Brain Network Dynamics Research Articles

Multimodal investigation of dynamic brain network alterations in autism spectrum disorder: Linking connectivity dynamics to symptoms and developmental trajectories

BackgroundAutism spectrum disorder (ASD) has been associated with disrupted brain connectivity, yet a comprehensive understanding of the dynamic neural underpinnings remains lacking. This study employed concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques to investigate dynamic functional connectivity (dFC) patterns and neurovascular characteristics in children with ASD. We also explored associations between neurovascular characteristics and the developmental trajectory of adaptive behavior in individuals with ASD. MethodsResting-state EEG and fNIRS data were simultaneously recorded from 58 ASD and 63 TD children. We implemented a k-means clustering approach to extract the dFC states for each modality. In addition, a multimodal covariance network (MCN) was constructed from the EEG and fNIRS dFC features to capture the neurovascular characteristics linked to ASD. ResultsEEG analyses revealed atypical properties of dFC states in the beta and gamma bands in children with ASD compared to TD children. For fNIRS, the ASD group exhibited atypical properties of dFC states such as duration and transitions relative to the TD group. The MCN analysis revealed significantly suppressed functional covariance between right superior temporal and left Broca's areas, alongside enhanced right dorsolateral prefrontal-left Broca covariance in ASD. Notably, we found that early neurovascular characteristics can predict the developmental progress of adaptive functioning in ASD. ConclusionThe multimodal investigation revealed distinct dFC patterns and neurovascular characteristics associated with ASD, elucidating potential neural mechanisms underlying core symptoms and their developmental trajectories. Our study highlights that integrating complementary neuroimaging modalities may aid in unraveling the complex neurobiology of ASD.

Locus coeruleus co-activation patterns at rest show higher state persistence in patients with dissociative seizures: A Pilot Study.

Dissociative seizures are paroxysmal disruptions of awareness and behavioral control in the context of affective arousal. Alterations in stress-related endocrine function have been demonstrated, but the timescale of dissociation suggests that the central locus coeruleus (LC) noradrenergic system is likely pivotal. Here, we investigate whether LC activation at rest is associated with altered brain network dynamics. A preliminary co-activation pattern (CAP) analysis of resting-state functional magnetic resonance imaging (fMRI) in 14 patients with dissociative seizures and 14 healthy controls was performed by using the LC as a seeding region. The red nucleus served as a control condition. Entry rates, durations, and state transition probabilities of identified CAPs were calculated. Analyses were corrected for demographic, technical, and clinical confounders including depression and anxiety. Three LC-related CAPs were identified, with the dominant two showing inverse activations and deactivations of the default mode network and the attention networks, respectively. Analysis of transition probabilities between and within the three CAPs revealed higher state persistence in patients compared to healthy controls for both CAP2LC (Cohen's d = -0.55; p = 0.01) and CAP3LC (Cohen's d = -0.57; p = 0.01). The control analysis using the red nucleus as a seed yielded similar CAPs, but no significant between-group differences in transition probabilities. Higher state persistence of LC-CAPs in patients with dissociative seizures generates the novel hypothesis that arousal-related impairments of network switching might be a candidate neural mechanism of dissociation. Dissociative seizures often arise during high affective arousal. The locus coeruleus is a brain structure involved in managing such acute arousal states. We investigated whether the activity of the locus coeruleus correlates with activity in other regions of the brain (which we refer to as "brain states"), and whether those brain states were different between patients with dissociative seizures and healthy controls. We found that patients tended to stay in certain locus coeruleus-dependent brain states instead of switching between them. This might be related to the loss of awareness and disruptions of brain functions ("dissociation") that patients experience during seizures.

Subject-based assessment of large-scale integration dynamics in epileptic brain networks: insights from the intrinsic ignition framework.

