Changes In Brain Networks Research Articles

Electroencephalography-Derived Sensory and Motor Network Topology in Multiple Sclerosis Fatigue

People with multiple sclerosis (MS) frequently complain of excessive fatigue, which is the most disabling symptom for half of them. While the few drugs used to treat MS fatigue are of limited utility, we recently observed the efficacy of a personalized neuromodulation treatment. Here, we aim at strengthening knowledge of the brain network changes that occur when MS fatigue increases, using graph theory. We collected electroencephalographic (EEG; 23 or 64 channels) data in resting state with eyes open in 27 relapsing-remitting (RR) patients with mild MS (EDSS ≤2), suffering a wide range of fatigue as scored by the modified Fatigue Impact Scale (mFIS) (2-69, within a total range 0-84). To estimate graph theory small-world index (SW), we calculated the lagged linear coherence between EEG cortical eLORETA sources, in the standard frequency bands delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-45 Hz). We calculated the SW of these undirected and weighted networks separately in the four left and right frontal (motor) and parieto-occipito-temporal (sensory) brain networks. A correlative analysis demonstrated increased fatigue symptoms along with the SW specifically in the Sensory network of the left dominant hemisphere in the beta1 band (Pearson’s r = 0.404, P = .020). Our study indicates a specific involvement of the dominant-hemisphere sensory network in MS fatigue. It suggests that compensatory neuromodulation interventions could enhance efficacy in relieving this debilitating symptom by targeting this area.

Aberrant Functional Whole-Brain Network Architecture in Patients With Schizophrenia: A Meta-analysis.

Findings from multiple lines of research provide evidence of aberrant functional brain connectivity in schizophrenia. By using graph-analytical measures, recent studies indicate that patients with schizophrenia exhibit changes in the organizational principles of whole-brain networks and that these changes relate to cognitive symptoms. However, there has not been a systematic investigation of functional brain network changes in schizophrenia to test the consistency of these changes across multiple studies. A comprehensive literature search was conducted to identify all available functional graph-analytical studies in patients with schizophrenia. Effect size measures were derived from each study and entered in a random-effects meta-analytical model. All models were tested for effects of potential moderator variables as well as for the presence of publication bias. The results of a total of n = 13 functional neuroimaging studies indicated that brain networks in patients with schizophrenia exhibit significant decreases in measures of local organization (g = -0.56, P = .02) and significant decreases in small-worldness (g = -0.65, P = .01) whereas global short communication paths seemed to be preserved (g = 0.26, P = .32). There was no evidence for a publication bias or moderator effects. The present meta- analysis demonstrates significant changes in whole brain network architecture associated with schizophrenia across studies.

Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI

Brain network alterations in patients with Alzheimer’s disease (AD) has been the subject of much investigation, but the biological mechanisms underlying these alterations remain poorly understood. Here, we aim to identify the changes in brain networks in patients with AD and mild cognitive impairment (MCI), and provide an accurate algorithm for classification of these patients from healthy control subjects (HC) by using a graph theoretical approach and advanced machine learning methods. Multivariate Granger causality analysis was performed on resting-state functional magnetic resonance imaging (rs-fMRI) data of 34 AD, 89 MCI, and 45 HC to calculate various directed graph measures. The graph measures were used as the original feature set for the machine learning algorithm. Filter and wrapper feature selection methods were applied to the original feature set to select an optimal subset of features. An accuracy of 93.3% was achieved for classification of AD, MCI, and HC using the optimal features and the naïve Bayes classifier. We also performed a hub node analysis and found that the number of hubs in HC, MCI, and AD were 12, 10, and 9, respectively, suggesting that patients with AD experience disturbance of critical communication areas in their brain network as AD progresses. The findings of this study provide insight into the neurophysiological mechanisms underlying MCI and AD. The proposed classification method highlights the potential of directed graph measures of rs-fMRI data for identification of the early stage of AD.

Brain network changes and memory decline in aging.

