Association between aphasia severity and brain network alterations after stroke assessed using the electroencephalographic phase synchrony index

AbstractElectroencephalographic synchrony can help assess brain network status; however, its usefulness has not yet been fully proven. We developed a clinically feasible method that combines the phase synchrony index (PSI) with resting-state 19-channel electroencephalography (EEG) to evaluate post-stroke motor impairment. In this study, we investigated whether our method could be applied to aphasia, a common post-stroke cognitive impairment. This study included 31 patients with subacute aphasia and 24 healthy controls. We assessed the expressive function of patients and calculated the PSIs of three motor language-related regions: frontofrontal, left frontotemporal, and right frontotemporal. Then, we evaluated post-stroke network alterations by comparing PSIs of the patients and controls and by analyzing the correlations between PSIs and aphasia scores. The frontofrontal PSI (beta band) was lower in patients than in controls and positively correlated with aphasia scores, whereas the right frontotemporal PSI (delta band) was higher in patients than in controls and negatively correlated with aphasia scores. Evaluation of artifacts suggests that this association is attributed to true synchrony rather than spurious synchrony. These findings suggest that post-stroke aphasia is associated with alternations of two different networks and point to the usefulness of EEG PSI in understanding the pathophysiology of aphasia.

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Mindfulness Meditation Is Related to Long-Lasting Changes in Hippocampal Functional Topology during Resting State: A Magnetoencephalography Study

Neural Plasticity ◽

10.1155/2018/5340717 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 9

Author(s):

Anna Lardone ◽

Marianna Liparoti ◽

Pierpaolo Sorrentino ◽

Rosaria Rucco ◽

Francesca Jacini ◽

...

Keyword(s):

Resting State ◽

Mindfulness Meditation ◽

Brain Connectivity ◽

Brain Networks ◽

Brain Network ◽

Long Term Effects ◽

Age Related ◽

Functional Topology ◽

The Right ◽

The Brain

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown that the brain connectivity changes in meditators. In the present work, we aim to describe the possible long-term effects of meditation on the brain networks. To this aim, we used magnetoencephalography to study functional resting-state brain networks in Vipassana meditators. We observed topological modifications in the brain network in meditators compared to controls. More specifically, in the theta band, the meditators showed statistically significant (p corrected = 0.009) higher degree (a centrality index that represents the number of connections incident upon a given node) in the right hippocampus as compared to controls. Taking into account the role of the hippocampus in memory processes, and in the pathophysiology of Alzheimer’s disease, meditation might have a potential role in a panel of preventive strategies.

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Altered Functional Brain Network in Systemic Lupus Erythematosus Patients Without Overt Neuropsychiatric Symptoms Based on Resting-State Functional Magnetic Resonance Imaging and Multivariate Pattern Analysis

Frontiers in Neurology ◽

10.3389/fneur.2021.690979 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yiling Wang ◽

Muliang Jiang ◽

Lixuan Huang ◽

Xia Meng ◽

Shu Li ◽

...

Keyword(s):

Systemic Lupus Erythematosus ◽

Lupus Erythematosus ◽

Resting State ◽

Neuropsychiatric Symptoms ◽

Brain Network ◽

Multivariate Pattern Analysis ◽

Systemic Lupus ◽

Functional Brain Network ◽

The Right ◽

Multivariate Pattern

Objective: This study aims to investigate the alterations in functional brain network in systemic lupus erythematosus patients without overt neuropsychiatric symptoms [neuropsychiatric systemic lupus erythematosus (non-NPSLE)] from the perspective of degree centrality (DC) and functional connectivity (FC) using resting-state functional magnetic resonance imaging (MRI) and multivariate pattern analysis (MVPA) approach.Methods: DC analysis was performed based on the resting-state functional MRI data derived from 47 non-NPSLE patients and 47 healthy controls (HCs). Nodes with abnormal DC were utilized as seeds for further FC analysis. The correlation between MRI variables and clinical or neuropsychological data was analyzed using Pearson correlation analysis. Finally, MVPA classification based on DC was performed.Results: When compared with the HCs, the non-NPSLE patients exhibited remarkably higher DC in the bilateral hippocampus (HIP), right insula (INS), and lower DC in the left superior parietal gyrus. Furthermore, the patients displayed significantly higher FC between the left HIP and the left INS/left dorsolateral middle frontal gyrus/left supramarginal gyrus and higher FC between the right HIP and the right middle temporal gyrus/right dorsolateral middle frontal gyrus/right dorsolateral inferior frontal gyrus/right supramarginal gyrus (all imaging variables mentioned earlier underwent cluster-level false discovery rate corrections, the voxel threshold was p < 0.001, cluster threshold was p < 0.05). Correlation analysis revealed significantly negative correlations between DC values of the right INS and disease activity and the DC values of the right HIP and the Montreal Cognitive Assessment scores. The accuracy, sensitivity, and specificity of MVPA classification based on DC were 72.34, 63.83, and 80.85%, respectively. The most discriminative power brain regions were chiefly located within the temporal, parietal, and frontal regions.Conclusion: Patients with non-NPSLE exhibited abnormal DC and FC in the brain network. MVPA based on DC possessed commendable classification ability. Our study may provide a novel perspective on the neuropathological mechanisms underlying subclinical brain damage in non-NPSLE.