This study examined the dynamic properties of brain regions involved in the genesis and spread of seizures in 10 individuals diagnosed with pharmacoresistant focal epilepsy. The patients and 30 healthy controls underwent resting-state functional magnetic resonance imaging scans and the brain's functional network dynamics were analyzed using the intrinsic ignition framework. Comparative statistical analyses examined the differences in the integration and metastability measures in both groups in the whole brain and specific local brain regions. Invasive electroencephalography evaluations validated the findings of significant global and regional changes in the patient's brain network dynamics. There was a marked increase in global integration and metastability across the brain, reflecting substantial alterations in the overall connectivity and flexibility of the functional networks. Specific brain regions exhibited paradoxical dynamics within the seizure onset zone, with decreased intrinsic ignition and increased metastability. Increased intrinsic ignition was observed in remote brain regions, suggesting a reorganization of the brain network hubs and potential pathways for seizure propagation. Using the intrinsic ignition framework provided insights into dynamic alterations in the brain networks of patients with epilepsy. These have increased our understanding of the mechanisms underlying epileptic seizures and may guide the development of diagnostic biomarkers and targeted therapeutic interventions.

Dynamic brain networks in spontaneous gestural communication

Gestures accent and illustrate our communication. Although previous studies have uncovered the positive effects of gestures on communication, little is known about the specific cognitive functions of different types of gestures, or the instantaneous multi-brain dynamics. Here we used the fNIRS-based hyperscanning technique to track the brain activity of two communicators, examining regions such as the PFC and rTPJ, which are part of the mirroring and mentalizing systems. When participants collaboratively solved open-ended realistic problems, we characterised the dynamic multi-brain states linked with specific social behaviours. Results demonstrated that gestures are associated with enhanced team performance, and different gestures serve distinct cognitive functions: interactive gestures are accompanied by better team originality and a more efficient inter-brain network, while fluid gestures correlate with individual cognitive fluency and efficient intra-brain states. These findings reveal a close association between social behaviours and multi-brain networks, providing a new way to explore the brain-behaviour relationship.

Altered dynamic large-scale brain networks and combined machine learning in primary angle-closure glaucoma

Primary angle-closure glaucoma (PACG) is a severe and irreversible blinding eye disease characterized by progressive retinal ganglion cell death. However, prior research has predominantly focused on static brain activity changes, neglecting the exploration of how PACG impacts the dynamic characteristics of functional brain networks. This study enrolled forty-four patients diagnosed with PACG and forty-four age, gender, and education level-matched healthy controls (HCs). The study employed Independent Component Analysis (ICA) techniques to extract resting-state networks (RSNs) from resting-state functional magnetic resonance imaging (rs-fMRI) data. Subsequently, the RSNs was utilized as the basis for examining and comparing the functional connectivity variations within and between the two groups of resting-state networks. To further explore, a combination of sliding time window and k-means cluster analyses identified seven stable and repetitive dynamic functional network connectivity (dFNC) states. This approach facilitated the comparison of dynamic functional network connectivity and temporal metrics between PACG patients and HCs for each state. Subsequently, a support vector machine (SVM) model leveraging functional connectivity (FC) and FNC was applied to differentiate PACG patients from HCs. Our study underscores the presence of modified functional connectivity within large-scale brain networks and abnormalities in dynamic temporal metrics among PACG patients. By elucidating the impact of changes in large-scale brain networks on disease evolution, researchers may enhance the development of targeted therapies and interventions to preserve vision and cognitive function in PACG.

Sex-differential markers of psychiatric risk and treatment response based on premature aging of functional brain network dynamics and peripheral physiology

BackgroundAging is a multilevel process of gradual decline that predicts morbidity and mortality. Independent investigations have implicated senescence of brain and peripheral physiology in psychiatric risk, but it is unclear whether these effects stem from unique or shared mechanisms. MethodsTo address this question, we analyzed clinical, blood chemistry and resting state functional neuroimaging data in a healthy aging cohort (N= 427; age 36-100 years) and two disorder-specific samples encompassing patients with early psychosis (100 patients, 16-35 years) and major depressive disorder (MDD) (104 patients, 20-76 years). ResultsWe identified sex-dependent coupling between blood chemistry markers of metabolic senescence (i.e., homeostatic dysregulation), functional brain network aging, and psychiatric risk. In females, premature aging of frontoparietal and somatomotor networks was linked to greater homeostatic dysregulation. It also predicted the severity and treatment resistance of mood symptoms (depression/anxiety [all three samples], anhedonia [MDD]) and social withdrawal/behavioral inhibition (avoidant personality disorder [healthy aging]; negative symptoms [early psychosis]). In males, premature aging of the default mode, cingulo-opercular, and visual networks was linked to reduced homeostatic dysregulation and predicted severity and treatment resistance of symptoms relevant to hostility/aggression (antisocial personality disorder [healthy aging]; mania/positive symptoms [early psychosis]), impaired thought processes (early psychosis, MDD) and somatic problems (healthy aging, MDD). ConclusionsOur findings identify sexually dimorphic relationships between brain dynamics, peripheral physiology, and risk for psychiatric illness, suggesting that the specificity of putative risk biomarkers and precision therapeutics may be improved by considering sex and other relevant personal characteristics.

