Widespread plasticity of cognition-related brain networks in single-sided deafness revealed by randomized window-based dynamic functional connectivity

Multilayer network instability underlying persistent auditory verbal hallucinations in schizophrenia

BACKGROUND

Auditory verbal hallucinations (AVHs) in schizophrenia (SCZ) are linked to brain network abnormalities. Resting-state fMRI studies often assume stable networks during scans, yet dynamic changes related to AVHs are not well understood.

METHODS

We analyzed resting-state fMRI data from 60 SCZ patients with persistent AVHs (p-AVHs), 39 SCZ patients without AVHs (n-AVHs), and 59 healthy controls (HCs), matched for demographics. Using graph theory, we constructed a time-varying modular structure of brain networks, focusing on multilayer modularity. Network switching rates at global, subnetwork, and nodal levels were compared across groups and related to AVH severity.

RESULTS

SCZ groups had higher switching rates in the subcortical network compared to HCs. Increased switching was found in two thalamic nodes for both patient groups. The p-AVH group showed lower switching rates in the default mode network (DMN) and two superior frontal gyrus nodes compared to HC and n-AVH groups. DMN switching rates negatively correlated with AVH severity in the p-AVH group.

CONCLUSIONS

Dynamic changes in brain networks, especially lower DMN and frontal region switching rates, may contribute to the development and persistence of AVHs in SCZ.

The relationship between changes in functional connectivity gradients and cognitive-emotional disorders in sudden sensorineural hearing loss

Sudden sensorineural hearing loss, a prevalent emergency in otolaryngology, is known to potentially precipitate cognitive and emotional disorders in affected individuals. Extensive research has documented the phenomenon of cortical functional reorganization in patients with sudden sensorineural hearing loss. However, the potential link between this neural functional remodelling and cognitive-emotional disorders remains unclear. To investigate this issue, 30 bilateral sudden sensorineural hearing loss patients and 30 healthy adults were recruited for this study. We collected clinical data and resting-state functional magnetic resonance imaging data from the participants. Gradient mapping analysis was employed to calculate the first three gradients for each subject. Subsequently, gradient changes in sudden sensorineural hearing loss patients were compared with healthy controls at global, regional and network levels. Finally, we explored the relationship between gradient values and clinical variables. The results revealed that at the global level, sudden sensorineural hearing loss did not exhibit significant differences in the primary gradient but showed a state of compression in the second and third gradients. At the regional level, sudden sensorineural hearing loss patients exhibited a significant reduction in the primary gradient values in the temporal pole and ventral prefrontal cortex, which were closely related to neuro-scale scores. Regarding the network level, sudden sensorineural hearing loss did not show significant differences in the primary gradient but instead displayed significant changes in the control network and default mode network in the second and third gradients. This study revealed disruptions in the functional hierarchy of sudden sensorineural hearing loss, and the alterations in functional connectivity gradients were closely associated with cognitive and emotional disturbances in patients. These findings provide new evidence for understanding the functional remodelling that occurs in sudden sensorineural hearing loss.

Dynamic functional network connectivity in children with profound bilateral congenital sensorineural hearing loss

BACKGROUND

Functional magnetic resonance imaging (fMRI) studies have revealed extensive functional reorganization in patients with sensorineural hearing loss (SNHL). However, almost no study focuses on the dynamic functional connectivity after hearing loss.

OBJECTIVE

This study aimed to investigate dynamic functional connectivity changes in children with profound bilateral congenital SNHL under the age of 3 years.

MATERIALS AND METHODS

Thirty-two children with profound bilateral congenital SNHL and 24 children with normal hearing were recruited for the present study. Independent component analysis identified 18 independent components composing five resting-state networks. A sliding window approach was used to acquire dynamic functional matrices. Three states were identified using the k-means algorithm. Then, the differences in temporal properties and the variance of network efficiency between groups were compared.

RESULTS

The children with SNHL showed longer mean dwell time and decreased functional connectivity between the auditory network and sensorimotor network in state 3 (P < 0.05), which was characterized by relatively stronger functional connectivity between high-order resting-state networks and motion and perception networks. There was no difference in the variance of network efficiency.

