Effective Connectivity Patterns Research Articles

New Insights into Freezing of Gait in Parkinson's Disease from Spectral Dynamic Causal Modeling.

Freezing of gait is one of the most disturbing motor symptoms of Parkinson's disease (PD). However, the effective connectivity between key brain hubs that are associated with the pathophysiological mechanism of freezing of gait remains elusive. The aim of this study was to identify effective connectivity underlying freezing of gait. This study applied spectral dynamic causal modeling (DCM) of resting-state functional magnetic resonance imaging in dedicated regions of interest determined using a data-driven approach. Abnormally increased functional connectivity between the bilateral dorsolateral prefrontal cortex (DLPFC) and the bilateral mesencephalic locomotor region (MLR) was identified in freezers compared with nonfreezers. Subsequently, spectral DCM analysis revealed that increased top-down excitatory effective connectivity from the left DLPFC to bilateral MLR and an independent self-inhibitory connectivity within the left DLPFC in freezers versus nonfreezers (>99% posterior probability) were inversely associated with the severity of freezing of gait. The lateralization of these effective connectivity patterns was not attributable to the initial dopaminergic deficit nor to structural changes in these regions. We have identified novel effective connectivity and an independent self-inhibitory connectivity underlying freezing of gait. Our findings imply that modulating the effective connectivity between the left DLPFC and MLR through neurostimulation or other interventions could be a target for reducing freezing of gait in PD. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Abnormal dynamics of functional connectivity density and effective connectivity in overactive bladder.

The study aims to analyze alterations in dynamic functional connectivity density (dFCD) and effective connectivity (dEC) patterns using functional magnetic resonance imaging (fMRI), hypothesizing that overactive bladder (OAB) patients will exhibit distinct dFCD and dEC patterns, reflecting altered neural communication underlying the OAB. Forty-three female OAB patients and 40 female healthy controls (HC) underwent resting-state fMRI. Sliding window correlation was used to calculate the variability of the dFCD. The changes in dFCD-based dEC between the two groups were examined using Granger causal analysis. To describe the time-varying Granger causality, a sliding-window approach was utilized to divide time courses into a group of windows. We adopted a k-means clustering method to cluster all matrices into discrete connectivity states. Compared with HC, OAB females consistently had a dFCD (decreased) in the left anterior cingulate cortex (ACC) and left medial prefrontal cortex (mPFC) (p < 0.05, GRF corrected). In state 1, OAB patients had excitatory effective connections from bilateral ACC to left mPFC in comparison to HC. In state 2, there was an increase in dEC from the SMA to the mPFC. Participants with OAB showed significantly more inhibitory dorsolateral prefrontal cortex (dlPFC) connections between the left ACC and the right ACC in state 4, as well as an excitatory dEC connection between the right dlPFC and the left ACC in state 2 (p < 0.05, GRF corrected). OAB patients demonstrate significant alterations in dFCD and dEC patterns, which may be indicative of the neural mechanisms involved in OAB pathophysiology.

Model-Based Approaches to Investigating Mismatch Responses in Schizophrenia.

Alterations of mismatch responses (ie, neural activity evoked by unexpected stimuli) are often considered a potential biomarker of schizophrenia. Going beyond establishing the type of observed alterations found in diagnosed patients and related cohorts, computational methods can yield valuable insights into the underlying disruptions of neural mechanisms and cognitive function. Here, we adopt a typology of model-based approaches from computational cognitive neuroscience, providing an overview of the study of mismatch responses and their alterations in schizophrenia from four complementary perspectives: (a) connectivity models, (b) decoding models, (c) neural network models, and (d) cognitive models. Connectivity models aim at inferring the effective connectivity patterns between brain regions that may underlie mismatch responses measured at the sensor level. Decoding models use multivariate spatiotemporal mismatch response patterns to infer the type of sensory violations or to classify participants based on their diagnosis. Neural network models such as deep convolutional neural networks can be used for improved classification performance as well as for a systematic study of various aspects of empirical data. Finally, cognitive models quantify mismatch responses in terms of signaling and updating perceptual predictions over time. In addition to describing the available methodology and reviewing the results of recent computational psychiatry studies, we offer suggestions for future work applying model-based techniques to advance the study of mismatch responses in schizophrenia.

Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis.

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.

Aberrant Brain Triple-Network Effective Connectivity Patterns in Type2 Diabetes Mellitus.