Alteration of dynamical degree centrality in brain functional network and its association with metabolic disorder in minimal hepatic encephalopathy.

To investigate dynamical degree centrality (dDC) alteration and its association with metabolic disturbance and cognitive impairment in minimal hepatic encephalopathy (MHE). Fifty-eight cirrhotic patients (22 with MHE, 36 without MHE [NHE]) and 25 healthy controls underwent resting-state functional magnetic resonance imaging, 1H-magnetic resonance spectroscopy, and neurocognitive examination based on the Psychometric Hepatic Encephalopathy Score (PHES). We obtained metabolite ratios in the bilateral posterior cingulate cortex and precuneus, including glutamate and glutamine (Glx)/total creatine (tCr), myo-inositol (mI)/tCr, total choline/tCr, and N-acetyl aspartate/tCr. For each voxel, degree centrality was calculated as the sum of its functional connectivity with other voxels in the brain; and sliding-window correlation was used to calculate dDC per voxel. We observed a stepwise increase in Glx/tCr and a decrease in mI/tCr from NHE to MHE. The intergroup dDC differences were observed in the bilateral posterior cingulate cortex and precuneus (region of interest [ROI1]), bilateral superior-medial frontal gyrus and anterior cingulate cortex (ROI2), and left caudate head. The dDC in ROI2 (r = 0.450, P < 0.001) and mI/tCr (r = 0.297, P = 0.024) was correlated with PHES. Significant correlations were found between dDC in ROI1 and Glx/tCr (r = - 0.413, P = 0.001) and mI/tCr (r = 0.554, P < 0.001). The dDC in ROI2, Glx/tCr, and mI/tCr showed potential for distinguishing NHE from MHE (areas under the curve = 0.859, 0.655, and 0.672, respectively). Our findings suggested dynamic brain network disorganization in MHE, which was associated with metabolic derangement and neurocognitive impairment.

A review of epileptic markers: from ion channels, astrocytes, synaptic imbalance to whole brain network dynamics

Epilepsy, a neurological disorder characterized by recurrent seizures, presents a complex interplay of cellular and molecular mechanisms. The symptoms manifest themselves at various scales, from ion channels to brain regions to behavior in humans. Various screening, treatment, and preventive measures use this knowledge to tackle the disorder effectively. This article aims to summarize the current state of the art in epileptic markers from ion channels, astrocytes, and synaptic imbalance to whole brain Network Dynamics. Recent research has shed light on the critical involvement of astrocytes, the multifunctional glial cells, in the pathogenesis and modulation of epileptic seizures in humans. Astrocytes, once considered as mere supportive cells, are now recognized as active participants in the regulation of neuronal excitability, synaptic transmission, and brain homeostasis. Ion channel imbalance is one of the widely studied areas in the context of epilepsy and is partially addressed in the abstract. Recent advances in computational neuroscience have led to the development of whole brain network models, providing valuable tools for studying the complex dynamics of epileptic seizures. These models integrate diverse biological factors, including neuronal connectivity, synaptic dynamics, and cellular properties, to simulate the spatiotemporal patterns of epileptic activity across brain regions. Through computational simulations and analysis, whole brain network models offer insights into seizure initiation, propagation, and termination mechanisms, shedding light on the dynamic interactions between epileptic foci and distributed brain networks. Moreover, these models facilitate the exploration of network-based biomarkers for seizure prediction and intervention optimization. Challenges and limitations, such as model complexity and validation against experimental data, are also discussed. Despite these challenges, whole brain network models represent a promising approach for advancing our understanding of epilepsy and identifying novel therapeutic strategies. Future research efforts should focus on refining model fidelity, incorporating multimodal data, and translating computational findings into clinically relevant applications, ultimately improving the management and treatment of epilepsy patients.

