Dynamics Of Brain Activity Research Articles

Similarities and differences of dynamic and static spontaneous brain activity between left and right temporal lobe epilepsy.

To comprehensively investigate the potential temporal dynamic and static abnormalities of spontaneous brain activity (SBA) in left temporal lobe epilepsy (LTLE) and right temporal lobe epilepsy (RTLE) and to detect whether these alterations correlate with cognition. Twelve SBA metrics, including ALFF, dALFF, fALFF, dfALFF, ReHo, dReHo, DC, dDC, GSCorr, dGSCorr, VMHC, and dVMHC, in 46 LTLE patients, 43 RTLE patients, and 53 healthy volunteers were compared in the voxel-wise analysis. Correlation analyses between metrics in regions showing statistic differences and epilepsy duration, epilepsy severity, and cognition scores were also performed. Compared with the healthy volunteers, the alteration of SBA was identified both in LTLE and RTLE patients. The ALFF, fALFF, and dALFF values in LTLE, as well as the fALFF values in RTLE, increased in the bilateral thalamus, basal ganglia, mesial temporal lobe, cerebellum, and vermis. Increased dfALFF in the bilateral basal ganglia, increased ReHoanddReHo in the bilateral thalamus in the LTLE group, increased ALFF and dALFF in the pons, and increased ReHoanddReHo in the right hippocampus in the RTLE group were also detected. However, the majority of deactivation clusters were in the ipsilateral lateral temporal lobe. For LTLE, the fALFF, DC, dDC, and GSCorr values in the left lateral temporal lobe and the ReHo and VMHC values in the bilateral lateral temporal lobe all decreased. For RTLE, the ALFF, fALFF, dfALFF, ReHo, dReHo, and DC values in the right lateral temporal lobe and the VMHC values in the bilateral lateral temporal lobe all decreased. Moreover, for both the LTLE and RTLE groups, the dVMHC values decreased in the calcarine cortex. The most significant difference between LTLE and RTLE was the higher activation in the cerebellum of the LTLE group. The alterations of many SBA metrics were correlated with cognition and epilepsy duration. The patterns of change in SBA abnormalities in the LTLE and RTLE patients were generally similar. The integrated application of temporal dynamic and static SBA metrics might aid in the investigation of the propagation and suppression pathways of seizure activity as well as the cognitive impairment mechanisms in TLE.

Lavender Essential Oil Inhalation Improves Attentional Shifting and Accuracy: Evidence from Dynamic Changes of Cognitive Flexibility and Power Spectral Density of Electroencephalogram Signals.

Cognitive flexibility, a vital component of executive function, entails the utilization of extended brain networks. Olfactory stimulation has been shown to influence various brain functions, particularly cognitive performance. To investigate aroma inhalation's effects on brain activity dynamics associated with cognitive flexibility, 20 healthy adults were recruited to complete a set-shifting task during two experimental conditions: no aroma stimuli vs. lavender essential oil inhalation. Using Thomson's multitaper approach, the normalized power spectral density (NPSD) was assessed for five frequency bands. Findings confirm that aroma inhalation significantly affects behavioral indices (i.e., reaction time (RT) and response accuracy) and electroencephalogram (EEG) signatures, especially in the frontal lobe. Participants showed a tremendous increase in theta and alpha NPSD, associated with relaxation, along with beta NPSD, associated with clear and fast thinking after inhaling the aroma. NPSD of the delta band, an indicator of the unconscious mind, significantly decreased when stimulated with lavender essential oil. Further, participants exhibited shorter RT and more accurate responses following aroma inhalation. Our findings revealed significant changes in oscillatory power and behavioral performance after aroma inhalation, providing neural evidence that olfactory stimulation with lavender essential oil may facilitate cognitive flexibility.

Altered Dynamics of Brain Spontaneous Activity and Functional Networks Associated With Cognitive Impairment in Patients With Type 2 Diabetes.