Aberrant brain functional connectivity network is thought to be related to cognitive impairment in patients with type2 diabetes mellitus (T2DM). This study aims to investigate the triple-network effective connectivity patterns in patients with T2DM within and between the default mode network (DMN), salience network (SN), and executive control network (ECN) and their associations with cognitive declines. In total, 92 patients with T2DM and 98 matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Spectral dynamic causal modeling (spDCM) was used for effective connectivity analysis within the triple network. The posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), lateral prefrontal cortex (LPFC), supramarginal gyrus (SMG), and anterior insula (AINS) were selected as the regions of interest. Group comparisons were performed for effective connectivity calculated using the fully connected model, and the relationships between effective connectivity alterations and cognitive impairment as well as clinical parameters were detected. Compared to HCs, patients with T2DM exhibited increased or decreased effective connectivity patterns within the triple network. Furthermore, diabetes duration was significantly negatively correlated with increased effective connectivity from the r-LPFC to the mPFC, while body mass index (BMI) was significantly positively correlated with increased effective connectivity from the l-LPFC to the l-AINS (r = - 0.353, p = 0.001; r = 0.377, p = 0.004). These results indicate abnormal effective connectivity patterns within the triple network model in patients with T2DM and provide new insight into the neurological mechanisms of T2DM and related cognitive dysfunction.

Resting-state effective connectivity is systematically linked to reappraisal success of high- and low-intensity negative emotions.

Emotion regulation is a process by which individuals modulate their emotional responses to cope with different environmental demands, for example, by reappraising the emotional situation. Here, we tested whether effective connectivity of a reappraisal-related neural network at rest is predictive of successfully regulating high- and low-intensity negative emotions in an emotion-regulation task. Task-based and resting-state functional magnetic resonance imaging (rs-fMRI) data of 28 participants were collected using ultra-high magnetic field strength at 7 Tesla during three scanning sessions. We used spectral dynamic causal modeling (spDCM) on the rs-fMRI data within brain regions modulated by emotion intensity. We found common connectivity patterns for both high- and low-intensity stimuli. Distinctive effective connectivity patterns in relation to low-intensity stimuli were found from frontal regions connecting to temporal regions. Reappraisal success for high-intensity stimuli was predicted by additional connections within the vlPFC and from temporal to frontal regions. Connectivity patterns at rest predicting reappraisal success were generally more pronounced for low-intensity stimuli, suggesting a greater role of stereotyped patterns, potentially reflecting preparedness, when reappraisal was relatively easy to implement. The opposite was true for high-intensity stimuli, which might require a more flexible recruitment of resources beyond what is reflected in resting state connectivity patterns. Resting-state effective connectivity emerged as a robust predictor for successful reappraisal, revealing both shared and distinct network dynamics for high- and low-intensity stimuli. These patterns signify specific preparatory states associated with heightened vigilance, attention, self-awareness, and goal-directed cognitive processing, particularly during reappraisal for mitigating the emotional impact of external stimuli. Our findings hold potential implications for understanding psychopathological alterations in brain connectivity related to affective disorders.

Repetitive transcranial magnetic stimulation regulates effective connectivity patterns of brain networks in the spectrum of preclinical Alzheimer's disease.

Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are considered as the spectrum of preclinical Alzheimer's disease (AD), with abnormal brain network connectivity as the main neuroimaging feature. Repetitive transcranial magnetic stimulation (rTMS) has been proven to be an effective non-invasive technique for addressing neuropsychiatric disorders. This study aims to explore the potential of targeted rTMS to regulate effective connectivity within the default mode network (DMN) and the executive control network (CEN), thereby improving cognitive function. This study included 86 healthy controls (HCs), 72 SCDs, and 86 aMCIs. Among them, 10 SCDs and 11 aMCIs received a 2-week rTMS course of 5-day, once-daily. Cross-sectional analysis with the spectral dynamic causal model (spDCM) was used to analyze the DMN and CEN effective connectivity patterns of the three groups. Afterwards, longitudinal analysis was conducted on the changes in effective connectivity patterns and cognitive function before and after rTMS for SCD and aMCI, and the correlation between them was analyzed. Cross-sectional analysis showed different effective connectivity patterns in the DMN and CEN among the three groups. Longitudinal analysis showed that the effective connectivity pattern of the SCD had changed, accompanied by improvements in episodic memory. Correlation analysis indicated a negative relationship between effective connectivity from the left angular gyrus (ANG) to the anterior cingulate gyrus and the ANG.R to the right middle frontal gyrus, with visuospatial and executive function, respectively. In patients with aMCI, episodic memory and executive function improved, while the effective connectivity pattern remained unchanged. This study demonstrates that PCUN-targeted rTMS in SCD regulates the abnormal effective connectivity patterns in DMN and CEN, thereby improving cognition function. Conversely, in aMCI, the mechanism of improvement may differ. Our findings further suggest that rTMS is more effective in preventing or delaying disease progression in the earlier stages of the AD spectrum. http://www.chictr.org.cn, ChiCTR2000034533.