Electrophysiological dynamics of salience, default mode, and frontoparietal networks during episodic memory formation and recall: A multi-experiment iEEG replication.

Dynamic interactions between large-scale brain networks underpin human cognitive processes, but their electrophysiological mechanisms remain elusive. The triple network model, encompassing the salience (SN), default mode (DMN), and frontoparietal (FPN) networks, provides a framework for understanding these interactions. We analyzed intracranial EEG recordings from 177 participants across four diverse episodic memory experiments, each involving encoding as well as recall phases. Phase transfer entropy analysis revealed consistently higher directed information flow from the anterior insula (AI), a key SN node, to both DMN and FPN nodes. This directed influence was significantly stronger during memory tasks compared to resting-state, highlighting the AI's task-specific role in coordinating large-scale network interactions. This pattern persisted across externally-driven memory encoding and internally-governed free recall. Control analyses using the inferior frontal gyrus (IFG) showed an inverse pattern, with DMN and FPN exerting higher influence on IFG, underscoring the AI's unique role. We observed task-specific suppression of high-gamma power in the posterior cingulate cortex/precuneus node of the DMN during memory encoding, but not recall. Crucially, these results were replicated across all four experiments spanning verbal and spatial memory domains with high Bayes replication factors. Our findings advance understanding of how coordinated neural network interactions support memory processes, highlighting the AI's critical role in orchestrating large-scale brain network dynamics during both memory encoding and retrieval. By elucidating the electrophysiological basis of triple network interactions in episodic memory, our study provides insights into neural circuit dynamics underlying memory function and offer a framework for investigating network disruptions in memory-related disorders.

InfoFlowNet: A multi-head attention-based self-supervised learning model with surrogate approach for uncovering brain effective connectivity

Deciphering brain network topology can enhance the depth of neuroscientific knowledge and facilitate the development of neural engineering methods. Effective connectivity, which gauges the directional influences among brain regions, is pivotal for these studies. This research introduces InfoFlowNet, a novel self-supervised learning model designed to infer causal relationships in electroencephalogram (EEG) data, aiming at capturing the complex information flow among brain regions. Specifically, InfoFlowNet employs convolution operations and multi-head self-attention mechanisms with a masking feature, which ignores self-connections to focus on inter-regional dynamics. Additionally, a new causal magnitude estimation is introduced by assessing the impact of shuffled surrogate data on signal prediction to quantify causal influence. Experiments using synthetic data and real EEG datasets demonstrate that InfoFlowNet effectively uncovers time-varying causal relationships. Compared with the Granger causality model (GCM) and the temporal causal discovery framework (TCDF), InfoFlowNet shows superior sensitivity in detecting significant causal edges. For instance, in a psychomotor vigilance task, InfoFlowNet identified 8 out of 16 significant causal edges, significantly outperforming GCM and TCDF. Furthermore, InfoFlowNet successfully passes the whiteness test on the model's residuals, and advanced analyses illustrate the advantages of utilizing multi-head attention and masking mechanisms to capture effective connectivity. In sum, InfoFlowNet represents a significant advancement in the analysis of effective connectivity, offering a deeper understanding of brain network dynamics. The model's ability to discern complex causal interactions supports its potential application in broader neuroscientific research and applications.

The digital twin in neuroscience: from theory to tailored therapy.

Digital twins enable simulation, comprehensive analysis and predictions, as virtual representations of physical systems. They are also finding increasing interest and application in the healthcare sector, with a particular focus on digital twins of the brain. We discuss how digital twins in neuroscience enable the modeling of brain functions and pathology as they offer an in-silico approach to studying the brain and illustrating the complex relationships between brain network dynamics and related functions. To showcase the capabilities of digital twinning in neuroscience we demonstrate how the impact of brain tumors on the brain's physical structures and functioning can be modeled in relation to the philosophical concept of plasticity. Against this technically derived backdrop, which assumes that the brain's nonlinear behavior toward improvement and repair can be modeled and predicted based on MRI data, we further explore the philosophical insights of Catherine Malabou. Malabou emphasizes the brain's dual capacity for adaptive and destructive plasticity. We will discuss in how far Malabou's ideas provide a more holistic theoretical framework for understanding how digital twins can model the brain's response to injury and pathology, embracing Malabou's concept of both adaptive and destructive plasticity which provides a framework to address such yet incomputable aspects of neuroscience and the sometimes seemingly unfavorable dynamics of neuroplasticity helping to bridge the gap between theoretical research and clinical practice.