Cognitive impairment is increasingly recognized as an important comorbidity and complication of type 2 diabetes (T2D), affecting patients' quality of life and diabetes management. Dynamic brain activity indicators can reflect changes in key neural activity patterns of cognition and behavior. To investigate dynamic functional connectivity (DFC) changes and spontaneous brain activity based on resting-state functional magnetic resonance imaging (rs-fMRI) in patients with T2D, exploring their correlations with clinical features. Retrospective. Forty-five healthy controls (HCs) (22 males and 23 females) and 102 patients with T2D (57 males and 45 females). 3.0 T/T1-weighted imaging and rs-fMRI with gradient-echo planar imaging sequence. Functional networks were created using independent component analysis. DFC states were determined using sliding window approach and k-means clustering. Spontaneous brain activity was assessed using dynamic regional homogeneity (dReHo) variability. One-way analysis of variance and post hoc analysis were used to compare the essential information including demographics, clinical data, and features of DFC and dReHo among groups. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve. P-values <0.05 were taken to indicate statistical significance. T2D group had significantly decreased mean dwell time and fractional windows in state 4 compared to HC. T2D with mild cognitive impairment showed significantly increased dReHo variability in left superior occipital gyrus compared to T2D with normal cognition. Mean dwell time and number of fractional windows of state 4 both showed significant positive correlations with the Montreal cognitive assessment scores (r = 0.309; r = 0.308, respectively) and the coefficient of variation of dReHo was significantly positively correlated with high-density lipoprotein cholesterol (r = 0.266). The integrated index had an area under the curve of 0.693 (95% confidence interval = 0.592-0.794). Differences in DFC and dynamic characteristic of spontaneous brain activity associated with T2D-related functional impairment may serve as indicators for predicting symptom progression and assessing cognitive dysfunction. 2 TECHNICAL EFFICACY: Stage 2.

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability

Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy – a critical period of rapid brain development and plasticity – microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.

Aberrant dynamic properties of whole-brain functional connectivity in acute mild traumatic brain injury revealed by hidden Markov models.

This study aimed to investigate the temporal dynamics of brain activity and characterize the spatiotemporal specificity of transitions and large-scale networks on short timescales in acute mild traumatic brain injury (mTBI) patients and those with cognitive impairment in detail. Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 71 acute mTBI patients and 57 age-, sex-, and education-matched healthy controls (HCs). A hidden Markov model (HMM) analysis of rs-fMRI data was conducted to identify brain states that recurred over time and to assess the dynamic patterns of activation states that characterized acute mTBI patients and those with cognitive impairment. The dynamic parameters (fractional occupancy, lifetime, interval time, switching rate, and probability) between groups and their correlation with cognitive performance were analyzed. Twelve HMM states were identified in this study. Compared with HCs, acute mTBI patients and those with cognitive impairment exhibited distinct changes in dynamics, including fractional occupancy, lifetime, and interval time. Furthermore, the switching rate and probability across HMM states were significantly different between acute mTBI patients and patients with cognitive impairment (all p < 0.05). The temporal reconfiguration of states in acute mTBI patients and those with cognitive impairment was associated with several brain networks (including the high-order cognition network [DMN], subcortical network [SUB], and sensory and motor network [SMN]). Hidden Markov models provide additional information on the dynamic activity of brain networks in patients with acute mTBI and those with cognitive impairment. Our results suggest that brain network dynamics determined by the HMM could reinforce the understanding of the neuropathological mechanisms of acute mTBI patients and those with cognitive impairment.

Decoding Semantics from Dynamic Brain Activation Patterns: From Trials to Task in EEG/MEG Source Space.

The temporal dynamics within the semantic brain network and its dependence on stimulus and task parameters are still not well understood. Here, we addressed this by decoding task as well as stimulus information from source-estimated EEG/MEG human data. We presented the same visual word stimuli in a lexical decision (LD) and three semantic decision (SD) tasks. The meanings of the presented words varied across five semantic categories. Source space decoding was applied over time in five ROIs in the left hemisphere (anterior and posterior temporal lobe, inferior frontal gyrus, primary visual areas, and angular gyrus) and one in the right hemisphere (anterior temporal lobe). Task decoding produced sustained significant effects in all ROIs from 50 to 100 ms, both when categorizing tasks with different semantic demands (LD-SD) as well as for similar semantic tasks (SD-SD). In contrast, a semantic word category could only be decoded in lATL, rATL, PTC, and IFG, between 250 and 500 ms. Furthermore, we compared two approaches to source space decoding: conventional ROI-by-ROI decoding and combined-ROI decoding with back-projected activation patterns. The former produced more reliable results for word category decoding while the latter was more informative for task decoding. This indicates that task effects are distributed across the whole semantic network while stimulus effects are more focal. Our results demonstrate that the semantic network is widely distributed but that bilateral anterior temporal lobes together with control regions are particularly relevant for the processing of semantic information.

