Tracking the Main States of Dynamic Functional Connectivity in Resting State

Characterizing brain stage-dependent pupil dynamics based on lateral hypothalamic activity

Pupil dynamics presents varied correlation features with brain activity under different vigilant levels. The modulation of brain dynamic stages can arise from the lateral hypothalamus (LH), where diverse neuronal cell types contribute to arousal regulation in opposite directions via the anterior cingulate cortex (ACC). However, the relationship of the LH and pupil dynamics has seldom been investigated. Here, we performed local field potential (LFP) recordings at the LH and ACC, and whole-brain fMRI with simultaneous fiber photometry Ca2+ recording in the ACC, to evaluate their correlation with brain state-dependent pupil dynamics. Both LFP and functional magnetic resonance imaging (fMRI) data showed various correlations to pupil dynamics across trials that span negative, null, and positive correlation values, demonstrating brain state-dependent coupling features. Our results indicate that the correlation of pupil dynamics with ACC LFP and whole-brain fMRI signals depends on LH activity, suggesting a role of the latter in brain dynamic stage regulation.

Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study

In addition to the standard observational assessment for autism spectrum disorder (ASD), recent advancements in neuroimaging and machine learning (ML) suggest a rapid and objective alternative using brain imaging. This work presents a pipelined framework, using functional magnetic resonance imaging (fMRI) that allows not only an accurate ASD diagnosis but also the identification of the brain regions contributing to the diagnosis decision. The proposed framework includes several processing stages: preprocessing, brain parcellation, feature representation, feature selection, and ML classification. For feature representation, the proposed framework uses both a conventional feature representation and a novel dynamic connectivity representation to assist in the accurate classification of an autistic individual. Based on a large publicly available dataset, this extensive research highlights different decisions along the proposed pipeline and their impact on diagnostic accuracy. A large publicly available dataset of 884 subjects from the Autism Brain Imaging Data Exchange I (ABIDE-I) initiative is used to validate our proposed framework, achieving a global balanced accuracy of 98.8% with five-fold cross-validation and proving the potential of the proposed feature representation. As a result of this comprehensive study, we achieve state-of-the-art accuracy, confirming the benefits of the proposed feature representation and feature engineering in extracting useful information as well as the potential benefits of utilizing ML and neuroimaging in the diagnosis and understanding of autism.

MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale.

Schizophrenia induces abnormal frequency-dependent patterns of dynamic brain network reconfiguration during an auditory oddball task

OBJECTIVE

Schizophrenia is a psychiatric disorder that has been shown to disturb the dynamic top-down processing of sensory information. Various imaging techniques have revealed abnormalities in brain activity associated with this disorder, both locally and between cerebral regions. However, there is increasing interest in investigating dynamic network response to novel and relevant events at the network level during an attention-demanding task with high-temporal-resolution techniques. The aim of the work was: (i) to test the capacity of a novel algorithm to detect recurrent brain meta-states from auditory oddball task recordings; and (ii) to evaluate how the dynamic activation and behavior of the aforementioned meta-states were altered in schizophrenia, since it has been shown to impair top-down processing of sensory information.

APPROACH

A novel unsupervised method for the detection of brain meta-states based on recurrence plots and community detection algorithms, previously tested on resting-state data, was used on auditory oddball task recordings. Brain meta-states and several properties related to their activation during target trials in the task were extracted from electroencephalography (EEG) data from patients with schizophrenia and cognitively healthy controls.

MAIN RESULTS

The methodology successfully detected meta-states during an auditory oddball task, and they appeared to show both frequency-dependent time-locked and non-time-locked activity with respect to the stimulus onset. Moreover, patients with schizophrenia displayed higher network diversity, and showed more sluggish meta-state transitions, reflected in increased dwell times, less complex meta-state sequences, decreased meta-state space speed, and abnormal ratio of negative meta-state correlations.

SIGNIFICANCE

Abnormal cognition in schizophrenia is also reflected in decreased brain flexibility at the dynamic network level, which may hamper top-down processing, possibly indicating impaired decision-making linked to dysfunctional predictive coding. Moreover, the results showed the ability of the methodology to find meaningful and task-relevant changes in dynamic connectivity and pathology-related group differences.

Dynamic functional connectivity profile of the salience network across the life span

The insular cortex and anterior cingulate cortex together comprise the salience or midcingulo-insular network, involved in detecting salient events and initiating control signals to mediate brain network dynamics. The extent to which functional coupling between the salience network and the rest of the brain undergoes changes due to development and aging is at present largely unexplored. Here, we examine dynamic functional connectivity (dFC) of the salience network in a large life span sample (n = 601; 6-85 years old). A sliding-window analysis and k-means clustering revealed five states of dFC formed with the salience network, characterized by either widespread asynchrony or different patterns of synchrony between the salience network and other brain regions. We determined the frequency, dwell time, total transitions, and specific state-to-state transitions for each state and subject, regressing the metrics with subjects' age to identify life span trends. A dynamic state characterized by low connectivity between the salience network and the rest of the brain had a strong positive quadratic relationship between age and both frequency and dwell time. Additional frequency, dwell time, total transitions, and state-to-state transition trends were observed with other salience network states. Our results highlight the metastable dynamics of the salience network and its role in the maturation of brain regions critical for cognition.

