Co-activation Patterns Research Articles

Singing training predicts increased insula connectivity with speech and respiratory sensorimotor areas at rest

The insula contributes to the detection of salient events during goal-directed behavior and participates in the coordination of motor, multisensory, and cognitive systems. Recent task-fMRI studies with trained singers suggest that singing experience can enhance the access to these resources. However, the long-term effects of vocal training on insula-based networks are still unknown. In this study, we employed resting-state fMRI to assess experience-dependent differences in insula co-activation patterns between conservatory-trained singers and non-singers. Results indicate enhanced bilateral anterior insula connectivity in singers relative to non-singers with constituents of the speech sensorimotor network. Specifically, with the cerebellum (lobule V-VI) and the superior parietal lobes. The reversed comparison showed no effects. The amount of accumulated singing training predicted enhanced bilateral insula co-activation with primary sensorimotor areas representing the diaphragm and the larynx/phonation area–crucial regions for cortico-motor control of complex vocalizations—as well as the bilateral thalamus and the left putamen. Together, these findings highlight the neuroplastic effect of expert singing training on insula-based networks, as evidenced by the association between enhanced insula co-activation profiles in singers and the brain's speech motor system components.

Decoding cerebro-spinal signatures of human behavior: Application to motor sequence learning

Mapping the neural patterns that drive human behavior is a key challenge in neuroscience. Even the simplest of our everyday actions stem from the dynamic and complex interplay of multiple neural structures across the central nervous system (CNS). Yet, most neuroimaging research has focused on investigating cerebral mechanisms, while the way the spinal cord accompanies the brain in shaping human behavior has been largely overlooked. Although the recent advent of functional magnetic resonance imaging (fMRI) sequences that can simultaneously target the brain and spinal cord has opened up new avenues for studying these mechanisms at multiple levels of the CNS, research to date has been limited to inferential univariate techniques that cannot fully unveil the intricacies of the underlying neural states. To address this, we propose to go beyond traditional analyses and instead use a data-driven multivariate approach leveraging the dynamic content of cerebro-spinal signals using innovation-driven coactivation patterns (iCAPs). We demonstrate the relevance of this approach in a simultaneous brain-spinal cord fMRI dataset acquired during motor sequence learning (MSL), to highlight how large-scale CNS plasticity underpins rapid improvements in early skill acquisition and slower consolidation after extended practice. Specifically, we uncovered cortical, subcortical and spinal functional networks, which were used to decode the different stages of learning with a high accuracy and, thus, delineate meaningful cerebro-spinal signatures of learning progression. Our results provide compelling evidence that the dynamics of neural signals, paired with a data-driven approach, can be used to disentangle the modular organization of the CNS. While we outline the potential of this framework to probe the neural correlates of motor learning, its versatility makes it broadly applicable to explore the functioning of cerebro-spinal networks in other experimental or pathological conditions.

Activity‐State Entropy: A novel brain entropy measure based on spatial patterns of activity

BackgroundBrain entropy is a measure of the complexity of brain activity that has been linked to various cognitive abilities. The measure is based on Shannon Entropy, a measure from Information Theory that quantifies the information capacity of a system from the probability distribution of its states. Most fMRI studies measure brain entropy at the voxel level as time-series entropy and assume that entropic time-series indicate complex large-scale spatiotemporal patterns of activity. New methodWe developed a novel measure of brain entropy called Activity-State Entropy. The method quantifies entropy based on underlying patterns of coactivation identified using Principal Components Analysis. These patterns, termed eigenactivity states, combine in time-varying proportions. ResultsWe showed that Activity-State Entropy is sensitive to the complexity of the spatiotemporal patterns of activity in simulated fMRI data. We then applied this measure to real resting-state fMRI data and found that the eigenactivity states that explained the most variance in the data were comprised of large clusters of coactivating voxels, including clusters within Default Mode Network regions. More entropic brains were increasingly influenced by eigenactivity states comprised of smaller and more sparsely distributed clusters. Comparison to existing methodsWe compared Activity-State Entropy to Sample Entropy and Dispersion Entropy, two time-series entropy measures commonly used in neuroimaging research, and found all three measures were positively correlated. ConclusionsActivity-State Entropy provides a measure of the spatiotemporal complexity of brain activity that complements time-series based measures of brain entropy.