Altered dynamic and static brain activity and functional connectivity in COVID-19 patients: a preliminary study.

This study aimed to investigate the effects of COVID-19 on brain functional activity through resting-state functional MRI (rs-fMRI). fMRI scans were conducted on a cohort of 42 confirmed COVID-19-positive patients and 46 healthy controls (HCs) to assess brain functional activity. A combination of dynamic and static amplitude of low-frequency fluctuations (dALFF/sALFF) and dynamic and static functional connectivity (dFC/sFC) was used for evaluation. Abnormal brain regions identified were then used as feature inputs in the model to evaluate support vector machine (SVM) capability in recognizing COVID-19 patients. Moreover, the random forest (RF) model was employed to verify the stability of SVM diagnoses for COVID-19 patients. Compared to HCs, COVID-19 patients exhibited a decrease in sALFF in the right lingual gyrus and the left medial occipital gyrus and an increase in dALFF in the right straight gyrus. Moreover, there was a decline in sFC between both lingual gyri and the right superior occipital gyrus and a reduction in dFC with the precentral gyrus. The dynamic and static combined ALFF and FC could distinguish between COVID-19 patients and the HCs with an accuracy of 0.885, a specificity of 0.818, a sensitivity of 0.933 and an area under the curve of 0.909. The combination of dynamic and static ALFF and FC can provide information for detecting brain functional abnormalities in COVID-19 patients.

Osl-dynamics, a toolbox for modeling fast dynamic brain activity

Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes.

The Commonality and Individuality of Human Brains When Performing Tasks.

It is imperative to study individual brain functioning toward understanding the neural bases responsible for individual behavioral and clinical traits. The complex and dynamic brain activity varies from area to area and from time to time across the entire brain, and BOLD-fMRI measures this spatiotemporal activity at large-scale systems level. We present a novel method to investigate task-evoked whole brain activity that varies not only from person to person but also from task trial to trial within each task type, offering a means of characterizing the individuality of human brains when performing tasks. For each task trial, the temporal correlation of task-evoked ideal time signal with the time signal of every point in the brain yields a full spatial map that characterizes the whole brain's functional co-activity (FC) relative to the task-evoked ideal response. For any two task trials, regardless of whether they are the same task or not, the spatial correlation of their corresponding two FC maps over the entire brain quantifies the similarity between these two maps, offering a means of investigating the variation in the whole brain activity trial to trial. The results demonstrated a substantially varied whole brain activity from trial to trial for each task category. The degree of this variation was task type-dependent and varied from subject to subject, showing a remarkable individuality of human brains when performing tasks. It demonstrates the potential of using the presented method to investigate the relationship of the whole brain activity with individual behavioral and clinical traits.

Dynamic reconfiguration of brain coactivation states that underlying working memory correlates with cognitive decline in clinically unimpaired older adults.

Impairments in working memory (WM) are evident in both clinically diagnosed patients with mild cognitive decline and older adults at risk, as indicated by lower scores on neuropsychological tests. Examining the WM-related neural signatures in at-risk older adults becomes essential for timely intervention. WM functioning relies on dynamic brain activities, particularly within the frontoparietal system. However, it remains unclear whether the cognitive decline would be reflected in the decreased dynamic reconfiguration of brain coactivation states during WM tasks. We enrolled 47 older adults and assessed their cognitive function using the Montreal Cognitive Assessment. The temporal dynamics of brain coactivations during a WM task were investigated through graph-based time-frame modularity analysis. Four primary recurring states emerged: two task-positive states with positive activity in the frontoparietal system (dorsal attention and central executive); two task-negative states with positive activity in the default mode network accompanied by negative activity in the frontoparietal networks. Heightened WM load was associated with increased flexibility of the frontoparietal networks, but the cognitive decline was correlated with reduced capacity for neuroplastic changes in response to increased task demands. These findings advance our understanding of aberrant brain reconfiguration linked to cognitive decline, potentially aiding early identification of at-risk individuals.