EEG‐based Analysis Reveals Different Effective Connectivity Patterns between Healthy Controls and MCI Patients in Motion Detection Tasks

AbstractBackgroundBrain connectivity has been proposed to identify people at risk for mild cognitive impairment (MCI). Existing work has mainly focused on functional connectivity which reflects the mutual dependence between brain regions. Effective connectivity, which represents the directed connectivity from one brain region to the other, is expected to provide more details on directed information flow in the brain network. The present study investigates how cognitive engagement changes the effective connectivity in the brain.MethodOur research focuses on task‐based EEG (64‐channel), where participants were asked to perform a motion direction discrimination task. The current dataset includes 56 consensus‐diagnosed, community‐dwelling African Americans (ages 60‐90 years, 28 healthy controls (HC) and 28 MCI patients) recruited through the Wayne State Institute of Gerontology and Michigan Alzheimer’s Disease Research Center with subjective cognitive complaints.We evaluated the effective connectivity at different time periods of the motion‐detection task across all the possible EEG region pairs using causalized convergent cross‐mapping (cCCM) of the current source density. For each task trial, the successive time periods being examined included: (I) stimulus onset to Go indication, (II) Go indication to motion‐stop, and (III) the button‐press period. For each subject, we calculated the mean, maximum and minimum of the cCCM vector for each EEG region pair and each time‐period, then selected the region pairs where the cCCM‐based effective connectivity of HC and MCI show a significant difference (p‐value&lt;0.05).ResultOur analysis indicated that typically, MCI participants show more active effective connectivity or need more effort from stimulus onset to Go indication and then to motion stop, but HC participants show higher maximum and minimum effective connectivity values during the button‐press period, where coherent coordination is required among many region pairs.ConclusionHC and MCI do show significant differences in effective connectivity during each of the three periods of a motion detection task (marked by stimulus onset, Go indication, and button‐press) over many region pairs. Our analysis provides a massive pool of possible features for future biomarker development for effective discrimination of HC and MCI. Funding: NSF‐2032709/Li; NIH‐1R21AG046637‐01A1/Kavcic and NIH‐1R01AG054484‐01A1/Kavcic; NIH‐P30AG053760/Paulson and NIH‐P30AG072931/Paulson.

Brain dynamics of mental workload in a multitasking context: Evidence from dynamic causal modeling

Multitasking is a common element in complex human-computer interactions and is known to impose deleterious mental workload demands. High mental workload is known to involve bilateral hemisphere activation, but the patterns of effective connectivity (directed causal influence or communication) among brain regions in such a context remain unclear. This study investigated the effect of mental workload on the causal influence brain regions exert over each other under a multitasking scenario. The Dynamic Causal Modeling (DCM) method was implemented to infer the flow of information and allocation of attentional resources. Thirty participants performed four subtasks with varying levels of workload on a computer-based multitasking program, simulating a pilot cockpit. Using eight brain regions commonly identified to be activated in multitasking conditions, nine candidate models were developed. Bayesian model averaging was then used to quantify the connectivity strengths among the brain regions. Linear regression was conducted to study the relationships between connection strengths and subtask performances. The results showed that the causal connections shifted from the left to both sides of the brain with increased workload. Linear regression analysis showed that the subtask performance could be predicted by connectivity strengths. Thus, by studying the brain dynamics of mental workload, we may be able to develop a predictor that supplements subjective self-report measures.

A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG

The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process.We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM’s ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM.Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.

Distinct Patterns of Connectivity between Brain Regions Underlie the Intra-Modal and Cross-Modal Value-Driven Modulations of the Visual Cortex.