Accurate localization and coactivation profiles of the frontal eye field and inferior frontal junction: an ALE and MACM fMRI meta-analysis

The frontal eye field (FEF) and the inferior frontal junction (IFJ) are prefrontal structures involved in mediating multiple aspects of goal-driven behavior. Despite being recognized as prominent nodes of the networks underlying spatial attention and oculomotor control, and working memory and cognitive control, respectively, the limited quantitative evidence on their precise localization has considerably impeded the detailed understanding of their structure and connectivity. In this study, we performed an activation likelihood estimation (ALE) fMRI meta-analysis by selecting studies that employed standard paradigms to accurately infer the localization of these regions in stereotaxic space. For the FEF, we found the highest spatial convergence of activations for prosaccade and antisaccade paradigms at the junction of the precentral sulcus and superior frontal sulcus. For the IFJ, we found consistent activations across oddball/attention, working memory, task-switching and Stroop paradigms at the junction of the inferior precentral sulcus and inferior frontal sulcus. We related these clusters to previous meta-analyses, sulcal/gyral neuroanatomy, and a comprehensive brain parcellation, highlighting important differences compared to their results and taxonomy. Finally, we leveraged the ALE peak coordinates as seeds to perform a meta-analytic connectivity modeling (MACM) analysis, which revealed systematic coactivation patterns spanning the frontal, parietal, and temporal cortices. We decoded the behavioral domains associated with these coactivations, suggesting that these may allow FEF and IFJ to support their specialized roles in flexible behavior. Our study provides the meta-analytic groundwork for investigating the relationship between functional specialization and connectivity of two crucial control structures of the prefrontal cortex.

A DEEP LEARNING FRAMEWORK TO LOCALIZE THE EPILEPTOGENIC ZONE FROM DYNAMIC FUNCTIONAL CONNECTIVITY USING A COMBINED GRAPH CONVOLUTIONAL AND TRANSFORMER NETWORK.

Localizing the epileptogenic zone (EZ) is a critical step in the treatment of medically refractory epilepsy. Resting-state fMRI (rs-fMRI) offers a new window into this task by capturing dynamically evolving co-activation patterns, also known as connectivity, in the brain. In this work, we present the first automated framework that uses dynamic functional connectivity from rs-fMRI to localize the EZ across a heterogeneous epilepsy cohort. Our framework uses a graph convolutional network for feature extraction, followed by a transformer network, whose attention mechanism learns which time points of the rs-fMRI scan are important for EZ localization. We train our framework on augmented data derived from the Human Connectome Project and evaluate it on a clinical epilepsy dataset. Our results demonstrate the clear advantages of our convolutional + transformer combination and data augmentation procedure over ablated and comparison models.

Dynamic functional hippocampal markers of residual depressive symptoms in euthymic bipolar disorder.

Bipolar disorder (BD) is a severe, chronic, affective disorder characterized by recurrent switching between mood states, psychomotor and cognitive symptoms, which can linger in euthymic states as residual symptoms. Hippocampal alterations may play a key role in the neural processing of BD symptoms. However, its dynamic functional connectivity (dFC) remains unclear. Therefore, the present study explores hippocampal dFC in relation to BD symptoms. We assessed hippocampus-based dFC coactivation patterns (CAPs) on resting-state fMRI data of 25 euthymic BD patients and 25 age- and sex-matched healthy controls (HC). Bilateral hippocampal dFC with somatomotor networks (SMN) was reduced in BD, compared to HC, while at the same time dFC between the left hippocampus and midcingulo-insular salience system (SN) was higher in BD. Correlational analysis between CAPs and clinical scores revealed that dFC between the bilateral hippocampus and the default-like network (DMN) correlated with depression scores in BD. Furthermore, pathological hyperconnectivity between the default mode network (DMN) and SMN and the frontoparietal network (FPN) was modulated by the same depression scores in BD. Overall, we observed alterations of large-scale functional brain networks associated with decreased flexibility in cognitive control, salience detection, and emotion processing in BD. Additionally, the present study provides new insights on the neural architecture underlying a self-centered perspective on the environment in BD patients. dFC markers may improve detection, treatment, and follow-up of BD patients and of disabling residual depressive symptoms in particular.