CONCLUSIONS

These results indicated the functional reorganization due to hearing loss. This study also provided new perspectives for understanding the state-dependent connectivity patterns in children with SNHL.

A survey of brain functional network extraction methods using fMRI data

Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.

Exploring functional connectivity alterations in sudden sensorineural hearing loss: A multilevel analysis

Sudden sensorineural hearing loss (SSNHL) constitutes an urgent otologic emergency, marked by a rapid decline of at least 30 dB across three consecutive frequencies within 72 h. While previous studies have noted brain region alterations encompassing both auditory and non-auditory areas, this research examines functional connectivity changes across integrity, network, and edge levels in SSNHL. The cohort included 184 participants: 107 SSNHL patients and 77 age- and sex-matched healthy controls. Our investigation comprises: (1) characterization of overall functional connectivity degree across 55 nodes in nine networks (p < 0.05, corrected for false discovery rate), exposing integrity level changes; (2) identification of reduced intranetwork connectivity strength within sensory and attention networks (somatomotor network, auditory network, ventral attention network, dorsal attention network) in SSNHL individuals (p < 0.05, Bonferroni corrected), and reduced internetwork connectivity across twelve distinct subnetwork pairs (p < 0.05, FDR corrected); (3) revelation of increased internetwork connectivity in SSNHL patients, primarily spanning dorsal attention network, fronto parietal network, default mode network, and limbic network, alongside widespread reductions in connectivity patterns among the nine distinct resting-state brain networks. The study further uncovers negative correlations between SSNHL duration and intranetwork connectivity of the auditory network (p < 0.001, R = -0.474), and between Tinnitus Handicap Inventory (THI) scores and internetwork connections linking auditory network and dorsal attention network (p < 0.001, R = -0.331). These observed alterations provide crucial insights into the neural mechanisms underpinning SSNHL and extend our comprehension of the brain's network-level responses to sensory loss. By unveiling the intricate interplay between sensory deprivation, adaptation, and cognitive processes, this study lays the groundwork for future research targeting enhanced diagnosis, treatment, and rehabilitation approaches for individuals afflicted by SSNHL.

Multilayer analysis of dynamic network reconfiguration in pediatric posttraumatic stress disorder

Neuroimage studies have reported functional connectome abnormalities in posttraumatic stress disorder (PTSD), especially in adults. However, these studies often treated the brain as a static network, and time-variance of connectome topology in pediatric posttraumatic stress disorder remain unclear. To explore case-control differences in dynamic connectome topology, resting-state functional magnetic resonance imaging data were acquired from 24 treatment-naïve non-comorbid pediatric posttraumatic stress disorder patients and 24 demographically matched trauma-exposed non-posttraumatic stress disorder controls. A graph-theoretic analysis was applied to construct time-varying modular structure of whole-brain networks by maximizing the multilayer modularity. Network switching rate at the global, subnetwork, and nodal levels were calculated and compared between posttraumatic stress disorder and trauma-exposed non-posttraumatic stress disorder groups, and their associations with posttraumatic stress disorder symptom severity and sex interactions were explored. At the global level, individuals with posttraumatic stress disorder exhibited significantly lower network switching rates compared to trauma-exposed non-posttraumatic stress disorder controls. This difference was mainly involved in default-mode and dorsal attention subnetworks, as well as in inferior temporal and parietal brain nodes. Posttraumatic stress disorder symptom severity was negatively correlated with switching rate in the global network and default mode network. No significant differences were observed in the interaction between diagnosis and sex/age. Pediatric posttraumatic stress disorder is associated with dynamic reconfiguration of brain networks, which may provide insights into the biological basis of this disorder.

