Detecting modular brain states in rest and task

Walking on the Edge: Brain Connectivity Changes in Response to Virtual Height Challenges

Virtual reality (VR) environments simulating height offer a unique platform to investigate neural adaptations to emotionally salient contexts during locomotion. These simulations allow for controlled analysis of motor-cognitive interactions under perceived threat. This secondary analysis of a previously dataset aimed to explore regional and global brain network adaptations, focusing on connectivity, modularity, and centrality, during gait under neutral and height-induced negative conditions. Seventy-five healthy participants performed a VR task involving a virtual plank at two heights: street level (neutral) and 80 floors up (negative). EEG was recorded using 32 scalp electrodes. Functional connectivity was analyzed using local efficiency, modularity, and eigenvector centrality across frontal, central, parietal, temporal, and occipital regions during two tasks: preparation (elevator) and active walking (plank). Repeated-measures ANOVAs examined the effects of task and condition. Frontal connectivity was significantly higher in the negative condition across tasks, suggesting increased cognitive-emotional regulation. Central connectivity showed a task × condition interaction, with elevated values during walking under threat, indicating increased sensorimotor integration. Occipital connectivity was higher during preparation, independent of condition, likely reflecting greater visual scene processing. Modularity was reduced in the negative condition, consistent with decreased functional segregation, while eigenvector centrality was greater in frontal and parietal regions during walking, highlighting their role as integrative network hubs. Height-related threat in VR modulates both regional and global brain network properties, enhancing integration in cognitive, motor, and visual systems. These findings advance our understanding of adaptive brain responses and support the use of VR in rehabilitation.

The Neurogenetics of Functional Connectivity Alterations in Autism: Insights From Subtyping in 657 Individuals

BACKGROUND

There is little consensus and controversial evidence on anatomical alterations in the brains of people with autism spectrum disorder (ASD), due in part to the large heterogeneity present in ASD, which in turn is a major drawback for developing therapies. One strategy to characterize this heterogeneity in ASD is to cluster large-scale functional brain connectivity profiles.

METHODS

A subtyping approach based on consensus clustering of functional brain connectivity patterns was applied to a population of 657 autistic individuals with quality-assured neuroimaging data. We then used high-resolution gene transcriptomic data to characterize the molecular mechanism behind each subtype by performing enrichment analysis of the set of genes showing a high spatial similarity with the profiles of functional connectivity alterations between each subtype and a group of typically developing control participants.

RESULTS

Two major stable subtypes were found: subtype 1 exhibited hypoconnectivity (less average connectivity than typically developing control participants) and subtype 2, hyperconnectivity. The 2 subtypes did not differ in structural imaging metrics in any of the analyzed regions (68 cortical and 14 subcortical) or in any of the behavioral scores (including IQ, Autism Diagnostic Interview, and Autism Diagnostic Observation Schedule). Finally, only subtype 2, comprising about 43% of ASD participants, led to significant enrichments after multiple testing corrections. Notably, the dominant enrichment corresponded to excitation/inhibition imbalance, a leading well-known primary mechanism in the pathophysiology of ASD.

CONCLUSIONS

Our results support a link between excitation/inhibition imbalance and functional connectivity alterations, but only in one ASD subtype, overall characterized by brain hyperconnectivity and major alterations in somatomotor and default mode networks.

Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states

Assessment of functional connectivity (FC) has revealed a great deal of knowledge about the macroscale spatiotemporal organization of the brain network. Recent studies found task-versus-rest network reconfigurations were crucial for cognitive functioning. However, brain network reconfiguration remains unclear among different cognitive states, considering both aggregate and time-resolved FC profiles. The current study utilized static FC (sFC, i.e., long timescale aggregate FC) and sliding window-based dynamic FC (dFC, i.e., short timescale time-varying FC) approaches to investigate the similarity and alterations of edge weights and network topology at different cognitive loads, particularly their relationships with specific cognitive process. Both dFC/sFC networks showed subtle but significant reconfigurations that correlated with task performance. At higher cognitive load, brain network reconfiguration displayed increased functional integration in the sFC-based aggregate network, but faster and larger variability of modular reorganization in the dFC-based time-varying network, suggesting difficult tasks require more integrated and flexible network reconfigurations. Moreover, sFC-based network reconfigurations mainly linked with the sensorimotor and low-order cognitive processes, but dFC-based network reconfigurations mainly linked with the high-order cognitive process. Our findings suggest that reconfiguration profiles of sFC/dFC networks provide specific information about cognitive functioning, which could potentially be used to study brain function and disorders.