Multi-Graph Assessment of Temporal and Extratemporal Lobe Epilepsy in Resting-State fMRI

Epilepsy is a common neurological disorder that affects millions of people worldwide, disrupting brain networks and causing recurrent seizures. In this regard, investigating the distinctive characteristics of brain connectivity is crucial to understanding the underlying neural processes of epilepsy. However, the various graph-theory frameworks and different estimation measures may yield significant variability among the results of different studies. On this premise, this study investigates the brain network topological variations between patients with temporal lobe epilepsy (TLE) and extratemporal lobe epilepsy (ETLE) using both directed and undirected network connectivity methods as well as different graph-theory metrics. Our results reveal distinct topological differences in connectivity graphs between the two epilepsy groups, with TLE patients displaying more disassortative graphs at lower density levels compared to ETLE patients. Moreover, we highlight the variations in the hub regions across different network metrics, underscoring the importance of considering various centrality measures for a comprehensive understanding of brain network dynamics in epilepsy. Our findings suggest that the differences in brain network organization between TLE and ETLE patients could be attributed to the unique characteristics of each epilepsy type, offering insights into potential biomarkers for type-specific epilepsy diagnosis and treatment.

Resting-state EEG microstates predict mentalizing ability as assessed by the Reading the Mind in the Eyes test

Microstates analysis of electroencephalography (EEG) has gained increasing attention among researchers and clinicians as a valid tool for investigating temporal dynamics of large-scale brain networks with a millisecond time resolution. Although microstates analysis has been widely applied to elucidate the neurophysiological basis of various cognitive functions in both clinical and non-clinical samples, its application in relation to socio-affective processing has been relatively under-researched. Therefore, the main aim of the current study was to investigate the relationship between EEG microstates and mentalizing (i.e., the ability to understand the mental states of others). Eighty-two participants (thirty-six men; mean age: 24.28 ± 7.35 years; mean years of education: 15.82 ± 1.77) underwent a resting-state EEG recording and performed the Reading the Mind in the Eyes Test (RMET). The parameters of the microstates were then calculated using Cartool v. 4.09 software. Our results showed that the occurrence of microstate map C was independently and positively associated with the RMET total score and contributed to the prediction of mentalizing performance, even when controlling for potential confounding variables (i.e., age, sex, education level, tobacco and alcohol use). Since microstate C is involved in self-related processes, our findings may reflect the link between self-awareness of one's own thoughts/feelings and the enhanced ability to recognize the mental states of others at the neurophysiological level. This finding extends the functions traditionally attributed to microstate C, i.e. mind-wandering, self-related thoughts, prosociality, and emotional and interoceptive processing, to include mentalizing ability.

Comparative study on structural and functional brain differences in mild cognitive impairment patients with tinnitus.

Tinnitus may be associated with various brain changes. However, the degenerative changes in patients with tinnitus have not been extensively investigated. We aimed to evaluate degenerative, structural, and functional brain changes in patients with mild cognitive impairment (MCI) who also suffer from tinnitus. This study included participants aged 60 to 80 years with MCI and a hearing level better than 40 dB. The participants were classified into two groups: MCI with tinnitus (MCI-T) and MCI without tinnitus (MCI-NT). All patients underwent Tinnitus Handicap Inventory (THI), 3 T brain MRI, F18-florapronol PET, and F18-FDG PET. The MCI-T group exhibited higher β-amyloid deposition in the superior temporal gyrus, temporal pole, and middle temporal gyrus compared to the MCI-NT group (p < 0.05 for all). Additionally, the MCI-T group showed increased metabolism in the inferior frontal gyrus, insula, and anterior cingulate cortex (ACC) (p < 0.005 for all). The THI score was strongly correlated with increased volume in the insula, ACC, superior frontal gyrus, supplementary motor area, white matter near the hippocampus, and precentral gyrus (p < 0.05 for all). Moreover, the MCI-T group demonstrated higher metabolic activity in the default mode network (DMN) and lower activity in the executive control network (ECN) (p < 0.05 for all). In the MCI-T group, the posterior DMN was positively correlated with the visual network and negatively with the ECN, whereas in the MCI-NT group, it correlated positively with the ECN. The MCI-T group exhibited greater β-amyloid accumulation in the auditory cortex and more extensive changes across various brain networks compared with the MCI-NT group, potentially leading to diverse clinical symptoms such as dementia with semantic deficits or depression. Tinnitus in MCI patients may serve as a biomarker for degenerative changes in the temporal lobe and alterations in brain network dynamics.