Synergistic integration of Multi-View Brain Networks and advanced machine learning techniques for auditory disorders diagnostics

In the field of audiology, achieving accurate discrimination of auditory impairments remains a formidable challenge. Conditions such as deafness and tinnitus exert a substantial impact on patients' overall quality of life, emphasizing the urgent need for precise and efficient classification methods. This study introduces an innovative approach, utilizing Multi-View Brain Network data acquired from three distinct cohorts: 51 deaf patients, 54 with tinnitus, and 42 normal controls. Electroencephalogram (EEG) recording data were meticulously collected, focusing on 70 electrodes attached to an end-to-end key with 10 regions of interest (ROI). This data is synergistically integrated with machine learning algorithms. To tackle the inherently high-dimensional nature of brain connectivity data, principal component analysis (PCA) is employed for feature reduction, enhancing interpretability. The proposed approach undergoes evaluation using ensemble learning techniques, including Random Forest, Extra Trees, Gradient Boosting, and CatBoost. The performance of the proposed models is scrutinized across a comprehensive set of metrics, encompassing cross-validation accuracy (CVA), precision, recall, F1-score, Kappa, and Matthews correlation coefficient (MCC). The proposed models demonstrate statistical significance and effectively diagnose auditory disorders, contributing to early detection and personalized treatment, thereby enhancing patient outcomes and quality of life. Notably, they exhibit reliability and robustness, characterized by high Kappa and MCC values. This research represents a significant advancement in the intersection of audiology, neuroimaging, and machine learning, with transformative implications for clinical practice and care.

Multilayer network analysis of dynamic network reconfiguration in patients with moderate-to-severe obstructive sleep apnea and its association with neurocognitive function

BACKGROUND

Brain functional network disruption and neurocognitive dysfunction have been reported in obstructive sleep apnea (OSA) patients. Nevertheless, most research studies static networks, while brain evolution continues dynamically.

PURPOSE

To investigate the characteristics of dynamical networks in moderate-to-severe OSA patients using multilayer network analysis of dynamic networks and compare their association with neurocognitive function.

METHODS

Twenty-seven moderate-to-severe OSA patients and twenty-five matched healthy controls (HCs) who completed the examination of the Epworth sleepiness scale (ESS), neurocognitive function, polysomnography, and functional magnetic resonance imaging (fMRI) were prospectively included. The dynamic variations of resting-state functional networks in both groups were described via network switching rate. Switching rates and their correlation with clinical parameters were analyzed.

RESULTS

At the global level, network switching rates were notably lower in the OSA group than in the HCs group (p = 0.002). More specifically, the differences include the default mode network (DMN), auditory network, and ventral attention network at the subnetwork level, and the right rolandic operculum, left middle temporal gyrus, and right precentral gyrus at the nodal level. Furthermore, these altered switching rates have a close correlation with ESS, sleep parameters, and neurocognitive function.

CONCLUSION

Patients with moderate-to-severe OSA showed lower network switching rates, especially in the DMN, auditory network, and ventral attention network. The disruption of dynamic functional networks may be a potentially crucial mechanism of neurocognitive dysfunction in moderate-to-severe OSA patients.

Functional to structural plasticity in unilateral sudden sensorineural hearing loss: neuroimaging evidence

A cortical plasticity after long-duration single side deafness (SSD) is advocated with neuroimaging evidence while little is known about the short-duration SSDs. In this case-cohort study, we recruited unilateral sudden sensorineural hearing loss (SSNHL) patients and age-, gender-matched health controls (HC), followed by comprehensive neuroimaging analyses. The primary outcome measures were temporal alterations of varied dynamic functional network connectivity (dFNC) states, neurovascular coupling (NVC) and brain region volume at different stages of SSNHL. The secondary outcome measures were pure-tone audiograms of SSNHL patients before and after treatment. A total of 38 SSNHL patients (21 [55%] male; mean [standard deviation] age, 45.05 [15.83] years) and 44 HC (28 [64%] male; mean [standard deviation] age, 43.55 [12.80] years) were enrolled. SSNHL patients were categorized into subgroups based on the time from disease onset to the initial magnetic resonance imaging scan: early- (n = 16; 1-6 days), intermediate- (n = 9; 7-13 days), and late- stage (n = 13; 14-30 days) groups. We first identified slow state transitions between varied dFNC states at early-stage SSNHL, then revealed the decreased NVC restricted to the auditory cortex at the intermediate- and late-stage SSNHL. Finally, a significantly decreased volume of the left medial superior frontal gyrus (SFGmed) was observed only in the late-stage SSNHL cohort. Furthermore, the volume of the left SFGmed is robustly correlated with both disease duration and patient prognosis. Our study offered neuroimaging evidence for the evolvement from functional to structural brain alterations of SSNHL patients with disease duration less than 1 month, which may explain, from a neuroimaging perspective, why early-stage SSNHL patients have better therapeutic responses and hearing recovery.