Graph-matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score.

Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter-individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome Project and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher-order networks, that is, default-mode and fronto-parietal, that underlie executive function and memory. These higher-order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter-subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior.

HPA–SAM co-activation among racially diverse, economically disadvantaged early adolescents: Secondary analysis with a preliminary test of a multisystem, person-centered approach

Investigating the co-activation of hypothalamic-pituitary-adrenal (HPA) and sympathetic-adrenomedullary (SAM) responses to acute stress can provide insight into how risk might become biologically embedded during early adolescence and improve understanding of what distinguishes physiological dysregulation from normative/expected physiological responses to stress. Evidence has thus far been mixed as to whether symmetric or asymmetric co-activation patterns are associated with higher exposure to chronic stress and poorer mental health outcomes during adolescence. This study expands on a prior multisystem, person-centered analysis of lower-risk, racially homogenous youth by focusing on HPA–SAM co-activation patterns in a higher-risk, racially diverse sample of early adolescents from low-income families (N = 119, Mage=11.79 years, 55.5% female, 52.7% mono-racial Black). The present study was conducted by performing secondary analysis of data from the baseline assessment of an intervention efficacy trial. Participants and caregivers completed questionnaires; youth also completed the Trier Social Stress Test-Modified (TSST–M) and provided six saliva samples. Multitrajectory modeling (MTM) of salivary cortisol and alpha-amylase levels identified four HPA–SAM co-activation profiles. In accordance with the asymmetric-risk model, youth exhibiting Low HPA–High SAM (n = 46) and High HPA–Low SAM (n = 28) profiles experienced more stressful life events, posttraumatic stress, and emotional and behavioral problems relative to Low HPA–Low SAM (n = 30) and High HPA–High SAM (n = 15) youth. Findings highlight potential differences in biological embedding of risk during early adolescence based on individuals’ exposure to chronic stress and illustrate the utility of multisystem and person-centered approaches in understanding how risk might get “underneath the skin” across systems.

Effect of Cognitive Load on Anticipatory Postural Adjustment Latency and its Relationship with Pain-Related Dysfunction in Non-specific Chronic Low Back Pain: A Cross-Sectional Study.

This study aimed to investigate the effect of cognitive load on anticipatory postural adjustment (APA) latency in patients with non-specific chronic low back pain (NCLBP) and its relationship with pain-related functional changes. A cross-sectional study was conducted from December 15, 2022 to January 25, 2023. Participants were divided into a healthy control group (n = 29) and an NCLBP group (n = 29). Each group was assigned a single task of rapid arm raising and a dual task of rapid arm raising combined with a cognitive load. The cognitive load task was conducted using visual conflict. The APA latency for bilateral trunk muscles was observed using electromyography. The duration of electromyography recording in each task cycle was 28s. Pain related-functional changes were evaluated using Roland-Morris Disability Questionnaire (RMDQ) before all tasks. The APA latency for the right multifidus was significantly delayed in the NCLBP group [25.38, 95% confidence interval (CI) 13.41-37.35] than in the healthy control group (- 5.80, 95% CI - 19.28 to 7.68) during dual task (p = 0.0416). The APA latency for the right multifidus (25.38, 95% CI 13.41-37.35) and transverse abdominis/internal oblique (29.15, 95% CI 18.81-39.50) were significantly delayed compared with on the left side in the NCLBP group during dual task (- 3.03, 95% CI - 15.18-9.13, p = 0.0220; 3.69, 95% CI - 6.81 to 14.18, p = 0.0363). The latency delay of the right and left multifidus APA in the NCLBP group under the dual-task was positively correlated with RMDQ scores (r = 0.5560, p = 0.0017; r = 0.4010, p = 0.0311). Cognitive load could induce APA delay in the right trunk muscles and co-activation pattern changes in bilateral trunk muscle APA in patients with NCLBP. The APA onset delay in multifidus is positively related to pain-related daily dysfunction. Trial Registration ChiCTR2300068580 (retrospectively registered in February 23, 2023).

