Brain Dynamics Research Articles

Altered Dynamic Brain Functional Network Connectivity Related to Visual Network in Spinal Cord Injury.

Visual feedback training (VFT) plays an important role in the motor rehabilitation of patients with spinal cord injury (SCI). However, the neural mechanisms are unclear. We aimed to investigate the changes in dynamic functional network connectivity (FNC) related to visual networks (VN) in patients with SCI and to reveal the neural mechanism of VFT promoting motor function rehabilitation. Dynamic FNC and the sliding window method were performed in 18 complete SCI (CSCI), 16 patients with incomplete SCI (ISCI), and 42 healthy controls (HCs). Then, k-mean clustering was implemented to identify discrete FNC states, and temporal properties were computed. The correlations between these dynamic features and neurological parameters in all patients with SCI were calculated. The majority of aberrant FNC was manifested between VN and executive control network (ECN). In addition, compared with HCs, temporal metrics derived from state transition vectors were decreased in patients with CSCI including the mean dwell time and the fraction of time spent in state 3. Furthermore, the disrupted FNC between salience network and ECN in state 2 and the number of transitions were all positively correlated with neurological scores in patients with SCI. Our findings indicated that SCI could result in VN-related FNC alterations, revealing the possible mechanism for VFT in rehabilitation of patients with SCI and increasing the training efficacy and promoting rehabilitation for SCI.

Exploring the brain physiological activity and quantified assessment of VR cybersickness using EEG signals

Cybersickness in virtual reality (VR) significantly impedes user experience enhancement. Sensory conflict theory explains cybersickness as arising from brain conflicts, making a brain physiology-based examination essential for cybersickness research. In this study, we analyze the impact of cybersickness on brain neural activity and achieve the quantified assessment of cybersickness using cybersickness-related electroencephalography (EEG) data. We conduct a cybersickness induction experiment by view rotation and simultaneously collect EEG signals from 36 subjects. We investigate both brain functional connectivity and neural oscillation power aiming to demonstrate the specific variation trends of brain physiological characteristics across varying degrees of cybersickness. Filtering raw EEG highlights cybersickness-related features, facilitating the quantified assessment of cybersickness through a Convolutional Temporal-Transformer Network, named CTTNet. The results demonstrate that cybersickness leads to a significant reduction in the power of the beta and gamma frequency bands in the frontal lobe, accompanied by weakened internal connectivity within these bands. Conversely, as the severity of cybersickness increases, connectivity between the posterior brain regions and the frontal lobe in the mid-to-high frequency bands is enhanced. CTTNet achieves accurate evaluation of cybersickness by effectively capturing temporal-spatial EEG features and the long-term temporal dependencies of cybersickness. A significant and robust relationship between cybersickness and cerebral physiological characteristics is demonstrated. These findings hold the potential to offer valuable insights for the future real-time assessment and mitigation of cybersickness, particularly focusing on brain dynamics.

Attentional failures after sleep deprivation represent moments of cerebrospinal fluid flow.

Sleep deprivation rapidly disrupts cognitive function, and in the long term contributes to neurological disease. Why sleep deprivation has such profound effects on cognition is not well understood. Here, we use simultaneous fast fMRI-EEG to test how sleep deprivation modulates cognitive, neural, and fluid dynamics in the human brain. We demonstrate that after sleep deprivation, sleep-like pulsatile cerebrospinal fluid (CSF) flow events intrude into the awake state. CSF flow is coupled to attentional function, with high flow during attentional impairment. Furthermore, CSF flow is tightly orchestrated in a series of brain-body changes including broadband neuronal shifts, pupil constriction, and altered systemic physiology, pointing to a coupled system of fluid dynamics and neuromodulatory state. The timing of these dynamics is consistent with a vascular mechanism regulated by neuromodulatory state, in which CSF begins to flow outward when attention fails, and flow reverses when attention recovers. The attentional costs of sleep deprivation may thus reflect an irrepressible need for neuronal rest periods and widespread pulsatile fluid flow.

Non-Equilibrium Quantum Brain Dynamics: Water Coupled with Phonons and Photons.

