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Loss of brain inter-frequency hubs in Alzheimer's disease.

ABSTRACT: Alzheimer's disease (AD) causes alterations of brain network structure and function. The latter consists of connectivity changes between oscillatory processes at different frequency channels. We proposed a multi-layer network approach to analyze multiple-frequency brain networks inferred from magnetoencephalographic recordings during resting-states in AD subjects and age-matched controls. Main results showed that brain networks tend to facilitate information propagation across different frequencies, as measured by the multi-participation coefficient (MPC). However, regional connectivity in AD subjects was abnormally distributed across frequency bands as compared to controls, causing significant decreases of MPC. This effect was mainly localized in association areas and in the cingulate cortex, which acted, in the healthy group, as a true inter-frequency hub. MPC values significantly correlated with memory impairment of AD subjects, as measured by the total recall score. Most predictive regions belonged to components of the default-mode network that are typically affected by atrophy, metabolism disruption and amyloid-β deposition. We evaluated the diagnostic power of the MPC and we showed that it led to increased classification accuracy (78.39%) and sensitivity (91.11%). These findings shed new light on the brain functional alterations underlying AD and provide analytical tools for identifying multi-frequency neural mechanisms of brain diseases.

SUBMITTER: Guillon J

PROVIDER: S-EPMC5589939 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Alzheimer's disease (AD) is characterized by a sequence of pathological changes, which are commonly assessed in vivo using various brain imaging modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Currently, the most approaches to analyze statistical associations between regions and imaging modalities rely on Pearson correlation or linear regression models. However, these models are prone to spurious correlations arising from uninformative shared variance and multicollinearity. Notably, there are no appropriate multivariate statistical models available that can easily integrate dozens of multicollinear variables derived from such data, being able to utilize the additional information provided from the combination of data sources. Gaussian graphical models (GGMs) can estimate the conditional dependency from given data, which is conceptually expected to closely reflect the underlying causal relationships between various variables. Hence, we applied GGMs to assess multimodal regional brain alterations in AD. We obtained data from N = 972 subjects from the Alzheimer's Disease Neuroimaging Initiative. The mean amyloid load (AV45-PET), glucose metabolism (FDG-PET), and gray matter volume (MRI) were calculated for each of the 108 cortical and subcortical brain regions. GGMs were estimated using a Bayesian framework for the combined multimodal data and the resulted conditional dependency networks were compared to classical covariance networks based on Pearson correlation. Additionally, graph-theoretical network statistics were calculated to determine network alterations associated with disease status. The resulting conditional dependency matrices were much sparser (≈10% density) than Pearson correlation matrices (≈50% density). Within imaging modalities, conditional dependency networks yielded clusters connecting anatomically adjacent regions. For the associations between different modalities, only few region-specific connections were detected. Network measures such as small-world coefficient were significantly altered across diagnostic groups, with a biphasic u-shape trajectory, i.e., increased small-world coefficient in early mild cognitive impairment (MCI), similar values in late MCI, and decreased values in AD dementia patients compared to cognitively normal controls. In conclusion, GGMs removed commonly shared variance among multimodal measures of regional brain alterations in MCI and AD, and yielded sparser matrices compared to correlation networks based on the Pearson coefficient. Therefore, GGMs may be used as alternative to thresholding-approaches typically applied to correlation networks to obtain the most informative relations between variables.

Project description:BackgroundMicroglia, the brain's principal immune cell, are increasingly implicated in Alzheimer's disease (AD), but the molecular interfaces through which these cells contribute to amyloid beta (Aβ)-related neurodegeneration are unclear. We recently identified microglial contributions to the homeostatic and disease-associated modulation of perineuronal nets (PNNs), extracellular matrix structures that enwrap and stabilize neuronal synapses, but whether PNNs are altered in AD remains controversial.MethodsExtensive histological analysis was performed on male and female 5xFAD mice at 4, 8, 12, and 18 months of age to assess plaque burden, microgliosis, and PNNs. Findings were validated in postmortem AD tissue. The role of neuroinflammation in PNN loss was investigated via LPS treatment, and the ability to prevent or rescue disease-related reductions in PNNs was assessed by treating 5xFAD and 3xTg-AD model mice with colony-stimulating factor 1 receptor (CSF1R) inhibitor PLX5622 to deplete microglia.FindingsUtilizing the 5xFAD mouse model and human cortical tissue, we report that PNNs are extensively lost in AD in proportion to plaque burden. Activated microglia closely associate with and engulf damaged nets in the 5xFAD brain, and inclusions of PNN material are evident in mouse and human microglia, while aggrecan, a critical PNN component, deposits within human dense-core plaques. Disease-associated reductions in parvalbumin (PV)+ interneurons, frequently coated by PNNs, are preceded by PNN coverage and integrity impairments, and similar phenotypes are elicited in wild-type mice following microglial activation with LPS. Chronic pharmacological depletion of microglia prevents 5xFAD PNN loss, with similar results observed following depletion in aged 3xTg-AD mice, and this occurs despite plaque persistence.InterpretationWe conclude that phenotypically altered microglia facilitate plaque-dependent PNN loss in the AD brain.FundingThe NIH (NIA, NINDS) and the Alzheimer's Association.

Project description:Empirical studies over the past two decades have provided support for the hypothesis that schizophrenia is characterized by altered connectivity patterns in functional brain networks. These alterations have been proposed as genetically mediated diagnostic biomarkers and are thought to underlie altered cognitive functions such as working memory. However, the nature of this dysconnectivity remains far from understood. In this study, we perform an extensive analysis of functional connectivity patterns extracted from MEG data in 14 subjects with schizophrenia and 14 healthy controls during a 2-back working memory task. We investigate uni-, bi- and multivariate properties of sensor time series by computing wavelet entropy of and correlation between time series, and by constructing binary networks of functional connectivity both within and between classical frequency bands ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]). Networks are based on the mutual information between wavelet time series, and estimated for each trial window separately, enabling us to consider both network topology and network dynamics. We observed significant decreases in time series entropy and significant increases in functional connectivity in the schizophrenia group in comparison to the healthy controls and identified an inverse relationship between these measures across both subjects and sensors that varied over frequency bands and was more pronounced in controls than in patients. The topological organization of connectivity was altered in schizophrenia specifically in high frequency [Formula: see text] and [Formula: see text] band networks as well as in the [Formula: see text]-[Formula: see text] cross-frequency networks. Network topology varied over trials to a greater extent in patients than in controls, suggesting disease-associated alterations in dynamic network properties of brain function. Our results identify signatures of aberrant neurophysiological behavior in schizophrenia across uni-, bi- and multivariate scales and lay the groundwork for further clinical studies that might lead to the discovery of new intermediate phenotypes.

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Loss of brain inter-frequency hubs in Alzheimer's disease.

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