Brain network dynamics in patients with single- and multiple-domain amnestic mild cognitive impairment.

Brain network dynamics have been extensively explored in patients with amnestic mild cognitive impairment (aMCI); however, differences in single- and multiple-domain aMCI (SD-aMCI and MD-aMCI) remain unclear. Using multicenter datasets, coactivation patterns (CAPs) were constructed and compared among normal control (NC), SD-aMCI, MD-aMCI, and Alzheimer's disease (AD) patients based on individual high-order cognitive network (HOCN) and primary sensory network (PSN) parcellations. Correlations between spatiotemporal characteristics and neuropsychological scores were analyzed. Compared to NC, SD-aMCI showed temporal alterations in HOCN-dominant CAPs, while MD-aMCI showed alterations in PSN-dominant CAPs. In addition, transitions from SD-aMCI to AD may involve PSN, while MD-aMCI to AD involves both PSN and HOCN. Results were generally consistent across datasets from Chinese and White populations. The HOCN and PSN are distinctively involved in aMCI subtypes and in the transformation between aMCI subtypes and AD, highlighting the necessity of aMCI subtype classification in AD studies. Individual functional network parcellations and coactivation pattern (CAP) analysis were performed to characterize spatiotemporal differences between single- and multiple-domain amnestic mild cognitive impairment (SD-aMCI and MD-aMCI), and between distinct aMCI subtypes and Alzheimer's disease (AD). The analysis of multicenter datasets converged on four pairs of recurrent CAPs, including primary sensory networks (PSN)-dominant CAPs, high-order cognitive networks (HOCN)-dominant CAPs, and PSN-HOCN-interacting CAPs. The HOCN and PSN are distinctively involved in aMCI subtypes and in the transformation between distinct aMCI subtypes and AD.

Task-specific topology of brain networks supporting working memory and inhibition.

Network neuroscience explores the brain's connectome, demonstrating that dynamic neural networks support cognitive functions. This study investigates how distinct cognitive abilities-working memory and cognitive inhibitory control-are supported by unique brain network configurations constructed by estimating whole-brain networks using mutual information. The study involved 195 participants who completed the Sternberg Item Recognition task and Flanker tasks while undergoing electroencephalography recording. A mixed-effects linear model analyzed the influence of network metrics on cognitive performance, considering individual differences and task-specific dynamics. The findings indicate that working memory and cognitive inhibitory control are associated with different network attributes, with working memory relying on distributed networks and cognitive inhibitory control on more segregated ones. Our analysis suggests that both strong and weak connections contribute to cognitive processes, with weak connections potentially leading to a more stable and support networks of memory and cognitive inhibitory control. The findings indirectly support the network neuroscience theory of intelligence, suggesting different functional topology of networks inherent to various cognitive functions. Nevertheless, we propose that understanding individual variations in cognitive abilities requires recognizing both shared and unique processes within the brain's network dynamics.

Comparison between EEG and MEG of static and dynamic resting-state networks.

The characterisation of resting-state networks (RSNs) using neuroimaging techniques has significantly contributed to our understanding of the organisation of brain activity. Prior work has demonstrated the electrophysiological basis of RSNs and their dynamic nature, revealing transient activations of brain networks with millisecond timescales. While previous research has confirmed the comparability of RSNs identified by electroencephalography (EEG) to those identified by magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), most studies have utilised static analysis techniques, ignoring the dynamic nature of brain activity. Often, these studies use high-density EEG systems, which limit their applicability in clinical settings. Addressing these gaps, our research studies RSNs using medium-density EEG systems (61 sensors), comparing both static and dynamic brain network features to those obtained from a high-density MEG system (306 sensors). We assess the qualitative and quantitative comparability of EEG-derived RSNs to those from MEG, including their ability to capture age-related effects, and explore the reproducibility of dynamic RSNs within and across the modalities. Our findings suggest that both MEG and EEG offer comparable static and dynamic network descriptions, albeit with MEG offering some increased sensitivity and reproducibility. Such RSNs and their comparability across the two modalities remained consistent qualitatively but not quantitatively when the data were reconstructed without subject-specific structural MRI images.