We investigate Quantum Electrodynamics (QED) of water coupled with sound and light, namely Quantum Brain Dynamics (QBD) of water, phonons and photons. We provide phonon degrees of freedom as additional quanta in the framework of QBD in this paper. We begin with the Lagrangian density QED with non-relativistic charged bosons, photons and phonons, and derive time-evolution equations of coherent fields and Kadanoff-Baym (KB) equations for incoherent particles. We next show an acoustic super-radiance solution in our model. We also introduce a kinetic entropy current in KB equations in 1st order approximation in the gradient expansion and show the H-theorem for self-energy in Hartree-Fock approximation. We finally derive conserved number density of charged bosons and conserved energy density in spatially homogeneous system.

Closed-Loop Auditory Stimulation (CLAS) During Sleep Augments Language and Discovery Learning.

Background/Objectives: Slow oscillation (SO) brainwaves observed during sleep have been shown to reflect the process of memory consolidation, that underlies the critical role of sleep in learning, memory, and other cognitive functions. Closed-loop auditory stimulation (CLAS) uses tones presented in phase with SOs to increase their amplitude and number, along with other brainwave signatures related to memory consolidation. Prior studies have found that CLAS maximizes the ability to perform rote memorization tasks, although this remains controversial. The present study examined whether CLAS affects a broader range of learning tasks than has been tested previously, including a rote language learning task requiring basic memorization and also two discovery learning tasks requiring insight, hypothesis testing, and integration of experience, all processes that benefit from memory consolidation. Methods: Twenty-eight healthy participants performed language and discovery learning tasks before sleeping in our laboratory for three continuous nights per week over two weeks, with verum or control CLAS using a prototype NeuroGevity system (NeuroGeneces, Inc., Santa Fe, NM, USA) in a crossed, randomized, double-blind manner. Results: Language learning showed a 35% better word recall (p = 0.048), and discovery learning showed a 26% better performance (p < 0.001) after three continuous nights of CLAS vs. control. EEG measures showed increased SO amplitude and entrainment, SO-spindle coupling, and other features that may underlie the learning benefits of CLAS. Conclusions: Taken together, the present results show that CLAS can alter brain dynamics and enhance learning, especially in complex discovery learning tasks that may benefit more from memory consolidation compared with rote word pair or language learning.

A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation

This study presents a method for generating synthetic electroencephalography (EEG) signals to test dynamic directed brain connectivity estimation methods. Current methods for evaluating dynamic brain connectivity estimation techniques face challenges due to the lack of ground truth in real EEG signals. To address this, we propose a framework for generating synthetic EEG signals with predefined dynamic connectivity changes. Our approach allows for evaluating and optimizing dynamic connectivity estimation methods, particularly Granger causality (GC). We demonstrate the framework’s utility by identifying optimal window sizes and regression orders for GC analysis. The findings could guide the development of more accurate dynamic connectivity techniques.

Dynamic evidence fusion neural networks with uncertainty theory and its application in brain network analysis

Deep learning has demonstrated significant potential and advantages, achieving notable success in the medical field, particularly in the application of brain network analysis. However, most models ignore the uncertainty caused by inconsistent view quality and fail to effectively leverage the potential correlations and temporal sequences present in multi-view data, preventing neural networks from fully showcasing their strengths. To this end, this paper proposes dynamic evidence fusion neural networks (DEF-NNs) with uncertainty theory, and applies it to brain network analysis. Our model is established within a multi-view learning framework that considers the functional connections under each window as a view. We employ a dynamic evidence learning module to capture the evidence for each time window of the dynamic brain network, utilizing three distinct convolutional filters to extract feature maps. Then, a dynamic evidence fusion mechanism is designed and a dynamic trust function is constructed according to the temporal nature of dFC data. The evidence generated by multiple windows is fused at the decision level of classification, dealing with the uncertainty caused by inconsistent view quality and improving the classification performance. We verified the effectiveness of DEF-NNs through comparison with advanced algorithms on three schizophrenia datasets, and the results show that DEF-NNs significantly improved the classification performance of brain disease diagnosis tasks.