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Modulation of the Visual to Auditory Human Inhibitory Brain Network: An EEG Dipole Source Localization Study

Brain Sciences ◽

10.3390/brainsci9090216 ◽

2019 ◽

Vol 9 (9) ◽

pp. 216 ◽

Cited By ~ 4

Author(s):

Rupesh Kumar Chikara ◽

Li-Wei Ko

Keyword(s):

Human Brain ◽

Inhibitory Control ◽

Source Localization ◽

Visual Stimulation ◽

Superior Temporal Gyrus ◽

Brain Network ◽

Dipole Source ◽

Beta Band ◽

Dipole Source Localization ◽

The Right

Auditory alarms are used to direct people’s attention to critical events in complicated environments. The capacity for identifying the auditory alarms in order to take the right action in our daily life is critical. In this work, we investigate how auditory alarms affect the neural networks of human inhibition. We used a famous stop-signal or go/no-go task to measure the effect of visual stimuli and auditory alarms on the human brain. In this experiment, go-trials used visual stimulation, via a square or circle symbol, and stop trials used auditory stimulation, via an auditory alarm. Electroencephalography (EEG) signals from twelve subjects were acquired and analyzed using an advanced EEG dipole source localization method via independent component analysis (ICA) and EEG-coherence analysis. Behaviorally, the visual stimulus elicited a significantly higher accuracy rate (96.35%) than the auditory stimulus (57.07%) during inhibitory control. EEG theta and beta band power increases in the right middle frontal gyrus (rMFG) were associated with human inhibitory control. In addition, delta, theta, alpha, and beta band increases in the right cingulate gyrus (rCG) and delta band increases in both right superior temporal gyrus (rSTG) and left superior temporal gyrus (lSTG) were associated with the network changes induced by auditory alarms. We further observed that theta-alpha and beta bands between lSTG-rMFG and lSTG-rSTG pathways had higher connectivity magnitudes in the brain network when performing the visual tasks changed to receiving the auditory alarms. These findings could be useful for further understanding the human brain in realistic environments.

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Degree Centrality of a Brain Network Is Altered by Stereotype Threat: Evidences From a Resting-State Functional Magnetic Resonance Imaging Study

Frontiers in Psychology ◽

10.3389/fpsyg.2021.705363 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xin Wu ◽

Yufang Zhao

Keyword(s):

Stereotype Threat ◽

Resting State ◽

Brain Regions ◽

Brain Network ◽

Angular Gyrus ◽

Control Group ◽

Degree Centrality ◽

Negative Stereotypes ◽

Manipulation Check ◽

The Right

Previous studies have found the effects of stereotype threat (ST) on cognitive processes, emotions, and motivations which could account for the underperformance in domain tasks. Efficient brain function does not require the function of different brain regions during specific tasks, but it does require the brain networks on which information is transported. Based on these, the effects of ST on the degree centrality under the resting state of brain regions related to these processes were investigated under math-related ST. The results showed that RSDC was decreased in the left hippocampus and left middle occipital gyrus (MOC), while RSDC was increased in the left precuneus, the right angular gyrus (AG), and the right superior parietal gyrus (SPG) under ST. Interestingly, we also found that the right-left anterior temporal lobe (ATL) and the right hippocampus were negatively correlated with manipulation check (MC) score in the ST group, while the right-left ATL and the right hippocampus were positively correlated with MC score in the control group. These results might reflect those individuals who attempted to inhibit the negative emotions induced by the negative stereotypes under ST conditions while increasing the self-relevant processes by retrieving episodic memory or autobiographical memory.