A comprehensive survey of complex brain network representation

Recent years have shown great merits in utilizing neuroimaging data to understand brain structural and functional changes, as well as its relationship to different neurodegenerative diseases and other clinical phenotypes. Brain networks, derived from different neuroimaging modalities, have attracted increasing attention due to their potential to gain system-level insights to characterize brain dynamics and abnormalities in neurological conditions. Traditional methods aim to pre-define multiple topological features of brain networks and relate these features to different clinical measures or demographical variables. With the enormous successes in deep learning techniques, graph learning methods have played significant roles in brain network analysis. In this survey, we first provide a brief overview of neuroimaging-derived brain networks. Then, we focus on presenting a comprehensive overview of both traditional methods and state-of-the-art deep-learning methods for brain network mining. Major models, and objectives of these methods are reviewed within this paper. Finally, we discuss several promising research directions in this field.

Abnormal multi-layered dynamic cortico-subcortical functional connectivity in major depressive disorder and generalized anxiety disorder

Comorbidity has been frequently observed between generalized anxiety disorder (GAD) and major depressive disorder (MDD), however, common and distinguishable alterations in the topological organization of functional brain networks remain poorly understood. We sought to determine a robust and sensitive functional connectivity marker for diagnostic classification and symptom severity prediction. Multi-layered dynamic functional connectivity including whole brain, network-node and node-node layers via graph theory and gradient analyses were applied to functional MRI resting-state data obtained from 31 unmedicated GAD and 34 unmedicated MDD patients as well as 33 age and education matched healthy controls (HC). GAD and MDD symptoms were assessed using Penn State Worry Questionnaire and Beck Depression Inventory II, respectively. Three network measures including global properties (i.e., global efficiency, characteristic path length), regional nodal property (i.e., degree) and connectivity gradients were computed. Results showed that both patient groups exhibited abnormal dynamic cortico-subcortical topological organization compared to healthy controls, with MDD > GAD > HC in degree of randomization. Furthermore, our multi-layered dynamic functional connectivity network model reached 77% diagnostic accuracy between GAD and MDD and was highly predictive of symptom severity, respectively. Gradients of functional connectivity for superior frontal cortex-subcortical regions, middle temporal gyrus-subcortical regions and amygdala-cortical regions contributed more in this model compared to other gradients. We found shared and distinct cortico-subcortical connectivity features in dynamic functional brain networks between GAD and MDD, which together can promote the understanding of common and disorder-specific topological organization dysregulations and facilitate early neuroimaging-based diagnosis.

Spatial-temporal data-augmentation-based functional brain network analysis for brain disorders identification

Introduction

Due to the lack of devices and the difficulty of gathering patients, the small sample size is one of the most challenging problems in functional brain network (FBN) analysis. Previous studies have attempted to solve this problem of sample limitation through data augmentation methods, such as sample transformation and noise addition. However, these methods ignore the unique spatial-temporal information of functional magnetic resonance imaging (fMRI) data, which is essential for FBN analysis.

Methods

To address this issue, we propose a spatial-temporal data-augmentation-based classification (STDAC) scheme that can fuse the spatial-temporal information, increase the samples, while improving the classification performance. Firstly, we propose a spatial augmentation module utilizing the spatial prior knowledge, which was ignored by previous augmentation methods. Secondly, we design a temporal augmentation module by random discontinuous sampling period, which can generate more samples than former approaches. Finally, a tensor fusion method is used to combine the features from the above two modules, which can make efficient use of spatial-temporal information of fMRI simultaneously. Besides, we apply our scheme to different types of classifiers to verify the generalization performance. To evaluate the effectiveness of our proposed scheme, we conduct extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and REST-meta-MDD Project (MDD) dataset.