Single Objective Light Sheet Microscopy allows high resolution in vivo brain imaging of Drosophila.

In vivo imaging of dynamic sub-cellular brain structures in Drosophila melanogaster is key to understanding several phenomena in neuroscience. However, a trade-off between spatial resolution, speed, photodamage, and setup complexity limits its implementation. Here, we designed and built a single objective light sheet microscope, customized for in vivo imaging of adult flies and optimized for maximum resolution. Unlike multi-objective light sheet setups, the microscope uses a single objective at the fly head interface, facilitating sample mounting and inspection, and reducing invasiveness. In contrast to two-photon microscopies, the light-sheet configuration with visible excitation offers reduced phototoxicity. We demonstrate in vivo imaging of the membrane, mitochondria, and dense-core vesicles in the axonal projections of small lateral ventral neurons. The achieved resolution was between 380 to 500 nm within a field of view of 70×50×12 μm 3 . This unique combination of easy sample mounting, high resolution, and the low-phototoxicity of light sheet illumination paves the way for new dynamic studies in the brain of living flies.

Functional connectotomy of a whole-brain model reveals tumor-induced alterations to neuronal dynamics in glioma patients

Abstract Brain tumors can induce pathological changes in neuronal dynamics that are reflected in functional connectivity measures. Here, we use a whole-brain modeling approach to investigate pathological alterations to neuronal activity in glioma patients. By fitting a Hopf whole-brain model to empirical functional connectivity, we investigate glioma-induced changes in optimal model parameters. We observe considerable differences in neuronal dynamics between glioma patients and healthy controls, both on an individual and population-based level. In particular, model parameter estimation suggests that local tumor pathology causes changes in brain dynamics by increasing the influence of interregional interactions on global neuronal activity. Our approach demonstrates that whole-brain models provide valuable insights for understanding glioma-associated alterations in functional connectivity.

Highly Sensitive and Biocompatible Microsensor for Selective Dynamic Monitoring of Dopamine in Rat Brain.

Highly selective and sensitive in vivo neurotransmitter dynamic monitoring of the central nervous system has long been a challenging endeavor. Here, an implantable and biocompatible microsensor with excellent performances was reported by electrodepositing poly(3,4-ethylenedioxythiophene)-electrochemically reduced graphene oxide (PEDOT-ERGO) nanocomposites and poly(tannic acid) (pTA) sequentially on the carbon fiber electrode (CFE) surface, and its feasibility in in vivo electrochemical sensing applications were demonstrated. Due to the synergistic electrocatalytic effect of PEDOT-ERGO nanocomposites with the negative-charged pTA on dopamine (DA) redox reaction, the microsensor exhibits high detection sensitivities of 1.1 and 0.37 nA μM-1 in the detection ranges of 0.02-0.5 and 0.5-20 μM with a low limit of detection of 9.2 nM. Also, the microsensor shows excellent selectivity, good sensing stability, repeatability, and reproducibility. In addition, the highly hydrophilic and negative-charged pTA inhibits the nonspecific adsorption of hydrophobic proteins, which endows the microsensor with good antifouling ability. Moreover, DA dynamics in rat brain were successfully monitored in real time, and the selective sensing ability of the microsensor in vivo was also demonstrated. The present study provides a new method for selective dynamics monitoring of DA in the brain, which would help to better understand the pathological and physiological functions of DA.

The control costs of human brain dynamics

Abstract The human brain is a complex system with high metabolic demands and extensive connectivity that requires control to balance energy consumption and functional efficiency over time. How this control is manifested on a whole-brain scale is largely unexplored, particularly what the associated costs are. Using network control theory, here we introduce a novel concept, time-averaged control energy (TCE), to quantify the cost of controlling human brain dynamics at rest, as measured from functional and diffusion MRI. Importantly, TCE spatially correlates with oxygen metabolism measures from positron emission tomography, providing insight into the bioenergetic footing of resting state control. Examining the temporal dimension of control costs, we find that brain state transitions along a hierarchical axis from sensory to association areas are more efficient in terms of control costs and more frequent within hierarchical groups than between. This inverse correlation between temporal control costs and state visits suggests a mechanism for maintaining functional diversity while minimizing energy expenditure. By unpacking the temporal dimension of control costs, we contribute to the neuroscientific understanding of how the brain governs its functionality while managing energy expenses.