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Test-retest reliability of functional brain network characteristics using resting-state EEG and graph theory

10.1101/385302 ◽

2018 ◽

Cited By ~ 2

Author(s):

Bahar Moezzi ◽

Brenton Hordacre ◽

Carolyn Berryman ◽

Michael C. Ridding ◽

Mitchell R. Goldsworthy

Keyword(s):

Graph Theory ◽

Resting State ◽

Brain Network ◽

Beta Band ◽

Graph Metrics ◽

Eeg Data ◽

Source Level ◽

Test Retest Reliability ◽

High Beta ◽

Healthy Participants

AbstractMetrics of brain network organization can be derived from neuroimaging data using graph theory. We explored the test-retest reliability of graph metrics of functional networks derived from resting-state electroencephalogram (EEG) recordings. Data were collected within two designs: (1) within sessions (WS) design where EEG data were collected from 18 healthy participants in four trials within a few hours and (2) between sessions (BS) design where EEG data were collected from 19 healthy participants in three trials on three different days at least one week apart. Electrophysiological source activity was reconstructed and functional connectivity between pairs of sensors or brain regions was determined in different frequency bands. We generated undirected binary graphs and used intra-class correlation coefficient (ICC) to estimate reliability. We showed that reliabilities ranged from poor to good. Reliability at the sensor-level was significantly higher than source-level. The most reliable graph metric at the sensor-level was cost efficiency and at the source-level was global efficiency. At the sensor-level: WS reliability was significantly higher than BS reliability; high beta band in WS design had the highest reliability; in WS design reliability in gamma band was significantly lower than reliability in low and high beta bands. At the source-level: low beta band in BS design had the highest reliability; there was no significant main effect of frequency band on reliability; reliabilities of WS and BS designs were not significantly different. These results suggest that these graph metrics can provide reliable outcomes, depending on how the data were collected and analysed.

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Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.715517 ◽

2021 ◽

Vol 13 ◽

Author(s):

Zhaoshun Jiang ◽

Yuxi Cai ◽

Xixue Zhang ◽

Yating Lv ◽

Mengting Zhang ◽

...

Keyword(s):

Machine Learning ◽

Clinical Trial ◽

Cardiac Surgery ◽

Prefrontal Cortex ◽

Random Forest ◽

Resting State ◽

Brain Network ◽

Support Vector ◽

Whole Brain ◽

The Right

Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain network features. A total of 74 elderly patients (≥ 60-years-old) undergoing non-cardiac surgery were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) before the surgery. Seed-based whole-brain functional connectivity (FC) was analyzed with 18 regions of interest (ROIs) located in the default mode network (DMN), limbic network, salience network (SN), and central executive network (CEN). Multiple machine learning models (support vector machine, decision tree, and random forest) were constructed to recognize the DNR based on FC network features. The experiment has three parts, including performance comparison, feature screening, and parameter adjustment. Then, the model with the best predictive efficacy for DNR was identified. Finally, independent testing was conducted to validate the established predictive model. Compared to the non-DNR group, the DNR group exhibited aberrant whole-brain FC in seven ROIs, including the right posterior cingulate cortex, right medial prefrontal cortex, and left lateral parietal cortex in the DMN, the right insula in the SN, the left anterior prefrontal cortex in the CEN, and the left ventral hippocampus and left amygdala in the limbic network. The machine learning experimental results identified a random forest model combined with FC features of DMN and CEN as the best prediction model. The area under the curve was 0.958 (accuracy = 0.935, precision = 0.899, recall = 0.900, F1 = 0.890) on the test set. Thus, the current study indicated that the random forest machine learning model based on rs-FC features of DMN and CEN predicts the DNR following non-cardiac surgery, which could be beneficial to the early prevention of DNR.Clinical Trial Registration: The study was registered at the Chinese Clinical Trial Registry (Identification number: ChiCTR-DCD-15006096).

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Brain networks and cognitive impairment in Parkinson′s disease

10.1101/2020.12.14.422706 ◽

2020 ◽

Author(s):

Rosaria Rucco ◽

Anna Lardone ◽

marianna Liparoti ◽

Emahnuel Troisi Lopez ◽

Rosa De Micco ◽

...

Keyword(s):

Cognitive Impairment ◽

Functional Connectivity ◽

Large Scale ◽

Brain Activity ◽

Brain Networks ◽

Motor Impairment ◽

Brain Network ◽

Topological Features ◽

Delta Band ◽

Wide Range

Aim The aim of the present study is to investigate the relations between both functional connectivity and brain networks with cognitive decline, in patients with Parkinson′s disease (PD). Introduction PD phenotype is not limited to motor impairment but, rather, a wide range of non-motor disturbances can occur, cognitive impairment being one of the commonest. However, how the large-scale organization of brain activity differs in cognitively impaired patients, as opposed to cognitively preserved ones, remains poorly understood. Methods Starting from source-reconstructed resting-state magnetoencephalography data, we applied the PLM to estimate functional connectivity, globally and between brain areas, in PD patients with and without cognitive impairment (respectively PD-CI and PD-NC), as compared to healthy subjects (HS). Furthermore, using graph analysis, we characterized the alterations in brain network topology and related these, as well as the functional connectivity, to cognitive performance. Results We found reduced global and nodal PLM in several temporal (fusiform gyrus, Heschl′s gyrus and inferior temporal gyrus), parietal (postcentral gyrus), and occipital (lingual gyrus) areas within the left hemisphere, in the gamma band, in PD-CI patients, as compared to PD-NC and HS. With regard to the global topological features, PD-CI patients, as compared to HS and PD-NC patients, showed differences in multi frequencies bands (delta, alpha, gamma) in the Leaf fraction, Tree hierarchy (both higher in PD-CI) and Diameter (lower in PD-CI). Finally, we found statistically significant correlations between the MoCA test and both the Diameter in delta band and the Tree Hierarchy in the alpha band. Conclusion Our work points to specific large-scale rearrangements that occur selectively in cognitively compromised PD patients and correlated to cognitive impairment.