Striatum-related spontaneous coactivation patterns predict treatment response on positive symptoms of drug-naive first-episode schizophrenia with risperidone monotherapy.

Evidence from functional magnetic resonance imaging (fMRI) studies of schizophrenia suggests that interindividual variation in the stationary striatal functional circuit may be correlated with antipsychotic treatment response. However, little is known about the role of the dynamic striatum-related network in predicting patients' clinical improvement. The spontaneous coactivation pattern (CAP) technique has recently been found to be important for elucidating the non-stationary nature of functional brain networks. Forty-two drug-naive first-episode schizophrenia patients underwent fMRI and T1W imaging before and after 8 weeks of risperidone monotherapy. The striatum was divided into 3 subregions, including the putamen, pallidum, and caudate. Spontaneous CAPs and CAP states were utilized to measure the dynamic characteristics of brain networks. We used DPARSF and Dynamic Brain Connectome software to analyze each subregion-related CAP and CAP state for each group and then compared the between-group differences in the neural network biomarkers. We used Pearson's correlation analysis to determine the associations between the neuroimaging measurements with between-group differences and the improvement in patients' psychopathological symptoms. In the putamen-related CAPs, patients showed significantly increased intensity in the bilateral thalamus, bilateral supplementary motor areas, bilateral medial, and paracingulate gyrus, left paracentral lobule, left medial superior frontal gyrus, and left anterior cingulate gyrus compared with healthy controls. After treatment, thalamic signals in the putamen-related CAP 1 showed a significant increase, while the signals of the medial and paracingulate gyrus in the putamen-related CAP 3 revealed a significant decrease. The increase in thalamic signal intensity in the putamen-related CAP 1 was significantly and positively correlated with the percentage reduction in PANSS_P. This study is the first to combine striatal CAPs and fMRI to explore treatment response-related biomarkers in the early phase of schizophrenia. Our findings suggest that dynamic changes in CAP states in the putamen-thalamus circuit may be potential biomarkers for predicting patients' variation in the short-term treatment response of positive symptoms.

A Postsynaptic Density Immediate Early Gene-Based Connectome Analysis of Acute NMDAR Blockade and Reversal Effect of Antipsychotic Administration.

Although antipsychotics' mechanisms of action have been thoroughly investigated, they have not been fully elucidated at the network level. We tested the hypothesis that acute pre-treatment with ketamine (KET) and administration of asenapine (ASE) would modulate the functional connectivity of brain areas relevant to the pathophysiology of schizophrenia, based on transcript levels of Homer1a, an immediate early gene encoding a key molecule of the dendritic spine. Sprague-Dawley rats (n = 20) were assigned to KET (30 mg/kg) or vehicle (VEH). Each pre-treatment group (n = 10) was randomly split into two arms, receiving ASE (0.3 mg/kg), or VEH. Homer1a mRNA levels were evaluated by in situ hybridization in 33 regions of interest (ROIs). We computed all possible pairwise Pearson correlations and generated a network for each treatment group. Acute KET challenge was associated with negative correlations between the medial portion of cingulate cortex/indusium griseum and other ROIs, not detectable in other treatment groups. KET/ASE group showed significantly higher inter-correlations between medial cingulate cortex/indusium griseum and lateral putamen, the upper lip of the primary somatosensory cortex, septal area nuclei, and claustrum, in comparison to the KET/VEH network. ASE exposure was associated with changes in subcortical-cortical connectivity and an increase in centrality measures of the cingulate cortex and lateral septal nuclei. In conclusion, ASE was found to finely regulate brain connectivity by modelling the synaptic architecture and restoring a functional pattern of interregional co-activation.

Objective biomarkers of depression: A study of Granger causality and wavelet coherence in resting-state fMRI.

The lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging. In this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality). Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects. We obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network. Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network. An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis. Based on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity.