Past reward associations may be signaled from different sensory modalities; however, it remains unclear how different types of reward-associated stimuli modulate sensory perception. In this human fMRI study (female and male participants), a visual target was simultaneously presented with either an intra- (visual) or a cross-modal (auditory) cue that was previously associated with rewards. We hypothesized that, depending on the sensory modality of the cues, distinct neural mechanisms underlie the value-driven modulation of visual processing. Using a multivariate approach, we confirmed that reward-associated cues enhanced the target representation in early visual areas and identified the brain valuation regions. Then, using an effective connectivity analysis, we tested three possible patterns of connectivity that could underlie the modulation of the visual cortex: a direct pathway from the frontal valuation areas to the visual areas, a mediated pathway through the attention-related areas, and a mediated pathway that additionally involved sensory association areas. We found evidence for the third model demonstrating that the reward-related information in both sensory modalities is communicated across the valuation and attention-related brain regions. Additionally, the superior temporal areas were recruited when reward was cued cross-modally. The strongest dissociation between the intra- and cross-modal reward-driven effects was observed at the level of the feedforward and feedback connections of the visual cortex estimated from the winning model. These results suggest that, in the presence of previously rewarded stimuli from different sensory modalities, a combination of domain-general and domain-specific mechanisms are recruited across the brain to adjust the visual perception.SIGNIFICANCE STATEMENT Reward has a profound effect on perception, but it is not known whether shared or disparate mechanisms underlie the reward-driven effects across sensory modalities. In this human fMRI study, we examined the reward-driven modulation of the visual cortex by visual (intra-modal) and auditory (cross-modal) reward-associated cues. Using a model-based approach to identify the most plausible pattern of inter-regional effective connectivity, we found that higher-order areas involved in the valuation and attentional processing were recruited by both types of rewards. However, the pattern of connectivity between these areas and the early visual cortex was distinct between the intra- and cross-modal rewards. This evidence suggests that, to effectively adapt to the environment, reward signals may recruit both domain-general and domain-specific mechanisms.

Inhibitory temporo-parietal effective connectivity is associated with explicit memory performance in older adults

Successful explicit memory encoding is associated with inferior temporal activations and medial parietal deactivations, which are attenuated in aging. Here we used dynamic causal modeling (DCM) of functional magnetic resonance imaging data to elucidate effective connectivity patterns between hippocampus, parahippocampal place area (PPA), and precuneus during encoding of novel visual scenes. In 117 young adults, DCM revealed pronounced activating input from the PPA to the hippocampus and inhibitory connectivity from the PPA to the precuneus during novelty processing, with both being enhanced during successful encoding. This pattern could be replicated in two cohorts (N= 141 and 148) of young and older adults. In both cohorts, older adults selectively exhibited attenuated inhibitory PPA-precuneus connectivity, which correlated negatively with memory performance. Our results provide insight into the network dynamics underlying explicit memory encoding and suggest that age-related differences in memory-related network activity are, at least partly, attributable to altered temporo-parietal neocortical connectivity.

Brain Connectivity Analysis in Distinct Footwear Conditions during Infinity Walk Using fNIRS.

Gait and balance are an intricate interplay between the brain, nervous system, sensory organs, and musculoskeletal system. They are greatly influenced by the type of footwear, walking patterns, and surface. This exploratory study examines the effects of the Infinity Walk, pronation, and footwear conditions on brain effective connectivity patterns. A continuous-wave functional near-infrared spectroscopy device collected data from five healthy participants. A highly computationally efficient connectivity model based on the Grange causal relationship between the channels was applied to data to find the effective relationship between inter- and intra-hemispheric brain connectivity. Brain regions of interest (ROI) were less connected during the barefoot condition than during other complex walks. Conversely, the highest interconnectedness between ROI was observed while wearing flat insoles and medially wedged sandals, which is a relatively difficult type of footwear to walk in. No statistically significant (p-value <0.05) effect on connectivity patterns was observed during the corrected pronated posture. The regions designated as motoric, sensorimotor, and temporal became increasingly connected with difficult walking patterns and footwear conditions. The Infinity Walk causes effective bidirectional connections between ROI across all conditions and both hemispheres. Due to its repetitive pattern, the Infinity Walk is a good test method, particularly for neuro-rehabilitation and motoric learning experiments.

Amygdala-frontoparietal effective connectivity in creativity and humor processing.