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Disrupted Functional Connectivity Within and Between Resting-State Networks in the Subacute Stage of Post-stroke Aphasia

Frontiers in Neuroscience ◽

10.3389/fnins.2021.746264 ◽

2021 ◽

Vol 15 ◽

Author(s):

Chao Zhang ◽

Yingying Xia ◽

Tao Feng ◽

Ke Yu ◽

Haiyan Zhang ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Network ◽

Lesion Volume ◽

Focal Lesions ◽

Verbal Comprehension ◽

Post Stroke ◽

Subacute Stage ◽

Frontoparietal Network ◽

The Relationship

Background: Post-stroke aphasia (PSA) results from brain network disorders caused by focal stroke lesions. However, it still remains largely unclear whether the impairment is present in intra- and internetwork functional connectivity (FC) within each resting-state network (RSN) and between RSNs in the subacute stage of PSA.Objectives: This study aimed to investigate the resting-state FC within and between RSNs in patients with PSA and observe the relationships between FC alterations and Western Aphasia Battery (WAB) measures.Methods: A total of 20 individuals with subacute PSA and 20 healthy controls (HCs) were recruited for functional MRI (fMRI) scanning, and only patients with PSA underwent WAB assessment. Independent component analysis was carried out to identify RSNs. Two-sample t-tests were used to calculate intra- and internetwork FC differences between patients with PSA and HCs. The results were corrected for multiple comparisons using the false discovery rate (FDR correction, p < 0.05). Partial correlation analysis was performed to observe the relationship between FC and WAB scores with age, gender, mean framewise displacement, and lesion volume as covariates (p < 0.05).Results: Compared to HCs, patients with PSA showed a significant increase in intranetwork FC in the salience network (SN). For internetwork FC analysis, patients showed a significantly increased coupling between left frontoparietal network (lFPN) and SN and decreased coupling between lFPN and right frontoparietal network (rFPN) as well as between lFPN and posterior default mode network (pDMN) (FDR correction, p < 0.05). Finally, a significant positive correlation was found between the intergroup difference of FC (lFPN-rFPN) and auditory-verbal comprehension (p < 0.05).Conclusion: Altered FC was revealed within and between multiple RSNs in patients with PSA at the subacute stage. Reduced FC between lFPN and rFPN was the key element participating in language destruction. These findings proved that PSA is a brain network disorder caused by focal lesions; besides, it may improve our understanding of the pathophysiological mechanisms of patients with PSA at the subacute stage.

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Alteration of Cortical Functional Networks in Default-Mode Network-Related Regions in Mood Disorders with Resting-State Electroencephalography

10.21203/rs.3.rs-119899/v1 ◽

2020 ◽

Author(s):

Sungkean Kim ◽

Ji Hyun Baek ◽

Se-hoon Shim ◽

Young Joon Kwon ◽

Hwa Young Lee ◽

...

Keyword(s):

Default Mode Network ◽

Resting State ◽

Functional Networks ◽

Global Level ◽

Beta Band ◽

Default Mode ◽

Source Level ◽

The Right ◽

High Beta ◽

Nodal Level

Abstract Studies comparing bipolar disorder (BD) and major depressive disorder (MDD) are scarce, and the neuropathology of these disorders is poorly understood. This study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in patients with BD and MDD. EEG was recorded in 35 patients with BD, 39 patients with MDD, and 42 healthy controls (HCs). Graph theory-based source-level weighted functional networks were assessed via strength, clustering coefficient (CC), and path length (PL) in six frequency bands. At the global level, patients with BD and MDD showed higher strength and CC, and lower PL in the high beta band, compared to HCs. At the nodal level, compared to HCs, patients with BD showed higher high beta band nodal CCs in the right precuneus, left isthmus cingulate, bilateral paracentral, and left superior frontal, belonging to the default-mode network (DMN); however, patients with MDD showed higher nodal CC only in the right precuneus compared to HCs. Although both MDD and BD patients had similar global level network changes, they had different nodal level network changes in DMN-related regions. Our findings might suggest more altered network in the DMN-related regions in patients with BD than in those with MDD.

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