One theory of age-related cognitive decline proposes that changes within the default mode network (DMN) of the brain impact the ability to successfully perform cognitive operations. To investigate this theory, we examined functional covariance within brain networks using regional cerebral blood flow data, measured by 15O-water PET, from 99 participants (mean baseline age 68.6±7.5) in the Baltimore Longitudinal Study of Aging collected over a 7.4year period. The sample was divided in tertiles based on longitudinal performance on a verbal recognition memory task administered during scanning, and functional covariance was compared between the upper (improvers) and lower (decliners) tertile groups. The DMN and verbal memory networks (VMN) were then examined during the verbal memory scan condition. For each network, group differences in node-to-network coherence and individual node-to-node covariance relationships were assessed at baseline and in change over time. Compared with improvers, decliners showed differences in node-to-network coherence and in node-to-node relationships in the DMN but not the VMN during verbal memory. These DMN differences reflected greater covariance with better task performance at baseline and both increasing and declining covariance with declining task performance over time for decliners. When examined during the resting state alone, the direction of change in DMN covariance was similar to that seen during task performance, but node-to-node relationships differed from those observed during the task condition. These results suggest that disengagement of DMN components during task performance is not essential for successful cognitive performance as previously proposed. Instead, a proper balance in network processes may be needed to support optimal task performance.

Movement Disorders Following Cerebrovascular Lesions in Cerebellar Circuits.

Cerebellar circuitry is important to controlling and modifying motor activity. It conducts the coordination and correction of errors in muscle contractions during active movements. Therefore, cerebrovascular lesions of the cerebellum or its pathways can cause diverse movement disorders, such as action tremor, Holmes’ tremor, palatal tremor, asterixis, and dystonia. The pathophysiology of abnormal movements after stroke remains poorly understood. However, due to the current advances in functional neuroimaging, it has recently been described as changes in functional brain networks. This review describes the clinical features and pathophysiological mechanisms in different types of movement disorders following cerebrovascular lesions in the cerebellar circuits.

The Whole-Brain "Global" Signal from Resting State fMRI as a Potential Biomarker of Quantitative State Changes in Glucose Metabolism.

The evolution of functional magnetic resonance imaging to resting state (R-fMRI) allows measurement of changes in brain networks attributed to state changes, such as in neuropsychiatric diseases versus healthy controls. Since these networks are observed by comparing normalized R-fMRI signals, it is difficult to determine the metabolic basis of such group differences. To investigate the metabolic basis of R-fMRI network differences within a normal range, eyes open versus eyes closed in healthy human subjects was used. R-fMRI was recorded simultaneously with fluoro-deoxyglucose positron emission tomography (FDG-PET). Higher baseline FDG was observed in the eyes open state. Variance-based metrics calculated from R-fMRI did not match the baseline shift in FDG. Functional connectivity density (FCD)-based metrics showed a shift similar to the baseline shift of FDG, however, this was lost if R-fMRI "nuisance signals" were regressed before FCD calculation. Average correlation with the mean R-fMRI signal across the whole brain, generally regarded as a "nuisance signal," also showed a shift similar to the baseline of FDG. Thus, despite lacking a baseline itself, changes in whole-brain correlation may reflect changes in baseline brain metabolism. Conversely, variance-based metrics may remain similar between states due to inherent region-to-region differences overwhelming the differences between normal physiological states. As most previous studies have excluded the spatial means of R-fMRI metrics from their analysis, this work presents the first evidence of a potential R-fMRI biomarker for baseline shifts in quantifiable metabolism between brain states.

Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia.

It is unclear how communication between brain networks responds to changing environmental demands during complex cognitive processes. Also, unknown in regard to these network dynamics is the role of neuromodulators, such as dopamine, and whether their dysregulation could underlie cognitive deficits in neuropsychiatric illness. We found that connectivity between brain networks changes with working-memory load and greater increases predict better working memory performance; however, it was not related to capacity for dopamine release in the cortex. Patients with schizophrenia did show dynamic internetwork connectivity; however, this was more weakly associated with successful performance in patients compared with healthy individuals. Our findings indicate that dynamic interactions between brain networks may support the type of flexible adaptations essential to goal-directed behavior.