Although both creativity and humor elicit experiences of surprise followed by appreciation, it remains unknown whether shared or distinct patterns of effective connectivity are involved in their processing. The present fMRI study used dynamic causal modeling and parametrical empirical Bayes analysis to examine the effective connectivity between the amygdala and frontoparietal network during two-stage creativity and humor processing. We examined processing during the setup and punch line stages for creativity and humor, including typical forms (alternate uses for creativity and incongruity-resolution humor), atypical forms (aesthetic uses for creativity and nonsense humor), and baseline forms. Our focus was on themesolimbic pathway during the punch line stage. We found that the amygdala plays a key role in expectation violation and appreciation. Broadly, amygdala-to-IFG connectivity was important for evaluating typical and atypical forms of both creativity and humor, while amygdala-to-precuneus connectivity was involved in evaluating typical forms. Amygdala-to-IFG connectivity was involved in the expectation violation to resolution stage of processing for typical and atypical forms of creativity and humor. Amygdala-to-precuneus connectivity was involved in processing the novelty and usefulness of typical forms of creativity (alternate uses) and understanding others' intentions in typical forms of humor (incongruity-resolution). Interestingly, VTA-to-amygdala connectivity was involved in processing the appreciation of both typical (incongruity-resolution humor) and atypical (nonsense humor) forms of humor while amygdala-to-VTA connectivity was involved in processing the appreciation of atypical (aesthetic uses) forms of creativity. Altogether, these findings suggest that the amygdala and frontoparietal circuitry are critical for creativity and humor processing.

Changes in both top-down and bottom-up effective connectivity drive visual hallucinations in Parkinson's disease.

Visual hallucinations are common in Parkinson's disease and are associated with a poorer quality of life and a higher risk of dementia. An important and influential model that is widely accepted as an explanation for the mechanism of visual hallucinations in Parkinson's disease and other Lewy body diseases is that these arise due to aberrant hierarchical processing, with impaired bottom-up integration of sensory information and overweighting of top-down perceptual priors within the visual system. This hypothesis has been driven by behavioural data and supported indirectly by observations derived from regional activation and correlational measures using neuroimaging. However, until now, there was no evidence from neuroimaging for differences in causal influences between brain regions measured in patients with Parkinson's hallucinations. This is in part because previous resting-state studies focused on functional connectivity, which is inherently undirected in nature and cannot test hypotheses about the directionality of connectivity. Spectral dynamic causal modelling is a Bayesian framework that allows the inference of effective connectivity-defined as the directed (causal) influence that one region exerts on another region-from resting-state functional MRI data. In the current study, we utilize spectral dynamic causal modelling to estimate effective connectivity within the resting-state visual network in our cohort of 15 Parkinson's disease visual hallucinators and 75 Parkinson's disease non-visual hallucinators. We find that visual hallucinators display decreased bottom-up effective connectivity from the lateral geniculate nucleus to primary visual cortex and increased top-down effective connectivity from the left prefrontal cortex to primary visual cortex and the medial thalamus, as compared with non-visual hallucinators. Importantly, we find that the pattern of effective connectivity is predictive of the presence of visual hallucinations and associated with their severity within the hallucinating group. This is the first study to provide evidence, using resting-state effective connectivity, to support a model of aberrant hierarchical predictive processing as the mechanism for visual hallucinations in Parkinson's disease.

Money or funny: Effective connectivity during service recovery with a DCM-PEB approach

While monetary compensation is considered the most effective service recovery strategy, relief theory claims that humor may also be useful in service recovery situations. This study investigated the effects of humor in service recovery using dynamic causal modeling and parametric empirical Bayes analysis to identify effective connectivity (EC) patterns in the dopaminergic reward system across four conditions representing different service recovery strategies: monetary compensation and humor (MH), monetary compensation and an apology (MA), non-monetary compensation using humor (H), and non-monetary compensation using an apology (CON, the control condition). The findings support the importance of the nucleus accumbens (NAc) in the monetary compensation (MH and MA) conditions and the amygdala in the non-monetary compensation (H and CON) conditions. Monetary compensation (MH and MA) resulted in right substantia nigra (rSN) to NAc EC, suggesting the processing of recovery satisfaction associated with perceived outcome fairness. Conversely, non-monetary compensation strategies (H and CON) resulted in left substantia nigra (lSN) to amygdala EC, suggesting the processing of satisfaction related to perceived interactional fairness. The use of humor for service recovery resulted in VTA-to-lSN-to-amygdala EC during humor appreciation, while the use of apologies (CON and MA) resulted in lSN-to-amygdala and lSN-to-VTA connectivity. Surprisingly, processing satisfaction in the MH condition did not activate the amygdala during humor appreciation. Coping humor could be norm-violating for service recovery, and its effectiveness depends on multiple factors. The results suggest that monetary compensation, humorous responses, and apologies play key roles in neurological responses to service recovery strategies.