Aberrant Flexibility of Dynamic Brain Network in Patients with Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a collection of neurodevelopmental disorders whose pathobiology remains elusive. This study aimed to investigate the possible neural mechanisms underlying ASD using a dynamic brain network model and a relatively large-sample, multi-site dataset. Resting-state functional magnetic resonance imaging data were acquired from 208 ASD patients and 227 typical development (TD) controls, who were drawn from the multi-site Autism Brain Imaging Data Exchange (ABIDE) database. Brain network flexibilities were estimated and compared between the ASD and TD groups at both global and local levels, after adjusting for sex, age, head motion, and site effects. The results revealed significantly increased brain network flexibilities (indicating a decreased stability) at the global level, as well as at the local level within the default mode and sensorimotor areas in ASD patients than TD participants. Additionally, significant ASD-related decreases in flexibilities were also observed in several occipital regions at the nodal level. Most of these changes were significantly correlated with the Autism Diagnostic Observation Schedule (ADOS) total score in the entire sample. These results suggested that ASD is characterized by significant changes in temporal stabilities of the functional brain network, which can further strengthen our understanding of the pathobiology of ASD.

Anhedonia severity mediates the relationship between attentional networks recruitment and emotional blunting during music listening

Emotion studies have commonly reported impaired emotional processing in individuals with heightened anhedonic depressive symptoms, as typically measured by collecting single subjective ratings for a given emotional cue. However, the interindividual variation in moment-to-moment emotional reactivity, and associated time-varying brain networks recruitment as emotions are unfolding, remains unclear. In this study, we filled this gap by using the unique temporal characteristics of music to investigate behavioural and brain network dynamics as a function of anhedonic depressive symptoms severity. Thirty-one neurotypical participants aged 18–30 years completed anhedonic depression questionnaires and then continuously rated happy, neutral and sad pieces of music whilst undergoing MRI scanning. Using a unique combination of dynamic approaches to behavioural (i.e., emotion dynamics) and fMRI (i.e., leading eigenvector dynamics analysis; LEiDA) data analysis, we found that participants higher in anhedonic depressive symptoms exhibited increased recruitment of attentional networks and blunted emotional response to both happy and sad musical excerpts. Anhedonic depression mediated the relationship between attentional networks recruitment and emotional blunting, and the elevated recruitment of attentional networks during emotional pieces of music carried over into subsequent neutral music. Future studies are needed to investigate whether these findings could be generalised to a clinical population (i.e., major depressive disorder).

Stacking models of brain dynamics to improve prediction of subject traits in fMRI

Abstract Beyond structural and time-averaged functional connectivity brain measures, modelling the way brain activity dynamically unfolds can add important information to our understanding and characterisation of individual cognitive traits. One approach to leveraging this information is to extract features from models of brain network dynamics to predict individual traits. However, these predictions are susceptible to variability due to factors such as variation in model estimation induced by the choice of hyperparameters. We suggest that, rather than merely being statistical noise, this variability may be useful in providing complementary information that can be leveraged to improve prediction accuracy. To leverage this variability, we propose the use of stacking, a prediction-driven approach for model selection. Specifically, we combine predictions developed from multiple hidden Markov models—a probabilistic generative model of network dynamics that identifies recurring patterns of brain activity—to demonstrate that stacking can slightly improve the accuracy and robustness of cognitive trait predictions. By comparing analysis from the Human Connectome Project and UK Biobank datasets, we show that stacking is relatively effective at improving prediction accuracy and robustness when there are enough subjects, and that the effectiveness of combining predictions from static and dynamic functional connectivity approaches depends on the length of scan per subject. We also show that the effectiveness of stacking predictions is driven by the accuracy and diversity in the underlying model estimations.