Atypical instantaneous spatio-temporal patterns of neural dynamics in Alzheimer’s disease

Cognitive functions produced by large-scale neural integrations are the most representative ‘emergence phenomena’ in complex systems. A novel approach focusing on the instantaneous phase difference of brain oscillations across brain regions has succeeded in detecting moment-to-moment dynamic functional connectivity. However, it is restricted to pairwise observations of two brain regions, contrary to large-scale spatial neural integration in the whole-brain. In this study, we introduce a microstate analysis to capture whole-brain instantaneous phase distributions instead of pairwise differences. Upon applying this method to electroencephalography signals of Alzheimer’s disease (AD), which is characterised by progressive cognitive decline, the AD-specific state transition among the four states defined as the leading phase location due to the loss of brain regional interactions could be promptly characterised. In conclusion, our synthetic analysis approach, focusing on the microstate and instantaneous phase, enables the capture of the instantaneous spatiotemporal neural dynamics of brain activity and characterises its pathological conditions.

A novel methodology for emotion recognition through 62-lead EEG signals: multilevel heterogeneous recurrence analysis.

Recognizing emotions from electroencephalography (EEG) signals is a challenging task due to the complex, nonlinear, and nonstationary characteristics of brain activity. Traditional methods often fail to capture these subtle dynamics, while deep learning approaches lack explainability. In this research, we introduce a novel three-phase methodology integrating manifold embedding, multilevel heterogeneous recurrence analysis (MHRA), and ensemble learning to address these limitations in EEG-based emotion recognition. The proposed methodology was evaluated using the SJTU-SEED IV database. We first applied uniform manifold approximation and projection (UMAP) for manifold embedding of the 62-lead EEG signals into a lower-dimensional space. We then developed MHRA to characterize the complex recurrence dynamics of brain activity across multiple transition levels. Finally, we employed tree-based ensemble learning methods to classify four emotions (neutral, sad, fear, happy) based on the extracted MHRA features. Our approach achieved high performance, with an accuracy of 0.7885 and an AUC of 0.7552, outperforming existing methods on the same dataset. Additionally, our methodology provided the most consistent recognition performance across different emotions. Sensitivity analysis revealed specific MHRA metrics that were strongly associated with each emotion, offering valuable insights into the underlying neural dynamics. This study presents a novel framework for EEG-based emotion recognition that effectively captures the complex nonlinear and nonstationary dynamics of brain activity while maintaining explainability. The proposed methodology offers significant potential for advancing our understanding of emotional processing and developing more reliable emotion recognition systems with broad applications in healthcare and beyond.

Data-driven modelling of brain activity using neural networks, diffusion maps, and the Koopman operator.

We propose a machine-learning approach to construct reduced-order models (ROMs) to predict the long-term out-of-sample dynamics of brain activity (and in general, high-dimensional time series), focusing mainly on task-dependent high-dimensional fMRI time series. Our approach is a three stage one. First, we exploit manifold learning and, in particular, diffusion maps (DMs) to discover a set of variables that parametrize the latent space on which the emergent high-dimensional fMRI time series evolve. Then, we construct ROMs on the embedded manifold via two techniques: Feedforward Neural Networks (FNNs) and the Koopman operator. Finally, for predicting the out-of-sample long-term dynamics of brain activity in the ambient fMRI space, we solve the pre-image problem, i.e., the construction of a map from the low-dimensional manifold to the original high-dimensional (ambient) space by coupling DMs with Geometric Harmonics (GH) when using FNNs and the Koopman modes per se. For our illustrations, we have assessed the performance of the two proposed schemes using two benchmark fMRI time series: (i) a simplistic five-dimensional model of stochastic discrete-time equations used just for a "transparent" illustration of the approach, thus knowing a priori what one expects to get, and (ii) a real fMRI dataset with recordings during a visuomotor task. We show that the proposed Koopman operator approach provides, for any practical purposes, equivalent results to the FNN-GH approach, thus bypassing the need to train a non-linear map and to use GH to extrapolate predictions in the ambient space; one can use instead the low-frequency truncation of the DMs function space of L2-integrable functions to predict the entire list of coordinate functions in the ambient space and to solve the pre-image problem.