Sensory, somatomotor and internal mentation networks emerge dynamically in the resting brain with internal mentation predominating in older age

Age-related changes in the brain are associated with a decline in functional flexibility. Intrinsic functional flexibility is evident in the brain's dynamic ability to switch between alternative spatiotemporal states during resting state. However, the relationship between brain connectivity states, associated psychological functions during resting state, and the changes in normal aging remain poorly understood. In this study, we analyzed resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP; N = 812) and the UK Biobank (UKB; N = 6,716). Using signed community clustering to identify distinct states of dynamic functional connectivity, and text-mining of a large existing literature for functional annotation of each state, our findings from the HCP dataset indicated that the resting brain spontaneously transitions between three functionally specialized states: sensory, somatomotor, and internal mentation networks. The occurrence, transition-rate, and persistence-time parameters for each state were correlated with behavioural scores using canonical correlation analysis. We estimated the same brain states and parameters in the UKB dataset, subdivided into three distinct age ranges: 50-55, 56-67, and 68-78 years. We found that the internal mentation network was more frequently expressed in people aged 71 and older, whereas people younger than 55 more frequently expressed sensory and somatomotor networks. Furthermore, analysis of the functional entropy - a measure of uncertainty of functional connectivity - also supported this finding across the three age ranges. Our study demonstrates that dynamic functional connectivity analysis can expose the time-varying patterns of transition between functionally specialized brain states, which are strongly tied to increasing age.

Extraction and Analysis of Dynamic Functional Connectome Patterns in Migraine Sufferers: A Resting-State fMRI Study

Migraine seriously affects the physical and mental health of patients because of its recurrence and the hypersensitivity to the environment that it causes. However, the pathogenesis and pathophysiology of migraine are not fully understood. We addressed this issue in the present study using an autodynamic functional connectome model (A-DFCM) with twice-clustering to compare dynamic functional connectome patterns (DFCPs) from resting-state functional magnetic resonance imaging data from migraine patients and normal control subjects. We used automatic localization of segment points to improve the efficiency of the model, and intergroup differences and network metrics were analyzed to identify the neural mechanisms of migraine. Using the A-DFCM model, we identified 17 DFCPs-including 1 that was specific and 16 that were general-based on intergroup differences. The specific DFCP was closely associated with neuronal dysfunction in migraine, whereas the general DFCPs showed that the 2 groups had similar functional topology as well as differences in the brain resting state. An analysis of network metrics revealed the critical brain regions in the specific DFCP; these were not only distributed in brain areas related to pain such as Brodmann area 1/2/3, basal ganglia, and thalamus but also located in regions that have been implicated in migraine symptoms such as the occipital lobe. An analysis of the dissimilarities in general DFCPs between the 2 groups identified 6 brain areas belonging to the so-called pain matrix. Our findings provide insight into the neural mechanisms of migraine while also identifying neuroimaging biomarkers that can aid in the diagnosis or monitoring of migraine patients.

Temporal dynamic changes of intrinsic brain activity in Alzheimer's disease and mild cognitive impairment patients: a resting-state functional magnetic resonance imaging study

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by memory impairment. Previous studies have largely focused on alterations of static brain activity occurring in patients with AD. Few studies to date have explored the characteristics of dynamic brain activity in cognitive impairment, and their predictive ability in AD patients.

METHODS

One hundred and eleven AD patients, 29 MCI patients, and 73 healthy controls (HC) were recruited. The dynamic amplitude of low-frequency fluctuation (dALFF) and the dynamic fraction amplitude of low-frequency fluctuation (dfALFF) were used to assess the temporal variability of local brain activity in patients with AD or mild cognitive impairment (MCI). Pearson's correlation coefficients were calculated between the metrics and subjects' behavioral scores.

RESULTS

The results of analysis of variance indicated that the AD, MCI, and HC groups showed significant variability of dALFF in the cerebellar posterior and middle temporal lobes. In AD patients, these brain regions had high dALFF variability. Significant dfALFF variability was found between the three groups in the left calcarine cortex and white matter. The AD group showed lower dfALFF than the MCI group in the left calcarine cortex.

CONCLUSIONS

Compared to HC, AD patients were found to have increased dALFF variability in the cerebellar posterior and temporal lobes. This abnormal pattern may diminish the capacity of the cerebellum and temporal lobes to participate in the cerebrocerebellar circuits and default mode network (DMN), which regulate cognition and emotion in AD. The findings above indicate that the analysis of dALFF and dfALFF based on functional magnetic resonance imaging data may give a new insight into the neurophysiological mechanisms of AD.

Altered dynamic effective connectivity of the default mode network in newly diagnosed drug-naïve juvenile myoclonic epilepsy

Juvenile myoclonic epilepsy (JME) has been repeatedly revealed to be associated with brain dysconnectivity in the default mode network (DMN). However, the implicit assumption of stationary and nondirectional functional connectivity (FC) in most previous resting-state fMRI studies raises an open question of JME-related aberrations in dynamic causal properties of FC. Here, we introduces an empirical method incorporating sliding-window approach and a multivariate Granger causality analysis to investigate, for the first time, the reorganization of dynamic effective connectivity (DEC) in DMN for patients with JME. DEC was obtained from resting-state fMRI of 34 patients with newly diagnosed and drug-naïve JME and 34 matched controls. Through clustering analysis, we found two distinct states that characterize the DEC patterns (i.e., a less frequent, strongly connected state (State 1) and a more frequent, weakly connected state (State 2)). Patients showed altered ECs within DMN subnetworks in the State 2, whereas abnormal ECs between DMN subnetworks were found in the State 1. Furthermore, we observed that the causal influence flows of the medial prefrontal cortex and angular gyrus were altered in a manner of state specificity, and associated with disease severity of patients. Overall, our findings extend the dysconnectivity hypothesis in JME from static to dynamic causal FC and demonstrate that aberrant DEC may underlie abnormal brain function in JME at early phase of illness.