Characterizing EEG signal dynamics in healthy, seizure-free, and seizure states using the chaos decision tree algorithm

Abstract Epilepsy is a multifaceted neurological condition marked by repetitive seizures that arise from irregular electrical activity in the brain. To understand this condition, a thorough examination of brain signals captured in different states is needed. In order to examine the dynamic behavior of brain signals in three different conditions: healthy, seizure-free, and seizure periods, this study uses the chaos decision tree algorithm. The findings show notable variations in these situations’ dynamics. Chaos is evident during seizure moments, showing extremely chaotic activity. The signals mostly exhibit stochastic behavior in the healthy condition, which is consistent with typical brain dynamics. It is noteworthy that an intermediate state exhibiting a blend of stochastic and chaotic signal dynamics is exhibited throughout the seizure-free time. Furthermore, the research shows that the frequency of chaotic signals rises with increasing proximity to the epileptogenic zone. These discoveries clarify the complex nature of epilepsy and offer insightful information about the dynamic properties of brain signals in various stages, aiding in improved understanding and potential diagnostic approaches.

How Freely Moving Mind Wandering Relates to Creativity: Behavioral and Neural Evidence.

Background: Previous studies have demonstrated that mind wandering during incubation phases enhances post-incubation creative performance. Recent empirical evidence, however, has highlighted a specific form of mind wandering closely related to creativity, termed freely moving mind wandering (FMMW). In this study, we examined the behavioral and neural associations between FMMW and creativity. Methods: We initially validated a questionnaire measuring FMMW by comparing its results with those from the Sustained Attention to Response Task (SART). Data were collected from 1316 participants who completed resting-state fMRI scans, the FMMW questionnaire, and creative tasks. Correlation analysis and Bayes factors indicated that FMMW was associated with creative thinking (AUT). To elucidate the neural mechanisms underlying the relationship between FMMW and creativity, Hidden Markov Models (HMM) were employed to analyze the temporal dynamics of the resting-state fMRI data. Results: Our findings indicated that brain dynamics associated with FMMW involve integration within multiple networks and between networks (r = -0.11, pFDR < 0.05). The links between brain dynamics associated with FMMW and creativity were mediated by FMMW (c' = 0.01, [-0.0181, -0.0029]). Conclusions: These findings demonstrate the relationship between FMMW and creativity, offering insights into the neural mechanisms underpinning this relationship.

Reconfiguration of functional brain network organization and dynamics with changing cognitive demands in children with attention-deficit/hyperactivity disorder

BackgroundThe pathophysiology of attention-deficit/hyperactivity disorder (ADHD) is characterized by atypical brain network organization and dynamics. Although functional brain networks adaptively reconfigure across cognitive contexts, previous studies have largely focused on network dysfunction during the resting-state. This preliminary study examined how functional brain network organization and dynamics flexibly reconfigure across rest and two cognitive control tasks with different cognitive demands in 30 children with ADHD and 36 typically developing (TD) children (8-12 years). MethodsWe leveraged graph theoretical analyses to interrogate the segregation (modularity, within-module degree) and integration (global efficiency, node dissociation index) of fronto-parietal, cingulo-opercular/salience, default mode, somatomotor, and visual networks. We also conducted edge timeseries analyses to quantify connectivity dynamics within and between these networks. ResultsAcross resting and task-based states, children with ADHD demonstrated significantly lower whole-graph modularity and greater node dissociation index between default mode and visual networks. Further, a significant task-by-diagnosis interaction was observed for fronto-parietal network within-module degree, which decreased from rest to task in children with ADHD but increased in TD children. Finally, children with ADHD displayed significantly more dynamic connectivity within and across cingulo-opercular/salience, default mode, and somatomotor networks, especially during task performance. Exploratory analyses revealed associations between network dynamics, cognitive performance, and ADHD symptoms. ConclusionsBy integrating static and dynamic network analyses across changing cognitive demands, this study provides novel insight into how context-specific, context-general, and timescale-dependent network connectivity is altered in children with ADHD. Our findings highlight the involvement and clinical relevance of both association and sensory/motor systems in ADHD.

