakefulness, after 24 h TSD, and after 30 h TSD. ANOVA was used to verify performance differences between 24 h and 30 h TSD in working memory tasks: (1) reaction time and accuracy hit rates, (2) P200, N200, and P300 amplitude and latency in measurements of event-related potential, as well as (3) effective connectivity strength between brain areas associated with working memory. Compared to 24 h TSD, 30 h TSD significantly decreased accuracy hit rates and induced a larger N200 difference waveform. The effective connectivity analysis showed that 30 h TSD also decreased beta frequency in effective connection strength from the right insular lobe to the left anterior cingulate cortex (ACC). Effective connection from the left ventrolateral prefrontal cortex to the left dorsolateral prefrontal cortex increased in the match condition of the 2-back task. In conclusion, 30 h TSD had a greater negative impact on working memory than 24 h TSD. This impairment of working memory is associated with decreased strength in the effective connection from the right insula to the left ACC.

Association between emotional intelligence and effective brain connectome: A large-scale spectral DCM study

IntroductionEmotional Intelligence (EI) is a well-documented aspect of social and interpersonal functioning, but the underlying neural mechanisms for this capacity remain poorly understood. Here we used advanced brain connectivity techniques to explore the associations between EI and effective connectivity (EC) within four functional brain networks. MethodsThe Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) was used to collect EI data from 55 healthy individuals (mean age = 30.56±8.3 years, 26 males). The MSCEIT comprises two area cores – experiential EI (T1) and strategic EI (T2). The T1 core included two sub-scales – perception of emotions (S1) and using emotions to facilitate thinking (S2), and the T2 core included two sub-scales – understanding of emotions (S3) and management of emotions (S4). All participants underwent structural and resting-state functional magnetic resonance imaging (rsfMRI) scans. The spectral dynamic causal modeling approach was implemented to estimate EC within four networks of interest – the default-mode network (DMN), dorsal attention network (DAN), control-execution network (CEN) and salience network (SN). The strength of EC within each network was correlated with the measures of EI, with correlations at pFDR < 0.05 considered as significant. ResultsThere was no significant association between any of the measures of EI and EC strength within the DMN and DAN. For CEN, however, we found that there were significant negative associations between EC strength from the right anterior prefrontal cortex (RAPFC) to the left anterior prefrontal cortex (LAPFC) and both S2 and T1, and significant positive associations between EC strength from LAPFC to RAPFC and S2. EC strength from the right superior parietal cortex (SPC) to RAPFC also showed significant negative association with S4 and T2. For the SN, S3 showed significant negative association with EC strength from the right insula to RAPFC and significant positive association with EC strength from the left insula to dorsal anterior cingulate cortex (DACC). ConclusionsWe provide evidence that the negative ECs within the right hemisphere, and from the right to left hemisphere, and positive ECs within the left hemisphere and from the left to right hemisphere of CEN (involving bilateral frontal and right parietal region) and SN (involving right frontal, anterior cingulate and bilateral insula) play a significant role in regulating and processing emotions. These findings also suggest that measures of EC can be utilized as important biomarkers to better understand the underlying neural mechanisms of EI.

Effective brain connectivity for fNIRS data analysis based on multi-delays symbolic phase transfer entropy

Objective. Recently, effective connectivity (EC) calculation methods for functional near-infrared spectroscopy (fNIRS) data mainly face two problems: the first problem is that noise can seriously affect the EC calculation and even lead to false connectivity; the second problem is that it ignores the various real neurotransmission delays between the brain region, and instead uses a fixed delay coefficient for calculation. Approach. To overcome these two issues, a delay symbolic phase transfer entropy (dSPTE) is proposed by developing traditional transfer entropy (TE) to estimate EC for fNIRS. Firstly, the phase time sequence was obtained from the original sequence by the Hilbert transform and state-space reconstruction was realized using a uniform embedding scheme. Then, a symbolization technique was applied based on a neural-gas algorithm to improve its noise robustness. Finally, the EC was calculated on multiple time delay scales to match different inter-region neurotransmission delays. Main results. A linear AR model, a nonlinear model and a multivariate hybrid model were introduced to simulate the performance of dSPTE, and the results showed that the accuracy of dSPTE was the highest, up to 74.27%, and specificity was 100% which means no false connectivity. The results confirmed that the dSPTE method realized better noise robustness, higher accuracy, and correct identification even if there was a long delay between series. Finally, we applied dSPTE to fNIRS dataset to analyse the EC during the finger-tapping task, the results showed that EC strength of task state significantly increased compared with the resting state. Significance. The proposed dSPTE method is a promising way to measure the EC for fNIRS. It incorporates the phase information TE with a symbolic process for fNIRS analysis for the first time. It has been confirmed to be noise robust and suitable for the complex network with different coupling delays.

The relationship between frontotemporal effective connectivity and performance during auditory working memory task in noise

The present study examined the relationship between effective connectivity among frontotemporal brain regions and auditory working memory (AWM) performance. Twenty healthy participants performed a word-based backward recall task in four signal-to-noise ratio (SNR) conditions during functional magnetic resonance imaging scans. Functional data were pre-processed and analyzed using Statistical Parametric Mapping. Optimum connectivity model and strength of effective connectivity were analyzed using Dynamic Causal Modelling. Group results indicated significant brain activation in left superior temporal gyrus (STG) and left inferior frontal gyrus (IFG). Behavioral results showed that performance was enhanced in good SNR but worsened in low SNR. Bayesian model selection showed strong evidence of a bidirectional connection between left STG and left IFG. Correlation analyses showed a moderate positive linear relationship between effective connectivity from left STG to left IFG and behavioral performance. These findings suggest that the strength of effective connectivity from left STG and left IFG may underpin successful AWM performance.

Continuous reorganization of cortical information flow in multiple sclerosis: A longitudinal fMRI effective connectivity study

Effective connectivity (EC) is able to explore causal effects between brain areas and can depict mechanisms that underlie repair and adaptation in chronic brain diseases. Thus, the application of EC techniques in multiple sclerosis (MS) has the potential to determine directionality of neuronal interactions and may provide an imaging biomarker for disease progression. Here, serial longitudinal structural and resting-state fMRI was performed at 12-week intervals over one year in twelve MS patients. Twelve healthy subjects served as controls (HC). Two approaches for EC quantification were used: Causal Bayesian Network (CBN) and Time-resolved Partial Directed Coherence (TPDC). The EC strength was correlated with the Expanded Disability Status Scale (EDSS) and Fatigue Scale for Motor and Cognitive functions (FSMC). Our findings demonstrated a longitudinal increase in EC between specific brain regions, detected in both the CBN and TPDC analysis in MS patients. In particular, EC from the deep grey matter, frontal, prefrontal and temporal regions showed a continuous increase over the study period. No longitudinal changes in EC were attested in HC during the study. Furthermore, we observed an association between clinical performance and EC strength. In particular, the EC increase in fronto-cerebellar connections showed an inverse correlation with the EDSS and FSMC. Our data depict continuous functional reorganization between specific brain regions indicated by increasing EC over time in MS, which is not detectable in HC. In particular, fronto-cerebellar connections, which were closely related to clinical performance, may provide a marker of brain plasticity and functional reserve in MS.