The investigation of an analysis method for co-activation of knee osteoarthritis utilizing normalization of peak dynamic method

BackgroundAssessing co-activation characteristics in knee osteoarthritis (knee OA) using method of quantification of the activity ratio (such as the co-contraction index (CCI) or the directed co-activation ratios (DCAR)) for surface electromyography (EMG) has been reported. However, no studies have discussed the differences in results between non-negative matrix factorization (NNMF) and the DCAR. Research questionDoes DCAR or NNMF reflect the characteristic co-activation pattern of knee OA while using EMG normalized by the peak dynamic method? MethodsTen elderly control participants (EC) and ten knee OA patients (KOA) volunteered to participate in this study. EMG data from 20 participants were obtained from our previous study. Patients with knee OA were recruited from a local orthopedic clinic. The DCAR of agonist and antagonist muscles and the number of modules using NNMF were calculated to evaluate multiple muscle co-activations. An independent t-test statistical parametric mapping approach was used to compare the DCAR between the two groups. The difference in the number of modules between EC and KOA was evaluated using the Wilcoxon rank-sum test. ResultsThere was no significant difference in the DCAR between the two groups. However, NNMF had significantly fewer modules with KOA than with EC. SignificanceThe NNMF with the ratio of the amplitude of each muscle and duration of activity as variables reflected the co-activation of KOA, characterized by the high synchronous and prolonged activity of each muscle. Therefore, the NNMF is suitable for extracting characteristic muscle activity patterns of knee OA independent of the normalization method.

The Role of Autonomic System Coordination in Relations Between Peer Factors and Aggressive Behavior in Early Childhood.

This study tested biological sensitivity to context theory in the peer context. Respiratory sinus arrythmia (RSA-R) and skin conductance level (SCL-R) reactivity to a peer stressor were collected for participants (N = 86; M age = 45.99months old; 70.2% White) in the summer (Time 1). Children's peer risk (i.e., physical and relational victimization) and protective (i.e., received prosocial behavior) factors were examined in the fall (T2) and relational and physical aggression were measured at T2 and in the spring (T3). Interactions were tested in regression analyses. Interactions emerged between relational victimization, RSA-R, and SCL-R in the prediction of T3 relational aggression and between received prosocial behavior, RSA-R, and SCL-R in the prediction of T3 relational and physical aggression, respectively. There was a positive relation between T2 relational victimization and T3 relational aggression for children with a coactivation pattern (i.e., increased RSA and SCL activity to a bullying stressor) but no relation for any other physiological pattern. Conversely, there was a negative relation between T2 received prosocial behavior and both forms of aggression at T3 for children with a reciprocal pattern (i.e., increased RSA and decreased SCL or decreased RSA and increased SCL activity) but no protective benefit of received prosocial behavior on subsequent aggression for children with a coactivation pattern. For children with a coinhibition pattern (i.e., decreased RSA and SCL activity), received prosocial behavior was negatively related to subsequent physical but not relational aggression. In sum, a coactivation pattern in response to stress may represent a vulnerability factor.

Chronology of auditory processing and related co-activation in the orbitofrontal cortex depends on musical expertise.

The present study aims to explore the extent to which auditory processing is reflected in the prefrontal cortex. Using magnetoencephalography (MEG), we investigated the chronology of primary and secondary auditory responses and associated co-activation in the orbitofrontal cortex in a large cohort of 162 participants of various ages. The sample consisted of 38 primary school children, 39 adolescents, 43 younger, and 42 middle-aged adults and was further divided into musically experienced participants and non-musicians by quantifying musical training and aptitude parameters. We observed that the co-activation in the orbitofrontal cortex [Brodmann-Area 10 (BA10)] strongly depended on musical expertise but not on age. In the musically experienced groups, a systematic coincidence of peak latencies of the primary auditory P1 response and the co-activated response in the orbitofrontal cortex was observed in childhood at the onset of musical education. In marked contrast, in all non-musicians, the orbitofrontal co-activation occurred 25-40 ms later when compared with the P1 response. Musical practice and musical aptitude contributed equally to the observed activation and co-activation patterns in the auditory and orbitofrontal cortex, confirming the reciprocal, interrelated influence of nature, and nurture in the musical brain. Based on the observed ageindependent differences in the chronology and lateralization of neurological responses, we suggest that orbitofrontal functions may contribute to musical learning at an early age.