Neuroplastic changes induced by long-term Pingju training: insights from dynamic brain activity and connectivity.

Traditional Chinese opera, such as Pingju, requires actors to master sophisticated performance skills and cultural knowledge, potentially influencing brain function. This study aimed to explore the effects of long-term opera training on the dynamic amplitude of low-frequency fluctuation (dALFF) and dynamic functional connectivity (dFC). Twenty professional well-trained Pingju actors and twenty demographically matched untrained subjects were recruited. Resting-state functional magnetic resonance imaging (fMRI) data were collected to assess dALFF differences in spontaneous regional brain activity between the actors and untrained participants. Brain regions with altered dALFF were selected as the seeds for the subsequent dFC analysis. Statistical comparisons examined differences between groups, while correlation analyses explored the relationships between dALFF and dFC, as well as the associations between these neural measures and the duration of Pingju training. Compared with untrained subjects, professional Pingju actors exhibited significantly lower dALFF in the right lingual gyrus. Additionally, actors showed increased dFC between the right lingual gyrus and the bilateral cerebellum, as well as between the right lingual gyrus and the bilateral midbrain/red nucleus/thalamus, compared with untrained subjects. Furthermore, a negative correlation was found between the dALFF in the right lingual gyrus and its dFC, and a significant association was found between dFC in the bilateral midbrain/red nucleus/thalamus and the duration of Pingju training. Long-term engagement in Pingju training induces neuroplastic changes, reflected in altered dALFF and dFC. These findings provide evidence for the interaction between artistic training and brain function, highlighting the need for further research into the impact of professional training on cognitive functions.

Spatiotemporal Dynamics of Periodic and Aperiodic Brain Activity Under Peripheral Nerve Stimulation with Acupuncture.

Brain activities are a mixture of periodic and aperiodic components, manifesting in the power spectral density (PSD) as rhythmic oscillations with spectral peaks and broadband fluctuations. Periodic oscillatory properties of brain response to external stimulation are widely studied, while aperiodic component responses remain unclear. Here, we investigate spatiotemporal dynamics of periodic and aperiodic brain activity under peripheral nerve stimulation with acupuncture by parameterization of power spectra of EEG signals. Regarding periodic brain activity, spectral peak in delta band emerges in frontal and central brain regions indicates a typical phenomenon of neural entrainment, which is formed by coupling periodic brain activity to external rhythmic acupuncture stimulation. In addition, the statistical results show that alpha periodic power is an important indicator for characterizing the modulatory effects of acupuncture on periodic brain activity. As for aperiodic brain activity, broadband EEG spectral trend analysis demonstrates a steeper aperiodic slope in left parietal lobe and a stronger negative correlation with the aperiodic offset under acupuncture compared with resting state, with the absolute value of correlation coefficient increasing from 0.27 to 0.50. Based on the two parameters that can best characterize the acupuncture effect, alpha periodic power and aperiodic slope, the accurate decoding of acupuncture manipulation is realized with AUC = 0.87. This work shows the modulatory effect of peripheral nerve stimulation with acupuncture on the brain activity by characterizing the periodic and aperiodic spectrum features of EEG, providing new insights into the comprehensive understanding of the response processes of human brain to acupuncture stimulation.