Analysis of task-related MEG functional brain networks using dynamic mode decomposition

Objective.Functional connectivity networks explain the different brain states during the diverse motor, cognitive, and sensory functions. Extracting connectivity network configurations and their temporal evolution is crucial for understanding brain function during diverse behavioral tasks.Approach.In this study, we introduce the use of dynamic mode decomposition (DMD) to extract the dynamics of brain networks. We compared DMD with principal component analysis (PCA) using real magnetoencephalography data during motor and memory tasks.Main results.The framework generates dominant connectivity brain networks and their time dynamics during simple tasks, such as button press and left-hand movement, as well as more complex tasks, such as picture naming and memory tasks. Our findings show that the proposed methodology with both the PCA-based and DMD-based approaches extracts similar dominant connectivity networks and their corresponding temporal dynamics.Significance.We believe that the proposed methodology with both the PCA and the DMD approaches has a very high potential for deciphering the spatiotemporal dynamics of electrophysiological brain network states during tasks.

A mixed-modeling framework for whole-brain dynamic network analysis

The emerging area of dynamic brain network analysis has gained considerable attention in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. This novel framework also allows for simulating dynamic brain networks with respect to desired covariates. Unlike current tools, which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks by using resting-state fMRI (rfMRI) data from 200 participants in the Human Connectome Project (HCP) study. We also demonstrate the utility of this model to simulate dynamic brain networks at both group and individual levels. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits as well as simulating dynamic brain networks.

In recent years, a growing body of studies have aimed at analyzing the brain as a complex dynamic system by using various neuroimaging data. This has opened new avenues to answer compelling questions about the brain function in health and disease. However, methods that allow for providing statistical inference about how the complex interactions of the brain are associated with desired phenotypes are to be developed for a more profound insight. This study introduces a promising regression-based model to relate dynamic brain networks to desired phenotypes and provide statistical inference. Moreover, it can be used for simulating dynamic brain networks with respect to desired phenotypes at the group and individual levels.

Cortical pathways during Postural Control: new insights from functional EEG source connectivity

Postural control is a complex feedback system that relies on vast array of sensory inputs in order to maintain a stable upright stance. The brain cortex plays a crucial role in the processing of this information and in the elaboration of a successful adaptive strategy to external stimulation preventing loss of balance and falls. In the present work, the participants postural control system was challenged by disrupting the upright stance via a mechanical skeletal muscle vibration applied to the calves. The EEG source connectivity method was used to investigate the cortical response to the external stimulation and highlight the brain network primarily involved in high-level coordination of the postural control system. The cortical network reconfiguration was assessed during two experimental conditions of eyes open and eyes closed and the network flexibility (i.e. its dynamic reconfiguration over time) was correlated with the sample entropy of the stabilogram sway. The results highlight two different cortical strategies in the alpha band: the predominance of frontal lobe connections during open eyes and the strengthening of temporal-parietal network connections in the absence of visual cues. Furthermore, a high correlation emerges between the flexibility in the regions surrounding the right temporo-parietal junction and the sample entropy of the CoP sway, suggesting their centrality in the postural control system. These results open the possibility to employ network-based flexibility metrics as markers of a healthy postural control system, with implications in the diagnosis and treatment of postural impairing diseases.

HD-EEG for tracking sub-second brain dynamics during cognitive tasks

This work provides the community with high-density Electroencephalography (HD-EEG, 256 channels) datasets collected during task-free and task-related paradigms. It includes forty-three healthy participants performing visual naming and spelling tasks, visual and auditory naming tasks and a visual working memory task in addition to resting state. The HD-EEG data are furnished in the Brain Imaging Data Structure (BIDS) format. These datasets can be used to (i) track brain networks dynamics and their rapid reconfigurations at sub-second time scale in different conditions, (naming/spelling/rest) and modalities, (auditory/visual) and compare them to each other, (ii) validate several parameters involved in the methods used to estimate cortical brain networks through scalp EEG, such as the open question of optimal number of channels and number of regions of interest and (iii) allow the reproducibility of results obtained so far using HD-EEG. We hope that delivering these datasets will lead to the development of new methods that can be used to estimate brain cortical networks and to better understand the general functioning of the brain during rest and task. Data are freely available from https://openneuro.org.