Results

Experimental results show that the proposed scheme achieves superior classification accuracy (ADNI: 82.942%, MDD: 63.406%) and feature interpretation on the benchmark datasets.

Discussion

The proposed STDAC scheme, utilizing both spatial and temporal information, can generate more diverse samples than former augmentation methods for brain disorder classification and analysis.

Multilayer Network Analysis of Dynamic Network Reconfiguration in Adults With Posttraumatic Stress Disorder

BACKGROUND

Brain functional network abnormalities are reported in posttraumatic stress disorder (PTSD). Most resting-state functional magnetic resonance imaging studies have assumed that the functional networks remain static during the scans. How these might change dynamically in PTSD remains unclear.

METHODS

Resting-state functional magnetic resonance imaging data were collected from 71 noncomorbid, treatment-naïve patients with PTSD and 70 demographically matched, trauma-exposed non-PTSD control subjects. Network switching rate was used to characterize dynamic changes of individual resting-state functional networks. Results were analyzed by comparing switching rates between the PTSD and trauma-exposed non-PTSD groups, testing for diagnosis × sex interactions, and examining correlations with PTSD symptom severity.

RESULTS

At the global level, the PTSD group showed significantly lower network switching rates than the trauma-exposed non-PTSD group. These were observed mainly in the frontoparietal, default mode, and limbic networks at the subnetwork level and in the frontal and temporal regions at the nodal level. These network switching rate alterations were correlated with PTSD symptom severity. There were no significant effects of sex.

CONCLUSIONS

These disruptions of dynamic functional network stability, reflected by lower network switching rates in the resting state, are a feature of PTSD and suggest that the frontoparietal, default mode, and limbic networks may play a critical role in the underlying neural mechanisms.

Intrinsic brain activity reorganization contributes to long-term compensation of higher-order hearing abilities in single-sided deafness

Single-sided deafness (SSD) is an extreme case of partial hearing deprivation and results in a significant decline in higher-order hearing abilities, including sound localization and speech-in-noise recognition. Clinical studies have reported that patients with SSD recover from these higher-order hearing abilities to some extent over time. Neuroimaging studies have observed extensive brain functional plasticity in patients with SSD. However, studies investigating the role of plasticity in functional compensation, particularly those investigating the relationship between intrinsic brain activity alterations and higher-order hearing abilities, are still limited. In this study, we used resting-state functional MRI to investigate intrinsic brain activity, measured by the amplitude of low-frequency fluctuation (ALFF), in 19 patients with left SSD, 17 patients with right SSD, and 21 normal hearing controls (NHs). All patients with SSD had durations of deafness longer than 2 years. Decreased ALFF values in the bilateral precuneus (PCUN), lingual gyrus, and left middle frontal gyrus were observed in patients with SSD compared with the values of NHs. Longer durations of deafness were correlated with better hearing abilities, as well as higher ALFF values in the left inferior parietal lobule, the angular gyrus, the middle occipital gyrus, the bilateral PCUN, and the posterior cingulate gyrus. Moreover, we observed a generally consistent trend of correlation between ALFF values and higher-order hearing abilities in specific brain areas in patients with SSD. That is, better abilities were correlated with lower ALFF values in the frontal regions and higher ALFF values in the PCUN and surrounding parietal-occipital areas. Furthermore, mediation analysis revealed that the ALFF values in the PCUN were a significant mediator of the relationship between the duration of deafness and higher-order hearing abilities. Our study reveals significant plasticity of intrinsic brain activity in patients with SSD and suggests that reorganization of intrinsic brain activity may be one of the compensatory mechanisms that facilitate improvement in higher-order hearing abilities in these patients over time.