Military Personnel with Traumatic Stress Show Changes in Large-Scale Brain Networks after Use of a Closed-Loop Neurotechnology (P4.043)

Military Personnel with Traumatic Stress Show Changes in Large-Scale Brain Networks after Use of a Closed-Loop Neurotechnology (P4.043)

Military Personnel with Traumatic Stress Show Changes in Large-Scale Brain Networks after Use of a Closed-Loop Neurotechnology (I7.009)

Military Personnel with Traumatic Stress Show Changes in Large-Scale Brain Networks after Use of a Closed-Loop Neurotechnology (I7.009)

Small-world brain functional network altered by watching 2D/3DTV

With the development of display technology, the healthy problems caused by watching 2D/3DTV have received more and more attention. This paper utilized resting-state functional magnetic resonance imaging to study the changes of small-world brain network before and after one-hour 2D/3DTV watching, and explored the brain fatigue mechanism caused by watching 2D/3DTV. We conclude that one-hour watching of 2DTV will not increase the burden of brain. On the contrary, one-hour watching of 3DTV requires the brain to regulate the efficiency of brain areas, such as temporal lobe and occipital lobe, which may explain the fact that watching 3DTV can easier cause brain fatigue than watching 2DTV.

Progranulin Mutations Affects Brain Oscillatory Activity in Fronto-Temporal Dementia.

Background: Mild cognitive impairment (MCI) is a clinical stage indicating a prodromal phase of dementia. This practical concept could be used also for fronto-temporal dementia (FTD). Progranulin (PGRN) has been recently recognized as a useful diagnostic biomarker for fronto-temporal lobe degeneration (FTLD) due to GRN null mutations. Electroencephalography (EEG) is a reliable tool in detecting brain networks changes. The working hypothesis of the present study is that EEG oscillations could detect different modifications among FTLD stages (FTD-MCI versus overt FTD) as well as differences between GRN mutation carriers versus non-carriers in patients with overt FTD.Materials and Methods: EEG in all patients and PGRN dosage in patients with a clear FTD were detected. The cognitive state has been investigated through mini mental state examination (MMSE).Results: MCI-FTD showed a significant lower spectral power in both alpha and theta oscillations as compared to overt FTD. GRN mutations carriers affected by FTLD show an increase in high alpha and decrease in theta oscillations as compared to non-carriers.Conclusion: EEG frequency rhythms are sensible to different stage of FTD and could detect changes in brain oscillatory activity affected by GRN mutations.

Violence-related content in video game may lead to functional connectivity changes in brain networks as revealed by fMRI-ICA in young men

In violent video games, players engage in virtual aggressive behaviors. Exposure to virtual aggressive behavior induces short-term changes in players’ behavior. In a previous study, a violence-related version of the racing game “Carmageddon TDR2000” increased aggressive affects, cognitions, and behaviors compared to its non-violence-related version. This study investigates the differences in neural network activity during the playing of both versions of the video game.Functional magnetic resonance imaging (fMRI) recorded ongoing brain activity of 18 young men playing the violence-related and the non-violence-related version of the video game Carmageddon. Image time series were decomposed into functional connectivity (FC) patterns using independent component analysis (ICA) and template-matching yielded a mapping to established functional brain networks.The FC patterns revealed a decrease in connectivity within 6 brain networks during the violence-related compared to the non-violence-related condition: three sensory-motor networks, the reward network, the default mode network (DMN), and the right-lateralized frontoparietal network.Playing violent racing games may change functional brain connectivity, in particular and even after controlling for event frequency, in the reward network and the DMN. These changes may underlie the short-term increase of aggressive affects, cognitions, and behaviors as observed after playing violent video games.

Brain Connectivity Dissociates Responsiveness from Drug Exposure during Propofol-Induced Transitions of Consciousness.