Measurement(s)	brain measurement • cognitive behavior trait
Technology Type(s)	electroencephalography (EEG)
Factor Type(s)	task • age • sex
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13560311

The dynamic modular fingerprints of the human brain at rest

The human brain is a dynamic modular network that can be decomposed into a set of modules, and its activity changes continually over time. At rest, several brain networks, known as Resting-State Networks (RSNs), emerge and cross-communicate even at sub-second temporal scale. Here, we seek to decipher the fast reshaping in spontaneous brain modularity and its relationships with RSNs. We use Electro/Magneto-Encephalography (EEG/MEG) to track the dynamics of modular brain networks, in three independent datasets (N = 568) of healthy subjects at rest. We show the presence of strikingly consistent RSNs, and a splitting phenomenon of some of these networks, especially the default mode network, visual, temporal and dorsal attentional networks. We also demonstrate that between-subjects variability in mental imagery is associated with the temporal characteristics of specific modules, particularly the visual network. Taken together, our findings show that large-scale electrophysiological networks have modularity-dependent dynamic fingerprints at rest.

Reproducibility of graph measures at the subject level using resting-state fMRI

INTRODUCTION

Graph metrics have been proposed as potential biomarkers for diagnosis in clinical work. However, before it can be applied in a clinical setting, their reproducibility should be evaluated.

METHODS

This study systematically investigated the effect of two denoising pipelines and different whole-brain network constructions on reproducibility of subject-specific graph measures. We used the multi-session fMRI dataset from the Brain Genomics Superstruct Project consisting of 69 healthy young adults.

RESULTS

In binary networks, the test-retest variability for global measures was large at low density irrespective of the denoising strategy or the type of correlation. Weighted networks showed very low test-retest values (and thus a good reproducibility) for global graph measures irrespective of the strategy used. Comparing the test-retest values for different strategies, there were significant main effects of the type of correlation (Pearson correlation vs. partial correlation), the (partial) correlation value (absolute vs. positive vs. negative), and weight calculation (based on the raw (partial) correlation values vs. based on transformed Z-values). There was also a significant interaction effect between type of correlation and weight calculation. Similarly as for the binary networks, there was no main effect of the denoising pipeline.

CONCLUSION

Our results demonstrated that normalized global graph measures based on a weighted network using the absolute (partial) correlation as weight were reproducible. The denoising pipeline and the granularity of the whole-brain parcellation used to define the nodes were not critical for the reproducibility of normalized graph measures.

Connectome spectral analysis to track EEG task dynamics on a subsecond scale

We present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in order to derive the Fourier modes of the brain structural connectivity graph, or "network harmonics". These network harmonics are naturally ordered by smoothness. Smoothness in this context can be understood as the amount of variation along the cortex, leading to a multi-scale representation of brain connectivity. We demonstrate that network harmonics provide a sparse representation of the EEG signal, where, at certain times, the smoothest 15 network harmonics capture 90% of the signal power. This suggests that network harmonics are functionally meaningful, which we demonstrate by using them as a basis for the functional EEG data recorded from a face detection task. There, only 13 network harmonics are sufficient to track the large-scale cortical activity during the processing of the stimuli with a 50 ​ms resolution, reproducing well-known activity in the fusiform face area as well as revealing co-activation patterns in somatosensory/motor and frontal cortices that an unconstrained ROI-by-ROI analysis fails to capture. The proposed approach is simple and fast, provides a means of integration of multimodal datasets, and is tied to a theoretical framework in mathematics and physics. Thus, network harmonics point towards promising research directions both theoretically - for example in exploring the relationship between structure and function in the brain - and practically - for example for network tracking in different tasks and groups of individuals, such as patients.