Interhemispheric connectivity dynamics of brain activity indused by cortical spreading depolarization in rats

Growing experimental and clinical evidence indicates the crucial role of network dysfunction in neurological disorders’ pathogenesis, including migraine, one of the most prevalent chronic brain diseases. Episodic headache attacks, frequently unilateral, accompanied by an aura, are associated with migraine. Migraine aura is a neurological condition characterized by the temporary development of unilateral sensory, motor, and/or speech disturbances. The symptoms are thought to indicate transient cerebral dysfunction in the cerebral cortex resulting from cortical spreading depolarization (SD), a wave of strong cellular depolarization that gradually spreads through the cortex at a rate of 3–5 mm/min. Electrophysiologically, the cortical SD wave is revealed by a high-amplitude slow negative shift in the extracellular potential and temporary suppression of the electrical activity of the cortex (EEG depression). The change in extracellular potential is associated with strong neuroglial depolarization and disruption of local ion homeostasis, which lasts for 1–2 min in healthy neuronal tissue. The SD results in a momentary suppression of the spontaneous electrical activity within the cortex, which is preceded by a brief excitation of the neurons. The neurological symptoms of the aura suggest a unilateral impairment of interhemispheric interactions during the early phase of a migraine attack. Our study investigated the effect of unilateral SD (a probable pathophysiological mechanism of migraine aura) on interhemispheric functional communication in freely behaving rats using local field potentials of the visual and motor cortex. Two methods were used to examine connectivity: mutual information function, computed using the method proposed in [1], and phase synchronization, calculated through the method [2], for four frequency bands: delta (1–4 Hz), theta (4–10 Hz), beta (10–25 Hz), and gamma (25–50 Hz). This was done by performing calculations on non-overlapping twenty-second intervals. Functional connectivity evolution was analyzed using local field potential records collected from homotopic points of the motor and visual cortex of two hemispheres in freely moving rats after inducing a single unilateral cortical SD in the somatosensory cortex. Cortical SD caused a significant wide-band decline (3–4 times) in interhemispheric functional connectivity in both the visual and motor cortex areas. Following the depolarization wave, the functional decoupling of the hemispheres began and progressively intensified, concluding by 5 min after the induction of the cortical SD wave. The network impairment displayed region- and frequency-specific features, with greater prominence observed in the visual cortex than in the motor cortex. The decline in functional connectivity was concurrent with abnormal animal behavior and aberrant activity in the ipsilateral cortex that appeared after the SD wave had ended. The study indicated that unilateral SD leads to a reversible decline in the functional interhemispheric connectivity in the awake animal cortex. Given the crucial role of synchronizing cortical oscillations for processing sensory information and integrating sensorimotor functions, the intracortical functional interactions disruption resulting from a unilateral SD wave, which we discovered in our present study, could contribute to the neuropathological mechanisms of migraine aura and sensory processing dysfunction during a migraine attack.

A Functional Magnetic Resonance Imaging Study Using Dynamic Amplitude of Low-Frequency Fluctuation to Assess Brain Activity in Patients with Moyamoya Disease.

Introduction: The purpose of this study was to monitor and record the dynamic brain activity of patients with moyamoya disease (MMD), as well as to study the relationship between brain abnormalities and presenting clinical features. Methods: A total of 16 patients with MMD (2 males and 14 females) were invited to participate in the study, as were healthy controls (HCs) with the same number and sex ratio. In this study, the dynamic amplitude of low-frequency fluctuation (dALFF) was utilized to assess changes in spontaneous brain activity. Moreover, we also used correlation analysis to study the relationship among the measured mean of dALFF, behavioral performances, and the retinal nerve fiber layer and the Hospital Anxiety and Depression Scale (HADS) score to explore the potential relationship between MMD and anxiety and depression. Results: Our study reveals that in MMD, dALFF levels decreased in the left lingual gyrus, right insula, and occipital lobe. Discussion: In this study, we found and discussed the potential relationship between the abnormal activities in multiple brain regions and related functional network disorders in patients with MMD, as well as the damage to brain regions that process emotion and vision, in the hopes of providing more ideas for the clinical diagnosis and treatment of MMD.

Associations between abnormal electroencephalogram microstates and childhood emotional abuse in adolescent depression.