Accurately measuring the neural correlates of consciousness is a grand challenge for neuroscience. Despite theoretical advances, developing reliable brain measures to track the loss of reportable consciousness during sedation is hampered by significant individual variability in susceptibility to anaesthetics. We addressed this challenge using high-density electroencephalography to characterise changes in brain networks during propofol sedation. Assessments of spectral connectivity networks before, during and after sedation were combined with measurements of behavioural responsiveness and drug concentrations in blood. Strikingly, we found that participants who had weaker alpha band networks at baseline were more likely to become unresponsive during sedation, despite registering similar levels of drug in blood. In contrast, phase-amplitude coupling between slow and alpha oscillations correlated with drug concentrations in blood. Our findings highlight novel markers that prognosticate individual differences in susceptibility to propofol and track drug exposure. These advances could inform accurate drug titration and brain state monitoring during anaesthesia.

Altered Effective Connectivity of Hippocampus-Dependent Episodic Memory Network in mTBI Survivors.

Traumatic brain injuries (TBIs) are generally recognized to affect episodic memory. However, less is known regarding how external force altered the way functionally connected brain structures of the episodic memory system interact. To address this issue, we adopted an effective connectivity based analysis, namely, multivariate Granger causality approach, to explore causal interactions within the brain network of interest. Results presented that TBI induced increased bilateral and decreased ipsilateral effective connectivity in the episodic memory network in comparison with that of normal controls. Moreover, the left anterior superior temporal gyrus (aSTG, the concept forming hub), left hippocampus (the personal experience binding hub), and left parahippocampal gyrus (the contextual association hub) were no longer network hubs in TBI survivors, who compensated for hippocampal deficits by relying more on the right hippocampus (underlying perceptual memory) and the right medial frontal gyrus (MeFG) in the anterior prefrontal cortex (PFC). We postulated that the overrecruitment of the right anterior PFC caused dysfunction of the strategic component of episodic memory, which caused deteriorating episodic memory in mTBI survivors. Our findings also suggested that the pattern of brain network changes in TBI survivors presented similar functional consequences to normal aging.

Musical training is related to altered functional connectivity during statistical learning: an MEG study

Musical training is related to altered functional connectivity during statistical learning: an MEG study

A Mathematical Model for Evaluating the Functional Connectivity Strongness in Healthy People.

The human brain is a really complex organization of connectivity whose principal elements are neurons, synapses and brain regions. Up to now this connectivity is not fully understood, and recent impulse in investigating its structure has been given by Graph Theory. However, some points remain unclear, mainly due to possible mismatching between the Mathematical and the Neuroscientific approach. It is known that neural connectivity is classified into three categories: structural (or anatomical) connectivity, functional connectivity and effective connectivity. The point is that these categories demand different kinds of graphs, except in the case of the resting state, and sometimes topological and metrical parameters are involved simultaneously, without a specific distinction of their roles. In this paper we propose a mathematical model for treating the functional connectivity, based on directed graphs with weighted edges. The function W(i,j,t), representing the weight of the edge connecting nodes i,j at time t, is obtained by splitting the model in two parts, where different parameters have been introduced step by step and rigorously motivated. In particular, there is a double role played by the notion of distance, which, according to the different parts of the model, assumes a discrete or an Euclidean meaning. Analogously, the time t appears both from a local and from a global perspective. The local aspect relates to a specific task submitted to an health volunteer (in view of possible future applications also to subjects affected by neurological diseases), while the global one concerns the different periods in the human life that characterize the main changes in the neural brain network. In the particular case of the resting state, we have shown that W reduces to the usually employed probabilistic growth laws for the edge formation. We tested the correctness of our model by means of synthetic data, where the selection of all involved parameters has been motivated according to what is known from the available literature. It turns out that simulated outputs fit well with the expected results, which encourages further analysis on real data, and possible future applications to neurological pathologies.

Compensation through Functional Hyperconnectivity: A Longitudinal Connectome Assessment of Mild Traumatic Brain Injury.