Fetal behavior during MRI changes with age and relates to network dynamics.

Fetal motor behavior is an important clinical indicator of healthy development. However, our understanding of associations between fetal behavior and fetal brain development is limited. To fill this gap, this study introduced an approach to automatically and objectively classify long durations of fetal movement from a continuous four-dimensional functional magnetic resonance imaging (fMRI) data set, and paired behavior features with brain activity indicated by the fMRI time series. Twelve-minute fMRI scans were conducted in 120 normal fetuses. Postnatal motor function was evaluated at 7 and 36 months age. Fetal motor behavior was quantified by calculating the frame-wise displacement (FD) of fetal brains extracted by a deep-learning model along the whole time series. Analyzing only low motion data, we characterized the recurring coactivation patterns (CAPs) of the supplementary motor area (SMA). Results showed reduced motor activity with advancing gestational age (GA), likely due in part to loss of space (r=-.51, p < .001). Evaluation of individual variation in motor movement revealed a negative association between movement and the occurrence of coactivations within the left parietotemporal network, controlling for age and sex (p=.003). Further, we found that the occurrence of coactivations between the SMA to posterior brain regions, including visual cortex, was prospectively associated with postnatal motor function at 7months (r=.43, p=.03). This is the first study to pair fetal movement and fMRI, highlighting potential for comparisons of fetal behavior and neural network development to enhance our understanding of fetal brain organization.

Antipsychotics-Induced Changes in Synaptic Architecture and Functional Connectivity: Translational Implications for Treatment Response and Resistance.

Schizophrenia is a severe mental illness characterized by alterations in processes that regulate both synaptic plasticity and functional connectivity between brain regions. Antipsychotics are the cornerstone of schizophrenia pharmacological treatment and, beyond occupying dopamine D2 receptors, can affect multiple molecular targets, pre- and postsynaptic sites, as well as intracellular effectors. Multiple lines of evidence point to the involvement of antipsychotics in sculpting synaptic architecture and remodeling the neuronal functional unit. Furthermore, there is an increasing awareness that antipsychotics with different receptor profiles could yield different interregional patterns of co-activation. In the present systematic review, we explored the fundamental changes that occur under antipsychotics' administration, the molecular underpinning, and the consequences in both acute and chronic paradigms. In addition, we investigated the relationship between synaptic plasticity and functional connectivity and systematized evidence on different topographical patterns of activation induced by typical and atypical antipsychotics.

Deficient dynamics of prefrontal-striatal and striatal-default mode network (DMN) neural circuits in internet gaming disorder

BackgroundStudies have proven that individuals with internet gaming disorder (IGD) show impaired cognitive control over game craving; however, the neural mechanism underlying this process remains unclear. Accordingly, the present study aimed to investigate the dynamic features of brain functional networks of individuals with IGD during rest, which have barely been understood until now. MethodsResting-state fMRI data were collected from 333 subjects (123 subjects with IGD (males/females: 73/50) and 210 healthy controls (males/females: 135/75)). First, the data-driven methodology, named co-activation pattern analysis, was applied to investigate the dynamic features of nucleus accumbens (the core region involved in craving/reward processing and addiction)-centered brain networks in IGD. Further, machine learning analysis was conducted to investigate the prediction effect of the dynamic features on participants' addiction severity. ResultsCompared to controls, subjects in the IGD group showed decreased resilience, betweenness centrality and occurrence in the prefrontal-striatal neural circuit, and decreased in-degree in the striatal-default mode network (DMN) circuit. Moreover, these decreased dynamic features could significantly predict participants' addiction severity. LimitationsThe causal relationship between IGD and the abnormal dynamic features cannot be identified in this study. All the subjects were university students. ConclusionsThe present results revealed the underlying brain networks of uncontrollable craving and game-seeking behaviors in individuals with IGD during rest. The decreased dynamics of the prefrontal-striatal and striatal-DMN neural circuits might be potential biomarkers for predicting the addiction severity of IGD and potential targets for effective interventions to reduce game craving of this disorder.