An Automated Squint Method for Time-syncing Behavior and Brain Dynamics in Mouse Pain Studies.

Spontaneous pain has been challenging to track in real time and quantify in a way that prevents human bias. This is especially true for metrics of head pain, as in disorders such as migraine. Eye squint has emerged as a continuous variable metric that can be measured over time and is effective for predicting pain states in such assays. This paper provides a protocol for the use of DeepLabCut (DLC) to automate and quantify eye squint (Euclidean distance between eyelids) in restrained mice with freely rotating head motions. This protocol enables unbiased quantification of eye squint to be paired with and compared directly against mechanistic measures such as neurophysiology. We provide an assessment of AI training parameters necessary for achieving success as defined by discriminating squint and non-squint periods. We demonstrate an ability to reliably track and differentiate squint in a CGRP-induced migraine-like phenotype at a sub second resolution.

FNIRS‐Based Energy Landscape Analysis to Signify Brain Activity Dynamics of Individuals With Depression

ABSTRACTBackgroundMajor depressive disorder (MDD) is one of the most common mental disorders, and the number of individuals with MDD (MDDs) continues to increase. Therefore, there is an urgent need for an objective characterization and real‐time detection method for depression. Functional near‐infrared spectroscopy (fNIRS) is a non‐invasive tool, which is widely used in depression research. However, the process of how the brain activity of MDDs changes in response to external stimuli based on fNIRS signals is not yet clear.MethodEnergy landscape (EL) can describe the brain dynamics under task conditions by assigning energy values to each state. The higher the energy value, the lower the probability of the state occurring. This study compares the EL features of 60 MDDs with 60 healthy controls (HCs).ResultsCompared to HCs, MDDs have more local minima, smaller energy differences, smaller variations in basin sizes, and longer duration in the basin of global minimum (GM). The classification results indicate that using the four features above for depression detection yields an accuracy of 86.53%. Simultaneously, there are significant differences between the two groups in the duration of the major states.ConclusionThe dynamic brain networks of MDDs exhibit more constraints and lower degrees of freedom, which might be associated with depressive symptoms such as negative emotional bias and rumination. In addition, we also demonstrate the strong depression detection capability of EL features, providing a possibility for their application in clinical diagnosis.

Evaluation of stereoscopic visual fatigue in virtual reality with exploration of brain dynamics

Virtual Reality (VR) provides a highly immersive experience with depth perception and three-dimensional representation, offering tremendous potential for applications in education, entertainment, healthcare, and other fields. However, stereoscopic visual fatigue (SVF) symptoms pose a limitation to its development. To investigate SVF induced by depth perception in VR environments and analyze its impact on the depth perception process, this study combined an improved Go/NoGo paradigm with repeated visual perception tasks to maintain participants’ task attention and induce SVF. The effectiveness of SVF induction and attention control was verified through subjective SVF ratings and behavioral data. Effective connectivity analysis identified key brain regions, critical electroencephalography (EEG) oscillations, and timing associated with the depth perception process following SVF. The results indicate that crucial brain regions are primarily located in the dorsal visual pathway, consistent with existing research on depth perception and attention control. The results also show that SVF is associated with a decline in depth perception awareness processing capabilities and is modulated by attention allocation and self-awareness. One-way repeated measures ANOVA results suggest that subdividing the entire α and β frequency bands into sub-bands can more accurately characterize SVF. The α2/θ and θ/(α2+β1) metrics provide a more precise representation of SVF in VR displays.

Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning

Backgroundand purpose: The investigation of functional plasticity and remodeling of the brain in patients with retinal detachment (RD) has gained increasing attention and validation. However, the precise alterations in the topological configuration of dynamic functional networks are still not fully understood. This study aimed to investigate the topological structure of dynamic brain functional networks in RD patients. MethodsWe recruited 32 patients with RD and 33 healthy controls (HCs) to participate in resting-state fMRI. Employing the sliding time window analysis and K-means clustering method, we sought to identify dynamic functional connectivity (dFC) variability patterns in both groups. The investigation into the topological structure of whole-brain functional networks utilized a graph theoretical approach. Furthermore, we employed machine learning analysis, selecting altered topological properties as classification features to distinguish RD patients from HCs. ResultsAll participants exhibited four distinct states of dynamic functional connectivity. Compared to the healthy control (HC) group, patients with RD experienced a significant reduction in the number of transitions among these four states. Additionally, the dynamic topological properties of RD patients demonstrated notable changes in both global and node-specific characteristics, with these changes correlating with clinical parameters. The support vector machine (SVM) model used for classification achieved an accuracy of 0.938, an area under the curve (AUC) of 0.988, and both sensitivity and specificity of 0.937. ConclusionThe alterations in the topological properties of the brain in RD patients may indicate the integration function and information exchange efficiency of the whole brain network were reduced. In addition, the topological properties hold considerable promise for distinguishing between RD and HCs.

Effects of List-Mode-Based Intraframe Motion Correction in Dynamic Brain PET Imaging.

Motion is unavoidable in dynamic [18F]-MK6240 Positron Emission Tomography (PET) imaging, especially in Alzheimer's disease (AD) research requiring long scan duration. To understand how motion correction affects quantitative analysis, we investigated two approaches: II-MC, which corrects for both inter-frame and intra-frame motion, and IO-MC, which only corrects for inter-frame motion. These methods were applied to 83 scans from 34 subjects, and we calculated distribution volume ratios (DVR) using the multilinear reference tissue model with two parameters (MRTM2) in tau-rich brain regions. Most of the studies yielded similar DVR results for both II-MC and IO-MC. However, in one scan of an AD subject, the inferior temporal region showed 14% higher DVR with II-MC compared to IO-MC. This difference was reasonable given the AD diagnosis, although similar results were not observed in other regions. Although discrepancies between IO-MC and II-MC results were rare, they underscore the importance of incorporating intra-frame motion correction for more accurate and dependable PET quantitation, particularly in the context of dynamic imaging. These findings suggest that while the overall impact of intra-frame motion correction may be subtle, it can improve the reliability of longitudinal PET data, ultimately enhancing our understanding of tau protein distribution in AD pathology.

Temporal dysregulation of the somatomotor network in agitated depression.

Agitated depression (A-MDD) is a severe subtype of major depressive disorder, with an increased risk of suicidality and the potential to evolve into bipolar disorder. Despite its clinical significance, the neural basis remains unclear. We hypothesize that psychomotor agitation, marked by pressured speech and racing thoughts, is linked to disruptions in brain dynamics. To test this hypothesis, we examined brain dynamics using time delay estimation and edge-centre time series, as well as dynamic connections between the somatomotor network (SMN) and the default mode network in 44 patients with A-MDD, 75 with non-agitated MDD (NA-MDD), and 94 healthy controls. Our results revealed that the neural co-activity duration was shorter in the A-MDD group compared with both the NA-MDD and controls (A-MDD versus NA-MDD: t = 2.295; A-MDD versus controls: t = 2.192, all P < 0.05). In addition, the dynamic of neural fluctuation in SMN altered in the A-MDD group than in the NA-MDD group (t = -2.616, P = 0.011) and was correlated with agitation severity (β = -0.228, P = 0.011). The inter-network connection was reduced in the A-MDD group compared with the control group (t = 2.102, P = 0.037), especially at low-amplitude time points (t = 2.139, P = 0.034). These findings indicate rapid neural fluctuations and disrupted dynamic coupling between the SMN and default mode network in A-MDD, potentially underlying the psychomotor agitation characteristic of this subtype. These insights contribute to a more nuanced understanding of the heterogeneity of depression and have implications for differential diagnosis and treatment strategies.