Mild traumatic brain injury (mTBI) is a major public health concern. Functional MRI has reported alterations in several brain networks following mTBI. However, the connectome-scale brain network changes are still unknown. In this study, sixteen mTBI patients were prospectively recruited from an emergency department and followed up at 4–6 weeks after injury. Twenty-four healthy controls were also scanned twice with the same time interval. Three hundred fifty-eight brain landmarks that preserve structural and functional correspondence of brain networks across individuals were used to investigate longitudinal brain connectivity. Network-based statistic (NBS) analysis did not find significant difference in the group-by-time interaction and time effects. However, 258 functional pairs show group differences in which mTBI patients have higher functional connectivity. Meta-analysis showed that “Action” and “Cognition” are the most affected functional domains. Categorization of connectomic signatures using multiview group-wise cluster analysis identified two patterns of functional hyperconnectivity among mTBI patients: (I) between the posterior cingulate cortex and the association areas of the brain and (II) between the occipital and the frontal lobes of the brain. Our results demonstrate that brain concussion renders connectome-scale brain network connectivity changes, and the brain tends to be hyperactivated to compensate the pathophysiological disturbances.

Motor and cortico-striatal-thalamic connectivity alterations in intrauterine growth restriction

Intrauterine growth restriction is associated with short- and long-term neurodevelopmental problems. Structural brain changes underlying these alterations have been described with the use of different magnetic resonance-based methods that include changes in whole structural brain networks. However, evaluation of specific brain circuits and its correlation with related functions has not been investigated in intrauterine growth restriction. In this study, we aimed to investigate differences in tractography-related metrics in cortico-striatal-thalamic and motor networks in intrauterine growth restricted children and whether theseparameters were related with their specific function in order toexplore its potential use as an imaging biomarker of altered neurodevelopment. We included a group of 24 intrauterine growth restriction subjects and 27 control subjects that were scanned at 1 year old; we acquired T1-weighted and 30 directions diffusion magnetic resonance images. Each subject brain was segmented in 93 regions with the use of anatomical automatic labeling atlas, and deterministic tractography was performed. Brain regions included in motor and cortico-striatal-thalamic networks were defined based in functional and anatomic criteria. Within the streamlines that resulted from the whole brain tractography, those belonging to each specific circuit were selected and tractography-related metrics that included number of streamlines, fractional anisotropy, and integrity were calculated for each network. We evaluated differences between both groups and further explored the correlation of these parameters with the results of socioemotional, cognitive, and motor scales from Bayley Scale at 2 years of age. Reduced fractional anisotropy (cortico-striatal-thalamic, 0.319 ± 0.018 vs 0.315 ± 0.015; P= .010; motor, 0.322 ± 0.019 vs 0.319 ± 0.020; P= .019) and integrity cortico-striatal-thalamic (0.407 ± 0.040 vs0.399 ± 0.034; P= .018; motor, 0.417 ± 0.044 vs 0.409 ± 0.046; P=.016) in both networks were observed in the intrauterine growth restriction group, with no differences in number of streamlines. More importantly, strong specific correlation was found between tractography-related metrics and its relative function in both networks in intrauterine growth restricted children. Motor network metrics were correlated specifically with motor scale results (fractional anisotropy: rho= 0.857; integrity: rho= 0.740); cortico-striatal-thalamic network metrics werecorrelated with cognitive (fractional anisotropy: rho= 0.793; integrity, rho= 0.762) and socioemotional scale (fractional anisotropy: rho= 0.850; integrity: rho= 0.877). These results support the existence of altered brain connectivity in intrauterine growth restriction demonstrated by altered connectivity in motor and cortico-striatal-thalamic networks, with reduced fractional anisotropy and integrity. The specific correlation between tractography-related metrics and neurodevelopmental outcomes in intrauterine growth restriction shows the potential to use this approach to develop imaging biomarkers to predict specific neurodevelopmental outcome in infants who are at risk because of intrauterine growth restriction and other prenatal diseases.

Brain network changes in relation to beginning apathy in PD patients

Introduction: Brain frequency and network analyses are known to differentiate between disease stages in Parkinson’s disease (PD) and are possible biomarkers. They correlate with cognitive decline. Little is known about changes in brain networks in relation to apathy.

Brain network changes in relation to beginning apathy in PD patients

Brain network changes in relation to beginning apathy in PD patients