Childhood traumatic experiences greatly influenced the brain network activities of patients with depression, and there is an urgent need to explore the temporal dynamics for these changes. This study aims to investigate the abnormalities of resting-state electroencephalogram (EEG) microstates in eye-open state of depressed adolescents and to explore the correlations between their EEG microstates and the childhood traumatic experience. Using resting-state EEG microstate analysis, we explored the temporal dynamics of brain activity in patients with adolescent depression. This study selected 66 adolescents with depression as a patient group, and 27 healthy adolescents as a healthy control group. A modified k-means clustering algorithm was used to classify the 64-channel resting-state EEG data into different microstates. Independent sample t-tests were used to compare the microstate parameters between the 2 groups and further assciations between these parameters and childhood traumatic experience in patients were explored via using Spearman correlation. In this study, significant differences were observed in the occurrence and transition probabilities of EEG microstates between the healthy control and the patient group. Notably, there was a statistically significant difference (P<0.05) in the occurrence of microstate A across 2 groups, exhibiting a negative correlation with the emotional abuse component within the childhood trauma scores (Spearman's rho=-0.31, P=0.013). Furthermore, patient-specific, non-random transitions from microstate B to A (Spearman's rho=-0.30, P=0.015) and C to A (Spearman's rho=-0.31, P=0.013) were inversely associated with the scores of emotional abuse factors from childhood trauma in the patient group, showing statistically significant differences when comparing to the healthy controls (P<0.05). Upon stratification into quartiles based on the emotional abuse factor scores, the occurrence of microstate A, as well as the transition rates from microstates B to A and C to A, retained statistical significance following adjustment for multiple comparisons (all P<0.05). The abnormal temporal dynamics in brain networks of adolescents with depression are linked to childhood emotional abuse. Those who have suffered severe emotional abuse may show greater impairments in the brain's visual and central executive networks. EEG microstate analysis could be a potential tool for detecting adolescent depression with severe childhood trauma.

Dynamic analysis of amplitude of low-frequency fluctuation in children with growth hormone deficiency

ObjectiveGrowth hormone (GH) affects brain activities and promotes growth and development. GH is a peptide hormone secreted by the anterior pituitary gland and is tied to behavior and cognitive function. Growth hormone deficiency (GHD) is the most common type of pathological short stature in children. Existing studies provide evidence that GHD may impact functional brain activities. The aim of this study was to investigate dynamic local brain activity in GHD children. MethodIn this study, we combined amplitude of low-frequency fluctuations (ALFF) and sliding-window techniques to examine the local brain activity of children with GHD. The resting-state functional magnetic resonance imaging (fMRI) data were collected from 26 children with GHD and 15 age- and sex-matched healthy controls (HC). ResultOur results showed significant abnormal temporal variability of dynamic ALFF in widespread regions in children with GHD, primarily in the frontal gyrus, temporal gyrus, and parietal lobule. ConclusionThe dALFF can capture dynamic changes in brain spontaneous activity, which are related to behavior and cognition. Based on this dynamic local brain activity, the results of this study provide a better understanding of the pathophysiological mechanism in children with GHD.

Dynamic brain activity states of memory impairment in stroke patients with varying motor outcomes.

The objective of this study was to characterize the alteration patterns of dynamic spatiotemporal activity in chronic subcortical stroke patients with varying motor outcomes, while investigating the imaging indicators relevant to the assessment of potential cognitive deficits in these patients. A total of 136 patients and 88 normal controls were included in the analysis of static and dynamic intrinsic brain activity, determined by amplitude of low-frequency fluctuations. The findings unveiled that subcortical stroke patients exhibited significantly aberrant temporal dynamics of intrinsic brain activity, involving regions within multiple brain networks. These spatiotemporal patterns were found to be contingent upon the side of the lesion. In addition, these aberrant metrics demonstrated potential in discerning cognitive deficits in stroke patients with memory impairment, with the dynamic indices exerting more influence than the static ones. The observe findings may indicate that subcortical stroke can trigger imbalances in the segregation and integration of spatiotemporal patterns across the entire brain with multi-domain networks, especially in patients with poor motor outcomes. It suggests that the temporal dynamics indices of intrinsic brain activity could serve as potential imaging indicators for assessing cognitive impairment in patients with chronic subcortical stroke, which may be associated with the motor outcomes.