Large-scale brain network dynamics in very preterm children and relationship with socio-emotional outcomes: an exploratory study

BackgroundChildren born very preterm (VPT; <32 weeks’ gestation) are at high risk of neurodevelopmental and behavioural difficulties associated with atypical brain maturation, including socio-emotional difficulties. The analysis of large-scale brain network dynamics during rest allows us to investigate brain functional connectivity and its association with behavioural outcomes.MethodsDynamic functional connectivity was extracted by using the innovation-driven co-activation patterns framework in VPT and full-term children aged 6–9 to explore changes in spatial organisation, laterality and temporal dynamics of spontaneous large-scale brain activity (VPT, n = 28; full-term, n = 12). Multivariate analysis was used to explore potential biomarkers for socio-emotional difficulties in VPT children.ResultsThe spatial organisation of the 13 retrieved functional networks was comparable across groups. Dynamic features and lateralisation of network brain activity were also comparable for all brain networks. Multivariate analysis unveiled group differences in associations between dynamical functional connectivity parameters with socio-emotional abilities.ConclusionIn this exploratory study, the group differences observed might reflect reduced degrees of maturation of functional architecture in the VPT group in regard to socio-emotional abilities. Dynamic features of functional connectivity could represent relevant neuroimaging markers and inform on potential mechanisms through which preterm birth leads to neurodevelopmental and behavioural disorders.ImpactSpatial organisation of the retrieved resting-state networks was comparable between school-aged very preterm and full-term children.Dynamic features and lateralisation of network brain activity were also comparable across groups.Multivariate pattern analysis revealed different patterns of association between dynamical functional connectivity parameters and socio-emotional abilities in the very preterm and full-term groups.Findings suggest a reduced degree of maturation of the functional architecture in the very preterm group in association with socio-emotional abilities.

The relationship between motor pathway damage and flexion-extension patterns of muscle co-excitation during walking.

Mass flexion-extension co-excitation patterns during walking are often seen as a consequence of stroke, but there is limited understanding of the specific contributions of different descending motor pathways toward their control. The corticospinal tract is a major descending motor pathway influencing the production of normal sequential muscle coactivation patterns for skilled movements. However, control of walking is also influenced by non-corticospinal pathways such as the corticoreticulospinal pathway that possibly contribute toward mass flexion-extension co-excitation patterns during walking. The current study sought to investigate the associations between damage to corticospinal (CST) and corticoreticular (CRP) motor pathways following stroke and the presence of mass flexion-extension patterns during walking as evaluated using module analysis. Seventeen healthy controls and 44 stroke survivors were included in the study. We used non-negative matrix factorization for module analysis of paretic leg electromyographic activity. We typically have observed four modules during walking in healthy individuals. Stroke survivors often have less independently timed modules, for example two-modules presented as mass flexion-extension pattern. We used diffusion tensor imaging-based analysis where streamlines connecting regions of interest between the cortex and brainstem were computed to evaluate CST and CRP integrity. We also used a coarse classification tree analysis to evaluate the relative CST and CRP contribution toward module control. Interhemispheric CST asymmetry was associated with worse lower extremity Fugl-Meyer score (p = 0.023), propulsion symmetry (p = 0.016), and fewer modules (p = 0.028). Interhemispheric CRP asymmetry was associated with worse lower extremity Fugl-Meyer score (p = 0.009), Dynamic gait index (p = 0.035), Six-minute walk test (p = 0.020), Berg balance scale (p = 0.048), self-selected walking speed (p = 0.041), and propulsion symmetry (p = 0.001). The classification tree model reveled that substantial ipsilesional CRP or CST damage leads to a two-module pattern and poor walking ability with a trend toward increased compensatory contralesional CRP based control. Both CST and CRP are involved with control of modules during walking and damage to both may lead to greater reliance on the contralesional CRP, which may contribute to a two-module pattern and be associated with worse walking performance.