Segregation Of Functional Brain Networks Research Articles

Developing Topics.

Alzheimer's disease is characterized by cortical hyperexcitability in the early stages, which may be attributed to the intricate interplay between tau and amyloid β pathologies. To combat the propagation of tau pathology and cognitive decline, it may be beneficial to maintain a high degree of functional brain network segregation. Additionally, the use of non-invasive brain stimulation techniques to alter cortical excitability could help alleviate cognitive deficits. However, it is unclear how these interventions are related to tau or amyloid biomarkers. In this study, 530 participants aged 40 to 65 from the Barcelona Brain Health Initiative cohort were analyzed to investigate the connection between plasma concentration of tau phosphorylated at amino acid 181 (pTau181), cortical excitability, and segregation of functional brain networks. Participants had functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and pTau181 available. A subset of 47 participants also underwent transcranial magnetic stimulation with concurrent EEG (TMS-EEG). Cortical excitability was assessed using the one-over-f slope of the EEG power spectrum (1/f-slope) and evoked response to a TMS perturbation (TEP) of the left prefrontal cortex (L-PFC) between 160-240ms post single-pulse TMS. The degree of segregation between major functional networks was measured from fMRI data using the system segregation statistic. A generalized linear model with a gamma distribution (Table 1) revealed that steeper 1/f-slope correlated with higher pTau181 concentration, particularly in participants older than 53 years of age (Figure 1.A), or system segregation below 0.28 (Figure 1.B). A multiple linear regression within the TMS-EEG subsample (Table 2) revealed that the TEP response correlated positively with pTau181 as age increased, and that this model, despite including a smaller sample size, better explained pTau181 concentrations. The study shows that cortical excitability is related to pTau181 based on age and system segregation. The TEP response is a more sensitive marker than unperturbed EEG metrics in explaining the relation between excitability and pTau181 concentrations. Combined TMS with EEG could potentially be an inexpensive and scalable approach for early identification and longitudinal tracking of middle-aged adults at risk for cognitive decline and dementia.

Age differences in predicting working memory performance from network-based functional connectivity

Deterioration in working memory capacity (WMC) has been associated with normal aging, but it remains unknown how age affects the relationship between WMC and connectivity within functional brain networks. We therefore examined the predictability of WMC from fMRI-based resting-state functional connectivity (RSFC) within eight meta-analytically defined functional brain networks and the connectome in young and old adults using relevance vector machine in a robust cross-validation scheme. Particular brain networks have been associated with mental functions linked to WMC to a varying degree and are associated with age-related differences in performance. Comparing prediction performance between the young and old sample revealed age-specific effects: In young adults, we found a general unpredictability of WMC from RSFC in networks subserving WM, cognitive action control, vigilant attention, theory-of-mind cognition, and semantic memory, whereas in older adults each network significantly predicted WMC. Moreover, both WM-related and WM-unrelated networks were differently predictive in older adults with low versus high WMC. These results indicate that the within-network functional coupling during task-free states is specifically related to individual task performance in advanced age, suggesting neural-level reorganization. In particular, our findings support the notion of a decreased segregation of functional brain networks, deterioration of network integrity within different networks and/or compensation by reorganization as factors driving associations between individual WMC and within-network RSFC in older adults. Thus, using multivariate pattern regression provided novel insights into age-related brain reorganization by linking cognitive capacity to brain network integrity.

Increasing isoflurane dose reduces homotopic correlation and functional segregation of brain networks in mice as revealed by resting-state fMRI

Effects of anesthetics on brain functional networks are not fully understood. In this work, we investigated functional brain networks derived from resting-state fMRI data obtained under different doses of isoflurane in mice using stationary and dynamic functional connectivity (dFC) analysis. Stationary network analysis using FSL Nets revealed a modular structure of functional networks, which could be segregated into a lateral cortical, an associative cortical network, elements of the prefrontal network, a subcortical network, and a thalamic network. Increasing isoflurane dose led to a loss of functional connectivity between the bilateral cortical regions. In addition, dFC analysis revealed a dominance of dynamic functional states (dFS) exhibiting modular structure in mice anesthetized with a low dose of isoflurane, while at high isoflurane levels dFS showing widespread unstructured correlation displayed highest weights. This indicates that spatial segregation across brain functional networks is lost with increasing dose of the anesthetic drug used. To what extent this indicates a state of deep anesthesia remains to be shown. Combining the results of stationary and dynamic FC analysis indicates that increasing isoflurane levels leads to loss of modular network organization, which includes loss of the strong bilateral interactions between homotopic brain areas.