Neural evidence suggests phonological acceptability judgments reflect similarity, not constraint evaluation

Acceptability judgments are a primary source of evidence in formal linguistic research. Within the generative linguistic tradition, these judgments are attributed to evaluation of novel forms based on implicit knowledge of rules or constraints governing well-formedness. In the domain of phonological acceptability judgments, other factors including ease of articulation and similarity to known forms have been hypothesized to influence evaluation. We used data-driven neural techniques to identify the relative contributions of these factors. Granger causality analysis of magnetic resonance imaging (MRI)-constrained magnetoencephalography (MEG) and electroencephalography (EEG) data revealed patterns of interaction between brain regions that support explicit judgments of the phonological acceptability of spoken nonwords. Comparisons of data obtained with nonwords that varied in terms of onset consonant cluster attestation and acceptability revealed different cortical regions and effective connectivity patterns associated with phonological acceptability judgments. Attested forms produced stronger influences of brain regions implicated in lexical representation and sensorimotor simulation on acoustic-phonetic regions, whereas unattested forms produced stronger influence of phonological control mechanisms on acoustic-phonetic processing. Unacceptable forms produced widespread patterns of interaction consistent with attempted search or repair. Together, these results suggest that speakers' phonological acceptability judgments reflect lexical and sensorimotor factors.

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.

Investigation on how dynamic effective connectivity patterns encode the fluctuating pain intensity in chronic migraine

Chronic migraine is characterised by persistent headaches for >15 days per month; the intensity of the pain is fluctuating over time. Here, we explored the dynamic interplay of connectivity patterns between regions known to be related to pain processing and their relation to the ongoing dynamic pain experience. We recorded EEG from 80 sessions (20 chronic migraine patients in 4 separate sessions of 25 min). The patients were asked to continuously rate the intensity of their endogenous headache. On different time-windows, a dynamic causal model (DCM) of cross spectral responses was inverted to estimate connectivity strengths. For each patient and session, the evolving dynamics of effective connectivity were related to pain intensities and to pain intensity changes by using a Bayesian linear model. Hierarchical Bayesian modelling was further used to examine which connectivity-pain relations are consistent across sessions and across patients.The results reflect the multi-facetted clinical picture of the disease. Across all sessions, each patient with chronic migraine exhibited a distinct pattern of pain intensity-related cortical connectivity. The diversity of the individual findings are accompanied by inconsistent relations between the connectivity parameters and pain intensity or pain intensity changes at group level. This suggests a rejection of the idea of a common neuronal core problem for chronic migraine.

Investigating the discrimination of linear and nonlinear effective connectivity patterns of EEG signals in children with Attention-Deficit/Hyperactivity Disorder and Typically Developing children

BackgroundAnalysis of effective connectivity among brain regions is an important key to decipher the mechanisms underlying neural disorders such as Attention Deficit Hyperactivity Disorder (ADHD). We previously introduced a new method, called nCREANN (nonlinear Causal Relationship Estimation by Artificial Neural Network), for estimating linear and nonlinear components of effective connectivity, and provided novel findings about effective connectivity of EEG signals of children with autism. Using the nCREANN method in the present study, we assessed effective connectivity patterns of ADHD children based on their EEG signals recorded during a visual attention task, and compared them with the aged-matched Typically Developing (TD) subjects. MethodIn addition to the nCREANN method for estimating linear and nonlinear aspects of effective connectivity, the direct Directed Transfer Function (dDTF) was utilized to extract the spectral information of connectivity patterns. ResultsThe dDTF results did not suggest a specific frequency band for distinguishing between the two groups, and different patterns of effective connectivity were observed in all bands. Both nCREANN and dDTF methods showed decreased connectivity between temporal/frontal and temporal/occipital regions, and increased connection between frontal/parietal regions in ADHDs than TDs. Furthermore, the nCREANN results showed more left-lateralized connections in ADHDs compared to the symmetric bilateral inter-hemispheric interactions in TDs. In addition, by fusion of linear and nonlinear connectivity measures of nCREANN method, we achieved an accuracy of 99% in classification of the two groups. ConclusionThese findings emphasize the capability of nCREANN method to investigate the brain functioning of neural disorders and its strength in preciously distinguish between healthy and disordered subjects.