The effect of perceived stress on cognition is mediated by personality and the underlying neural mechanism

Perceived stress impairs cognitive function across the adult lifespan, but the extent to which cognition decline is variable across individuals. Individual differences in the stress response are described as personality traits. Substantial individual differences in the magnitude of cognitive impairment that is induced by short-term perceived stress are poorly understood. The present study tested the hypothesis that the relationship between short-term perceived stress and different aspects of cognition is mediated by personality traits. The study included 1066 participants with behavior and neuroimaging data from the Human Connectome Project after excluding individuals with missing variables. In the result, the parallel multiple mediation model demonstrated that the influence of perceived stress on the total and crystalized cognition is mainly mediated by neuroticism (indirect effect = -0.04, p < 0.05) and conscientiousness (indirect effect = 0.05, p < 0.05) in adults. Cortical thickness value (n = 1066) of the right superior frontal gyrus (SFG) showed not only positive correlations with short-term perceived stress and neuroticism, but negative associations with cognition. The chain mediation model found that the right SFG and neuroticism play a small but significant chain mediating effect between stress and total cognition. The strength of the resting-state functional connectivity (n = 968) between the left orbitofrontal cortex versus the left superior medial frontal cortex was positively correlated with crystallized cognition and negatively associated with conscientiousness. These results extend previous findings by the impacts of short-term perceived stress on cognitive function is mediated by neuroticism and the right SFG was the underlying neural mechanism.

Dynamic Alterations in Functional Connectivity Density in Amyotrophic Lateral Sclerosis: A Resting-State Functional Magnetic Resonance Imaging Study

Background and Aims

Current knowledge on the temporal dynamics of the brain functional organization in amyotrophic lateral sclerosis (ALS) is limited. This is the first study on alterations in the patterns of dynamic functional connection density (dFCD) involving ALS.

Methods

We obtained resting-state functional magnetic resonance imaging (fMRI) data from 50 individuals diagnosed with ALS and 55 healthy controls (HCs). We calculated the functional connectivity (FC) between a given voxel and all other voxels within the entire brain and yield the functional connection density (FCD) value per voxel. dFCD was assessed by sliding window correlation method. In addition, the standard deviation (SD) of dFCD across the windows was computed voxel-wisely to measure dFCD variability. The difference in dFCD variability between the two groups was compared using a two-sample t-test following a voxel-wise manner. The receiver operating characteristic (ROC) curve was used to assess the between-group recognition performance of the dFCD variability index.

Results

The dFCD variability was significantly reduced in the bilateral precentral and postcentral gyrus compared with the HC group, whereas a marked increase was observed in the left middle frontal gyrus of ALS patients. dFCD variability exhibited moderate potential (areas under ROC curve = 0.753-0.837, all P < 0.001) in distinguishing two groups.

Conclusion

ALS patients exhibit aberrant dynamic property in brain functional architecture. The dFCD evaluation improves our understanding of the pathological mechanisms underlying ALS and may assist in its diagnosis.

Intersubject correlation analysis reveals the plasticity of cerebral functional connectivity in the long‐term use of social media

Owing to the limitations of cross‐sectional studies, it is unclear whether social media induce brain changes, or if individuals with certain biological traits are more likely to use social media. Functional connectivity (FC) can reflect cerebral functional plasticity, and if social media can influence cerebral FC, then the FC of light social media users should be more similar to that of heavy users after they “heavily” used social media for a long period. We combined longitudinal study design and intersubject correlation (ISC) analysis to investigate this similarity. Thirty‐five heavy and 21 light social media users underwent cognitive tests and functional MRIs. The 21 light social media users underwent another functional MRI scan after completing an additional four‐week social media task. We conducted the ISC at the group, individual, and brain‐region levels to investigate the similarity of FC and locate the brain regions most affected by social media. The FC of light social media users was more similar to that of heavy social media users after they completed the four‐week social media task. Then, social media had an impact on half of the brain, involving almost all brain networks. Finally, cerebral FC that mostly affected by social media was associated with selective attention. We concluded that the impact of social media use on cerebral functional connectivity changes is revealed by ISC method and longitudinal design, which may provide guidance for clinical practice. The methods used in the current research could also be applied to similar domains.