Reduced integration and improved segregation of functional brain networks in Alzheimer’s disease

Objective. Emerging evidence shows that cognitive deficits in Alzheimer’s disease (AD) are associated with disruptions in brain functional connectivity. Thus, the identification of alterations in AD functional networks has become a topic of increasing interest. However, to what extent AD induces disruption of the balance of local and global information processing in the human brain remains elusive. The main objective of this study is to explore the dynamic topological changes of AD networks in terms of brain network segregation and integration. Approach. We used electroencephalography (EEG) data recorded from 20 participants (10 AD patients and 10 healthy controls) during resting state. Functional brain networks were reconstructed using EEG source connectivity computed in different frequency bands. Graph theoretical analyses were performed assess differences between both groups. Main results. Results revealed that AD networks, compared to networks of age-matched healthy controls, are characterized by lower global information processing (integration) and higher local information processing (segregation). Results showed also significant correlation between the alterations in the AD patients’ functional brain networks and their cognitive scores. Significance. These findings may contribute to the development of EEG network-based test that could strengthen results obtained from currently-used neurophysiological tests in neurodegenerative diseases.

Change in brain network topology as a function of treatment response in schizophrenia: a longitudinal resting-state fMRI study using graph theory.

A number of neuroimaging studies have provided evidence in support of the hypothesis that faulty interactions between spatially disparate brain regions underlie the pathophysiology of schizophrenia, but it remains unclear to what degree antipsychotic medications affect these. We hypothesized that the balance between functional integration and segregation of brain networks is impaired in unmedicated patients with schizophrenia, but that it can be partially restored by antipsychotic medications. We included 32 unmedicated patients with schizophrenia (SZ) and 32 matched healthy controls (HC) in this study. We obtained resting-state scans while unmedicated, and again after 6 weeks of treatment with risperidone to assess functional integration and functional segregation of brain networks using graph theoretical measures. Compared with HC, unmedicated SZ showed reduced global efficiency and increased clustering coefficients. This pattern of aberrant functional network integration and segregation was modulated with antipsychotic medications, but only in those who responded to treatment. Our work lends support to the concept of schizophrenia as a dysconnectivity syndrome, and suggests that faulty brain network topology in schizophrenia is modulated by antipsychotic medication as a function of treatment response.

7. Physiological and pathological aging: A cortical connectivity analysis by graph theory model applied to brain network

Cortical connectivity in physiological and pathological aging. Physiological aging was evaluated in 113 EEGs, divided in three groups: Young (18–45 years), Adult (50–70) and Elderly (>70). Pathological aging was evaluated in 378 EEGs, divided in: Alzheimer disease (AD), mild cognitive impaired (MCI) and normal elderly subjects. Graph theory parameters were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA in delta, theta, alpha1, alpha2, beta1, beta2 and gamma bands. In physiological aging, normalized Characteristic Path Length ( λ ) presented the pattern Young > Adult > Elderly in the higher alpha band, the pattern Elderly > Young in delta and theta bands. Normalized Clustering coefficient ( γ ) and Small-World ( σ ) showed no significant age-modulation. In pathological aging, λ presented differences between healthy and dementia in theta band while γ showed a significant increment for AD in delta, theta and alpha1 bands; finally, σ presented a significant rising in MCI respect to AD in theta band.Functional integration was prejudiced in both physiological and pathological aging, while functional segregation of brain networks is more evident only in the pathological aging. Graph theory can disclose brain network connectivity in physiological and pathological aging, allowing understanding functional integration and segregation processes.